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Hartley, J.R., & Sleeman, D.H. (1973). Towards intelligent teaching systems. International Journal of Man-Machine Studies, 5, 215-236. Holzkamp, K. (1995). Lernen: Subjektwissenschaftliche Grundlegung, Frankfurt, Germany: Campus Verlag. Jeremić, Z. (2005). An Intelligent Tutoring System for learning Software Patterns (in Serbian), Master Thesis, Belgrade, Serbia. Jeremić, Z., & Devedžić, V. (2004). Design Pattern ITS: Student Model Implementation. Proceedings of the IEEE ICALT 2004 Conference, CA: IEEE Computer Society, 864-865. Jeremić, Z., & Devedžić, V. (2004). Student Modeling in Design Pattern ITS. Paper presented at the KES 2004 Conference, September 22-24, 2004, Wellington, New Zealand. Jeremić, Z., Devedžić, V., & Gašević, D. (2004). An Intelligent Tutoring System for Learning Design Patterns, retrieved March 20, 2009, from http://www.ii.uam.es/~rcarro/AHCW04/Jeremic.pdf. Jeremić, Z., Jovanović, J., & Gašević, D. (2008). A Semantic-rich Framework for Learning Software Patterns. Paper presented at the IEEE ICALT 2008 Conference, July 1-5, 2009, Santander, Spain. Kay, J., & Kummerfeld, R.J. (1994). An Individualised Course for the C Programming Language, retrieved March 20, 2009, from http://www.cs.su.oz.au/~bob/kay-kummerfeld.html. Kim, D.W. (1995). WING-MIT: Das auf einer multimedialen und intelligented Benutzerschnittstelle basierende tutorielle Hilfesystem für das Werkstoffinformationssystem WING-M2. Paper presented at the Workshop Adaptivität und Benutzermodellierung in interactiven Systemen, October 11-13, 1995, München, Germany. Kirkpatrick, D.L. (1979). Techniques for evaluating training programs. Training and Development Journal, 33 (6), 78-92. Kirkpatrick, D. (1994). Evaluating Training Programs, San Francisco, CA: Berrett-Koehler. Le Rouzic, V., & Cusick, M.C. (1998). Immediate Evaluation of Training Events at the Economic Development Institute of the World Bank: Measuring Reaction, Self-Efficacy and Learning in a Worldwide Context. Paper presented at annual American Evaluation Association Meeting, November 4-7, 1998, Chicago, USA. Marczyk, G., DeMatteo, D., & Festinger, D. (2005). Essentials of Research Design and Methodology, NJ: John Wiley. McEvoy, G.M., & Buller, P.F. (1990). Five uneasy pieces in the training evaluation puzzle. Training and Development Journal, 44 (8), 39-42. Millan, E., & Perez-de-la-Cruz, J.L. (2002). A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation. User Modeling and User-Adapted Interaction, 12 (2-3), 281-330. Miller, S., & Butz, B. (2004). Evaluation of an Interactive Multimedia Intelligent Tutoring System, retrieved March 20, 2009, from http://www.temple.edu/imits/shock/Evaluation2.pdf. Miskelly, T. (1998). Interactive Student Modeling. Paper presented at the 36 th Annual ACM Southeast Regional Conference, April 1-3, 1998, Marietta, GA, USA. Mitrovic, A., Marting, B., & Mayo, M. (2002). Using Evaluation to Shape ITS Design: Results and Experiences with SQL-Tutor. User Modelling and User Adapted Interaction, 12 (2-3), 243-279. O’Rourke, N., Hatcher, L., & Stepanski, E.J. (2005). A Step-by-Step Approach to Using SAS® for Univariate & Multivariate Statistics (2 nd Ed.), Cary, NC: SAS Publishing. Phillips, J. J. (1997). A rational approach to evaluating training programs including calculating ROI. Journal of Lending and Credit Risk Management, 79 (11), 43-50. Prentzas, J., Hatzilygeroudis, I., & Garofalakis, J. (2002). A Web-Based Intelligent Tutoring System Using Hybrid Rules as Its Representational Basis. Lecture Notes In Computer Science, 2363, 119-128. Rising, L. (1998). The Patterns Handbook: Techniques, Strategies and Applications, New York: Cambridge University Press. Reeves, T.C., & Hedberg, J.G. (2003). Interactive learning systems evaluation, Englewood Cliffs, NJ: Educational Technology. Weibelzahl, S., Weber, G. (2003). Evaluation the Inference Mechanism of Adaptive Learning Systems. Lecture Notes in Artificial Intelligence, 2702, 154-168. Wenger, E. (1987). Artificial Intelligence and Tutoring Systems, Los Altos, CA: MorganKaufmann. 129
APPENDIX A Computer Science and Software engineering, Military academy in Belgrade, Department of Defense Serbia 2006 COURSE EXPERIENCE QUESTIONNAIRE This questionnaire is intended at collecting the information which will help us to evaluate the DEPTHS learning system from the perspective of its technical characteristics and performance as well as from the perspective of the quality and adequacy of the course it provides. The information you will provide in this questionnaire will be kept strictly confidential. Neither your name nor any details that might be used to identify you will be published. We guarantee that the data will be used exclusively for requirements and statistical analysis. 1=Never/Not at all/Not good at all 5=Always/Very much/Extremely good 1. SECTION: General, e-learning related questions How familiar are you with the electronic educational tools? 1 2 3 4 5 2. How often do you use a computer for learning? To... a)...search the Internet for relevant web pages. 1 2 3 4 5 b)...download papers or similar items (with a known Internet address) 1 2 3 4 5 c)...access a learning management system offering a course of interest 1 2 3 4 5 d)... use a web-based learning application 1 2 3 4 5 3. Computer-based learning can replace lectures and exercises 1 2 3 4 5 4. Computer-based learning should be used only to supplement classroom lectures and exercises. 1 2 3 4 5 5. I have difficulties or I just don’t like working with learning programs which are not in my mother-tongue. 1 2 3 4 5 6. Web-based learning should be nothing more than the distribution of notes over the Internet. SECTION: Evaluation questions regarding the system 1 2 3 4 5 7. Did you enjoy learning with DEPTHS? 1 2 3 4 5 8. Do you find the system easy to use? 1 2 3 4 5 9. Do you find navigational tools confusing? 1 2 3 4 5 10. Did you experience any technical problems while using the system? 1 2 3 4 5 11. If your answer to the previous question was 5 or 4 please describe the kind of problems you had. SECTION: Evaluation questions regarding the course ___________________ ___________________ 12. The learning goal, outcome, objective, and/or expectations for the course were clearly stated. 1 2 3 4 5 13. The offered course met the stated learning goal, outcome, and/or objective 1 2 3 4 5 14. The course content was well organized. 1 2 3 4 5 15. The amount of content was suitable for the length of the course. 1 2 3 4 5 16. The content of this course was too difficult. 1 2 3 4 5 17. Helpful examples were used. 1 2 3 4 5 18. I was encouraged to take responsibility for my learning. 1 2 3 4 5 19. During the course, you usually had a clear idea of where you’re going and what was expected from you. 1 2 3 4 5 20. Students have a great deal of choice over how they are going to learn in this course. 1 2 3 4 5 21. To what extent do you feel you have learned from the program? 1 2 3 4 5 22. This course can be improved by: ___________________ ___________________ 23. Please feel free to make any additional comments about this course. ___________________ ___________________ 130
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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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Page 85 and 86: edited and presented to the class u
Page 87 and 88: Figure 10. Screen captures from the
Page 89 and 90: Some of the concerns that emerge fr
Page 91 and 92: Ajayi, L. (2009). An Exploration of
Page 93 and 94: As a result of the confluence of di
Page 95 and 96: peers (Schellens et al. 2005). Sche
Page 97 and 98: The integration of discussion board
Page 99 and 100: ecause the technology allowed the p
Page 101 and 102: The pre-service teachers perceived
Page 103 and 104: Furthermore, the study demonstrated
Page 105 and 106: Schellens, T., van Keer, H., & Valc
Page 107 and 108: McKean, 2003). Instructors will see
Page 109 and 110: program at The Ohio State Universit
Page 111 and 112: Knowledge and skills of developing
Page 113 and 114: 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
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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
Page 161 and 162: In this study, forty subjects were
Page 163 and 164: The potential for LSRS to promote l
Page 165 and 166: They pointed out that LSRS helped t
Page 167 and 168: Cover, T. M., & Thomas, J. A. (1991
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
Page 177 and 178: learning, such as posting question
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
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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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them. The computers were networked
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“The major difference was that I
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questionnaire. We do not consider t
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Faruque, F., Hewlett, P. O., Wyatt,
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etween a user and the system. Addit
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Figure 2. A concept map representin
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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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