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From the evaluation results, we found that the use of the learning sequence by learners was able to significantly promote their learning transfer, a fact that has several important pedagogical implications. For teachers, the system offers learning sequencing support by providing good recommendations and constructing a learning picture that is able to clarify the learning objectives for students. The evaluation results showed that most lecturers responded positively to the use of the system for their teaching, as it allowed them to effectively utilize the limited time they had available for student assistance. In the future, we will make an effort to provide sufficient learning objects to allow instructors to create more meaningful learning maps. For learners, applicable learning sequences can be dynamically generated to target individual learners. Learning sequences inferred by the Markov chain model can narrow down and focus the whole learning process. The results revealed that there was not only a statistic significant difference in evaluating learning effectiveness; but that the learners also felt that they received benefits from the experienced learning sequences. Therefore, such learning recommendations in particular, may reduce the workload involved in searching for and accessing information, and lead to the development of better time management skills in a professional context. In addition, it is important to emphasize that the method of investigation is not without problems. Although we have measured whether learners acquired the essential elements of learning transfer, the detailed cognition and knowledge transfer processes seem not indicated from the results of the present study. We are hopeful that future research will provide more detailed results which may clearly reveal the transfer process of knowledge during students’ learning. By observing patterns of group learning, the possible pitfalls in terms of student perceptions of the worth of the learning sequences become clear, and it is obviously important that efforts should be directed towards the provision of high quality materials, rather than the provision of large quantities of material. We also may expand to a number of unanticipated nodes (or paths) for a subject, which means that the provision of learners’ selection would need to be carefully arranged so that all individual learners would be assured of some viable opportunities for extra learning over their course of study. Therefore, we will weight up the options for these recommended learning sequences and deploy the proposed system to the real-world learning environment found in our university e-learning system. Then, we believe much more research work is still needed to investigate for the scalability, performance, and fault-tolerance of the system. Given a system capable of exploiting learning patterns in e-Learning settings, statistical heuristics might provide a default track to groups’ paths. To promote learning, we will hopefully find more detailed and precise criteria to accommodate their needs, and then provide a foundation upon which an even more intelligent and responsive system can be built. Acknowledgements This work was supported in part by the National Science Council (NSC), Taiwan, ROC, under Grant NSC 95-2221- E-006-307-MY3. References ADL (2004). Sharable content object reference model (SCORM) 2004 2 nd Edition, retrieved December 27, 2008 from http://www.adlnet.gov/downloads/DownloadPage.aspx?ID=236. Borich, G. D., & Tombari, M. L. (1995). Educational Psychology: A Contemporary Approach, New York: Harper Collins College. Chan, H. G., Tsai, P. H., & Huang, T. Y. (2006). Web-based learning in a geometry course. Educational Technology & Society, 9 (2), 133-140. Chen, G. D., Chang, C. K., & Wang, C. Y. (2008). Ubiquitous learning website: Scaffold learners by mobile devices with information-aware techniques. Computers & Education, 50 (1), 77-90. Chen, J. N., Huang, Y. M., & Chu, W. C. (2005). Applying dynamic fuzzy Petri Net to Web learning system. Interactive Learning Environments, 13 (3), 159 - 178. 161
Cover, T. M., & Thomas, J. A. (1991). Elements of information theory, New York: John Wiley. De Corte, E. (2003). Transfer as the productive use of acquired knowledge, skills, and motivations. Current Directions in Psychological Science, 12, 142-146. Garland, D., & Martin, B. N. (2005). Do gender and learning style play a role in how online courses should be designed? Journal of Interactive Online Learning, 4 (2), 67-81. Gross, D., & Harris, C. M. (1998). Fundamentals of queuing theory, Virginia: John Wiley. Haskell, R. E. (2001). Transfer of learning: cognition, instruction, and reasoning, San Diego, CA: Academic Press. Heller, J., Levene, M., Keenoy, K., Hockemeyer, C., & Albert, D. (2004). An e-Learning perspective of cognitive and pedagogical aspects of trails, retrieved December 27, 2008 from http://www.dcs.bbk.ac.uk/trails/docs/D22-01- 01-F.pdf. Herring, S. (1994). Gender differences in computer mediated communication: Bringing familiar baggage to the new frontier. Paper presented at the American Library Association Annual Convention, June 27, Miami, Florida, USA. Hsu, L. L., & Hsieh S. I. (2005). Concept maps as an assessment tool in a nursing course. Journal of Professional Nursing, 21 (3), 141-149. Huang, Y. M., Chen, J. N., Huang, T. C., Jeng, Y. L., & Kuo, Y. H. (2008). Standardized course generation process using Dynamic Fuzzy Petri Nets. Expert Systems with Applications, 34 (1), 72-86. IEEE (2002). Draft standard for learning object metadata, retrieved December 27, 2008 from http://ltsc.ieee.org/wg12/files/LOM_1484_12_1_v1_Final_Draft.pdf. IMS (2003). Learner Information Package (LIP) Final Specification v1.0, retrieved December 27, 2008 from http://www.imsglobal.org/profiles/. Kabassi, K., & Virvou, M. (2003). Using Web services for personalised Web-based learning. Educational Technology & Society, 6 (3), 61-71. Lee, C. I., & Tsai, F. Y. (2004). Internet project-based learning environment: The effects of thinking styles on learning transfer. Journal of Computer Assisted Learning, 20, 31–39. McSporran, M., & Young, S. (2001). Does gender matter in online learning? Association of Learning Technology Journal, 9 (2), 3-15. Perkins, D. N., & Salomon, G. (1992). Transfer of learning, retrieved December 27, 2008 from http://learnweb.harvard.edu/alps/thinking/docs/traencyn.htm. Rajapakse, J. C., & Ho, L. S. (2005). Markov encoding for detecting signals in genomic sequences. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2 (2), 131 -142. Richman-Hirsch, W. L. (2001). Posttraining interventions to enhance transfer: The moderating effects of work environments. Human Resource Development Quarterly, 12 (2), 105-120. Sarukkai, R. (2000). Link prediction and path analysis using Markov chains. Computer Networks, 33 (1-6), 377-386. Scanlon, E. (2000). How gender influences learners working collaboratively with science simulations. Learning and Instruction, 10, 463-481. Thorndike, E. L., & Woodworth, R. S. (1901). The influence of improvement in one mental function upon the efficiency of other functions. Psychological Review, 8, 247-261. Tian, B., Azimi-Sadjadi, M. R., Vonder Haar, T. H., & Reinke, D. (2000). Temporal updating scheme for probabilistic neural network with application to satellite cloud classification. IEEE Transactions on Neural Networks, 11 (4), 903 - 920. Tseng, S. S., Sue, P. C., Su, J. M., Weng, J. F., & Tsai, W. N. (2007). A new approach for constructing the concept map. Computers & Education, 49 (3), 691-707. Weinman, J., & Cain, L. (1999). Technology: The new gender gap. Technos, 8 (1), 9-12. Zhu, X. H. (2007). Extending the SCORM specification for references to the open content object. Educational Technology & Society, 10 (1), 248-264. 162
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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
Page 115 and 116: Bull, K., Montgomery, D., Overton,
Page 117 and 118: frustrating the student). ITSs make
Page 119 and 120: • Link removal - advanced student
Page 121 and 122: Expert Module Expert Module’s mai
Page 123 and 124: Based on the questionnaire results
Page 125 and 126: students were told that their perfo
Page 127 and 128: thirds of the interviewed students
Page 129 and 130: Throughout the semester, students i
Page 131 and 132: human teacher or question developer
Page 133 and 134: To test the learning effectiveness
Page 135 and 136: APPENDIX A Computer Science and Sof
Page 137 and 138: Thus, the use of computers changes
Page 139 and 140: Specifically, two objectives were p
Page 141 and 142: Nonetheless, both teachers assign a
Page 143 and 144: 48% 35% 53% similar similar 69% 42%
Page 145 and 146: Table 4: Students’ attitudes Stat
Page 147 and 148: activities. Specifically, the histo
Page 149 and 150: Huang, Y.-M., Huang, T.-C., Wang, K
Page 151 and 152: • Could recommended learning sequ
Page 153 and 154: Analysis of sequences prediction me
Page 155 and 156: Figure 4. The sorting of learning s
Page 157 and 158: The figures 5a and 5b illustrate th
Page 159 and 160: Learning sequence recommendation sy
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Page 163 and 164: The potential for LSRS to promote l
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Page 169 and 170: Related Efforts An Internet forum i
Page 171 and 172: with handheld devices and they were
Page 173 and 174: agent would direct learners to a le
Page 175 and 176: Mobile RSS Aggregator In order to s
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Page 179 and 180: With novel technological support, m
Page 181 and 182: Shen, L., Wang, M., & Shen, R. (200
Page 183 and 184: as an ‘affective loop’, which r
Page 185 and 186: Figure 2 is an example of two-dimen
Page 187 and 188: preference was gathered through dat
Page 189 and 190: compute the heart rate (HR) as a fu
Page 191 and 192: sessions. Of all the 18 learning se
Page 193 and 194: Burleson, W., Picard, R. W., Perlin
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
Page 203 and 204: them. The computers were networked
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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
Page 213 and 214: Figure 2. A concept map representin
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that a selected programming exercis
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differences between our system and
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1(students can complete without any
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The PADS sometimes assigned very di
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Corno, L. (2000). Looking at Homewo
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Hwang, W.-Y., Hsu, J.-L., Tretiakov
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listed these functions as allowing
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In the research presented in this a
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Research Tools Web-based learning e
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Table 1. Operational definitions of
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action, interaction, and outeractio
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for them was getting the answer as
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Table 6 shows the learners’ prefe
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Bell, P., & Winn, W. (2000). Distri
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Askar, P., & Altun, A. (2009). CogS
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elations, interactions and activiti
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Figure 3. A comparison of knowledge
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ontology will easily be extensible
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Figure 7. Visual Representation of
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Step 5: Use Boolean to combine the
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2) CogSkillNet provides classroom i
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Neo, M., & Neo, T.-K. (2009). Engag
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• Conversation and collaboration
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IMAGE 2 2. Problem identification I
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Methodology At the end of the proje
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presentation skills (m = 3.72), and
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6. Can’t be denying that, sometim
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Lambert, N. M., & McCombs, B. J. (1
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not detected in a previous study, t
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et al. (2001) studied the learning
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all the students. At the midpoint o
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The observed pairs were aware of be
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Table 2 shows the results from a mo
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Table 5: The distribution of time (
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As discussed in the section on prev
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McDowell, C., Werner, L., Bullock,
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Comprehension monitoring is the awa
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or roles in a context, such as “I
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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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