- Page 1 and 2: July 2006 Volume 9 Number 3
- Page 3 and 4: Guidelines for authors Submissions
- Page 5 and 6: Component Exchange Community: A mod
- Page 7 and 8: students negotiate in the peer-asse
- Page 9 and 10: of relevant test sheets from larger
- Page 11 and 12: of each generated test sheet is clo
- Page 13 and 14: C. An Illustrative Example Herein,
- Page 15 and 16: Figure. 2. Teacher interface for de
- Page 17 and 18: Table 4 shows the experimental resu
- Page 19 and 20: Acknowledgement This study is suppo
- Page 21 and 22: Lai, K. R., & Lan, C. H. (2006). Mo
- Page 23 and 24: Web-based peer assessment has recen
- Page 25 and 26: Ψ : Π p → [ 0, 1] is an evaluat
- Page 27: K Thus, agent K selected the most l
- Page 31 and 32: Lai, K. R., & Lin, M. W. (2004). Mo
- Page 33 and 34: identifiers to enable efficient ret
- Page 35 and 36: Research Questions We aim to design
- Page 37 and 38: conceptual clustering (FCA) Unsuper
- Page 39 and 40: (a) (b) (c) Figure 6. (a) The train
- Page 41 and 42: Given source ontological element Oe
- Page 43 and 44: Generally, a threshold value of 0.8
- Page 45 and 46: Learning Object Interoperability in
- Page 47 and 48: Klein, M. (2001). Combining and rel
- Page 49 and 50: whatever technologies are available
- Page 51 and 52: constructive feedback elicited from
- Page 53 and 54: Computer Science courses by means o
- Page 55 and 56: University of Joensuu (we used the
- Page 57 and 58: of service would help designers to
- Page 59 and 60: Gerdt, P., Kurhila, J., Meisalo, V.
- Page 61 and 62: Sierra, J. L., Fernández-Valmayor,
- Page 63 and 64: activity could be better based on t
- Page 65 and 66: García Santesmases Computing museu
- Page 67 and 68: epositories, enable different acces
- Page 69 and 70: general-purpose LMS, like WebCT. In
- Page 71 and 72: In Figure 8 we show some snapshots
- Page 73 and 74: Rehberg, S., Ferguson, D., McQuilla
- Page 75 and 76: Campbell et al, 2000; Lankes, 1995)
- Page 77 and 78: Figure 2 shows the entire layout of
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that learners cannot understand the
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of each learning factor in learning
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ac1 aci acc Class1 Classi Classc ar
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the non-fired linguistic terms. Thi
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various linguistic terms to describ
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Number of patterns with unknown lea
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Table 13. The discovered learning p
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Morimoto, Y., Ueno, M., Kikukawa, I
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Figure 2 outlines a model illustrat
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Lesson form Assessment Portfolios W
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Formal description method Developme
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Although Figure 3 is a description
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Figure 6. Screen of teacher’s por
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Von Brevern, H. & Synytsya, K. (200
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he or she resolved a correspondent
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SSTA builds the necessary formal gr
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Task Decomposition Task decompositi
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Preliminary task decomposition buil
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on mere engineering principles. Ins
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Cutshall, R., Changchit, C., & Elwo
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Data Collection Surveys were distri
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Figure 2. The factors perceived as
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References Al-Khaldi, M. A., & Al-J
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Appendix 1: Critical Success Factor
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Cagiltay, N. E., Yildirim, S., & Ak
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as well as a way of approaching lea
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case studies also yield to better u
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opportunities to choose any subject
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If everything had been designed in
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Teacher or self-study preferences:
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go between these two approaches and
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Microsoft (2000). Microsoft's clip
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One of the visualization techniques
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the interrelationships among concep
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The control group. On the first day
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among concepts although they had no
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learners to be attentive to learnin
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Jonassen, D. H. (2000). Computers a
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1985; Voyer et al.,1995) show that
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Procedure This study employed a fac
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Total (Females+Males) Spatial Visua
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Given the absence of any significan
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Ellis, T. (2001). Animating to impr
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Delwiche, A. (2006). Massively mult
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Gee (2003) argues that all knowledg
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attempted to guess which students w
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computers. There might also have be
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On the whole, the gaming community
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Bradley, C., & Froomkin, M. (2003).
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Pearce, C. (2001) Story as play spa
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How to encourage students to do exe
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dependent on field. This is an impo
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(preferences, history, etc.). The u
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expected from them. The teaching te
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Hussain, S., Lindh, J., & Shukur, G
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Moore & Ray (1999) argue for more a
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Quantitative methods The quantitati
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marginal homogeneity by comparing g
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with the attitude questions include
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materials to help teachers instruct
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Appendix A Statement 1. LEGO materi
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alternatives to high-quality, but r
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Because number of statements made i
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questions. If the student was instr
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Participant 45.16 19 2.38 6.50 .000
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condition, however, do not drastica
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Breiter, A., & Light, D. (2006). Da
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Today, MIS literature has moved for
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(Drucker, 1989). Therefore, data, p
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of the information provided. Second
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Supporting Conversations: Most of t
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Gorry, G. A., & Scott Morton, M. S.
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Deng, Y.-C., Lin, T., Kinshuk, Chan
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2. Being the platform to foster 1:1
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1. Learning devices which are hardw
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component provider is not required
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e-Bay Model vs. Amazon-model A comp
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A Scenario of Collaboration Figure
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Quality: researchers with higher po
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Gibbs, W. J., Olexa, V., & Bernas,
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Visualization of Online Discussions
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MTRDS Design MTRDS is a Web-based p
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ather than of initiating dialogue,
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dominance or apprehension, and part
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Jeong, A. (2004). Methods and tools
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Ng’ambi, D., & Johnston, K. (2006
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understanding of how students acqui
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A message is not only an expressive
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Figure 2: DFAQ screen showing the n
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Comparing the results of the same s