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https://ptsldigital.ukm.my/jspui/handle/123456789/513484Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Nor Azan Mat Zin, Associate Prof. Dr. | - |
| dc.contributor.author | Saman Shishehchi (P49059) | - |
| dc.date.accessioned | 2023-10-16T04:37:10Z | - |
| dc.date.available | 2023-10-16T04:37:10Z | - |
| dc.date.issued | 2014-03-12 | - |
| dc.identifier.other | ukmvital:74840 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/513484 | - |
| dc.description | Learners have different learning abilities due to prior knowledge background, personalities and intellectual abilities. Previous researches indicate that personalization in computer based educational system help enhance learning. In adaptive systems the recommender module helped personalized learning and this improve the efficiency of learning. However the recommender module is rarely developed for educational system. There are some tutoring and recommender systems developed to teach programming languages. However, they only teach how to program and recommend the best solution for learner's error in the programming syntax. Majority of them use only quizzes to evaluate learner's performance whereas this can be done accurately using more criteria. Furthermore, despite the significance of initial knowledge evaluation in adaptive educational systems, they suffer from lack of structural knowledge evaluation method. Hence this research proposes an adaptive educational recommender system framework for Visual Basic.Net subject using ontological domain and semantic rule (VBNR). The framework consists of knowledge-base, initial knowledge evaluation and recommender modules. The knowledge-base module is the VBNET ontology which represents knowledge about the learners and learning domain as well as the relationship among them. The learner's knowledge (static and dynamic) and domain knowledge (learning topics in VB.Net and related learning activities) are integrated in this ontology. The knowledge evaluation module has two tests whereby the questions are constructed based on Bloom's taxonomy. The recommender module has three levels of recommender: learning topic, learning path and learning activity recommenders. Furthermore, there are rules to update the ontology once information is created or changed. The system analyses learner's request and extract all logical relations among the request with the other learning topics from the ontology. Then suitable learning activities are recommended based on learner's current knowledge level and performance. The time and the quizzes score are considered to enhance the quality of performance evaluation. Evaluation of the system was carried out by comparing this system against the nonrecommender learning system where 39 students were involved as respondents. The similarity of learner's request and initial knowledge level were observed between comparable learners. Results demonstrates that this recommender system enhance the efficiency of learning; respondents who used the system spent 10.3hours compared to those who used non-recommender system (14.6hours). The recommender system reduce the time spent learning the same content. There is also a significant difference in the percentage gain in knowledge level of topics between learners who used the recommender system (21%) and those who used the non-recommender system (5%). Furthermore, usability test of the system give the mean values for each construct (Usefulness, Ease of use, Ease of learning and Satisfaction) as> 4 (out of 5 maximum scale), and the average mean is 4.48, which indicate that the system is useful, easy to use and learn and users are satisfied with the system.,PhD | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | - |
| dc.rights | UKM | - |
| dc.subject | Semantic recommender | - |
| dc.subject | Recommender framework | - |
| dc.subject | Visual basic.net | - |
| dc.subject | Tutoring system | - |
| dc.subject | Universiti Kebangsaan Malaysia -- Dissertations | - |
| dc.title | Semantic recommender framework for visual basic.net tutoring system | - |
| dc.type | Theses | - |
| dc.format.pages | 211 | - |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_74840+Source01+Source010.PDF Restricted Access | 3.6 MB | Adobe PDF |  View/Open |
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