(AIMS 2020) A Novel Method to Estimate Students' Knowledge Assessment

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Abstract: Performance of learning can be enriched with proper and timely feedback. This paper proposes a solution based on a Bayesian network in machine learning that can examine and judge students' written response to identify evidences that students fully comprehend concepts being considered in a certain knowledge domain. In particular, it can estimate probabilities that a student has known concepts in computer science at different cognitive ability levels in a sense. Thus, the method can offer learners personalized feedbacks on their strengths and shortcomings, as well as advising them and instructors of supplementary education actions that may help students to resolve any lacks to improve their knowledge and exam score.

Authors: Phuoc Hung Pham and Javed I. Khan (Kent State University, USA)

Email: hung205a2@gmail.com, javed@kent.edu

Who can view: Everyone

Webinar Price: Free

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Phuoc Hung Pham

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Phuoc Hung Pham received the B.S. degree in Computer Engineering from Ho Chi Minh National University, University of Sciences, Vietnam, Master's degree in Computer Science from Dongguk University, Korea, Ph.D degree in Computer Engineering from KyungHee University, Korea. He used to be a director, a project manager in some software companies. At present, he is also working as a Lecturer in Department of Computer Science at Kent State University, USA where he has been working on several large-scale R&D funded projects, including their proposals. His research interests include Data Mining, Resource Allocation, Parallel and Distributing Computing, High Performance Computing, Cluster and Grid Computing, Cloud Computing, Fog Computing.

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