Personalized Online Learning, Test Fairness, and Educational Measurement: Considering Differential Content Exposure Prior to a High Stakes End of Course Exam

Autor/inn/en	Katz, Daniel; Huggins-Manley, Anne Corinne; Leite, Walter
Titel	Personalized Online Learning, Test Fairness, and Educational Measurement: Considering Differential Content Exposure Prior to a High Stakes End of Course Exam
Quelle	In: Applied Measurement in Education, 35 (2022) 1, S.1-16 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Leite, Walter) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2022.2034824
Schlagwörter	High Stakes Tests; Test Bias; Testing Problems; Prior Learning; Summative Evaluation; Individualized Instruction; Artificial Intelligence; Algebra; Elementary Secondary Education; Mathematics Tests; Florida + Suchen Sie Ihr Suchwort? Testkritik; Vorkenntnisse; Individualisierender Unterricht; Künstliche Intelligenz
Abstract	According to the "Standards for Educational and Psychological Testing" (2014), one aspect of test fairness concerns examinees having comparable opportunities to learn prior to taking tests. Meanwhile, many researchers are developing platforms enhanced by artificial intelligence (AI) that can personalize curriculum to individual student needs. This leads to a larger overarching question: When personalized learning leads to students having differential exposure to curriculum throughout the K-12 school year, how might this affect test fairness with respect to summative, end-of-year high-stakes tests? As a first step, we traced the differences in content exposure associated with personalized learning and more traditional learning paths. To better understand the implications of differences in content coverage, we conducted a simulation study to evaluate the degree to which curriculum exposure varied across students in a particular AI-enhanced learning platform for Algebra instruction with high-school students. Results indicate that AI-enhanced personalized learning may pose threats to test fairness as opportunity-to-learn on K-12 summative high-stakes tests. We discuss the implications given different perspectives of the role of testing in education (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01