October 2011 Volume 14 Number 4 - Educational Technology ...

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The Architecture of SWiCLE Figure 4 illustrates the architecture of SWiCLE. As the figure shows, SWiCLE comprises three layers. The storage layer serves as the underlying learning repositories. The application layer consists of modules about processing and exchanging of structural information, which is accomplished by Semantic Web technologies. The UI layer provides various user interfaces for decentralized learners. The storage layer is comprised of knowledge base and content database. Knowledge base consists of ontology and semantic link networks. The former is an explicit specification of a conceptualization with respect to the specific application domains, and the latter represents various semantic relationships between learning objects. Content database deposits learning materials authored or uploaded by learners and instructors. In the application layer, semantic template comprises predefined parts of text into pages, and placeholders that are instantiated with user-supplied text when the template is included into a page. By simply adding typed links or attributes to the template text, the semantic template also allows the encapsulation of semantic annotation. Inline query enables editors to add dynamically created lists or tables to a page, thus making up-to-date query results available to readers who are not even aware of semantic queries. Parser is responsible for converting the text written by the user into learning objects. It parses the text for semantic annotations, layout directives, and links. Render takes charge of filling the page dynamically based on semantic templates, which determines the display layout delivered to learners. As extended from RAP (http://sourceforge.net/projects/rdfapi-php), the rest modules are used to create and store knowledge base. OWL Query & Export and RDF Query & Export give direct access to the formalized knowledge, which opens SWiCLE up to a wide range of external applications that will be able to use it as a background knowledge base. Regarding the UI layer, several distinctive functionalities are provided. Semantic search comprises three search modes that are detailed in the former section. With the assistance of Ontology & Content authoring module, experts or administrators can construct and modify the domain ontology, and learners can create and edit learning objects along with properties via templates. Semantic linking allows learners to annotate links between learning objects via a special type of markup, while Comment empowers learners to evaluate learning objects with certain grading and remark. Relational navigation function offers additional information on the relation the semantic link describes, which changes the way content is presented based on semantic links and enables the content aggregation from different pages. This can include enriching pages by displaying of semantically related pages in a separate link box, displaying of information that can be derived from the underlying knowledge base, or even rendering its content of a page in a different manner that is more suitable for the context (e.g. multimedia content vs. text content). Multi-view filter offers faceted browsing to learners with advanced text search and filtering functionalities. With this function, property values of learning objects that occur more often can be grouped. Learners can select the values they would like to see by checking them, and then query results that do not hold the selected values will vanish. Relevancerecommendation function is to proactively recommend learning companions and related resources that are contributed or browsed by other closely-related learning-peers. This function is implemented based on analyzing learners’ collaborative structure. Applications With an integrated, scalable and easy-to-use interface, SWiCLE serves as an entry point for learners to conveniently author, access, reuse and aggregate resources via diverse intelligent facilities, such as semantic search, relational navigation, relevance-recommendation, multi-view filter, etc. Application Scenario 1: An online course for hybrid learning As a preliminary application built on SWiCLE, we have developed an online course “Introduction to Artificial Intelligence”. By adopting task-driven collaborative learning strategy, the online course is flexibly designed to support hybrid learning. Figure 5 shows the semantic linked pages guided for relational navigation. During learning process, learners can browse learning content sequentially or click the marked blue text in the right flowing panel to study other content such as key concepts, previous task, etc. Meanwhile, learners can collaboratively edit each page 55
and comment on each page as well. To motivate students’ collaboration, the final assignment of this course comprises three stages: individual design; mutual evaluation; and modification. Figure 6 shows one student’s final assignment and mutual discussion with other students on the assignment. Additionally, as figure 7 illustrates, the course management module is especially designed for teachers allowing them to lookup students’ activities, manage course resources and etc. Herein the interface shows students’ contributions. Figure 5. Semantic linked pages for relational navigation Figure 6. Collaborative discussions on a student’s final assignment Twenty-two junior undergraduate students enrolled in the one-semester optional course. The course was carried out from March 2010 to June 2010 and took place 3 hour per week in a classroom. Besides listening in the classroom, students were required to finish one or two tasks for each learning unit through the online course platform. Each task is composed of several sequential learning steps. 56
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October 2011 Volume 14 Number 4
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Supporting Organizations Centre for
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Time-Quality Tradeoff of Waiting St
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Ibáñez, M. B., García, J. J., Ga
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Page 13 and 14: The participants were twelve non-na
Page 15 and 16: Huang, H. M., Rauch, U., & Liaw, S.
Page 17 and 18: Theoretical background Feedback, is
Page 19 and 20: There are already a number of tools
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Page 31 and 32: Collins, 1998). The appraisal theor
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Page 37 and 38: Acknowledgements This research was
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Page 43 and 44: The annotation part of the SDM onto
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Page 47 and 48: Figure 4. Semantic document browser
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Page 51 and 52: participants performed less mouse c
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Page 57 and 58: Semantic Search In contrast to the
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Page 67 and 68: Discussion and Conclusion Collabora
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Page 71 and 72: Obtaining: the first step of the li
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Page 85 and 86: Overview of the model The first res
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Page 89 and 90: Discussion This study represents a
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Page 95 and 96: Interestingly, the Universal Design
Page 97 and 98: Figure 1. Framework of the TriAcces
Page 99 and 100: Experiment This experiment aimed to
Page 101 and 102: Table 2. Descriptive statistics for
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Page 105 and 106: answer. However, students can choos
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correct for a four-option MC item i
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from −3 to 3 for any four-option
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Kansup, W. (1973). A comparison of
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The theory of mental models explain
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computer-mediated counter-arguments
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Learning in this study refers to, w
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the experiment process was anonymou
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supported. However, significant dif
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Cannon-Bowers, J. A., Salas, E., &
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Lee, Y.-H., Hsieh, Y.-C., & Hsu, C.
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Many researchers have conducted emp
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Trialability Some studies have empi
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Results Instrument validation Two c
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In this study, Amos 6.0 was employe
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Our results strongly supported the
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Fornell, C., & Larcker, D. F. (1981
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Kimmerle, J., Moskaliuk, J., & Cres
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What becomes clear from this short
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Figure 1. Distribution of informati
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Results In order to test the hypoth
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esults of the present study with th
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Kolbitsch, J., & Maurer, H. (2006).
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A conceptual framework of this stud
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eveal positive effects on students
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learning resources; and able to acc
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Discussion This study suggests that
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146). Also, role of constructivist
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eform strategy, namely, the MOE-Int
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McNaughton, S. (2002). Meeting of m
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Wu, T.-T., Sung, T.-W., Huang, Y.-M
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Ubiquitous English-Reading Learning
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include the number of times and ran
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initial value for the predetermined
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The following Eq. (18) expresses th
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Limitations This experiment adopted
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Table 3 shows the analysis of the p
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enhance learning outcomes, it impor
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Ogata, H., & Yano, Y. (2004a). Cont
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Communication seems to be more effe
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the audience only listened, one whe
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Step 5) Writing the story On the ba
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As indicated in Table 2, the differ
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References Armstrong, S. (2003). Th
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Goh, T.-T. (2011). Exploring Gender
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OPAC has been transformed into AirP
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Perceived Self-Efficacy Figure 3. H
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were asked to rate their confidence
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(2001). The correlation table begin
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Discussion and Implications Note. S
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will become important to identify i
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Parker, A. (2007). SMS - Its use in
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Find the perimeter of the rectangle
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Wait Time Management Issues related
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Precision Reduced_Precision ( T T0
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learning concept. If the Specialty
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Two criteria have been adopted to e
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Reduced Recall 0.07 0.06 0.05 0.04
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In this work, both recall-oriented
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Tsai, C.-C., Chuang, S.-C., Liang,
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confidence and self-belief in their
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Peng et al. (2006) To explore ISE &
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Torkzadeh, Chang, and Demirhan (200
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educational level, locus of control
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Internet-based learning self-effica
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Though Lee and Lee (2008) did not d
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Regarding the category of ISE resea
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Bandura, A. (1997). Self-efficacy:
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Pintrich, P. R., & de Groot, E. V.
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In formal learning, Safran, Helic,
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that culture is essential when user
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Table 3. Analysis of usage frequenc
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7 1.16 137.73 0.25 0.29 0.25 Social
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discussions freely. So Korean stude
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Johnson, L., Levine, A., & Smith, R
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Figure 1. The UE Faculty Web Portal
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2006), and consistency (Sindhuja &
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Independent Variables Dependent Var
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Meanwhile, as shown in Table 4, Ava
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design methods in the development o
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Pearson, J. M., & Pearson, A. (2008
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Clewley, N., Chen, S. Y., & Liu, X.
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As shown in this Table, Holists wil
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satisfaction or dissatisfaction wit
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8 I would prefer to learn from huma
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the examples need to be more practi
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Brotherton, J. A,. & Abowd, G. D. (
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Cobos, D. L. (2011). Book review: T
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October 2011 Volume 14 Number 4 - Educational Technology ...

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