January 2012 Volume 15 Number 1 - Educational Technology ...

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Pintrich, P. R. & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. Puzziferro, M. (2008). Online technologies self-efficacy and self-regulated learning as predictors of final grade and satisfaction in college-level online courses. American Journal of Distance Education, 22(2), 72-89. Roca, J. C., Chiu, C. M., & Martinez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human-Computer Studies, 64(8), 683-696. Sass, D. A., & Smith, P. L. (2006). The effects of parceling unidimensional scales on structural parameter estimates in structural equation modeling. Structural Equation Modeling, 13(4), 566-586. Seok, I. (2008). Analyzing characters of the learning flow. Journal of Educational Technology, 24(1), 187-212. Shea, P., Li, C. S., & Pickett, A. (2006). A study of teaching presence and student sense of learning community in fully online and web-enhanced college courses. Internet and Higher Education, 9, 175-190. Shin, N. (2006). Online learner’s flow experience: An empirical study. British Journal of Educational Technology, 37 (5), 705- 720. Skaberg, Y. X., & Kimmel, J. R. (2003). Visitors’ flow experience while browsing a Web site: its measurement, contributing factors and consequences. Computers in Human Behavior, 20(3), 403-422. Skinner, E. A., Wellborn, J. G., & Connell, J. P. (1990). What it takes to do well in school and whether I’ve got it: The role of perceived control in children’s engagement and school achievement. Journal of Educational Psychology, 82, 22–32. Spielberger, C., & Vagg, P. (1995 ). Test anxiety: A transactional process model. In C. Spielberger & P. Vagg (Eds.), Test anxiety: Theory, assessment, and treatment (pp. 3-14). Washington, DC: Taylor & Francis. Spinath, B., Spinath. F. M., Harlaar, N., & Plomin, R. (2006). Predicting school achievement from general cognitive ability, selfperceived ability, and intrinsic value. Intelligence, 34(4), 363-374. Strother, J. B. (2002). An Assessment of the effectiveness of e-learning in corporate training programs. The International Review of Research in Open and Distance Learning, 3(1). http://www.icaap.org/iuicode?149.3.1.x Wigfield, A., & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30, 1-35. Wild, R.H., Griggs, K.A., & Downing, T. (2002). A framework for e-learning as a tool for knowledge management. Industrial Management & Data Systems, 102(7), 371-380. Wolters, C. (2004). Advancing achievement goal theory: Using goal structures and goal orientations to predict students’ motivation, cognition, and achievement. Journal of Educational Psychology, 96(2), 236-250. Zhang, D. & Nunamaker, J. F. (2003). Powering E-Learning In the New Millennium: An Overview of E-Learning and Enabling Technology. Information Systems Frontiers, 5(2), 207–218. Zimmerman, B. J., & Schunk, D. H. (1989). Self-regulated learning and academic achievement: Theory, research, and practice. New York: Springer Verlag. 325
Despotović-Zrakić, M., Marković, A., Bogdanović, Z., Barać, D., & Krčo, S. (2012). Providing Adaptivity in Moodle LMS Courses. Educational Technology & Society, 15 (1), 326–338. Providing Adaptivity in Moodle LMS Courses Marijana Despotović-Zrakić, Aleksandar Marković, Zorica Bogdanović * , Dušan Barać and Srdjan Krčo University of Belgrade, Faculty of Organizational Sciences, 11000 Belgrade, Jove Ilića 154, Serbia // maja@myelab.net // aca@fon.rs // zorica@myelab.net // dusan@myelab.net // krco@myelab.net * Corresponding author ABSTRACT In this paper, we describe an approach to providing adaptivity in e-education courses. The primary goal of the paper is to enhance an existing e-education system, namely Moodle LMS, by developing a method for creating adaptive courses, and to compare its effectiveness with non-adaptive education approach. First, we defined the basic requirements of a course adaptation and the steps required to implement it. Based on the collected data, we identified the characteristic groups of students and then classified all participating students into these groups. The final adaptation of courses was performed in the distance education system of the University of Belgrade, in the Laboratory for e-business, Faculty of Organizational Sciences. Findings of the experimental study showed that the students’ effectiveness and achievements in learning were higher when they attended courses adapted using the described method, in comparison to the non-adaptive e-learning courses. Keywords Adaptive e-learning systems, e-Learning course adaptation, Group personalization, Moodle LMS Introduction Learning management systems (LMS) are powerful integrated systems that support a number of activities performed by teachers and students during the e-learning process 00. Teachers use an LMS to develop web-based course notes and quizzes, to communicate with students and to monitor and grade student progress. Students use it for learning, communication, and collaboration. One of a widely used open-source systems for learning process management is Moodle (Modular Object Oriented Developmental Learning Environment) 0. Moodle LCMS is in use for the last five years in the teaching process at the University of Belgrade, in the Laboratory for e-business of the Faculty of Organizational Sciences. Each school year, more than 700 students use this system on all the study levels (undergraduate, master, and PhD studies). Elearning courses include the following areas: Internet technologies, E-business, Computer simulation, Mobile computing, and Internet marketing 0. In addition, we use the concept of blended learning throughout the whole teaching process 0. Adaptive e-learning systems In most cases, LMS users belong to heterogeneous groups with different individual, sometime even adverse, characteristics and needs. The adaptation of e-education systems to an individual or to a group based on their characteristics, expectations, knowledge, and preferences of the students is the next step in the evolution of the elearning systems. The lack of adaptive learning environments or an environment with adaptive features is partly due to the “one-sizefits-all” concept 000 in use today which presents the same static content to all students. Several studies suggested that this type of e-learning system organization were not successful 00. Therefore, the emphasis is moving towards learner-oriented platforms and putting student’s expectations, motivation, habits, learning styles, needs, etc. in the focus of interest 0. According to 0, an e-learning system is considered to be adaptive if it is capable of: monitoring the activities of its users; interpreting these on the basis of domain-specific models; inferring user requirements and preferences out of the interpreted activities, appropriately representing these in associated models; and, finally, acting upon the available knowledge on its users and the subject matter at hand, to dynamically facilitate the learning process. Since the system behavior adapts to a person, this kind of adaptation is also called personalization. Thus, adaptive e- ISSN 1436-4522 (online) and 1176-3647 (print). © International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. 326
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January 2012 Volume 15 Number 1
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Supporting Organizations Centre for
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Analyzing the Learning Process of a
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Angeli, C., & Valanides, N. (2012).
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ased on an integration and evaluati
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ased on the assumption that no sing
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Diagram type D thinking (shown in F
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collaborative task in terms of inte
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Table 4. Descriptive statistics for
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Hong, N. S., Jonassen, D. H., & McG
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commercial off-the-shelf digital ga
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The post-questionnaire was used aft
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Phase Table 1. Phases and activitie
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Research results Describing student
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Assessment results for each one of
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References Annetta, L.A., Minogue,
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Huang, T.-W. (2012). Aberrance Dete
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Detection power Typically, the rela
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Results Detection rates As seen in
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2 .00 .05 3 .02 .10 1 .60 .40 ECI 4
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their easily understandable devices
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Ifenthaler, D. (2012). Determining
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activities is assumed to be reflect
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that the problem representations (i
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deductive reasoning inventory (33 m
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HIMATT structural measures The part
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outcomes, r = .297, p < .01. Accord
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Azevedo, R. (2009). Theoretical, co
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Seel, N. M. (1999b). Educational se
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Initially this paper explores defin
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gamers were boys, and 35% were girl
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Teachers underwent eight hours of p
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A correlation analysis was used to
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playing the game. This would mean P
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Yu, F. Y. (2012). Any Effects of Di
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Considering that identity concealme
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In the nickname group, the student
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Attitudes toward assessors Percepti
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Liu, Z. F., Chiu, C. H., Lin, S. S.
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Appendix A: Attitudes toward Peer-A
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Appendix C: Perception toward the I
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Han, H., & Johnson, S. D. (2012). R
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Research participants The target po
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that its value of cross-loading is
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The canonical variable for the emot
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Literature in the field of online l
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Cornelius, R. R. (1996). The scienc
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Shih, W.-C., Tseng, S.-S., Yang, C.
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To provide personalized (e-)learnin
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Figure 3. An illustrative example o
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elementary schools. Thus they are b
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Table 5. Descriptive statistics for
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References Abowd, G. D., & Atkeson,
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Tseng, K.-H., Chang, C.-C., Lou, S.
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Figure 1. The five stages of KT (Gi
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The use of learning strategies, mon
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Additionally, the above two scales
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Figure 3. Canonical structure betwe
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highly positive perception of CM an
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Langan-Fox, J., Platania-Phung, C.,
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Gömleksiz, M. N. (2012). Elementar
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any statistically significant diffe
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consideration in an educational set
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conditions of a learning environmen
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Kahle, J. B. (1983). The disadvanta
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Appendix A Science and Technology S
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(Guuawardena, Nola, Wilson, Lopez-I
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Questionnaire on student satisfacti
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G4 53 (12.5) 48.9 (13.4) 0.39 G5 51
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across the five levels of knowledge
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Santhanam, R., Sasidharan, S., Webs
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This paper reports only the early e
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Figure 1 illustrates how the abovem
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teacher/instructor continues to dom
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The instructor plays a significant
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Data collection and data sources Fi
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y turning over the responsibility t
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Brophy, J. (1999). Toward a model o
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Lawanto, O., Santoso, H. B., & Liu,
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such as grades and evaluation by ot
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students’ interest, expectancy fo
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constructs that measure students’
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Bandura, A. (1978). Reflections on
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Lin, J. M.-C., & Liu, S.-F. (2012).
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Table 1. The MSWLogo commands learn
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other words, we had prepared a set
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lab. When she returned and found th
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Attitudes toward Programming and Co
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Table 5 successfully, it took four
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Kuter, S., Altinay Gazi, Z., & Alti
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not only the means for trainees’
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Data Collection Techniques and Anal
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organizations was highlighted by on
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provides the means for professional
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Kohonen, V. (2001). Towards experie
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As Rogers (1995) postulates in his
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Instrument and data collection Afte
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“Raising our computer skills in C
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teachers deploy ICT tools in langua
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Ma, W., Anderson, R., & Streith, K.
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APPENDIX A. Questionnaire for Dista
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consistency with other ideas, and a
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conceptions, justifying their belie
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each group. The experimental group
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specifically, instructional approac
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Table 3 shows that the mean frequen
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previous Web-based instructional le
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Millar, R., & Osborne, J.F. (1998).
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Several studies explore the roles t
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understand the content structures o
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teacher was not allowed to provide
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two coding schemes, as illustrated
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also very limited. Teachers and sof
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learning activities. British Journa
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2009; Bernard & Cathryn, 2006) or p
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In this study, the focus of the ins
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The student’s degree of mastery i
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The questionnaire for the acceptanc
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the t-test result, it is found that
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Scale Questionnaire item Mean S.D.
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Hwang, G. J. (2003). A conceptual m
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sharing, problem solving, and achie
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Virtual learning environment In the
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Class Table 1. The survey questions
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“…the distance and the lack of
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collaboration within EVS, thus prev
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Huang, T.-H., Liu, Y.-C., & Chang,
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y personalised context examples in
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Students’ Problem-Solving Guidanc
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Research Method Figure 8. The scree
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Table 4. Abstract of Pairwise Compa
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questions designed for the system h
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Lee, J. (2012). Patterns of Interac
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This study focused on online fora i
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Results and Discussion Due date-cen
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In addition, all groups developed 8
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future. Therefore, they had an oppo
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surprising that most interaction wa
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Pawan, F., Paulus, T. M., Yalcin, S
Page 279 and 280: systems by using 18 personalization
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Page 291 and 292: Finally, Figure 10(A) and Figure 10
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Page 295 and 296: mental models, are cognitive struct
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Page 299 and 300: Data analysis To test the model fit
Page 301 and 302: may be considered an effective inte
Page 303 and 304: Hsu, I.-C. (2012). Intelligent Disc
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Page 313 and 314: Similarly, the relation metadata of
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Page 317 and 318: References ADL. (2006). Sharable Co
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Page 321 and 322: Mediating effect of learning flow A
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Page 333 and 334: Learning is a cognitive activity th
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Page 341 and 342: Conclusion Conducted research showe
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Page 347 and 348: Reflection Types This study attempt
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Page 351 and 352: Results Learning Outcomes Pre- and
Page 353 and 354: Map Analysis in Transfer Test Figur
Page 355 and 356: Generic Prompts and Specific Prompt
Page 357 and 358: Biswas, G., Schwartz, D., Bransford
Page 359 and 360: Chen, Y.-H., Looi, C.-K., Lin, C.-P
Page 361 and 362: of correct response, answer until c
Page 363 and 364: Figure 7 shows a screenshot of one
Page 365 and 366: Collaboration Questionnaire results
Page 367 and 368: following diagrams. The double-arro
Page 369 and 370: “Most students were encouraged to
Page 371 and 372: Bangert-Drowns, R.L., Kulick, C. C.
Page 373 and 374: primary research questions. First,
Page 375 and 376: Individual learning Figure 2. Scree
Page 377 and 378: Experimental Tools This study emplo
Page 379 and 380: Table 3 shows that almost all items
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I usually engaged myself in listeni
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References Bloom, B. S. (Ed.). (195
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APPENDIX 2. Taxonomy for Informatio
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understand how we can effectively u
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5. Is there a relationship between
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correlation between teachers’ IWB
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Q7. IWB provides advantages to me t
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training regarding this topic. This
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Conclusion This study provides a so
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Smith, H. J., Higgins, S., Wall, K.
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