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knowledge structure that connects the subjects (Question 2, Table 8). The reason they pointed out is the deficiency of helpful information, which is able to construct the knowledge structure within a curriculum. Also, they signified that LSRS should provide the substantial meaning behind recommended learning sequences rather than only provide the concepts that are similar between each pair of subjects. Thus, the transfer of learning would perform well after taking the meaning of learning sequences into account. Other than that, over half of teachers decided to take the learning transfer into consideration during future course-design, but some of them thought that the situation should depend on the features of the subjects (Question 3, Table 8). The opposite teachers believed that some subjects possess very high levels of independence from, rather than connections with, other subjects. Therefore, from the teachers’ perspective, LSRS would be better if it provided more meaningful learning sequences, which should depend on the different features of each subject. Table 8. The research questions to teachers and their responses Teachers’ Choices Question (1) The LSRS made teaching for learning transfer Strongly approve & approve (%) Neutral (%) Oppose & Strongly oppose (%) Mean possible in the Web-based learning 70 26 4 4.0 environment. (2) The LSRS appropriately illustrated knowledge structure of the subjects. the 26 31 43 2.7 (3) I will take the suggestions provided from LSRS into consideration while I am designing the courses for the next semester. (4) With the system’s assistance, students can 57 4 39 3.3 track the related learning elements (concepts) among different subjects. 65 31 4 3.9 Question (1) My interaction with LSRS was clear and understandable. (2) The learning sequences provided by LSRS rendered an understandable relation between each of the two subjects. (3) The learning sequences introduced by LSRS effectively helped me to explore a new subject or knowledge domain. (4) During this semester, LSRS provides a macro view of the curriculum which consists of three subjects chosen. Table 9. The research questions to learners and their responses Learners’ Choices Strongly approve & approve (%) Neutral (%) Oppose & Strongly oppose (%) Mean 88 10 2 4.3 51 5 44 3.1 61 30 9 3.8 44 30 26 3.3 The results indicate that 61% of learners gave positive views of the recommended learning sequences, which can help them explore a new subject (Question 3, Table 9). Some of them expressed the view that they had no idea how to choose the most useful subjects, while the fact that only three subjects can be chosen was stipulated in advance. Herein, LSRS provided them a few directions to help them choose the subjects and connect their existing knowledge from previous subjects with a new subjects or knowledge domain. However, 9% of students expressed the opinion that the learning sequences only displayed the connections between each pair of subjects rather than explaining how to actually apply the learned knowledge or skills to the next subject. The results also corresponded with the statistical findings mentioned earlier, which were regarded as a limitation of LSRS. Other than that, 44% expressed positive views about the assistance of LSRS for enhancing their cognition in a semester curriculum (Question 4, Table 9). 159
They pointed out that LSRS helped them to choose a coherent curriculum. Despite the fact that nearly 50% of students gladly accepted the curriculums recommended by LSRS, 26% of students were still not predisposed to choosing the curriculums arranged in advance. They expressed the idea that the subjects within the recommended learning sequences were usually found in the similar domain, while regarding subjects from different domains, LSRS seemed not to offer a macro view of the curriculum. The most important finding from this result suggests that LSRS should improve its recommendation ability for cross-domain subjects. According to above mentioned limitations, additional research focusing on these aspects would be of great interest and value in understanding the role of crossdomain subjects and their influence on transfer of learning. The usability of LSRS for experimental group learners The responses to questions 1 and 2 (see Table 9) summarize the results obtained for evaluation of the perceived usability of LSRS, and the clearness and understandability of the learning sequences. Opinions extracted from questions 1 and 2 in the semi-open ended questionnaire denoted how the learner perceived the system user interface and the objects display presented in LSRS. 88% of learners responded that the LSRS user interface is very friendly and that the system is easy to use (Question 1, Table 9). Additionally, 51% of learners expressed the positive views about the clear display of the learning sequences while 44% of learners disagreed on this point (Question 2, Table 9). The mixed results imply that the system can still be greatly improved. Some of the opposite learners expressed that, although LSRS provides a clear display of learning sequences and the connections among subjects, the presented learning sequences should include more information about the linking relations, such as which textbooks have caused the relations or related assessments between two subjects. In this regard, more research is needed on the effects of more displayed information toward effective transfer of learning. The LSRS’s effectiveness for learners of different genders Gender based differences in learning and behavior have long been recognized as an important issue on educational research (Herring, 1994; Scanlon, 2000; Weinman & Cain 1999). Furthermore, the importance of investigating whether or not gender differences exist in web-based learning settings is also reported in the literature (Garland & Martin, 2005; Lee & Tsai, 2004; McSporran & Young, 2001). However, the findings of these studies have been mixed. In this study, an independent-sample t-test was conducted to examine whether or not differences in learning transfer related to gender exist in the web-based learning environment. The results revealed no significant difference between males and females. Perhaps future research could examine different samples from different learning domains, and provide more detailed results, which may differentiate these views from one another. Overall, the above evaluations reflect positively on our efforts since most respondents gave positive feedback regardless of whether they were teachers or learners. Both positive and negative feedback gives us more information to analyze so that we can offer more precise levels of individualization for each user in the future. Conclusion When effective group patterns are successfully applied, the likelihood of positive learning achievement and students’ satisfaction with group activities is significantly increased. Alternatively, when students encounter obstacles and feel frustrated because they do not use the experienced group-learning patterns for assistance, or do not believe that continued searching the available material is useful, web based learning can become a cause of stress, and its benefits to users are reduced. Therefore, it is all the more important for teachers to have a well planned learning sequence in hand when designing a course and ensure it leads learners to achieve transfer of learning. In this paper, we have proposed a dependable learning sequence recommendation system. By employing the Markov chain model and an entropy-based approach, we discovered learning sequences and formed a recommended learning map that allowed learners to set their own learning pace in Web-based educational settings. The proposed approach successfully discovered applicable learning sequences so that learners could avoid redundant or otherwise ineffective learning behaviors. 160
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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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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
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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
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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
Page 161 and 162: In this study, forty subjects were
Page 163: The potential for LSRS to promote l
Page 167 and 168: Cover, T. M., & Thomas, J. A. (1991
Page 169 and 170: Related Efforts An Internet forum i
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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
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Page 189 and 190: compute the heart rate (HR) as a fu
Page 191 and 192: sessions. Of all the 18 learning se
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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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C5.0 can help teachers to generate
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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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