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

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prompted to consider how their beliefs, experiences and knowledge have shaped the theories they apply to teaching and learning. Working from awareness and understanding of their own metacognitive strategies and beliefs, student teachers can then be challenged to consider a wider range of discourses, an aspect that Phelan (2001) argues is vital in recognizing the discourses that shape and often restrict thinking. From our study, it could be possible to conclude that the focus for reflection on metacognitive strategies and beliefs could shape or restrict students’ reflection during the reflective practices. A similar study is conducted by Andre (1979) who investigates the effects of asking students questions at different levels of cognitive complexity during learning. It is shown that higher level questions can have facilitative effects on both reproductive and productive knowledge, but the conditions under which such facilitation occurs are not well understood. A more recent study by Chen et al. (2009) finds that the main factor affecting reflection levels is high level prompts followed by high quality observation that has a moderating effect on learners' reflection levels. Conclusion Overall, the results of the study suggest that the use of agent tutee as an active and inquisitive learning partner to ask meaningful questions could be some helpful to students learn by reflective teaching. The preliminary quantitative and qualitative results suggest that the use of an agent prompts as computer-based scaffolds, could be useful in particular ways, such as improving students’ learning outcomes and eliciting higher levels of reflection. The study suggests that generic prompts could be more beneficial in fostering double-loop learning. There remains significant support challenges that must be overcome before a system similar to that used in this study, with agent prompts, can be incorporated into regular classroom use. Generally, the goal of supporting students needs to shift from a notion of leading to one of facilitating and enabling. This means designing intelligent tutee systems that are not necessarily recipients of information but, rather, they are designed to promote reflection. Instead of attempting to create an agent tutee that will always know the correct answer, designers need to design agent tutee systems that encourage students to do both reflection-in-action and reflection-on-action (Schön, 1987) and pay attention to double-loop reflection (Argyris and Schön, 1974, 1978, 1996), i.e., thinking out of the box. Acknowledgements We thank Dr. Gautam Biswas in Vanderbilt for providing the Teachable Agent software. References Akbar, H. (2003). Knowledge Levels and their Transformation: Towards the Integration of Knowledge Creation and Individual Learning. Journal of Management Studies, 40(8), 1997-2001. Aleven, V. A. W. M. M., & Koedinger, K. R. (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26, 33. Amulya, J. (2004). What is Reflective Practice? Retrieved 18 April 2009, from http://www.itslifejimbutnotasweknowit.org.uk/files/whatisreflectivepractice.pdf Andre, T. (1979). Does Answering Higher-Level Questions WhileReading Facilitate Productive Learning? Review of Educational Research, 49(2), 280-318. Argyris, C., & Schön, D. (1974). Theory in practice: Increasing professional effectiveness. San Francisco: Jossey-Bass. Argyris, C., & Schön, D. (1978). Organizational learning: A theory of action perspective Reading. Mass: Addison Wesley. Argyris, C., & Schön, D. (1996). Organizational learning II: Theory, method and practice Reading. Mass: Addison Wesley. Azevedo, R., & Hadwin, A. F. (2005). Scaffolding self-regulated learning and metacognition - Implications for the design of computer-based scaffolds. Instructional Science, 33, 367--379. Biggs, J., & Collis, K. (1982). Evaluating the Quality of Learning: the SOLO taxonomy New York: Academic Press. 351
Biswas, G., Schwartz, D., Bransford, J., & TAG-V. (2001). Technology Support for Complex Problem Solving: From SAD Environments to AI. In F. Feltovich (Ed.), Smart Machines in Education (pp. 71-98). Menlo Park, CA: AAAI Press. Bloom, B. S. (1956). Taxonomy of educational objectives: The classification of educational goals. New York Toronto: Longmans, Green. Bolton, G. (2001). Reflective Practice. London: Paul Chapman Publishing. Boud, D., Keogh, R., & Walker, D. (1985). Reflection: turning experience into learning. London: Kogan Page. Bransford, J. D., Brown, L. A., & Cocking, R. R. (2000). How People Learn. Washington, D.C: National Academy Press. Brockbank, A., & McGill, I. (1998). Facilitating Reflective Learning in Higher Education Society for Research into Higher Education. Buckingham: Open University Press. Chen, N. S., Kinshuk, Wei, C. W., & Liu, C. C. (2011). Effects of matching teaching strategy to thinking style on learner’s quality of reflection in an online learning environment. Computers & Education, 56(1), 53-64. Chen, N. S., Wei, C. W., Wu, K. T., & Uden, L. (2009). Effects of high level prompts and peer assessment on online learners' reflection levels. Computers & Education, 52(2), 283-291. Chi, M. T. H., Siler, S., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human learning. Cognitive Science, 25, 471-533. Cohen, J. (1986). Theoretical considerations of peer tutoring. Psychology in the Schools, 23, 175-186. Cohen, P. A., Kulik, J. A., & Kulik, C. C. (1982). Educational outcomes of tutoring: A meta-analysis of findings. American Educational Research Journal 19(2), 237-248. Courtney, J., Croasdell, D., & Paraadice, D. (1998). Inquiring Organisations. Australian Journal of Information Systems, 6(1). Davis, E. A., & Linn, M. (2000). Scaffolding students’ knowledge integration: Prompts for reflection in KIE. International Journal of Science Education, 22(8), 819-837. Dewey, J. (1910/1933). How we think. Boston: Heath. Gama, C. A. (2004). Integrating Metacognition Instruction in Interactive Learning Environments. PhD Thesis, University of Sussex. Gartner, A., Kohler, M., & Riessman, F. (1971). Children teach children: Learning by teaching. New York: Harper & Row. Ge, X., & Land, S. M. (2004). A conceptual framework of scaffolding ill-structured problem solving processes using question prompts and peer interactions. Educational Technology Research and Development 52(2), 5-27. Graesser, A. C., Person, N. K., & Magliano, J. P. (1995). Collaborative dialogue patterns in naturalistic one-to-one Tutoring. Applied Cognitive Psychology, 9, 495-522. Graesser, A. C., VanLehn, K., Rose, C., Jordan, P., & Harter, D. (2001). Intelligent tutoring systems with conversational dialogue. AI Magazine, 22, 39–51. Hmelo, C., & Day, R. (1999). Contextualized questioning to scaffold learning from simulations. Computers & Education, 32, 151-164 Katz, S., O'Donnell, G., & Kay, H. (2000). An approach to analyzing the role and structure of reflective dialogue. International Journal of Artificial Intelligence and Education 11, 320-333. Kemmis, S. (1985). Action research and the politics of reflection. In D. Boud, R. Keogh & D. Walker (Eds.), Reflection: Turning experience into learning (pp. 139-163). New York: Nichols. King, A., Staffieri, A., & Adelgais, A. (1998). Mutual peer tutoring: Effects of structuring tutorial interaction to scaffold peer learning. Journal of Educational Psychology, 90(1), 134-152. Knight, B. (1996). Reflecting on 'reflective practice. Studies in the Education of Adult, 28(2), 162-184. Kolb, D. A. (1984). Experiential Learning: Experience the Source of Learning and Development. NJ: Prentice Hall. Lai, G. (2008). Examining the effects of selected computer-based scaffolds on preservice teachers’ levels of reflection as evidenced in their online journal writing. PhD Thesis, Georgia State University, Atlanta, GA. Leelawong, K. (2005). Using the learning-by-teaching paradigm to design intelligent learning environments. PhD Thesis, Vanderbilt University. Lynch, M., & Joham, C. (2004). Reflection in Self-organised Systems, Information Systems Foundations: Constructing and Criticising ISF Proceedings. Canberra: ANU. 352
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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
Page 305 and 306: (Gradinarova, Zhelezov et al. 2006)
Page 307 and 308: RLO denotes a remote learning objec
Page 309 and 310: Figure 2. The flow-oriented LOFinde
Page 311 and 312: 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: Generic Prompts and Specific Prompt
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 and 364: Figure 7 shows a screenshot of one
Page 365 and 366: Collaboration Questionnaire results
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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