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

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Third, findings for the semantic HIMATT measures (CCM, PPM, BPM) are in line with the above-discussed results. Solutions of GP learners are semantically more similar to the expert solution than those of other learners. However, the overall similarity of the learners’ problem representation to the expert representation is low. Hence, the learners of the present study are far from being experts and should be given more time and resources to improve their overall performance. Accordingly, we believe that further studies are needed to better understand the underlying cognitive processes of learning-dependent progression from novice to expert and, as a consequence, to provide more effective instructional materials. Furthermore, correlational analysis showed that metacognitive awareness and deductive reasoning skills were not associated with the problem scenario representation as expressed by concept maps. These results complement previous research studies which have found similar results (e.g. Hilbert & Renkl, 2008; Ifenthaler, et al., 2007; O'Donnell, et al., 2002). However, we found significant correlations between domain-specific knowledge and deductive reasoning skills. Accordingly, deductive reasoning skills have positive effects on the declarative learning outcomes. One final consideration based on our findings is that when we train novices to become experts, we often think about training general abilities to efficiently facilitate the process. While this works well for training abilities themselves, these methods may have limits when we train experts who have to decide and act within complex domains (Chi & Glaser, 1985; Ifenthaler, 2009). Reviewing the results of our experimental investigation, we suggest that a generic prompt which includes general instructions for planning and reflecting on their ongoing problem-solving activities are most effective for learners which already have a solid set of skills (in our case students at a university). In contrast, if learners do not have a specific set of problem-solving skills, directed prompts may be more effective. Accordingly, future studies should focus on the effectiveness of different prompts for different types of learners (novices, advanced learners, expert learners). The present research is limited to the single problem scenario on differences between an influenza and HIV infection as well as their effects to the human immune system. The limited time and resources for solving the problem may have also had an influence on our results. Also, further empirical investigations should focus on the “best” point in time when to present a prompt (Thillmann, et al., 2009). In addition, the present research is limited by our use of concept maps to elicit the problem scenario. However, such graphical representations are a widely accepted method for illustrating the meaning of locally discussed information (Eliaa, Gagatsisa, & Demetriou, 2007; Hardy & Stadelhofer, 2006; Ruiz-Primo, Schultz, Li, & Shavelson, 2001). In order to improve the external validity of our research, we suggest applying additional methodologies such as think-aloud protocols (Ericsson & Simon, 1993), standardized questionnaires and interviews (Zimmerman, 2008), and log files or click streams (Chung & Baker, 2003; Veenman, et al., 2004) within multimedia learning environments. Especially thinking aloud protocols applied during the reflection phase could give more insights into the metacognitive procedures induced by different types of prompts. Lastly, the timing of the prompts should be investigated in future studies (Thillmann, et al., 2009). Accordingly, future studies will include not only prompts for reflecting on the problem-solving process but also reflection prompts provided before the learners enter the problem scenario. Conclusions To sum up, since cognitive and educational researchers are not able to measure internal cognitive structures and functions directly, studies like ours will always be biased. A major bias includes the limited possibilities for externalizing learners’ internal cognitive structures (Ifenthaler, 2008, 2010b). However, we are adamant in our belief that it is essential to identify economic, fast, reliable, and valid methodologies to elicit and analyze these cognitive structures and functions (Zimmerman, 2008). In conclusion, new ways of assessment and analysis could make more precise results available, which may in turn lead to superior instructional interventions in the future. References Azevedo, R. (2008). The role of self-regulation in learning about science with hypermedia. In D. Robinson & G. Schraw (Eds.), Recent innovations in educational technology that facilitate student learning (pp. 127-156). Charlotte, NC: Information Age Publishing. 49
Azevedo, R. (2009). Theoretical, conceptual, methodological, and instructional issues in research on metacognition and selfregulated learning: A discussion. Metacognition and Learning, 4(1), 87-95. Bannert, M. (2007). Metakognition beim Lernen mit Hypermedia. Erfassung, Beschreibung, und Vermittlung wirksamer metakognitiver Lernstrategien und Regulationsaktivitäten. Münster: Waxmann. Bannert, M. (2009). Promoting self-regulated learning through prompts. Zeitschrift für Pädagogische Psychologie, 23(2), 139- 145. Boekaerts, M. (1999). Self-regulated learning: where we are today. International Journal of Educational Research, 31(6), 445- 457. Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145-182. Chi, M. T. H., De Leeuw, N., Chiu, M.-H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding Cognitive Science, 18(3), 439-477. Chi, M. T. H., & Glaser, R. (1985). Problem solving ability. In R. J. Sternberg (Ed.), Human abilities: An information processing approach (pp. 227-257). San Francisco, CA: W. H. Freeman & Co. Chung, G. K. W. K., & Baker, E. L. (2003). An exploratory study to examine the feasibility of measuring problem-solving processes using a click-through interface. Journal of Technology, Learning and Assessment, 2(2), Available from http://www.jtla.org. Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.), Knowing, learning, and instruction (pp. 453-494). Hillsdale, NJ: Lawrence Erlbaum. Davis, E. (2003). Prompting middle school science students for productive reflection: Generic and directed prompts. Journal of the Learning Sciences, 12(1), 91-142. Davis, E., & Linn, M. C. (2000). Scaffolding students’ knowledge integration: Prompts for reflection in KIE. International Journal of Science Education, 22, 819-837. Dummer, P., & Ifenthaler, D. (2005). Planning and assessing navigation in model-centered learning environments. Why learners often do not follow the path laid out for them. In G. Chiazzese, M. Allegra, A. Chifari & S. Ottaviano (Eds.), Methods and technologies for learning (pp. 327-334). Sothhampton: WIT Press. Eliaa, I., Gagatsisa, A., & Demetriou, A. (2007). The effects of different modes of representation on the solution of one-step additive problems. Learning and Instruction, 17(6), 658-672. Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data. Cambridge, MA: MIT Press. Ertmer, P. A., & Newby, T. J. (1996). The expert lerner: Strategic, self-regulated, and reflective. Instructional Science, 24(1), 1- 24. Ge, X., & Land, S. M. (2004). A conceptual framework for scaffolding ill-structured problem-solving processes using question prompts and peer interactions. Educational Technology Research and Development, 52(2), 5-22. Hardy, I., & Stadelhofer, B. (2006). Concept Maps wirkungsvoll als Strukturierungshilfen einsetzen. Welche Rolle spielt die Selbstkonstruktion? Zeitschrift für Pädagogische Psychologie, 20(3), 175-187. Herl, H. E., Baker, E. L., & Niemi, D. (1996). Construct validation of an approach to modeling cognitive structure of U.S. history knowledge. Journal of Educational Research, 89(4), 206-218. Hilbert, T. S., & Renkl, A. (2008). Concept mapping as a follow-up strategy to learning from texts: what characterizes good and poor mappers? Instructional Science, 36, 53-73. Ifenthaler, D. (2008). Practical solutions for the diagnosis of progressing mental models. In D. Ifenthaler, P. Pirnay-Dummer & J. M. Spector (Eds.), Understanding models for learning and instruction. Essays in honor of Norbert M. Seel (pp. 43-61). New York: Springer. Ifenthaler, D. (2009). Model-based feedback for improving expertise and expert performance. Technology, Instruction, Cognition and Learning, 7(2), 83-101. Ifenthaler, D. (2010a). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58(1), 81-97. doi: 10.1007/s11423-008-9087-4 Ifenthaler, D. (2010b). Scope of graphical indices in educational diagnostics. In D. Ifenthaler, P. Pirnay-Dummer & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 213-234). New York: Springer. 50
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January 2012 Volume 15 Number 1
Page 3 and 4: Supporting Organizations Centre for
Page 5 and 6: Analyzing the Learning Process of a
Page 7 and 8: Angeli, C., & Valanides, N. (2012).
Page 9 and 10: ased on an integration and evaluati
Page 11 and 12: ased on the assumption that no sing
Page 13 and 14: Diagram type D thinking (shown in F
Page 15 and 16: collaborative task in terms of inte
Page 17 and 18: Table 4. Descriptive statistics for
Page 19 and 20: Hong, N. S., Jonassen, D. H., & McG
Page 21 and 22: commercial off-the-shelf digital ga
Page 23 and 24: 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: outcomes, r = .297, p < .01. Accord
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
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
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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
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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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