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dc.contributor.authorRafferty, Anna N.
dc.contributor.authorGerard, Libby
dc.contributor.authorMcElhaney, Kevin
dc.contributor.authorLinn, Marcia C.
dc.identifier.citationRafferty, A. N., Gerard, L., McElhaney, K., & Linn, M. C. (2014). Promoting Student Learning through Automated Formative Guidance on Chemistry Drawings. In Joseph L. Polman, Eleni A. Kyza, D. Kevin O'Neill, Iris Tabak, William R. Penuel, A. Susan Jurow, Kevin O'Connor, Tiffany Lee, and Laura D'Amico (Eds.). Learning and Becoming in Practice: The International Conference of the Learning Sciences (ICLS) 2014. Volume 1. Colorado, CO: International Society of the Learning Sciences, pp. 386-393.en_US
dc.description.abstractWe investigated the effect of automated guidance on student-generated chemistry drawings in computer-based learning activities. Expert teachers provide guidance on generative tasks such as drawings or essays that encourages students to refine their understanding, often by gathering more evidence. We developed algorithms to score student drawings and designed guidance for each score level. The guidance was intended to promote coherent understanding. We compared computer-generated guidance to teacher guidance in two studies, conducted with over 300 students in secondary classrooms. The studies suggest that automated guidance is as effective as teacher guidance for improving student understanding. Teachers appreciated the assessment of class progress provided by the automated guidance. They reported that it took them several hours to grade their five classes of 30 to 40 students. Thus, automated guidance can reduce the time teachers spend evaluating student work, creating more time for planning lessons, facilitating inquiry, or guiding individual students.en_US
dc.publisherBoulder, CO: International Society of the Learning Sciencesen_US
dc.titlePromoting Student Learning through Automated Formative Guidance on Chemistry Drawingsen_US
dc.typeBook chapteren_US
Appears in Collections:ICLS2014

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