Machine Learning Analyses of Process Data and Test Performance

Autor/in	He, Dan
Titel	Machine Learning Analyses of Process Data and Test Performance
Quelle	(2023), (54 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, University of Kansas
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	979-8-3797-2210-4
Schlagwörter	Hochschulschrift; Dissertation; Artificial Intelligence; Data Analysis; Algorithms; Classification; Prediction; Performance; Test Items + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Künstliche Intelligenz; Auswertung; Algorithm; Algorithmus; Classification system; Klassifikation; Klassifikationssystem; Vorhersage; Achievement; Leistung; Test content; Testaufgabe
Abstract	This dissertation examines the effectiveness of machine learning algorithms and feature engineering techniques for analyzing process data and predicting test performance. The study compares three classification approaches and identifies item-specific process features that are highly predictive of student performance. The findings suggest that educators could use these features to offer more personalized and effective formative feedback to students. Overall, this research highlights the potential of machine learning in education and contributes to the growing body of literature on the use of process data to enhance learning outcomes. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01