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conducting an interaction. On the contrary, for the future interaction, learners are also motivated to participate in outeraction, leading to multiple individuals available, another prerequisite of distributed cognition. Limitations While there are interesting implications for research and practice to our findings, there are also some methodological limitations inherent in our approach. While the findings are consistent with our suggestion of the interplay among the three kinds of communication, the crosssectional nature of our data limits the extent to which our logical explanations can be conclusively supported by the data. Additional research, ideally involving longitudinal data, is needed to fully address the issue of the interplay direction. Another methodological limitation concerns the generalizability of our findings. Specifically, there are issues of generalizability that relate to the restrictions that a student sample brings. The idiosyncratic nature of the sample is unavoidable in most forms of research, but we are encouraged by age of our respondents. Because the participants were all the freshmen of university, their age are so close, which could result in a restriction of range. Replication of this study in samples with a broader range of age would be a good way to address this limitation of the current study. Finally, the limitation of using a particular blended learning system developed in this study may have affected the way in which the participants learned from the course material, leading to the variation of learning performance. Conclusions In this study, in context of an blended learning course, we demonstrated that intra-action, interaction, and outeraction are positively correlated. More importantly, in statistically significant way, higher incidence of communication corresponded to better learning achievement. The tendency of higher level of intra-action activity to correspond to higher learning achievement is particularly clear. This is consistent with the widely held view that intra-action activities, such as note-taking, promote learning. Unstructured interviews conducted with students supported the view that the positive relationship between high level of intra-action and high learning outcomes is to a large degree a cause-effect one. For annotation as intra-action activity, our research indicated that only explicit annotations can be viewed as a reliable measure of intra-action, as inexplicit annotation are often applied as motor activity with no cognitive purpose. This result has implications for future blended learning systems design, as well as for the way existing systems are configured by teachers and blended learning material designers - enabling explicit annotation is likely to have more positive effect on learning outcomes than enabling inexplicit annotation. In a wider context, our results suggest that designers of blended learning courses should reconsider their practices, and put more emphasis on offering intra-action tools to students. We notice that while interaction and outeraction carry risks, such as bulling among students or inappropriate and unfair criticism of the course, intra-action tools are virtually risk free as their inappropriate use is hardly possible. Our study underlines the importance of intra-action in blended learning environments. While the study is, due to ethics considerations, of correlational nature, by showing the importance of intra-action it opens way to further studies of intra-action, which eventually may justify experimental designs demonstrating cause-effect between intraaction and learning outcomes in a more direct manner, but at the expense of certain inconveniencies for some of the human subjects involved. Acknowledgement This study was supported by the National Science Council under the grant number: NSC-96-2524-S-008-002. References Allport, G. W. (1937). Personality: A psychological interpretation, New York: Holt. 237
Bell, P., & Winn, W. (2000). Distributed cognitions, by nature and by design. In D. H. Jonassen & S. M. Land (Eds.), Theoretical Foundations of Learning Environments (pp. 123-145), Mahwah, NJ: Lawrence Erlbaum. Berge, Z. L. (1999). Interaction in post-secondary web-based learning. <strong>Educational</strong> <strong>Technology</strong>, 39 (1), 5-11. Berg, G. A. (2002). Why distance learning? Higher education administrative practices, Westport: ACE/Oryx Press. Bransford, J. D., Brown, A., & Cocking, R. (2000). How people learn: Mind, brain, experience and school, expanded edition, Washington, DC: National Academy Press. Brown, A. L., & Smiley, S. S. (1978). The development of strategies for studying texts. Child Development, 19, 1076-1088. Cameron, A. F., & Jane, W. (2005). Unintended consequences of emerging communication technologies: Instant messaging in the workplace. Computers in Human Behavior, 21 (1), 85-103. Daniel, B. K., Zapata-Rivera, J. D., & McCalla, G. I. (2003). A Bayesian computational model of social capital in virtual communities. In M. Huysman & E. Wenger & V. Wulf (Eds.), Communities and technologies (pp. 287-305), London: Kluwer. Doll, W. J., & Torkzadeh, G. (1998). The measurement of end-user computering satisfaction. MIS Quarterly, 12 (2), 259-274. Fazio, R. H., & Olson, M. A. (2003). Implicit measures in social cognition research: Their meaning and uses. Annual Review of Psychology, 54, 297-327. Garrison, D. R., & Shale, D. (1990). Education at a distance: From issues to practice, Melbourne, FL: Krieger. Giere, R. (2002). Distributed cognition in epistemic cultures. Philosophy of Science, 69, 637-644. Gutwin, C., & Greenberg, S. (1998). Effects of awareness support on groupware usability. Proceedings of the ACM Conference on Human Factors in Computing Systems (pp. 511-518), New York: ACM Press. Hinds, P. J., & Pfeffer, J. (2003). Why organizations don’t know what they know: Cognitive and motivational factors affecting the transfer of expertise. In M. Ackerman, V. Pipek & V. Wulf (Eds.), Beyond knowledge management: Sharing expertise (pp. 3-26), Cambridge, MA: MIT Press. Hollan, J., Hutchins, E., & Kirsh D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction, 7 (2), 174-196. Holmberg, B. (1989). Theory and practice of distance education, London: Routledge. Howe, M. J. A. (1977). Learning and the acquisition of knowledge by students: Some experimental investigations. In M. J. A. Howe (Ed.), Adult learning: Psychological research and applications (pp.1-16), London: Wiley. Hutchins, E. (1995). Cognition in the wild, MIT Press: Cambridge, MA. Hwang, W. Y., Wang, C. Y., & Sharples, M. (2007). A study of multimedia annotation of web-based materials. Computers & Education, 48 (4), 680-690. Jonassen, D. H. (1991). Evaluating constructivistic learning. In: T. M. Duffy & D. H. Jonassen (Eds.), Constuctivism and the technology of instruction: A conversation (pp.129-135), New York: Lawrence Erlbaum. Laurillard, D. (1997). Rethinking university teaching: A framework for the effective use of educational technology, London: Routledge. Lipman, M. (1991). Thinking in education, Cambridge: Cambridge University Press. Marshall, C. C. (1997). Annotation: From paper books to the digital library. In Proceedings of the Second ACM Conference on Digital Libraries, July 23-26, 1997, Philadelphia, Pa, USA. McClelland, J. L., Rumelhart, D. E.. & the POP Research Group. (l986). Parallel distributed processing: Exploration in the microstructure of cognition, Cambridge. MA: MIT Press. Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital and the organizational advantage. Academy of Management Review, 23 (2), 242-266. 238
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April 2009 Volume 12 Number 2
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Abstracting and Indexing Educationa
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Engaging students in multimedia-med
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this study discusses computer-based
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Sampling for preliminary study One
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Part 2 Pearson correlation .349** 1
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Figure 1. Box and whisker plot of t
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characteristics and learning styles
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However, critical reflection and in
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novels, so they collected any relat
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1. Theories of teaching: Comments m
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grammatical errors and basic writin
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Ho, B., & Richards, J. C. (1993). R
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Hwang, K.-A., & Yang, C.-H. (2009).
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e deduced from the Affective Domain
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Detection procedure Figure 1 shows
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avoided. Image processing is perfor
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falling asleep) as defined in this
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classmates or left their seats duri
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help teachers to recognize the lear
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Hsieh, P.-H., & Dwyer, F. (2009). T
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significant comprehension effects o
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Treatment 3 (keyword group): Studen
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Criteria of achievement measures Th
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esults. A correlational analysis de
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A 2 x 1 ANOVA analyzed the effect o
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References Aragon, S. R. (2004). In
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Raphael, T. (1982). Improving quest
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educing maintenance costs. The cont
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Synchronization functions According
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inconsistency) and navigate directl
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the suitability of the instructiona
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Design of manipulation process The
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Table 1: Comparative analysis of au
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the convenience of mobile-based ass
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According to evaluation result, the
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Tan, O. S., Parsons, R. D., Hinson,
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At a deeper level, though, there wa
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theoretical assumptions. Two theore
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The students were not going to have
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slightly different. The group who c
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every item, not giving any result.
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edited and presented to the class u
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Figure 10. Screen captures from the
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Some of the concerns that emerge fr
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Ajayi, L. (2009). An Exploration of
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As a result of the confluence of di
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peers (Schellens et al. 2005). Sche
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The integration of discussion board
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ecause the technology allowed the p
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The pre-service teachers perceived
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Furthermore, the study demonstrated
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Schellens, T., van Keer, H., & Valc
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McKean, 2003). Instructors will see
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program at The Ohio State Universit
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Knowledge and skills of developing
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their monthly reflections or assign
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Bull, K., Montgomery, D., Overton,
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frustrating the student). ITSs make
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• Link removal - advanced student
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Expert Module Expert Module’s mai
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Based on the questionnaire results
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students were told that their perfo
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thirds of the interviewed students
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Throughout the semester, students i
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human teacher or question developer
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To test the learning effectiveness
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APPENDIX A Computer Science and Sof
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Thus, the use of computers changes
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Specifically, two objectives were p
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Nonetheless, both teachers assign a
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48% 35% 53% similar similar 69% 42%
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Table 4: Students’ attitudes Stat
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activities. Specifically, the histo
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Huang, Y.-M., Huang, T.-C., Wang, K
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• Could recommended learning sequ
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Analysis of sequences prediction me
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Figure 4. The sorting of learning s
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The figures 5a and 5b illustrate th
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Learning sequence recommendation sy
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In this study, forty subjects were
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The potential for LSRS to promote l
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They pointed out that LSRS helped t
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Cover, T. M., & Thomas, J. A. (1991
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Related Efforts An Internet forum i
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with handheld devices and they were
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agent would direct learners to a le
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Mobile RSS Aggregator In order to s
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learning, such as posting question
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With novel technological support, m
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Shen, L., Wang, M., & Shen, R. (200
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as an ‘affective loop’, which r
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Figure 2 is an example of two-dimen
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preference was gathered through dat
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compute the heart rate (HR) as a fu
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Page 195 and 196: Wu, C.-C., & Lai, C.Y. (2009). Wire
Page 197 and 198: The wireless handheld learning envi
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Page 201 and 202: Nursing dictionary We constructed a
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Page 207 and 208: questionnaire. We do not consider t
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Page 211 and 212: etween a user and the system. Addit
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Page 223 and 224: The PADS sometimes assigned very di
Page 225 and 226: Corno, L. (2000). Looking at Homewo
Page 227 and 228: Hwang, W.-Y., Hsu, J.-L., Tretiakov
Page 229 and 230: listed these functions as allowing
Page 231 and 232: In the research presented in this a
Page 233 and 234: Research Tools Web-based learning e
Page 235 and 236: Table 1. Operational definitions of
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Page 241: Table 6 shows the learners’ prefe
Page 245 and 246: Askar, P., & Altun, A. (2009). CogS
Page 247 and 248: elations, interactions and activiti
Page 249 and 250: Figure 3. A comparison of knowledge
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Page 253 and 254: Figure 7. Visual Representation of
Page 255 and 256: Step 5: Use Boolean to combine the
Page 257 and 258: 2) CogSkillNet provides classroom i
Page 259 and 260: Neo, M., & Neo, T.-K. (2009). Engag
Page 261 and 262: • Conversation and collaboration
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Page 265 and 266: Methodology At the end of the proje
Page 267 and 268: presentation skills (m = 3.72), and
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Page 271 and 272: Lambert, N. M., & McCombs, B. J. (1
Page 273 and 274: not detected in a previous study, t
Page 275 and 276: et al. (2001) studied the learning
Page 277 and 278: all the students. At the midpoint o
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Page 281 and 282: Table 2 shows the results from a mo
Page 283 and 284: Table 5: The distribution of time (
Page 285 and 286: As discussed in the section on prev
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Page 289 and 290: Comprehension monitoring is the awa
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Correlation coefficient was also co
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(a) Text 1 (b) Text 2 (c) Text 3 (d
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Table 3 shows that the average read
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Figure 10. Trace results of the les
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monitor their reading process and h
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Conn, S. R., Roberts, R. L., & Powe
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The technology-mediated groups, wit
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hybrid model of supervision was pos
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Our research indicates that the hyb
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Janoff, D. S., & Schoenholtz-Read,
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goal of using storyboards. But, thi
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In general, learning activities nee
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Furthermore, free subjects, such as
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Storyboarding Roots and related wor
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Referees, on the other hand, may wa
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Symbols Interpretation Defines a un
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Figure 4. Top-level storyboard for
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may take subjects such as practical
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network security information envir
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The concrete procedure and interact
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(e.g., for graduation) using such i
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Thus, academic education modeling b
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Feyer, T., Kao, O., Schewe, K.-D.,
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Richards, G. (2009). Book review: T
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expands the child’s proximal envi
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Chapter 6 exemplifies the activitie
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