Scaffolding Deep Comprehension Strategies Through Point&Query, AutoTutor, and iSTART
Abstract
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 worthwhile 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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Much research on self-explanation indicates that prompting students to provide verbal explanations of actions, whether their own or ones given in a worked example, can often aid robust learning.
“Much research on “self-explanation” indicates that prompting students to provide verbal explanations of actions (whether their own or ones given in a worked example) can often aid robust learning (Aleven & Koedinger, 2002;Chi et al., 1994;Graesser, McNamara, & VanLehn, 2005;Hausmann & VanLehn, 2007).”
From The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning (2012) · cites this paper as Graesser, McNamara, & VanLehn, 2005
Understanding and sense-making processes include explicit comprehension strategies, verbally-mediated self-explanation, explicit hypothesizing and scientific discovery, deductive proof, and explicit argumentation or collaborative discourse.
“Understanding and sense-making processes include explicit comprehension strategies (e.g., Graesser et al., 2005;Kintsch, 1998;Palinscar & Brown, 1984), verbally-mediated selfexplanation (e.g., Ainsworth, & Loizou, 2003;Chi, Bassok, Lewis, Reimann & Glaser, 1989;Lewis, 1988;VanLehn, 1999), explicit hypothesizing and scientific discovery processes (Levine, 1966;Klahr & Dunbar, 1988), deductive proof”
From The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning (2012) · cites this paper as Graesser et al., 2005
Cites
Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry
Flavell, J. H. (1979) · American Psychologist · doi:10.1037/0003-066x.34.10.906 · Cited by 11,282 · cited 1× · see citing works
“Metacognition is defined here as conscious and deliberate thoughts about a person’s behavior, emotions, and other thoughts (Flavell, 1979; Hacker, 1998).”
BackgroundInquiry, modeling, and metacognition: making science accessible to all students
White, B. Y., & Frederiksen, J. R. (1998) · Cognition and Instruction · doi:10.1207/s1532690xci1601_2 · Cited by 1,394 · see citing works
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Shute, V. J. (2008) · Review of Educational Research · doi:10.3102/0034654307313795 · Cited by 4,631 · see citing works
Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012) · Cognitive Science · doi:10.1111/j.1551-6709.2012.01245.x · Cited by 772 · see citing works
“Understanding and sense-making processes include explicit comprehension strategies (e.g., Graesser et al., 2005; Kintsch, 1998; Palincsar & Brown, 1984), verbally mediated self-explanation (e.g., Ainsworth & Loizou, 2003; Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Lewis, 1988; VanLehn, 1999),…”
BackgroundInfluentialAzevedo, R., Moos, D. C., Greene, J. A., Winters, F. I., & Cromley, J. G. (2008) · Educational Technology Research and Development · doi:10.1007/s11423-007-9067-0 · Cited by 380 · see citing works
Wittwer, J., & Renkl, A. (2008) · Educational Psychologist · doi:10.1080/00461520701756420 · Cited by 277 · see citing works
GREENE, J. A., & AZEVEDO, R. (2010) · Educational Psychologist · doi:10.1080/00461520.2010.515935 · Cited by 267 · see citing works
GRAESSER, A., & McNAMARA, D. (2010) · Educational Psychologist · doi:10.1080/00461520.2010.515933 · Cited by 153 · see citing works
Self-citationWhy does peer instruction benefit student learning?
Tullis, J. G., & Goldstone, R. L. (2020) · Cognitive Research: Principles and Implications · doi:10.1186/s41235-020-00218-5 · Cited by 153 · see citing works
Automatically Measuring Question Authenticity in Real-World Classrooms
Kelly, S., Olney, A. M., Donnelly, P., Nystrand, M., & D’Mello, S. K. (2018) · Educational Researcher · doi:10.3102/0013189x18785613 · Cited by 114 · see citing works
Wiley, J., Hastings, P., Blaum, D., Jaeger, A. J., Hughes, S., Wallace, P., Griffin, T. D., & Britt, M. A. (2017) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-017-0138-z · Cited by 49 · see citing works
Roelle, J., Lehmkuhl, N., Beyer, M.-U., & Berthold, K. (2015) · Journal of Educational Psychology · doi:10.1037/edu0000010 · Cited by 42 · see citing works
CreAItive Collaboration? Users' Misjudgment of AI-Creativity Affects Their Collaborative Performance
Bangerl, M. M., Disch, L., David, T., & Pammer-Schindler, V. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713886 · Cited by 13 · see citing works
- Authors
- Graesser, A. C., McNamara, D. S., & VanLehn, K.
- Affiliations
- University of Memphis (US)
- University of Pittsburgh (US)
- Venue
- Educational Psychologist
- Publisher
- Informa UK Limited
- Published
- 2005-09-01
- Volume
- 40
- Issue
- 4
- Pages
- 225-234
- ISSN
- 0046-1520 (ISSN-L)
- 1532-6985
- OpenAlex
- W2091097903
- Cited by
- 285
- References
- 2
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 7 · 2023: 7 · 2022: 9 · 2021: 7 · 2020: 13 · 2019: 7 · 2018: 7 · 2017: 13 · 2016: 10 · 2015: 26 · 2014: 23 · 2013: 27 · 2012: 15
- FWCI
- 13.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Educational Psychologist (publishedVersion)
- Funders
- National Science Foundation
- Multidisciplinary University Research Initiative
- Office of Naval Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- point queries, AutoTutor, iSTART, self-explanation, metacognition, inquiry-based learning, explanation construction, computer-based tutoring, chatbots, mixed-initiative dialogue, metacomprehension, science and technology learning, deep comprehension
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