TY - JOUR
T1 - Scaffolding deep comprehension strategies through Point&Query, AutoTutor, and iSTART
AU - Graesser, Arthur C.
AU - McNamara, Danielle S.
AU - VanLehn, Kurt
N1 - Funding Information:
The research on AutoTutor was supported by the National Science Foundation (NSF; SBR 9720314, REC 0106965, REC 0126265, ITR 0325428) and the DoD Multidisciplinary University Research Initiative administered by the Office of Naval Research (ONR) under Grant N00014–00–1–0600. This research on iSTART was supported by NSF Interagency Education Research Initiative Grant REC–0089271. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Department of Defense, ONR, or NSF.
PY - 2005/9
Y1 - 2005/9
N2 - It is well-documented that most students do not have adequate proficiencies in inquiry and metacognition, particularly at deeper levels of comprehension that require explanatory reasoning. The proficiencies are not routinely provided by teachers and normal tutors so it is worth-while to turn to computer-based learning environments. This article describes some of our recent computer systems that were designed to facilitate explanation-centered learning through strategies of inquiry and metacognition while students learn science and technology content. Point&Query augments hypertext, hypermedia, and other learning environments with question-answer facilities that are under the learner control. AutoTutor and iSTART use animated conversational agents to scaffold strategies of inquiry, metacognition, and explanation construction. AutoTutor coaches students in generating answers to questions that require explanations (e.g., why, what-if, how) by holding a mixed-initiative dialogue in natural language. iSTART models and coaches students in constructing self-explanations and in applying other metacomprehension strategies while reading text. These systems have shown promising results in tests of learning gains and learning strategies.
AB - It is well-documented that most students do not have adequate proficiencies in inquiry and metacognition, particularly at deeper levels of comprehension that require explanatory reasoning. The proficiencies are not routinely provided by teachers and normal tutors so it is worth-while to turn to computer-based learning environments. This article describes some of our recent computer systems that were designed to facilitate explanation-centered learning through strategies of inquiry and metacognition while students learn science and technology content. Point&Query augments hypertext, hypermedia, and other learning environments with question-answer facilities that are under the learner control. AutoTutor and iSTART use animated conversational agents to scaffold strategies of inquiry, metacognition, and explanation construction. AutoTutor coaches students in generating answers to questions that require explanations (e.g., why, what-if, how) by holding a mixed-initiative dialogue in natural language. iSTART models and coaches students in constructing self-explanations and in applying other metacomprehension strategies while reading text. These systems have shown promising results in tests of learning gains and learning strategies.
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U2 - 10.1207/s15326985ep4004_4
DO - 10.1207/s15326985ep4004_4
M3 - Review article
AN - SCOPUS:33144481491
SN - 0046-1520
VL - 40
SP - 225
EP - 234
JO - Educational Psychologist
JF - Educational Psychologist
IS - 4
ER -