Paper Type
ERF
Abstract
Student success in higher education remain a topic of interest for academics and educators. While research has been conducted around topics such as attrition, retention, and educational data mining, one of the hardest questions to answer is what effect does a student's schedule have on their success? This question is hard to address given the amount of variables around what makes a good schedule for any given student and the inherent heterogeneity of students. Institutions of higher education happen to capture a tremendous amount of data around a student, whether it is intentional or not. This paper presents a design theoretic approach to seizing the opportunity this data to create a smart advising system while removing a human element of error.
Recommended Citation
Bradberry, Caleb and Pittges, Jeff, "A Design Theoretic Approach for Smarter Advising using Educational Data Mining" (2020). AMCIS 2020 Proceedings. 18.
https://aisel.aisnet.org/amcis2020/ai_semantic_for_intelligent_info_systems/ai_semantic_for_intelligent_info_systems/18
A Design Theoretic Approach for Smarter Advising using Educational Data Mining
Student success in higher education remain a topic of interest for academics and educators. While research has been conducted around topics such as attrition, retention, and educational data mining, one of the hardest questions to answer is what effect does a student's schedule have on their success? This question is hard to address given the amount of variables around what makes a good schedule for any given student and the inherent heterogeneity of students. Institutions of higher education happen to capture a tremendous amount of data around a student, whether it is intentional or not. This paper presents a design theoretic approach to seizing the opportunity this data to create a smart advising system while removing a human element of error.
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