Combining Machine Learning and Qualitative Methods to Elaborate Students' Ideas about the Generality of Their Model-Based Explanations

Autor/inn/en	Rosenberg, Joshua M.; Krist, Christina
Titel	Combining Machine Learning and Qualitative Methods to Elaborate Students' Ideas about the Generality of Their Model-Based Explanations
Quelle	In: Journal of Science Education and Technology, 30 (2021) 2, S.255-267 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Rosenberg, Joshua M.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1059-0145
DOI	10.1007/s10956-020-09862-4
Schlagwörter	Artificial Intelligence; Educational Technology; Technology Uses in Education; Models; Man Machine Systems; Coding; Science Instruction; Student Evaluation; Evaluation Methods; Generalization + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Analogiemodell; Mensch-Maschine-System; Codierung; Programmierung; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Schulnote; Studentische Bewertung
Abstract	Assessing students' participation in science practices presents several challenges, especially when aiming to differentiate meaningful (vs. rote) forms of participation. In this study, we sought to use machine learning (ML) for a novel purpose in science assessment: developing a construct map for students' "consideration of generality," a key epistemic understanding that undergirds meaningful participation in knowledge-building practices. We report on our efforts to assess the nature of 845 students' ideas about the generality of their model-based explanations through the combination of an embedded written assessment and a novel data analytic approach that combines unsupervised and supervised machine learning methods and human-driven, interpretive coding. We demonstrate how unsupervised machine learning methods, when coupled with qualitative, interpretive coding, were used to revise our construct map for generality in a way that allowed for a more nuanced evaluation that was closely tied to empirical patterns in the data. We also explored the application of the construct map as a framework for coding used as a part of supervised machine learning methods, finding that it demonstrates some viability for use in future analyses. We discuss implications for the assessment of students' meaningful participation in science practices in terms of their considerations of generality, the role of unsupervised methods in science assessment, and combining machine learning and human-driven approach for understanding students' complex involvement in science practices. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01