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Vocabulary Use for Improving Reading Comprehension in Third ... I think he's secretly an educational superhero. ... acquisition and reading comprehension.

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UNION UNIVERSITY SCHOOL OF EDUCATION We hereby recommend that the Dissertation by Megan Lawler Salemi Entitled Utilizing Fluency, Flexibility, Originality, and Elaboration to Enhance Creativity and Vocabulary Use for Improving Reading Comprehension in Third through Sixth Grade Students

Be accepted in partial fulfillment of the requirements for the Degree of

Doctor of Education In Educational Leadership

_________________________________________________________________ Jennifer F. Grove, Ed.D., Assistant Dean (Date)

Dissertation Committee _________________________________________________________________ Stephen R. Marvin, Ed.D., Chairperson (Date) _________________________________________________________________ Patricia P. Ray, Ed.D. (Date) _________________________________________________________________ John L. Malone, Ed.D. (Date)

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STATEMENT OF PERMISSION TO USE

In presenting this dissertation in partial fulfillment of the requirements for the Doctor of Education Degree at Union University, I agree that the Library shall make it available to borrowers under the rules of the Library. Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of the source is made. Permission for extensive quotation from or reproduction of this dissertation may be granted by my research chair or, in his absence, by the Head of the Interlibrary Services when, in the opinion of either, the proposed use of the material is the scholarly purposes. Any copying or use of the material in this dissertation for financial gain shall not be allowed without my written permission.

Signature ________________________________________________ Date ____________________________________________________

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Utilizing Fluency, Flexibility, Originality, and Elaboration to Enhance Creativity and Vocabulary Use for Improving Reading Comprehension in Third through Sixth Grade Students

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Doctor of Education Degree Union University

Megan Lawler Salemi December 2010

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ii

Copyright © 2010 Megan Lawler Salemi

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iii

DEDICATION

This entire process is dedicated to the other “jars of clay.” 2 Corinthians 4:7-18

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iv

ACKNOWLEDGMENTS

This dissertation process has been a humbling and educational one, in which I have acutely learned that I am unable to do anything well exclusively on my own. I thank God for patience, determination, and favor. I owe sincerest gratitude to my chairs. Without Dr. Rosetta Mayfield’s extensive support, there is no way that I would be in a position to submit this dissertation. It is a significant task to mentor someone through such a personal and emotional process as a dissertation, and her guidance and steadfastness have been essential to my completion. I owe a debt of gratitude also to Dr. Stephen Marvin. I appreciate his willing spirit not only to fill a need that he saw, but to edit and respond to my dissertation needs as if I was his own student from the beginning. His encouragement came ringing true at some discouraging moments. I think he’s secretly an educational superhero. I also appreciate the support and patience of my family and friends, namely my husband, Jay Salemi, my mother, Terry Lawler, my father, Jerry Lawler, and Tanner Jackson, my editor. My school community’s offerings and support were instrumental in the development and completion of this research, namely the teachers, students, and families who participated, Susan Flippen, Tom Benton, and Trent Williamson. Finally, thank you to the countless people who calmed me down, encouraged me, fed me, or performed a seemingly insignificant task that allowed me to proceed with this endeavor.

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v

ABSTRACT

The increased use of standardized testing to measure student and teacher success has caused a shift in the way teachers approach students and learning. Students in regular education classrooms, particularly those from lower socioeconomic backgrounds, may not receive the highest quality instruction due to the testing needs of the school or other students. However, educators must find ways to include all learners in the highest quality instruction while meeting district testing needs. Creativity research provides a framework for understanding the brain and learning in a way that may help increase students’ test scores and ensure that they receive high quality instruction. In this study, creativity was operationally defined by four of its factors: fluency, flexibility, originality, and elaboration. The purpose of this research was to investigate the effects instruction emphasizing fluency, flexibility, originality, and elaboration had on students’ vocabulary acquisition and reading comprehension. Creativity was measured by the Torrance Tests of Creative Thinking and the Khatena-Torrance Creative Perception Inventory. Vocabulary acquisition and reading comprehension were measured by the STAR Reading Test. It was hypothesized that students instructed in creativity during their vocabulary lessons would score higher on creativity measures and vocabulary measures. Consequently, those students should also score higher on reading comprehension measures. Eighty-seven third through sixth grade students from a small, private school in the Mid-South participated in the research. Forty-four students participate in the

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vi treatment group and 43 participated in the control group. A significant difference in scores was found between the treatment and control groups on the Torrance Test of Creative Thinking Verbal Form. The treatment group performed significantly higher than the control group after the treatment was administered. No other significant differences were found. Further implications of the results are discussed.

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vii

TABLE OF CONTENTS

CHAPTER 1.

PAGE

INTRODUCTION ...............................................................................................1 Statement of the Problem ....................................................................................2 Purpose of the Study ...........................................................................................5 Research Questions .............................................................................................6 Definitions of Terms ...........................................................................................6

2.

REVIEW OF LITERATURE ..............................................................................9 Traditional versus Nontraditional Instructional Practices .....................................9 The Need for Creativity and Vocabulary Instruction ..........................................12 Creative Thinking Instruction ............................................................................22 Fluency, Flexibility, Originality, and Elaboration ..............................................47 Vocabulary Development with Creativity ..........................................................54 Critique, Statement of the Problem, Purpose .....................................................57 Conclusion ........................................................................................................62

3.

METHODS .......................................................................................................64 Purpose of the Study .........................................................................................64 Design ...............................................................................................................65 Participants .......................................................................................................66 Procedures ........................................................................................................66 Instrumentation .................................................................................................69 Data ..................................................................................................................72 Limitations ........................................................................................................74

4.

RESULTS .........................................................................................................76 Opening Statement ............................................................................................76 Data and Statistical Results ...............................................................................77 Additional Variables .........................................................................................83 Closing Statement .............................................................................................83

5.

CONCLUSIONS AND DISCUSSION ..............................................................85

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viii

Opening Statement ............................................................................................85 Conclusions ......................................................................................................86 Recommendations .............................................................................................88 Implications ......................................................................................................89 For Future Studies .............................................................................................90 Discussion .........................................................................................................91 Closing Summary ..............................................................................................92 REFERENCES .............................................................................................................93 APPENDICES ............................................................................................................112

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1

CHAPTER 1 INTRODUCTION

Achievement gaps among different groups of students have haunted reformers searching for equal educational opportunities for all students (Kozol, 1991; Hilliard, 2000; Lomax, West, Harmon, Viator, & Madaus, 1995). These gaps have been documented by researchers in both intelligence and achievement (Hilliard, Perry, & Steele, 2003; Lomax et al., 1995). Low performance on standardized measures has prompted legislators to seek solutions regardless of the validity of the claims researchers purport exist between students from minority groups and European Americans. The No Child Left Behind Act of 2001 (NCLB) provides support for a variety of underperforming groups of students, such as students who are homeless, living in economically disadvantaged communities, and in abusive situations. In addition to providing resources for students who might be left behind academically, NCLB (2002) outlines requirements for measuring the academic progress of schools receiving Federal funds. The purpose of the NCLB legislation is to guarantee that all students, regardless of background, receive an equal opportunity to a high-quality education that meets minimum performance standards on state achievement measures. This legislation also attempts to regulate teaching in order to reduce any gaps in performance. Additionally, the Act functions as a benchmark to hold public schools accountable for the academic

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2 performance of all students (NCLB, 2002, sect. 1001). The reality of this Act shifts the focus from authentic learning and critical thinking to a bottom line of raising students’ test scores on state mandated standardized tests. Since the enactment of NCLB into law, “accountability” has become an institutionalized concept, signifying a great shift in teaching and learning in schools. The shift toward increasing student test scores on standardized tests may particularly deter minority or low socioeconomic students from achieving high school graduation, entrance into college, or economic stability in the workforce (Lee, Daniels, Puig, Newgent, & Nam, 2008; Fram, Miller-Cribbs, & Horn, 2007). Further, a disparity in academic achievement often predicts future economic, personal and family success (Teske, Fitzpatrick, & Kaplan, 2006; Rouse & Barrow, 2006). Statement of the Problem Teachers are often faced with a conflict between learners’ needs and state mandated requirements (Brimijoin, 2005; Longo, 2010). Many teachers continue to teach using best practices to foster critical thinking, problem solving, and creativity for minority and low socioeconomic students. Research shows that “high-stakes” standardized testing to produce maximum learning at minimum performance standards hinders teachers’ ability to use these strategies (Hurren, Rutledge, & Garvin, 2006; Caughy & O’Campo, 2006; Hilliard et al., 2003). For minority and low socioeconomic students, defining success may not be as simple as taking one standardized test; therefore, utilizing instructional and assessment methods that define and measure success is necessary (Hilliard et al., 2003; Tate 2003). Specific instructional strategies must also be

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3 indentified that assist minority and low socioeconomic students in eliminating an achievement gap. One area deserving attention is vocabulary instruction as a specific skill to increase reading comprehension. Vocabulary and reading are historically and causally linked (Baumann, Edwards, Boland, Olejnick & Kame’enui, 2003). Students struggling in reading comprehension should benefit from different types of instruction in vocabulary. The NCLB Act was originally implemented as an attempt to reduce the achievement gaps between different groups of students; however, this act has shifted the focus of education from teaching necessary skills and information to teaching the specific requirements of a test. Creativity provides a framework that could serve as a way to impact student achievement on a summative assessment while also attending to the learner’s educational needs regardless of race or socioeconomic background (Ford, Moore, & Milner, 2005; Tieso, 2005; Respress & Lutfi, 2006; Cheng, Wang, Liu, & Chen, 2010). A review of the research in creativity sheds light on the complicated nature of learning and the brain and underscores the limited scope of a standardized test at measuring teaching and learning (Chavez-Eakle, Graff- Guerroro, Vaugier, & CruzFuentes, 2007; Douville, 2004; Jitendra, Sczesniak, & Deatline-Buchman, 2005) . Research suggests that teaching students how to be creative in their thinking and their approaches to learning will provide the high-quality education the NCLB Act (2002) and other assessments seek to validate. In addition, creative thinking can significantly increase all students’ ability to achieve higher than minimum proficiency on standardized

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4 tests by engaging higher order thought processes with content knowledge and increased problem solving skills (Goldberg & Bush, 2003; Wolfe, 2002). Research also highlights the central role vocabulary plays in high achievement on standardized assessments (Manzo, Manzo, & Thomas, 2006; Barry, Heubsch, & Burhop, 2008; Parcel & Geschwender, 1995). Researchers in the fields of creativity and vocabulary advise that updated study is needed to connect these areas of research with student achievement (Sternberg, 2002, 2007; Dudek, Strobel, & Runco, 1993; National Institute of Child Health and Human Development, 2000; Shaunessy, Karnes, & Cobb, 2004). Current studies of creativity aimed at increasing achievement scores focus on components of the creative thinking – producing a substantial number of ideas, using metacognition, or utilizing high level problem solving (Goldberg & Bush, 2003; Douville, 2004). Other studies distinguish creativity as a whole-brain process that should be utilized holistically to produce creative thoughts and learning (Starchenko, Bekhtereva, Pakhomov, & Medvedev, 2003; Abraham & Windmann, 2007; Treffinger & Isaksen, 2005). Current studies of vocabulary studies also contain a wide array of definitions and perspectives (Manzo et al., 2006; NICHHD, 2000). Fluency, elaboration, and originality are three components regularly cited in creativity research. The Torrance Tests of Creative Thinking (TTCT), one of the most widely used creativity measures, provide verbal and performance tasks based on divergent thinking elements: fluency, flexibility, originality, and elaboration (Torrance, 1974). These elements were first identified by J. P. Guilford who advanced theories of

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5 intelligence advocating for the inclusion of creativity during the 1950s. Based on extensive research, Torrance eliminated flexibility measures from his test battery due to its high correlation with fluency measures. The TTCT adopt Guilford’s definitions of fluency as the number of relevant responses, originality as the number of unusual and relevant responses determined by statistical infrequency, and elaboration as the number of details used to extend a response (Torrance, 1974; Guilford, 1950). These measures have become widely accepted as the basis for defining and measuring creativity (Wang & Horng, 2002; Russo, 2004; Cramond, Matthews-Morgan, Bandalos, & Zuo, 2005; Matud, Rodriguez, & Grande, 2007). Purpose of the Study The purpose of this research is to investigate the effects instruction emphasizing fluency, originality and elaboration will have on students’ vocabulary acquisition and reading comprehension. Students in urban school settings with higher minority subgroup populations may benefit from teaching creativity to increase achievement. Not only do students in urban school settings often begin schooling with disproportionate family resources, they also may suffer from a lack of supplied educational resources (Crosnoe & Huston, 2007). Finding brain-based, cost-effective methods with high student expectations could serve dual roles for educators. These methods could assist educators in spending appropriate time teaching students to meet or exceed minimum testing standards while also increasing student learning.

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6 Research Questions Through a review and synthesis of the literature about creativity and education, several research questions emerge: 1.

Is there a relationship between the effect of instruction emphasizing fluency, flexibility, originality, and elaboration on vocabulary achievement and reading comprehension as measured by the STAR Reading test?

2.

Will the use of fluency, flexibility, originality, and elaboration in vocabulary instruction have a statistically significant impact on students’ creativity scores as measured by the Torrance Tests of Creative Thinking?

3.

Is there an effect on how students rate their own creativity as a result of instruction emphasizing fluency, flexibility, originality, and elaboration as measured by the Khatena-Torrance Creative Perception Inventory?

Definition of Terms Advanced. A student’s score is advanced on a standardized state achievement test if it is a certain percentage above the minimum proficiency required by that state (Mendoza, 2006). Below proficient. A student’s score on a standardized state achievement test if it is a certain percentage below the minimum proficiency required by that state (Mendoza, 2006). Creative framework. A method of understanding and using understandings of creativity in order to enhance academic and problem solving performance (Treffinger et al., 2003a).

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7 Creative problem solving. Creative Problem Solving (CPS) is one current model constructed to teach and measure creativity as a problem solving process. It involves directions and materials to clarify problems, think of ideas, and decide on and carry out solutions (Treffinger & Isaksen, 2005). Creativity. Creativity has many, varied definitions across a variety of disciplines. Creativity will generally be defined as a complex, mental process employing numerous areas in the brain that produces a product, often thought to be novel in nature. Elaboration, fluency, and originality are three different components identified as assisting in developing creative ideas (Torrance, 1974; Plucker & Runco, 1998). Divergent thinking. Divergent thinking, also referred to as brainstorming, is the production of numerous responses to a single prompt (Guilford, 1950; Osborne, 1963). Elaboration. Elaboration refers to the number of details used to extend a response (Torrance, 1974). Flexibility. Flexibility refers to the number of different categories to which responses could belong. Flexibility represents a change in thought (Torrance, 1974). Fluency. Fluency refers to the number of relevant responses (Torrance, 1974). Originality. Originality refers to the number of unusual, but relevant ideas as measured by the statistical infrequency of the idea (Torrance, 1974). Partially proficient. A student’s score is partially proficient on a standardized state achievement test that is within a predetermined acceptable range in some subtest areas, but not in other subtest areas (Mendoza, 2006).

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8 Proficient. A student’s score is proficient on a standardized state achievement test if it meets the minimum requirements for acceptability as established by that state (Mendoza, 2006). Thinking styles. Thinking styles are “one’s preferred way of using the abilities we have” (Zhang, 2002, p. 247).

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9

CHAPTER 2 REVIEW OF LITERATURE

While current legislation has defined student and school success as scoring minimum proficiency on one yearly state-created, mandated test, researchers and education professionals have been critical of using a single measure to describe or predict a student’s or school’s success (NCLB, 2002, sect. 1001). One review of achievement data gathered globally revealed that no single predictor of student achievement could be identified (e.g. race, gender, socioeconomic status) (Heyneman, 2005). These conclusions suggest educational institutions should not focus solely on numerical achievement improvement. A review of the research in creativity suggests that creativity can be utilized to facilitate student learning (Douville, 2004; Hoh, 2005; Saunders-Wickes & Ward, 2006; Xin, 2007). The goal of creative education should not be to think creatively to perform better on a test; instead, the goal should be to enhance the thinking processes of students so that they solve problems and make decisions in creative, productive ways throughout their lives (Treffinger et al., 2003a). Traditional versus Nontraditional Instructional Practices Anecdotal reports from classroom teachers, specialists, and administrators in lowperforming and high-performing schools in four districts in Colorado indicated that instructional time was manipulated to serve students’ testing needs as opposed to their

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10 overall learning needs (Mendoza, 2006). Anecdotal information was gathered from regular classroom teachers of reading, writing, and mathematics. Mendoza asked participants to report the amount of time and energy spent teaching these subjects. An analysis of teachers’ averages showed students received instructional attention based on their performance status on previous testing. Students scoring below proficient received 24% of instructional attention, students scoring partially proficient received 39%, students scoring proficient received 26%, and students scoring advanced received 11%. Sixty-three percent of instructional time was spent on students who needed to pass a test. An alternative interpretation is 63% of instructional time was not used effectively for proficient or advanced students. This report suggests that all students are not receiving a high-quality education, but rather calculated instruction based on school data and school needs to perform at minimum proficiency (Mendoza, 2006). Professionals also suggested that focus has shifted to assist low-performing students in meeting statistical proficiency on achievement tests under the NCLB Act (Longo, 2010). Consequently, students labeled gifted or above average may receive less instructional time and resources. An investigation of state funding options for equitable education funding revealed that only two states provided adequate funding for students receiving gifted education (Baker & McIntire, 2003). Research showed that students from a low socioeconomic status and African Americans are further excluded from gifted education due to cultural and testing bias (Ambrose, 2004; Milner & Ford, 2007; Shaunessy et al., 2004; Ford et al., 2005). Research has shown that using minimum proficiency requirements on a summative state assessment does not benefit students

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11 (Mendoza, 2006; Lomax et al., 1995). The repercussions associated with not meeting NCLB requirements reinforce instructional practices that increase students’ scores on the yearly summative assessment (Liston, Whitcomb, & Borko, 2007; McMillian, 2003). Educators desiring more for their students than a proficiency score are viewed as nontraditional thinkers in the current direction of educational policy (Doherty & Hilberg, 2007). In terms of effective instructional practice beyond a summative assessment, research addresses the role of language and discussion. An important element in effective classrooms is the communication between students and their peers, as well as between students and teachers (Vygotsky, 1978). In Doherty and Hilberg’s (2007) replication of a previous study, they supported the original findings that student achievement in comprehension, language, reading and spelling could be predicted by teacher’s use of five standards of effective pedagogy. Low income, Latino students participated in the replication study. An increase in these minority and low income students’ achievement scores indicated that effective pedagogy and classroom organization can supersede test-taking strategies or instruction aimed at test performance. It is critical that educators desiring effective instructional practices while needing to maintain proficient test scores examine their instructional practices and classroom organization. In the study, researchers described five standards that should propel classroom instruction. These standards are joint activities involving discussions between teachers and students, language and literacy implementation across all content areas, connecting learning to all areas of students’ lives, requiring students to elaborate new learning, and planned small-group instruction (Doherty & Hilberg, 2007).

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12 Language and discussion reflect a significant portion of student achievement (Vygotsky, 1978; Graves, 2007). Instructional practices aimed at increasing student learning and achievement must take vocabulary instruction into account. Students must receive opportunities to communicate to solve problems and learn new information (Wu & Chiou, 2008). Creative production involves various areas of the brain (Starchenko et al., 2003; Chavez-Eakle et al., 2007). Creative production involves fluency, originality, and elaboration of ideas (Mouchiroud & Lubart, 2001; Wu & Chiou, 2008). All of these skills are necessary for vocabulary development and increased communication between students and their peers, as well as between students and teachers (Graves, 2007). Teachers aspiring to provide quality instruction that also allows students to meet proficiency on standardized tests could attain this goal by utilizing fluency, originality, and elaboration of ideas within the context of creativity. The Need for Creativity and Vocabulary Instruction The impact of standardized testing on students’ neurological development has been considered by researchers. Brain development and language acquisition research suggests that a single summative evaluation may actually have negative effects on students’ knowledge retention, mastery of content, and transfer of learning to new contexts. The diverse ways of representing knowledge disappears when only a multiple choice answer is desired. The Center for Educational Policy’s 5th annual report on NCLB indicated that more instructional time is spent on content that is tested, such as reading and math. The adjustments in instructional time to produce higher test scores resulted in a 32% average decrease in all other educational areas, especially social studies, science, art,

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13 music, physical education, lunch, and recess. The report further explains that school districts reported 84% of the reading and math instructional time was spent on teaching skills directly related to the standardized test (Center on Educational Policy, 2007). This additional instructional time in reading and math may be providing students with initial understandings in these content areas; however, learning occurs when students are able to transfer their knowledge to other situations. If learning is contextualized within the confines of a standardized test, students may not be able to use what they have learned outside of that context. There may be no transfer of their learning (National Research Council, 2000). Additional content areas, such as science, social studies, art, and music, provide different learning experiences for students to transfer their initial understanding and master content knowledge (Plucker & Zabelina, 2009). During instructional time complex ways of understanding, such as metaphors, analogies, imagery in spoken and written forms, and illustrations are necessary for students’ comprehension regardless of the measurement (Williamson, Bondy, Langly, & Mayne, 2005; Hurren et al., 2006). In one case study, researchers examined two teachers in a low performing urban elementary school. These two teachers’ students scored significantly higher on the state assessment than other students in their school. Instead of using teaching material matching the standardized test, both teachers demanded a deep level of understanding from their students through explanations, written defenses, and performance-based assessments of their learning. These teachers were aware of the standards their students needed to meet, but with high-quality, brain-based strategies,

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14 100% of their students passed the state assessments. Most of their students scored above proficient (Williamson et al., 2005). Educational brain research acknowledges the fact that a variety of on-going formative assessments is necessary to provide environments in which mastery of content knowledge and deep understanding can occur. A fixed summative assessment can inhibit understanding and mastery of content. Further, reliance on a single, rigid performance style may also have unforeseen consequences for students and schools. Even though standardized test scores at the state level are increasing at the elementary and middle school levels, content mastery on higher-order thinking skills for high school students on the national level is decreasing (Center on Educational Policy, 2007). Other studies of the effects of NCLB have indicated that many schools’ test scores are increasing because their dropout rates are increasing. Students unable to meet the standard requirements for testing tend to drop out of school, especially if a diploma hinges on meeting this testing requirement (Balfanz, Legters, West, & Weber, 2007). Similarly, experiences and environments affect how the brain develops. New learning is developed and enhanced when it is applied to a variety of contexts (National Research Council, 2000; Brinkman, 2010). For example, students may learn how to divide fractions in a math class. However, to engage in a deeper level of understanding and facilitate transfer of learning, students would benefit from manipulating recipes with fractional parts. Deeper learning would occur if students then used the fractional manipulations to create the recipe. Language acquisition and memory studies also show “the developing and the mature brain are structurally altered when learning occurs”

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15 (NRC, 2000, p. 125-126). Early language experiences can affect word learning. Children who had larger word knowledge were able to learn and produce more original words than their same-age peers (Hoh, 2005; Mills et al., 2004). Based on their findings, researchers from the NRC (2000) concluded that the type of instruction impacts the learning and the learner. The most efficient and productive method for ensuring students perform well in school is to provide high-quality brain-based instruction with high performance expectations. In the midst of teacher accountability of student performance on a standardized, summative assessment, many educators are seeking to provide this quality, brain-based educational experience to students that will extend beyond the competencies required to pass a test (Brimijoin, 2005; Hurren et al., 2006). Learning opportunities shape the brain and impact future learning significantly because of dendrites. Dendrites are parts of the nerve that branch out from the center of the neuron. A neurological misconception is that all of the brain’s connective abilities form shortly after birth. When connections are not made among areas of the brain through dendrites, the brain will prune connections that are not used to increase efficiency (Willis, 2007). While an overwhelming abundance of dendrites are formed during infant development and early childhood, there are many other periods during development that dendrites reconnect and adapt. Pruning actively occurs to dendrites that are not exercised so that new connections can be formed through new experiences (NRC, 2000). In one ten-year longitudinal study, thirteen children and adolescents’ brains were scanned every 2 years using magnetic resonance imagery (MRI). Researchers asserted

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16 that mapping the brain development during childhood and early adulthood of the same subjects could provide comparison images from which to draw conclusions about brain development and disorders. Results affirmed that although brain development in individuals is nonlinear, there are patterns of development that typically occurred in most participants (Gogtay et al., 2004). Specifically they found that higher-order processing areas developed after lowerorder processing areas developed. First, typically the frontal lobe matured from the back to the front, then the superior and inferior frontal gyri matured, and the prefrontal cortex matured last. The frontal lobe is a part of the cerebral cortex that regulates problem solving and reasoning. The superior and inferior frontal gyri assist in the cognitive understandings of nuances in language and thought. A gyrus is a fold in the brain that creates efficiency in processing complex information in the cerebral cortex. The prefrontal cortex regulates self-control of impulses and discretion in decision-making. These areas of the brain that regulate decision-making are called executive functioning areas (Gogtay et al., 2004). Executive functioning areas of the brain, the frontal lobe and prefrontal cortex, regulate complex thought processes, such as problem solving, fluency and flexibility of ideas, and judgment (Starchenko et al., 2003; Chavez-Eakle et al., 2007; Abraham & Windmann, 2007). In many cases, the type of test used to measure thinking dictate the area of the brain engaged during the thought processes (Rosalind, Chavez, Grazioplene, & Jung, 2010). Other studies have found correlations between cortical brain development and intelligence measures, particularly the vocabulary subtest of the Weschler

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17 intelligence tests and the Creative Achievement Questionnaire (Shaw et al., 2006; Jung et al., 2010). Other researchers have confirmed that learning experiences shape the brain and future learning. One area that has been investigated is the role the formation of myelin sheaths (myelination) around nerve fibers play in language development. Myelin is a fatty substance which protects nerves in the brain and ensures correct electrical transmissions of information across neurological networks. In one study, 241 neurologically healthy children from birth to 8.25 years old contributed magnetic resonance images (MRI) to researchers in a Tokyo hospital. Researchers analyzed seven language-related areas of participants’ brains for myelination as compared to 25 adolescents and adults, who served as a control group. The myelination process for all participants fit a similar curve: myelination began at birth, matured at approximate 1.5 years, and slowed into adulthood. This study suggests that rapid myelin production until 1.5 years facilitated increased vocabulary growth during this developmental period; however, results also suggest that vocabulary and language development continue into adulthood, consistent with myelination (Su, Kuan, Kaga, Sano, & Mima, 2008). Additional studies confirm that a wide distribution of brain activity affecting vocabulary occurs during early childhood. Children from approximately 12-14 months use frontal, anterior temporal, temporal, parietal, and occipital lobes to process unfamiliar and nonsensical words (Mills et. al, 2004). Vocabulary development in early age ranges increases dramatically, and brain activity significantly reorganizes during this time. Electroencephalographs (EEG) of children with small vocabularies for their age range

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18 show neural activity across many areas of the brain when presented with stimuli as opposed to children with large vocabularies for their age range. Electroencephalograph scans of children with smaller vocabularies indicated neural activity in fewer, more localized brain areas when presented with stimuli. In one study, children age 14-20 months listened to 30 words. Researchers measured the response to these stimuli using event-related potential (ERP), via electroencephalographs. Ten words were words known to the participant, 10 words were unknown to the participant, and the 10 remaining words were phonetically similar unknown words (Mills et al., 2004). Researchers hypothesized that the participants with less developed vocabulary knowledge would become confused by the 10 phonetically similar but unknown words. Consequently, those participants’ ERPs would show a great distribution of brain activity across many lobes. The participants with more developed vocabulary knowledge would be less confused by the 10 unknown, but phonetically similar words because their vocabulary knowledge should be more localized in specific brain areas. This localization of brain activity should be reflected on ERPs. Their results indicate that children as young as 20-months-old create neural distinctions for phonetically similar words whether those words are familiar to them or not (Mills et al., 2004). This study supports the assertion that learning reorganizes neural networks and structure underscoring the educational need for effective vocabulary instruction (NRC, 2000). Adolescent studies of vocabulary learning also demonstrate similar results that various areas of the brain are required for vocabulary learning. Contrary to previous

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19 neurological understanding about brain development, recent brain research validates that the brain does not fully develop in humans until early adulthood (Shaw et al., 2006; Gogtay et al., 2004). Changes in neurological understandings have prompted researchers to advocate for increased study of late childhood and adolescent brain development. These researchers suggest that developmental changes in children and adolescents’ brains are influenced by learning and environmental experiences (Giedd et al., 1999; Durston et al., 2001). One study of adolescent vocabulary development reveals that diverse learning activities increased vocabulary for students in seventh and ninth grade. With multiple and varied learning experiences in higher-order thinking using difficult vocabulary, students’ scores in word learning increased. Students’ scores on verbal ability tests increased as well (Khan, 1972). A more recent study explored correlations between the selected subtests of the Delis-Kaplan Executive Function System and Wechsler Abbreviated Scale of Intelligence (WAIS). The Delis-Kaplan measures higher-order cognitive skills. The WAIS measures general intelligence through verbal and performance tasks. In the Switching Condition Subtest of the Delis-Kaplan, for example, participants must draw as many designs as possible in 60 seconds while alternating between connecting filled and empty dots. The purpose of the study was to determine if a relationship existed between measures of higher-order, creative brain production and lower-level, knowledge-based brain production. The researchers argued that students performing high on knowledge-based measures, such as the IQ test, might not perform as well on measures of critical thinking (Delis et al., 2007).

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20 Similarly, they argued that high performers on higher-order thinking measures might score lower on knowledge-based IQ measures. Four hundred seventy students 8 to 19-years-old took both measures. Pearson correlations among the subtests of the Delis Kaplan Executive Function System and the WAIS were very weak (highest r = .427). Correlations on the verbal subtest of the WAIS, the subtest focused most on vocabulary and vocabulary comprehension, were highest with the Sort Recognition Description subtest and lowest with the Color/Word Interference subtest of the Delis-Kaplan. In the Sort Recognition Description subtest, participants provide eight rules for sorting six cards into two groups with three cards in each group. The Color/Word Interference subtest required participants to read color names written in the named color or in a different color. For example, a participant would see the word “red” written in yellow and need to say “red” (Delis et al., 2007). Further, a large number of participants demonstrated significantly higher scores on either the Delis-Kaplan or the WAIS. This result indicated that participants in this sample had experience with either knowledge-based learning or higher-order thinking learning, but not both. Researchers concluded that the results indicated that instructional practices in education should shift to include instruction in both knowledge-based learning and higher-order thinking in order to increase both areas of learning (Delis et al., 2007). Interaction between knowledge-based learning and higher-order thinking is essential for vocabulary development. An extensive meta-analysis by a subgroup of the National Reading Panel revealed a variety of instructional methods that impact

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21 vocabulary development. These methods include explicit instruction, indirect instruction, multimedia methods, capacity methods, and association methods. Their results indicated, that of the 73 studies analyzed, only one reported that vocabulary had no significant impact on long term reading comprehension (NICHHD, 2007). The other 72 studies have validated their assertion that vocabulary directly impacts reading comprehension. The vocabulary subtest of the Iowa Silent Reading Test served as a significant predictor of reading comprehension results among independent reading variables, such a semantics, deduction, and verbal knowledge for a sample of 165 underachieving college freshmen (Farley & Elmore, 1992). While many reports list vocabulary instruction and development as essential components of effective reading instruction, vocabulary acquisition and retrieval for short- and long-term comprehension remains a complex process (Braze, Tabor, Shankweiler, & Mencl, 2007). Neurological understanding of vocabulary comprehension, its processes, and effects on reading can help to suggest which instructional methods could most benefit students (Tuller, Jantzen, Olvera, Steinberg, & Kelso, 2007). The National Institute of Child Health and Human Development’s (2000) sub-report on Vocabulary Instruction recommended that the focus of future reading research should include the effectiveness of vocabulary instruction using active learning tasks, evaluation of the size, acquisition, and retention of vocabulary, specific grade-level vocabulary instructional needs, and the integration of vocabulary instruction for the greatest educational gains.

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22 Creative Thinking Instruction Similar to the area of vocabulary, research on creativity reveals the complicated nature between learning and the brain. The research underscores the limited scope of a standardized test or single method for measuring teaching and learning, particularly for vocabulary instruction and retention of knowledge. Creativity research provides a framework for finding mental processes that are likely to influence student achievement, while also attending to the learner’s educational and neurological needs. In order to understand a creative framework, attention must be given to the various approaches used in defining creativity and describing the most widely applied creative teaching methods. The definition of creativity has evolved across numerous studies in psychology, education, cultural understanding, and neuroscience; thus, defining creativity fundamentally depends on the approach taken. Guilford’s (1950) speech to the American Psychological Association annual meeting sparked interest in the academic research of creativity. Beginning in 1950, and continuing through the 1980s, creativity was studied primarily through a psychological lens. Whether an individual was considered creative depended on intelligence, birth order, or personality type; all of which were thought to be unalterable, predisposed individual constructs (Runco & Albert, 1987; Albert & Runco, 1989; Barron & Harrington, 1981). The advent of intelligence tests began to differentiate creativity from intelligence. Although correlated and useful in the production of creative thought, high intelligence, as measured by a high Intelligence Quotient (IQ) score, is not currently assigned as a prerequisite for creativity (Cramond et al., 2005). Torrance’s research and creation of a test specifically designed to measure creativity, independent of

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23 an intelligence quotient, established an original way of thinking about creativity, how creativity was produced, and how it was measured. These tests measure creativity using fluency, flexibility, originality, and elaboration of ideas. The Torrance Test of Creative Thinking continues to be used as the primary assessment measure for student qualification into gifted and exceptional programs. Although the test has remained consistent since 1980, the norms have been updated (Torrance, 1974; Cramond et al., 2005). Torrance’s landmark research in creativity provided specific criteria to describe creativity as a mental process. His contributions to the field, spanning four decades, focused on developing creativity measurements that were less rigid than multiple-choice, single answer assessments (Torrance, 1974; Torrance, 1995). While Torrance was conducting his research on creativity, other researchers were studying the mental health of creative individuals. They produced theories that creative people were mentally disordered. This research sought to understand creativity through describing unique and talented individuals’ mental deficiencies or disorders (Becker, 1978). Through either the intellectual or personality lens, creativity was seen as a fixed entity dependent on individual differences (Martindale & Hasenfus, 1978). A landmark study of the brain’s role in creativity stimulated the study of creativity as scientific; consequently, researchers began studying the anatomy of creativity as a thinking process instead of an individual difference. Creativity was not a fixed trait genetically determined at birth, but it was a way of thinking and could be altered. Neurology and psychology eventually merged to suggest that a variety of brain processes in diverse areas of the brain produced creative thought (Martindale & Hasenfus, 1978).

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24 In addition to the previous research on the components of creativity, neuroscience has enriched the study of creativity in a dramatic way. Neuroscience research establishes that creativity is an adaptable process. This finding is important to educators since creativity centers of the brain are also the higher-order processing centers of the brain. Educators can use creativity to facilitate learning in classrooms at higher levels than those expected on standardized tests. In their seminal study using brain encoding as a method to study creativity, Martindale & Hasenfus (1978) found that the pattern of brain waves changed when participants were asked to create mental stories. His research suggested that creativity was not fixed intelligence or a pervasive trait, but rather a mental process requiring mental flexibility and communication of those abilities. More recent research verifies Martindale’s & Hasenfus’s (1978) findings that creativity is multi-faceted brain activity and not a fixed individual trait (Albrecht, 2002; El-Murad & West, 2004). Neuroscience creativity research studies where specific components of creativity occur. This type of approach is similar to the one taken within educational research; however, unlike the findings and uses of educational creativity research, neuroscience research has found that creativity is a complex mental process involving many brain areas (Mashal, Faust, Hendler, & Jung-Beeman, 2007; Grabner, Fink, & Neubauer, 2007; Abraham & Windmann, 2007). Synthesis of neuroscience creativity studies indicated the gyrus, frontal cortex, and right hemisphere are localized areas critical to specific aspects of creative thinking and production (Mihov, Denzler, & Forster, 2010; Moore et al., 2009). Similar to vocabulary acquisition, comprehension, and long-term memory, creativity’s components

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25 can be localized in specific brain centers; however, the variety of neuroscience research suggests all localized brain productions only contribute to the larger process of creativity, and are not creativity themselves. Starchenko et al. (2003) used positron-emission tomography to track brain patterns when participants linked sequential words with associated words (creative task), categorized information (complexity isolator), and read aloud (control). Using positron-emission tomography provides an alternate method of measurement to the electroencephalograph method. This enables researchers to have convergent validity across methods. Their comparison of levels of activation in each task pinpointed the supramarginal gyrus as one area consistently activated during the creative task. They identified the supramarginal gyrus as a brain section necessary for flexibility in thinking. Due to the other brain areas activated during the three tasks, the researchers concluded that creativity includes selective brain attention, internal selection, and flexibility of thought (Starchenko et al., 2003). Other neuroscientists have used brain cerebral blood flow (CBF) to add to the study of the process and neural activity associated with creativity. Researchers used a radioactive tracing agent injected into participants’ blood streams. Chavez-Eakle et al. (2007) confirmed the use of the gyral brain areas in creative thought. They used Single Photon Emission Computerized Tomography (SPECT) to trace the image of the radioactive tracer in the cerebral blood flow of 100 participants. SPECT uses Gamma rays to produce a scan similar to x-ray imagery. Forty nationally and internationally awarded scientists and artists were selected to participate using the Torrance Tests of Creative Thinking (TTCT) (Torrance, 1974). Researchers administered two subtasks of

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26 the TTCT, “Just Suppose” and “Unusual Uses,” in a dark room while participants’ CBF was measured using SPECT. Image processing analysis was performed and a regression formula was used to compare CBF as the dependent variable and the creativity measures as independent variables. The results of Chavez-Eakle et al.’s (2007) study supported the results of Starchenko et al. (2003) that the frontal gyral areas contribute to the fluency of creative ideas. The prefrontal cortex also emerged as a neural area affecting creative function. The way the prefrontal cortex structures and monitors conscious thoughts are critical to creativity. The prefrontal cortex serves as the brain center for planning and monitoring the execution of tasks (Abraham & Windmann, 2007). Their research supports the contention that creativity is not one type of thinking or thought product (i.e. creativity equals brainstorming as many ideas as possible), but a complex mental process requiring various areas of the brain. Additionally, CBF further supports the PET scan methods in identifying similar areas for the production of original ideas. Chavez-Eakle et al.’s (2007) results also confirmed that participants identified as creative according to other measures had increased CBF in areas of the brain previous research identified. The major conclusion of Chavez-Eakle et al.’s research suggested that creative brain function requires a neural network that accesses designated brain areas repeatedly. The more these designated brain areas function together, the more developed the neural network becomes. A developed neural network facilitates creative thought (Chavez-Eakle et al., 2007).

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27 While neurology can specify neural areas involved in creative production, there are dangers of assigning specific areas of the brain to creative function. An opposing viewpoint about neurology and creativity highlights that the same localized areas assigned to producing creative aspects more regularly produce unoriginal ideas (Dietrich, 2007). A review of neuroscience literature aids the discussion of creativity by providing support that creativity is more than an unchangeable trait or disposition, more than discrete aspects or products, and more than one area of the brain to engage with another. Instead, creative brain research underscores the ability to find mental structures and processes already identified and merge them with educational understanding of teaching and learning to infuse creativity into curricula and education (Dietrich, 2007). In part due to its many elements, educational research in creativity has focused on a single component of creativity at a time, and its impact on students’ learning, as opposed to creativity as a predetermined individual difference or disorder. Some of those aspects have included language production, processing speed, use of imagery, and problem-solving skills (Douville, 2004; Hoh, 2005; Saunders-Wickes & Ward, 2006, Xin, 2007). Educational researchers have focused on components of creativity, such as metacognition or problem solving, that when taught may directly increase student performance on standardized tests. Instruction in creativity in discrete content areas has shown to increase creative production in those areas (Cheng et al., 2010). Goldberg and Bush (2003) determined that explicit instruction in metacognitive thinking within the context of mathematics problem solving instruction increased third graders’ ability to solve mathematics problems with greater accuracy and understanding than if they had not

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28 used metacognitive thinking skills. Jitendra et al. (2005) studied 77 third grade participants’ word problem solving skills through winter and spring. They compared these curriculum performance measures to each student’s performance on an end-of-theyear state assessment. Researchers concluded that word problem solving performance was correlated with performance on the achievement test (r = .71). Students who were better word problem solvers were more likely to score higher on the achievement test. Researchers have also conducted studies on thinking styles and critical thinking. After reviewing the literature on thinking styles, Zhang (2002) categorized the variety of thinking styles into two categories: holistic/creative versus simplistic/norm affirming. Zhang defined thinking styles as the “preferred way of using the abilities we have” (p. 247). His literature review expressed the need to use a variety of specific thinking skills or strategies to learn and understand. Zhang labeled the interchange between thinking styles in an individual as similar to the process used in creative thinking. The executive functioning brain areas are used both in managing thinking styles and creativity. Given the available evidence about the instructional benefits specific components of creativity have on learning, brain-based instruction has also emerged. Some educators have begun to reject the idea that minimum proficiency should be the desired educational performance goal (Brimijoin, 2005; Hurren et al., 2006). Instead some educators and researchers have written guides attempting to increase the quality of instruction for all students through a creative brain-based approach (Treffinger et al., 2003a; Treffinger et al., 2003b; Treffinger et al., 2003c; Tate, 2003). Study of isolated components of creativity does not enhance the understanding of creativity as a mental

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29 process. Even though studies validate that increased use of creative components is correlated with increased academic success, primary researchers in the field of creativity agree that creativity should be studied and defined as a process (Douville, 2004; Hoh, 2005; Saunders-Wickes & Ward, 2006; Xin, 2007; Runco, 1993; Sternberg & Lubart, 1993; Torrance, 1995). Furthermore, educators can teach creativity aspects to increase students’ discrete skill performance, but a thorough understanding of creativity and teaching students to think creatively will better benefit overall instruction and student performance. In a book aimed at classroom teachers, Classroom Instruction that Works, the focus of the research is not on specific vocabulary acquisition strategies or memory devices, but instead on outlining and describing overarching thinking and organizational strategies (Marzano, Pickering, & Pollock, 2004). Educational creativity research needs a similar construct about teaching a process of creative thinking, which includes specific component study and brain-based strategies. A unified theory of creativity is needed because the approaches, components, confounding variables, and methodologies used to study creativity are numerous and varied. Although other approaches to studying creativity exist, the historically significant and formative approaches to the field have been presented to discuss the current understanding of creativity research. The major contributions in the field of psychology have focused on the measurement of an individual or product’s level of creativity. These measurements, while beneficial to identifying and distinguishing creative people or situations, have not helped to define creativity in educational settings or extend the

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30 understanding of it. Therefore, it has been difficult for educators to widely, effectively implement creativity instruction in educational settings (Plucker & Runco, 1998). Many of the educational contributions to understanding creativity and learning have provided a plethora of narrow definitions of creativity; ultimately, these single component views of creativity undermine a larger, deeper understanding of creativity. Additionally, these narrowly focused studies may have further confounded the understanding of creativity in educational practice (Sternberg & Lubart, 1999). Similarly, employing a neuroscience understanding of creativity alone will offer only one view of how creativity operates as a biological function. A neuroscience approach does not explain individual differences in creative productions or answer the question of whether creativity can be taught (Sternberg & Lubart, 1999). The creative cognitive approach attempts to combine psychological understandings of creativity with recent brain research; however, this approach is limited by ignoring environmental and individual difference (Dietrich, 2007; Sternberg & Lubart, 1999). Sternberg & Lubart (1999) have proposed the investment theory which attempts to unify previous research into a single coherent theory. Their theory merges psychological and neurological understandings to define, study, and evaluate creativity. Investment theory defines creativity as the convergence of and interaction between “intellectual abilities, knowledge, styles of thinking, personality, motivation, and environment” (Sternberg & Lubart, 1995, p. 3). Sternberg and Lubart contend that an Intelligence Quotient is an arbitrary measure, especially when used as a measure for the identification of creative people for gifted programs. While they admit that creativity

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31 might be a complement to some people’s high intelligence, they also cited instances in which people were creative with normal intelligence or creative in nontraditional ways. Their theory describes an evolving process of exchange between these six factors (intelligence, knowledge, thinking style, personality, motivation, and environment) to produce creative thought, products, or solutions. They assert that one factor could supersede or compensate for another factor given the situation or the individual. Investment theory research has been analyzed and validated in a variety of studies and discussions (Sternberg, 2007; Sternberg & Lubart, 1995; Zhang & Sterberg, 2000; Sternberg & Grigorenko, 2004; Sternberg, 2000). According to this theory, intellectual abilities are not static or an exclusive prerequisite for creative thought; moreover, one’s knowledge, although partially dependent upon intelligence, can enhance or impede creativity (Sternberg, 2002). The more a student knows about a topic, the more interested they may become and learn more about that topic. Students with prior knowledge are also more likely to make connections or generate ideas to support creative thought. This knowledge is true of vocabulary also. Students with larger vocabularies are more likely to make connections to new words or new word meanings (Mills et al., 2004). Investment theory categorized divergent thinking, metacognitive abilities, and other higher-order thinking processes within the intellectual abilities category. Parts of intellectual ability are the ability to see nuances to problems, comparisons across problems, and combinations of problems. Memory and retrieval processes are critical in the knowledge ability. Sternberg and Lubart (1999) acknowledged that creative individuals typically spend time in areas of interest; however,

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32 they also suggest that learned abilities and information in a specific field can spark creative products and processes in individuals. Their research into a unified theory of creativity suggests that creativity can be learned. Some researchers have attempted to develop the components of investment theory (Zhang, 2002). Thinking style is one component. Zhang categorized numerous thinking style descriptors into two groups: legislative and executive. Legislative thinking styles included those that referenced risk-taking, global thinking, and liberal styles. This style prefers open-ended questions or challenges. Executive thinking styles included those favoring authority and confirming norms. This style prefers logic and rule following to solve problems. Further, he qualified executive thinking styles as those focusing on understanding at a minimal level. Zhang hypothesized that people who used the legislative thinking style would naturally exhibit more creativity than people who used the executive thinking style. He further suggested that using the legislative thinking style, even if it was not the preferred style of an individual would help to facilitate and enhance creativity. Zhang obtained two thinking styles inventories and compared them to Torrance’s Styles of Learning and Thinking (SOLAT), which attempts to measure left and right brain hemisphere use in thinking. Participants were 371 male and female first-year students and the University of Hong Kong. Zhang found that the SOLAT measure correlated across factors with the thinking styles inventory; there were no gender differences found among Zhang’s analyses. Zhang’s thinking style measures’ correlation (p = .32) with the SOLAT data moderately confirms that students scoring high on legislative thinking styles measures also scored high on the SOLAT’s holistic scale.

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33 Torrance also used the SOLAT holistic scale to identify creative thinking. These results indicate that creative thinking, and consequently teaching students to think creatively, involves holistic, global, and complex information processing. These results further validated Sternberg and Lubart’s suggestion that creativity can be taught (Zhang, 2002; Sternberg & Lubart, 1999). Personality, motivation and environment cannot be ignored with respect to understanding creativity. Within investment theory, the two personality characteristics highlighted as most significant were tolerance of ambiguity and moderate risk-taking. Tolerance of ambiguity is the ability to view many choices as possibly the best and select one choice using self-control. Moderate risk-taking is the willingness to take risks in thinking, learning, and areas of production that may or may not offer benefit in the long run (Sternberg & Lubart, 1999). As highlighted above, numerous research studies focused on correlating people generally believed to be creative individuals, Albert Einstein or Ludwig van Beethoven, with personality characteristics. Creative individuals have been labeled as emotionally sensitive, imaginative, independent, and lacking warmth (Walker, Koestner, & Hum, 1995; Ludwig, 1995; Marchant-Haycox & Wilson, 1992; Dudek et al., 1993). These previous studies assume that creativity is a fixed personality trait that cannot be altered or increased. Current research, however, supports that people allowed to pursue interests they love tend to perform with increased creativity (Collins & Amabile, 1999; Torrance, 1987). Cultures also have different ways of defining creativity. Many Eastern cultures view creative expression without respect to intelligence, academic

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34 aptitude, or a creative product. These cultures often view creativity as a reinterpretation of a traditional understanding which could advance the common good or provide inspiration (Seo, Lee & Kim, 2005). Additionally, the school environment and the socioeconomic status of students affect performance on creativity measures (Dudek et al., 1993, Sternberg, 2002; Sternberg & Grigorenko, 2004). Studies have revealed that creativity can be taught (Torrance, 1987; Sternberg, 2002). However, with the multitude of factors influencing creativity, it raises the question: Can teachers systematically and demonstrably enhance creativity in students? Creativity must be accurately operationally defined and measured before its enhancement can be validated. The definition and measurement of creativity depends heavily upon the researchers and their perspectives. As a response to the growing trend to quantify and standardize student achievement, creativity advocates developed the Creative Problem Solving Model (CPS). The CPS Model attempts to provide educators with an accessible tool to teach creativity within educational contexts. Researchers utilized creativity research that promoted student achievement (Treffinger & Isaksen, 2005; Treffinger et al., 2002). This model relies on a unified theory of creativity, and explores the evaluative and sequential thought processes associated with creative thinking (Treffinger & Isaksen, 2005). Creative Problem Solving is constructed to teach and measure creativity as a problem solving process (Treffinger & Isaksen, 2005). Originally developed in 1952, the CPS Model has undergone decades of refinement for application and use within education for gifted students. The current CPS Model Version 6.1 provides a framework for individuals or groups to think creatively to

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35 solve problems. Treffinger’s and Isaksen’s (2005) current version of CPS involves three steps to solving problems creatively. Problems are termed “tasks” throughout the model, and they refer to any challenge that is open-ended, complex, important to the user of the model, or ambiguous. In this way, the CPS model can be used with academic tasks, personal tasks, social tasks, or leadership tasks. It can be used individually or with groups. There are three steps in the CPS model: Understanding the Challenge, Generating Ideas, and Preparing for Action. In step one, Understanding the Challenge, participants identify the broad problem and context. To engage in this step of the model, students must work through three stages: Constructing Opportunities, Exploring Data, and Framing Problems. Constructing Opportunities underscores the assumption that to creatively solve problems, students must want to solve these problems. Research supports that creativity is improved by motivation (Collins & Amabile, 1999). In this stage, the students state the task. An example could be, “I wish, want, or need…” The statement of the task should summarize the major theme or issue of the task. Students then will use WIBAI and WIBNI (“Wouldn’t it be awful if…” and “Wouldn’t it be nice if…”) to create statements about the task. These statements should be revised repeatedly at this stage. The final part of this stage involves the students choosing one WIBNI to provide direction for the remaining two steps in the CPS model. After completing the Constructing Opportunities stage, students will have a clear idea about what direction the rest of the CPS model will take (Treffinger et al., 2006).

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36 The students progress to Exploring Data. Students use questions to more fully understand the task. Data is gathered form a variety of perspectives about information, feelings, observations, impressions, and questions. Although this stage is framed as an exploration, a considerable amount of divergent thinking (originality) occurs during this stage. The final stage of Understanding the Challenge is Framing Problems. Students should restate the original task and review the WIBNI, adding key findings from the Exploring Data stage. Students then use introductory statements to frame the problem in a concise way. Example statements are: “In what ways might…”, “How might…”, and “How to….” At this point the problem should be framed in an open-ended way. Students should leave Step One: Understanding the Challenge with an open-ended question (Treffinger et al., 2006). In step two, Generating Ideas, participants brainstorm possible solutions to the framed problem. Divergent thinking is a main component of this step; however, during this step, participants should evaluate possible solutions to determine the feasibility of each solution. Students are prompted to look beyond the obvious solutions (originality), name many and different possibilities (fluency), and think of useful ideas. In step three, Preparing for Action, participants select the most realistic solutions and develop a plan of action to execute the solution. Participants are encouraged to work cooperatively to build consensus for the selected solution if they are working in a group. Developing Solutions and Building Acceptance are the two stages in this step. Students should only engage in step three when they have promising ideas for their task and are ready to put them into use. Developing Solutions involves identifying criteria that must

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37 be met for the solution to their task. Students should ask, “Will it…” Then, goals and strategies for turning the ideas into solutions are stated. The second stage of this step is Building Acceptance, which seeks to fully consider the implementation of the solution. Students are developing an action plan in this step, as well as alternative plans based on potential changes to the task (Treffinger et al., 2006). The CPS Model 6.1 fundamentally suggests that creativity can be enhanced. The CPS Model 6.1 does not explicitly use investment theory as a conceptual basis; however, assumptions associated with the model indicate a similar, unified understanding of creativity to investment theory. For example, researchers hypothesized that individuals and groups can be creative in varying degrees in a variety of circumstances, individuals will exhibit creative solutions in areas in which they are interested, and creativity and productivity can and should exist (Treffinger & Isaksen, 2005). These ideas are supported by the underpinnings of investment theory and research suggesting domain-specific creativity (Sternberg & Lubart, 1999; Han & Marvin, 2002). The CPS Model’s design increases individuals’ access to creativity by providing explicit instruction in creativity, coordinating the CPS prompts with current instruction and national standards associated with the NCLB Act, and accounting for individual differences (Treffinger & Isaksen, 2005). Educators outside of the field of gifted education have used the Creative Problem Solving Model as a problem-based learning tool (Sewell, Fuller, Murphy, & Funnell, 2002; Morgan, 2003). Treffinger et al. (2006) created a kit with a leader’s guide to assist educators with instruction, materials, and resources in order to implement the Creative Problem Solving

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38 Model in regular education classrooms. The kit is designed to make each step of the creative problem solving process explicit. For example, during the brainstorming phase of step two, tool cards offer suggestions and instructional strategies to assist in students’ generation of ideas. One such strategy is to look for connections, in which the card provides a definition of the strategy and ways to explain it to students. An example is given on the tool card with which students can practice the new strategy (Treffinger et al., 2006). Several positive effects of the CPS model are evident when it has been used with general education students. Sewell et al. (2002) studied the application of an adapted CPS Model for use in two classroom case studies. They found that teaching Creative Problem Solving increased their adolescent students’ motivation, peer interactions, and achievement. Teachers modified the existing CPS model into a six-step problem-solving process. Steps one through six included: Sensing problems, fact-finding, problem finding, idea finding, solution finding, and making it happen. Case studies were used to describe the use of the six-step process. In the first case study, a teacher used the CPS Model with her Year 3 (3rd grade) students to reduce litter around their school. She walked with students around the school asking them to note what areas needed improvement (sensing problems). Students agreed that litter on the playground was the most concerning problem, and the teacher framed questions about the litter (fact-finding). After research about the effects of littering (problem finding), students brainstormed possible solutions to reduce littering (idea finding), and created criteria for selecting a final solution

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39 (solution finding). Finally, students presented their solution during a student assembly (making it happen) (Sewell et al., 2002). In the second case study, a different teacher-researcher used the modified CPS Model with older students in Year 6/7 (6th/7th grade) to solve three different problems at their school creatively. As high-stakes testing becomes the norm, educators must engage learners in ways authentic to their biology to increase their achievement. These case studies and students’ responses to the CPS Model suggest that creative problem solving could help educators use brain-based instruction while increasing student achievement and creativity. Sewell et al. (2002) interviewed students participating in each case study and found that they reported higher levels of purpose in their learning than they reported prior to the CPS instruction, cooperation among classmates, and ownership of their learning. Moreover, teaching creative problem solving can be effective in addressing adolescents’ learning needs while increasing achievement (Sewell et al., 2002). All adolescents, not only those labeled as creative or gifted, need different types of instruction during their transition from elementary to middle school. Veenman, Kok, and Blote (2005) found that adolescents, aged 12-13, in Delft, The Netherlands, scored higher on achievement measures when cued using previously taught metacognitive strategies. These findings support similar research with younger students that instruction in metacognitive strategies, a previously described component of creativity, can be effective at raising student achievement for adolescents (Jitendra et al., 2005). Other researchers have explored the use of problem-based learning or critical problem solving methods to increase adolescents’ achievement, through analysis of

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40 motivation, problem-solving skills and critical thinking skills (Paynton, Carlson, Hopper, & Carey, 2006; Meece, 2003; Song & Grabowski, 2006). Song and Grabowski (2006) theorized that adolescents needed different types of instruction to support internal motivation for problem solving during the physiological, cognitive shift from concrete to abstract thinking during adolescent development. Their results showed that students assigned to an experimental group in which researchers stressed learning new ideas scored higher on a measure of intrinsic motivation adapted by the researchers than those students assigned to an experimental group in which researchers stressed producing a specific product. These results suggest that students in middle grades have increased desire to perform when learning opportunities are flexible and provide real-world context. One limitation of the study was the lack of a clearly identified learning process for students to work within as they solved problems. Attention should be given to the creative needs of students with respect to their developmental and neurological stages. Researchers believe that adolescent needs and desires change across time and context, although certain biological and psychological developmental stages are common to most adolescents. One study hoped to provide insight into adolescence for educators. Researchers provided open-ended questions for participants to answer. Scorers identified common phrases based on previous adolescent research that they expected to see in students’ responses to these writing prompts. The scorers’ coding attempted to ensure a certain level of uniformity in rating students’ descriptive responses. Participants were 88% of 14-18-year-old adolescent students in health classes in a western state. No reward or punishment was given for choosing to

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41 participate in the study, which suggests that adolescents have high internal motivation to perform when the questions or problem constructs are applicable to their lives (Girod, Pardales, Cavanaugh, & Wadsworth, 2005). Participants were told that they were participating in a study to provide teachers information about what adolescents think teachers should know about them. First, patterns in participants’ choice of which questions to answer were analyzed. Although questions were written to reflect current research on adolescent concerns and issues, researchers found that students choose certain questions more frequently than others. The three questions participants most frequently chose to answer were: “What makes a good teacher? What can teachers do to be more helpful to you?,” “What’s important to you? What kind of things do you worry about and why?,” and “Why do some teens use drugs and alcohol, and what do teachers need to know about it?”. Analyzing these patterns led researchers to conclude that participants most likely chose the first question because of the provided description of the purpose of the study. Students were told that the target audience would be teachers who wanted to help adolescents learn, so the researchers hypothesized that this context may have increased the frequency of the first question. The second question was chosen by a wide variety of participants, regardless of age, health class, or high school. The third question was chosen 60 out of 69 times by students at one high school known for high drug use. Within each written response, researchers looked for patterns that emerged in students’ responses that may not have been a part of their original coding system. These patterns were found among a

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42 statistically significant number of responses and were described by researchers as three conflicting relationships: fearfulness versus risk-taking, boredom versus stress, and frivolity versus maturity (Girod et al., 2005). This study, as well as others, suggests that adolescents have a variety of instructional needs that can enhance their creativity and achievement: metacognitive, flexible learning, and practical application of problem-solving. When these needs are met, adolescent students have performed higher on measures of achievement and quality than expected (Paynton et al., 2006; Meece, 2003; Song & Grabowski, 2006; Girod et al., 2005). Attending to physical and social-emotional changes in middle school years at the expense of employing rigorous and challenging content negatively affects all students. Further, it alienates students labeled as gifted and potentially damages these students’ abilities to express their creativity (Tieso, 2007). Differences in the social and emotional aspects of adolescents may also contribute to creativity. Five hundred ten typical and gifted elementary and secondary students in five schools across the east coast United States took the Overexcitability Questionnaire II and a demographic questionnaire to examine differences in overexcitability. Overexcitability was defined as acting with emotional intensity when faced with stress or crisis. Original research in this area correlated overexcitability with gifted ability (Hollingworth, 1942). Outlying data was eliminated and researchers conducted a multivariate analysis of variance (MANOVA). Results indicated that most gifted students’ measures of overexcitability regressed toward the mean in the middle-school years. This finding suggested that overexcitability

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43 makes teaching and learning difficult for students labeled as gifted in their middle school years. Results further showed that significant differences existed between males and females, among age groups, and between typical and gifted students (Tieso, 2007). Students labeled gifted in elementary school scored significantly higher on the Overexcitabilities Questionnaire II (OEQII) than same-age students labeled typical. These results showed no significant differences between gifted and typical peers on the OEQII, and in some cases middle school students labeled as gifted scored lower than students labeled typical. Tieso hypothesized that students labeled as gifted scored lower in middle school because instruction shifts from skill-based instruction to content-based instruction. In elementary school, the skill-based instruction focus on individual learner needs and generally used performance on a task as a measure of success. In middle school, content becomes the focus and understanding is generally expressed in paper-and-pencil measures (Tieso, 2007). This research suggests the need to teach creativity and impose higher expectations is amplified during the middle school years. The shift from skill-based, individualized instruction to content-based, standardized instruction can affect students’ views about their creativity. SaundersWickes and Ward (2006) found that students, who identified themselves as more creative, scored higher on creative measures than those students rating themselves lower as creative individuals. Her findings suggest that gifted students’ beliefs about themselves as gifted and creative played a significant role in their performance on these measures. No current study has validated her results with a group of non-gifted adolescents’ implicit

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44 theories of creativity; however, a limited number of studies have demonstrated the need for additional research in adolescents’ creativity. Moreover, research addresses early childhood and adolescent creativity, but a deficit exists in the research about preadolescence, or middle childhood – approximately ages 9-11 years old (Hoff, 2003). One study in this developmental stage sought to determine a relation between children’s concepts of creativity and their own creative productions. Researchers gave an Activity Questionnaire measuring involvement in creative activities and hobbies. This questionnaire consisted of two different creativity tests – measuring fluency and flexibility of ideas, a drawing task, a questionnaire describing imagery, and a questionnaire describing self-image to 110 10-year-old participants to (Hoff, 2003). Relation was found between the creativity measures and children’s self-concept of their own creativity. The current version of the Creative Problem Solving Model (CPS) (Treffinger & Isaksen, 2005) addresses each of the areas of limitation in the previous studies, as well as many others (Riley & Karnes, 2005; Williams, 2005). Additionally, it allows for participants to “address meaningful and important concerns and challenges” (Treffinger & Isaksen, 2005, p. 349). The current CPS Model provides an initial structure to teach a creative thinking process; however, it also allows for flexibility and individual or group differences as the model is used in the solution of a problem. The focus on the latest edition of the CPS Model focus on real-life problems posed within educational settings. Instead of basic problem solving practice, the CPS Model integrates current global problems with which students should grapple and improve their creative and problem

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45 solving processes. For these reasons, the use of the CPS Model should increase adolescent student internal motivation, which in turn will increase their beliefs about themselves as creative individuals. To improve students’ creativity, the CPS Model must be effectively integrated into instructional contexts. Selby, Treffinger, Isaksen, and Lauer (2004) identified necessary components for the successful implementation of the Creative Problem Solving Model (CPS) in classrooms. They found that if an individual, teacher or student, is flexible to change the CPS Model will be more effective. Although this finding is not surprising since flexibility of thought is one part of creative thinking, it helps validate the CPS Model for teaching creativity (Vartanian, Martindale, & Kwiatkowski, 2003). Selby et al. (2004) also suggested that an individual’s preferred manner of processing information and making decisions will also affect the use of the CPS Model. However, their research concluded that use of the CPS Model in classrooms, regardless of an individual’s orientation would increase that individual’s approach to change. Additionally, use of the model would increase an individual’s ability to choose a variety of information processing techniques and a use variety of decision-making strategies. Similar to previous creativity research studies, the CPS Model studies have primarily been used with students qualifying for gifted education. There continues to be limited research on the efficacy of the CPS Model to improve student motivation and learning outcomes, particularly achievement, among classrooms with learners of all ability levels (Selby et al., 2004).

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46 In order to increase regular students’ creativity using the CPS Model, specific characteristics of creativity must be defined and measured. As explored above, creativity encompasses a broad range of definitions and characteristics to test. While researchers acknowledge the limiting nature of defining creativity by a few characteristics, the need for research enhancing the creativity of students in educational contexts justifies this limitation (Treffinger et al., 2002). Four general areas of creative thought and production have been identified by the National Research Center on the Gifted and Talented: generating ideas, digging deeper into ideas, openness and courage to explore ideas, and listening to one’s inner voice. These characteristics develop over time and may not all be fully developed in pre-adolescent children, approximately ages 9-11 (Treffinger et al., 2002). In general, neurological research claims that pre-adolescent children’s brains are still developing during this age range. The frontal, parietal, and temporal lobes continue to develop until the late teen years. These areas regulate self-control, judgment, and emotions. Due to their developing brains, pre-adolescents do not reason or use insight the same way an adult would (Giedd et al., 1999). However, brain research supports that preadolescent brains are developed enough to generate creative ideas and dig deeper into ideas (Starchenko et al., 2003; Chavez-Eakle et al., 2007; Abraham & Windmann, 2007; Gogtay et al., 2004). The primary characteristics of the generating ideas category include fluency, flexibility, originality, elaboration, and metaphorical thinking. Divergent thinking is often included in this category. Chief characteristics of the digging deeper into ideas category

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47 include convergent thinking, critical thinking, reorganizing and redefining, resolving ambiguities, and seeing relationships (Treffinger et al., 2002). Recognizing the noted factors, educators’ main focus in teaching pre-adolescent students how to think more creatively should focus on the generating ideas category. Pre-adolescent students are biologically and emotionally prepared for this level of creative thinking. Attention to this area of creative thinking should provide the neural connections to pursue higher levels of creativity as students are able (Wu & Chiou, 2008; Gogtay et al., 2004). Some researchers in psychology and education highlighted divergent thinking as synonymous with generating ideas and deemed it an important component of creativity deserving further study (Guilford, 1950; Torrance, 1974; Vartanian et al., 2003). Divergent thinking has been one way to define creativity. Researchers have sought to enhance and measure divergent thinking through testing. One reason divergent thinking has received so much attention is that it can be developed and validated through quantitative measures. For example, divergent thinking can be quantified by the total number of responses given (fluency), the total number of responses that are statistically different from a group (originality), or the number of problems found given a context (elaboration) (Osborn, 1963; Treffinger et al., 2006). Fluency, Flexibility, Originality, and Elaboration Since its identification as a measure of creativity, divergent thinking has been a primary measure for tests of creativity because it has been easy to quantify (Mouchiroud & Lubart, 2001; Torrance, 1987). Another reason divergent thinking has been prominent in the field is its relation to originality and fluency, two predictive measures of creativity

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48 (Mouchiroud & Lubart, 2001; Torrance, 1987; Runco, 1993). Recent studies, however, have shown that divergent thinking represents only one component of the creative thinking process (originality) and emphasize that it should not be viewed as synonymous with creativity itself. Instead, divergent thinking is now thought to represent the fluency, flexibility, originality, and elaboration of ideas. The Torrance Tests of Creative Thinking (TTCT) are the most widely used divergent thinking tests today (Saunders-Wickes & Ward, 2006; Clapham, 2004). The understanding of divergent thinking has evolved into a useful predictive tool for measuring one part of creative thinking. Fluency is one characteristic required for generating ideas. It refers to the number of relevant responses a person provides when given a prompt (Torrance, 1974). In their experiment to measure divergent thinking as critical to the study of creativity, Vartanian et al. (2003) highlighted fluency as a predictor of creative thought. They tested 73 male undergraduate students at the University of Maine by first administering a divergent thinking test to calculate number of responses (fluency), and then administering a personality measure. Finally, they administered Watson’s 2-4-6 task, a well-researched psychological measure of inductive reasoning, which required participants to generate a rule describing the relationship between three given numbers within 10 minutes. For example, if the given numbers were 2, 4, and 6, the participant might generate a rule that the numbers increased by 2. In this task, participants received feedback about their first rule generation. Participants were given three new numbers that either confirmed or disconfirmed their generated rule (Vartanian et al., 2003).

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49 Vartanian et al. (2003) found that participants who generated more potential solutions to the number task were more successful in solving that task. Those participants had more options from which to choose when presented with the next set of three numbers. They concluded that the most significant variable in whether or not a participant generated the correct rule was the frequency of disconfirmatory responses, which the researchers defined as responses that eliminated hypothesized rules. Ultimately, the more responses to the prompt, the more likely the participant had options to confirm or reject when presented with a rule. Their research further suggests that creativity can be evidenced in single instances, such as a single divergent thought. However, to think creatively as a mental process, participants were required to produce a large number of diverse thoughts (fluency) and show evidence of flexibility of thought between opposing ideas. Participants’ thoughts were flexible if their ideas were diverse from one another (Vartanian et al., 2003; Torrance, 1974). Fluency and flexibility also coincide with one another as creative processes. Some researchers question whether measures of divergent thinking, such as fluency, flexibility, and originality, can be judged independently of one anther. Instead of individual scores for the number of responses, the statistically different response, etc., researchers suggest one score for a participants’ complete list of responses. In this way, the number of responses (fluency) will not necessarily confound the number of statistically different responses (originality). Fluency and originality scores are also related. Divergent thinking continues to be measured by many as the number of original responses given. Because this measure includes the total number of responses, a participants’ fluency confounds

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50 originality scores. If a participant can provide more responses (fluency), there is an increased likelihood that a larger number of original responses will be given (originality). The underpinnings of divergent thinking as a measure of creative thinking rely on the responses given being original, not great in number (Runco & Mraz, 1993; Mouchiroud & Lubart, 2001). Torrance found the correlations between fluency and flexibility to be consistently high (Torrance, 1974). For example, if a participant has 5 responses to a prompt, these responses represent low fluency. If all 5 of those responses represent a statistically different response, the participant has high originality. These two independent scores would in effect cancel each other out. In fact, a participant who provides 5 highly original responses is possibly more creative than a participant who provides 50 unoriginal responses (Runco & Mraz, 1993; Mouchiroud & Lubart, 2001). The impact of divergent thinking on creativity was studied by E. P. Torrance, a leading researcher in the field of creativity. The Torrance Test of Creative Thinking (TTCT) (Torrance, 1987) tests participants’ creative thinking ability on several subtests involving identifying different and novel uses for ordinary objects. As indicated by brain research, specific areas of the brain, such as the supramarginal gyrus facilitate flexibility in thinking, or the ability to generate a variety of ideas (Starchenko et al., 2003). Mouchiroud and Lubart (2001) focused on the additional components of mental fluency and flexibility in creating divergent responses to a prompt. They conducted two studies to determine if a relationship existed between participants’ divergent thinking scores and the scorers’ beliefs about what constituted an ordinary or a creative response. In each of three studies, Mouchiroud and Lubart (2001) tested elementary school students attending

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51 public schools in France. All groups responded to subtests of the TTCT (Torrance, 1987) that measured divergent thinking. In the first group, the researchers established a positive relationship between cognitive ability and creative idea generation about object uses (originality). The researchers’ purpose in establishing the correlation between cognitive ability and creative generation was to use additional experimental groups to test whether the relationship remained stable under different conditions. In the second group, students brainstormed responses to social scenarios instead of object uses. While a moderately significant relationship existed between the number of responses (fluency) and the overall creative score (originality), the second study illustrated the variability among raters when abstract brainstorming scenarios were used, such as social scenarios. Further, the originality of the responses changed based on the social construct of the scenario (Mouchiroud & Lubart, 2001). Their results showed that divergent thinking provides a valid and reliable measure for the originality component of creativity. IQ, fluency, and originality components of the TTCT were the best predictors of the quantity of creative production in Torrance’s 40-year longitudinal study (fluency R2 = .63, originality R2 = .46). IQ, flexibility, and originality were the best predictors of the quality of creative production in the same longitudinal study (flexibility R2 = .58). IQ scores accounted for only 9% of the variance in creative achievement over a 40-year span. When IQ was coupled with the TTCT, which measured divergent thinking measures of fluency, flexibility, originality, and elaboration, the predictive relationship between these measures and creative achievement was 54% (Cramond et al., 2005). This

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52 study demonstrates that fluency, flexibility, originality, and elaboration can influence creativity over time. Elaboration is defined as the value related to an idea by the number of details added. The details used to extend the response to a useable, valuable idea express a part of divergent thinking that fluency, flexibility, and originality do not (Wu & Chiou, 2008; Torrance, 1974). Fluency and flexibility most often refer to the quantity of ideas, while originality and elaboration refer to the quality of the ideas. Studies of the TTCT have found that test-takers tend to score higher on the fluency and flexibility subtests or the originality and elaboration subtests (Kim, Cramond, & Bandalos, 2006). Although divergent thinking can provide a quantifiable measure of creativity, one acknowledged limitation of the study was the possibility that the participants mentally evaluated the creative quality of their responses prior to offering them. Researchers raised the issue that a variety of thought processes could have been used to produce one divergent thinking response, one of those processes being mental self-evaluation. Mouchiroud & Lubart (2001) hypothesized that when participants were given a time limit, and asked to produce the most original responses, participants would self-evaluate responses to ensure the highest quality of originality. Although the researchers did not test for participants’ self-evaluation, they discussed self-evaluation of ideas as a possible confounding variable. If participants evaluated responses prior to providing them, they would be using divergent thinking and self-evaluation which requires more than one area of the brain (Chavez-Eakle et al., 2007).

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53 This type of evaluative, or convergent thinking, is the opposite mental activity of divergent thinking. Convergent thinking interferes with the reliability of divergent thinking tests, which measure fluency, flexibility, or originality. When two divergent thinking measures on the TTCT (Torrance, 1987) were compared to one another, a weak correlation between verbal and figural measures of divergent thinking was found. These findings point to the vastly complicated nature of measuring creativity (Clapham, 2004). Difficulty in measuring creativity has also inhibited its wide-range use in educational settings. The NCLB Act typifies an educational trend that demands a quantifiable, standard way to describe achievement. Historically, creativity measurement has not consistently or authentically provided a quantifiable, standard way to describe creativity (Clapham, 2004). Problem-finding tasks have been correlated with increased divergent thinking. Based on this correlation, verbal subtests and one nonverbal subtest of the WallachKogan Creativity Test, another widely used divergent thinking measure, were adjusted to represent problem-finding tasks and given to 109 second grade students in five urban schools (Han & Marvin, 2002). The purpose of their study was to investigate whether specific areas of the brain or a general distribution of brain areas were used in divergent thinking processes when correlated with performance tasks in math, art, and verbal comprehension. Cronbach’s alpha coefficient for the Wallach-Kogan Creativity Test and the Real-World Divergent Thinking Test was .94, indicating a significant reliability between these two tests to predict similarly to one another. Significant differences existed in different domains: math, art, and verbal comprehension. Researchers concluded that

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54 although creative processes access different areas of the brain, that some students exhibit creativity in one domain, such as art, but not other domains. A small number of students in this study demonstrated creativity in all domains. Because a significant number of students demonstrated creativity in only one domain, researchers suggested that creative expression can be limited to one area (Han & Marvin, 2002). Vocabulary Development with Creativity Memory and language are both complex neurological processes that involve many areas of the brain (NRC, 2000; Mills et al., 2004; Jincho, Namiki, & Mazuka, 2008). One academic area in which brain research has been applied is vocabulary study. Similar to general learning acquisition occurring in diverse parts of the brain, vocabulary learning utilizes different parts of the brain (Jincho et al., 2008; Mills et al., 2004). Many linguistic and psycholinguistic studies of vocabulary involve using event-related brain potentials (ERPs) measured commonly through electroencephalography (EEG). Neurologically, EEG describes electrical measurements of the brain. EEG measurements are often used in linguistic studies because EEG recordings trace continuous brain activity on imaging devices. When one instance is isolated on an EEG measurement, it is called an eventrelated potential (ERP). ERPs are considered a single image of brain activity at a specific instance. For some researchers, this idea has translated in to ERPs representing an explicit thought at a particular time. (Brown, Hagoort, & ter Keurs, 1999; Engel, Josiane, Santos, Gathercole, 2008). Researchers studying vocabulary or linguistic acquisition from a neurological perspective have used ERP-type recordings to categorize words on the basis of their

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55 linguistic function. Measurements were taken while participants viewed two types of words: open-class and closed-class. Open-class words are those representing concrete concepts with no context provided, such as nouns, adjectives, adverbs, and verbs. This class provides meaning to words and semantics to sentences and reading. Closed-class words are those representing little meaning independently; however, closed-class words, such as articles, prepositions, and conjunctions offer great syntactical support (Brown et al., 1999). Event-related potential images for these two types of words, or vocabulary, are quite different. These differences suggest that vocabulary development and processing, like much learning, structurally depends upon and alters widely distributed areas of the brain (Brown et al., 1999; van Berkum, Jos, Hagoort, & Brown, 1999). Similar to creativity, vocabulary involves a variety of definitions and complexities that make it difficult to measure. The term vocabulary can imply different meanings, such as word definition recall, understanding words in context, and using words in speech. Despite this diversity of meaning, the effect of vocabulary on reading comprehension and language, have been well documented. Vocabulary has been termed as the “middle ground in learning to read” (National Institute of Child Health and Human Development, 2000, p. 1). Often vocabulary is associated with single word comprehension or recall. Some studies highlight the importance of this vocabulary skill in reading comprehension, especially for struggling readers (Braze et al., 2007). Vocabulary, when defined as mental retrieval of individual words, is typically understood in two categories: receptive and expressive vocabulary. Receptive vocabulary is that which is comprehended as we hear or read words. Expressive vocabulary is that

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56 which is used fluently in speaking (NICHHD, 2000). Therefore, many vocabulary studies, and explanations about vocabulary comprehension, focus on either receptive or expressive vocabulary. Comprehension of vocabulary is a construct assessed in many ways: word definitions, correct multiple choice test answer, or verbally providing word meanings in context or in isolation. A further complication of measuring vocabulary is that it is almost impossible to realistically test the actual number of words a person knows and the level to which he or she comprehends them (NICHHD, 2000). Working memory has been identified as a critical brain function in vocabulary and language acquisition (Baddeley, 2003; Majerus, Poncelet, Greffe, & Van der Linden, 2006). Working memory includes two temporary storage centers in the brain, the central executive and the episodic buffer. The central executive component shifts attention from the storage centers to the buffer, and manages the organization of data. The phonological loop is one of the brain’s temporary storage centers for verbal information. It inputs auditory or visual speech into a one-to-two second temporary storage center, while the brain internally repeats and reprocesses the speech in a loop-like method (Baddley, 2003; Engel et al., 2008). The phonological loop plays a significant role in verbal working memory. Children with identified reading disabilities in the third through seventh grade performed significantly lower on a word recall task than children with no reading disabilities. Because the participants with reading disabilities had similar articulation rates as participants in the control group, researchers concluded that the phonological loop functioned correctly for participants with reading disabilities.

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57 However, the differences in phonological storage represented a linguistic, or vocabulary word, deficit (Kibby, Marks, Morgan, & Long, 2004). Verbal working memory (VWM), as a subsystem of working memory, and cumulative linguistic knowledge (CLK) were compared in one study as influences on reading comprehension and vocabulary. Vocabulary was measured by vocabulary size, general word knowledge, and orthographic knowledge (syllabic understanding). Sixty-two undergraduate students read 70 unrelated sentences with an underlined word in each sentence and were asked to remember the 70 words. A reading comprehension, Japanese vocabulary test, and WAISR vocabulary subtest were also administered. A Pearson correlation coefficient showed that all variables were highly positively correlated, although the sentence/word test was not as highly correlated. The strong positive correlation between the reading comprehension and vocabulary tests indicates that when vocabulary increases, reading comprehension also increases. Further, because cumulative linguistic knowledge and verbal working memory also increased, this study suggests that CLK is independent of VWM. It also suggests that both play a significant and independent role in reading comprehension performance (Jincho et al., 2008). Critique, Statement of the Problem, Purpose Pre-adolescent and adolescent learners have specific vocabulary needs. A quasiexperimental research study was conducted that investigated fifth grader’s new vocabulary acquisition and comprehension. Examination of the four-year vocabulary program revealed that poor readers in third through fifth grade increased word recognition skills significantly. These readers’ intelligence was average or above average,

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58 and word recognition was measured by comparisons/contrasts of known to unknown words, word analysis, organization of language, and strong word decoding skills (Gaskins et al., 1988). This study revealed that students taught morphemic and contextual analysis scored higher on learning new words with similar morphology and context than did a control group in which lesson vocabulary was imbedded in the content instruction. The students who were taught the analysis also scored higher on vocabulary tests over time than did the students in the control group (Baumann, Edwards, Boland, Olejnik, & Kame’enui, 2003). Many standardized tests measures adolescents’ vocabulary knowledge through items testing comparisons and contrasts in word meaning, word decoding for precision, and application or organization of language. Additionally, below proficient English-speaking third through fifth grade students’ language and vocabulary benefited the most from effective pedagogy in a study examining the relationship between pedagogy, classroom organization and achievement testing (Doherty & Hilbert, 2007). This research revealed that effective pedagogy has five standards. The first standard involves teachers and students working together in the context of community toward a common goal. Integrated reading is the second standard. The third standard is writing and speaking across all content. The fourth standard is intentional use of students’ prior knowledge, requiring student elaboration of content extending beyond the text. Planned discussions between the teacher and small-groups of students make up the fifth standard. This pedagogical model was also effective for nonnative English learners and typical English speaking students, although the gains on the summative state assessment were not as significant as for the below proficient English

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59 speaking students. The less significant results for below proficient English speaking students was not surprising since many standards involved discussions and the context of community. Researchers also found that classrooms organized into diverse learning activities increased student achievement (Doherty & Hilbert, 2007). Student and teacher perceptions about their learning environment also influence performance on achievement and creative tasks. Student and teacher perceptions should be taken into account when evaluating creative and academic achievement (Fleith, 2000). Based on the literature, preadolescent and adolescent learners’ vocabulary needs are distinct and require considerable manipulation of vocabulary in isolation and in context. Creativity research has evolved from studying isolated components of creativity to creating larger frameworks or theories to describe and manipulate the construct of creativity. The previous isolated research has demonstrated the need for a unified framework. Investment theory, this unified framework, provides the most thorough and accurate definition of creativity as the integration of internal and external components to thinking, learning and problem solving. Vocabulary instruction serves as one critical component for reading, especially in the middle grades (NICHHD, 2000). The Creative Problem Solving Model (CPS) provides an organized, yet flexible, framework that directs users through a creative problem solving method to make creative thinking accessible to a variety of individuals. A summary of the benefits of creative thinking previously mentioned include: a joining of high-stakes testing objectives with best teaching practices for students (Hurren et al., 2006), instructional time distributed equitable to all learners (Mendoza, 2006; Ambrose 2004; Milner & Ford, 2007;

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60 Shaunessy et al., 2004; Moore, Ford, & Milner, 2005), more accurately measuring successful learning (Williamson et al., 2005), shaping the brain and neural networks at higher functioning and interconnected levels as learning occurs (NRC, 2000), and increasing student motivation and performance (Song & Grabowski, 2006). Creativity instruction and vocabulary development are keys to students’ academic and reading development. Another advantage of teaching creative problem solving is the ability to work toward a well-integrated form of vocabulary instruction that also fosters creativity. Step one of the CPS Model (i.e., Understanding the Problem) might connect with prior knowledge, community of learners, and integration of vocabulary across the curriculum (Doherty & Hilbert, 2007). Step two (i.e., Generating Ideas) links with word and structural analysis, in which students analyze word parts to understand word meaning. Discussions about vocabulary and comparisons/contrasts for vocabulary are included in this step. Students would generate ideas about word meaning, usage, and connotation. Finally, morphemic and contextual awareness would connect to the generating ideas step. Understanding how the sounds and meaning of words allow them to develop the context of a sentence is an idea generating process (Doherty & Hilbert, 2007; Baumann et al., 2003; Gaskins et al., 1988). Step three (Preparing for Action) correlates to small-group and teacher groups, specific vocabulary tasks, using vocabulary in context, and writing (Doherty & Hilbert, 2007). In addition, the CPS Model may help teachers differentiate instruction in classrooms. Tomlinson et al. (2003) cited multiple studies in which students needing

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61 modifications for learning did not receive them, regardless of whether the modifications were for below proficient students or above proficient students. Tomlinson et al. (2003) suggested six effective practices of differentiated instruction that supported all learners’ needs and increased achievement. Among them were proactive curriculum and instruction, knowledge and learner centered instruction, variable materials, and variable pacing. The Creative Problem Solving Model (CPS) involves all of these components by using the learners’ own interests and knowledge to brainstorm new tasks that could arise in a context, generate possible solutions to those problems, and plan a course of action to solve the problems. Students exposed to a revised curriculum, which placed an emphasis on critical thinking skills, abstract questions, and the removal of emphasis on rote memory learning, scored higher on a math post-test measure than did students who learned the same material with the textbook unmodified (Tieso, 2007). The CPS Model involves all of these components, and the use of it within vocabulary instruction may show similar results to this study. Socioeconomic status plays a significant role in vocabulary development due to limited experiences (Engel et al., 2008). In addition to these advantages of teaching creative problem solving within vocabulary instruction, there are other advantages that extend beyond the classroom. Sternberg (2005) hypothesized that students growing up in lower socioeconomic backgrounds would score higher on creative and practical measures, as opposed to analytical ones, such as the Stanford Abilities Test (SAT) or grade point average (GPA) regularly used for college admission. He created the Rainbow Project to analyze the SAT scores and GPAs of 793 first-year students at eight four-year

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62 colleges and five community colleges. These students were also given an additional creative, analytical, and practical measure. For example, a student might be asked to write a short story based on a prompt, complete a movie where the ending has been left off, or how to resolve an interpersonal conflict on a classroom assignment. The Rainbow Project results indicated that alternative creative, analytical, and practical measures of performance reduced ethnic and socioeconomic bias. Hispanic students benefited the most by alternative measures, and black students benefited a statistically significant amount. Further, they did an equal, and at times better, job of predicting future success in college as measured by student’s grade point average. These results lead to the conclusion that teaching creative problem solving in classrooms may prepare students for successful college experiences. Furthermore, additional study may support the use of alternative assessment measures for college admission. Sternberg’s results suggested that testing students in alternative ways self-corrected test bias for racial and economic factors (Sternberg, 2005). Conclusion Identifying instruction that creates situations for students to exhibit an observable creative product deserves more study. Educators need to determine whether the use of instruction focusing on creativity will increase student achievement overall, and on standardized measures. Although research has shown that a single standardized assessment does not provide an accurate picture of successful teaching and learning, the fact remains that students and teachers will continue to be evaluated in this way. The Creative Problem Solving Model (CPS) can provide a framework to create and provide

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63 instruction that values the holistic definition of creativity proposed by investment theory (Sternberg & Lubart, 1995), differentiating instruction for all learners (Tieso, 2005; Tomlinson et al., 2003), and creating equitable learning situations for all learners in which they are evaluated using project-based or performance measures (Sternberg, 2005). While this literature review has outlined the complex history of defining and studying creativity, the future of creativity research is promising. Neuroscientists, psychologists, and educators are acknowledging the necessity of a holistic approach to defining creativity. Additionally, researchers are exploring the benefits to using and teaching creativity in the classroom. The Creative Problem Solving Model Version 6.1 (CPS) has been developed and used among gifted student populations with great academic and social success. Limited tests of the use of the CPS Model among typical student populations have been performed; however, these limited research results have suggested that the use of the CPS Model among this student population would benefit all students academically and socially, similar to the results found with gifted students. Further, the use of the CPS Model as an instructional tool could benefit adolescent students, particularly in vocabulary instruction. Limited research exists validating the use of the effects of the CPS Model on content specific objectives. Using alternative teaching methods that promote creativity and real-world application for vocabulary can positively affect adolescents’ intrinsic motivation and performance in reading during these years.

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64

CHAPTER 3 METHODS

Purpose of the Study The purpose of this study was to investigate the effects instruction in creativity had on students’ vocabulary achievement and reading comprehension and examine whether instruction in creativity can increase students’ creativity. Students’ achievement in urban school settings with higher minority subgroup populations may increase through instruction in creativity. Not only do students in urban school settings often begin schooling with unequal resources, they also often suffer from a lack of supplied educational resources (Crosnoe & Huston, 2007). Components of creativity, such as fluency, flexibility, originality, and elaboration, require students to uses brain processes similar to those that can mitigate achievement differences due to socioeconomic background and lack of prior knowledge (Starchenko, Bekhtereva, Pakhomov, & Medvedev, 2003; Chavez-Eakle, Graff-Guerroro, GarciaReyna, Vaugier, & Cruz-Fuentes, 2007; Mouchiroud & Lubart, 2001; Wu & Chiou, 2008; Graves, 2007). Vocabulary instruction emphasizing creative thought processes could serve dual roles for educators. These methods could assist educators in spending appropriate time teaching students to meet or exceed minimum testing standards while also increasing student learning.

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65 Creativity research has focused on gifted populations. Contributions to creativity research in educational settings should include all groups of students. Inclusion of minority students could provide valuable information regarding how to close achievement gaps for underperforming minority students. Research involving low socioeconomic students also adds to the limited amount of creativity research in this area. Educators seek instructional methods to best teach all students. Investigating how instruction in creativity will affect one area of students’ learning can help educators determine the usefulness of instruction in creativity in classrooms. If instruction in creativity increases vocabulary achievement for minority and/or low socioeconomic students, instruction in creativity may be applied in other academic disciplines to facilitate underperforming students’ learning. Likewise, if instruction in creativity increases vocabulary achievement, and in turn increases students’ reading comprehension achievement, then instruction in creativity may be a powerful tool for educators. Design A quasi-experimental mixed-method study was used to investigate the effects instruction with creativity had on students’ vocabulary acquisition and reading comprehension. A nonequivalent intact control group design was used to implement creativity instruction with students who represent minority or low socioeconomic students. Third, fourth, fifth, and sixth grade classes at two small, private, urban elementary schools located in West Tennessee were randomly assigned to a control receiving their typical vocabulary instruction or an experimental group receiving a

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66 creativity treatment. There were approximately 145 students in these four grades at these schools. Participants One school’s population was primarily composed of African-American students of average achievement performance from low- to middle-socioeconomic backgrounds. At least 80% of the students in the school qualified as poor according to national poverty guidelines and/or the percentage of students qualifying as poor in this school was equal or greater than the percentage of students qualifying as poor in the local neighborhood. Ninety-eight percent of the school’s students were non-white. Additionally, the school’s population was convenient for the researcher to sample due to existing relationships with the school administration. The second school’s population was primarily composed of Caucasian students of average achievement performance from middle- to uppersocioeconomic backgrounds. One randomly selected teacher from each grade was asked to participate. Those randomly selected teacher’s students were also asked to participate in the study. Procedures Permission was granted by the Institutional Review Board (IRB) from Union University in Jackson, Tennessee and the principal of each school to conduct treatments within vocabulary classes, and access was granted to all test data. Before the research began, students were assigned to heterogeneous classrooms by the schools’ administrations. The researcher recorded 3rd through 6th grade teachers’ names on separate slips of paper. Teachers were grouped by grade. One teacher from each grade

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67 was randomly drawn from a hat. This randomly selected teacher was asked by letter to participate in the treatment group of the research until one teacher per grade agreed to participate. If the selected teacher declined, the researcher repeated the random selection process. This random selection process was used to determine which teachers were asked to participate in the treatment group and the control group. Students of randomly selected teachers for the treatment group were asked to participate in the treatment group of the study. Likewise, students of randomly selected teachers for the control groups were asked to participate in the control group. Parental consent and student assent was gained. The researcher pretested both the treatment and control groups using the selected vocabulary, reading, and creativity pretests. The STAR Reading test (Renaissance Learning, Inc., 2002) served as the vocabulary and reading comprehension pretests, and the Torrance Tests of Creative Thinking Verbal Form A and Figural Form A (Scholastic Testing Service, Inc., 2006) served as the creativity pretest for fluency, flexibility, originality and elaboration of ideas (Torrance, 1974). Students in the treatment group rated themselves on their belief about their own creativity using the Khatena-Torrance Creative Perception Inventory: Something About Myself [Khatena-Torrance CPI SAM] (Khatena-Torrance, 1998). Students in the treatment classes will rate their own creativity on the Khatena-Torrance CPI SAM. Permission was granted for the use of these instruments’ by their publishers (Appendix B). The classes in the control and treatment groups continued vocabulary instruction as usual. Teachers continued to instruct students in vocabulary, in addition to their

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68 reading instruction. In addition to students’ regular vocabulary instruction, classes in the treatment groups received one activity per week focusing on fluency, flexibility, originality, and elaboration for the teacher to use during vocabulary instructional time. These activities were provided to the teacher by the researcher and were adapted from the Creative Problem Solving Kit (Treffinger, Nassab, Schoonover, Selby, Shepardson, Wittig, & Young, 2006). For example, when focusing on fluency, the teacher in the treatment group may prompt students to name as many synonyms as he or she can for the vocabulary word being taught. The participating teachers were instructed to substitute their regular vocabulary lesson with the provided vocabulary lesson. The teacher was allowed to select the day of the week in which to insert the creativity lesson; however, the same creativity lessons were provided to all teachers each week across grade levels. To design these creativity lessons, the researcher used activities focusing on developing fluency, flexibility, originality, and elaboration from the Creative Problem Solving Kit (Treffinger, Nassab, Schoonover, Selby, Shepardson, Wittig, & Young, 2006), a research-based instructional kit aimed at teaching creativity to students. These lessons were designed using the operational definitions of fluency, flexibility, originality, and elaboration. Fluency refers to the number of relevant responses. Flexibility refers to the number of different categories to which responses could belong. Flexibility represents a change in thought. Originality refers to the number of unusual, but relevant ideas as measured by the statistical infrequency of the idea. Elaboration refers to the number of details used to extend a response (Torrance, 1974).

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69 Teachers were encouraged to provide feedback via email at any time throughout the treatment. Although participating teachers could not be guaranteed anonymity, the researcher provided teachers with confidentiality. Teachers’ responses were reported using a coded number for each teacher instead of a name. After 6 weeks of treatment, the treatment and control group received the second STAR test (Renaissance Learning, Inc., 2002) as the vocabulary and reading comprehension post-test, the Torrance Tests of Creative Thinking Forms B (Torrance, 1974) as creativity posttests for fluency, flexibility, originality, and elaboration, and the Khatena-Torrance Creative Perception Inventory post-test (Khatena-Torrance, 1998). One school in the study did not complete post-testing. Instrumentation The instruments noted below were utilized to address each of the follow research

questions: Research question 1. Is there a relationship between the effect of instruction emphasizing fluency, flexibility, originality, and elaboration on vocabulary achievement and reading comprehension as measured by the STAR Reading test? The STAR Reading test measures students’ reading comprehension (Renaissance Learning, 2002). One primary use of STAR Reading is for teachers to determine students’ general reading level. It has been particularly useful in assessing student growth in reading and comparing students’ reading comprehension to national norms. The STAR Reading test is computer-based and utilizes item response theory adjusting questions based on a student’s previous response as correct or incorrect. It can be used up to five

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70 times per year, allowing it to serve as both a pre-test and post-test. Students answer 25 questions per testing session. The test is appropriate for students as young as 1 st grade. Questions require students to choose a word that would correctly fit the context of a sentence or paragraph as well as answer questions related to syntax and semantics. Reliability ranges from .79 to .91, with an overall reliability of .94 for all grade levels. Content validity was established through item comparisons with well-researched gradelevel vocabulary lists and a comparison of students’ scores on the STAR reading test and other standardized reading tests. Content validity for 3rd – 12th grades were between .60 .90. The testing protocol indicates that questions associated with 3rd grade and above are more authentic measures of reading ability versus simple vocabulary understanding due to the longer passages and necessity of students to use higher order reading strategies, such as making inferences, finding cause and effect, and finding the main idea (Renaissance Learning, 2002). Research question 2. Will the use of fluency, flexibility, originality, and elaboration in vocabulary instruction have a statistically significant impact on students’ creativity scores as measured by the Torrance Tests of Creative Thinking? Subtests for the Torrance Tests of Creative Thinking are based on fluency, flexibility, originality, and elaboration. Fluency refers to the number of relevant responses. Flexibility refers to the number of different categories to which responses could belong. Flexibility represents a change in thought. Originality refers to the number of unusual, but relevant ideas as measured by the statistical infrequency of the idea. Elaboration refers to the number of details used to extend a response (Torrance, 1974).

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71 Reliability and validity information are excellent for the Torrance Tests of Creative Thinking. Inter-rater reliability ranged from .86 to .99. Test-retest reliability ranged from .50 to .93. The reliability among subtests was also strong, ranging from .74 to .80. Validity information has been difficult to provide due to the various definitions of creativity across the field. Research question 3. Is there an effect on how students rate their own creativity as a result of instruction emphasizing fluency, flexibility, originality, and elaboration as measured by the Khatena-Torrance Creative Perception Inventory? The Khatena-Torrance Creative Perception Inventory (Khatena-Torrance, 1998) uses two self-report checklists to determine how creative individuals perceive themselves. Authors wrote and grounded questions on the inventory based on Torrance’s extensive research. Additionally, authors correlated questions with other creativity tests. On one of the self-reporting instruments, What Kind of Person are You?, inter-rater reliability is r = .97-.99. Internal consistency was determined using split-half reliability comparing the even numbered test items to the odd numbered test items. Internal consistency is r = .98. On the second self-reporting instrument, Something About Myself, a Pearson Product-moment correlation coefficient of .99 indicates very high inter-rater reliability. The split-half method was also used on this self-report checklist to measure internal consistency. Internal consistency for adolescents is r = .92. For non-gifted students in grades 1-12, r = .70 for Form A and r = .65 for Form B. Validity measures are difficult to report on creativity measures because of a lack of consistent definition of creativity across researchers and instruments. The authors tested construct validity

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72 through a variety of experimental studies using theoretical research in creativity to define creativity or creative patterns of behavior. Concurrent validity is reported at r = .14-.18. Data Selection of data to collect and analyses to perform were based on the three research questions. Research question 1. Is there a relationship between the effect of vocabulary instruction emphasizing fluency, flexibility, originality, and elaboration on vocabulary achievement as measured by the STAR Reading test? The first and second dependent variables measured were pre- and post-test scores on the STAR Reading test. In each question, the independent variable of the creativity treatment was used to determine if there were changes in the dependent variables. A 2group Multivariate Analysis of Variance (MANOVA) was used to determine the degree to which creativity instruction can effect growth in vocabulary achievement and reading comprehension. Creativity instruction emphasizing fluency, flexibility, originality, and elaboration was used as the only independent variable. Four different control groups were be compared to four different treatment groups based on students’ scores on interval test and survey data. These groups’ data were analyzed within the MANOVA as the control group and the treatment group. These two groups created the need for a 2-group MANOVA. The MANOVA was performed to analyze the differences among groups based on the two dependent variables. A MANOVA is the best analysis to control for Type I error while also discriminating differences between correlated variables. There is

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73 significant covariance noted in research between reading and vocabulary (NICHHD, 2000). Research question 2. Will the use of fluency, flexibility, originality, and elaboration in vocabulary instruction have a statistically significant impact on students’ creativity scores as measured by the Torrance Tests of Creative Thinking (TTCT)? The third dependent variable measured will be students’ scores on the TTCT (Scholastic Testing Service, 2006). Creativity instruction emphasizing fluency, flexibility, originality, and elaboration was used as the independent variable in this question also. Because the same independent variable was utilized in both questions one and two, students’ scores on the TTCT was included in the same 2-group MANOVA used in question one. By analyzing creativity scores on the TTCT as a third dependent variable using the same MANOVA procedure, covariances between all dependent variables were analyzed. This data analysis drew attention to the interactions between vocabulary, reading comprehension, and creativity. Research question 3. Is there an effect on how students rate their own creativity as a result of instruction emphasizing fluency, flexibility, originality, and elaboration as measured by the Khatena-Torrance Creative Perception Inventory? The fourth dependent variable was students’ ratings on their own creativity. This dependent variable was also included in the same 2-group MANOVA analysis used in questions one and two. In this way, interactions between students’ perceptions of their own creativity and the other dependent variables were analyzed. The MANOVA is the best statistical test to use in analyzing the effect that instruction in creativity emphasizing

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74 fluency, flexibility, originality, and elaboration have on the four dependent variables. Additionally, the MANOVA identified the interactions these four dependent variables had with one another. Because an effect was found through the 2-group MANOVA, post hoc procedures were used to determine the degree to which creativity instruction emphasizing fluency, flexibility, originality, and elaboration affected the dependent variables. Particular attention was paid to the effect on vocabulary knowledge and reading comprehension. The researcher provided weekly activities to teachers based on the Creative Problem Solving Kit activities (Appendix A). Qualitative data, such as emails, any teacher’s questions, and teacher’s comments, regarding these activities were communicated to the researcher through emails weekly. All data was kept secure in a locked closet in the researcher’s classroom. Data will be reported to the participating schools and local community. Limitations One limitation of this study involves a small sample size. While the researcher planned for two participating schools, conditions beyond the researcher’s control eliminated one of the schools from being included in data analysis. Another limitation of this study involves the selection of the participating school. The primary reason for this school’s inclusion is that most participants attending the schools represent minority and/or low socioeconomic students. Additional study is needed to provide information about these students and creativity. The school is small and may represent a skewed population of students. It is also a small, private school that offers a safe, secure learning

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75 environment. Small class sizes and continuity across learning environments may foster creativity more naturally than larger, public school environments (Maslow, 1987; NRC, 2000). The majority of students attending these schools are African Americans from lower socioeconomic backgrounds. However, because both schools are private schools that provide a college-preparatory education to lower income students, the population of students may not represent a cross-section of all African American students from lower socioeconomic backgrounds. To minimize this limitation, the researcher will generalize results only to similar groups of students. Another possible limitation is the reliance on teachers to administer the creativity activities independently. Teacher integrity and consistency in administration is an issue. Providing convenient and easy-to-administer activities are two ways the researcher addressed this limitation. Feedback through email aimed to foster teacher compliance and consistency.

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76

CHAPTER 4 RESULTS

Opening Statement This research was conducted to investigate whether fluency, flexibility, originality, and elaboration would enhance creativity and vocabulary use for improving reading comprehension in third through sixth grade students. Three research questions were posed to investigate the effects of fluency, flexibility, originality, and elaboration. Research Question 1: Is there a relationship between the effect of instruction emphasizing fluency, flexibility, originality, and elaboration on vocabulary achievement and reading comprehension as measured by the STAR Reading test? Research Question 2: Will the use of fluency, flexibility, originality, and elaboration in vocabulary instruction have a statistically significant impact on students’ creativity scores as measured by the Torrance Tests of Creative Thinking? Research Question 3: Is there an effect on how students rate their own creativity as a result of instruction emphasizing fluency, flexibility, originality, and elaboration as measured by the Khatena-Torrance Creative Perception Inventory? To analyze these questions, participating students were assigned to either a control or treatment group. Students in the control group received vocabulary instruction from their teacher as prescribed by the general curriculum. Students in the treatment group received vocabulary instruction from their teacher as prescribed by the general

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77 curriculum and a supplemental vocabulary lesson once per week that utilized four components of creativity - flexibility, fluency, originality, or elaboration (Torrance, 1974). The null hypothesis is that there is no variance in the data among dependent variables. Box’s M was 25.15, F(10, 34498.65) = 2.39, and p = .008. Since p = .008 > p = .05, Box’s test showed significance. This means that the treatment and control groups differ in their covariance matrices. Data and Statistical Results Research question 1. Is there a relationship between the effect of instruction emphasizing fluency, flexibility, originality, and elaboration on vocabulary achievement and reading comprehension as measured by the STAR Reading test? Data was collected from the STAR Reading test completed by participants in the treatment and control groups after the treatments were concluded. The STAR Reading test measures student reading level based on students’ answers to vocabulary-based questions. Mean scores by grade were as follows: third grade students scored a mean standard score of 504, fourth grade students scored a mean standard score of 585, fifth grade students scored a mean standard score of 588, and sixth grade students scored a mean standard score of 786. The relative increase in reading scores is expected with increasing grade (Renaissance Learning, Inc., 2002). Mean differences were noted between the treatment and control groups’ on the STAR Reading test. The treatment group’s mean score was 617.41 (SD = 198.30) as compared to the control group’s mean score of 621.44 (SD = 143.33). The MANOVA revealed no significant differences between the control and treatment groups on the STAR Reading test, F(.05, 1, 85) = .012, p = .914. The MANOVA did reveal

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78 significant differences among each grade. Post hoc analysis using the Bonferroni method was performed to describe the differences in grade that existed. Significant differences on the STAR Reading post-test existed between the sixth grade and all other grades (p = .000). The sixth grade also significantly differed from the third grade on the KhatenaTorrance WKOPAY? survey (p = .021). The sixth grade scores were higher, with a mean difference of 281.44. Analysis revealed that fluency, flexibility, originality, and elaboration had no significant effect on vocabulary achievement and reading comprehension as measured by the STAR Reading test. Research question 2. Will the use of fluency, flexibility, originality, and elaboration in vocabulary instruction have a statistically significant impact on students’ creativity scores as measured by the Torrance Tests of Creative Thinking? Data was collected on two subtests of the Torrance Tests of Creative Thinking [TTCT]. The Verbal Form required participants to provide written responses to seven timed figural and written prompts. The Figural Form required participants to provide drawn responses to three timed figural and written prompts. An analysis of the TTCT Verbal post-test, the treatment group’s mean score was 84.84 (SD = 25.06), while the control group’s mean score was 103.23 (SD = 16.87). On the TTCT Figural post-test, the treatment group’s mean score was 85.48 (SD = 14.34), while the control group’s mean score was 82.72 (SD = 16.26). Mean differences were noted between the treatment and control groups’ on these post-test measures. In order to determine if any significant difference existed between the treatment and control groups, a 2-group Multivariate Analysis of Variance (MANOVA) was performed. The MANOVA conducted revealed significant differences

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79 between the treatment group and the control group on the dependent measures. Wilks’s Λ = .805, F(.05, 1, 85) = 4.963, p < .01. The critical F value for these degrees of freedom was approximately 3.97 (Stevens, 2002). Therefore, there was a significant difference between the means of the treatment group and the control group. Since significance was found, a post hoc analysis using the Bonferroni method was conducted to determine how the dependent variables were affected by the independent variable. The univariate test revealed that the mean difference in scores of the treatment and control groups on the Torrance Test of Creative Thinking Verbal was significant, F(.05, 1, 85) = 15.686, p = .000. There was a significant difference between the fourth and sixth grades on the TTCT Verbal post-test (p = .035). The sixth grade’s mean score was higher with a mean difference of 19.49. Finally, there was also a significant difference between the third and sixth grades on the TTCT Figural post-test (p = .012). The sixth grade mean score was higher than the third grade mean score by 14.03 points. The mean differences for the other dependent variables was not significant at the p = .05 level. The Torrance Test of Creative Thinking Figural was not significant, F(.05, 1, 85) = .704, p = .404, and the Khatena-Torrance What Kind of Person Are You? survey was not significant, F(.05, 1, 85) = 1.863, p = .176. R2 = .156 for the TTCT Verbal form. R2 = .00 for the STAR Reading test, R2 = .008 for the TTCT Figural form, and R2 = .021 for the Khatena-Torrance survey. Fluency, flexibility, originality, and elaboration had a statistically significant impact on students’ creativity scores as measured by the Torrance Tests for Creative Thinking Verbal form. There was no significant difference found on

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80 the Figural form. Figure 1 below represents the differences in the treatment and control groups.

Figure 1. Comparison of post-test standard scores of all dependent variable measures.

Research question 3. Is there an effect on how students rate their own creativity as a result of instruction emphasizing fluency, flexibility, originality, and elaboration as measured by the Khatena-Torrance Creative Perception Inventory? To answer this question, students rated their own creativity on the Khatena-Torrance Creative Perception Inventory (Khatena-Torrance, 1998). Participants selected statements that described them by placing a mark beside the statement. Third graders scored a mean standard score of 61 on this post-test with a standard deviation of 11 points. Fourth

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81 graders’ mean standard score on this measure was 55 with a standard deviation of 9 points. Fifth graders’ mean standard score was 58 with a standard deviation of 12 points. Sixth graders’ mean standard score was 52 with a standard deviation of 12 points. As compared to the mean standard scores of the survey’s pre-test measure, Something About Myself, students tended to score higher on the post-test. On the Khatena-Torrance WKOPAY post-test, the treatment group’s mean score was 57.91 (SD = 10.67), while the control group’s mean score was 54.65 (SD = 11.58). No significant differences were found between the control and treatment groups on this measure. To summarize, the mean standard scores for each grade’s pre-test and post-tests are presented in Table 2.

Table 1 Comparison of Grade Level Mean Standard Scores on Pre-test and Post-test Measures ________________________________________________________________________ Third Fourth Variable M SD M SD ________________________________________________________________________ Khatena-Torrance 46.05 11.91 43.60 10.11 Something About Myself Pre-test Khatena-Torrance What Kind of Person Are You? Post-test

61.33

11.16

54.85

8.96

TTCT Figural Pre-test

80.47

8.13

81.90

15.70

TTCT Figural Post-test

76.62

20.93

87.30

16.42

TTCT Verbal Pre-test

87.86

14.93

99.35

14.93

TTCT Verbal Post-test

91.71

18.40

107.75

21.33

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82

Table 1 (continued). ________________________________________________________________________ Third

Fourth

Variable M SD M SD ________________________________________________________________________ STAR Reading Pre-test

496.76

110.16

563.25

141.75

STAR Reading Post-test 504.42 134.21 584.85 136.56 ________________________________________________________________________ Fifth

Sixth

Variable M SD M SD ________________________________________________________________________ Khatena-Torrance 44.61 12.12 39.04 11.58 Something About Myself Pre-test Khatena-Torrance What Kind of Person Are You? Post-test

57.70

12.33

51.57

10.14

TTCT Figural Pre-test

69.30

11.19

82.70

9.99

TTCT Figural Post-test

81.65

9.53

90.62

8.87

TTCT Verbal Pre-test

86.09

17.99

96.00

16.48

TTCT Verbal Post-test

94.83

16.13

85.26

30.76

STAR Reading Pre-test

600.74

120.06

723.70

198.72

STAR Reading Post-test

587.96

110.64

785.87

166.32

Note. N = 21 for Third; 20 for Fourth; 23 for Fifth; 23 for Sixth

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83 Additional Variables Gender and grade differences were also noted. No significant differences were found based on gender. For gender, Wilks’s Λ = .977, F(.05, 1, 85) = .482, p = .75. There were significant differences based on grade, however. For grade, Wilks’s Λ = .473, F(.05, 1, 85)

= 5.709, p < .01. There was a significant difference on the all post-test measures by

grade: STAR Reading F(.05, 1, 85) = 16.71, p = .00; Khatena-Torrance WKOPAY? F(.05, 1, 85) = 3.261, p = .026; TTCT Verbal F(.05, 1, 85) = 2.763, p = .047; TTCT Figural F(.05, 1, 85) = 3.937, p = .011. The MANOVA revealed a relationship between grade and the dependent variables: STAR Reading post-test R2 = .375; Khatena-Torrance WKOPARY? survey R2 = .105; TTCT Verbal post-test R2 = .091; TTCT Figural post-test R2 = .125. Closing Statement In summary, the MANOVA revealed that a significant difference existed between the treatment and control groups’ means. Further exploration through the post-hoc analysis showed that the significant difference occurred on the Torrance Test of Creative Thinking Verbal Form. Therefore, utilizing fluency, flexibility, originality, and elaboration to enhance creativity and vocabulary use was effective as measured by the Torrance Tests of Creative Thinking Verbal Form. A relationship between grade in school and scores on each of the instruments was also noted, with an increase in grade indicating an increase in score. Participants scored higher on the STAR Reading test, the Torrance Tests of Creative Thinking, and the Khatena-Torrance Creative Perception Inventory as they advanced in school. Since these tests are deemed appropriate for use with students as young as third grade, it will be important for future research to consider

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84 revalidating these tests with students in the general education program. Norm groups should also include low socioeconomic students. Gender had no statistically significant effect on students’ scores on the STAR Reading test, the Torrance Tests of Creative Thinking, or the Khatena-Torrance Creative Perception Inventory. No other significant differences were found in the post hoc procedure.

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85

CHAPTER 5 CONCLUSIONS AND DISCUSSION

Opening Statement Elements of creativity have been shown to reinforce skills used in knowledge acquisition and may help students from lower socioeconomic backgrounds increase achievement (Starchenko, Bekhtereva, Pakhomov, & Medvedev, 2003; Chavez-Eakle, Graff-Guerroro, Garcia-Reyna, Vaugier, & Cruz-Fuentes, 2007; Mouchiroud & Lubart, 2001; Wu & Chiou, 2008). Educators must determine, however, how to implement instruction in creativity into their classrooms. Instruction in creativity may be applied in a variety of academic disciplines to aid and enhance students’ learning (Cheng, Wang, Liu, & Chen, 2010). The purpose of this study was to investigate the effects instruction in creativity had on students’ vocabulary achievement and reading comprehension and to examine whether instruction in creativity increased students’ creativity. To date, most of the research in creativity included gifted students and adults as participants. It is important that research on creativity in educational settings include diverse learners in order to provide the highest quality instruction to the greatest number of students. This study implemented instruction in creativity with students from a low socioeconomic

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86 background and diverse ethnic groups. The results of this study contribute to the limited creativity research focusing on this group of students. Conclusions In order to focus this research, three research questions were posed. Each of these questions sought to discuss the role instruction in creativity played in student learning. Research question 1. Is there a relationship between the effect of instruction emphasizing fluency, flexibility, originality, and elaboration on vocabulary achievement and reading comprehension as measured by the STAR Reading test? Instruction in creativity in vocabulary lessons should increase students’ achievement on the STAR Reading test. The MANOVA analysis revealed that instruction in creativity during vocabulary lessons did not increase students’ performance on the STAR Reading test. There was a statistically significant difference between the standard scores of third grade students and sixth grade students on the STAR Reading post-test. However, due to the increase in general reading ability one would expect students to gain each year, this statistical difference cannot be attributed to the instruction in creativity treatment. Further, since the significant difference on the STAR Reading post-test was shown for both the treatment and control groups, the conclusion cannot be made that the treatment had any effect on these scores. Research question 2. Will the use of fluency, flexibility, originality, and elaboration in vocabulary instruction have a statistically significant impact on students’ creativity scores as measured by the Torrance Tests of Creative Thinking? Instruction in creativity should increase students’ ability to perform creatively. An increase in

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87 achievement as a result of instruction in creativity was expected, but this instruction should have affected students’ creativity performance, as well. Two subtests of the Torrance Tests of Creative Thinking [TTCT] were used to assess student performance. The Verbal Form and Figural Form are both part of the complete battery of the Torrance Tests of Creative Thinking (Scholastic Testing Services, Inc., 2006). There were no significant differences between the treatment group and the control group on the Figural Form of this test. However, there was a significant difference between the mean scores on the Verbal Form for the treatment and control group. Because the instruction in creativity was implemented into students’ vocabulary instructional time, and therefore focused on verbal skills, this significant difference indicated that instruction in creativity in a content area could increase student performance in that area. Research question 3. Is there an effect on how students rate their own creativity as a result of instruction emphasizing fluency, flexibility, originality, and elaboration as measured by the Khatena-Torrance Creative Perception Inventory? Instruction in creativity should increase students’ ratings of how creative they thought they were. No statistically significant differences were found. A comparison of the mean standard scores for students on the Khatena-Torrance showed an increase in students’ scores from the pre-test to the post-test. However, the MANOVA did not reveal that the differences in these mean scores were significant. Additionally, the results of the MANOVA showed no significant differences in scores based on gender. However, grade did affect students’ performance on all instruments. While much of this increase can be attributed to general knowledge gain in

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88 typical school instruction, higher test scores for older students were noted. With more experience in reading and vocabulary, the instruction in creativity in vocabulary lessons may have been more accessible to older students that were more proficient readers and with larger vocabularies (Graves, 2007). Further research may provide insight into the relationship between verbal creativity tests and the age of test-takers. Recommendations The significant differences found on the verbal form of the TTCT suggest that implementation of instruction in creativity in individual disciplines within the regular school context can have an affect on student performance on creativity measures in that area. One confounding variable in the implementation of this research was the limited amount of time to conduct the research. Due to external factors outside of the researcher’s control, the research was conducted over a 6 week period instead of the originally proposed 4 months. Therefore, it is even more significant to the body of creativity research that significant differences were found between the treatment and control group on the TTCT Verbal Form. Teachers should implement instruction in creativity in vocabulary lessons. This instruction in creativity during vocabulary lessons in third through sixth grade classrooms showed no hindrance to student learning in vocabulary or other content areas. STAR Reading test scores show no differences between the treatment and control groups; therefore, this additional instruction did not detract from student performance on this vocabulary and reading assessment. Utilizing fluency, flexibility, originality, and elaboration in other linguistically based content areas will benefit low socioeconomic and

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89 minority students in increasing creativity in linguistic formats. This benefit may extend to other content areas, and teachers of low socioeconomic and minority students should pursue the use of instruction in creativity in linguistic and other formats in a variety of content areas. Implications Significant differences between groups were shown when utilizing fluency, flexibility, originality, and elaboration to enhance creativity in third through sixth grade students. One major implication of this study is that creativity scores can be increased through treatment for non-gifted, low socioeconomic students. These students benefited from instruction in creativity in the regular classroom setting. Students in the treatment group scoring statistically significantly higher on the TTCT Verbal Form than students in the control group indicates that instruction in creativity can increase students’ creativity. Whether creativity can be increased has been a point of discussion in the field of creativity research. It is also noteworthy that this significant difference occurred on the TTCT Verbal Form. Treatments were conducted during vocabulary lessons, a languagebased content area. Because language and discussion reflect a significant portion of student achievement, it is encouraging to practitioners that instruction in creativity can show increases on verbal forms of any test (Vygotsky, 1978; Graves, 2007). The results of this study indicate that creativity, as indicated on a creativity test, can be increased when the instruction in creativity correlates to the type of creativity test administered. Moreover, it is very possible that instruction in creativity could lead to greater increases in all academic areas given that language plays such a large role in student learning.

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90 For Future Studies In order to determine the effect instruction in creativity may have in other academic areas, further research in this area should separate each aspect of creativity – fluency, flexibility, originality, and elaboration – to determine what additional differences exist. Measuring creativity and attempts to increase it have been areas of disagreement in the field of creativity research. Isolating each aspect of creativity within future studies may provide more information about the use of instruction in creativity in educational settings. The differences found in this study may be seen in other subject areas, in addition to language-based subject areas. It would be important to determine if instruction in creativity in other content areas, such as math, science, or social studies, would affect student performance in a similar way. Additionally, future research may investigate if the instruction in creativity was presented in both verbal and figural representations, would this increase students’ scores on both the Verbal and Figural Forms of the TTCT? Another important recommendation for future research would be to replicate this study over a longer period of time. Other significant differences may be found if the length of the treatment was increased. Students in lower socioeconomic situations may need longer instruction in creativity since this learning represents attainment of different skill sets (Mouchiroud & Lubart, 2001). Statistically significant differences were found on one instrument after 6 weeks. Consequently, other statistically significant differences may result after an extended treatment period. Additional research should also increase the sample size in order to generalize results to a larger population of students. A larger

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91 sample size would also allow future researchers to more accurately compare students among different socioeconomic groups. Discussion The significant differences found between the treatment and control groups on the TTCT Verbal Form show great promise for the use of instruction in creativity in the regular education classroom for future research. The significance found in this study indicates that students can improve scores on creativity tests as a result of instruction in creativity. Further, participants in this study represented non-gifted, low socioeconomic, minority students receiving instruction in the regular classroom. This population deserves further study within the field of creativity research, as norm groups and the body of literature do not represent this group adequately. This study adds validity to the significance of studying the effects of instruction in creativity in the regular education setting. Low socioeconomic and minority students can benefit from instruction in creativity. Despite the limitations of this study, small sample size and limited duration of the research, the significant finding suggests that all students may benefit from instruction in creativity. Compounded with the lack of difference between groups on other tests, these results show that the addition of this instruction will not detract from student learning. Instead, instruction in creativity enhances student learning and performance. It is important to note that all students involved in the research were students from a low socioeconomic background and all received instruction in a regular education classroom. These factors increase the importance of the significant findings on the TTCT Verbal test.

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92 Closing Summary All students, particularly those from low socioeconomic backgrounds, may benefit from instruction in creativity in the regular classroom setting. Utilizing fluency, flexibility, originality, and elaboration enhanced creativity in third through sixth grade low socioeconomic, minority students in a regular education setting. A statistically significant difference existed between the control and treatment groups on the Verbal Form of the creativity measure. While no other statistically significant differences were found in this study, a longer treatment time and larger sample size may show even greater differences on a variety of measurements.

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93

REFERENCES

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94

REFERENCES

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112

APPENDICES

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113

APPENDIX A WEEKLY ACTIVITIES PROVIDED TO TEACHERS

Vocabulary Lesson Supplement using Creativity Instruction Please include this supplement in one of your regularly scheduled vocabulary lessons during this week. Week 1 – Generating Categories Look at your list of vocabulary words. Try to generate as many categories for this group of words. Try to think of at least 20 categories. Not all of the words need to fit inside each category, but you should try to create categories that stretch your thinking and include as many of the words as possible. This is called fluency. For example, if your vocabulary list included crevice, vertical, harsh, makeshift, and challenge, you might create the category “mountain climbing”. All of these words have some relationship with mountain climbing. Week 2 – Variety of Possibilities Look at your list of vocabulary words. For each word, try to think of the different people that might use that word. Creative thinking involves looking at something from a variety of points of view. This is called flexibility. For example, if your vocabulary list includes the word vertical, you might list these possibilities: A geometry teacher teaches about vertical lines. A mountain climber climbs vertical slopes. Someone who sleepwalks does not want to be vertical. A tree stays vertical almost all of the time. Week 3 – Attribute Listing Look at your list of vocabulary words. For each word, try to think of the different ways to use that word in a sentence. Creative thinking involves looking at something from a variety of points of view. This is called flexibility. For example, if your vocabulary list includes the word vertical, you might list these possibilities:

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114 I can use vertical as an adjective. “The vertical mountain slope looked dangerous to the climber.” I can use vertical as an adverb. “The horse slept vertically without falling over.” Week 4 – Attribute Listing Look at your list of vocabulary words. For each word, try to think of the different ways to represent that word. Creative thinking involves looking at something from a variety of points of view. This is called flexibility. For example, if your vocabulary list includes the word vertical, you might use these methods as representations of the word vertical: Write the word Represent the word by writing the definition Use the word in a sentence Draw a picture of the word Act out the word Week 5 – Brainstorming Look at your list of vocabulary words. For each word, try to think of the different ways to use that word. Sometimes sentence stems, such as “In What Ways Might…”, “How might…”, or “How to…” help you brainstorm. For example, if your vocabulary list includes the word “harsh”, you might use the stems listed about to talk about how to use “harsh”. Something could be harsh because it is hard to the touch. Something could be harsh because it is like sandpaper to the touch. Something could be harsh because it hurts your feelings. Harsh things are undesirable. Harsh things can improve your character. Week 6 – Brainstorming Look at your list of vocabulary words. For each noun, try to think of the different ways to use that word. Sometimes sentence stems, such as “In What Ways Might…”, “How might…”, or “How to…” help you brainstorm. For example, if your vocabulary list includes the word hammer, you might use the stems listed about to talk about how to use hammer in the most unusual ways. 1. to hammer a nail 2. to build a house 3. to draw lines in the sand 4. to get wrinkles out of clothes 5. to hit ping pong balls 6. to play golf 7. as a bat

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115 8. juggle three of them at once 9. stir a pot of spaghetti 10. use the opposite end to brush your hair

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116

APPENDIX B TEST USE PERMISSION

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117

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