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

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We defined the following statistical hypotheses: Null hypothesis H1: There is no significant statistical difference in the pre-test results between the experimental and the control group Null hypothesis H2: There is no significant statistical difference in the post-test results between the experimental and the control group Null hypothesis H3: There is no significant statistical difference between the pre-test and the post-test results of the experimental group Null hypothesis H4: There is no significant statistical difference between the pre-test and the post-test results of the control group The comparative descriptive statistics is shown in Table 4. Table 4. Comparative descriptive statistics of pre-test and post-test results N Mean Std. Deviation Results Pre Post Pre Post Pre Post Experimental group 218 218 8.05 8.25 1.132 1.096 Control group 100 100 8.14 8.02 1.078 1.228 The results of the independent t-test showed that the difference between the experimental and control group results achieved in pre-test is not statistically significant (t=-0.706, df=316, p>0.05), which supports the null hypothesis H1. The results of the independent t-test also showed that difference between the experimental and control group results achieved in post-test is statistically significant (t=1.700, df=316, p
Conclusion Conducted research showed that adaptation of e-courses leads to better results. Proposed method for course adaptation is based on adapting presentation of content, communication methods, and organization of online activities to students’ learning styles and preferences. Analyses of e-learning system adaptivity have shown that cognitive characteristics of students, such as learning style, are of the greatest importance for successful adaptation. Described method for course adaptation is based on using data mining techniques to classify students into clusters with regards to Felder-Silverman learning styles model. Type of presentation of teaching materials had the highest impact on clustering and classification. E-learning courses were adapted using relations between Felder-Silverman’s learning style model and activities in Moodle learning management system. Research results proved that teaching resources and activities adapted to learning styles lead to significant improvement in learning results. Analysis of results showed that students who attended adapted online courses achieved better results than students who attended nonadapted online course. T-test showed that there was statistically significant difference in results achieved by students in experimental group on post-test compared to results of students in control group. Higher number of students passed the test, and they achieved higher grades. Analyses of satisfaction of students in experimental group showed that they had positive attitude towards adapted online course, and they thought that course materials and activities had been adapted with respect to their learning styles. One of the greatest advantages of described method for course adaptation is that it does not require programming new software. Adaptation can be performed without any programming knowledge, and teachers can create adapted courses by adjusting teaching materials and activities in Moodle LMS. The authors of this paper believe that this approach can easily be modified for application in other LMSs as well. The biggest disadvantage of the described approach is that it does not enable real time adaptation. Therefore, it is necessary for teachers to monitor students’ progress and move them to another cluster, if necessary. This type of adaptation requires more teachers’ effort in both creating adaptive course and exploitation. In this research, course personalization has been realized with respect to learning styles as only criterion. Future work will be directed toward providing further data about students with respect to different characteristics, such as pre-knowledge, expectations, etc. Future work will also be directed towards qualitative analysis of described method, as well as suitability for teaching other subjects and application in other learning management systems, Acknowledgement The authors of this paper are grateful to MNTRS for financial support grant no. 174031. References Aroyo, L., Dolog, P., Houben, G-J., Kravcik, M., Naeve, A., Nilsson, M., et al. (2006). Interoperability in Personalized Adaptive Learning. Journal of Educational Technology & Society, 9 (2), 4–18. Aydin, C.C., & Tirkes, G. (2010). Open source learning management systems in e-learning and Moodle. In Proceedings of IEEE EDUCON 2010 - IEEE Engineering Education 2010, Madrid, 14–16 April, 593–600. Brown, E., Cristea, A., Stewart, C., & Brailsford, T.J. (2005). Patterns in Authoring of Adaptive Educational Hypermedia: A Taxonomy of Learning Styles. Educational Technology and Society 8 (3), 77–90. Brusilovsky, P., & Miller, P. (2001). Course Delivery Systems for the Virtual University. In: Tschang, T., & Della Senta, T. (eds.) Access to Knowledge: New Information Technologies and the Emergence of the Virtual University (pp. 167–206). Amsterdam: Elsevier Science and International Association of Universities. Brusilovsky, P. (1996). Methods and Techniques of Adaptive Hypermedia. User Modeling and User-Adapted Interaction, Springer Verlag, 6(2-3), 87–129. Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User Adapted Interaction, Springer Verlag, 11 (1-2), 87–110. 336
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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
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systems by using 18 personalization
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Short-Term Sensory Memory is a temp
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with respect to articles by using a
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system also computes a review value
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DFL. d ( yi ) is the degree of mem
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Page 291 and 292: Finally, Figure 10(A) and Figure 10
Page 293 and 294: Appendix # Question Description Par
Page 295 and 296: mental models, are cognitive struct
Page 297 and 298: Based on the theoretical implicatio
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
Page 305 and 306: (Gradinarova, Zhelezov et al. 2006)
Page 307 and 308: RLO denotes a remote learning objec
Page 309 and 310: Figure 2. The flow-oriented LOFinde
Page 311 and 312: if x include with y, and y include
Page 313 and 314: Similarly, the relation metadata of
Page 315 and 316: (defrule student-advisor (triple (p
Page 317 and 318: References ADL. (2006). Sharable Co
Page 319 and 320: determinants of how people think, b
Page 321 and 322: Mediating effect of learning flow A
Page 323 and 324: The instrument used to measure lear
Page 325 and 326: addition, the cross-loadings of the
Page 327 and 328: and ease of use had significant eff
Page 329 and 330: Connell, J. P., Spencer, M. B., & A
Page 331 and 332: Despotović-Zrakić, M., Marković,
Page 333 and 334: Learning is a cognitive activity th
Page 335 and 336: The survey consisted of 30 question
Page 337 and 338: Created data mining model needs to
Page 339: dealt with the matter taught at the
Page 343 and 344: Romero, C., Ventura S., García E.,
Page 345 and 346: prompts as scaffolding strategy to
Page 347 and 348: Reflection Types This study attempt
Page 349 and 350: All the three groups completed the
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
Page 381 and 382: I usually engaged myself in listeni
Page 383 and 384: References Bloom, B. S. (Ed.). (195
Page 385 and 386: APPENDIX 2. Taxonomy for Informatio
Page 387 and 388: understand how we can effectively u
Page 389 and 390: 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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