Automated, adaptive guidance for K-12 education
Libby Gerard, Graduate School of Education, United States ; Camillia Matuk, School of Culture, Education, and Human Development, Media and Games Network, United States ; Kevin McElhaney, Center for Technology in Learning, United States ; Marcia C. Linn, Graduate School of Education, United States
Educational Research Review Volume 15, Number 1, ISSN 1747-938X Publisher: Elsevier Ltd
This paper distinguishes features of automated adaptive guidance used in K-12 instructional settings and recommends directions for design. We use meta-analysis to synthesize 24 independent comparisons between automated adaptive guidance and guidance provided during typical teacher-led instruction, and 29 comparisons that isolate the effects of specific adaptive guidance design features in computer-based instruction. We find automated adaptive guidance to be significantly more effective than guidance provided in typical instruction, particularly for students with low prior knowledge. Automated adaptive guidance is most effective when students are generating and integrating ideas (e.g. writing essays, making concept diagrams) as opposed to selecting from the given options. Guidance that promoted self-monitoring was more likely to improve learning outcomes than guidance that addressed only content knowledge. Our findings have implications for researchers who investigate K-12 teaching and learning, designers who create and refine instructional materials using automated guidance, and practitioners who deliver or customize instruction featuring automated guidance.
Gerard, L., Matuk, C., McElhaney, K. & Linn, M.C. (2015). Automated, adaptive guidance for K-12 education. Educational Research Review, 15(1), 41-58. Elsevier Ltd.
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The Teacher Responding Tool: Scaffolding the teacher practice of responding to student ideas in mathematics classrooms
James P. Bywater, Jennifer L. Chiu, James Hong & Vidhya Sankaranarayanan
Computers & Education Vol. 139, No. 1 (October 2019) pp. 16–30
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