Using a Randomized Experiment to Compare the Performance of Two Adaptive Assessment Engines [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).

Autor/inn/en	Matayoshi, Jeffrey; Uzun, Hasan; Cosyn, Eric
Titel	Using a Randomized Experiment to Compare the Performance of Two Adaptive Assessment Engines [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).
Quelle	(2022), (7 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Knowledge Level; Mathematical Models; Learning Experience; Comparative Analysis; Learning Management Systems; Classification; Artificial Intelligence; Student Evaluation; Computer Assisted Testing; Middle School Students; Chemistry; Science Projects; Undergraduate Students; Algebra; Science Achievement; Mathematics Achievement; Recall (Psychology); Accuracy + Suchen Sie Ihr Suchwort? Wissensbasis; Mathematical model; Mathematisches Modell; Lernerfahrung; Classification system; Klassifikation; Klassifikationssystem; Künstliche Intelligenz; Schulnote; Studentische Bewertung; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Chemie; Science; Project; Wissenschaft; Projekt; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Abberufung
Abstract	Knowledge space theory (KST) is a mathematical framework for modeling and assessing student knowledge. While KST has successfully served as the foundation of several learning systems, recent advancements in machine learning provide an opportunity to improve on purely KST-based approaches to assessing student knowledge. As such, in this work we compare the performance of an existing KST-based adaptive assessment to that of a newly developed version--with this new version combining the predictive power of a neural network model with the strengths of existing KST-based approaches. Using a cluster randomized experiment containing data from approximately 140,000 assessments, we show that the new neural network assessment engine improves on the performance of the existing KST version, both on standard classification metrics, as well as on measures more specific to the student learning experience. [For the full proceedings, see ED623995.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01