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|Title:||Neural network model of memory reinforcement for text-based intelligent tutoring system|
|Advisor:||Montazemi, Ali R.|
|Department:||Electrical and Computer Engineering|
|Keywords:||Electrical and Computer Engineering;Electrical and Computer Engineering|
|Abstract:||<p>Information technology is generally believed to enhance learning processes. As a result, researchers are beginning to seek new theories and approaches towards developing information technology enabled learning environments. The focus of the research in this thesis is to provide the structure of an intelligent tutoring system (called MIS-Tutor) that we developed in support of mastery learning for students registered in a university-level management information system course. We adopted formative evaluation, spanning over 4 years and that included the participation of 1,328 students, to evaluate the effectiveness of the MIS-Tutor. An important component of this intelligent tutoring system is the ability to adapt its interaction to individual learning behaviour. This is achieved by means of neural network models to reinforce long-term memory retention and to assess the required time-on-task for each student. The results of our formative evaluation support the effectiveness of the proposed models in support of students' mastery learning.</p>|
|Appears in Collections:||Open Access Dissertations and Theses|
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