Process Mining Combined with Expert Feature Engineering to Predict Efficient Use of Time on High-Stakes Assessments

Autor/in	Levin, Nathan A.
Titel	Process Mining Combined with Expert Feature Engineering to Predict Efficient Use of Time on High-Stakes Assessments
Quelle	In: Journal of Educational Data Mining, 13 (2021) 2, S.1-15 (15 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Levin, Nathan A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Learning Analytics; Data Collection; Competition; Prediction; Time Factors (Learning); High Stakes Tests; Middle School Students; Grade 8; Mathematics; Computer Assisted Testing; Classification; Efficiency; Scores; Computation; Engineering; Models + Suchen Sie Ihr Suchwort? Data capture; Datensammlung; Wettkampf; Vorhersage; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; School year 08; 8. Schuljahr; Schuljahr 08; Mathematik; Classification system; Klassifikation; Klassifikationssystem; Effectiveness; Effektivität; Wirkungsgrad; Maschinenbau; Analogiemodell
Abstract	The Big Data for Education Spoke of the NSF Northeast Big Data Innovation Hub and ETS co-sponsored an educational data mining competition in which contestants were asked to predict efficient time use on the NAEP 8th grade mathematics computer-based assessment, based on the log file of a student's actions on a prior portion of the assessment. In this work, a combined approach of process mining and expert feature engineering was used to build a large set of features that were then trained with an Extreme Gradient Boosting machine learning model to classify students based on whether they would use their time efficiently. Predictions were evaluated throughout the competition on half of a hidden data set and then the final results were based on the second half of the hidden data set. The approach used here earned the top score in the competition. The work presented elaborates on the combined technique for analyzing computer-based assessment log-file data with the hope that this approach will offer valuable insights for future predictive model building in educational data mining. (As Provided).
Anmerkungen	International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01