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Why is a need for ontology to represent cognitive skills? CogSkillNet ontology has been proposed to address two major needs. First, the ontology design in an expectationbased education context includes concept space and skills space (See Figure 2). The elements in an expectation represent a conceptual model, which formally constraints the semantics of the concept taxonomy based on explanations formulated in natural language. Skills are dynamic in nature. Therefore, such dynamic relations are needed to be included in an ontology in order to show the execution of a skill within a cognitive process. Therefore, there is a need to define skills and their relational axioms in a dynamic approach. Figure 2. Structure of an expectation: Concepts and Skills The second one is related to the architectural specifications. From the proposed specifications, the IMS Learning Design (IMS LD) (2003) has emerged as the de facto standard that addresses the ontological modeling of any specification for learning design. XML-Schema language is suggested and used when modeling the IMS LD specification in order to facilitate the interoperability between software systems. CogSkillNet presents a new idea of modeling skills for educational context. In their comparison of conceptual modeling methods in databases (DB), knowledge representation formalisms (KB), and Semantic Web ontology languages (SW-onto), Ding, Kolari, Ding, Avancha, Finin, and Joshi (2006) assert that the Semantic Web significantly improves visibility and extensibility aspects of knowledge sharing in comparison with the previous approaches (See Figure 3). Its URI-based vocabulary and XML-based grammar are key enablers to web-scale knowledge management and sharing. Thus, by using semantic web ontology languages, CogSkillNet is aimed to increase usage, sharing and management of learning and assessment objects in educational contexts. 243
Figure 3. A comparison of knowledge representation formalisms (KB), conceptual modeling methods in databases (DB), and Semantic Web ontology languages (SW-onto) Method Built-in semantics Simple It is a well-accepted fact that there is not a single way of developing ontology (Noy & McGuinness, 2001). Building object-oriented applications with reusable learning objects based on semantic webbing techniques require a sequence of steps to build such an application (Askar, Altun, Kalinyazgan, & Pekince, 2007). In order to reach CogSkillNet ontology, a five-step ontology development process, with an iterative approach to ontology development, has been applied (adapted from McGuiness, 1999). These steps were: 1. Enumerate important terms in the ontology 2. Define the classes and the class hierarchy 3. Define the properties/relations of classes 4. Create Instances 5. Create axioms/rules When designing the ontology, Protégé was used as an ontology editor. The visualizations are captured by TGViz and Jambalaya plug-ins embedded in Protégé. In the following section, these steps are explained in detail. CogSkillNet Development Process Enumerating Important Terms Rich DB OODB RDB Description Logic Frame Semantic Net OWL RDFS RDF Local Web Visibility A term in CogSkillNet refers to an entity, which represents a textual representation of a cognitive skill. Skills are conceptual representations of cognitive processes in an action. At the first step, important terms regarding the cognitive skills in K-12 educational curricula have been determined by using bootstrapping method. Bootstrapping is a technique that enables the designers to pull out their corpus from domain-specific corpora. These terms were clustered according to their functional and semantic relations based on pre-determined expectations in K-12 educational curricula. These clusters help disambiguate the repeated skills within each subject matter; for example, does the action word “state” in a document refer to an idea (i.e., liberal arts), a calculation result (i.e., mathematics), or a measurement (i.e., an interdisciplinary critical thinking activity). Consequently, when a single skill is repeated several times, semantic relations for each skill become evident. KB SW-onto 244
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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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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 213 and 214: Figure 2. A concept map representin
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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 241 and 242: Table 6 shows the learners’ prefe
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Page 245 and 246: Askar, P., & Altun, A. (2009). CogS
Page 247: elations, interactions and activiti
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Page 253 and 254: Figure 7. Visual Representation of
Page 255 and 256: Step 5: Use Boolean to combine the
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
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Page 265 and 266: Methodology At the end of the proje
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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
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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
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Page 289 and 290: Comprehension monitoring is the awa
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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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