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  1.  65
    Categorization and representation of physics problems by experts and novices.Michelene T. H. Chi, Paul J. Feltovich & Robert Glaser - 1981 - Cognitive Science 5 (2):121-52.
    The representation of physics problems in relation to the organization of physics knowledge is investigated in experts and novices. Four experiments examine the existence of problem categories as a basis for representation; differences in the categories used by experts and novices; differences in the knowledge associated with the categories; and features in the problems that contribute to problem categorization and representation. Results from sorting tasks and protocols reveal that experts and novices begin their problem representations with specifiably different problem categories, (...)
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  2.  57
    Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems.Michelene T. H. Chi, Miriam Bassok, Matthew W. Lewis, Peter Reimann & Robert Glaser - 1989 - Cognitive Science 13 (2):145-182.
    The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...)
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  3.  25
    Eliciting Self-Explanations Improves Understanding.Michelene T. H. Chi, Nicholas De Leeuw, Mei-Hung Chiu & Christian Lavancher - 1994 - Cognitive Science 18 (3):439-477.
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  4.  17
    Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems.Michelene T. H. Chi, Miriam Bassok, Matthew W. Lewis, Peter Reimann & Robert Glaser - 1989 - Cognitive Science 13 (2):145-182.
    The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...)
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  5. Eliciting Self‐Explanations Improves Understanding.Michelene T. H. Chi, Nicholas Leeuw, Mei‐Hung Chiu & Christian Lavancher - 1994 - Cognitive Science 18 (3):439-477.
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  6.  91
    Active‐Constructive‐Interactive: A Conceptual Framework for Differentiating Learning Activities.Michelene T. H. Chi - 2009 - Topics in Cognitive Science 1 (1):73-105.
    Active, constructive, and interactive are terms that are commonly used in the cognitive and learning sciences. They describe activities that can be undertaken by learners. However, the literature is actually not explicit about how these terms can be defined; whether they are distinct; and whether they refer to overt manifestations, learning processes, or learning outcomes. Thus, a framework is provided here that offers a way to differentiate active, constructive, and interactive in terms of observable overt activities and underlying learning processes. (...)
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  7.  52
    Learning from human tutoring.Michelene T. H. Chi, Stephanie A. Siler, Heisawn Jeong, Takashi Yamauchi & Robert G. Hausmann - 2001 - Cognitive Science 25 (4):471-533.
    Human one‐to‐one tutoring has been shown to be a very effective form of instruction. Three contrasting hypotheses, a tutor‐centered one, a student‐centered one, and an interactive one could all potentially explain the effectiveness of tutoring. To test these hypotheses, analyses focused not only on the effectiveness of the tutors' moves, but also on the effectiveness of the students' construction on learning, as well as their interaction. The interaction hypothesis is further tested in the second study by manipulating the kind of (...)
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  8. Misconceived Causal Explanations for Emergent Processes.Michelene T. H. Chi, Rod D. Roscoe, James D. Slotta, Marguerite Roy & Catherine C. Chase - 2012 - Cognitive Science 36 (1):1-61.
    Studies exploring how students learn and understand science processes such as diffusion and natural selection typically find that students provide misconceived explanations of how the patterns of such processes arise (such as why giraffes’ necks get longer over generations, or how ink dropped into water appears to “flow”). Instead of explaining the patterns of these processes as emerging from the collective interactions of all the agents (e.g., both the water and the ink molecules), students often explain the pattern as being (...)
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  9.  51
    Translating the ICAP Theory of Cognitive Engagement Into Practice.Michelene T. H. Chi, Joshua Adams, Emily B. Bogusch, Christiana Bruchok, Seokmin Kang, Matthew Lancaster, Roy Levy, Na Li, Katherine L. McEldoon, Glenda S. Stump, Ruth Wylie, Dongchen Xu & David L. Yaghmourian - 2018 - Cognitive Science 42 (6):1777-1832.
    ICAP is a theory of active learning that differentiates students’ engagement based on their behaviors. ICAP postulates that Interactive engagement, demonstrated by co‐generative collaborative behaviors, is superior for learning to Constructive engagement, indicated by generative behaviors. Both kinds of engagement exceed the benefits of Active or Passive engagement, marked by manipulative and attentive behaviors, respectively. This paper discusses a 5‐year project that attempted to translate ICAP into a theory of instruction using five successive measures: (a) teachers’ understanding of ICAP after (...)
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  10.  39
    Observing Tutorial Dialogues Collaboratively: Insights About Human Tutoring Effectiveness From Vicarious Learning.Michelene T. H. Chi, Marguerite Roy & Robert G. M. Hausmann - 2008 - Cognitive Science 32 (2):301-341.
    The goals of this study are to evaluate a relatively novel learning environment, as well as to seek greater understanding of why human tutoring is so effective. This alternative learning environment consists of pairs of students collaboratively observing a videotape of another student being tutored. Comparing this collaboratively observing environment to four other instructional methods—one‐on‐one human tutoring, observing tutoring individually, collaborating without observing, and studying alone—the results showed that students learned to solve physics problems just as effectively from observing tutoring (...)
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  11.  19
    Translating a Theory of Active Learning: An Attempt to Close the Research‐Practice Gap in Education.Michelene T. H. Chi - 2021 - Topics in Cognitive Science 13 (3):441-463.
    Closing the research‐practice gap cannot be achieved by one of the most promising methods, which is to distill and synthesize decades of research to see how the robust findings can work in practice. An alternative approach is proposed, which is to translate a theory of active learning for practitioners.
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  12.  22
    Assimilating evidence: The key to revision?Michelene T. H. Chi - 1989 - Behavioral and Brain Sciences 12 (3):470-471.