The Malaysian Online Journal of Educational Technology

ISSN: 2289-2990

The Malaysian Online Journal of Educational Technology Volume 2, Issue 1 January 2014

2014 Editor-in-Chief Professor Dr. Saedah Siraj Editors Dr. Norlidah Alias Dr. Onur Isbulan Associate Editors Professor Dr. Raja Maznah Binti Raja Hussain, Associate Prof. Dr. Habib Bin Mat Som, Dr. Chin Hai Leng Dr. Dorothy Dewitt

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The Malaysian Online Journal of Educational Technology

Volume 2, Issue 1

Copyright © 2013 - THE MALAYSIAN ONLINE JOURNAL OF EDUCATIONAL TECHNOLOGY All rights reserved. No part of MOJET’s articles may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher. Contact Address: Professor Dr. Saedah Siraj MOJET, Editor in Chief University of Malaya, Malaysia

Published in Malaysia

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Message from the editor-in-chief The Malaysian Online Journal of Educational Technology (MOJET) highlights the current issues in educational technology. MOJET is an international, professional referred journal in the interdisciplinary fields sponsored by Faculty of Education, University of Malaya. This journal serves as a platform for presenting and discussing the emerging issues on educational technology for readers who share common interests in understanding the developments of the integration of technology in education. The journal is committed to providing access to quality researches raging from original research, theoretical articles and concept papers in educational technology. In order to produce high quality journal, extensive effort has been put in selecting valuable researches that contribute to the journal. I would like to take this opportunity to express my appreciation to editorial board, reviewers and researchers for their valuable contributions to make this journal a reality. Professor Dr. Saedah Siraj January 2014 Editor in chief Message from the editor The Malaysian Online Journal of Educational Technology (MOJET) is aimed at using technology in online teaching and learning through diffusing information from a community of researchers and scholars. The journal is published electronically four times a year. The journal welcomes the original and qualified researches on all aspects of educational technology. Topics may include, but not limited to: use of multimedia to improve online learning; collaborative learning in online learning environment, innovative online teaching and learning; instructional design theory and application; use of technology in instruction; instructional design theory, evaluation of instructional design, and future development of instructional technology. As editor of the journal, it is a great pleasure to see the success of this journal publication. On behalf of the editorial team of The Malaysian Online Journal of Educational Technology (MOJET), we would like to thank to all the authors and editors for their contribution to the development of the journal. Dr. Norlidah Alias January 2014 Editor

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Editor-in-Chief Professor Dr. Saedah Siraj, University of Malaya, Malaysia Editors Dr. Norlidah Alias, University of Malaya, Malaysia Dr. Onur Isbulan, Sakarya University, Turkey

Associate Editors Professor Dr. Raja Maznah Binti Raja Hussain, University of Malaya, Malaysia Associate Prof. Dr. Habib Bin Mat Som, Sultan Idris Education University, Malaysia Dr. Chin Hai Leng, University of Malaya, Malaysia Dr. Dorothy Dewitt, University of Malaya, Malaysia

Advisory Board

Professor Dr. Mohd Hamdi Bin Abd Shukor, University of Malaya, Malaysia Professor Emeritus Dato’ Dr. Abu Bakar Nordin, University of Malaya, Malaysia Professor Dr. Aytekin Isman, Sakarya University, Turkey Professor Dr. Fatimah Binti Hashim, University of Malaya, Malaysia Professor Dr. Mohammed Amin Embi, National University of Malaysia, Malaysia Professor Dr. Moses Samuel, University of Malaya, Malaysia Professor Dr. Omar Abdul Kareem, Sultan Idris University of Education, Malaysia Professor Dr. Richard Kiely, the University College of St. Mark and St. John, United Kingdom Dr. Zawawi Bin Ismail, University of Malaya, Malaysia Editorial Board

Professor Emiritus Dr. Rahim Md. Sail, University Putra of Malaysia, Malaysia Professor Datuk Dr. Tamby Subahan Bin Mohd. Meerah , National University of Malaysia, Malaysia Associate Professor Dato’ Dr. Abdul Halim Bin Tamuri, National University of Malaysia, Malaysia Professor Dr. Abdul Rashid Mohamed, University of Science, Malaysia Professor Dr. Bakhtiar Shabani Varaki , Ferdowsi University of Mashhad, Iran. Professor Dr. H. Hamruni, Ma, Sunan Kalijaga Islamic University, Indonesia Professor Dr. Ibrahem Narongsakhet, Prince of Songkla University, Thailand Professor Dr. Iskandar Wiryokusumo M.Sc, PGRI ADI Buana University, Surabaya, Indonesia Professor Dr. Mohammad Ali, M.Pd, Ma, University of Islamic Education, Indonesia Professor Dr. Ramlee B. Mustapha , Sultan Idris University of Education, Malaysia Professor Dr. Rozhan M. Idrus, University of Science, Malaysia Associate Professor Dr. Abdul Jalil Bin Othman, University of Malaya, Malaysia Associate Professor Dr. Ajmain Bin Safar, University of Technology, Malaysia Associate Professor Dr. Esther Sarojini Daniel, University of Malaya, Malaysia Associate Professor Dr. Fadzilah Siraj, Northern University of Malaysia, Malaysia Associate Professor Dr. Haji Izaham Shah Bin Ismail, Mara University of Technology, Malaysia Associate Professor Dr. Mohamad Bin Bilal Ali, University of Technology, Malaysia Associate Professor Dr. Norazah Mohd Nordin, National University of Malaysia, Malaysia www.mojet.net


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Associate Professor Dr. Rohaida Mohd Saat, University of Malaya, Malaysia Assist. Prof. Dr. Mubin KIYICI, Sakarya University, Turkey Dr. Adelina Binti Asmawi Dr. Farrah Dina Binti Yusop, University of Malaya, Malaysia Dr. Husaina Banu Kenayathula, University of Malaya, Malaysia Dr. Ismail Bin Abbas, Institute of Teacher Education, Malaysia Dr. Khamsiah Binti Ismail, Institute of Education, International Islamic University Malaysia Dr. Lorraine Pe Symaco, University of Malaya, Malaysia Dr. Misnan Bin Jemali, Sultan Idris University of Education, Malaysia Dr. Mohammad Bin Ab Rahman, Institute of Teacher Education Malaysia Dr. Mohammad Attaran, University of Malaya, Malaysia Dr. Mohd. Awang Bin Idris, University of Malaya, Malaysia Dr. Mohd Burhan Bin Ibrahim, Institute of Education, International Islamic University Malaysia Dr. Mojgan Afshari, University of Malaya, Malaysia Dr. Muhamad Arif Ismail, National University of Malaysia, Malaysia Dr. Muhamad Faizal Bin A. Ghani , University of Malaya, Malaysia Dr. Nabeel Abdallah Mohammad Abedalaziz, University of Malaya, Malaysia Dr. Nazean Binti Jomhari, University of Malaya, Malaysia Dr. Rafiza Binti Abd Razak, University of Malaya, Malaysia Dr. Rose Amnah Binti Abd. Rauf, University of Malaya, Malaysia Dr. Siti Hendon Sheikh Abdullah, Institute of Teacher Education, Malaysia Dr. Tee Meng Yew, University of Malaya, Malaysia Dr. T. Vanitha Thanabalan, English Language Teaching Centre, Malaysia Ministry of Education Dr. Zahra Naimie, University of Malaya, Malaysia En. Mohd Khairul Azman Bin Md Daud, University of Malaya, Malaysia En. Mohd Sharil Nizam Shaharom, University of Malaya, Malaysia En. Norhashimi Saad, University of Malaya, Malaysia En. Norjoharuddeen Mohd Nor, University of Malaya, Malaysia Pn. Norini Binti Abas, University of Malaya, Malaysia

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Table of Contents BETWEEN SCHOOL FACTORS AND TEACHER FACTORS WHAT INHIBITS MALAYSIAN SCIENCE TEACHERS FROM USING ICT

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Tunku Badariah Tunku Ahmad E-LEARNING NEEDS ASSESSMENT AMONG STUDENTS IN THE COLLEGES OF EDUCATION

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Hamid Mohammad Azimi, Hazri Jamil EVALUATING THE IMPACT OF TECHNOLOGY INTEGRATION IN TEACHING AND LEARNING

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Nafisat Afolake Adedokun-Shittu, Abdul Jaleel Kehinde Shittu IMPLEMENTATION OF PTECHLS MODULES IN RURAL MALAYSIAN SECONDARY SCHOOL: A NEEDS ANALYSIS

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Norlidah Alias, Dorothy DeWitt, Saedah Siraj, Mohd Nazri Abdul Rahman, Rashidah Begum Gelamdin, Rose Amnah Abd Rauf THE EFFECT OF FIELD SPECIALIZATION VARIATION ON TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE (TPACK) AMONG MALAYSIAN TVET INSTRUCTORS

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Junnaina Husin Chua, Hazri Jamil

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Between School Factors and Teacher Factors: What Inhibits Malaysian Science Teachers From Using ICT?

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[1] [email protected] Institute of Education International Islamic University Malaysia (IIUM)

Tunku Badariah Tunku Ahmad [1]

ABSTRACT Despite the Malaysian government’s efforts to increase the use of ICT in school, teachers’ uptake of the technology remains slow and dismal. In this study, teachers’ perceptions of the barriers that inhibited their use of ICT in the science classroom were explored. One hundred and fifty-one (N = 151) science teachers from selected secondary schools in Kuala Lumpur and Selangor took part in the survey. Teachers’ perceptions of the barriers were captured using a selfdeveloped questionnaire consisting of 27 Likert-type items. The questionnaires were administered with the help of school principals. Principal components analysis (PCA) with Promax rotation extracted four underlying dimensions of barriers to ICT use, namely teachers’ self-handicapping thoughts, school support, attitude toward ICT use, and negative beliefs about ICT use. Three of these factors were teacher related. Self-handicapping thoughts emerged as the largest inhibitor, explaining about 38.2% of teachers’ lack of ICT utilization in the science classroom. The results corroborated previous findings that teacher factors tend to outweigh school factors in hampering teachers’ uptake of technology.

Keywords:

ICT utilization, science teaching, barriers to ICT use, Malaysian science teachers, Principal Components Analysis

INTRODUCTION In Malaysian schools where traditionalist pedagogical approaches prevail over other methods (Sharifah Maimunah, 2003), the use of Information and Communications Technology (ICT) can significantly enhance the quality of teaching and students’ learning experience, especially in science subjects. Somekh (2008) argues that ICT is a powerful driver for educational change if used in the right manner, and helps to create a less stressful environment for both teachers and students. The benefits of using ICT are immense for teachers and students of science. For teachers, the Internet expands the instructional resources available to them (Bingimlas, 2009), while also allowing them to empower students to become active and skilful information seekers rather than remaining passive recipients of scientific facts (Pickersgill, 2003). Teachers can make science more engaging and comprehensible to students by employing ICT in four distinct ways as categorized by Ball (2003): as a tool, as a reference source, as a means of communication and as a means for exploration. For students, ICT can support development of science process skills and conceptual understanding, besides enhancing opportunities to engage in effective communication about science at several levels (Murphy, 2006). A comprehensive review of 557 research papers concludes that students can acquire science ideas quite successfully through ICT models and simulations (Hogarth, Bennett, Lubben, Campbell, & Robinson, 2006). In steps that acknowledge the importance of ICT-enhanced education and mirror global trends in ICT uptake and integration, the Malaysian Ministry of Education formulated multiple strategies and plans to encourage teachers to integrate ICT into classroom teaching. The strategies included the 1997 Smart School Project, the 2003 provision of laptops to Mathematics and Science teachers and the ICT in Education policy, all of which constituted the Ministry’s efforts to galvanize greater ICT use by teachers. Extensive amounts of money and resources were expended to this end, but success was marginal. Research shows that the ICT integration level in classroom science is still below the expected standards (Hamid, 2011; Lau, 2006; Shahril, 2007). By and large, science teachers reported using ICT for ancillary services and activities but not for teaching. This is despite their having the necessary equipment (Mohd Darus, 2004), skills

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(Multimedia Development Corporation, 2006), and belief that ICT can improve the quality of education (Mohamed Zaki, 2013). These findings support Redecker (2009) who found that, in practice, ICT is either scarcely used or only used to supplement traditional and frontal teaching. Lıterature Revıew Integrating ICT into classroom learning is not as easy as it sounds. The process is indeed complex and teachers often encounter difficulties. Schoepp (2005) called these difficulties “barriers” and defined them as conditions that make it difficult for a person or organization to progress or achieve an objective. Numerous studies across contexts and cultures have identified what these barriers are among teachers, for example, mismatch between available ICT and existing curricula (Albirini, 2006); lack of institutional support (Ageel, 2011); lack of funds and budget allocation (Alwani & Soomro, 2010); insufficient training (Al-Oteawi, 2002; Taylor & Corrigan, 2007); computer anxiety, ICT efficacy, and lack of confidence (Becta, 2004); teacher beliefs and attitudes (Chen, Tan, & Lim, 2012); resistance to change (Gomes, 2005); overwhelming workload and commitment (Hennessy, Ruthven, & Brindley, 2005); overloaded curriculum and lack of subject-specific guidance for using ICT (Osborne & Hennessy, 2003); outdated hardware and Internet facilities (Ozen, 2012); time constraint and unfamiliarity with new equipment (Peralta & Costa, 2007); absence of technical support (Toprakci, 2006); and readiness as well as motivation (Ward, 2003). In a comprehensive review, Mumtaz (2000) summed up three factors that impede teachers’ ICT uptake: the school/institution, resources and the teachers themselves. Although these discoveries are insightful, ICT utilization barriers can be more fully understood if they are clustered into categories or underlying dimensions. The literature offers some useful classifications of factors impeding teachers’ ICT utilization. Ertmer (1999) grouped factors related to teacher variables (e.g., attitudes, beliefs, practices, resistance, personal experience, and awareness) as intrinsic or first order barriers, while placing factors such as inadequate and/or inappropriate configuration of ICT infrastructure, access, time, technical support, resources, and training in the extrinsic or second order category. Chen et al. (2012) discovered that extrinsic barriers (i.e., time and curriculum constraints) tend to play a greater role than intrinsic barriers in hampering teachers’ use of ICT in the classroom. Becta (2004) summarized the research conducted in several different countries over a ten-year period (19932003) and proposed two categories of barriers, namely school-level barriers (such as lack of instructional time, access to resources, hardware, and effective training; inappropriate organization; poor quality software; and technical problems), and teacher-level barriers (such as lack of preparation time, confidence and access to ICT resources; resistance to change; negative attitudes; and no perception of benefits). According to Veen (1993), teacher-level factors (e.g., beliefs about ICT benefits and computer skills) tend to outweigh school factors in influencing ICT use. Since the nature of ICT barriers and how they operate to inhibit ICT use are highly context-specific, Becta (2004) suggested that it might be useful to further compare them between specific subjects (e.g., Science, Mathematics or English). In this study, the Becta categorization of barriers (teacher factors and school factors) was used as the conceptual framework to guide the analysis and discussion of inhibitors. Problem Statement An extensive body of research is available to inform us about factors inhibiting teachers in general from using ICT in teaching. Much of this body of research, however, has studied these inhibitors as single or individual indicators rather than as groups of factors, or constructs, that share a set of common attributes. Thus the approach to identifying inhibitors has largely been piecemeal. Furthermore, although the literature is replete with studies on ICT barriers, consensus is lacking regarding which factors (teacher-related or school-related) are more instrumental in impeding science teachers’ utilization of ICT in the classroom. For instance, the findings of Albirini (2006), Ageel (2011), Alwani and Soomro (2010), Osborne and Hennessy (2003), and Ozen (2012) among others pointed to extrinsic or non-teacher factors, but those of Veen (1993), Becta, (2004), Ward (2003) and Gomes (2005) pinned the inhibitors down to teacher variables. This could be due in part to the piecemeal approach adopted by most studies. The lack of agreement in the findings is also understood as suggesting that ICT utilization barriers are context- and culture-specific, and often influenced by personal, sociocultural and system variables such as local policies, subjective norm, prior experience and institutional support. Furthermore, a large amount of research into ICT use inhibitors among science teachers has been conducted in non-Malaysian settings. As such, the findings may not be completely applicable to the context of Malaysian secondary science teachers. Thus, research to understand these barriers in a specific context among specific groups of teachers is much needed in order to design appropriate intervention programs to galvanize their ICT uptake.

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OBJECTIVES OF THE STUDY The objectives of this study were threefold: (i) to determine Malaysian science teachers’ use of selected ICT applications; (ii) to explore if the ICT inhibitors identified from the literature and teacher interviews constituted meaningful and interpretable categories of barriers as proposed by Becta (2004); and (iii) to identify whether school factors or teacher factors were more instrumental in impeding teachers’ use of ICT in the science classroom. METHOD Respondents The study was a cross-sectional survey involving 151 science teachers who were purposively sampled from a number of secondary schools in Malaysia. The criteria set in identifying the respondents that befit the purpose of the study were: (i) they must be science teachers teaching in public secondary schools; and (ii) they must be teachers who were not using ICT in teaching science at the time of data collection. Science teachers who did employ ICT in teaching were excluded from the survey. Female teachers made up 76% of the respondents (n = 115). The sample’s mean age was approximately 35 years with an average teaching experience of 9.5 years. Fifty-one percent (n = 77) had received some form of ICT training at their respective schools, while the other half reported having received none. A majority were degree holders (88%), while the rest held either a master’s degree (3.3%) or a diploma (8.7%). Instrument The data collection instrument was a self-developed questionnaire containing three parts. The first part requested demographic details, while the second required the respondents to indicate whether they used the given ICT tools or applications. Eleven were listed, namely e-mail, blogs, Skype, social networks, online libraries, e-news, Internet browsing, database, spreadsheet, presentation software, and word processor. The third section contained items on two categories of ICT barriers, namely teacher factors (such as ICT efficacy, beliefs about ICT and interest) and school factors (such as scheduling, workload and technical support). The items were drawn from a review of previous works (e.g., Becta, 2004; Mumtaz, 2000), as well as from several interviews and focus group discussions with Malaysian science teachers. The initial pool consisted of 28 Likert-type items with response categories ranging from Strongly Disagree to Strongly Agree. After content validation with five experts in the field, the questionnaire was refined and pilot tested with 31 science teachers. Results of the pilot test indicated that one item was problematic; the item was therefore removed. A reliability check was run on the remaining 27 items yielding an alpha value of .93. Therefore, the study proceeded with the 27 items for data collection. Out of the 27 items, 14 were related to school factors while the remaining 13 were teacher-related. Data Collection and Analysis With the help of school principals, 200 questionnaires were distributed to selected secondary schools in Kuala Lumpur and Selangor. E-mails and text messages were later sent as reminders to the respondents to return the questionnaire. Out of the 200 distributed, 152 were collected, constituting a response rate of 76%. The data were analyzed using descriptive statistics (to address research objective 1) and Principal Components Analysis (to address research objective 2). RESULTS Use oc ICT Applıcatıons Among The Respondents Figure 1 presents a visual summary of the respondents’ use of selected applications. Four applications turned out to be utilized by a large number of science teachers (between 66% and 79%), namely email, the Internet for browsing purposes, PowerPoint presentation software, and e-news. Just over half reported using social networking sites (55.9%) and databases (51.3%), while about one third (33.6%) used online libraries. Few reported using spreadsheet (21%), Skype (15.1%) and blogs (13.8%). The figures suggest that using ICT was common and quite widespread among the respondents, although the type of application used might vary. This was not a surprising finding considering that the sample consisted of relatively young teachers (mean age was 35) who were more likely to be familiar with ICT than older teachers.

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55.9% 15.1% 33.6% 13.8%

66.4% 74.3% 51.3% 21% 73% 62.5% 78.9% percentage

Figure 1: Malaysian Science Teachers’ Use of Selected ICT Applications (Percentage)

Based on the percentage of teachers who reported using the given ICT applications, it could be inferred that most had the basic skills to enable them to use ICT in science teaching. Quite a large majority were familiar with the use of email (78.9%), the Internet (74.3%) and PowerPoint (73%). This means that most respondents could at least use PowerPoint, including its animation features, to present science concepts to the class, search the Internet for supporting materials, learning activities and reading lists, and use email to communicate with students about science ideas and homework. This information rules out lack of ICT skills as a possible barrier to ICT use. Barrıers to ICT Use Principal Components Analysis (PCA) with Promax rotation was applied on the data to extract underlying factors that represented barriers to the respondents’ use of ICT in the science classroom. The PCA procedures would allow the study to reduce the number of items or variables in the questionnaire down to their principal components, which constituted inhibitors to ICT use. Matsunaga (2010) suggests the use of the Promax rotation technique rather than the more popular Varimax rotation as it is the most suitable and robust technique for data obtained in social science research. The PCA procedures applied on the data produced acceptable results in terms of sampling adequacy and item correlations. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.88, indicating that the sample size relative to the number of questionnaire items was adequate for applying PCA. The Bartlett’s test of sphericity was statistically significant (χ2 =2107.79, 351, p = .001), indicating that the overall correlations within the correlation matrix were adequate. Except for one item (“There are not enough ICT technicians to help”), the communalities of the variables were acceptable at above 0.5. In summary, these results show factorability of the data, hence justifying the use of PCA in the analysis. The first run of PCA produced a six-factor structure of ICT barriers that explained close to 64.2% of the variance. However, the PCA had to be revised due to eight items that either cross loaded or failed to load on any of the factors. These items were all school-related inhibitors. The problematic items were then identified and removed from subsequent analysis.

The revised PCA after removing eight problematic items produced a clean four-factor solution with no contamination. The Kaiser-Meyer-Olkin (KMO) measure was 0.86, while the Bartlett’s test of sphericity was statistically 4

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significant (χ2 =1443.56, 171, p = .001). The correlations among items were significant with communalities ranging between .324 and .797. Only three items (“I find it troublesome to use ICT,” “There aren’t enough ICT technicians to help,” and “The school provides no ICT training for teachers”) had a communality of less than 0.5. Table 1 shows the inter-item correlation matrix, descriptive statistics and communalities. Table 1: Inter-Item Correlation Matrix, Descriptive Statistics and Communalities

ITEM

B1

B2

B3

B4

B5

B6

B7

B8

B9

B10

B11

B12

B13

B14

B15

B16

B17

B18

B19

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 B17 B18 B19 M SD Comm

1.00 .698 .445 .686 .418 .132 .204 .265 .197 .365 .189 .485 .452 .360 .339 .414 .401 .315 .479 3.68 1.04 .659

1.00 .520 .483 .411 .038 .137 .198 .127 .348 .246 .421 .475 .387 .341 .352 .381 .331 .482 3.44 1.07 .624

1.00 .505 .495 .104 .096 .149 .095 .294 .110 .340 .498 .303 .322 .427 .481 .446 .431 3.15 1.04 .549

1.00 .525 .088 .242 .291 .127 .421 .172 .523 .483 .362 .479 .514 .454 .454 .509 3.56 1.16 .725

1.00 .102 .299 .230 .214 .268 .152 .491 .447 .378 .329 .355 .411 .482 .501 2.81 1.15 .619

1.00 .539 .465 .428 .354 .264 .066 .130 .184 .168 .132 .085 .238 -.016 2.45 1.12 .410

1.00 .649 .327 .532 .332 .241 .242 .402 .296 .234 .121 .193 .126 2.65 1.25 .324

1.00 .253 .429 .302 .295 .319 .425 .280 .207 .080 .220 .239 2.95 1.43 .676

1.00 .324 .232 .156 .151 .286 .102 .166 .105 .182 .068 2.89 1.03 .727

1.00 .280 .284 .295 .427 .369 .363 .349 .370 .217 2.67 1.19 .636

1.00 .135 .167 .178 .301 .192 .211 .198 .111 2.47 1.15 .679

1.00 .591 .513 .316 .377 .247 .323 .621 3.58 1.16 .578

1.00 .617 .553 .529 .393 .407 .662 3.45 1.08 .573

1.00 .380 .352 .266 .432 .477 3.72 1.05 .738

1.00 .770 .455 .490 .493 2.87 1.02 .797

1.00 .489 .500 .436 2.81 1.04 .728

1.00 .553 .307 2.49 1.10 .732

1.00 .425 2.81 1.13 .491

1.00 3.50 1.03 .694

The Promax rotation extracted a four-factor structure of underlying ICT barriers. No item cross-loaded. The structure was represented by 19 items and explained close to 63% of the variance. The four factors are shown in Table 2 along with their representative items, factor loadings, eigenvalues, individual variance explained and internal consistency index.

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Table 2: Factor Solution with Items, Factor Loadings, Eigenvalues, Variance Explained and Reliability Index

Factor Loading

Factor and Items Factor 1: Self-Handicapping Thoughts I don’t know how to teach using ICT I don’t have the required ICT skills I have no time to learn ICT skills I don’t feel confident to use ICT to teach I find it troublesome to use ICT

.835 .814 .632 .569 .478

Factor 2: School Support There are not enough computers for teachers My school doesn’t provide enough ICT facilities There are no ICT facilities in the class or lab The school provides no ICT training for teachers Class time is too short to use ICT There aren’t enough ICT technicians to help

.824 .792 .662 .641 .582 .499

Factor 3: Attitude Toward ICT Use I’m not interested in using ICT to teach I don’t see how ICT helps students to understand science ICT doesn’t improve my teaching I think the science curriculum is inappropriate for ICT use

Eigenvalue

Variance Explained

Cronbach’s alpha

7.23

38.2%

.84

2.32

12.2%

.79

1.29

6.8%

.85

1.09

5.8%

.82

.762 .731 .687 .671

Factor 4: Beliefs about ICT My students learn equally well without ICT I teach just as well without ICT Using ICT requires a lot of time My workload doesn’t allow me to use ICT

.878 .827 .663 .648

The factors were then labelled based on the common idea shared by the items that loaded into them. Five items empirically grouped together to create Factor 1. The items revealed debilitating ideas that the respondents had about themselves and their ability to use ICT for teaching. Thus, the factor was labelled Self-Handicapping Thoughts. It alone accounted for 38.2% of the variance. The second factor consisted of six items that pointed to lack of support from the school in terms of infrastructure, technical help and scheduling; thus the factor was labelled School Support. It explained 12.2% of the variance. The empirical grouping of items that loaded on the third factor revealed the respondents’ less than favorable attitude toward ICT use. This factor explained 6.8% of the variance and was named Attitude toward ICT Use. The last factor which accounted for 5.8% of the variance consisted of four items that underscored the respondents’ deep-seated beliefs about the necessity of using ICT and the amount of time it would require. The factor was hence labelled Beliefs about ICT Use. All four underlying dimensions displayed high internal consistency indices ranging between .79 and .85 with items that loaded in the same consistent direction, resulting in a solution that was free from variable-specific factors. DISCUSSION AND CONCLUSION The descriptive results generally showed that the science teachers surveyed were not unfamiliar with ICT. Based on the figures that reported using the myriad applications asked, it cannot be concluded that they did not have the basic ICT skills needed for science teaching. The least ICT they could employ was PowerPoint as a content delivery tool and an aid to the explication of complex science concepts. They could also refer students to myriad science reading materials on the Internet to augment classroom teaching. However, the use of ICT in this manner should be viewed with

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some concern as it may lead to nothing more than ICT-supported traditional or frontal teaching as highlighted by Redecker (2009). Instead, teachers should be encouraged to use ICT in more innovative ways. The fact that these teachers actually used ICT for various purposes provides some empirical support for an earlier observation that teachers tend to use ICT for ancillary services and activities, but not so much for teaching. In this study, the science teachers surveyed reported not using ICT at all in their science classes. The reasons were indicated by the data. It appeared that they were impeded by four major barriers: their own self-handicapping thoughts; lack of school support; negative attitude toward ICT utilization; and negative beliefs. Three of these were teacher-level barriers (Becta, 2004) that stemmed from within the teachers themselves. Cumulatively they accounted for 50.7% of the reason Malaysian science teachers avoided using ICT to teach science. The results supported Veen (1993) who earlier maintained that teacher factors tend to outweigh school-related factors in influencing teachers’ use of ICT. Lack of confidence and interest intertwined with insufficient ICT efficacy and negative beliefs worked their way up to become the largest inhibitors of science teachers’ ICT utilization.

However, one particular finding merits further attention, and that concerns teachers’ report about not knowing how to teach using ICT (the first item in Table 2). This brings to light the importance of subject-specific guidance highlighted by Osborne and Hennessy (2003) which also includes technological pedagogical content knowledge (TPCK). Providing teachers with ICT facilities and access alone is insufficient. In order to empower teachers to use ICT successfully, subject-specific ICT training must be designed and imparted with appropriate care and rigor. This effort can be taken up at the school level if necessary with the cooperation of head teachers and school principals. Teacher training programs should also take this finding into account in designing technology training for teachers. Science teachers’ needs for training in ICT use may differ from those of other teachers. For example, science teachers may benefit more from skills in using screencasts, simulation and spreadsheet than other ICT applications such as database, programming or word processing. Training in all ICT applications available is not the answer to science teachers’ lack of ICT uptake. Moreover, technology is only “good” insofar as teachers know how to use it in meaningful ways that encourage student learning. In this regard, future research should explore the role of TPCK and relevant ICT training in influencing science teachers’ use of ICT.

Of the four categories of barriers, only one was school-related, and within this category, access to ICT facilities, technical support and scheduling of class time were discovered to be the main inhibitors. Teachers attributed their lack of ICT use to their respective schools’ failure to provide enough computing facilities, access to computers, technical help when needed, adequate ICT training and sufficient class time to accommodate ICT use. The findings agreed with Ageel (2011), Al-Oteawi (2002), Taylor and Corrigan (2007) and Toprakci (2006), but the extent to which they accurately reflected the actual situation in the schools involved could not be ascertained as the study did not acquire institutional data to cross-validate the teachers’ self-reports. Future studies to examine ICT utilization barriers should therefore take the cross-validation and triangulation factor into account when designing their research in order to give more weight and credibility to the data. In addition, school administration and management should identify the ICT requirements of science teachers through an ICT needs analysis. The analysis will help schools to carefully assess and identify whether the ICT facilities they wish to purchase would be relevant to what science teachers need to enhance their instructional quality.

Eight school-related items failed to load on any factor although they were included in the PCA procedures. This may be attributed to several factors. Firstly, the eight items were found to be too weakly correlated to be able to form a factor or to load on the extracted school-related category of barriers. Secondly, they did not constitute a reliable construct to represent meaningful barriers. Thirdly, each of the items could have measured different aspects of schoolrelated barriers that did not share a common attribute, which would have been detected during the pilot test had the sample size been large enough. Fourthly, the pilot test conducted could not examine the structure of school-related barriers due to the small sample size of 31 science teachers. Further studies looking into ICT barriers should address these inadequacies and methodological concerns.

On the measurement side, given the substantial number of problematic items found in the questionnaire (i.e., items with significant cross-loadings) which affected the number of factors to be retained and the proportion of variance explained, many items had to be revised and reworded to be more closely in line with the categories of barriers established in the literature (e.g., Becta 2004). The present study could be treated as a pilot test to establish the reliability of the data and refine the items that measure ICT adoption barriers among science teachers. Upon revision and improvement of the items, new samples of science teachers could be surveyed from within and outside of Malaysia 7

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(for comparative analysis) to generate more comprehensive data that can better explain the reasons for science teachers’ slow uptake of ICT. REFERENCES Ageel, M. (2011, September). The ICT proficiencies of university teachers in Saudi Arabia: A case study to identify challenges and encouragements. Hummingbird: University of Southampton’s Doctoral Research Journal, 2, 55-60. Retrieved from http://www.southampton.ac.uk/gradschools/pdfs/ hummingbird/40891_Hummingbird_WEB_bkmk.pdf Albirini, A. (2006). Teachers’ attitudes toward information and communication technologies: The case of Syrian EFL teachers. Computers and Education, 47(4), 373-398. Al-Oteawi, S. M. (2002). The perceptions of administrators and teachers in utilizing information technology in instruction, administrative work, technology planning and staff development in Saudi Arabia.Unpublished Doctoral Dissertation, College of Education, Ohio University, Columbus. Alwani, A. E. S., & Soomro, S. (2010). Barriers to effective use of information technology in science education at Yanbu, Kingdom of Saudi Arabia. In S. Soomro (Ed.), E-learning experiences and future (pp. 35-46). Vukovar, Croatia: INTECH. Ball, S. (2003). ICT that works. Primary Science Review, 76, 11-13. Bingimlas, K. A. (2009). Barriers to the successful integration of ICT in teaching and learning environments: A review of the Literature. Eurasia Journal of Mathematics Science and Technology Education, 5(3), 234-245. British Educational Communications and Technology Agency (Becta). (2004, June). A review of the research literature on barriers to the uptake of ICT by teachers. Retrieved from http://dera.ioe.ac.uk/1603/1/ becta_2004_barrierstouptake_litrev.pdf Chen, W., Tan, A., & Lim C. (2012). Extrinsic and intrinsic barriers in the use of ICT in teaching: A comparative case study in Singapore. In M. Brown, M. Hartnett & T. Stewart (Eds.), Proceedings of ASCILITEAustralian Society for Computers in Learning in Tertiary Education Annual Conference, Wellington, 2012 (pp. 191-196). Retrieved from www.editlib.org/p/42584 Ertmer, P. A. (1999). Addressing first- and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47-61. Retrieved from http://dx.doi.org/10.1007/BF02299597 Gomes, C. (2005). Integration of ICT in science teaching: A study performed in Azores, Portugal. Paper Presented at the 3rd International Conference on Multimedia and Information & Communication Technologies in Education (m-ICTE2005), Caceres (Spain), June 8-10th 2005. Hamid, R. H. (2011). Teachers’ beliefs and use of ICTs in Malaysian Smart Schools: A case study. In G. Williams, P. Statham, N. Brown & B. Cleland (Eds.), Changing Demands, Changing Directions: Proceedings ASCILITE, Hobart 2011 (pp. 522-525). Retrieved http://www.ascilite.org.au/conferences/hobart11/downloads/papers/Hamid-poster.pdf

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Hennessy, S., Ruthven, K., & Brindley, S. (2005). Teacher perspectives on integrating ICT into subject teaching: commitment, constraints, caution, and change. Journal of Curriculum Studies, 37, 155–192. Retrieved from http://www.educ.cam.ac.uk/research/projects/istl/WP042.pdf Hogarth, S., Bennett, J., Lubben, F., Campbell, B., & Robinson, A. (2006). ICT in science teaching. Technical report. In: Research Evidence in Education Library. London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London. Lau, K. H. (2006). Integration of ICT in the teaching of science: An investigation of 45 primary school teachers. Unpublished Master’s Thesis. Faculty of Education, Open University Malaysia, Kuala Lumpur. Mohamed Zaki, F. Z. (2013). ICT and internet usage in early childhood education: A comparative study of Australian and Malaysian teachers’ beliefs and current practices. Unpublished Master of Education Thesis, Faculty of Education, Queensland University of Technology, Australia. Mohd Darus, N. (2004). Review of the implementation of the willingness of teachers in teaching of Science and Mathematics in English. Unpublished Master of Education Project Paper. Faculty of Education, Universiti Kebangsaan Malaysia, Bangi. Multimedia Development Corporation. (2006). Impact assessment study on the Smart School Integrated Solution and other ICT initiatives. Putrajaya: Government of Malaysia. Mumtaz, S. (2000). Factors affecting teachers’ use of Information and Communications Technology: A review of the literature. Journal of Information Technology for Teacher Education, 9(3), 319-341. Retrieved from http://www.tandfonline.com/doi/pdf/10.1080/14759390000200096 Murphy, C. (2006). Report 5: Literature Review in Primary Science and ICT. Bristol, UK: FutureLab Series. Osborne, J., & Hennessy, S. (2003). Report 6: Literature Review in Science Education and the Role of ICT: Promise, Problems and Future Directions. Bristol, UK: FutureLab Series. Retrieved from http://hal.archives-ouvertes.fr/docs/00/19/04/41/PDF/osborne-j-2003-r6.pdf Ozen, R. (2012, February). Distance education for professional development in ICT integration: A study with primary school teachers in Turkey. International Journal of Business and Social Science, 3(3), 185-195. Retrieved from http://www.ijbssnet.com/journals/Vol_3_No_3_February_2012/19.pdf Peralta, H., & Costa, F. A. (2007).Teacher’s competence and confidence regarding the use of ICT. Sísifo: Educational Sciences Journal, 3, 75–84. Retrieved from http://repositorio.ul.pt/bitstream/10451/ 7008/1/(2007)PERALTA,H%26COSTA,F(ICTCompetenceConfidence)S%C3%8DSIFO3eng.pdf Pickersgill, D. (2003). Effective use of the internet in science teaching. School Science Review, 84(309), 77-86. Redecker, C. (2009). Review of learning 2.0 practices: Study on the impact of Web 2.0 innovations on education and training in Europe. JRC Scientific and Technical Reports. European Commission: Luxembourg. Retrieved from http://ftp.jrc.es/EURdoc/JRC49108.pdf Schoepp, K. (2005). Barriers to technology integration in a technology-rich environment. Learning and Teaching in Higher Education: Gulf Perspectives, 2(1), 1-24.

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Shahril, S. (2007). Sikap dan persepsi guru sains terhadap penggunaan komputer dalam pengajaran dan pembelajaran di makmal sains.Unpublished Master’s Thesis, Faculty of Education, Universiti Putra Malaysia, Serdang. Sharifah Maimunah Syed Zin. (2003). The teaching of maths and science through English in Malaysian schools. Curriculum Development Centre, Ministry of Education Malaysia. Somekh, B. (2008). Factors affecting teachers’ pedagogical adoption of ICT. In J. Voogt & G. Knezek (Eds.), International handbook of information technology in primary and secondary education (pp. 449460). New York, NY: Springer. Taylor, N., & Corrigan, G. (2007). New South Wales primary school teachers’ perceptions of the role of ICT in the primary science curriculum: A rural and regional perspective. International Journal of Science and Mathematics Education, 5(1), 85-109. Toprakci, E. (2006). Obstacles at integration of schools into information and communication technologies by taking into consideration the opinions of the teachers and principals of primary and secondary schools in Turkey. E-Journal of Instructional Science and Technology (E-JIST), 9(1), 1-16. Veen, W. (1993). The role of beliefs in the use of information technology: Implications for teacher education, or teaching the right thing at the right time. Journal of Information Technology for Teacher Education, 2 (2), 139-153. Retrieved from http://dx.doi.org/10.1080/0962029930020203 Ward, L. (2003). Teacher practice and the integration of ICT: Why aren’t our secondary school teachers using computers in their classrooms? In Educational Research, Risks, & Dilemmas.Proceedings of the Conference of the Joint New Zealand Association for Research in Education and Australian Association for Research in Education, Auckland. Retrieved from http://publications.aare.edu.au/03pap/ war03165.pdf

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E-Learning Needs Assessment among Students in the Colleges of Education Hamid Mohammad Azimi [1], Hazri Jamil [2]

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[1] Department of Studies in Education, University of Mysore, India [email protected] & [email protected]

ABSTRACT The purpose of this survey study was to clearly identify major gaps and needs of e-learning components among students in theColleges of Education (one year Bachelor of Education or B.Ed. degree programme) affiliated by University of Mysore, India. A questionnaire was designed and validated by experts. A pilot test was carried out on a sample of 45 students and the Cronbach alpha value for the instrument was .89. Data were collected from 346 students selected through stratified random sampling method to gauge students’ needs on learning elearning components. Findings highlighted that in the ranking of needs for learning of e-learning components, Internet tools and video streaming ranked on the highest level also instructional theories and mobile technology graded as the lowest one. t- Test revealed a non-significant association between gender and needs to learning e-learning components. Moreover, One Way ANOVA test showed there is no significant difference among type of colleges (government / private aided and unaided) and different subjects (science / art / language) in needs for learning of e-learning components.

Keywords:

TPACK; professional knowledge; specialization; technology integration; technological knowledge.

INTRODUCTION Advances in information technology, and changes in society, are creating new paradigms for education and training. These massive changes have tremendous impact on our educational and training systems (Reigeluth & Khan, 1994). To stay viable in this global competitive market, providers of education and training must develop efficient and effective learning systems to meet societal needs. The higher education sector can take greatest advantage of the increased use of technology, especially the Internet, in delivering educational products. Distance learning via the Internet will drive tremendous growth (Cappelli, 2003). Usage of new technologies, Internet and e-learning in development of higher education enable education of citizens familiar with Information and Communications Technology (ICT) and needs of living in the 21st century.The present study is a survey type involving descriptive research among students of colleges of education. The study includes assessing and evaluation of needs on e-learning system components from the viewpoint of students of colleges of education affiliated with the University of Mysore, India.

E-Learning Electronic learning or E-learning concept has been around for decades and is one of the most significant recent developments in the Information Systems (IS) industry (Wang, 2003). E-learning has been viewed as synonymous with Web-based learning (WBL), Internet-based training (IBT), advanced distributed learning (ADL), Web-based instruction (WBI), online learning (OL) and open/flexible learning (OFL) (Khan, 2001). E-learning system is implemented through several ways; however, the best practices among the various educational institutions have recommended developing a Web-based learning management system (LMS). E-learning has been defined a number of different ways in the literature. In general, e-learning is the expression broadly used to describe “instructional content or learning experience delivered or enabled by electronic technologies” (Ong, Lai, & Wang, 2004). Some definitions of e-learning are more restrictive than this one, for example limiting elearning to content delivery via the Internet (Jones, 2003). The broader definition can include the use of the Internet, intranets/extranets, audio- and videotape, satellite broadcast, interactive television (TV), and CD-ROM, not only for 11

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content delivery, but also for interaction among participants (Industry Canada, 2001). More recently, this definition can be further expanded to include mobile and wireless learning applications (Kinshuk, Suhonen, Sutinen, & Goh, 2003; Lehner, Nösekabel, & Lehmann, 2003). Many researchers in the field of integrating ICT in educational settings have attempted to define the concept of e-learning. Liaw, Huang, and Chen (2007) define e-learning as the convergence of technology and learning, and as the use of network technologies to facilitate learning anytime, anywhere. Davis (2001) has also defined e-learning as technology-enabled learning that covers various concepts, or a phenomenon delivering instructions through technology. Welsh, Wan Berg, Brown, and Simmering (2003, p. 246) define e-learning as the use of computer network technology through the Internet to deliver information and instruction to learners. Rosenberg (2001) refers to e-learning as using Internet technologies to deliver various solutions to learners. Holmes and Gardner (2006) point out that elearning provides access to resources that promote learning on an anyplace and anytime basis. E-learning is simply defined as a delivery of course content via electronic media such as Internet, Intranet, Extranet, satellite broadcast, audio/video tapes, interactive TV and CDROMs (Urdan & Weggen, 2000). However, the most well-known definition that educators agree on is that e-learning is a set of synchronous and asynchronous instruction delivered to learners over technology (Colvin & Mayer, 2008). E-learning encompasses related terms such as online learning, virtual learning, webbased learning, and distance learning (Panda & Mishra, 2007). Obringer (2001) mentioned that the history of e-learning goes back to 1983 when Nova Southern University in Fort Lauderdale, Florida, offered online courses to students for credit, and since then, schools have made a serious move toward implementing e-learning into curricula. In 2005, nearly 32.2 million students took at least one e-learning course (Lin, Lin, & Laffey, 2008). In general, e-learning is the future of learning that focuses on both the individual learner needs as well as the delivered content (Colvin & Mayer, 2008). Given the variety of definitions of e-learning, it is difficult to estimate the size of the market. However, e-learning is believed to be the fastest growing sub-sector of the $2.3T USD global education market, with the market for online higher education expected to grow to $69B USD by 2015 (Hezel Associates, 2005). Many reasons account for the growth of the higher education e-learning industry, both from the institutions’ and students’ perspectives. Globally, the demand for post secondary education is increasing. For example, in the United States, college enrollment among high school graduates increased from 56% in 1980 to 67% in 2003 (Morrison, 2003). With the limited capacity of existing classrooms at academic institutions and the prohibitive cost of building new facilities, e-learning is an attractive alternative (Werbach, 2000). According to Kleiman (2004), “e-learning can contribute to addressing each challenge by enhancing the preparation of new teachers, providing high quality and readily accessible professional development opportunities for active teachers, and making the teaching profession more attractive (e.g., by providing online resources for teachers and new connections to colleagues and mentors) to help address the teacher recruitment and retention problem”. E-Learning Components Khan (2001) pointed out that an e-learning program can be described in terms of various components and features conducive to learning. Components are integral parts of an e-learning system. Features are characteristics of an e-learning program contributed by those components. Components, individually and jointly, can contribute to one or more features. Khan (2005) has organized e-learning components into seven categories, namely: 1. Instructional Design (ID) 2. Multimedia Component 3. Internet Tools 4. Computers and Storage Devices 5. Connections and Service Providers 6. Authoring/Management Programs, Enterprise Resource Planning (ERP) Software, and Standards 7. Server and Related Applications Needs Assessment Mitchell (1993) describes needs assessment/analysis as “an examination of the existing need for training within an organization”. It identifies performance areas or programs within an organization where training should be applied. A needs analysis identifies the problem or need and then proceeds to identify the aims, content, implementation, target population and outcome of an intervention (Cohen, Manion, & Morrison, 2007). Needs assessments have occurred in various settings including community organizations (Rahtz & Sirgy, 2000; Torma, 1998), government agencies (Holton, Bates, & Naquin, 2000; Noll & O’Dell, 1997), health care facilities (Barry, Doherty et al., 2000; Thorton, 1995; Lockwood & Marshall, 1999) as well as education institutions (McCaslin & Lave, 1976; Stabb et al., 1995). In higher education, the needs assessment process appears in several contexts. This process 12

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has been applied to distance education, to various student organizations or faculty work groups (Bishop, Bauer, & Becker, 1998; Kruse, Elacque, & Rapaport, 1998). Witkin (1984) utilized a general definition of needs assessment namely that any systematic approach used in setting priorities for future action constitutes needs assessment. Kaufman (1985) contends, in a specific way, that needs assessment focuses on identifying and justifying gaps in results and how these gaps are prioritized for attention. The results of needs assessment will be an important part of the information used in decision-making about training, but it will not be the only information used. Needs assessment is one of the main investigative tools institutions use to identify actual needs, gaps, and hidden parts in the system and other activities. Needs assessment can help institutions to match the needs of their target audience with the e-learning courses and programs they plan to market. Any institution venturing into e-learning should conduct a needs assessment survey to find out its expected customers’ (i.e., learners’) willingness to enrol in its e-learning courses. Needs assessment will help institutions analyze the short-term and long term needs for their e-learning initiatives, and in turn will be instrumental in developing their e-learning strategies. Needs assessment can also provide information about the technological and other support services needed for their e-learning initiatives. Through a comprehensive needs assessment process, an institution can establish its e-learning goals (Khan, 2005). One Year Bachelor of Education (B.Ed.) Program The Bachelor of Education program, generally known as (B.Ed)., is a professional course that prepares teachers for upper primary or middle level (classes VI-VIII), secondary (classes IX-X) and senior secondary (classes XI-XII) levels. This program is offered by teacher training colleges which mainly designed to prepare effective secondary school teachers. The program essentially aims at providing student teachers with an insight into the educational scenario in the world with a specific reference to India. NCTE (The National Council for Teacher Education) prescribed minimum percentage of marks for admission as 45% in the qualifying examination. The duration of study for the B.Ed. degree is extended over a period of one academic year as a regular course of not less than 180 working days of which at least 40 days shall be for practice teaching in about ten schools at upper primary / secondary / senior secondary levels. The medium of instruction and examination in the B.Ed. program is Kannada (local language of Karnataka state, India) or English. Need and Significance of the Study The present research is among the first efforts to determine the needs assessment of e-learning among students of colleges of teacher education. The results of this study will be significant for several reasons.Teachers play a very important role in a student’s life. It is, to a great extent, the teachers who decide the shape a student’s life will take. So, it is very necessary to be adequately equipped with resources that will make the teacher a perfect role model to the students. To achieve this, the Bachelor of Education program was introduced, which will teach a person about teaching and the various aspects associated with teaching. Candidates who complete the Bachelor of Education training are awarded the B.Ed. degree. Curriculum, administration, and assessment are all affected as members of the educational community experience changes in communication and commerce resulting from the explosive expansion of the Internet (Austin & Mahlman, 2001). Thus, many educators are looking at how ICT and Internet-based learning can provide flexibility and convenience. Internet-based learning can overcome some traditional barriers such as time and place. A student can study independently online or take an instructor-led online class, which combines the benefits of self-study with those of more traditional classroom-based learning (Ryan, 2001). For working adults occupying an increasingly large percentage of our college population, and with greater numbers of students having computers and Internet experience prior to entering college, opportunities are being made to better meet their needs, interests, and work schedules through online classes (Cooper, 2001). As university-level technology education programs begin to offer more online classes and degree programs, technology education professors may be in the position to develop online offerings (Flowers, 2001). Technological advancement has been the major inspiration for change, beginning with the integration of radio broadcasting in the 1920s (Huynh, Umesh, & Valachich, 2003). More recently, the advent of the Internet has enabled tremendous innovation in the delivery of post secondary education (Gunasekaran, McNeil, & Shaul, 2002; Teo & Gay, 2006). With time, more people gain access to the Internet, the cost of computer ownership decreases, and overall computer literacy increases (Huynh et al., 2003). These trends provide educational institutions with an ideal channel for delivering educational content. Integrating e-learning technology in education, having skilled faculties and students as future teachers should be an integral part of the Teacher Training colleges’ curriculum to develop in Information Technology (IT) and knowledge based societies. Having a clear profile of needs assessment on e-learning components of students (as future teachers) of colleges of education provides vital information about the situation in colleges of education. Through a comprehensive needs assessment process, an institution can establish its e-learning goals. Findings of the study would facilitate the decision13

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making process and planning of usage and implementation of e-learning in teacher education colleges. Clarifying potential differences or similarities on gender, type of institution, and type of subject would show a mirror with a full feature of selected sample and finally population of B.Ed. colleges in the area and even at state level. Therefore, according to the literature we reviewed regarding assessment of needs of e-learning at the teacher training level, with confidence and certainty it can be said that; this research project was the first one in the field around the state and even the country. According to the advantages of using e-learning, importance of having basic information on B.Ed. colleges mentioned in the above paragraphs; conducting this study was not only essential but indispensable and vital to planning for developing and preparing teacher education to enter the ICT world and information & knowledge based society. Studies Related to E-learning Needs A survey study was undertaken to analyze the needs assessment in Open and Distance Learning (ODL). Glasgow (2011) found the existence of a relationship (correlation) between program choice and level of educational attainment. Respondents with the highest qualifications opted for the academic programs while those with lower qualifications selected technical, vocational and skill based programs. However, respondents with the lowest qualifications (incomplete primary/secondary education and ODL certification) were the ones who selected literacy courses. Ailing Qiao and Nan Wang (2009) explored in their study that the majority of respondents were required to learn computing skills on web design software, Learning Management System, and electronic resources for teaching; only a few needed to learn basic computing skills such as e-mail and Internet. A more important issue was that respondents wanted to learn how to integrate ICT in classroom teaching effectively and efficiently. Omwenga (2004) carried out a needs assessment of five universities in East Africa in order to determine their state of readiness to embrace ICT and educational technology. He reported on students’ access to computer facilities, the percentage of staff with computers in the offices; the networking of computers in the faculties of science and engineering, nature of link with the Internet, general computer literacy of staff and students and factors affecting ICT use as educational technology. This work determined in each university the resources (both human and material) required to enable the institution to use ICT as an educational technology; indicates the resources required for each level of ICT use as an educational technology and the level of within classroom interaction, at the level of interaction within departments, faculty and campus and the level of interaction with the wider world. Martin, Klein, and Igoe (2003) reported on the needs assessment conducted among the current graduate students, past graduate students (professionals) and faculty of Arizona State University to find their views on the course “Instructional Media Design” being offered online. Findings indicated that only 14% of the participants preferred a totally online setting for the course, more than 60% preferred a blended approach of online and classroom based learning. The review of related literature has elicited widely accepted definitions of key terms and the variables used in the study. As made clear from the comprehensive literature review, just a few researchers worked on the e-learning needs assessing in higher educational level especially in teacher training colleges, while the present study was going to shed some light on the students, different subjects of studies in colleges, comparing institutional types of colleges with reference to their financial in/dependency on governmental supports. In the literature review, extant studies regarding awareness, perceptions and attitudes, gender differences address these issues, but remain inadequate to address Teacher Education in e-learning needs. RESEARCH METHODOLOGY The objective of this study was to investigate: The differences between the following categories of students with reference to their e-learning system components needs (a) Male and female students (b) Government, aided an unaided colleges students (c) Science, Art and Language subjects students H 0 . There is no significant difference between the following categories of students with reference to their eLearning system components needs (a) Male and female students (b) Government, aided and un-aided colleges students (c) Science, Art and Language subjects students

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Location of the Study The present study is colleges of education affiliated with the University of Mysore, Karnataka state in India. There are a total of 194 colleges affiliated with the University. Numbers of Education Colleges are 35 which are distributed in different districts such as Mysore city, Mandiya, Kollegal, Acetate Town and Hassan. Details of selected colleges have been mentioned in the sample section. Sample of the Study Determining an effective sample size is not an easy matter. Krejcie and Morgan (1970), quoted in Cavana, Delahaye, and Sekaran (2000), greatly simplified the sample size decision by providing a table which ensures a good decision model. According to Krejcie and Morgan’s table, the optimal (effective and valid) sample size to represent students’ population of 3500 is 346. This calculation of sample size agrees with Wimmer and Dominick’s calculation (2005) at 95% confidence and 5% margin of error. To gather sufficient variables and to allow for the substantial sample size needed to provide an overview of needs assessment of e-learning, the survey method was clearly the most suitable approach. In particular, surveys are especially suitable when there is a need to study a large number of variables and to manage a large sample size (Galliers, 1991). Using surveys to obtain a broad perspective across a large number of organizations is a technique which has also been used successfully by other researchers. The survey approach, therefore, appeared to be the most appropriate approach for this research project. To this end, data were collected by means of paper-based questionnaire– the survey was designed and randomly distributed to students studying in colleges of education affiliated with the University of Mysore. All the students, who were in educational colleges affiliated to University of Mysore, constituted the population of the present study. Sample size was calculated according Krejcie and Morgan (1970). E-Learning Needs Tool Needs assessment is a form of applied research and furnishes information applicable for solving real problems (Powell, 1997). According to Westbrook (1997), qualitative research yields results that “centres on understanding rather than on predicting” (p. 144). Needs assessments are usually qualitative in nature, although some quantitative data may be collected for demographic purposes. Examples of qualitative data would be feelings, thoughts and ideas. Examples of quantitative data obtained in the needs assessment would be age, area of residence, academic level, and gender. Several data collection methods, including four primary varieties of data collection can be used. These include: surveys, focus groups, individual interviews, and the Delphi technique. Each of these varieties of data collection has unique aspects. Regarding the targeted teacher training colleges of Mysore University, limitations of research and consulting with experts in the field and specialists, appropriate method was selected. In the present study, the researcher used a researcher made test to measure e-learning components of the e-learning for students. The needs assessment questionnaire for students has two divisions: Part A: Demographic Information Part B: Needs on e-Learning system components Demographic Information had three main parts: 1) Gender: Male / Female 2) Type of institution: Government/PrivateAided /Unaided 3) Subject taught: Science /Art / Language The Second Division of the tool was on finding out needs on three learning system components. How much a student needs to know about e-learning components? 1) Instructional Design (ID) 2) Multimedia Component 3) Internet Tools 4) Computers and Storage Devices 5) Connections and Service Providers E-learning components based on Khan (2005) had seven categories, but after validity and reliability of the tool, the last two categories of that model were omitted, since they were so technical and difficult and were not understandable for students, so only the above five categories were analyzed. Scoring for each item starts with 15

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minimum needs for learning (< 25%) and maximum needs on learning (100%). Validity and Reliability of Tool The toolwas in English language but itwas translated to Kannada language (local language of Karnataka state) to be more understandable and for easy answering. Before piloting the instrument, the tool was scanned and reviewed with the help of eight experts conversant in both English and Kannada language; and involved in the field of education, higher education, ICT and e-learning. A pilot test was carried out to determine item reliability for the constructs being measured. The Cronbach alpha value for the pilot test was .89; hence the instrument was classified as having acceptable reliability. According to the Cronbach alpha Reliability Classification Index, these values are classified as acceptable and therefore no changes were made to the items (Kamarul Azmi Jasmi, 2010; Pallant, 2002; Sekaran, 2003). The survey took 24 weeks to complete, from June to December 2012. The questionnaire returned by the participants was checked for any incomplete answers. PASW Statistics 18 software was used for data analysis. The results were analyzed and interpreted using the statistical techniques of independent samples t-test, one way ANOVA . FINDINGS A total of 374 students were selected through stratified random sampling of which 143 students were males (38.2%) and remaining 231 (61.8%) were females. Of the 374 students selected, 28 of them were studying in government college, 72 of them were studying in private aided colleges and a large majority of 274 of them were studying in private unaided colleges. Further, contingency coefficient test revealed a significant association between gender type and college type (CC=.151; p = .013), indicating more number of male students in private unaided colleges compared to government and private aided colleges, where we find more of female students. Of the 374 sample students selected, the majority of them were from the arts stream (53.7%), followed by 24.3% of them from language and the remaining 21.9% from the science stream. When the contingency coefficient test was applied to see the association between subjects and type of institute, a significant association was observed (CC = .177; p = .016). It was found that more language students were from government colleges; in contrast, more number of students from arts stream were in private aided and private unaided colleges. In the following Table 1, ranking of students in e-learning components needs was delineated.

Table 1.Descriptive Statistics Needs on Learning e-Learning Components Ranking

Instructional Design (ID)

Multimedia Component

Internet Tools

Computers and Storage Devices Connections and Service Providers

Needs on learning e-learning components Learning theories Instructional theories Instructional strategies and techniques Text Graphics Audio Streaming Video Streaming Links (e.g., Hypertext links, Hypermedia links, 3-D links, image maps, etc.) Asynchronous Text-based (e.g., Chat, Messaging, etc.) Synchronous Audio-Video Conferencing Tools

Mean 3.46 3.50 3.56 3.63 3.56 3.59 3.63

Rank 15 14 11 2.5 12 8 2.5

3.61

6

3.63 3.62 3.57

4 5 10

Internet Navigation Tools

3.65

1

Search Tools & Engines Operating Systems (Unix, Windows, Macintosh, Linux) Hard drives, CD ROMs, DVDs, and so on Tablets, iPods

3.60 3.44 3.54 3.58

7 16 13 9

Mobile technology(e.g., connected wireless, wireless LAN, WAN, PAN or personal area network)

3.39

17

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When the needs on learning e-learning components were ranked, Navigation under Internet tools ranked 1, followed by Video streaming and text under Multimedia components ranked 2.5 each, and Asynchronous under Internet tools again ranked 4. The least ranking needs on learning e-learning components were Mobile technology under Connections and service providers (ranked 17), Operating systems under computer and storage devices (ranked 16), Learning theories under Instructional design (ranked 15), Instructional theories under Instructional design (ranked 14). and Hard drives, CD ROMs, DVDs under computers and storage devices ranked 13. H 0 . There is no significant difference between the following categories of students with reference to their elearning system components needs H 0 a. Male and female students Table 2.Mean e-learning system components needs scores of male and female students and results of independent samples ‘t’ test Gender Male Female

N 143 231

SD

Mean 61.13 60.21

t value 11.83 11.31 .751

Note: NS-Non-significant at 0.05 levels

p value .453

Between male and female students, a non-significant difference existed in their mean e-learning needs as the observed t value of .751 was found to be non-significant (p = .453). Further, the mean values clearly revealed that male (mean 61.13) and female (mean 60.21) students had statistically equal scores on e-learning needs.

H 0 b. Government, aided and un-aided colleges students

Table3.Mean e-learning components system needs scores of students studying in different types of colleges and results of one way ANOVA Type of Institution

N

Government Private-Aided Private-Unaided

28 72 274

Total

S.D

Mean 64.78 60.51 60.14

374

F value

p value

10.51 10.81 11.72

60.56

11.50

2.077

.127

Note: NS-Non-significant at the .05 level. One way ANOVA revealed a non-significant difference in mean e-learning needs of the students studying in different types of colleges. The F value of 2.077 was found to be non-significant with probability value of .127. The mean e-learning need scores of the students studying in government, private aided and private unaided colleges were 64.78, 60.51 and 60.14 respectively, which were statistically the same. H 0 c. Science, Art and Languages subjects students Table 4 Mean e-Learning System Components Need Scores of Students in Different Streams and Results of One Way ANOVA Teaching subjects Science Arts Languages

N 82 201 91

Total

374

S.D

Mean 59.20 61.40 59.93

F value

p value

1.242

.290

12.49 10.99 11.66

60.5611.50

Note: NS-Non-significant at .05 level.

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Students studying different subjects did not differ significantly in their e-learning needs as the obtained F value of 1.242 failed to reach the significance level criterion of .05 (p = .290). The mean e-learning needs of the students studying science, art and language were 59.20, 61.40 and 59.93 respectively, which were statistically the same, contributed for the nonsignificant F value. DISCUSSION AND CONCLUSIONS The purpose of this investigation was to find out needs of e-learning components and examine how certain demographic variables (male and female, type of institution and teaching-learning subject) affect e-learning needs assessment among students in colleges of education or secondary level (B.Ed.) affiliated with the University of Mysore. When the needs on learning e-learning components were ranked for students, it was observed that navigation under Internet tools ranked 1, followed by video streaming and text under multimedia components ranked 2.5 each, and the least ranking needs on learning e-learning components were instructional theories ranked 17, mobile technology ranked 16, Asynchronous. The least priorities were given to mobile technology under connections and service providers ranked 17, operating systems under computer and storage devices ranked 16, learning theories under Instructional design ranked 15. In needs to learning e-learning components system between male and female students, there was a nonsignificant difference. To studying in different types of colleges (government/ private aided / private unaided) had not a non-significant difference in mean of e-learning components needs for the students. To studying in different subjects (science/ art / language) had not a non-significant difference in mean of e-learning components needs for the students. Findings of this study support by Ailing Qiao and Nan Wang (2009). They showed that the majority of respondents were required to learn web design software, Learning Management System, and electronic resources e-mail and Internet. Instructional Design (ID) had medium ranking in needs of our samples which in Ailing’s study the pedagogy for integrating classroom teaching and online learning had a high priority in teacher training in ICT. Based on the findings of this study, students, faculty members and management of colleges of education and educators can plan and conduct needed and related training programs to expand their own knowledge and proficiency in e-learning, Internet technologies and lead to more efficient utilization. Moreover, students (as future teachers) should be made aware of the potential of various e-learning technologies for enhancing the teaching and learning process. Clarifying the incentives and eliminating obstacles to fully integrate e-learning is needed. This study, while obviously focused on the one year B.Ed. college program experience, also has potential benefit to other teacher training colleges such as high schools, D.Ed. and B.P. Ed. colleges or even PG educational colleges and departments in M.A and M. Phil. Level. Decision makers and Heads can decide for the planning and designing workshops and intensive courses. It is suggested that institutions plan and conduct some non-credit courses and intensive workshops in faculties to improve students’ acceptance of e-learning. REFERENCES Ailing Qiao & Nan Wang. (2009). An Investigation of Teachers’ Needs on Using ICT in Teaching and Learning. Proceedings of 2009 International Conference on Computer Engineering and ApplicationsIPCSIT vol.2 (2011) © (2011) IACSIT Press, Singapore Austin, J. T., & Mahlman, R. A. (2000). Using the Internet for career - technical assessment: A pilot project in Ohio. Retrieved from http://scholar.lib.vt.edu/ejournals/JCTE/v16n2/austin.html Badrul, H. Khan. (2001). Managing Evaluation .George Washington University, USA

E-Learning:

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Badrul, H. Khan. (2005). E-Learning Quick Checklist. Hershey, PA: Information Science Publishing. Retrieved from http://BooksToRead.com/checklist Barry, M. M., Doherty, A., Hope, A., Sixsmith, J., Kelleher, C. C. (2000). A community needs assessment for rural mental health promotion. Health Education Research, 15, 293-304.

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Bishop, J., Bauer, K., & Becker, E. (1998). A survey of counseling needs of male and female college students. Journal of College Student Development, 39, 205-210. Cappelli, G. (2003). E-learning in the postsecondary education market: A view from WallStreet. In M. S. Pittinsky (Eds.), The wired tower: Perspectives on the impact ofthe Internet on higher education (pp. 41-63). Upper Saddle River, NJ: PrenticeHall. Cavana, R., Delahaye, B., & Sekaran, U. (2000). Applied Business Research: Qualitative and Quantitative Methods. USA: John Wiley & Sons Inc. Cohen, L., Manion, L., & Morrison, K. (2007) Research Methods in Education, 6th edition. London: Routledge. Colvin, R., &Mayer, R. (2008). E-learning and the science of instruction. California: John Wiley. Cooper, L. W. (2001). A comparison of online and traditional computer applications classes. THE Journal, 28(8), 52-58. Davis, S. (2001, January 1). What E-Learning Can Learn from History. USDLA Journal, 15(10). (ERIC Document Reproduction Service No. EJ641496) Retrieved September 21, 2012, from ERIC database. Galliers, R. D. (1991). Strategic Information Systems Planning. European Journal of Information Systems, 1(1), pp. 55-64. Flowers, J. (2001). Online learning needs in technology education. Journal of Technology Education, 13(1), 17-30. Glasgow, F. (2011). Needs Assessment in Open and Distance Learning (ODL): Case of the Institute of Distance andContinuing Education (IDCE), University of Guyana. Indian Journal of Open Learning, 20(1), 1530. Gunasekaran, A., McNeil., R. D., & Shaul, D. (2002). E-learning: research and applications. Industrial and Commercial Training, 34(2), pp. 44 – 53. Gunawardena, C., Nolla, A., Wilson, P., Lopez-Islas, J., Ramirez-Angel, N., &Megchun-Alpizar, R.(2001). Journal: Distance Education - DISTANCE EDUC, 22(1), pp. 85-121, 2001 Hezel Associates. (2005). Global E-learning Opportunity for U.S. Higher Education," vol. 2006: Hezel Associates, LLC, 2005. Holmes, B.,& Gardner, J. (2006).E-learning: Concepts and practice. Great Britain: Sage Publications. Holton, E. F., III, Bates, R. A., & Naquin, S. S. (2000). Large-scale performance- driven training needs assessment: A case study. Public Personnel Management, 29, 249-267. Huynh, Minh Q., Umesh, U. N., & Valacich, J. S. (2003). E-Learning as an Emerging Entrepreneurial Enterprise in Universities and Firms. Communications of the Association for Information Systems, 12, Article 3. Retrieved from http://aisel.aisnet.org/cais/vol12/iss1/3 Jones, A. J. (2003). ICT and Future Teachers: Are we preparing for e-Learning? IFIP Working Groups 3.1 and 3.3 Conference: ICT and the Teacher of the Future, Melbourne, Australia.

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Kamarul Azmi b. Jasmi. (2010). Guru cemerlang pendidikan Islam sekolah menengah di Malaysia: Satu kajian kes. Ph.D thesis, National University of Malaysia. Kleiman, G. M. (2004). Meeting the Need for High Quality Teachers: e-Learning Solutions, White Paper, Retrieved from http://www.ed.gov/about/offices/list/os/technology/plan/2004/site/documents/KleimaneetingtheNeed.pdf Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610. Kruse, B. G., Elacqua, T. C., & Rapaport, R. J. (1998). Classroom accommodations for students with disabilities: A needs assessment. Journal of College Student Development, 39, 296-298. Lehner, F., Nösekabel, H., & Lehmann, H. (2003). Wireless e-learning and communication environment. In Z. Maamar, W. Mansoor, & W.-J. van den Heuvel (Eds.), Proceedings of the Workshop at ISMIS ’02, Lyon. Liaw, S., Huang, H., & Chen, G. (2007, December 1). Surveying Instructor and Learner Attitudes toward E-Learning. Computers & Education, 49(4), 1066-1080. (ERIC Document Reproduction Service No. EJ773936) Retrieved from ERIC database. Lin, Y. M., Lin, G. Y., & Laffey, J. M. (2008). Building a social and motivational framework for understanding satisfaction in online learning. Journal of Educational ComputingResearch, 38(1), 1-27. Lockwood, A., & Marshall, M. (1999). Can a standardized needs assessment be used to improve the care of people with severe mental disorders? A pilot study of ‘needs feedback.’ Journal of Advanced Nursing, 30, 1408-1415. Martin, F., Klein, J., & Igoe, A. (2003, November). Teaching Instructional Media design: A needs assessment report. Proceedings of E-learn 2003, Phoenix, AZ. McCaslin, N. L., & Lave, J. (1976). Needs Assessment and Career Education: An Approach for States. Columbus, OH: Centre for Vocational Education, Ohio State University. Mitchell, G. (1993). The Trainer's Handbook, The AMA Guide to Effective Training, 2nd Edit. AMACOM, NY, 423 pp. Morrison, J. (2003). U.S. Higher Education in Transition. On the Horizon, 11(1), 6-10. Noll, P. F., & O’Dell, W. (1997). Florida’s affordable housing needs assessment methodology. Journal of the American Planning Association, 63, 495-508. Obringer, L. (2001). How e-learning http://communication.howstuffworks.com/elearning.htm

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Omwenga, E. ( 2004). A Training Needs Assessment Study on the use of Information and Communications Technologies (ICT) in supporting the provision of Science and Engineering Education in Five East African Universities. Retrieved from http://webmail.fandm.edu/Session/2067200dal35cnfox0Uw6venHilgjfwj/MessagePart/inbox/607902B/Needs %20Assessment%20report%20Oct04.pdf Ong, C. S., Lai, J. Y., & Wang, Y. S. (2004). Factors affecting engineers’ acceptance of Asynchronous E-learning Systems in High-Tech Companies. Information and Management, 41(6), 795-804, p. 01.

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Pallant, J. (2002). SPSS survival manual. Sydney, Australia: Ligare Sydney. Panda, S., & Mishra, S. (2007). E-Learning in a mega open university: Faculty attitudes, barriers, and motivation. Educational Media International, 44(4), 323-338. Powell, R. R. (1997). Basic research methods for librarians (3rd ed.). Greenwich, CT: Ablex. Rahtz, D. R., & Sirgy, M. J. (2000). Marketing of health care within a community: A quality-of-life needs assessment model and method. Journal of Business Research, 48, 165-176. Reigeluth, C. M., & Khan, B. H. (1994). Do instructional systems design (ISD) and educational systems design (ESD) really need each other? Paper presented at theAnnual Meeting of the Association for Educational Communications and Technology (AECT), Nashville, TN, February. Rosenberg, M. J. (2001). E-learning: Strategies for delivering knowledge in the digital age. Boston, MA: McGraw-Hill Professional. Ryan, S. (2001). Is online learning right for you? American Agent & Broker, 73(6), 54-58. Teo, C. B., & Gay, R. K. L. (2006). A Knowledge-Driven Model to Personalize e-Learning.ACM Journal of Educational Resources inComputing, 6(1), 1-15. Sekaran, U. (2003). Research methods for business: A skill building approach (4th ed.). New York, NY: Wiley. Stabb, S., Harris, M., Joseph, E., Talley, H. E., Robin, A., Buhrke, E. F., . . . Prieto, S. (1995). Multicultural needs assessment for college and university student populations. Retrieved from http://www.copyrightencyclopedia.com/the-clinical-use-and-interpretation-of-the-wechsler-4/#ixzz2GjRsma84 Thornton, P. M. (1995). Linking market research to strategic planning. Nursing Homes Long Term Care Management, 44, 34-36. Jerabek, McMain, and Van Roekel 59 Torma, C. (1998). Youth recreation needs assessment, Albuquerque. Planning, 64, 14. Urdan, T. A., & Weggen, C. (2000). Corporate E-learning: Exploring a New Frontier: Wrhambrecht+co. Wang, Y. (2003). Assessment of learner satisfaction with asynchronous electronic learning systems.Information & Management, 41(1), 75–86. Werbach, K. (2000). Clicks and Mortar Meets Cap and Gown: Higher Education Goes Online. Release 1.0, 18 (8), 1-22. Welsh, E., Wanberg, C., Brown, K., &Simmering, M. (2003), E-learning: emerging uses, empirical results and future directions. International Journal of Training andDevelopment, 7(4), 245-258. Westbrook, L. (1997). Qualitative Research. In R. R. Powell, Basic Research Methods for Librarians (3rd ed.) (pp. 143-162). Greenwich, CT: Ablex Publishing.

Bass.

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Wimmer, R., & Dominick, J. (2005). Sample size calculator. Mass Media Research: An Introduction (8th Edition). CA: Thomson Wadsworth. Retrieved 2012-06-12 from http://www.rogerwimmer.com/mmr/samplesizecalculator.htm

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Evaluating the Impact of Technology Integration in Teaching and Learning Nafisat Afolake Adedokun-Shittu [1] Abdul Jaleel Kehinde Shittu[2]

Volume 2, Issue 1

[1] College of Business, Universiti Utara Malaysia [2] [email protected] College of Arts and Science, Universiti Utara Malaysia²

ABSTRACT This article reports the impacts of technology integration on teaching and learning from a study that examines the impact of ICT deployment in teaching and learning at a University in Nigeria. The survey data were drawn from 593 respondents (students and lecturers) and the survey instrument employed for both the students and the lecturers is a 64-item questionnaire that was each subdivided into seven parts. The study finds many ICT impacts in teaching and learning such as: learning aid and resourcefulness, comfort with ICT, psychoanalytical and psychosocial aid, task enabler, interdependence with ICT and learning collaboration. It also identifies factors that need to be recognized in ICT impact study settings such as: perception, integration, motivation and challenges.

Keywords:

Impact, technology integration, teaching and learning, evaluation

INTRODUCTION This topic is concerned with how the impact of teaching and learning with the various forms of technology can be evaluated to ensure that the goals of technology integration in education are achieved. This should not be confused with evaluating the various forms of technology used in teaching and learning. Greenberg (2004) in Adedokun-Shittu and Shittu (2011) proposed that instead of comparing the effectiveness of varying technologies, efforts should be geared toward determining the optimal combinations of all; that would best produce excellent learning outcomes for a particular audience. He further lamented that most researchers fail to control for essential factors such as prior student knowledge, pedagogical methods techniques, and teachers’ and students’ ability. Thus, this article is focused on discussing some issues critical to evaluating the impact of technology integration in teaching and learning in order to respond to this gap identified by Greenberg. The factors researchers must consider while evaluating ICT impact include: the learning environment, the status of ICT integration in the learning environment, the students’ and teachers’ disposition toward technology, access to technology and training facilities and many others. Several authors (AdedokunShittu & Shittu, 2011; Ecclestone, 2008) have reported that researchers often neglect most important factors such as students’ and teachers’ technology proficiencies, pedagogical techniques and the peculiarities of learning environment in impact studies. To exemplify how impact evaluation can be carried out to reflect these factors, this article reports a study conducted by Adedokun-Shittu (2012) on ICT impact on teaching and learning in higher education. The study finds many ICT impacts in teaching and learning such as: learning aid and resourcefulness, comfort with ICT, psychoanalytical and psychosocial aid, task enabler, interdependence with ICT and learning collaboration. It also identifies factors that need to be recognized in ICT impact study settings such as: perception, integration, motivation and challenges.

The Impact Study Outline The study addressed in this article studied the impact of ICTs on teaching and learning in a higher institution of learning in Nigeria. Nigeria being a developing nation recognizes the relevance of ICTs in national development and particularly in education. Hence the deployment of ICT in Nigerian education generally and specifically in higher institutions has received considerable attention. The formulation of the National Policy on Computer Education in 1988 contained information on application of computers at various levels of the country’s education with issues related to basic objectives, hardware and software requirements (Federal Republic of Nigeria, FRN, 1988, cited in Yusuf, 2005). In 23

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line with the recommendations of the National policy on IT, the National Universities Commission (NUC), the government agency responsible for registering and regulating universities has prescribed a minimum level of PC ownership for universities as follows: one to every four students, one PC to every two lecturers below the grade of Lecturer 1, one PC to each Senior Lecturer, and one notebook to a Professor/Reader. Many universities in Nigeria have thus achieved a significant ratio than the prescribed and some have taken steps ahead in building campus-wide area networking and developing e-learning course deliveries. Even though ICT facilities have been deployed in many learning institutions, few or no impact studies have been conducted to determine ICTs effect on teaching and learning outcomes. This is due to the fact that technology integration in higher education in Nigeria is still at a preliminary stage (Adegun, 2007). As such, this study looks into the impacts derived from employing ICTs in teaching and learning in a Nigerian University through a quantitative means. The survey instrument employed for both the students and the lecturers is a 64-item questionnaire that was each subdivided into seven parts. The first part (demographic variable) is a 14 item-question which contains choice and fill in questions. The next two parts covering perception (21 items) and integration (12 items) are based on the fivepoint Likert scale. Motivation (6 items) and constraints (4 items) are rated on a preference scale (1 - least preferred to 5 – most preferred). The problems (3 items) part of the instrument is rated on a frequency scale (1 – Never and 5 Always). Constraints and problems were re-coded as challenges. The last part renamed as ICT rate (1 item) carries a value scale (1 – of no value to 5 – extremely valuable). These four scales are run through linear regression using rate of ICT value to teaching and learning (ICT rate) as the dependent variable. Both the students’ and lecturers’ survey instrument display a high reliability (alpha coefficients of .900 and .909) respectively. Linear regression was conducted to see the interaction of the four scales of items (perception, integration, motivation and challenges) with respect to the value of ICT in teaching and learning (ICTrate). All the independent factors significantly and positively interact except integration that has a negative value (-.112). Perception has the highest beta value of .243, while they all have R value of = .417, and p < .000. The negative relationship revealed by students and lecturers ICT integration in teaching and learning is a function of the fact that some factors such as access hinder their integration. Thus, ICT impact study cannot be conducted without taking some factors such as access, constraints, problems, and technical issues into consideration. The impacts reported in this study (learning aid and resourcefulness, comfort with ICT, psychoanalytical and psychosocial aid, task enabler, inter-dependence with ICT and learning collaboration) were extracted through factor analysis. This study is however limited by the quantitative method it employed because rich and more in-depth findings could be gathered if supported by qualitative approach. Likewise evaluation studies are better conducted employing mixed method approach because of its comprehensiveness and in order to achieve valid and well-substantiated conclusions (Cresswell, 2009; Stufflebeam, Harold, & McKee, 2003). Similarly, studies (Centre for Global Development, 2006; Independent Evaluation Group, 2006; World Bank, 2004) strongly recommend mixed method for impact study given the lack of credibility flaw identified against many existing impact studies that focus mainly on quantitative method.

Impacts of Technology in Teaching and Learning Education technology has been confirmed to have great potentials that impact on teaching and learning. It motivates and engages students to learn and helps broaden their skills, helps to simulate the workplace experiences thereby preparing students for the challenges of the labor market. This revolutionalizes the school environment, facilitates teaching by providing resourceful teaching aids for teachers and connects the school to the outside world. Trucano (2005) ascertained that technology empowers teachers and learners and promotes the growth of skills necessary for the 21st century workplace. Wright, Stanford, and Beedle (2007) describe ICTs as giving opportunities for students to explore, discover, create, communicate effectively and freely with instructors, complete and receive assignments and feedback online, initiate and participate in online discussions. Both lecturers and students in the study of discourse in this article agree on the significant impact ICT has on students and their learning and on teaching and teaching styles. Among the impacts of ICT in teaching and learning reported were; learning aid and resourcefulness, comfort with ICT, psychoanalytical and psychosocial aid, task enabler, interdependence with ICT and learning collaboration. Spector (2008) advocates how student collaboration is achieved through technology-mediated communication such as e-mail and teleconferencing across space and time in local and wider communities. Kozma (Kozma, 2003; Kozma & McGhee, 2003) illustrated a student learning approach in which students collaborate with their peers in given projects. He named this approach the Student Collaborative Research Cluster. These classroom practices support the development of skills needed by a society focused on sustained economic development and social transformation: information management skills, communication and collaboration skills, interpersonal and self-directional skills, and ability to create and innovatively apply new knowledge to solve complex problems. Similarly, King (2005) and Simonson, Smaldino, Albright, and Zvacek (2003) ascertained that ICTs foster collaborative learning. 24

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Considering teachers’ professional development cannot be achieved in isolation, Kozma (2003) also exemplified how teachers collaborated with students, colleagues in the school and others outside the school such that ideas on solving classroom problems could be shared and disseminated. Collaboration among lecturers as a benefit of ICT use in teaching is also found in Abolade and Yusuf (2005); they found that ICT allows for networking with other teachers, thus connecting teachers and allowing them to exchange ideas, share resources, and improve teaching practices. The psychoanalytical and psychosocial impacts described in both lecturers and students’ findings in this study have a backing in the work of Lajbcyier and Spratt (2007). They argue that the social presence develop critical thinking and cognitive skills and promote higher order learning in a community of learners. Some of these impacts of ICT in teaching and learning such as interaction and social negotiation of meaning were also affirmed in Madden, Baptista Nunes, McPherson, Ford, and Miller (2008). Other impacts in the findings such as learning aid and resourcefulness, task enabler, comfort with ICT are supported in Abolade and Yusuf (2005) having described ICT as essential tools in any educational system which has the potential of being used to meet the learning needs of individual students, promote equality of educational opportunities; offer high quality learning materials, increase self-efficacy and independence of learning among students, and improve teachers’ professional development. They also affirmed that ICT provides opportunity for connecting schools to the world, as learning is expanded beyond the classroom; that allows students and teachers to access information and resources.

Issues in Evaluating Technology Impact in Teaching and Learning Evaluating technology impact in teaching and learning requires a broad range of issues which are often undermined when ICT impact researches are being carried out. Thus, the task in this part of the article is to carefully consider some of them and make recommendations for further researches on technology impact in teaching and learning. The impact of ICT in education is often difficult to establish especially when other factors that can affect achievement are isolated. Critical to evaluating ICT impact in teaching and learning are issues such as: the environment in which teaching and learning will take place, the status of technology integration in the learning environment, the students’ and teachers’ technology proficiencies, their disposition towards technology, access to technology and training facilities, teachers’ teaching methodology, and students’ learning approach. Researchers must focus on each of these issues to determine if the adoption of technology in teaching and learning produces the desired and maximum impact or otherwise what factors hinder realization of expected outcomes (Adedokun-Shittu & Shittu, 2011; Newby, Stepich, Lehman, Russell, & Leftwich, 2011). The study under discourse in this article finds that technology impact in teaching and learning can be generated through examining teachers’ and students’ perception of technology use in teaching and learning, assessing their level of technology integration, determining the motivating factors that propel them and ascertaining the challenges that restrain them. This is supported by the suggestions given by Kankaanranta (2005) on technology evaluation in schools such as justifying investment returns by examining if the desired impacts are achieved, assessing technology infusion in the curriculum to analyze whether the intended curriculum is implemented and ultimately attained and determining if the pedagogical uses of ICT emphasize how it is employed in the class by the teachers and how it is received by the students. Some of the perception of technology in teaching and learning held by the lecturers and students in this study include; technology changes the nature of student/lecturer interaction, improves higher-order and critical thinking, improves quality education, transforms the learning environment into a learner-centered one, increases students’ motivation and engagement, increases students’ positive effects on learning, enhances students’ assessment and independent learning, reduces both students and lecturers’ burden, facilitates learning and enhances performance. It is also seen as a tool for increased access to resourceful information, improved research output, resource sharing and student/lecturer collaboration (Adedokun-Shittu, Shittu & Adeyemo, 2013; Jimoh, Shittu & Kawu, 2012). Evaluating both lecturers’ and students’ perception in this context has thus confirmed that they believe technology has positive effects on their teaching and learning. Researchers (e.g., Adedokun-Shittu et al., 2013; Kozma, 2005; McNamara, 2003; UNESCO, 2002) have also found ICT as a way to promote educational change, improve learners’ skills and prepare them for the global economy and the information society. However, these positive effects do not magically occur without proper policy considerations on how to integrate technology in the learning environment. As such, it is practically crucial to establish how technology is implemented in the learning institution, while conducting technology impact studies (Adedokun-Shittu & Shittu, 2011). As mentioned above, the second factor identified in this study is integration. Among the technology integration aspects evaluated in this study are technology use in classroom teaching and learning, its alignment with the curriculum

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and students’ assessment and its blend with the existing traditional teaching and learning practices. It was found that technology integration in teaching and learning across these dimensions has not fully materialized in this university. The regression analysis employed reveals that integration has a negative correlation (-.112) with the value of technology in this learning environment. This implies that the level of technology integration in the university is still at an unprecedented level such that its value could not be attached to established technology impact in teaching and learning. This is in support of Adegun’s (2007) finding that technology integration in Nigerian education is still at a rudimentary stage. As a form of recommendation, Kozma (2005) suggests some policy considerations for ICT integration in education that can help in generating the desired outcome. He recommends that the technology plan should describe how technology will be coordinated with changes in curriculum, pedagogy, assessment, teacher professional development and school restructuring. Following the integration issue is the question on what incentives could motivate teachers and students to effectively integrate technology to maximize its impact. The motivating factors examined in this study include: training especially for teachers, adequate access to technology facilities, ease of use of the facilities and compatibility with teaching and learning needs, relative advantage and usefulness of technology in teaching and learning. Both the students and lecturers in this study possess an average ICT skill essential for their appreciation of ICT deployment in the university and a judicious use of limited ICT facilities provided. They also acknowledge a substantial engagement with ICT for academic purposes though with varying degree based on the faculty, department, lecturers’ teaching style and course requirement. They have also received some forms of training though insufficient for the desired integration. The World Bank (2003) demands that teachers need to be lifelong learners to keep abreast of new knowledge, pedagogical ideas and technology relevant to successful implementation of educational reforms. Similarly, Haddad (2003) affirms developmental training is required for teachers for successful technological reforms in schools. Thus, training and mentoring on how to utilize and infuse technology in pedagogy and curriculum is essential for teachers. With respect to this, Kozma and McGhee (2003) suggest that teachers should collaborate with students, colleagues in the school and others outside the school on how classroom problems can be solved such that it will serve as mentoring and motivation for them. Access to the desired content through ICT facilities can also enhance the quality of students’ learning since this will provide both the students and the teachers with access to curricular materials and other resources. Haddad (2003) notes that ICT is relevant in the teachers’ professional development; it is also a source of knowledge given that they require a large, rich, and easily accessible-knowledge base. Voogt and Pelgrum (2005) and Ololube, Eke, Uzorka, Ekpenyong, and Nte (2009) also assert that access to ICT can be used to improve the delivery and access to quality education. Robinson (2007) and Adedokun-Shittu et al. (2013) however declare that access to technology alone does not guarantee integration and technology alone will not guarantee students’ learning. Thus, bearing this in mind Robinson (2007) suggests that technology integration be understood as an integral component of a more comprehensive package of education reform which will include curriculum, assessment, instruction and other practices within the context of the entire school. He further asserted that integration is unlikely to happen where access is restricted to specific classrooms; thus technology integration requires provision of ample access to all and addressing individual actual needs and perceptual barriers. This leads to the last factor vital to technology impact evaluation reported in this study. Investigating the challenges confronting teachers and learners in their engagement with technology in teaching and learning is another essential factor to evaluate in ICT impact studies. Software, accessibility and technical problems and constraints such as power failure, internet interruption, and inadequate training were some of the challenges uncovered in this study. These are all consistent with the description of Ajayi (2002) in Adedokun-Shittu (2012) stating that the status of ICT use in higher institutions in Nigeria is characterized by poor availability and quality of infrastructure, inadequate institutional capacity, inadequate human resource capacity, low bandwidth of connectivity, and poor penetration of ICT into higher institutions. Abolade and Yusuf (2005) also supported this by highlighting as part of the factors militating against effective ICT integration in Nigerian higher education to include lack of technically experienced lecturers, inadequate course content for ICT training, limited ICT facilities and infrastructure and electricity supply problems. Wright et al. (2007) also identified access and connection speed as challenges confronting higher education with the increasing demand for ICTs in teaching and learning. Other challenges as given by Jimoh et al. (2012) are: lack of access to computers and software, insufficient time to plan instruction, inadequate technical and administrative support.

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CONCLUSION This article identified some of the issues fundamental to evaluating impacts of technology in teaching and learning such as; teachers and students’ views, technology needs, and proficiencies, technology integration levels in learning institutions, incentives such as adequate access and training and the imminent challenges of technology in teaching and learning. It is also recommended that studies evaluating technology impact should employ a combination of quantitative and qualitative approaches such that each can compensate for the weakness of the other, thereby given a detailed and credible result. Some of the impacts of ICTs in teaching and learning reported in this article are: ICTs as learning aid and resourcefulness, ICTs as psychoanalytical and psychosocial aid, ICTs as task enabler, comfort with ICT, interdependence with ICT and learning collaboration. REFERENCES Abolade, A.O. & Yusuf, M. O. (2005). Information and communication technology and Nigerian teacher education. African Journal of Educational Studies, 3(10), 19-23. Adedokun-Shittu, N. A. (2012). The deployment of ICT facilities in teaching and learning in higher education: A mixed method study of its impact on lecturers and students at the University of Ilorin, Nigeria. Ph.D. thesis, International Islamic University Malaysia. Adedokun-Shittu, N. A., Shittu, A. J. K., & Adeyemo, A. A. (2013). Impact factors of Information and Communication Technologies. International Conference on Computing, E-Learning and Emerging Technology (ICCEET2013) Sydney, Australia. October, 30 – 31, 2013. Retrieved from http://www.icceet.com Adedokun-Shittu, N. A & Shittu, A. J. K. (2011). Critical issues in evaluating Education Technology. In M. S. Al-Mutairi & L. A. Mohammad (Eds.), Cases on ICT utilization, practice and solutions: Tools for managing day- to- day issues (pp. 47–58). IGI Global. Adegun, O. A. (2007). Managing e-learning to achieve education for all in Nigeria. In Proceeding 12th Cambridge International Conference on Open and Distance Learning, London. Ajayi, G. O. (2002). Bridging the digital divide: The Nigerian case study. In Proceedings Developing Country Access on Online Scientific Publishing Sustainable Alternatives. Trieste, Italy. 4-5 October, 2002. Centre for Global Development. (CGD). (2006). When will we ever learn? Improving lives through impact evaluation. Washington DC: Center for Global Development. Cornford, J., & Pollock, N. (2003). Putting the university online: Information, technology and organisational change. Buckingham: SRHE. Creswell, J. W. (2009). Research design qualitative, quantitative and mixed methods approaches (3rd ed.). Thousand Oaks, CA: Sage. Ecclestone, K. (2008). The impact of assessment on pedagogy can have damaging consequences. In Nash I., Jones S., Ecclestone K. & A. Brown (Eds.), Challenge and change in further education: A commentary by the Teaching and Learning Research Programme. Retrieved from http://www.tlrp.org/pub/research.html Forcier, R. C. & Descy, D. E. (2008). The computer as an educational tool: Productivity and problem solving (5th ed.). Englewood Cliffs, NJ: Pearson. Greenberg, G. (2004, July/August). The digital convergence: extending the portfolio model’ [online], Educause Review, 39(4), pp. 28–36. Retrieved from http://net.educause.edu/ir/library/pdf/ERM0441.pdf

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Haddad, W. D. (2003). Is instructional technology a must for learning? Retrieved from http://www.techknowlogia.org/TKL_Articles/PDF/455.pdf Independent Evaluation Group (IEG) (2006). Impact evaluation experience of the independent evaluation group of the world bank. Washington DC: World Bank. Jimoh, R. G., Shittu, A. J. K., & Kawu, Y. K. (2012). Students’ perception of computer based test (CBT) for examining undergraduate chemistry courses. Journal of Emerging Trends in Computing and Information Sciences, 3(2), 125-134. Kankaanranta, M. (2005). Innovative Pedagogical Practices in Technology-Enhanced Education Finnish Perspective. Retrieved from http://e.finland.fi/netcomm/news/showarticle.asp?intNWSAID=41844 Kozma. R. B. (2005). National policies that connect ICT-based education reform to economic and social development. Human Technology, 1(2), 117-156. Retrieved from http://www.humantechnology.jyu.fi/articles/volume1/2005/kozma.pdf Kozma, R., & McGhee, R. (2003). ICT and innovative classroom practices. In R. Kozma (Ed.), Technology, innovation, and educational change: A global perspective. Eugene, OR: International Society for Educational Technology. Madden, A. D., Baptista Nunes, J. M., McPherson, M., Ford, N., & Miller, D. (2008). Mind the Gap!: New 'Literacies" Create New Divides. In C. Van Slyke (Ed.), Information Communication Technologies: Concepts, methodologies, tools, and applications (pp. 2297-2310). Hershey, PA: IGI Global. Newby, T. J., Stepich, D., Lehman, J., Russell, J. W. & Leftwich, A.T. (2011) Educational Technology for teaching and learning (4th ed.). Englewood Cliffs, NJ: Pearson. Ololube, N. P., Eke, P., Uzorka, M. C., Ekpenyong, N. S., & Nte, D. N. (2009). Instructional technology in higher education: A case of selected universities in the Niger Delta. Asia-Pacific Forum on Science Learning and Teaching, 10(2). Simonson, M., Smaldino, S., Albright, M., & Zvacek, S. (2003). Teaching and learning at a distance: Foundations of distance education (2nd ed.) Upper Saddle River, NJ: Merrill Prentice Hall. Spector, M. J. (2008). Handbook of research on educational communications and technology. Taylor & Francis. Trucano, M. (2005). Knowledge Maps: ICTs in Education. Washington, DC: infoDev. Retrieved from http://www.infodev.org/ files/1062_file_ Knowledge Maps _ICTs_and_the_Education_MDGs.pdf Voogt, J., & Pelgrum, H. (2005, October). ICT and curriculum change. An Interdisciplinary Journal on Humans in ICT Environments, 1(2), 157-175. World Bank. (2003). Infrastructure services: The building blocks of development. Washington, DC: World Bank. World Bank. (2004). Monitoring and evaluation: Some tools methods and approaches. Washington, D.C. World Bank Group. http://www.worldbank.org/oed/ecd/. Wright, V. H., Stanford, R., & Beedle, J. (2007). Using a blended model to improve delivery of teacher education curriculum in global settings. In L. Tomei, Integrating Information and Communications Technologies into the classroom (pp. 51-61). Hershey, PA: Information Science Publishing.

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Yusuf, M. O. (2005). Information and communication technology and education. Analysing the Nigerian national policy for information technology. International Education Journal, 6(3), 316 – 321. Retrieved from http://iej.cjb.net Yusuf, M. O. (2010). Higher Educational Institutions and Institutional Information and Communication Technology (ICT) Policy. In E. Adomi (Ed.), Handbook of research on Information Communication Technology Policy: Trends, issues and advancements (pp. 243-254). IGI Global. Yusuf, M. O., & Afolabi, A. O. (2010). Effects of computer assisted instruction (CAI) on secondary school students’ performance in biology. The Turkish Online Journal of Educational Technology, 9(1), 6269. Retrieved from http://www.tojet.net/volumes/v9i1.pdf

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Implementation of Ptechls Modules in Rural Malaysian Secondary School: A Needs Analysis

Norlidah Alias [1], Dorothy DeWitt [2], Saedah Siraj [3], Mohd Nazri Abdul Rahman [4], Rashidah Begum Gelamdin [5], Rose Amnah Abd Rauf [6]

ABSTRACT Research has shown that the strategy of matching learning style with certain technology enhances students’ learning experience. This study seeks to identify the learning styles among students in a rural secondary school, based on the Felder Silverman Model (1988) which comprises four dimensions (visual/verbal, active/reflective, sequential/global, sensing/intuitive). A PTechLS module developed by Norlidah Alias (2010) will be implemented in secondary school. The main objective of this study is to analyze the needs for a pedagogical module based on technology and learning style (PTechLS) for the Form 4 Physics curriculum before the implementation. Data were collected through surveys among 47 students in a rural secondary school in the Jempol district in Negeri Sembilan. Two instruments were used: learning style instrument and computer skills and usage questionnaire. From the learning style instrument, most of the students were identified as active (89.3%), reflective (10.7%), sensing (78.7%), intuitive (21.3%), visual (95.7%), verbal (4.3%), sequential (70.2%) and global (29.8%) learners. The computer skills and usage questionnaire shows that students in the selected rural school have access to technology and are already using it for learning. Therefore, the researchers suggest the implementation of PTechLS modules among rural Malaysian secondary schools.

Keywords:

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[1] [email protected] Department of Curriculum & Educational Technology, Faculty of Education, University of Malaya, Kuala Lumpur, Malaysia. [2] [email protected] Department of Curriculum & Educational Technology, Faculty of Education, University of Malaya, Kuala Lumpur, Malaysia. [3] [email protected] Department of Curriculum & Educational Technology, Faculty of Education, University of Malaya, Kuala Lumpur, Malaysia.

[5] [email protected] Department of Curriculum & Educational Technology, Faculty of Education, University of Malaya, Kuala Lumpur, Malaysia. [6] [email protected] Department of Curriculum & Educational Technology, Faculty of Education, University of Malaya, Kuala Lumpur, Malaysia.

PTechLS, Physics Curriculum, A needs Analysis

INTRODUCTION The strategy of matching learning style with certain technology enhances students learning experience (Norlidah Alias, 2010; Norlidah Alias & Saedah Siraj, 2012; Norlidah Alias, Dorothy DeWitt, & Saedah Siraj, 2013). Learning style defines how a learner concentrates, processes and retains information during learning (Dunn, 1990). Identifying a learner’s unique learning style is important in ensuring that learners are engaged in learning (Graf, Kinshuk, & Liu, 2009; Larkin-Hein & Budny, 2001; Naimie, Siraj, Ahmad Abuzaid, & Shagholi, 2010; Yang & Tsai, 2008). It has been observed that when instruction is aligned with the learners’ learning styles learning achievements will increase together with affective and motivational advantages (Aviles & Moreno, 2010; Franzoni & Assar, 2009; Lau & Yuen, 2010; Saeed, Yang, & Sinnapan, 2009). Previous research shows that matching the Physics concept, technology and learning styles can increase the students’ mastery of concepts (Hein, 1997; Ross & Lukow, 2004; Tsoi, Goh, & Chia, 2005). A Physics pedagogical module based on learning style and appropriate technology (PtechLS) was developed by Norlidah Alias (2010) to enhance the learning of abstract concepts in Physics by matching learning style and appropriate technology. The module was later experimented among 120 urban students in the Klang Valley of Malaysia (Norlidah Alias & Saedah Siraj, 2012) involving 30 participants of each learning style (visual/verbal, active/reflective). The results of the study suggested that the module is effective for visual, active, reflective and not for verbal learners. The researchers also compared the module effectiveness

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according to gender. The verbal and reflective modules were effective for female learners and not for male learners. The module was later extended to other science subjects such as Biology and Chemistry and further will be implemented in a rural school in Negeri Sembilan. Before implementing thePTechLS modules, a needs analysis was conducted to analyse the needs for a PTechLS among the rural school students in the context of the study. The Aim of Research The aim of this research is to analyse the needs of the PTechLS among the students in the rural school in the context of the study. In order to achieve this aim, the researcher set two research objectives. The first objective is to identify the learning styles of the students in the rural school in the context of the study. The second objective of this research is to identify the computer skills and usage of the students in the rural school in the context of the study. This study seeks to answer the following research questions: •

What are the learning styles of the students in the rural school in the context of the study?

•

What are the technology tools which students in the rural school in the context of the study accessed?

•

What are the technology skills of the students in the rural school in the context of the study? Scope and Limitations

In this study, a sample size of 47 students at a rural secondary school in the state of Negeri Sembilan was selected as the population reflected the proportion of the Malay community in Malaysia. INSTRUMENTS

Two instruments were used in this study: First is the Index of Learning Styles (ILS) (Felder & Silverman, 1988) for identifying the students’ learning styles. The survey instrument used was the Learning Style Index (LSI) developed by Felder and Soloman (1988) which had been translated into Bahasa Malaysia by Nabihah Badar and Saedah Siraj (2005) and administrated to 120 Form Four students in the Klang district. The instrument has a Cronbach alpha reliability score of .72. The second instrument is the Technology Skills and Usage Questionnaire (TechSU) which covers the students’ access to technology equipment; the skills and usage of the students related to technology; and the perception of the use of computers and mobile phones for learning. The TechSU questionaire was adapted from the Computer Skills and Usage Questionnaire originally designed to determine teachers’ and students’ skills and use of computers based on the Smart School Teachers’ Training Curriculum, Information Technology and Skills Curriculum and National Educational Technology Standards (Norizan Ahmad, 2005). The TechSU questionnaire took into account the latest trends and progress of technology as in the National Educational Technology Standards for Students (NETSS) (International Society for Technology in Education, 2005). The instrument used in this study was validated by three experts in education technology and had a high Cronbach alpha coefficient of .882 on the items for technology skills usage, and perceptions of use of technology in learning. The responses to the items were on a Likert scale of 1 to 4 ranging from ‘never doing a particular item’; to ‘frequently used, which is equivalent to using more than once a week’ for frequency of use; and another scale of 1 to 4 ranging from ‘no knowledge of a particular item’; to ‘feels that the response is very true’ for perception on the use of technology.

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The Malaysian Online Journal of Educational Technology RESULTS AND DISCUSSION

The Learning Style of the Students Table 1 displays the findings on the rural students’ preferred learning styles. Table 1: The learning style of the students in the rural school Learning Style 4ST (n = 47)

Active

Reflective

Sensing

Intuitive

Visual

Verbal

Sequential

Global

89.3%

10.7%

78.7%

21.3%

95.7%

4.3%

70.2%

29.8%

Findings from survey conducted among 47 participants based on the learning style instrument suggested that most of the students in the rural school in the context of the study were identified as active (89.3%), reflective (10.7%), sensing (78.7%), intuitive (21.3%), visual (95.7%), verbal (4.3%), sequential (70.2%) and global (29.8%). Table 2 shows the access to computers and mobile phones among the rural school students. Table 2: The access to computers and mobile phones among students in the rural school Equipment

No.

Percent (%)

Computer Mobile Phone

34 14

73.8 29.8

Mobile Phone/ Tablet with internet

20

42.6

The survey using the TechSU questionaire showed that a large number of students had access to computers (73.8%), and mobile devices such as mobile phones and tablets (72.4%). Many had access to both computers and mobile devices. Most of the mobile devices accessed by the students could acces the internet (42.6%).The students also had access to other technology equipment such as VCD/DVD players (57.4%) and other portable audio players such as MP3 or iPods (21.3%). Table 3 gives the frequency of basic technology operations among the rural students. Table 3: The mean frequency of use of basic technology operations among students in the rural school Basic technology operations Writing reports using wordprocessing software (eg. MS Word)

Mean 1.4681

S.D .77603

Input data in spreadsheet (eg. MS Excel)

1.4043

.64806

Drawing graphs using spreadsheet (eg. MS Excel)

1.4468

.80240

Printing documents with a printer

1.7447

.84617

Using a scanner or digital camera

2.0213

1.11295

Nota: 1: Never 2: Once in 2-3 months 3: Once a month 4: Once a week or more

The basic operations which were most used are scanning and using digital cameras (Mean = 2.0213. S.D.=1.11295) and printing documents using a printer (Mean = 1.7447, S.D. =0.84617) Next, Table 4 shows the frequency of yechnology usage for problem solving among the rural students in the study. 32

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Table 4: The mean frequency of use of technology for problem solving among students in the rural school Techology for problem solving

Mean

S.D

Obtaining information from CD-ROM (reference or courseware) Obtaining information from search engines such as Yahoo or Google Evaluating validity of information obtained from the internet Using graphical management software

1.4681

.77603

2.6383

1.30926

2.2340

1.25478

1.7660

1.06756

Using concept mapping software

1.4681

.77603

Nota: 1: Never 2: Once in 2-3 months 3 Once a month 4: Once a week or more

The technology most used for problem-solving is the internet as the students frequently use it to obtain information (Mean = 2.6383, S.D. =1.30926) and evaluate the validity of the information (Mean = 2.2340. S.D. =1.25478). Table 5 gives the mean for frequency in using communication tools among the rural students surveyed. Table 5: The mean frequency of use of communication tools among students in the rural school

Communication tool

Mean

S.D

Sending e-mails to other students or friends peers regarding school work

1.6596

1.00599

Receiving information regarding school work through e-mails from peers or experts

1.7447

1.09282

2.5106

1.19550

2.2979

1.17797

2.1064

1.22002

Sending and receiving information from peers or experts through on-line discussions (bulletin board, newsgroup, Yahoo messenger, blogging) Sharing information with peers or experts through online discussions (bulletin board, newsgroup, Yahoo messenger) Having discussions and exchange of data among peers and experts through on-line discussions

Nota: 1: Never 2: Once in 2-3 months 3 Once a month 4: Once a week or more

Communication tools are used for sending and receiving information (Mean = 2.5106, S.D. =1.19550), sharing information (Mean = 2.2979, S.D. = 1.17797) and discuss among peers and experts (Mean = 2.1064. S.D.=1.2002). The students frequently use basic technology tools such as scanners, digital cameras and printers as well as search engines. In addition, communication tools for sharing information and discussions were also frequently used. The data indicates that social applications which provided for interaction and communication is frequently used among students in the rural school. This is further verified as a majority of students were able to access computers and have mobile devices which could access the internet.

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Hence, there is a possibility of employing tools for communication online for instruction. These tools could be used to cater to specific learning styles. Communication tools which can be employed for specific learning styles, according to experts in a Delphi study indicate the following: Web quests, and microblogs such as Facebook and Twitter can be used as digital resources for active learners, while courseware, audios and video clips for reflective learners (Norlidah Alias, DeWitt, & Saedah Siraj, 2013). Visual learners prefer video clips, courseware and social media as learning resources, while verbal learners in addition prefer video conferencing, webquest, audio recordings, and specifically Facebook and Twitter as social media (Norlidah Alias et al., 2013). Most of the learners use social media, and access the internet for resources. This is reflected by the technology tools used most frequently by the learners, namely communication tools and the internet. IMPLICATION AND CONCLUSIONS

This paper has described an effort to identify the needs of PTechLS modules among students in the Malaysian rural secondary educational setting by identifying the students employing the Isman model. The needs were addressed by identifying the learning styles, the computer skills and usage of the students in the rural school in the context of the study. From the learning style instrument, most of the students were identified as visual (95.7%), sensing (78.7%), active (89.3%) and sequential (70.2%). The computer skills and usage questionnaire shows that students in the selected rural school have access to technology and are already using it for learning and social interaction using the internet. Therefore, the researchers suggest the implementation of PTechLS modules among rural Malaysian secondary schools. The impact of the project will be that matching the learning style of the student to the activities using the appropriate technology tools will benefit the students. During the project implementation, the teachers were made aware of the concept of learning styles and specifically the preferred learning styles of students in their classes. Discussion was conducted among the teachers on how to address the different learning styles during face to face activities in the classroom. This awareness will assist teachers in designing activities which will address individual learning styles. The impact of this project will be on the implementation of technology use for the apporpriate learning style. The students will be able to utilise ICT for learning according to their individual learning style. This will be conducted in the next phase of the study. ACKNOWLEDGEMENT

Funding of this research work is generously supported by the Knowledge Transfer Grant (KTP072012B), Ministry of Education Malaya, Malaysia. REFERENCES Aviles, R. M. H., & Moreno, A. H. (2010). Creating the conditions for educational change: Learning styles and gender. International Journal of Learning and Change, 4(3), 252-262. Dunn, R. (1990). Understanding the Dunn and Dunn Learning Styles Model and the need for individual diagnosis and prescription. Reading, Writing and Learning Disabilities, 6, 223-247. Franzoni, A. N., & Assar, S. (2009). Student Learning Styles Adaptation Method based on teaching strategies and electronic media. Educational Technology & Society, 12(4), 15-29.

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Graf, S., Kinshuk, & Liu, T.-C. (2009). Supporting teachers in identifying students’ learning style in Learning Management System: An automatic student modelling approach. Educational Technology & Society, 12(4), 3-14. Hein, T. L. (1997). Digital video, learning styles and students understanding of kinematics graph. Doctoral thesis, Kansas State University. Larkin-Hein, T., & Budny, D. D. (2001). Research on learning style: Applications in the physics and engineering classrooms. IEEE Transactions on Education, 44(3), 276–281. Lau, W. W. F, & Yuen, H. K. (2010). Promoting conceptual change of learning sorting algorithm through the diagnosis of mental models: The effects of gender and learning styles. Computers & Education, 54, 275-288. Norlidah Alias. (2010). Pembangunan Modul Pedagogi Berasaskan Teknologi dan Gaya Pembelajaran Felder-Silverman Kurikulum Fizik Sekolah Menengah. [Development of pedagogical Module Based on Technology and Learning Style for Secondary School Physics Curriculum] Unpublished doctoral thesis, University of Malaya. Norlidah Alias & Saeddah Siraj. (2012, October). Design and development of Physics Module based on Learning Style and Appropriate Technology by employing Isman Instructional Design Model. TOJET: The Turkish Online Journal of Educational Technology, 12(4). Norlidah Alias, Dorothy DeWitt & Saedah Siraj (2013). Development of Science Pedagogical Module based on learning styles and technology. Kuala Lumpur: Pearson Malaysia. Naimie, Z., Siraj, S., Ahmad Abuzaid, R., & Shagholi, R. (2010, October). Hypothesized Learners’ Technology Preferences based on Learning Style Dimensions. TOJET: The Turkish Online Journal of Educational Technology, 9(4). Ross, C. M., & Lukow, J. E. (2004). Are learning styles a good predictor for integrating instructional technology into a curriculum? Journal of Scholarship of Teaching and Learning 4(1). Retrieved from http://www.iupui.edu/~josotl/2004vol4no1/RossLukow.pdf Saedah Siraj, & Nabihah Badar. (2005). Malaysian secondary students’ preference in learning Physics: Implication to the teaching strategies. The International Journal of Learning, 10, 3559-3572. Retrieved from http://www.Learning-Journal.com Saeed, N., Yang, Y., & Sinnapan, S. (2009). Emerging Web technologies in Higher Education. A case of incorporating blogs, podcasts and social bookmarks in a Web Programming Course based on students’ learning styles and technology preferences. Educational Technology & Society, 12(4), 98-109. Yang, F.-Y., & Tsai, C.-C. (2008). Investigating university student preferences and beliefs about learning in the Web-based context. Computers and Education, 50(4), 1284-1303. Tsoi, M. F., Goh, N. K., & Chia, L. S. (2005). Multimedia learning design pedagogy: A hybrid learning model. US-China Education Review, 2(9), 59-62.

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The Effect of Field Specialization Variation on Technological Pedagogical Content Knowledge (TPACK) Among Malaysian TVET Instructors Junnaina Husin Chua [1], Hazri Jamil [2]

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[1] [email protected] Arrumugam Pillai Industrial Training Institute Nibong Tebal, Penang, Malaysia [2] [email protected] School of Education Studies, Universiti Sains Malaysia, Penang, Malaysia

ABSTRACT Technological Knowledge is directly related to productivity, enhanced performance and service quality. Technology integration in the Technical and Vocational Education and Training (TVET) curriculum is expected due to high application of technical knowledge and technology applications. TPACK is a professional knowledge framework that gives flexibility and provides dynamic strategies to TVET instructors to enhance and therefore improve the teaching and learning process. This study analyzed the impact of Field Specialization variation on the level of knowledge gained. It is found that regardless of the large variation and multiple perspectives of specialization existing among TVET instructors, specialization is not a factor that influenced the level of knowledge gained. Therefore, this study contributes to the understanding that there are other factors that may influence the knowledge gained among Malaysian TVET instructors.

Keywords:

TPACK; professional knowledge; specialization; technology integration; technological knowledge.

INTRODUCTION The level of knowledge gained by instructors is an element used to measure the quality and effectiveness of training provided by TVET institutions (Scheerens, Luyten, & Ravens, 2011). Knowledge contributes to the differences occurred in actions taken and decisions made (Clarke & Hollingsworth, 2002) which was built up from the experiences, belief and the culture around them. According to Coggshall, Behrstock-Sherratt, Drill, Menon, and Cushing (2011), 90% of teachers across different generations view that technology use can assist teaching; however, only half of the teachers felt that technology use in teaching and learning is very effective. Studies have found that the level of knowledge gained varied from one cohort to another. Shaharom Noordin and Faridah Sapiee (2010) found that physics pre-service teachers had moderate level of knowledge while Yeo Kee Jiar and Siti Sara Abdul Halim (2010) found student teachers’ knowledge to be at high level. Others found that the level of knowledge among school teachers was unsatisfactory (Ehlers, 2010; Richards, 2010). Female teachers were reported to dominate good pedagogical knowledge but have difficulty in gaining technology knowledge as compared to male teachers (Low, 1999; Shaharom Noordin & Faridah Sapiee, 2010). Therefore, in order to address quality problems effectively, a good understanding on the competency level of the cohort studied is required. The variation in the level of knowledge gained by different cohorts leads to a question on what affects the variation. Is field of specialization one of the critical factors? What about other demographical aspects such as age or gender?

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Professional Knowledge In Technical and Vocational Education and Training (TVET), besides the standard teaching and learning professional knowledge, instructors also need to acquire specialized knowledge related to teaching and learning of a particular job title. It covers the concepts behind each theoretical and practical application as well as the knowledge on how to expand existing knowledge to create a new one (Muttaqin, 2007). The teaching and learning strategy chosen should account for any recent changes and current practices by industry as well as students’ knowledge background. In order to stay relevant and competitive with the explosion of knowledge across national boundaries, TVET instructors must acquire multiple specializations, engage in high level of thinking and participate in a transformative learning processes (Mishra, Koehler, & Henriksen, 2011). To do that, a dynamic framework on professional knowledge is needed to enable new knowledge formation and provide flexible teaching strategies. As knowledge evolves rapidly in line with technology advancement, instructors need to manipulate existing knowledge and continuously develop new knowledge (Niess, 2011). Technology Integration Technology in education involves the use of digital or analog equipment (Plair, 2010) as well as the use of information and communications technology (ICT) such as animation and simulation software (Khan, 2011) to facilitate teaching and learning process implementation as well as daily tasks. According to Ertmer and Ottenbreit-Leftwich (2010), the new definition of effective teaching is the one that uses relevant ICT resources as a meaningful pedagogical tool to help students understand. Having the capability to integrate technology in teaching and learning will help in delivering effective teaching (Hairani, 2006; Md. Johan Othman & Dinyati Lukman, 2011; Sidhu & Kang, 2010) Technology used in TVET is divided into two main categories, namely the standard technology and the specific technology. Standard technology refers to analog equipment such as books, chalk, chalkboard, or digital devices such as internet, computer hardware and software as well as digital media (Lux, 2010; Mishra & Koehler, 2006). Specific technology refers to the equipment and machines used specifically to perform a certain job scope (Guthrie, Harris, Simons, & Karmel, 2009). An example of specific technology will be the knowledge on offset printing machine for printing work or how to operate the splicing machine for a fiber optic installation module. Mordini (2007) in Gupta, Fischer, and Frewer (2011) stated that technology has a social function that is capable of transforming society both through the manipulation of physical or symbolic objects and acculturation. Technology functions as the bridging gap between theory and practical teaching (Eidsheim, 2009). This view is supported by Madden (2012) who found that smart phone usage can improve content delivery and students’ focus toward learning. Technology enables instructors to create transformative learning by applying constructive learning and integrating technology into their teaching. Technology can be applied in the analysis process or in decision-making as well as in enhancing teaching techniques (Means, Padilla, DeBarger, & Bakia, 2009). Technology is often associated with increased productivity. Similarly in education, technology acts as an enabler to help instructors to perform comprehensive teaching and promote brain-based learning (Knight & Elliott, 2009). Technological application is found effective in increasing student motivation and understanding (Buzan, 2006; Jensen, 2000; Knight & Elliott, 2009). Technological Pedagogical Content Knowledge (TPACK) Theories and models on professional knowledge are very broad and had been studied from various perspectives (Ohi, 2007). The nearest to the TVET instructor’s profession is the Technological Pedagogical Content Knowledge (TPACK) framework proposed by Mishra and Koehler (2006). TPACK is a specialized knowledge referring to the knowledge and ability to integrate technology based on certain pedagogical strategy to teach a specific content knowledge. It is an expansion of the professional knowledge framework introduced by Shulman (Shulman, 1986, 1987). According to Shulman (1986), mastering Content Knowledge (CK) alone does not ensure effective teaching. Shulman listed seven fundamental types of knowledge required by each teaching personnel (Shulman, 1987; Tengku Zawawi Tengku Zainal, Ramlee Mustapha, & Abdul Razak Habib, 2009) and Pedagogical Content Knowledge (PCK) is one of them. PCK is a specific knowledge where Content Knowledge is matched with Pedagogical Knowledge (PK) (Shulman, 1987). Recognizing the role and importance of technology applications in education, Mishra and Koehler (2006, 2008) introduced a conceptual framework on TPACK by adding the technological knowledge elements to the Shulman (1987) PCK framework. This framework was agreed by many other researchers such as Knight and Elliott (2009) and Shin et al. (2009) which states Content Knowledge alone is not enough to help TVET instructors prepare students for the future. TPACK is a framework that allows instructors to carry out the teaching and learning process effectively through technology integration (Sahin, 2011; Schmidt et al., 2010).

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Mishra and Koehler (2006) integrated the TK dimension into the PCK model (Shulman, 1987b); they introduced four other new fundamental knowledge dimensions, namely: (a) Technological Knowledge (TK), (b) Technological Pedagogical Knowledge (TPK), (c) Technological Content Knowledge (TCK), and (d) Technological Pedagogical Content Knowledge (TPACK) in addition to the existing three professional knowledge types (PK, CK, and PCK) proposed by Shulman (1987). TPACK in Teaching and Learning TPACK provides instructors with strategies to match learning content with specific teaching techniques using appropriate technology (Archambault & Crippen, 2009; Koh, Chai, & Tsai, 2010). As in other professions, the ability to use technology to increase teaching and learning effectiveness is essential and expected (Ertmer & Ottenbreit-Leftwich, 2010). Technology applications in the classroom are now a necessity and accordingly, all instructors are expected to acquire technological knowledge and apply technology integration in the classroom. Guthrie et al. (2009) reported that the TVET teaching and learning process requires high usage of technology since the syllabuses were designed based on hands-on, conscious creation, and collaborative experience concepts. In addition, rapid technological development, increase in enrollment, and financial constraints had forced TVET institutions to switch to software based applications such as animation and simulation software usage to complete the teaching and learning process (Eidsheim, 2009). With TPACK, instructors are able to re-evaluate the purpose of learning and make students think outside the box (Mishra et al., 2011). This particular transition is important since the current group of students comes from the “Net-Generation” who are digitally literate and fond of using ICT applications (Pittman, McLaughlin, & Bracey-Sutton, 2008; Short & Reeves, 2009). The so called “Net-generation” was also identified to have short attention span and technology has been identified to have the capability to boost their concentration level in the classroom (Mayes, Calhoun, Bixler, & Zimmerman, 2009). Hence, TPACK could be the bridging tool to reduce the existing digital divide between instructors and students besides improving TVET effectiveness (Jamalludin Harun & Nur Khairul Safrah Jamri, 2010). TPACK also has been identified as an agent of multidisciplinary integration (Francis, 2010). Coggshall et al. (2011) also reported that teachers from the Y generation cohort (those born between 1977 and 1995) are the most knowledgeable teachers compared to the previous generation due to their high interest in technology. However, the same study showed that the Y Generation teachers still feel hesitant to use technology in their profession. This situation was also detected by other researchers (Johari Hassan & Fazliana Rashida Abdul Rahman, 2011; M. Al-Muz-Zammil & Abd. Muezzam Shah, 2010; Wahid, 2010) who found that although the use of ICT for personal purposes and shared digital literacy among teachers and prospective teachers is high, the rate of use in the learning process, however, is still at a moderate level or lower. A study conducted in Australia on prospective teachers found the knowledge of technology perception of 345 final year students at two universities in the whole Queensland was still at a low level even though the percentage of computer ownership (99.4%) and access to broadband internet (96.5 %) was high. A similar finding was reported by Ertmer and Ottenbreit-Leftwich (2010) in the U.S. whereby 88% of teachers use technology in administrative work and 93% of them use technology to communicate, but the use of technology in teaching and learning is still low. One study in Malaysia had also found that although the level of ICT facilities provided was high, the level of ICT use by instructors in teaching and learning was still at a low or moderate level depending on the level of study (Johari Hassan & Fazliana Rashida Abdul Rahman, 2011; Md. Johan Othman & Dinyati Lukman, 2011; Naser Jamil, Leong, & Fong, 2010).

Field of Specialization Field of specializations in TVET normally was determined based on job titles. Its database keeps on expanding in line with economic growth and technological advancement. According to the Malaysian Standard Classification of Occupations (MASCO), there are ten main groups of occupation classification sub-divided into 4310 occupation codes (Department of Labour, 2008). The National Occupational Skill Standard (NOSS) developed by the Department of Skill Development was clustered into 29 main sectors covering 1310 job titles (Department of Skills Development, 2011). The competency of TVET graduates is assessed based on NOSS and currently available from Level 1 to level 5. Extant theory and empirical evidence showed that specialization is capable of enhancing knowledge growth (Carnabuci & Bruggeman, 2009) and specialization can be seen either as a property or as a process. Specialization as a property in the technological domain refers to the combination of ideas forming a uniform entity of related knowledge. Alternatively, specialization as a process describes the expansion of knowledge from a related area. Andersson and Ejermo (2008) supported the statement by Carnabuci and Bruggeman and mentioned that knowledge specialization is 38

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determined by the technology field or domain of the knowledge itself. Due to the existing variation and the massive knowledge specialization, an understanding to what extent the variation affects the professional knowledge gained is needed. One needs to know whether field of specialization is one of the critical success factors affecting TVET quality (Bhuasiri, Xaymoungkhoun, Zo, Rho, & Ciganek, 2012; Pittman et al., 2008).

Purpose of Study The guiding research question which this study explored was: What is the level of knowledge (TPACK) gained by TVET instructor currently and how does TPACK level vary across field of specialization? Therefore, this study attempted to measure the current status of the professional knowledge gained and its variation based on field of specialization for the sample of TVET instructors who are different from previous samples in terms of curriculum, specialization, qualification and the teaching and learning orientation. The findings are expected to enable better understanding of teacher thinking besides providing feedback to TVET instructors on their current performance.

METHODOLOGY An exploratory mixed method study was carried out using survey and interview. The level of professional knowledge gained and the emerging factors that influence instructor knowledge were obtained using questionnaires while in-depth understanding regarding the factors influencing professional knowledge was obtained via semistructured interview. A total of 300 instructors (220 male and 80 female) from nine TVET institutions were chosen based on random stratified proportional sampling method. The stratification was made based on specialization cluster and level of instruction namely certificates, diploma or advanced diploma. The survey used was adopted from Lux (2010), Schmidt et al. (2010), Nurhayati (2006) and Siti Atiqah (2008) and then adapted to suit the Malaysian TVET system. It was sectioned into three main aspects namely the demographical information, professional knowledge and the factors influencing professional knowledge. Demographical information included, among others, age, gender, education level and field of specialization. The field of specialization was studied based on six main clusters offered in the TVET institution studied namely (a) Mechanical and Production (MP), (b) Electrical and Electronics (EE), (c) Civil and Building (CB), (d) Printing (P), (e) Information and Communications Technology (ICT), and (f) Non-metal Construction (NMC). Professional knowledge was measured using 29 questions on a Likert scale using the TPACK model (α = .93) covering all seven components of knowledge as proposed by Koehler, Shin, and Mishra (2012). The overall professional knowledge was measured by taking the mean of all seven dimensions as suggested by Lux (2010). Personal and organizational factors were evaluated through 59 questions based on three constructs each (α = .86). Content validity and pilot study were carried out to ensure the data obtained are precise and reliable. In-depth study was conducted using a semi-structured questionnaire on three respondents with teaching experience of more than 20 years from the same specialization area. The interview sessions were recorded, transcribed, and cross checked. The findings of analysis were peer reviewed to confirm the themes identified.

RESULTS AND DISCUSSION Descriptive analysis found that the level of professional knowledge among TVET instructors was at a moderate level (M = 3.16, SD = 0.38). Even though the difference was not significant (t(298) = 1.60, p = .11), an analysis done on all seven TPACK domains indicated that male instructors gained higher knowledge compared to female instructors. Field of specialization was not a contributing factor to the variation in professional knowledge level (F(5,300) = 0.73, p = .60) among TVET instructors in Malaysia. This information could be used to plan better professional development programs for the TVET instructors. The finding on TPACK level indicated that even though the pedagogical aspect was not emphasized in preparing novice instructors as claimed by Ehlers (2010), the level of professional knowledge among TVET instructors is still acceptable. Consequently, in-depth study needs to be done to investigate why the skill gap among TVET graduates still exists even when the instructor level of knowledge is at an acceptable level. As mentioned by Fitz-Gibbon and Kochan (2000) as well as Scheerens (2000) an effective training program depends on multiple aspects at input, process and output levels. The knowledge gap existing between genders as report by Shaharom Noordin and Faridah Sapiee (2010) and Coleman, Atkinson, and Thrasher (2011) did not exist in this cohort studied. From the interview responses, it was gathered that pedagogical aspect was considered less important in TVET

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teaching and learning by the instructors since the curriculum involves a lot of machine operations and hands- on activities. Technological knowledge both on standard and specific technologies was said to be more important than pedagogical knowledge. Analysis on the sources of knowledge revealed that professional knowledge development is best done through practical activities either via On-Job Training or Off-Job Training. As suggested by Guthrie et al. (2009), respondents to this study also agreed that a good relationship with the related industries enables TVET instructor to expand their knowledge specifically on innovation capability, organizational culture and actual work operations. Table 1: One Way ANOVA Analysis a)

Specialization

N

Mean

MP EE CB P NMC ICT Total

122 123 6 5 8 36 300

3.21 3.13 3.03 3.08 3.12 3.15 3.16

Descriptive

Standard Deviation 0.42 0.38 0.22 0.33 0.30 0.35 0.385 b)

Sum of Squares Type III

Standard Error 0.035 0.035 0.16 0.17 0.14 0.064

95% Confidence Lower Upper 3.13 3.27 3.06 3.19 2.72 3.34 2.74 3.42 2.85 3.39 3.02 3.27

ANOVA

Df

Mean Square

F

Sig.

0.73

.60

Specialization

0.545

5

0.11

Error TOTAL

43.74 44.29

294 299

0.15

Another possible explanation for the result obtained could be the limitation of the data collection method. The fact that 66% (n = 201) of the respondents were aged between 18-35 years might have contributed to the moderate level of TPACK since this later generation is known to be more technology savvy (Becker, Fleming, & Keijsers, 2012). Yang and Chen (2010) also reported that digital technology is capable of reducing the gender knowledge gap. Areas of specialization used in this study were too narrow and only focused on manufacturing clusters offered in the TVET institutions studied. In addition, the majority of the instructors (N = 242; 80.6%) in this study came from Mechanical and Production (MP) as well as Electrical and Electronics (EE) clusters. Therefore, the result might be influenced by the dominance of EE instructors’ TPACK level. As mentioned by Carnabuci and Bruggeman (2009), knowledge specialization in this study only considered specialization as a property and no consideration was made on specialization as a process.

CONCLUSION The professional knowledge of TVET instructors was found to be at satisfactory level and therefore eliminated the presumption that low quality of the Malaysian TVET system was caused by low instructor knowledge. The research findings suggested that other factors might influence the professional knowledge of TVET instructors in Malaysia and the variation in specialization field does not influence instructors’ capability which was claimed to contribute to low performance of TVET graduates. Further investigation should be carried out to identify what other major factors influence the TPACK level among TVET instructors in Malaysia.

REFERENCES Andersson, M., & Ejermo, O. (2008). Technology specialization and the magnitude and quality of exports. Economics of Innovation and New Technology, 17(4), 355-375. [doi: 10.1080/10438590701279714].

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Archambault, L., & Crippen, K. (2009). Examining TPACK Among K-12 Online Distance Educators in the United States. Contemporary Issues in Technology and Teacher Education, 9(1), 71-88. Becker, K., Fleming, J., & Keijsers, W. (2012). E-learning: Ageing workforce versus technology-savvy generation. Education + Training, 54(5), 385 - 400. Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J. J., & Ciganek, A. P. (2012). Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty. Computers & Education, 58(2), 843-855. [doi: 10.1016/j.compedu.2011.10.010] Buzan, T. (2006). Use your head. London, UK: BBC Active. Carnabuci, G., & Bruggeman, J. (2009). Knowledge specialization, knowledge brokerage and the uneven growth of technology Domains. Social Forces, 88(2), 607-641. Clarke, D., & Hollingsworth, H. (2002). Elaborating a model of teacher professional growth. Teaching and Teacher Education, 18(8), 947-967. [doi: 10.1016/S0742-051X(02)00053-7] Coggshall, J. G., Behrstock-Sherratt, E., Drill, K., Menon, R., & Cushing, E. (2011, April). Workplaces that support high-performing teaching and learning: Insights from Generation Y teachers. [Report] American Institutes for Research & American Federation of Teachers. Coleman, P., Atkinson, J. K., & Thrasher, E. (2011). A study of the gender differences on spreadsheet grades for undergraduate students. Journal of Instructional Pedagogies, 5, 1-9. Department of Labour. (2008). Malaysian Standard Classification of Occupation. Retrieved from http://static.jobsmalaysia.gov.my/html/jobsm/masco/ms/Prinsip-pengelasan-pekerjaan.pdf Department of Skills Development. (2011). National Occupational Skill Standard (NOSS) registry. Putrajaya: Author. Ehlers, M. (2010). City & Guilds Centre for Skills Development country report Malaysia. International Center for Technical and Vocational Education and Training (UNESCO-UNEVOC). Eidsheim, N. S. (2009). Desktop simulation: Towards a new strategy for arts technology education. Journal for Learning through the Arts, 5(1). Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255-284.

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Guthrie, H., Harris, R., Simons, M., & Karmel, T. (2009). Teaching for Technical and Vocational Education and Training (TVET). In L. J. Saha & A. G. Dworkin (Eds.), International handbook of research on teachers and teaching (Vol. 21, pp. 851-865). New York, NY: Springer. Hairani, R. (2006). Kesan Latihan Dalam Perkhidmatan ke atas kualiti pengajaran dan pembelajaran dalam bidang Teknik Dan Vokasional. Unpublished Master thesis, Kolej Universiti Tun Hussein Onn, Johor. Jamalludin Harun, & Nur Khairul Safrah Jamri. (2010). Kajian berkaitan pengaplikasian teori pembelajaran dalam pembangunan bahan pembelajaran digital di kalangan pelajar tahun akhir Fakulti Pendidikan. Repositori Universiti Teknologi Malaysia. Jensen, E. (2000). Brain-Based Learning: A reality check. Educational Leadership, 57(7), 76. Johari Hassan, & Fazliana Rashida Abdul Rahman. (2011). Penggunaan ICT dalam proses pengajaran dan pembelajaran di kalangan pendidik. Repositori Universiti Teknologi Malaysia, 1-9. Khan, S. (2011). New pedagogies on teaching Science with computer simulations. Journal of Science Education and Technology, 20(3), 215-232. Knight, J. A., & Elliott, J. F. (2009). TVET tacher education: A vision beyond tradition. Journal of Technical Education and Training, 1, 73-83. Koehler, M. J., Shin, T. S., & Mishra, P. (2012). How do we measure TPACK? Let me count the ways. In R. N. Ronau, Rakes, C. R., & Niess, M. L. (Ed.), Educational technology, teacher knowledge, and classroom impact: A research handbook on frameworks and approaches (pp. 16-31). Hershey, PA: IGI Global. Koh, J. H. L., Chai, C. S., & Tsai, C. C. (2010). Examining the technological pedagogical content knowledge of Singapore pre-service teachers with a large-scale survey. Journal of Computer Assisted Learning, 26(6), 563-573. Low, S. N. (1999). Gender differences in computer readiness among Smart School teachers. Serdang: Universiti Putra Malaysia. Lux, N. J. (2010). Assessing Technological Pedagogical Content Knowledge. Unpublished 3430401, Boston University, MA. M.Al-Muz-Zammil, Y., & Abd. Muezzam Shah, A. (2010). Penggunaan ICT Dalam Kalangan Guru Pelatih Kemahiran Hidup Fakulti Pendidikan, UTM. Retrieved from http://eprints.utm.my/10142/ Madden, L. (2012). Cell phones transform a Science Methods course. The Educational Forum, 76(4), 442-445. [doi: 10.1080/00131725.2012.707571].

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Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing data-informed decision making in schools: Teacher access, supports and use. Report prepared for U.S. Department of Education, Office of Planning, Evaluation and Policy Development. Prepared by SRI International, Menlo Park, CA. Mishra, P., & Koehler, M. J. (2006). Technological Pedagogical Content Knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. Mishra, P., & Koehler, M. J. (2008). Introducing Technological Pedagogical Content Knowledge. Paper presented at the Annual Meeting of the American Educational Research Association. Retrieved from http://punya.educ.msu.edu/presentations/AERA2008/MishraKoehler_AERA2008.pdf Mishra, P., Koehler, M. J., & Henriksen, D. (2011). The Seven Trans-Disciplinary Habits of Mind: Extending the TPACK Framework Towards 21 st Century learning. Educational Technology, 11(2), 22-28. Muttaqin, A. Z. (2007). Pengajaran Mekatronika menggunakan gambar animasi Makromedia Flash di jurusan Teknik Mesin. Paper presented at the Seminar Nasional Aplikasi Teknologi Informasi 2007. Naser Jamil, A.-Z., Leong, L. M., & Fong, S. F. (2010). Teachers’ attitudes and levels of technology use in classrooms: The case of Jordan schools. International Education Studies, 2(3), 211-218. Niess, M. L. (2011). Investigating TPACK: Knowledge growth in teaching with technology. Journal of Educational Computing Research 44(3), 299-317. Nurhayati, B. (2006). Faktor-Faktor yang mempengaruhi profesionalisme dan kinerja guru Biologi di SMAN Kota Makassar Sulawesi Selatan. Mimbar Pendidikan, 4(25), 64-70. Ohi, S. (2007). Teacher’s professional knowledge and the teaching of reading in the early years. Australian Journal of Teacher Education, 1-14. Pittman, J., McLaughlin, R. T., & Bracey-Sutton, B. (2008). Critical Success Factors in Moving Toward Digital Equity. In J. Voogt & G. Knezek (Eds.), International handbook of Information Technology in primary and secondary education (Vol. 20, pp. 803-817). New York, NY: Springer. Plair, S. K. (2010). On becoming technology fluent: Digital classrooms and middle aged teachers. Unpublished 3435097, Michigan State University. Richards, J. C. (2010). Competence and performance in language teaching. RELC Journal, 41(2), 101-122. Sahin, I. (2011). Development of Survey Of Technological Pedagogical And Content Knowledge (TPACK). The Turkish Online Journal of Educational Technology, 10(1), 97-105. Scheerens, J. (2000). Improving school effectiveness. Paris, France: UNESCO. Scheerens, J., Luyten, H., & Ravens, J. (2011). Perspectives on educational quality. In J. Scheerens, H. Luyten & J. van Ravens (Eds.), Perspectives on educational quality (Vol. 1, pp. 3-33). Houten, The Netherlands: Springer. Schmidt, D., Baran, E., Thompson, A., Mishra, P., Koehler, M., & Tae, S. S. (2010). Technological Pedagogical Content Knowledge (TPACK): The development and validation of an assessment instrument for preservice teachers. Journal of Research on Technology in Education, 42(2), 123-149.

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Shaharom Noordin, & Faridah Sapiee. (2010). Tahap pencapaian pengetahuan pedagogi dan kandungan dalam kalangan bakal guru Fizik. Repositori Universiti Teknologi Malaysia. Shin, T. S., Koehler, M. J., Mishra, P., Schmidt, D. A., Baran, E., & Thompson, A. D. (2009). Changing Technological Pedagogical Content Knowledge (TPACK) through course experiences. In I. Gibson, R. Weber, K. McFerrin, R. Carlsen & D. A. Willis (Eds.), Society for Information Technology and Teacher Education International Conference book (pp. 4152–4156). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE). Short, J. C., & Reeves, T. C. (2009). The graphic novel: A "Cool'' format for communicating to Generation Y. Business Communication Quarterly, 72(4). Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4-14. Shulman, L. S. (1987). Knowledge and teaching: Foundations of the New Reform. Harvard Educational Review, 57(1), 1. Sidhu, M. S., & Kang, L. C. (2010). Emerging trends and technologies for enhancing Engineering education: An overview. International Journal of Information and Communication Technology Education (IJICTE), 6(4), 38-48. Siti Atiqah, S. (2008). Faktor yang mempengaruhi keberkesanan pengajaran dan pembelajaran di dalam Bengkel Vokasional di dua buah Sekolah Menengah Teknik di Negeri Sembilan. Universiti Teknologi Malaysia. Tengku Zawawi Tengku Zainal, Ramlee Mustapha, & Abdul Razak Habib. (2009). Pengetahuan Pedagogi Isi Kandungan Guru Matematik Bagi tajuk Pecahan: Kajian kes di sekolah rendah. Jurnal Pendidikan Malaysia, 34(1), 131-153 Wahid, H. (2010, 27/09/2010). 'Virus' gagalkan program ICT sekolah. Utusan Malaysia Online. Yang, J. C., & Chen, S. Y. (2010). Effects of gender differences and spatial abilities within a Digital Pentominoes game. Computers & Education, 55(3), 1220-1233. Yeo Kee Jiar, & Siti Sara Abdul Halim. (2010). Tahap pengetahuan pedagogi pelajar tahun akhir, Fakulti Pendidikan dalam pengajaran dan pembelajaran. Repositori Universiti Teknologi Malaysia, 1-8.

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