Analysis of Click-Stream Data to Predict STEM Careers from Student Usage of an Intelligent Tutoring System

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Published Aug 22, 2020
Jihed Makhlouf Tsunenori Mine

Abstract

In recent years, we have seen the continuous and rapid increase of job openings in Science, Technology, Engineering and Math (STEM)-related fields. Unfortunately, these positions are not met with an equal number of workers ready to fill them. Efforts are being made to find durable solutions for this phenomena, and they start by encouraging young students to enroll in STEM college majors. However, enrolling in a STEM major requires specific skills in math and science that are learned in schools. Hopefully, institutions are adopting educational software that collects data from the students' usage. This gathered data will serve to conduct analysis and detect students' behaviors, predict their performances and their eventual college enrollment. As we will outline in this paper, we used data collected from the students' usage of an Intelligent Tutoring System to predict whether they would pursue a career in STEM-related fields. We conducted different types of analysis called "problem-based approach" and "skill-based approach". The problem- based approach focused on evaluating students' actions based on the problems they solved. Likewise, in the skill-based approach we evaluated their usage based on the skills they had practiced. Furthermore, we investigated whether comparing students' features with those of their peer schoolmates can improve the prediction models in both the skill-based and the problem-based approaches. The experimental re- sults showed that the skill-based approach with school aggregation achieved the best results with regard to a combination of two metrics which are the Area Under the Receiver Operating Characteristic Curve (AUC) and the Root Mean Squared Error (RMSE).

How to Cite

Makhlouf, J., & Mine, T. (2020). Analysis of Click-Stream Data to Predict STEM Careers from Student Usage of an Intelligent Tutoring System. JEDM | Journal of Educational Data Mining, 12(2), 1-18. https://doi.org/10.5281/zenodo.4008050
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Keywords

STEM career, predictive analytics, educational data mining, intelligent tutoring system

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Section
Special Issue on ASSISTments Longitudinal Data