||A Semantic-Aware Personalized Course Recommendation and Composition for e-Learning Systems
||Department of Engineering Science
The energetic development of the Internet, especially on the web page interaction technology, has made distant e-learning systems become more and more realistic and popular in the past ten years. Problems due to technology shortcomings, however, gradually emerge when using current e-learning systems, among which, how to compose personalized courses dynamically in accordance with a user’s intention is the largest challenge. One of the advantageous prospects of e-learning systems comes from the easiness of achieving personalized learning, which is virtually impossible in traditional classrooms; but the lack of proper technologies has been blocking the dream from coming true. This thesis proposed a semantic-aware approach that makes an e-learning system able to infer a user’s query and then recommend a personalized course according to the user’s preference and intention. The proposed approach uses a semi-automatic ontology constructing mechanism, developed in this research, to build domain knowledge ontology of different courses. By using a constructed ontology, the approach can analyze a user’s query and understand what concepts in a specific domain the user is intending to learn. It then uses a hybrid recommendation model, developed also for the proposed approach, to recommend suitable learning objects according to a user’s preference and intention. In the last phase of the approach, the personalized course composition, an adapted discrete particle swarm optimization is used to promote the performance of picking suitable learning objects and a smooth reading order is constructed for the user’s comfortable learning and reading. From the experimental results, it shows that personalized courses dynamically composed by the proposed approach can satisfy different users’ needs with their feedbacks indicate that the recommended domain concepts conformed to their learning intentions and the picked learning objects fit their preferences.
List of Tables VI
List of Figures VII
CHAPTER 1 Introduction 1
1.1 Motivation 1
1.2 Goals 3
1.3 Contributions 4
1.4 Organization of this dissertation 5
CHAPTER 2 Literature Review 6
2.1 Learning Object Metadata (LOM) 6
2.2 Ontology Overview 8
2.2.1 Ontologies and their applications 8
2.2.2 An ontology construction process 9
2.2.3 Semi-automatic approach for constructing the ontology 11
2.3 Recommendation Technologies 13
2.4 Particle Swarm Optimization Algorithm 15
2.5 Overview of the Learning Models 16
CHAPTER 3 Personalized Course Recommendation and Composition 21
3.1 Ontology Creator 23
3.2 Learning Objects Classifier 26
3.3 Query Parser 28
3.4 Learner Objects Recommender 34
3.4.1 Recommendation based on a user’s preference 35
3.4.2 Recommendation based on similar users 38
3.4.3 Integrated ranking strategy 41
3.5 Course Composer 42
3.5.1 Objective and fitness functions 42
3.5.2 Discrete particle swarm optimization (DPSO) 43
3.5.3 A Greedy-like algorithm for sequencing the reading order 52
3.6 Feedback Calculation 54
CHAPTER 4 Implementation and Performance Evaluating 55
4.1 Overview of the System Web Portal 55
4.2 Evaluation Measures 58
4.2.1 Recall and precision 58
4.2.2 Mean absolute error 59
4.3 Experimental Setting 60
4.4 Experiment Results and Discussion 61
4.4.1 Evaluate the variation of fitness values 61
4.4.2 Evaluations of recall, precision, and f-measure 62
4.4.3 Evaluate satisfied degree of the reading order 63
4.4.4 Recommendation based on feedbacks with different weights 65
4.4.5 Construction of a learner’s preference pattern 70
CHAPTER 5 Conclusions and Future Work 72
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