Abstract
In this paper, a three pronged solution to faculty evaluation is proposed. Almost in every
university, faculty and course evaluations are filled by students after the completion of courses. Due to the
large volume of such evaluations, it becomes very difficult for management to carefully analyze them. This
paper proposes a framework based on machine learning techniques that can be adopted for effective evaluation
of faculty. It uses k-means clustering to group the evaluations and points out the specific area on which
management needs to work on with faculty. Along with the quantitative evaluation of faculty, students
also provide feedback in the form of comments. The proposed solution performs sentiment analysis on those
comments. If there is a high emotion (positive or negative) associated with comments, an email can be sent in
real-time to higher management. Another important component of proposed solution is providing summary of
the topics discussed in the lectures via transcribing their recorded lecture and then applying machine learning
on transcripts.
Noman Islam . (2018) A Novel Framework Using Machine Learning to Effectively Analyze the Faculty Evaluations, Journal of Education & Social Sciences, Volume 6, Issue 2.
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