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Step 2: Determine the type of the action Step 3: If encapsulated or integrated Then get the Sub-actions Repeat until all sub-actions are processed Loop all actions until reaches to base actions Else Base actions Figure 8. Learning and Assessment Objects in an Expectation Space Furthermore, the system further contextualizes the educational expectations (See Figure 9) by incorporating the nature of actions into the process as shown in the following algorithm. Input: Contextualize the expectation Output: Contextualized e-learning environment Step 1: Determine the action Step 2: Determine the nature of actions Step 3: Get the initiators Step 4: Get the terminators 249
Step 5: Use Boolean to combine the initiators and terminators with actions Step 5: Determine the context Step 6: If context is multiple Then provide pathways accordingly Action 1 Action 2 Action 3 . . Action N Sub-Action 1 Sub-Action 2 Figure 9. The structure of action processes Figure 10. A Sample View from CogSkillNet Let’s illustrate this with an example. Let’s assume a student is given a task (either as in an LO or as an AO) to make a generalization either in an assessment object or in a learning object to study. Generalization as a cognitive skill is not self-exploratory but tacit by itself. Since it is not a base action, it includes sub-processes and it is difficult for a student to analyze this process by himself/herself. If the system provides the learners sub-processes of generalization, which are information gathering, determining similarities and determining differences, the task will be more explicit for the learner to process as in the following algorithm. Input: An Expectation (i.e., students learn to generalize the principles of sliding frictions) Output: Suggest learning paths based on learners’ progress and responses . . . Sub-Action N Context 1 Context 2 . . . Context N 250
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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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sessions. Of all the 18 learning se
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Burleson, W., Picard, R. W., Perlin
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Wu, C.-C., & Lai, C.Y. (2009). Wire
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The wireless handheld learning envi
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the lengthiness of the scales, whic
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Nursing dictionary We constructed a
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Page 207 and 208: questionnaire. We do not consider t
Page 209 and 210: Faruque, F., Hewlett, P. O., Wyatt,
Page 211 and 212: etween a user and the system. Addit
Page 213 and 214: Figure 2. A concept map representin
Page 215 and 216: C5.0 can help teachers to generate
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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
Page 237 and 238: action, interaction, and outeractio
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Page 241 and 242: Table 6 shows the learners’ prefe
Page 243 and 244: Bell, P., & Winn, W. (2000). Distri
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 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
Page 263 and 264: IMAGE 2 2. Problem identification I
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
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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
Page 287 and 288: McDowell, C., Werner, L., Bullock,
Page 289 and 290: Comprehension monitoring is the awa
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Page 293 and 294: Correlation coefficient was also co
Page 295 and 296: (a) Text 1 (b) Text 2 (c) Text 3 (d
Page 297 and 298: Table 3 shows that the average read
Page 299 and 300: Figure 10. Trace results of the les
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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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