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

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Number of Participants (N=24) Tests Table 3. T-test of pre and post tests in collaborative group Number of Participant t-test of collaborative group Min. Max. Average SD Progress t p Pre-test of high-achievers Post-test of high- achievers 6 63.34 48.34 76.68 100 70.56 77.78 3.141 10.576 Pre-test of medium achievers Post-test of medium-achievers 9 45.01 40.01 66.68 98.35 56.48 62.96 5.182 14.454 Pre-test of low-achievers Post-test of low-achievers 9 6.67 16.67 55.01 76.68 30.56 53.89 10.111 9.874 7.22 1.308 .248 6.47 .880 .404 23.33 4.834 .001** The low-achiever groups in Class A were found to have the highest increase in post-test scores at a high level of significance (P=0.001). This indicates that low-achievers of these collaborative groups derived the most benefits in this study. Table 4 showed further analyses conducted on three different types of test questions on “addition and subtraction”. Students had better scores in all three types of questions in the post-test. But the low-achiever groups achieved significantly highest improvement in questions of “basic computing”, “unknown constant” and “Cross Number Puzzle” with the increase of average score 9.63, 7.38 and 6.32 respectively. This suggests that these low-achievers benefited the most from the “Cross Number Puzzle” in improving their basic arithmetic skills. Table 4. Low achieved students’ progress in Pre and Post tests Low achievers in Class A ( N=9 ) Pre -test Post -test Average increased scores Ratio of progress in different questions Basic computing skills Score of question 1 to 5 (33.33) 18.52 28.15 9.63 41.3% Unknow constant Score of question 6 to12 (46.67) 8.90 16.28 7.38 31.7% Cross number puzzle Score of question 13 to 15 (20.00) 3.14 9.46 6.32 27.0% On the other hand, the average score of Class B (the individual group) is 4.17 higher in the post-test (57.21) than in the pre-test (53.04) at a significant level of .026 (p .05) shows that the individual group (Class B) and the collaborative group (Class A) can be regarded homogeneous. However, Table 5 below provides the result of F-test (F = 4.479, p =. 039, p
Collaboration Questionnaire results illustrated 85% students tried to do cooperation and discussion before they submitted the answer when they played the “Cross number puzzle” game. There was one high-achiever student who did not discuss with others when he did his calculations. He explained in the follow-up interview that he was quite confident and only shared his results with others when he completed all his calculations. 87.5% students claimed that it was much easier to complete the calculations with collaboration than doing them individually. Those students without confidence in mathematics found it easier to share their own ideas with others and complete the calculations together. All students agreed that they derived benefits from discussion with other classmates. “Tips” usage in Class A and Class B As we mentioned before, students in Class A play the game collaboratively in groups while students in Class B completed the game individually. We can easily conclude from Table 5 below that feedback in the form of “Tips” was much more frequently used in Class B than in Class A. It suggests that when a student encounters problems and difficulties and in the situation where there is no help from peers, he or she would search help from the “feedback” system. On the other hand, students in Class A would discuss their strategies to solve the problem within a group first, allocating cooperative work among group members. They only referred to the “feedback” system when every student in the group was uncertain on how to proceed They used the “tips” less often then students in Class B. Students in Class B seldom initiatively asked for help from their classmates but only referred to the tips when they encountered difficulties. However either in Class A or Class B, high-achiever students seemed to have used the “tips” far less than low-achiever students. Low achieving students relied more on “tips”. Table 6. “Tips” usage in Class A and Class B Number of use in different group Class A (N=24) Class B (N=28) High-achiever 0.54 0.84 Medium-achiever 0.71 1.31 Low-achiever 1.25 2.09 Average usage 0.86 1.40 We analyze further students’ collaborative activities (in Class A) in the process of game playing which included three tasks to be completed in the game. Pre-activity: Result number unknown Before formal learning activities, a pre-activity was run with two objectives: familiarization with the GS system operation and warming-up the student work in doing arithmetic calculations. The question model is A ±B =□ which is the level one difficulty. 23 of 24 students in collaborative groups finished this activity. Learning Activity 1: Remove the operator Four different patterns of collaborative problem solving were found in their activities of “remove the operator”: whole-group-deciding, two-member-deciding, leader-deciding and individual deciding. Group 6 had the decision made by all group members. Three groups, Group 1, Group 3 and Group7 decided the answer on an individual basis. Two groups followed the two-member deciding pattern and the other rest two groups the leader-deciding pattern. The following figures (Figure 12 to Figure 15) shows different layout of the game in different collaborative methods. For example, in figure 11, three students in group 6 (one student in one color of “+”) posted their answer as 4777 +++ 4611 +++ 1799 = 11154, six “+” and one “=”. All these three students could perform the addition operation correctly. Therefore we could judge that this group’s answer was decided by the whole group. In Figure 14, there is only one answer being pasted, and when we referred to the video recording, we found this group was clearly one of leader-deciding. 360
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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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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
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 and 340: dealt with the matter taught at the
Page 341 and 342: Conclusion Conducted research showe
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: Figure 7 shows a screenshot of one
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
Page 391 and 392: correlation between teachers’ IWB
Page 393 and 394: Q7. IWB provides advantages to me t
Page 395 and 396: training regarding this topic. This
Page 397 and 398: Conclusion This study provides a so
Page 399: Smith, H. J., Higgins, S., Wall, K.
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