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without the sources of specific knowledge or relevant scientific evidence. It implies that students who hold less alternative conceptions are more likely to perform better in the post-Physical Science Dependent Argumentation Test. Acknowledgements This research is based in work supported and funded by the National Science Council (NSC 95-2522-S-009-001- MY3), Taiwan, R.O.C. References Cobern, W. W. (1993). Contextual constructivism: The impact of culture on the learning and teaching of science. In K. Tobin (Ed.), The practice of constructivism in science education (pp. 51-69). Hillsdale, NJ: Lawrence Erlbaum Associates. Delfino, M., & Persico, D. (2007). Online or face-to-face? Experimenting with different techniques in teacher training. Journal of Computer Assisted Learning, 23(5), 351-365. De Vries, E., Lund, K., & Baker, M. (2002). Computer-mediated epistemic dialogue: Explanation and argumentation as vehicles for understanding scientific notions. The Journal of the Learning Sciences, 11(1), 63-103. Driver, R. (1995). Constructivist approaches to science learning. In L. P. Steffe & J. Gale (Eds.), Constructivism in education (pp. 385-400). Hillsdale, NJ: Erlbaum. Driver, R., & Bell, B. (1986). Students thinking and the learning of science: A constructivist view. School Science Review, 67(6), 443-456. Driver, R., Leach, J., Millar, R., & Scott, P. (1996). Young people’s images of science. Philadelphia, PA: Open University Press. Hewson, M., & Hewson, P. W. (1983). Effect of instruction using students' prior knowledge and conceptual change strategies on science learning. Journal of Research in Science Teaching, 20(8), 731-743. Hewson, P. W., & Hewson, M. G. (1988). An appropriate conception of teaching science: A view from studies of science learning. Science Education, 72(5), 597-614. Janssen, J., Erkens, G., & Kanselaar, G. (2006). Visualization participation to facilitate argumentation. Paper presented at the 7th International Conference of the Learning Sciences June/July, Bloomington, IN. Jiménez-Aleixandre, M. P., & Rodriguez, A. B. (2000). Doing the lesson or doing science: argument in high school genetics. Science Education, 84(6), 757-792. Kirschner, P. A., Buckingham Shum, S. J., & Carr, C. S. (2003). Visualizing Argumentatio: Software Tools for Collaborative and Educational Sense-Making. London: Springer Verlag. Kirtman, L. (2009). Online versus in-class courses: An examination of differences in learning outcomes. Issues in Teacher Education, 18(2), 103-116. Kolsto, S. D., & Ratcliffe, M. (2008). Social aspects of argumentation. In S. Erduran & M.P. Jiménez-Aleixandre (Eds.), Argumentation in science education: perspectives from classroom-based research (pp.114-133). Berlin, Germany: Springer. Kuhn, D., Shaw, V., & Felton, M. (1997). Effects of dyadic interaction on argumentative reasoning. Cognition and Instruction, 15(3), 287-315. Larson, D. K., & Sung, C. H. (2009) Comparing Student Performance: Online versus Blended versus Face-to-Face. Journal of Asynchronous Learning Networks, 13(1), 31-42. Latour, B. (1987). Science in action: how to follow scientists and engineers through society. Cambridge, MA: Harvard University Press. Liao, Y.W. & She, H.C. (2009). Enhancing Eight Grade Students’ Scientific Conceptual Change and Scientific Reasoning through a Web-based Learning Program. Educational Technology & Society, 12 (4), 228-240. Lim, J., Kim, M., Chen, S. S., & Ryder, C. E. (2008). An empirical investigation of student achievement and satisfaction in different learning environments. Journal of Instructional Psychology, 35(2), 113-119. Menchaca, M. (2008). Learner and instructor identified success factors in distance education. Distance Education, 29(3), 231-252. 209
Millar, R., & Osborne, J.F. (1998). Beyond 2000: Science Education for the Future. London: King's College London. Osborne, J., Erduran, S., & Simon, S. (2004). Enhancing the quality of argumentation in school science. Journal of Research in Science Teaching, 41(10), 994-1020. Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66(2), 211-227. Ravenscroft, A. (2000). Designing Argumentation for Conceptual Development. Computers & Education, 34(3-4), 241-255. Ravenscroft, A. (2007). Promoting Thinking and Conceptual Change with Digital Dialogue Games. Journal of Computer Assisted Learning (JCAL), 23(6), 453-465. Russell, M., Carey, R., Kleiman, G., & Venable, J. D. (2009). Face-to-face and Online Professional Development for Mathematics Teachers: A Comparative Study. Journal of Asynchronous Learning Networks, 13(2), 71-87. Salcedo, C. S. (2010). Comparative Analysis Of Learning Outcomes In Face-To-Face Foreign Language Classes Vs. Language Lab And Online. Journal of College Teaching & Learning, 7(2), 43-54. Sandoval, W. A. & Reiser, B. (2004). Explanation-Driven Inquiry: Integrating Conceptual and Epistemic Scaffolds for Scientific Inquiry. Science Education, 88(3), 345-372. Scagnoli, N. I., Buki, L. P., & Johnson, S. D. (2009). The influence of online teaching on face-to-face teaching practices. Journal of Asynchronous Learning Networks, 13(2), 115-128. She, H.C. (2004). Fostering “Radical” conceptual change through Dual Situated Learning Model. Journal of Research in Science Teaching, 41(2), 142-164. She, H.C. & Lee, C.Q. (2008). SCCR Digital Learning System for Scientific Conceptual change and Scientific Reasoning. Computers & Education, 51(2), 724-742. She, H.C. & Liao, Y.W. (2010). Bridging Scientific Reasoning and Conceptual Change through Adaptive Web-based Learning. Journal of Research in Science Teaching, 47(1), 91-119. Venville, G., & Treagust, D. F. (1998). Exploring conceptual change in genetics using a multidimensional interpretive framework. Journal of Research in Science Teaching, 35(9), 1031-1055. Vosniadou, S., & Brewer, W. F. (1987). Theories of knowledge restructuring in development. Review of Educational Research, 57(1), 51-67. Wray, D., & Lewis, M. (1997). Extending literacy: Children reading and writing non-fiction. London: Routledge. Wuensch, K. W., Aziz, S., Ozan, E., Kishore, M., & Tabrizi, M. H. N. (2009). Technology and pedagogy: The association between students’ perceptions of the quality of online courses and the technologies employed. Journal of Online Learning and Teaching, 5(2), 253-262. Yeh, K.H. & She, H.C. (2010). On-line Synchronous Scientific Argumentation Learning: Nurturing Students’ Argumentation Ability and Conceptual Change in Science Context. Computers & Education, 55(2), 586-602. Zohar, A., & Nemet, F. (2002). Fostering students' knowledge and argumentation skills through dilemmas in human genetics. Journal of Research in Science Teaching, 39(1), 35-62. 210
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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
Page 163 and 164: constructs that measure students’
Page 165 and 166: Bandura, A. (1978). Reflections on
Page 167 and 168: Lin, J. M.-C., & Liu, S.-F. (2012).
Page 169 and 170: Table 1. The MSWLogo commands learn
Page 171 and 172: other words, we had prepared a set
Page 173 and 174: lab. When she returned and found th
Page 175 and 176: Attitudes toward Programming and Co
Page 177 and 178: Table 5 successfully, it took four
Page 179 and 180: Kuter, S., Altinay Gazi, Z., & Alti
Page 181 and 182: not only the means for trainees’
Page 183 and 184: Data Collection Techniques and Anal
Page 185 and 186: organizations was highlighted by on
Page 187 and 188: provides the means for professional
Page 189 and 190: Kohonen, V. (2001). Towards experie
Page 191 and 192: As Rogers (1995) postulates in his
Page 193 and 194: Instrument and data collection Afte
Page 195 and 196: “Raising our computer skills in C
Page 197 and 198: teachers deploy ICT tools in langua
Page 199 and 200: Ma, W., Anderson, R., & Streith, K.
Page 201 and 202: APPENDIX A. Questionnaire for Dista
Page 203 and 204: consistency with other ideas, and a
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Page 207 and 208: each group. The experimental group
Page 209 and 210: specifically, instructional approac
Page 211 and 212: Table 3 shows that the mean frequen
Page 213: previous Web-based instructional le
Page 217 and 218: Several studies explore the roles t
Page 219 and 220: understand the content structures o
Page 221 and 222: teacher was not allowed to provide
Page 223 and 224: two coding schemes, as illustrated
Page 225 and 226: also very limited. Teachers and sof
Page 227 and 228: learning activities. British Journa
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
Page 237 and 238: the t-test result, it is found that
Page 239 and 240: Scale Questionnaire item Mean S.D.
Page 241 and 242: Hwang, G. J. (2003). A conceptual m
Page 243 and 244: sharing, problem solving, and achie
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
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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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the experimental group can understa
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Finally, Figure 10(A) and Figure 10
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Appendix # Question Description Par
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mental models, are cognitive struct
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Based on the theoretical implicatio
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Data analysis To test the model fit
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may be considered an effective inte
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Hsu, I.-C. (2012). Intelligent Disc
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(Gradinarova, Zhelezov et al. 2006)
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RLO denotes a remote learning objec
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Figure 2. The flow-oriented LOFinde
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if x include with y, and y include
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Similarly, the relation metadata of
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(defrule student-advisor (triple (p
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References ADL. (2006). Sharable Co
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determinants of how people think, b
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Mediating effect of learning flow A
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The instrument used to measure lear
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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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