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

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The first canonical variable for types of interaction variables had a weighted difference of social type (-1.034), work type (.808), and management type (.966). All the variables seem to have an important contribution to the correlation. However, the canonical cross-loadings of the social type variable (r=-.005) and the management type variable (r=.114) were very small. Rather, the management type variable had the highest cross-loading (r=.319). This indicates that the management type variable was the most important variable in the canonical correlation, while the contribution of social type and work type variables to the relationship was small. Moreover, the squared multiple correlations show that the first canonical variable of types of interaction has some predictive power for management type interaction in the synchronous text interaction (.102). There was no predictable power for social type (.000) and work type (.013) interaction in the synchronous text interaction. The first canonical variable for the social bond variables had a weighted difference of bond to peer (.296), bond to instructor (-.246), and bond to online program (.970), with greater emphasis on bond to program. The canonical cross-loading of the bond to program (.383) also had the highest correlation with the canonical variable of the social bond variable and the same sign as the canonical weight. Some of the remaining variables appear to have high weights. However, their contribution was not worthy to consider, since their canonical cross-loadings were lower than .30 and their squared multiple correlations indicate that they have less predictable power for the canonical correlation. Relationship between emotional intelligence and social bond No statistically significant canonical correlations between the emotional intelligence variables and the social bond variables were found. Four branch score variables, including EI-B1 (Perceiving Emotion), EI-B2 (Using Emotion), EI-B3 (Understanding Emotion), and EI-B4 (Managing Emotion) were input as a set of the emotional intelligence variables. For the social bond variables, peer, instructor, and program variables were input. Gender variable was also input as a control variable. Seeing that there was no significant canonical correlation between the emotional intelligence variables and social bond variables, the Pearson partial correlations were further analyzed to determine the correlation between the task scores of emotional intelligence and social bond variables. According to the Pearson partial correlation, bond to peer was the only variable to be associated with the four emotional intelligence variables. Another canonical correlation analysis was conducted to further examine the relationship between the emotional intelligence variables and the bond to peer variable. Multiple regression analysis could be used to examine this one-dimensional relationship but canonical correlation analysis was used since canonical correlation also has an ability to do univariate multiple regression analysis and produce the same result as multiple regression analysis. Table 5 shows the canonical partial correlation between the four emotional intelligence variables and the bond to peer variable. One canonical correlation was derived because the smaller set had one variable. The canonical correlation was found to be statistically significant (r=.349, p=.039) at the .05 alpha level. The squared canonical correlation for the first dimension was .129. This squared canonical correlation equals to R 2 in univariate multiple regression analysis. Table 5. Canonical Partial Correlations between EI and the Bond to Peer # Canonical Correlation Squared Canonical (R 2 ) Eigenvalues of Inv(E)*H = CanRsq/(1-CanRsq) Test of H0 Eigenvalue Cumulative F DF 1 DF 2 Sig. 1 .349 .129 .147 1.00 2.66 4 72 .039* Note. Significance level *p
The canonical variable for the emotional intelligence variable had a weighted difference of EI-B1 (.964), EI-B2 (- .317), EI-B3 (.332), and EI-B4 (.600). EI-B1 and EI-B4 appear to be important variables in the correlation. However, while the canonical cross-loading of the variable EI-B1 was .310, the cross-loading of the EI-B4 was not greater than .30. Moreover, the squared multiple correlations also indicate that EI-B1 has greater predictable power of the canonical correlation. Table 6. Relative Importance of Variables for the First Canonical Correlation between EI and the Bond to Peer Variate/ Canonical Canonical Cross- Squared Multiple Redundancy Variables Weights Loading Correlations Index Dependent .129 Peer 1.000 .359 .129 Independent .040 EI-BI .964 .310 .096 EI-B2 -.317 .145 .021 EI-B3 .332 .039 .002 EI-B4 .600 .200 .040 In sum, there was a negative association between students’ emotional intelligence and the amount of interaction in the synchronous online environment. Students’ ability to perceive emotion by facial expressions and the number of text and audio messages were found to be critical factors in the relationship. Also, there was a positive relationship between students’ social bond and their types of interactions in the synchronous online environment. Students’ bonding to their online program and their management type interaction were found to be contributing variables in the association. Lastly, emotional intelligence with more emphasis on the ability to perceive emotion had a positive relationship with the bond to peer variable. Conclusions and discussion Conclusion 1: Students who have higher emotional intelligence have a greater degree of social bond in online learning. Specifically, individuals with a higher ability to perceive emotion tend to be more attached to peers in online learning environments. The results indicate that students who have higher ability to perceive emotions have a greater sense of bond to their online learning peers. Previous literature supports the conclusion about the relationship between emotional intelligence and social bond in online learning. In traditional in-person relationships, students with a high ability to perceive emotions of self and others’ have better relationships with friends (Brackett et al., 2004). This positive relationship was found in the online learning environment in this study. It was interesting that, while students’ ability to perceive emotion was found to be associated with their emotional attachment to peers, the other emotional abilities (i.e., understanding emotion, using emotion, and managing emotion) were found to have an insignificant contribution to the relationship. In traditional school settings, the ability to manage emotion was found to be associated with students’ social relationship with friends and school membership (Lopes et al., 2003; Zins et al., 2004). However, this relationship was not found in this study. These inconsistent results may indicate the limited function of emotional intelligence in online learning environments. As Mayer et al. (2004) discussed, the four emotional abilities (i.e., perceiving emotion, using emotion, understanding emotion, and managing emotion) represent a hierarchy. In other words, they argued that individuals should be able to perceive their and others’ emotions in order to use, understand, and manage their emotion. The insignificant contribution of the other three emotional abilities in the relationship may result from this hierarchy of emotional abilities in online learning environments. Expressing one’s emotion or the ability to perceive others’ emotions in online environments is challenging (Wang & Reeves, 2007). Since emotional information processing in the emotion system (i.e., perceiving emotion) is challenged in online environments due to the limited emotional cues, the other emotional abilities (i.e., using emotion, understanding emotion, and managing emotion) may be challenged to help develop social bonds. The combination of the limited environmental characteristic of online learning and hierarchical representation of emotional abilities may reduce the association of using emotion, understanding emotion, and managing emotion with social bonding. 84
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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
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 and 76: Attitudes toward assessors Percepti
Page 77 and 78: Liu, Z. F., Chiu, C. H., Lin, S. S.
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
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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
Page 127 and 128: conditions of a learning environmen
Page 129 and 130: Kahle, J. B. (1983). The disadvanta
Page 131 and 132: Appendix A Science and Technology S
Page 133 and 134: (Guuawardena, Nola, Wilson, Lopez-I
Page 135 and 136: Questionnaire on student satisfacti
Page 137 and 138: 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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January 2012 Volume 15 Number 1 - Educational Technology ...

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