Gender Differences in Predicting STEM Choice by Affective States and Behaviors in Online Mathematical Problem Solving: Positive-Affect-to-Success Hypothesis

Autor/in	Chiu, Mei-Shiu
Titel	Gender Differences in Predicting STEM Choice by Affective States and Behaviors in Online Mathematical Problem Solving: Positive-Affect-to-Success Hypothesis
Quelle	In: Journal of Educational Data Mining, 12 (2020) 2, S.48-77 (30 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Chiu, Mei-Shiu)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Gender Differences; Predictor Variables; STEM Education; Course Selection (Students); Psychological Patterns; Middle School Mathematics; Problem Solving; Intelligent Tutoring Systems; Higher Education; Academic Aspiration; Learner Engagement; Affective Behavior + Suchen Sie Ihr Suchwort? Geschlechterkonflikt; Prädiktor; STEM; Course selection; Student; Students; Kurswahl; Problemlösen; Intelligentes Tutorsystem; Hochschulbildung; Hochschulsystem; Hochschulwesen; Affective disturbance; Active behaviour; Affektive Störung
Abstract	This study aims to identify effective affective states and behaviors of middle-school students' online mathematics learning in predicting their choices to study science, technology, engineering, and mathematics (STEM) in higher education based on a "positive-affect-to-success hypothesis." The dataset (591 students and 316,974 actions) was obtained from the ASSISTments project. In the ASSISTments intelligent tutoring system, students completed mathematical problem-solving tasks, and the data was processed to infer their action-level affective states and behaviors, which were averaged to form student-level measures. The students' future STEM choice was predicted by the student- and action-level affective states and behaviors using logistic regression (LR), ordinary least squares regressions with standardized scores (ORz), and random forest with permutation importance and SHAP values (RFPS). The results revealed that student- and action-level gaming behavior consistently predict STEM choice. In addition to gaming, female students are more likely to study STEM if they are less bored and more off-task, and male students if more concentrated and less frustrated. ORz generates theoretically plausible results and identifies sufficiently distinguishable affective states and behaviors. Suggestions for educational practice and research are provided for adaptive teaching. (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