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

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De Wever, B., Schellens, T., Valcke, M., & Van Keer, H. (2006). Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review. Computers & Education, 46(1), 6-28. Dysthe, O. (2002). The learning potential of a web-mediated discussion in a university course. Studies in Higher Education, 27(3), 339-352. Fahy, P., Crawford, G., & Ally, M. (2001). Patterns of interaction in a computer conferencing transcript. International Review of Research in Open and Distance Learning, 2(1), 1-24. Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in higher education. American Journal of Distance Education, 15(1), 7-23. Guldberg, K. K., & Pilkington, R. M. (2006). A community of practice approach to the development of non-traditional learners through networked learning. Journal of Computer Assisted Learning, 22(3), 159-172. Guldberg, K. K., & Pilkington, R. M. (2007). Tutor roles in facilitating reflection on practice through online discussion. Educational Technology & Society, 10(1), 61-72. Gunawardena, C. N., Lowe, C., & Anderson, T. (1997). Analysis of a global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of Educational Computing Research, 17(4), 397-431. Guzdial, M., & Turns, J. (2000). Effective discussion through computer-mediated anchored forum. The Journal of the Learning Sciences, 9(4), 437-469. Hara, N., Bonk, C. J., & Angeli, C. (2000). Content analysis of online discussion in an applied educational psychology course. Instructional Science, 28(2), 115-152. Hathorn, L. G., & Ingram, A. L. (2002). Cooperation and collaboration using computer-mediated communication. Journal of Educational Computing Research, 26(3), 325-347. Henri, F. (1992). Computer conferencing and content analysis. In A. R. Kaye (Ed.), Collaborative learning through computer conferencing. The Najadan Papers (pp. 117-136). London: Springer-Verlag. Henri, F., & Rigault, C. R. (1996). Collaborative distance learning and computer conferencing. In T. T. Liao (Ed.), Advanced educational technology: Research issues and future potential (pp.45-76). Berlin: Springer-Verlag. Islas, J. R. L. (2004). Collaborative learning at Monerrey Tech-Virtual University. In. T. M. Duffy and J. R. Kirkley (Eds.), Learner-centered theory and practice in distance education: Cases from higher education (pp.297-319). Mahwah, NJ: Lawrence Erlbaum Associates. Järvelä, S., & Häkkinen, P. (2002). Web-based cases in teaching and learning-the quality of discussions and a stage of perspective taking in asynchronous communication. Interactive Learning Environments, 10(1), 1-22. Kanuka, H., & Anderson, T. (1998). Online social interchange, discord, and knowledge construction. The Journal of Distance Education, 13(1), 57-74. Lally, V. (2001). Analysing teaching and learning interaction in a networked collaborative learning environment: issues and work in progress. In Euro CSCL 2001 (pp. 397-405). Maastrict: McLuhan Institute, University of Maastrict. Retrieved March, 9, 2010, from http://www.ll.unimaas.nl/euro-cscl/presentations.htm. Li, Q. (2004). Knowledge building community: Key for using online forums. Techtrends, 48(4), 24-28. Li, Q. (2008-2009). Knowledge building in an online environment. A design-based research study. Journal of Educational Technology Systems, 37(2), 195-216. Marra, R., Moore, J. L., & Klimczak, A. K. (2004). Content analysis of online discussion forums: A comparative analysis of protocols. Educational Technology Research & Development, 52(2), 23-40. Mazur, J. (2004). Conversation analysis for educational technologists: Theoretical and methodological issues for researching the structures, processes and meaning of online talk. In D. H. Jonassen (Ed.), Handbook for research in educational communications and technology, (2nd ed., pp. 1073-1098). Hilsdale, NJ. Lawrence Erlbaum Associates. Moore, J. L., & Marra, R. M. (2005). A comparative analysis of online discussion participation protocols. Journal of Research on Technology in Education, 38(2), 191-212. Murphy, E. (2004). Recognizing and promoting collaboration in an asynchronous discussion. British Journal of Educational Technology, 35(4), 421-431. Newman, D. R., Webb, B., & Cochrane, C. (1995). A content analysis method to measure critical thinking in face to face and computer supported learning. Interpersonal Computing and Technology, 3(2), 56-77. 271
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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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Page 229 and 230: 2009; Bernard & Cathryn, 2006) or p
Page 231 and 232: In this study, the focus of the ins
Page 233 and 234: The student’s degree of mastery i
Page 235 and 236: The questionnaire for the acceptanc
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Page 239 and 240: Scale Questionnaire item Mean S.D.
Page 241 and 242: Hwang, G. J. (2003). A conceptual m
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Page 245 and 246: Virtual learning environment In the
Page 247 and 248: Class Table 1. The survey questions
Page 249 and 250: “…the distance and the lack of
Page 251 and 252: collaboration within EVS, thus prev
Page 253 and 254: Huang, T.-H., Liu, Y.-C., & Chang,
Page 255 and 256: y personalised context examples in
Page 257 and 258: Students’ Problem-Solving Guidanc
Page 259 and 260: Research Method Figure 8. The scree
Page 261 and 262: Table 4. Abstract of Pairwise Compa
Page 263 and 264: questions designed for the system h
Page 265 and 266: Lee, J. (2012). Patterns of Interac
Page 267 and 268: This study focused on online fora i
Page 269 and 270: Results and Discussion Due date-cen
Page 271 and 272: In addition, all groups developed 8
Page 273 and 274: future. Therefore, they had an oppo
Page 275: surprising that most interaction wa
Page 279 and 280: systems by using 18 personalization
Page 281 and 282: Short-Term Sensory Memory is a temp
Page 283 and 284: with respect to articles by using a
Page 285 and 286: system also computes a review value
Page 287 and 288: DFL. d ( yi ) is the degree of mem
Page 289 and 290: the experimental group can understa
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
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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
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
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and ease of use had significant eff
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Connell, J. P., Spencer, M. B., & A
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Despotović-Zrakić, M., Marković,
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Learning is a cognitive activity th
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The survey consisted of 30 question
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Created data mining model needs to
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dealt with the matter taught at the
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Conclusion Conducted research showe
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Romero, C., Ventura S., García E.,
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prompts as scaffolding strategy to
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Reflection Types This study attempt
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All the three groups completed the
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Results Learning Outcomes Pre- and
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Map Analysis in Transfer Test Figur
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Generic Prompts and Specific Prompt
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Biswas, G., Schwartz, D., Bransford
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Chen, Y.-H., Looi, C.-K., Lin, C.-P
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of correct response, answer until c
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Figure 7 shows a screenshot of one
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Collaboration Questionnaire results
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following diagrams. The double-arro
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“Most students were encouraged to
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Bangert-Drowns, R.L., Kulick, C. C.
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primary research questions. First,
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Individual learning Figure 2. Scree
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Experimental Tools This study emplo
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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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