Chemistry Education Research and Practice

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University chemistry students’ learning approaches and willingness to change major Mika Lastusaari*a and Mari Murtonenb

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A questionnaire with 22 Likert type items was developed to collect cross-sectional data from university chemistry students of different study years (N = 118). The aim was to obtain information on their learning approaches as well as their study preferences. Students willing to change from their major subject to medical education represented a considerable portion (N = 49) of the whole subject group, thus the special characteristics of these students were also analyzed. The factor analyses of the dataset revealed six distinct approaches: submissive surface, memorizing surface, technical surface, active deep, processing deep and practical deep. Statistically significant changes in the learning approaches with study years were found for the technical surface and practical deep approaches. The former was most common for introductory level students desiring to change their major subject. The scores in the practical deep approach increased with a greater number of study years. Significant gender differences were observed for the technical surface and processing deep approaches. Male students scored higher Received 25th March 2013, Accepted 14th May 2013

on the processing deep scale and female students willing to change their major scored highest on the technical surface scale. Finally, the students were grouped based on a cluster analysis yielding four

DOI: 10.1039/c3rp00045a

groups: submissive, diligent, enthusiastic and technical students. Those willing to change their major belonged mostly to the superficially-oriented technical group, while advanced level chemistry majors

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occupied the deeply-oriented enthusiastic group.

Introduction Understanding the principles of chemistry is essential for the mastery of not only chemistry itself but also other natural sciences, such as physics, biology, biochemistry and geology (Tai et al., 2005). From the point of view of learning, chemistry can be considered to exist in three forms: macro, submicro and representational (Johnstone, 2000). The macro level is the tangible and descriptive form that people are accustomed to in everyday life. However, to fully understand chemistry, it has to be interpreted or explained in terms of the unseen, i.e. in the submicro, atomic level. This then has to be recorded in a representational language and notation, e.g. symbols, formulae and graphs. Each of these three forms complements each other and no form is superior to another (Johnstone, 2000). Transferring the understanding from the macro level towards the submicro and representational remains a great challenge in learning (and teaching) chemistry. In this matter, the learning approach adopted by the student plays an important role. a

Department of Chemistry, University of Turku, Turku, Finland. E-mail: [email protected] b Centre for Learning Research, University of Turku, Turku, Finland

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Students can adopt different approaches to learning, defined classically as the deep and surface approaches by ¨ljo ¨ in 1976 (see e.g. Case and Gunstone, 2003). Marton and Sa Those who employ surface-level processing focus on the text to be studied and try to memorize as much as possible. The intention is then to cope with the course requirements. This involves e.g. treating the course material as unrelated pieces of knowledge, carrying out procedures routinely, finding it difficult to make sense of new material, seeing little value in courses and tasks, studying without considering the purpose or strategy, feeling undue pressure and worry about work and syllabusboundness (Lovatt et al., 2007; Almeida et al., 2011). The deep level processing, on the other hand, involves the goal of grasping the underlying meaning of the text (Case and Gunstone, 2003). Students adopting this approach relate ideas to previous knowledge, look for patterns and underlying principles, use evidence and relate it to conclusions, examine logic and arguments cautiously and, critically, are aware of the understanding developed while learning and become actively interested in the studied content (Almeida et al., 2011). An extension to this model, identified by Biggs in 1978, includes a third group of students with a strategic approach to learning. This approach encompasses the intention to achieve the highest possible grades.

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Paper The student will then typically put consistent effort into studying, manage time and effort efficiently, find the right conditions and materials for studying, be alert to the assessment requirements and criteria as well as gear work to the perceived preferences of lectures (Almeida et al., 2011). In addition to the deep/surface definition of learning approaches, others also exist. However, these can all be considered to be similar to the deep/surface classification, simply with a different nomenclature. An example is provided by the four approaches identified by Booth in 1992: the opportunistic approach (similar to the surface approach), which can be either expedient or constructual and the interpretative approach (similar to the deep approach), which can be operational or structural (Case and Gunstone, 2003). Moreover, intermediates between the deep and surface approaches have been categorized. These include the procedural approaches identified in engineering education: procedural deep and the algorithmic (procedural surface) approaches (Case and Gunstone, 2003). The latter involves the memorizing of mathematical formulae and the substitution of appropriate values into them as required by the assessment tests. In the former approach the student relates formulae to each other to gain understanding at some future point through applications and problem-solving procedures (Case and Marshall, 2004). It has also been proposed that there is, in fact, a continuous distribution of learning approaches with the deep and surface forms appearing at the extremes (Case and Gunstone, 2003). The deep/surface classification of learning approaches has provided a ready explanation for the different learning outcomes exhibited by students at different levels and suggested possible teaching strategies that could promote deep learning (Case and Gunstone, 2003). This is especially so, since the surface and strategic approaches can be considered to result from context rather than as natural ways of learning, such as the deep approach. For example, a study of Australian science students at universities reported a dependence between the teachers’ approaches to teaching and the students’ learning approaches (Trigwell et al., 1999). If the teaching is focussed on transmitting knowledge, the students are very likely to adapt a surface approach. On the other hand, with teaching oriented towards students and to changing students’ conceptions, students often use deep approaches. However, it has been pointed out in a study of chemistry students at the university level that, in addition to surface learners finding it very difficult to adapt to a learning environment promoting deep learning, some deep learners may find the same (Almeida et al., 2011). Thus, students may use both deep and surface approaches at different points in their studies, depending on the learning task and the conditions under which the task is performed, but the usual tendency is to adopt a particular approach and not change it (Laird et al., 2005). Generally speaking, students using the deep approach tend to earn higher grades as well as retain, integrate and transfer information at higher rates than those using the surface approach (Laird et al., 2005). This has been found to be true in many fields of study,

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Chemistry Education Research and Practice including chemistry (Almeida et al., 2011). On the other hand, the ‘‘paradox of the Chinese learner’’ implies that Asian students make extensive use of memorization, but still display extremely high quality learning outcomes (Case and Gunstone, 2003). This has led to the classification of two forms of memorization, one with and the other without comprehension involved, thereby also adapting the paradox to the deep/surface classification. It has been suggested that for some science tasks, especially in fields such as chemistry, a deep approach will initially demand a narrow focus on details, whereas in humanities a deep approach is required from the very start (Case and Gunstone, 2003). This evolution of the approach to learning can be attributed to the structure of the studies: the first year will include a wide spectrum of different things to be learned, thus leaving little time to really concentrate on the understanding. During the next years, the courses will become narrower but deeper in knowledge, allowing better combining of pieces into a whole. A study with more than 50 000 participants from colleges and universities across the United States indicated that, indeed, students of e.g. the arts and humanities use a deep approach to learning far more commonly than do students of e.g. physical sciences (including chemistry) or engineering (Laird et al., 2005). Studies on the evolution of learning approaches with study years have yielded practically all kinds of results. A longitudinal study with participants from different university departments in the Netherlands showed an increase in the use of the deep approach from the first to the third semester (Vermetten et al., 1999). Simultaneously, the surface approaches did not decrease but remained constant, indicating the independence of the surface and deep approaches. Another 30-month longitudinal study of commencing science students participating in a chemistry course in Australia reported a small initial decrease in the deep approach and a small increase in the surface approach (Zeegers, 2001). Finally, both returned to their initial values. For a group of students of psychology and medicine in Finland, no change was observed between novice and 5th year ¨nne, 1996). Finally, many students (Lonka and Lindblom-Yla studies also report the overall decline of the deep approach with increasing study years, which is explained as being due to e.g. the students responding to the learning environment by reproducing what they perceived the teacher wanted or was likely to accept (Jackling, 2005). Another factor found to be important in the evolution of learning approaches is the age of the students: Older students tend to adopt a deep approach more readily and a surface approach less readily than younger students (Sadler Smith, 1996). The desire to change the major subject is one of the four main categories of reasons for not continuing studies in a study program at the university level. According to Hackman and Dysinger (1970), the categories are characterized by the differences in academic competence and commitment. ‘‘Persisters’’ score high in both categories, whereas ‘‘voluntary withdrawals’’ score low. ‘‘Academic dismissals’’ show high commitment but low competence, and ‘‘transfers/returnees’’ commonly show

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Chemistry Education Research and Practice high competence but low commitment. A more recent study by ¨kinen et al. (2004) focussing on 1600 first year students in Ma 6 faculties in one Finnish university showed similar results: changing the major subject or quitting altogether was observed to be most common for students with a non-committing study orientation. A considerable portion of these students had chosen their major subject based on easy admittance to the university and the fact that they would then have at least some position as a student. Interest in this chosen secondary choice subject played no role. 80% of these students studied in the Faculty of Mathematics and Natural Sciences or the Faculty of Humanities. However, non-committing approaches were observed to be most common in the Faculty of Mathematics and Natural Sciences, though this was not accompanied by the increased anxiety or submissiveness commonly associated with non-committed students prone to quit their studies. This result was well in line with the statistics on changing the major subject, because most of the subjects taught in this faculty have been observed to function as good one- or two-year training courses for students aiming for studies in medicine or polytechnics, i.e. in their primary choice of subject. Keeping their final goal in mind, the students may be non-committed to the ‘‘training’’ subject, but this will not lead to anxiety ¨kinen et al., 2004). (Ma As witnessed, for example, in the results of Hackman and ¨kinen et al. (2004), the driving force Dysinger (1970) and Ma behind the choice of the major subject can be considered to be motivation. This can be either intrinsic or extrinsic. Intrinsic motivation refers to doing something not for a separable outcome but because it is itself rewarding, whereas the extrinsic form focuses on the outcome, e.g. the achieved profession (Mikkonen et al., 2009). However, even if profession-oriented, i.e. extrinsically motivated, students are often most successful ¨kinen et al., 2004), it has been in earning grade points (Ma pointed out that both extrinsic and intrinsic motivation are necessary to maintain motivation over time (Mikkonen et al., 2009). A similar observation was reported by Woosley and Jackson (2002) in a study of 75 students who had changed their major subject in Ball State University (Muncie, Indiana, USA). The results suggested that most students changed their major because of the attractions associated with the new major. The main attractions were career possibilities, more interesting courses and available job openings in the field. The major changers were not dissatisfied with their old major, but they felt more motivated, both extrinsically and intrinsically, to study in the new major. The learning approach differences between withdrawing and non-withdrawing students have been studied by Van Bragt et al. (2011). They reported that, for a group of 1176 Dutch higher education students, non-withdrawing students showed higher scores in deep approaches than the withdrawing ones. The continuing students showed a high level of future occupational identity, perception and experience of learning environment quality as well as pragmatic and personal orientation. Similarly, in another report from Colorado State University, all 808 students who had changed their major subject were able to

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Paper be more specific about their probable career choice than in their previous choice of major (Titley et al., 1976). Even if a student has not chosen the highest motivating major subject and even if these secondary choices of study are very often applied for without a serious intention to actually take part in the studies (Ozga and Sukhnandan, 1998), starting studies in the secondary choice does not automatically lead to ¨¨ ¨, quitting or change of the major subject (Liljander and Ma atta 1994). This leaves a chance for the educators to influence the students’ perspectives. In this work, the approaches to learning of chemistry majors and minors were studied at the Department of Chemistry at the University of Turku. The work is cross-sectional, covering students from all study years. The main aim was to find the answers to the following questions: What are the learning approaches of chemistry students at the university level? Do these approaches change with an increase in the years of study? Do other factors affect the learning approaches?

Methodology Subjects At the University of Turku, high school graduates can be admitted as chemistry students to the Department of Chemistry either by passing an entrance examination or via their high school grades. The structure of the study program follows the pan-European convention, with three years for a bachelor’s degree (Bachelor of Science, BSc) and an additional two years for a master’s (Master of Science, MSc). Altogether, 65 students are accepted each year to start their bachelor studies and each of them can continue with the MSc studies, if they so desire. The University of Turku also has a strong tradition of medical education, producing medical doctors at the Faculty of Medicine. Admission for this can be achieved only by an entrance exam. Typically, the Department of Chemistry will lose 10–15 students annually to the Faculty of Medicine after the first year. Students who have studied chemistry or physics at the University of Turku have ca. a 30% higher chance of being admitted to the Faculty of Medicine than other students. The subjects in the present study comprised 118 students with differing numbers of study years attending lectures and practicals at the Department of Chemistry, Faculty of Mathematics and Natural Sciences, University of Turku, Finland. All data were collected during 2012. Thus, the study was crosssectional. The participants were divided into three groups based on their study years as follows: introductory (1st year; N: 75), basic (2nd to 3rd year; N: 28) and advanced level (4th to 5th year; N: 25). Thus, the first two groups correspond to bachelor’ degree students and the last group to master’s degree students. The participants were further classified according to their gender and willingness to change their major subject. The major subject was not used for classification because the number of participants from some major subjects was too small (mathematics: 14, biochemistry: 7, geology: 4, physics: 1). The study year groups contained 53 (introductory), 18 (basic) and 21 (advanced) chemistry majors. As the yearly intake of

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chemistry majors in Turku is 65, the present study included 82% of the new students. The statistics from 2010 indicate that 24 students completed the BSc degree and 20 the MSc degree with chemistry as their major subject at the University of Turku (Statistics Finland, 2012). This suggests that the basic and advanced level year groups also represent the student volumes very well. On the national scale, the University of Turku hosted 19% of BSc students and 14% of MSc students in Finland in 2010, nationally producing 18% and 17% of the BSc and MSc degrees in chemistry, respectively. Thus, the rate of completing a degree as well as withdrawing from studies at University of Turku corresponds well to the general trend in Finland.

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Materials There are standardized general questionnaires, such as the Study Process Questionnaire (SPQ) by Biggs, the Approaches to Study Skills Inventory for Students (ASSIST) by Entwistle (see e.g. Lovatt et al., 2007), Learning and Study Strategies Inventory (LASSI), Motivated Strategies for Learning Questionnaire (MSLQ) (see e.g. Zeegers, 2001) and the Inventory of Learning Styles in Higher Education (ILS) (Vermunt, 1994), to probe the deep/surface approaches to studying. These questionnaires had Table 1

an impact on the formulation of the questionnaire that was used in this study. However, to address studying chemistry in particular, a self-made two-page questionnaire was created in Finnish. It contained five background questions (gender, major subject, intention to change the major subject, intended major subject and year of study). This was followed by 22 statements about preparation for a chemistry examination, chemistry lectures, ways of studying chemistry and chemistry practicals. The statements are shown in Table 1. The answers were collected anonymously on a five-point Likert scale from complete disagreement (value: 1) to complete agreement (5) with the statement. The Likert approach was chosen to allow easy and objective quantification of the data. The statements were chosen to reflect the learning approach in particular, and also those covering regulation strategies, study preferences and opinions on practicals were included. Three statements (I learn everything exactly as it has been presented in the course book, I try to connect things from a course as parts of a bigger picture, I look for justifications and evidence to make my own conclusions about things to be learned) were adopted from the ILS by Vermunt (1994). Data were collected during laboratory practicals and breaks between lectures. It took around five to ten minutes to complete the questionnaire.

The final rotated component matrix. The highest loads (marked in italic text) for each factor were used in the assessment of the six approaches to learning

Component Statement

SubSurf MemSurf TechSurf ActDeep ProcDeep PractDeep

Many things that I learn remain isolated and do not link as a part of a bigger picture. When reading the course book, I often do not understand how a new topic relates with any old one. I have to memorize things without having the opportunity to understand them. During a lecture, I often do not understand what a new thing is connected with.

0.68

I learn everything exactly as it has been presented in the course book. I remember many isolated things from the lectures. I learn a new thing easily, if it is presented as text. It is important that the things presented in the course book are dealt with during the lectures. I find it easy to learn things by memorizing them. I make my own notes when studying for an examination. I make mnemonics to learn things better. I underline while reading for chemistry examination.

0.35

0.81 0.74 0.81 0.61 0.60 0.48 0.65

0.36

0.52 0.30

0.75 0.73 0.78

I make myself acquainted with the subject of the next lecture beforehand. I usually search and read additional material concerning the course.

0.80 0.72

I try to connect things from a course as parts of a bigger picture. I often chew over the thoughts awoken by scientific texts as well as connections between them. I look for justifications and evidence to make my own conclusions about things to be learned. I like to do practicals. I have often understood a chemical phenomenon only after doing practical work on it. When doing a practical, I usually try to understand what its different parts are based on. One can learn a chemical phenomenon only by doing practical work on it. My most important goal for a practical is to have it done in a way accepted by the teacher.

0.70 0.78 0.79 0.74 0.69 0.35

0.75 0.57 0.55

0.34

Note: loadings less than 0.30 are omitted.

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Statistical procedures Correlations were computed and principal component analyses were carried out to examine the interactions between the individual 22 items. Finally, a six-principal-component Varimax (with Kaiser normalization) solution was chosen from which six sum variables were constructed. Means and Cronbach a-values (Cronbach, 1951; Bland and Altman, 1997) were calculated for each sum variable. A one-way ANOVA was applied to compare the different student groups. A K-means cluster analysis was carried out to classify students into groups with similar approaches to learning. Several cluster analyses were run using a method for maximizing between-cluster distances. The theoretically most interesting solution of four clusters was chosen for further analyses. The statistical significance between the groups in the selected solution reached p o 0.001 level on all variables. The IBM SPSS Statistics v20 program was used for the analyses. As the Likert scales are ordinal by nature, measurement theory formally forbids the use of interval scale descriptors such as the mean and standard deviation for summarizing the data set (Michell, 1986). Thus, there has been considerable debate on the use of these descriptors (see e.g. Schmid et al., 2012, for a short review). However, in many cases, these descriptors provide fruitful information or even reveal facts obscured by the formally allowed descriptors (Crisp et al., 2011). Therefore, this approach was also adopted in the present work.

Results and discussion Learning approaches Table 1 shows the principal component loadings of the 22 individual items in a six-component Varimax solution explaining 61% of the total variance. The solution was satisfactory theoretically and statistically with all eigenvalues exceeding 1.1. Many recommendations have been proposed for factor analyses concerning the validity of a factor solution. These are usually based on sample size, average number of items per factor, pattern of loadings and the average size of the loading (see e.g. Velicer and Fava, 1998, for a review). It has been pointed out, however, that none of these recommendations are valid universally, but the most important indicator is actually the level of communality for each item (MacCallum et al., 2001). In the present work, the mean communality obtained was 0.61. This high level of communality indicates that a good recovery of population factors can be achieved with samples with N well below 100 (MacCallum et al., 1999). With communalities around 0.5, a somewhat larger sample (N: 100–200) would be required. Thus, the present communalities indicate that the sample size (N = 118) is well above the minimum requirements. It was found that each component described a distinct approach to learning. These were named based on the statements involved as follows: submissive surface (4 items), memorizing surface (5), technical surface (3), active deep (2), processing deep (3) and practically oriented deep (5) approaches. The corresponding scales and approaches were given the abbreviations SubSurf, MemSurf, TechSurf, ActDeep, ProcDeep

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Paper and PractDeep, respectively, which will be used in the tables and figure below. This new classification was then used for further analysis of the data. The items included in the individual scales described the types of students using the six learning approaches. These were as follows: a student using the SubSurf approach can be characterized as a person who is just studying chemistry and has very little interest in learning on any level. He/she is not willing to make too much effort in making the learning outcomes better. The MemSurf learner puts a lot of effort in passing the assessment tests. He/she uses mostly memorization to store information and does not pay much attention to connecting the memorized bits to larger units of knowledge. The TechSurf student goes a little bit further by actively using different techniques for surface learning instead of just pure memorization. The ActDeep learner actively seeks for additional material to complement his/her view of the studied subject and wants to have an understanding of the material to be learned already before the lecture. The ProcDeep approach involves the active collecting and cognitive processing of information for the construction of a total conception of the matter to be studied. Finally, the PractDeep learner uses chemistry practicals as the means to achieve deeper understanding of the studied matter. In the context of learning chemistry, the approaches could be visualized as follows. Let us take the valence shell electron pair repulsion (VSEPR) model as an example. In a regular inorganic chemistry textbook, there might be the following text and an associated table. In this case, these are from Atkins et al. (2006). ‘‘The primary assumption of the VSEPR model is that regions of enhanced electron density, by which we mean bonding pairs, lone pairs, or the concentrations of electrons associated with multiple bonds, take up positions as far as possible so that the repulsion between them is minimized. . .’’ ‘‘The basic arrangement of regions of electron density according to the VSEPR model: Number of electron regions: 2 3 4 5 6

Arrangement: Linear Trigonal planar Tetrahedral Trigonal bipyramidal Octahedral’’

The SubSurf student would approach this by trying to memorize the table as such. She/he would probably have no idea of what ‘‘number of electron regions’’ or the arrangements given actually mean and she/he would probably not try to memorize the text. The MemSurf student remembers things easily, so she/he would memorize both the text and the table so that she/he could reproduce it later. This student feels that memorizing is the best way to learn things. The TechSurf approach would include possibly underlining parts of the text to focus memorization on those parts. A student using this approach could make her/his own summary of the text by

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e.g. combining the underlined parts. For the table, this approach could include making a mnemonic of the arrangements to remember them more easily. This could involve e.g. memorizing a string like ‘‘lin-trig-tet-tribi-oct’’ or building a sentence from the first letters of each word, for example ‘‘Lisa tried teaching tricky biology in October’’. None of these three approaches would involve any actual understanding of the matter. Thus, the students would not be able to answer any questions involving applied knowledge. The ActDeep learner would be interested enough in the subject so that she/he would read this part of the course book before the actual lecture. Also, this kind of approach would involve seeking additional material so that the student would be able to read about the matter from different points of view. This would help the learner to understand the matter rather than help her/him to memorize it. The ProcDeep approach would involve processing the text cognitively to connect it to a bigger picture. This type of learner could e.g. establish a thought that usually molecules containing four ligands around the central atom, e.g. SO42 , will have a tetrahedral geometry and that it is only natural due to the electrostatic repulsion between the O2 ions. These deductions would be made by connecting prior knowledge to the new matter. This student would clearly not need any memorization to understand the VSEPR model. Finally, the PractDeep approach would involve a desire to do laboratory work to complement theory. A practical work with the thermochromic compound [(CH3CH2)2NH2]2[CuCl4], which changes color upon heating due to the change in the geometry (from tetragonal to square planar) around the Cu2+ ion, could be used as an example. Involving characterization with different spectroscopic techniques, this kind of work could trigger enhanced understanding in a student with the PractDeep approach. All these three approaches would involve actual understanding of the matter. Thus, the students would be able to apply this knowledge. Scales and their reliabilities Table 2 shows the mean scores and standard deviations for each scale describing an individual approach. For the entire studied population of chemistry students at the University of Turku, the features of TechSurf and MemSurf were the most common on average, whereas those of SubSurf and especially ActDeep were quite uncommon (Table 2). The Cronbach a values (also shown in Table 2) varied from 0.54 to 0.81. Conventionally, Cronbach a values between 0.70 and 0.95 are considered to be acceptable (Bland and Altman, 1997). However, the a value is highly dependent

Table 2 Statistical descriptors for the six scales describing different learning approaches for chemistry students at the University of Turku (N: 118)

on the number of items in such a way that the a value will increase with an increasing number of items. This explains the somewhat lower a value (0.59) for ActDeep. For the MemSurf scale, the a value is still a little lower (0.54). The items in this scale, however, form a uniform group and none of them could fit in any of the other scales. The lower a value thus indicates that even if the items describe the same type of approach, the degree by which they do this is not as uniform as in the other five scales. On the other hand, this value is not too low compared to those presented before in ¨nne similar types of data handling, e.g. by Lonka and Lindblom-Yla (1996). Therefore, the Cronbach a values may be concluded as indicating a high reliability for the scales assigned to each approach based on the principal component factor analysis. Learning approach differences by study level and gender Study level. To probe whether students at different levels in their studies (introductory, basic or advanced) differed from each other in their approaches, mean scores were calculated for each scale and ANOVA analyses were carried out for each study level. Table 3 shows these results. On the SubSurf scale, basic level students scored the highest. Measured by one-way ANOVA, there were no statistically significant differences between the levels, however. On the MemSurf scale, the score decreased with increasing study years. On this scale, the effect of level was not significant either. Statistical significance was observed in the TechSurf scale. This had the highest score for the introductory level students, decreased for the basic level students and finally increased slightly for the advanced level. The high score for the introductory level group was due to the fact that the students planning to change their major subject adopted this approach very readily (see Willingness to change major below). These students were almost completely first year students. On the ActDeep scale, the score decreased from the introductory to the basic level and thereafter increased again for the advanced level group, which scored the highest. There were no statistically significant differences observed, however. Similarly, on the ProcDeep scale, the highest score was obtained for the advanced level and the lowest for the basic level, but there were no significant differences between the levels here either. Statistically the scores on the PractDeep scale had the highest changes by the study level with the score increasing with additional study years. This evolution was well in line with the educational path of a chemistry student at the University of Turku, as it is only

Table 3 Statistical descriptors and one-way ANOVA results for the differences by study level on the six scales describing different learning approaches

Mean score/standard deviation

ANOVA

Scale

Number of items

Mean score

Standard deviation

Cronbach a

Scale

Introductory Basic (N = 75) (N = 28)

Advanced (N = 25) df F

Significance

SubSurf MemSurf TechSurf ActDeep ProcDeep PractDeep

4 5 3 2 3 5

2.57 3.37 3.49 2.12 3.13 2.98

0.77 0.52 1.06 0.80 0.79 0.72

0.81 0.54 0.69 0.59 0.74 0.72

SubSurf MemSurf TechSurf ActDeep ProcDeep PractDeep

2.50/0.77 3.43/0.48 3.74/0.99 2.15/0.73 3.12/0.75 2.84/0.66

2.47/0.69 3.24/0.60 3.23/1.17 2.26/0.96 3.34/0.86 3.40/0.76

0.10 0.28 0.02 0.25 0.23 0.004

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2.85/0.82 3.35/0.53 3.15/0.99 1.91/0.81 2.97/0.82 2.94/0.70

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at the master’s degree stage (advanced level) that the students participate actively in the research projects of the Chemistry Department. When viewed qualitatively, the evolution of the score of each scale by study level revealed a similar trend: the middle year group (basic level) always scored lowest in the active scales (TechSurf, ActDeep, ProcDeep) and highest in the passive ones (SubSurf, MemSurf). Although there was no statistical significance shown by the ANOVA results, the trend is quite clear. This observation was very similar to that reported by Zeegers (2001) in a 30-month-long longitudinal study of university students attending an introductory chemistry course. This trend may be a reflection of the U-shaped evolution of learning, which states that the learner first learns the correct behavior, then abandons it and finally adopts it again. U-shaped learning is fairly common, and it has been claimed that it is necessary for obtaining full learning power (Carlucci et al., 2008). It is possible that the driving force for this decline is the increased work load perceived by some chemistry students at the University of Turku during this stage of their studies. It seems that as the work load increases after the first study year or the students adapt to the teachers’ expectations for acceptance, they seek to alleviate the effort needed by decreasing the degree of energy-demanding active approaches. As the students begin to understand how they should study chemistry by the beginning of their master’s studies, they adopt the active approaches in an even purer form. Gender. To check whether female students differed from male students in their approaches to learning, mean scores were calculated for each scale and ANOVA analyses were carried out. Table 4 shows the results. The results indicated that the ProcDeep was adopted far more by male than female students and that this difference was statistically significant. However, if the students planning to change their major subject were not considered, there was no statistically significant gender difference for ProcDeep, although males still scored a little higher (means: 3.0 for females and 3.3 for males; F = 2.1 and significance = 0.16). Male students also scored higher on the other two deep scales (ActDeep and PractDeep), but in these cases, the differences were not statistically significant. On the surface scales, female students scored statistically significantly higher in TechSurf. This was not affected by the removal of the students planning to change their major subject (means: 3.7 for females and 2.8 for males; F = 14.1 and

significance = 0.00). Thus, this seems to be an approach clearly more preferred by female than male students. Female students also scored higher in the SubSurf and MemSurf scales, but the differences were not statistically significant. Different learning approach groups of students Subject grouping. To discover whether the students could be grouped according to their utilization of the different learning approaches, a k-means cluster analysis was carried out on the students’ scores in the learning approach sum scales. A fourcluster model was applied, since that was the smallest number of clusters supported by the ANOVA results (significances o0.05 for all sum scales in all clusters). The size of the groups varied from 19 to 46 students. Each cluster was named in a manner describing the common elements concerning the typical learning approaches of the students in the group. The figure below depicts the differences in the scores in the learning approach sum scales between the groups (Fig. 1). Group 1 (N: 19) described submissive students. These students had problems with understanding, and they felt that they needed to memorize things. They did not try to be active by using learning techniques (TechSurf), finding additional material (ActDeep) or trying to understand things (ProcDeep). These students had a low trust in themselves as learners, and they seemed to have given up even trying to learn. Group 2 (N: 19) contained diligent students. These students trusted in themselves (low in SubSurf), they learned a lot by memorizing (MemSurf), but they also tried to understand things deeply (ProcDeep). They seemed to be diligent but not especially active students. Group 3 (N: 33) contained enthusiastic students. These students were eager to learn: they used memorizing techniques (MemSurf and TechSurf), but they also sought information themselves (ActDeep) and tried to deeply understand the things to be learnt (ProcDeep). They were also very interested in doing practicals and felt that it helped them to learn (PractDeep). Group 4 (N: 46) comprised technical students. These students tried to memorize things (MemSurf) and they liked to use study techniques (TechSurf). They were not interested in trying to

Table 4 Statistical descriptors and one-way ANOVA results for the effect of gender on the six learning approaches at the University of Turku

Mean score/standard deviation

ANOVA

Scale

Female (N = 76)

Male (N = 42)

df F

Significance

SubSurf MemSurf TechSurf ActDeep ProcDeep PractDeep

2.62/0.78 3.41/0.52 3.75/0.94 2.09/0.74 2.98/0.67 2.89/0.70

2.49/0.77 3.30/0.53 3.04/1.13 2.17/0.91 3.40/0.90 3.15/0.73

1 1 1 1 1 1

0.40 0.29 0.00 0.63 0.005 0.06

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0.72 1.12 13.22 0.23 8.11 3.66

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Fig. 1 Final cluster centers for the cluster analysis of the chemistry students at the University of Turku.

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Chemistry Education Research and Practice

understand things (ActDeep and ProcDeep), and they also did not see the benefit of practicals. Overall group compositions. The four groups were further analyzed using differences in the frequencies of the study level, willingness to change major and gender. These frequencies are shown in Table 5. Those willing to change their major subject will be discussed in more detail later in the text (see Willingness to change major). Group 1, submissive students, constantly contained a small number of students from each study level. These were mostly students who were not willing to change their major subject. The group had a slight dominance of female students (63%) with the dominance originating from the introductory level students. Group 2, diligent students, was composed mostly of introductory and basic level students. The highest frequency was obtained for basic level students who did not want to change their major subject. This group contained somewhat more male students (68%) than females. Also, as in Group 1, the highest difference in gender frequencies originated from the introductory level students with no plans to change their major subject. Group 3, enthusiastic students, was dominated by introductory level students who wanted to change their major and advanced level students who did not want to change major. This group was almost equal in gender distribution, although females were a majority (55%). Finally, Group 4, technical students, was highly enriched with introductory level students willing to change their major subject. Moreover, the group was clearly dominated by females (87%). Excepting those students willing to change their major, introductory level students had no preferred group. Basic level

Table 5

students had a slight preference towards Groups 2 and 4. Thus, they had not yet found their calling in chemistry and therefore had more superficial approaches. The advanced level students, however, belonged mostly to Group 3. Thus, they felt that chemistry was the correct subject of study for them, and they were also enthusiastic about it. Willingness to change major Effect on learning approaches. Of the 118 students participating in this study, a total of 49 answered ‘‘yes’’, when asked whether they would change their major subject, if possible. 40 (82%) of these were chemistry majors. The students were also asked what they would like to change their major to. Almost all mentioned medical education or some related domain. Only two of the students mentioned something totally different, namely, kindergarten teacher and architecture. These two were not included in the further analyses. To address the question of whether students willing to change their major subject differed from those not willing to change their major subject in their approaches to learning, mean scores were calculated for each scale and ANOVA analyses were carried out. The results are shown in Table 6. For the MemSurf, TechSurf and PractDeep scales, statistically significant differences were obtained. There were no significant differences in the mean scores and standard deviations if only chemistry majors were taken into consideration of those willing to change their major (3.50/0.44 for MemSurf, 3.94/0.99 for TechSurf and 2.81/0.62 for PractDeep). Thus, those willing to change their major could be considered as a group unaffected by their major subject. They scored higher in MemSurf and TechSurf but lower in PractDeep. It seems that those aiming to be medical doctors used memorization more extensively than

Composition (in frequencies) of the four student groups by study level, willingness to change major and gender

Group Study level

Willingness to change major

1

2

3

4

Total

Introductory

Wants to change major (female/male) Does not want to change (female/male) Total

3 (2/1) 6 (5/1) 9

4 (2/2) 4 (0/4) 8

13 (6/7) 4 (2/2) 17

25 (22/3) 6 (6/0) 31

45

Wants to change major (female/male) Does not want to change (female/male) Total

1 (1/0) 4 (2/2) 5

1 (1/0) 7 (3/4) 8

0 (0/0) 5 (2/3) 5

1 (1/0) 9 (8/1) 10

3

Wants to change major (female/male) Does not want to change (female/male) Total

0 (0/0) 5 (2/3) 5

1 (0/1) 2 (0/2) 3

0 (0/0) 11 (8/3) 11

0 (0/0) 5 (3/2) 5

1

Wants to change major (female/male) Does not want to change (female/male) Total (female/male)

4 (3/1) 15 (9/6) 19 (12/7)

6 (3/3) 13 (3/10) 19 (6/13)

13 (6/7) 20 (12/8) 33 (18/15)

26 (23/3) 20 (17/3) 46 (40/6)

49

Basic

Advanced

Total

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20 65

25 28

23 24

68 117

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Table 6 Statistical descriptors and one-way ANOVA results for the differences in willingness to change the major subject on the six learning approaches at the University of Turku

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Mean score/standard deviation

ANOVA

Scale

Wants to Does not want to change (N = 49) change (N = 69) df F

Significance

SubSurf MemSurf TechSurf ActDeep ProcDeep PractDeep

2.50/0.70 3.49/0.45 3.78/1.01 2.22/0.77 3.14/0.75 2.74/0.62

0.36 0.03 0.01 0.23 0.91 0.002

2.63/0.82 3.29/0.55 3.29/1.05 2.04/0.82 3.13/0.83 3.16/0.74

1 1 1 1 1 1

0.83 4.66 6.46 1.46 0.01 10.32

those aiming to become chemists. Moreover, they had less interest in doing chemistry practicals. The students in the former group probably had little interest in really understanding chemistry, since their goal was elsewhere. This drove them towards a more superficial approach. These results agree well ¨kinen et al. (2004) on the lack of commitment with those of Ma and Mikkonen et al. (2009) on the strongly driving extrinsic motivation of these profession-oriented students. On the SubSurf scale, those aiming to be medical doctors scored lower. This may be due to their actively goal-oriented learning strategies, which are prone to expel submissiveness. Therefore, they also scored higher on the ActDeep scale. On the ProcDeep scale, there was practically no difference between the scores of the two groups. Presence in the learning approach groups. For the group of students willing to change their major subject, most students (N = 25 with 21 (84%) chemistry majors) belonged to Group 4 (technical students) (see Table 5). Thus, they were students with low commitment towards studying chemistry. A considerable number (N = 13 with 12 (92%) chemistry majors) of the major change hopefuls belonged to Group 3. These were good students who seemed to be interested also in chemistry, even if their goal was elsewhere. This group of good students constituted about 30% of the students willing to change their major. Since the Medical Faculty admission number at the University of Turku for students with previous studies in the Department of Chemistry has been around 10 to 15, it seems reasonable to assume that it is these Group 3 students that have the highest chance of admission. On the other hand, these students could also be most easily influenced to continue with chemistry instead of striving towards the Medical Faculty. This is because they are clearly willing to learn chemistry deeply albeit that their goal is elsewhere.

Conclusions and implications The present work focused on obtaining more information on the learning approaches of chemistry students at the university level by carrying out a cross-sectional study. A questionnaire aimed especially at learning and studying chemistry was constructed for this purpose. Students willing to change their major subject to medical education represented a considerable portion of the whole subject group, thus the special characteristics of these students were also analyzed.

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The results for the entire subject group revealed six distinct approaches to learning: submissive surface, memorizing surface, technical surface, active deep, processing deep and practical deep. It was found that the technical surface and practical deep approaches showed statistically significant differences between students at different study levels. The technical surface approach was most common for introductory level students desiring to change their major subject. Moreover, these students scored higher on the memorizing surface and technical surface scales as well as lower on the practical deep scale than those not willing to change their major subject. This suggested that the major change hopefuls were surface learners with little interest in learning chemistry. The scores in the practical deep approach increased with increasing numbers of study years. This indicated that the understanding of the connection between theory and practice in chemistry increased with additional study years and that the goal of chemical education was met in this respect very well among the subjects. Significant gender differences were observed for the technical surface and processing deep approaches. Male students scored higher on the processing deep scale and female students willing to change their major scored highest on the technical surface scale. The subjects could be grouped into four different student groups, namely submissive, diligent, enthusiastic and technical. For the students not willing to change their major subject, advanced level students mostly belonged to the group of enthusiastic students. For the introductory and basic level students, there was no clear preference for any group. A clear majority of the students wishing to change their major were labeled as technical, although a considerable number were also in the group of enthusiastic students. Knowledge of these results may be beneficial for designing the teaching of chemistry at the university level. Since a nonnegligible portion of the first year students wishing to change their major subject from chemistry to medical education showed enthusiasm in learning chemistry deeply, there seems to be an opportunity for the teachers to convince them to actually stay with chemistry. This implies that more emphasis should be placed on the introductory courses to make them more stimulating and interesting. It would be especially important to show how the course contents relate to the future careers of the students if they become chemists. This could be made easier with a low amount of staff resources by e.g. integrating comprehensible talks from doctoral students or senior staff members highlighting the benefits of persisting with chemistry. If more staff resources were available, the often passive first year mass lectures could be changed to more engaging and interactive teaching by also using peers and student tutors. However, competing against the higher status of the medical doctor profession, not to mention its higher expected salary, remains a great challenge. The present study shows that the basic level students (2nd and 3rd study year) would also benefit from modifications in the teaching strategies, since there was an indication of U-shaped learning observed. As this group seems to be somewhat passive,

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Paper activating teaching could be used. Of course, chemistry teaching already inherently contains laboratory practicals, which by definition are active, but they could be modified by putting them to a more meaningful context. Real examples of e.g. industrially or biologically important chemical processes or reactions could be used as the basis for the laboratory practicals. Again, this could be incorporated into regular laboratory work with a quite low demand on extra resources. One must keep in mind, however, that the present results also show that the advanced level students are overall active and enthusiastic, and it seems that they have found their calling in chemistry. Therefore, the present results indicate no need for changes in teaching chemistry for those satisfied with their choice of major. Finally, one must note that the present study deals only with students from one university and that the cross-sectional data only cover a time span of one year. Even if the sample group is large enough for statistical analysis and the study program corresponds to today’s European standards, one cannot state that the results are completely generalizable. However, the questionnaire seems to address the research questions it was developed for very well. In the future, it would be interesting to expand the study to other universities to enable comparison of data and validation of the questionnaire. The U-shaped learning curve suggested by the present study should be verified by collecting a larger sample set. Moreover, the learning approach differences between those students who are admitted to the Faculty of Medicine and those who do not should be studied further.

Acknowledgements Prof. K. Haapakka, Dr H. Neuvonen, Dr A. Lehtonen, ¨nnberg and Dr M. Karonen (University of Turku) are Dr T. Lo gratefully acknowledged for their help with the data collection. All the students are thanked for eager participation in the study.

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