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317
The Architecture of Cognition
, 1983
"... Spanning seven orders of magnitude: a challenge for ..."
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Cited by 1608 (40 self)
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Spanning seven orders of magnitude: a challenge for
An effective metacognitive strategy: Learning by doing and explaining with a computer-based cognitive tutor
- Cognitive Science
, 2002
"... Recent studies have shown that self-explanation is an effective metacognitive strategy, but how can it be leveraged to improve students ’ learning in actual classrooms? How do instructional treatments that emphasizes self-explanation affect students ’ learning, as compared to other instructional tre ..."
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Cited by 220 (54 self)
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Recent studies have shown that self-explanation is an effective metacognitive strategy, but how can it be leveraged to improve students ’ learning in actual classrooms? How do instructional treatments that emphasizes self-explanation affect students ’ learning, as compared to other instructional treatments? We investigated whether self-explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem-solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better-integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self-explanation can be achieved in a relatively simple computer-based approach that scales well for classroom use. © 2002
Learning from human tutoring
, 2001
"... 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 ..."
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Cited by 207 (20 self)
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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 tutoring tactics tutors were permitted to use. In order to promote a more interactive style of dialogue, rather than a didactic style, tutors were suppressed from giving explanations and feedback. Instead, tutors were encouraged to prompt the students. Surprisingly, students learned just as effectively even when tutors were suppressed from giving explanations and feedback. Their learning in the interactive style of tutoring is attributed to construction from deeper and a greater amount of scaffolding episodes, as well as their greater effort to take control of their own learning by reading more. What they learned from reading was limited, however, by their reading abilities.
Focus on formative feedback
- Review of Educational Research
, 2008
"... This article reviews the corpus of research on feedback, with a focus on for-mative feedback—defined as information communicated to the learner that is intended to modify his or her thinking or behavior to improve learning. According to researchers, formative feedback should be nonevaluative, sup-po ..."
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Cited by 171 (8 self)
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This article reviews the corpus of research on feedback, with a focus on for-mative feedback—defined as information communicated to the learner that is intended to modify his or her thinking or behavior to improve learning. According to researchers, formative feedback should be nonevaluative, sup-portive, timely, and specific. Formative feedback is usually presented as infor-mation to a learner in response to some action on the learner’s part. It comes in a variety of types (e.g., verification of response accuracy, explanation of the correct answer, hints, worked examples) and can be administered at various times during the learning process (e.g., immediately following an answer, after some time has elapsed). Finally, several variables have been shown to inter-act with formative feedback’s success at promoting learning (e.g., individual characteristics of the learner and aspects of the task). All of these issues are discussed. This review concludes with guidelines for generating formative feedback.
Evaluation of a Constraint-Based Tutor for a Database Language
- International Journal of Artificial Intelligence in Education
, 1999
"... Abstract: We propose a novel approach to intelligent tutoring in which feedback messages are associated with constraints on correct problem solution. The knowledge state of the student is represented by the constraints that he or she does and does not violate during problem solving. Constraint-base ..."
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Cited by 122 (42 self)
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Abstract: We propose a novel approach to intelligent tutoring in which feedback messages are associated with constraints on correct problem solution. The knowledge state of the student is represented by the constraints that he or she does and does not violate during problem solving. Constraint-based tutoring has been implemented in SQL-Tutor, an intelligent tutoring system for teaching the database query language SQL. Empirical evaluation shows that (a) students find the system easy to use, and (b) they do better on a subsequent classroom examination than peers without experience with the system. Furthermore, learning curves are smooth when plotted in terms of individual constraints, supporting the psychological appropriateness of the constraint construct.
ActiveMath: A Generic and Adaptive Web-Based Learning Environment
- International Journal of Artificial Intelligence in Education
, 2001
"... ActiveMath is a generic web-based learning system that dynamically generates interactive (mathematical) courses adapted to the students goals, preferences, capabilities, and knowledge. The content is represented in an semantic xml-based format. For each user, the appropriate content is retrieved fr ..."
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Cited by 94 (28 self)
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ActiveMath is a generic web-based learning system that dynamically generates interactive (mathematical) courses adapted to the students goals, preferences, capabilities, and knowledge. The content is represented in an semantic xml-based format. For each user, the appropriate content is retrieved from a knowledge base and the course is generated individually according to pedagogical rules. Then the course is presented to the user via a standard web-browser. One of the exceptional features of ActiveMath is its integration of stand-alone mathematical service systems. This offers the means for exploratory learning, realistically complex exercises as well as for learning proof methods. The article provides a comprehensive account of the current version of ActiveMath.
Lifelike Pedagogical Agents for Mixed-Initiative Problem Solving in Constructivist Learning Environments. User Modeling and User-Adapted Interaction
, 1999
"... Abstract. Mixed-initiative problem solving lies at the heart of knowledge-based learning environments. While learners are actively engaged in problem-solving activities, learning environments should monitor their progress and provide them with feedback in a manner that contributes to achieving the t ..."
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Cited by 86 (6 self)
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Abstract. Mixed-initiative problem solving lies at the heart of knowledge-based learning environments. While learners are actively engaged in problem-solving activities, learning environments should monitor their progress and provide them with feedback in a manner that contributes to achieving the twin goals of learning effectiveness and learning efficiency. Mixed-initiative interactions are particularly critical for constructivist learning environments in which learners participate in active problem solving. We have recently begun to see the emergence of believable agents with lifelike qualities. Featured prominently in constructivist learning environments, lifelike pedagogical agents could couple key feedback functionalities with a strong visual presence by observing learners ’ progress and providing them with visually contextualized advice during mixed-initiative problem solving. For the past three years, we have been engaged in a large-scale research program on lifelike pedagogical agents and their role in constructivist learning environments. In the resulting computational framework, lifelike pedagogical agents are specified by (1) a behavior space containing animated and vocal behaviors, (2) a design-centered context model that maintains constructivist problem representations, multimodal advisory contexts, and evolving problem-solving tasks, and (3) a behavior sequencing engine that in realtime dynamically selects and assembles agents ’ actions to create pedagogically effective, lifelike behaviors. To empirically investigate this framework, it has been instantiated in a full-scale implementation of a lifelike pedagogical agent for DESIGN-A-PLANT, a learning environment developed for the domain of botanical anatomy and physiology for middle school students. Experience with focus group studies conducted with middle school students interacting with the implemented agent suggests that lifelike pedagogical agents hold much promise for mixed-initiative learning. Key words: Lifelike agents, pedagogicalagents, animated agents, knowledge-basedlearning environments, mixed-initiative interaction, intelligent tutoring systems, intelligent multimedia presentation,
Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes
- In
, 2001
"... The advent of second-generation intelligent computer tutors raises an important instructional design question: when should tutorial advice be presented in problem solving? This paper examines four feedback conditions in the ACT Programming Tutor. Three versions offer the student different levels of ..."
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Cited by 73 (6 self)
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The advent of second-generation intelligent computer tutors raises an important instructional design question: when should tutorial advice be presented in problem solving? This paper examines four feedback conditions in the ACT Programming Tutor. Three versions offer the student different levels of control over error feedback and correction: (a) immediate feedback and immediate error correction; (b) immediate error flagging and student control of error correction; (c) feedback on demand and student control of error correction. A fourth, No-tutor condition offers no step-by-step problem solving support. The immediate feedback group with greatest tutor control of problem solving yielded the most efficient learning. These students completed the tutor problems fastest, and the three tutor-
When does feedback facilitate learning of words
- Journal of Experimental Psychology: Learning, Memory, & Cognition
, 2005
"... Some researchers have suggested that although feedback may enhance performance during associative learning, it does so at the expense of later retention. To examine this issue, subjects (N � 258) learned Luganda–English word pairs. After 2 initial exposures to the materials, subjects were tested on ..."
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Cited by 68 (13 self)
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Some researchers have suggested that although feedback may enhance performance during associative learning, it does so at the expense of later retention. To examine this issue, subjects (N � 258) learned Luganda–English word pairs. After 2 initial exposures to the materials, subjects were tested on each item several times, with the presence and type of feedback varying between subjects. A final test followed after 1 week. Supplying the correct answer after an incorrect response not only improved performance during the initial learning session—it also increased final retention by 494%. On the other hand, feedback after correct responses made little difference either immediately or at a delay, regardless of whether the subject was confident in the response. Practical and theoretical implications are discussed. Despite more than a century of work, research on learning and memory has provided designers of classroom curricula or computer-aided instruction systems with surprisingly few bits of concrete guidance on how to speed learning and retard forgetting. This is true even for rather cut and dry learning situations in which people merely seek to acquire discrete bits of information such as facts, foreign language vocabulary, and the like. In part, this lack
Cognitive Tutor: Applied research in mathematics education
"... For 25 years, we have been working to build cognitive models of mathematics, which have become a basis for middle- and high-school curricula. We discuss the theoretical background of this approach and evidence that the resulting curricula are more effective than other approaches to instruction. We a ..."
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Cited by 68 (20 self)
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For 25 years, we have been working to build cognitive models of mathematics, which have become a basis for middle- and high-school curricula. We discuss the theoretical background of this approach and evidence that the resulting curricula are more effective than other approaches to instruction. We also discuss how embedding a well specified theory in our instructional software allows us to dynamically evaluate the effectiveness of our instruction at a more detailed level than was previously possible. The current widespread use of the software is allowing us to test hypotheses across large numbers of students. We believe that this will lead to new approaches both to understanding mathematical cognition and to improving instruction. For 25 years, we have been working to understand mathematical cognition through the use of cognitive modeling and applying that knowledge to constructing curricula (both text and software) that are more educationally effective than preexisting approaches. This work has been successful on many levels. It has advanced knowledge of cognition in general and of mathematical cognition in particular; the resulting curricula have proven to be educationally effective in school settings; and the curricula, as commercial products, have found a strong following in the school marketplace. We believe that our development model, which involves a close and continuing relationship among basic research, applied research, and field testing, can serve as a model for other efforts to apply cognitive psychology to education. In this article, we describe some of the history of our efforts, our view of the relationship between basic research and development, and some directions for further research.
