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

Autor/inn/en	Makhlouf, Jihed; Mine, Tsunenori
Titel	Analysis of Click-Stream Data to Predict STEM Careers from Student Usage of an Intelligent Tutoring System
Quelle	In: Journal of Educational Data Mining, 12 (2020) 2, S.1-18 (18 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Learning Analytics; STEM Education; Science Careers; Career Choice; Intelligent Tutoring Systems; Student Behavior; Problem Solving; Skill Development; Predictor Variables; Middle School Students; Majors (Students); Longitudinal Studies + Suchen Sie Ihr Suchwort? STEM; Intelligentes Tutorsystem; Student behaviour; Schülerverhalten; Problemlösen; Kompetenzentwicklung; Qualifikationsentwicklung; Prädiktor; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung
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 results 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). (As Provided).
Anmerkungen	International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://jedm.educationaldatamining.org/index.php/JEDM
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01