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shown when a student logs into the WCSS. The student can click one of the three hyperlinks to link to a respective exercise web page. An exercise web page (Figure 4) consists of four sections: problem description, concept, discussion, and submission. A student receiving an exercise first must understand the programming problem and then plan the program based on the problem description. If the student does not know how to solve the problem, then the concepts shown on the concept section can remind the student which concepts should be adopted to solve the problem. Students who are not familiar with these concepts can learn about them on the e-dictionary system through the hyperlinks in the concept section. Students who still cannot solve the problem after using the e-dictionary system can post questions on the discussion forum via the hyperlink in the discussion section. Meanwhile, two capable classroom peers chosen for each question by the peer recommendation system are notified to resolve the assigned questions together with the questioner. Ideally, the student should be able to complete the exercise through this process. Furthermore, the exercise web page presents hyperlinks to the e-dictionary and peer recommendation system, giving students access to assistance, including learning products distributed in different web pages and online capable classroom peers, through links on a single web page. Personalized assignment dispatching system Figure 4. The layout of an exercise web page The PADS applies decision tree analysis methodology to construct a decision model that can determine whether a programming exercise has an appropriate difficulty level for a student. The decision tree analysis originates from the machine learning discipline, which can induce rules for accurately predicting a target attribute from feature attributes. C5.0 is a machine learning tool for generating decision trees and was developed by Quinlan (Quinlan, 1996). A trial version is available on the Internet at http://www.rulequest.com. The induction performed by C5.0 is based on the value of entropy (Mitchell, 1997). For instance, programming exercises can be clustered into a set of groups based on the values of feature attributes. If programming exercises in a cluster have the same value of the target attribute, then the cluster has the lowest entropy, while if every programming exercise in a cluster has a different value of the target attribute, then the class has the highest entropy. The decision tree tool is used to generate a tree with the minimum entropy. 209
C5.0 can help teachers to generate a decision tree from the observed data. The procedure used by C5.0 in generating a decision model comprises several serial steps. First, the feature attributes and the target attribute must be selected. Second, teachers must determine how to measure each attribute. Third, the teachers collect observed data composed of a set of instances, described by a fixed set of attributes and their values. Finally, the observed data are input into C5.0, and a decision model is generated. The detailed steps follow. Determine attributes and measure the level of the attributes The first step in generating a decision model is to determine the feature attributes and a target attribute. The feature attributes adopted in the decision tree analysis methodology should be associated with the target attribute. The target attribute in this case is whether a programming exercise is suitably difficult for a student. Specific attributes are applied to evaluate the difficulty level of a problem in some studies. For example, Lee and Heyworth (2000) evaluated the complexity of a logarithm problem to a student according to the number of steps needed by the computer system to solve the problem, the number of operators (+, −, *, /, etc.) in the problem expression and the degree of familiarity of the problem to the student; Beck et al (1997) used the proficiency of sub-concepts in a problem to determine its difficulty level. Similarly, to solve a programming problem, a student must be familiar with the related programming concepts and possess an appropriate level of ability in algorithm analysis. Thus, a programming exercise that is suitably difficult for a student may be of a higher algorithm or programming concept complexity level than one that the student can complete without any assistance, but it can be completed with assistance. Therefore, this work selected five attributes: the student’s proficiency level in main concepts used for completing the exercise, the student’s proficiency level in sub-concepts used for completing the exercise, the complexity level in the algorithm of the exercise, the number of lines in program code used for completing the exercise, and the student’s proficiency level in algorithm analysis, as feature attributes. In addition, two other attributes, the coursework grade of the student’s last assignment and login times in the web-based learning system during the last two weeks, were also selected as feature attributes. These two attributes were chosen because the prior coursework performance and the learning motivation (We assume that students performing more login times may associate with that they have a higher learning motivation in learning) may be both related to the target attribute. For example, students having greater learning motivation are willing to spend more time and efforts to solve a more difficult problem. Additionally, students with high prior coursework grade may understand more strategies and knowledge in solving a difficult programming problem. Therefore, this work used seven feature attributes to generate a decision model. Nine experts (TAs who had learned Java programming for two years and worked as TA for over one year) identified concepts for each exercise. In an exercise, the main concepts are those concepts that are central to the exercise, and sub-concepts are those concepts that have a secondary relationship to it. Item–Objective Consistency (IOC) (Rovinelli & Hambleton, 1977) was used to identify the main concepts and sub-concepts. Figure 5 presents an example that demonstrates how IOC is applied. The X-axis represents the nine experts; the Y-axis represents all concepts that the teacher has instructed in the last two weeks, and the Z-axis represents the ten candidate exercises. Each square contains one of three values, +1, 0, and −1. The value +1 indicates that the expert explicitly confirms that the concept is required by the exercise. The value 0 indicates that the expert non-explicitly confirm the concept demanded by the exercise. The value −1 indicates that the expert can explicitly confirm that the concept is not required by the exercise. The nine experts calculated the IOC for each concept for each exercise after filling in the squares. A concept is a candidate for main concepts when IOC ≥ 0.8 or a candidate for sub-concepts when 0.5 ≤ IOC
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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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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 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
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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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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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