Automated,adaptive guidance for K-12 education |
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| Institution: | 1. Graduate School of Education, University of California Berkeley, 4433 Tolman Hall, Berkeley, CA 94720, USA;2. School of Culture, Education, and Human Development, Media and Games Network, New York University Steinhardt, 2 Metrotech Center, Brooklyn, NY 11201, USA;3. Center for Technology in Learning, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA;4. Graduate School of Education, University of California Berkeley, 4611 Tolman Hall, Berkeley, CA 94720, USA |
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| Abstract: | 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. |
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