Preparing students for future learning with Teachable Agents |
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| Authors: | Doris B Chin Ilsa M Dohmen Britte H Cheng Marily A Oppezzo Catherine C Chase and Daniel L Schwartz |
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| Institution: | (1) Stanford Center for Innovations in Learning, Stanford University, 450 Serra Mall, Building 160, Stanford, CA 94305-2055, USA;(2) School of Education, Stanford University, 485 Lasuen Mall, Stanford, CA 94305-3096, USA;(3) Center for Technology in Learning, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025-3493, USA |
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| Abstract: | One valuable goal of instructional technologies in K-12 education is to prepare students for future learning. Two classroom
studies examined whether Teachable Agents (TA) achieves this goal. TA is an instructional technology that draws on the social
metaphor of teaching a computer agent to help students learn. Students teach their agent by creating concept maps. Artificial
intelligence enables TA to use the concept maps to answer questions, thereby providing interactivity, a model of thinking,
and feedback. Elementary schoolchildren learning science with TA exhibited “added-value” learning that did not adversely affect
the “basic-value” they gained from their regular curriculum, despite trade-offs in instructional time. Moreover, TA prepared
students to learn new science content from their regular lessons, even when they were no longer using the software. |
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