Predicting engineering students' optimal group size using socio-educational features
Pages 97 - 104
Abstract
Socio-educational background plays an influential role in the success of a studentś engineering schooling. These socio-educational backgrounds are of more diverse nature in developing countries like Bangladesh. The fact that, tertiary education is given in a foreign language adds another dimension to challenge of imparting a successful engineering education. If the students could be grouped according to their socio-educational features, then it would have been easier to anticipate the needs of students coming from diverse backgrounds. In this work, we classify the students (N=237) of the department of Computer Science and Engineering of a university in the Bangladeshi capital of Dhaka based on their socio-educational features using K-means clustering and then propose a classifier that could work as a predictor that could work as a predictor for predicting student needs coming from different backgrounds.
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Published In
May 2019
190 pages
ISBN:9781450366793
DOI:10.1145/3309700
Copyright © 2019 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
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Published: 22 May 2020
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