Towards Interpretable Automated Machine Learning for STEM Career Prediction

Autor/inn/en	Liu, Ruitao; Tan, Aixin
Titel	Towards Interpretable Automated Machine Learning for STEM Career Prediction
Quelle	In: Journal of Educational Data Mining, 12 (2020) 2, S.19-32 (14 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Tan, Aixin)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Career Choice; Prediction; Automation; Artificial Intelligence; Science Careers; Mathematics; Technical Occupations; Engineering; Middle School Students + Suchen Sie Ihr Suchwort? Vorhersage; Künstliche Intelligenz; Mathematik; Technical occupation; Technischer Beruf; Maschinenbau; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin
Abstract	In this paper, we describe our solution to predict student STEM career choices during the 2017 ASSISTments Datamining Competition. We built a machine learning system that automatically reformats the data set, generates new features and prunes redundant ones, and performs model and feature selection. We designed the system to automatically find a model that optimizes prediction performance, yet the final model is a simple logistic regression that allows researchers to discover important features and study their effects on STEM career choices. We also compared our method to other methods, which revealed that the key to good prediction is proper feature enrichment in the beginning stage of the data analysis, while feature selection in a later stage allows a simpler final model. (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