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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 40849 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Habibi, Jafar
- Abstract:
- In the pas few years, the use of web-based education systems have grown exponentially spurred by the fact that neither students nor teachers are bound to a specific location and that this form of computer-based education is virtually independent of any specific hardware platforms. These systems can offer a great variety of channels and workspaces to facilitate information sharing and communication between participants in a course, let educators distribute information to students, produce content material, prepare assignments and tests, engage in discussions, manage distance classes and enable collaborative learning with virtual classroom sessions, forums, chats, file storage areas, news services, etc. These e-learning systems accumulate a vast amount of information which is very valuable for analyzing students’ behavior and could create a gold mine of educational data. In the last years, researchers have begun to investigate various data mining methods to help teachers improve e-learning systems. Data mining can be applied to explore, visualize and analyze e-learning data in order to identify useful patterns, to evaluate web activity to get more objective feedback for instruction, and knowing more about how the students learn. This study aims to investigate how these methods can be applied in a real e-learning environment. In particular, this research is focused on the extraction of interesting rules about the relations between the performance of students and their final grades. In order to meet these goals, at the start a Data Warehouse has been build based on the students’ web usage data including comprehensive information about the participation of students in online learning activities. Then, data mining methods are employed in order to study and analyze the behavior of students in a profound manner. First, a case study has been performed to evaluate the role of each online learning activity in the performance of students. A number of ‘Feature Selection’ and ‘Attribute Evaluation’ tools together with real usage data of students were used in order to perform the case study. The results indicated that participation in virtual classroom sessions has the greatest impact on learning effectiveness. Second, a novel method has been proposed to solve the problem of discovering ‘Rare Association Rules’ with high interpretability based on evolutionary algorithms. In addition, some illustrative examples of the rules discovered are presented in order to demonstrate their usefulness in educational environment.
- Keywords:
- Electronic Learning ; Business Intelligence ; Evolutionary Algorithm ; Feature Selection ; Educational Data Mining ; Data Warehouse
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