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of acquainted partners is valued, as was witnessed in this study. Finally, though a prior study found that participants revealed significantly different preferences toward different identity revelation modes (Yu & Liu, 2009), in light of the non-significance results of the present study, learning technologists should not feel their need to base their decision on learning systems on the availability of identity revelation mode. Furthermore, instructors should not feel compelled to resort to anonymity or nickname as the mode of choice for the enhancement of learners’ perceptions of the interacting activities or academic performance. Acknowledgements This paper was funded by a 3-year research grant from the National Science Council, Taiwan, ROC (NSC 96-2520- S-006-002-MY3). The author would like to thank the instructor, Shannon Huang and research assistants, Meiju Chen, Knem Chen and Yu-Zen Liao for their assistance during the process. References Abramovich, S., & Cho, E. K. (2006). Technology as a medium for elementary preteachers’ problem-posing experience in Mathematics. Journal of Computers in Mathematics and Science Teaching, 25(4), 309-323. Barlow, A., & Cates, J. M. (2006). The impact of problem posing on elementary teachers’ beliefs about mathematics and mathematics teaching. School Science and Mathematics, 106(2), 64-73. Brindley, C., & Scoffield, S. (1998). Peer assessment in undergraduate programmes. Teaching in Higher Education, 3(1), 79-90. Brown, S. I., & Walter, M. I. (2005). The Art of Problem Posing, 3 rd Edition. Hillsdale, New Jersey: Lawrence Erlbaum Associates. Cooper, W. H., Gallupe, R. B., Pollard, S., & Cadsby, J. (1998). Some liberating effects of anonymous electronic brainstorming. Small Group Research, 29(2), 147-178. DeSanctis, G., & Gallupe, B. (1987). A foundation for the study of group decision support systems. Management Science, 33(5), 589-609. Dunn, S., & Hansford, B. (1997). Undergraduate nursing students' perceptions of their clinical learning environment. Journal of Advanced Nursing, 25(6), 1299-1306. Falchikov, N., & Goldfinch, J. (2000). Student peer assessment in higher education: A meta-analysis comparing peer and teacher marks. Review of Educational Research, 70(3), 287-322. Fellenz, M. R. (2004). Using assessment to support higher level learning: The multiple choice item development assignment. Assessment and Education in Higher Education, 29(6), 703-719. Festinger, L., Pepitone, A., & Newcomb, T. (1952). Some consequences of deindividuation in a group. Journal of Abnormal and Social Psychology, 47(2), 382-389. Frailich, M., Kesner, M., & Hofstein, A. (2007). The influence of web-based chemistry learning on students' perceptions, attitudes, and achievements. Research in Science & Technological Education, 25(2), 179-197. Franzoi, S. L. (2006). Social Psychology, 4 th edition. New York: McGraw Hill. Gatfield, T. (1999). Examining student satisfaction with group projects and peer assessment. Assessment & Evaluation in Higher Education, 24(4), 365-377. Gergen, K. J., Gergen, M. M., & Barton, W. N. (1973). Deviance in the dark. Psychology Today, 7, 129-130. Ghaith, G. (2003). The relationship between forms of instruction, achievement and perceptions of classroom climate. Educational Research, 45(1), 83-93. Hanrahan, S. J., & Isaacs, G. (2001). Assessing self- and peer-assessment: The students’ views. Higher Education Research & Development, 20(1), 53-70. Kiesler, S., Siegel, J., & McGuire, T. W. (1984). Social psychology aspects of computer-mediated communication. American Psychologist, 39(10), 1123-1134. Lea, M., Spears, R., & de Groot, D. (2001). Knowing me, knowing you: Anonymity effects on social identity processes within groups. Personality and Social Psychology Bulletin, 27(5), 526-537. 71
Liu, Z. F., Chiu, C. H., Lin, S. S. J., & Yuan, S. M. (2001). Web-based peer review: The learner as both adapter and reviewer. IEEE Transaction on Education, 44(3), 246-251. Lurie, S., Nofziger, A., Meldrum, S., Mooney, C., & Epstein, R. (2006). Effects of rater selection on peer assessment among medical students. Medical Education, 40(11), 1088-1097. Moral-Toranzo, F., Canto-Ortiz, J., & Gómez-Jacinto, L. (2007). Anonymity effects in computer-mediated communication in the case of minority influence. Computers in Human Behavior, 23(3), 1660-1674. Moses, B. M., Bjork, E., & Goldenberg, E. P. (1993). Beyond problem solving: Problem posing. In Brown, S. I. & Walter, M. I. (Eds.) Problem posing: Reflections and applications (pp. 178-188). Hillsdale, New Jersey: Lawrence Erlbaum Associates. Newby, T. J., Stepich, D., Lehman, J., & Russell, J. D. (2006). Educational Technology for Teaching and Learning, 3 rd edition. Merrill, New Jersey: Upper Saddle River. Palme, J., & Berglund, M. (2002). Anonymity on the Internet. Retrieved August 15, 2009, from http://people.dsv.su.se/~jpalme/society/anonymity.html Pinsonneault, A., & Heppel, N. (1997-98). Anonymity in group support systems research: A new conceptualization, measure, and contingency framework. Journal of Management Information Systems, 14(3), 89-108. Postmes, T., & Lea, M. (2000). Social processes and group decision making: anonymity in group decision support systems. Ergonomics, 43(8), 1252-1274. Purchase, H. C. (2000). Learning about interface design through peer assessment. Assessment & Evaluation in Higher Education, 25(4), 341-352. Smaldino, S. E., Lowther, D. L., & Russell, J. D. (2007). Instructional Technology and Media for Learning, 9 th edition. Upper Saddle River, New Jersey: Prentice Hall. Stanier, L. (1997). Peer assessment and group work as vehicles for student empowerment: A module evaluation. Journal of Geography in Higher Education, 21(1), 95-98. Topping, K. J. (1998). Peer assessment between students in colleges and universities. Review of Educational Research, 68(3), 249-276. Tsai, C. C., Lin, S. S. J., & Yuan, S. M. (2002). Developing science activities through a networked peer assessment system. Computers & Education, 38(1-3), 241-252. van Gennip, N. A. E., Segers, M., & Tillema, H. H. (2009). Peer assessment for learning from a social perspective: The influence of interpersonal and structural features. Learning and Instruction, 4(1), 41-54. Vreman-de Olde, C., & de Jong, T. (2004). Student-generated assignments about electrical circuits in a computer simulation. International Journal of Science Education, 26(7), 859-873. Wen, M., & Tsai, C. (2006). University students’ perceptions of and attitudes toward (online) peer assessment. Higher Education, 51(1), 27-44. Whiten, D. J. (2004). Exploring the strategy of problem posing. In Bright, G. W. & Rubenstein, R. N. (Eds.) Professional development guidebook for perspectives on the teaching of mathematics: Companion to the sixty-sixth yearbook (pp. 1-4). Reston, Virginia: National Council of Teachers of Mathematics. Wilson, E. V. (2004). ExamNet asynchronous learning network: Augmenting face-to-face courses with student-developed exam questions. Computers & Education, 42(1), 87-103. Wubbels, T., & Brekelmans, M. (2005). Two decades of research on teacher–student relationships in class. International Journal of Educational Research, 43(1-2), 6-24. Yu, F. Y. (2001). Competition within computer-assisted cooperative learning environments: cognitive, affective and social outcomes. Journal of Educational Computing Research, 24 (2), 99-117. Yu, F. Y. (2003). The mediating effects of anonymity and proximity in an online synchronized competitive learning environment. Journal of Educational Computing Research, 29(2), 153-167. Yu, F. Y. (2005). Promoting metacognitive strategy development through online question-generation instructional approach. Proceeding of International Conference on Computers in Education 2005 (pp. 564-571). Singapore: Nanyang Technological University. Yu, F. Y. (2009). Scaffolding student-generated questions: Design and development of a customizable online learning system. Computers in Human Behavior, 25(5), 1129-1138. Yu, F. Y., Han, C. L., & Chan, T. W. (2008). Experimental comparisons of face-to-face and anonymous real-time team 72
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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
Page 25 and 26: Phase Table 1. Phases and activitie
Page 27 and 28: Research results Describing student
Page 29 and 30: Assessment results for each one of
Page 31 and 32: References Annetta, L.A., Minogue,
Page 33 and 34: Huang, T.-W. (2012). Aberrance Dete
Page 35 and 36: Detection power Typically, the rela
Page 37 and 38: Results Detection rates As seen in
Page 39 and 40: 2 .00 .05 3 .02 .10 1 .60 .40 ECI 4
Page 41 and 42: their easily understandable devices
Page 43 and 44: Ifenthaler, D. (2012). Determining
Page 45 and 46: activities is assumed to be reflect
Page 47 and 48: that the problem representations (i
Page 49 and 50: deductive reasoning inventory (33 m
Page 51 and 52: HIMATT structural measures The part
Page 53 and 54: outcomes, r = .297, p < .01. Accord
Page 55 and 56: Azevedo, R. (2009). Theoretical, co
Page 57 and 58: Seel, N. M. (1999b). Educational se
Page 59 and 60: Initially this paper explores defin
Page 61 and 62: gamers were boys, and 35% were girl
Page 63 and 64: Teachers underwent eight hours of p
Page 65 and 66: A correlation analysis was used to
Page 67 and 68: playing the game. This would mean P
Page 69 and 70: Yu, F. Y. (2012). Any Effects of Di
Page 71 and 72: Considering that identity concealme
Page 73 and 74: In the nickname group, the student
Page 75: Attitudes toward assessors Percepti
Page 79 and 80: Appendix A: Attitudes toward Peer-A
Page 81 and 82: Appendix C: Perception toward the I
Page 83 and 84: Han, H., & Johnson, S. D. (2012). R
Page 85 and 86: Research participants The target po
Page 87 and 88: that its value of cross-loading is
Page 89 and 90: The canonical variable for the emot
Page 91 and 92: Literature in the field of online l
Page 93 and 94: Cornelius, R. R. (1996). The scienc
Page 95 and 96: Shih, W.-C., Tseng, S.-S., Yang, C.
Page 97 and 98: To provide personalized (e-)learnin
Page 99 and 100: Figure 3. An illustrative example o
Page 101 and 102: elementary schools. Thus they are b
Page 103 and 104: Table 5. Descriptive statistics for
Page 105 and 106: References Abowd, G. D., & Atkeson,
Page 107 and 108: Tseng, K.-H., Chang, C.-C., Lou, S.
Page 109 and 110: Figure 1. The five stages of KT (Gi
Page 111 and 112: The use of learning strategies, mon
Page 113 and 114: Additionally, the above two scales
Page 115 and 116: Figure 3. Canonical structure betwe
Page 117 and 118: highly positive perception of CM an
Page 119 and 120: Langan-Fox, J., Platania-Phung, C.,
Page 121 and 122: Gömleksiz, M. N. (2012). Elementar
Page 123 and 124: any statistically significant diffe
Page 125 and 126: 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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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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