A Multimodal Assessment Framework for Integrating Student Writing and Drawing in Elementary Science Learning

Autor/inn/en	Smith, Andy; Leeman-Munk, Samuel; Shelton, Angi; Mott, Bradford; Wiebe, Eric; Lester, James
Titel	A Multimodal Assessment Framework for Integrating Student Writing and Drawing in Elementary Science Learning
Quelle	In: IEEE Transactions on Learning Technologies, 12 (2019) 1, S.3-15 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Smith, Andy) ORCID (Shelton, Angi) ORCID (Mott, Bradford) ORCID (Wiebe, Eric)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2018.2799871
Schlagwörter	Elementary School Students; Elementary School Science; Science Instruction; Writing (Composition); Freehand Drawing; Student Evaluation; Evaluation Methods; Measurement Techniques; Educational Technology; Technology Uses in Education; Intelligent Tutoring Systems; Formative Evaluation + Suchen Sie Ihr Suchwort? Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Schreibübung; Drawing; Zeichnen; Schulnote; Studentische Bewertung; Messtechnik; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Intelligentes Tutorsystem
Abstract	Science learning is inherently multimodal, with students utilizing both drawings and writings to explain observations of physical phenomena. As such assessments in science should accommodate the many ways students express their understanding, especially given evidence that understanding is distributed across both drawing and writing. In recent years advanced automated assessment techniques that evaluate expressive student artifacts have emerged. However, these techniques have largely operated individually, each considering only a single mode. We propose a framework for the multimodal automated assessment of students' writing and drawing to leverage the synergies inherent across modalities and create a more complete and accurate picture of a student's knowledge. We introduce a multimodal assessment framework as well as two computational techniques for automatically analyzing student writings and drawings: a convolutional neural network-based model for assessing student writing, and a topology-based model for assessing student drawing. Evaluations with elementary students' writings and drawings collected with a tablet-based digital science notebook demonstrate that (1) each of the framework's two modalities provide an independent and complementary measure of student science learning, and (2) the computational methods are capable of accurately assessing student work from both modalities and offer the potential for integration in technology-rich learning environments for real-time formative assessment. (As Provided).
Anmerkungen	Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01