File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/ICALT.2012.66
- Scopus: eid_2-s2.0-84867011829
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Toward a complete e-learning system framework for semantic analysis, concept clustering and learning path optimization
Title | Toward a complete e-learning system framework for semantic analysis, concept clustering and learning path optimization |
---|---|
Authors | |
Keywords | Concept clustering Learning objects Learning path optimization Learning styles Complete system |
Issue Date | 2012 |
Publisher | IEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000009 |
Citation | The IEEE 12th International Conference on Advanced Learning Technologies (ICALT 2012), Rome, Italy, 4-6 July 2012. In Proceedings of the 12th ICALT, 2012, p. 592-596 How to Cite? |
Abstract | Most online e-learning systems often demand the pre-requisite requirements between course modules and/or some relationship measures between involved concepts to be explicitly inputed by the course instructors so that an optimizer can be ultimately used to find an optimal learning sequence of involved concepts or modules for each individual learner after considering his/her past performance, learner's profile, learning style, etc. However, relying solely on the course instructor's input on the relationship among the involved concepts can be imprecise possibly due to the individual biases by human experts. Furthermore, the decision will become more complicated when various instructors hold conflicting views on the relationship among the involved concepts that may hinder any reasonable deduction. Therefore, we propose in this paper a complete system framework that can perform an explicit semantic analysis on the course materials, possibly aided by the relevant Wiki articles for any missing information about the involved concepts, to formulate the individual concepts, and followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures. Lastly, an evolutionary optimizer will be used to return the optimal learning sequence after considering multiple experts' recommended learning sequences possibly containing conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework. Our empirical evaluation clearly revealed the possible advantages of our proposal with many possible directions for future investigation. © 2012 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/165160 |
ISBN |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tam, V | en_US |
dc.contributor.author | Lam, EYM | en_US |
dc.contributor.author | Fung, ST | en_US |
dc.date.accessioned | 2012-09-20T08:15:55Z | - |
dc.date.available | 2012-09-20T08:15:55Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | The IEEE 12th International Conference on Advanced Learning Technologies (ICALT 2012), Rome, Italy, 4-6 July 2012. In Proceedings of the 12th ICALT, 2012, p. 592-596 | en_US |
dc.identifier.isbn | 978-0-7695-4702-2 | - |
dc.identifier.uri | http://hdl.handle.net/10722/165160 | - |
dc.description.abstract | Most online e-learning systems often demand the pre-requisite requirements between course modules and/or some relationship measures between involved concepts to be explicitly inputed by the course instructors so that an optimizer can be ultimately used to find an optimal learning sequence of involved concepts or modules for each individual learner after considering his/her past performance, learner's profile, learning style, etc. However, relying solely on the course instructor's input on the relationship among the involved concepts can be imprecise possibly due to the individual biases by human experts. Furthermore, the decision will become more complicated when various instructors hold conflicting views on the relationship among the involved concepts that may hinder any reasonable deduction. Therefore, we propose in this paper a complete system framework that can perform an explicit semantic analysis on the course materials, possibly aided by the relevant Wiki articles for any missing information about the involved concepts, to formulate the individual concepts, and followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures. Lastly, an evolutionary optimizer will be used to return the optimal learning sequence after considering multiple experts' recommended learning sequences possibly containing conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework. Our empirical evaluation clearly revealed the possible advantages of our proposal with many possible directions for future investigation. © 2012 IEEE. | - |
dc.language | eng | en_US |
dc.publisher | IEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000009 | en_US |
dc.relation.ispartof | IEEE International Conference on Advanced Learning Technologies Proceedings | en_US |
dc.subject | Concept clustering | - |
dc.subject | Learning objects | - |
dc.subject | Learning path optimization | - |
dc.subject | Learning styles | - |
dc.subject | Complete system | - |
dc.title | Toward a complete e-learning system framework for semantic analysis, concept clustering and learning path optimization | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Tam, V: vtam@eee.hku.hk | en_US |
dc.identifier.email | Lam, EYM: elam@eee.hku.hk | en_US |
dc.identifier.email | Fung, ST: h0609963@hku.hk | - |
dc.identifier.authority | Tam, V=rp00173 | en_US |
dc.identifier.authority | Lam, EYM=rp00131 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ICALT.2012.66 | - |
dc.identifier.scopus | eid_2-s2.0-84867011829 | - |
dc.identifier.hkuros | 206479 | en_US |
dc.identifier.spage | 592 | en_US |
dc.identifier.epage | 596 | en_US |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 130402 | - |