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Nardi, B. A., Whittaker, S., & Bradner, E. (2000). Interaction and outeraction: Instant messaging in action. Proceedings of the Conference on Computer Supported Cooperative Work (pp.79-88), New York: ACM press. Neter, J. & Wasserman, W & Kutner, M. K. (1985). Applied linear statistical models: Regression, analysis of variance, and experimental designs, Homewood, Il: Richard D Irwin. Parker, A. (1999). Interaction in distance education: The critical conversation. Educational Technology Review, 12 (1), 13-17. Riding, R. J., & Rayner, S. (1998). Cognitive styles and learning learning strategies, London: David Futton. Ron M. (2003). Enterprise IM goes mainstream. EContent, Aug/Sep, 32-37. Frees, S. E., & Drew Kessler, G. (2004). Developing Collaborative Tools to Promote Communication and Active Learning in Academia. Paper presented at the Frontiers in Education Conference, 20-23 October, Savannah, GA. Semin, G. R. (2007). Grounding communication: Synchrony. In A. Kruglanski & E. T. Higgins (Eds.), Social psychology: Handbook of basic principles (2nd ed., pp. 630-644). New York: Guilford. Semin, G. R., de Montes, G. L., & Valencia, J. F. (2003). Communication constraints on the linguistic intergroup bias. Journal of Experimental Social Psychology, 39, 142-148. Sims, R. (1999). Interactivity on stage: Strategies for learner-designer communication. Australian Journal of Educational Technology, 15 (3), 257-272. Vygotsky, L. S. (1978). Mind in society, Cambridge, MA: Harvard University Press. Wagner, E. D. (1994). In support of a functional definition of interaction. The American Journal of Distance Education, 8 (2), 6-26. Wenger, E. (2001). Supporting communities of practice: A survey of community-oriented technologies, retrieved March 16, 2009 from http://www.ewenger.com/tech/index.htm. Wenger, E., McDermott, R., & Snyder, W. (2002). Cultivating communities of practice: A guide to managing knowledge, Cambridge, MA: Harvard Business School Press. Wortzel, R. (1979). New life style determinants of women’s food shopping behavior, Journal of Marketing. 43, 28- 29. 239
Askar, P., & Altun, A. (2009). CogSkillnet: An Ontology-Based Representation of Cognitive Skills. Educational Technology & Society, 12 (2), 240–253. CogSkillnet: An Ontology-Based Representation of Cognitive Skills Petek Askar and Arif Altun Hacettepe University Dept. of Computer Education and Instructional Technologies // paskar@hacettepe.edu.tr // altunar@hacettepe.edu.tr ABSTRACT A number of studies emphasized the need to capture learners’ interaction patterns in order to personalize their learning process as they study through learning objects. In education context, learning materials are designed based on pre-determined expectations and learners are evaluated to what extent they master these expectations. Representation of these expectations in learning and assessment objects, on the other hand, is a new challenge for e-learner providers. In order to address this challenge, POLEonto (Personlized Ontological Learning Environment) proposes a new method to separate these expectations by determining domain concepts (ConceptNet) and cognitive skills (CogSkillNet) for expectations via creating cognitive skill and concept ontology for K-12 education. In this paper, we report only the development and design processes of CogSkillNet within POLEonto environment; and, we further discuss how CogSkillNet can be modeled in the e-learning domain. We also describe how ontological representations play a role in creating personalized navigational guidance for allowing visualization of cognitive skills and providing useful navigational feedback to learners. Keywords Ontology, Ontology Development, Cognitive Skills, Educational Expectations, Instructional Design Introduction Semantic web opened a new paradigm to design adaptive e-learning systems. By using the capabilities of semantic web approach, World Wide Web led the interchange of information about data (i.e., metadata) as well as documents. Such capabilities also indicated a new kind of challenge for e-learning providers to design a common framework that allows learning objects to be shared and reused within and across applications. An ontology is an explicit specification of a conceptualization (Gruber, 1995) or a model (Musen, 1998), which is used for structuring and modeling of a particular domain that is shared by a group of people in an organization (O’Leary, 1998). Domain ontologies provide explicit and formal descriptions of concepts in a domain of discourse, their properties, relationships among concepts and axioms (Guarino, 1995). Ontologies are being developed in order to make learning objects reusable (Qin & Hernández, 2006), interoperable (Biletskiy & Hirtle, 2006), and to increase the reliability of using computational (intelligent) agents in e-learning environments (Koper & Tattersall, 2005). Consequently, an ontology design is needed to equip the learning objects with the ability of semantic relations among different types of objects in educational settings. In order to model the semantic relationships, various models have been designed by various researchers and committees. In their model, for example, Bailey, et. al. (2006) use the nugget metaphor and focus on the need for assisting teachers in the thought processes involved in selecting appropriate methods, tools, student activities and assessments to suit the required learning objectives. Similarly, IMS Learning Design (LD) model proposes an application of a pedagogical model for a specific learning objective, target group, and a specific context or knowledge domain (See, Koper and Olivier, 2004). Another model is proposed by IEEE Learning Technology Standards Committee (LTSC). In this model, learning object metadata covers areas of learning technology, digital rights, metadata, and structured definitions related to instruction. These attempts to model the learning processes invited several criticisms. For example, Paquette (2007) points out that the actors’ competencies in IMS-LD are indirectly defined in learning units or activities only if educational objectives are associated. Such unstructured model for competencies makes the system unable to provide personalized learning activities for its users, since this association between learning objects within a unit of learning is a key concept for semantic web applications (Berners-Lee, Hendler, & Lassilla, 2001). Waldman, Hopkins, & Burgess (2004), on the other hand, claim that the use of metadata in IEEE-LTSC model is based on metadata schemas, which are user-defined; therefore, reusability and interoperability of learning objects and the quality of user ISSN 1436-4522 (online) and 1176-3647 (print). © International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. 240
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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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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 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
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Page 257 and 258: 2) CogSkillNet provides classroom i
Page 259 and 260: Neo, M., & Neo, T.-K. (2009). Engag
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Page 265 and 266: Methodology At the end of the proje
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Page 281 and 282: Table 2 shows the results from a mo
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Page 285 and 286: As discussed in the section on prev
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Page 289 and 290: Comprehension monitoring is the awa
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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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