Effective Tutoring Techniques: A Comparison of Human Tutors and Intelligent Tutoring Systems

Merrill, Douglas C.; Reiser, Brian J.; Ranney, Michael; Trafton, J. Gregory

doi:10.1207/s15327809jls0203_2

Cited by 203 publications

(102 citation statements)

References 37 publications

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“…Researchers have argued that immediate feedback offered by Cognitive Tutors is qualitatively different from that offered by human tutors. For instance, research by Merrill and colleagues comparing human and computer tutors shows that human tutors do not intervene immediately on errors that may provide learning opportunities (Merrill, Reiser, Ranney, & Trafton, 1992). Other researchers have observed that human tutors let learners engage in activities associated with error detection and correction (Fox 1993).…”

Section: Delayed Feedbackmentioning

confidence: 99%

Fostering the Intelligent Novice: Learning From Errors With Metacognitive Tutoring

Mathan¹,

Koedinger²

2005

Educational Psychologist

139

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This article explores 2 important aspects of metacognition: (a) how students monitor their ongoing performance to detect and correct errors and (b) how students reflect on those errors to learn from them. Although many instructional theories have advocated providing students with immediate feedback on errors, some researchers have argued that immediate feedback eliminates the opportunity for students to practice monitoring for and learning from errors. Thus, they advocate delayed feedback. This article provides evidence that this line of reason is flawed and suggests that rather than focusing on the timing of feedback, instructional designers should focus on the "model of desired performance" with respect to which feedback is provided. Instead of delaying feedback, we suggest broadening our model of correct behavior or desired performance to include some kinds of incorrect, but reasonable behaviors. This article explores the effects of providing feedback on the basis of a so-called intelligent novice cognitive model. A system based on an intelligent novice model allows students to make certain reasonable errors, and provides guidance through the exercise of error detection and correction skills. There are two pedagogical motivations for feedback based on an intelligent novice model. First, jobs today not only require ready-made expertise for dealing with known problems, but also intelligence to address novel situation where nominal experts are thrown back to the state of a novice. Second, the opportunity to reason about the causes and consequences of errors may allow students to form a better model of the behavior of domain operators. Results show that students receiving intelligent novice feedback acquire a deeper conceptual understanding of domain principles and demonstrate better transfer and retention of skills over time.

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Section: Delayed Feedbackmentioning

confidence: 99%

Fostering the Intelligent Novice: Learning From Errors With Metacognitive Tutoring

Mathan¹,

Koedinger²

2005

Educational Psychologist

139

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“…There is a large literature that has examined what human tutors do-such as analyzing what they say, how they said it, and when it is best said. For example, Merrill et al (1992) reviewed research on human tutoring strategies in various domains and compared them to an intelligent tutoring approach called "model tracing. "…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Automated Diagnosis and Instruction for Mathematics Problem‐solving

Underwood

2007

ETS Research Report Series

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ETS and the ETS logo are registered trademarks ofEducational Testing Service (ETS). AbstractThe past several decades have seen numerous approaches toward automated diagnosis and instructional support of students engaged in mathematics problem-solving. These approaches typically involve detailed analysis of potential solution paths for problems, formal representations of correct and incorrect answers, and support in the form of feedback or explanations to students during the process of solving a problem. The approaches of each of a number of representative systems (ACED, ALEKS, Cognitive Tutors, Andes, and Assistments)will be described through a critical evaluation of how they represent content knowledge, their approaches to diagnosis, and their approaches to instructional support. Finally, recommendations are made for new approaches to automated diagnosis and instructional support of mathematics problem-solving.

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“…Similarly, in the tutorial dialog study by Fox (Fox, 1993), tutors show immediate recognition of every step the student makes and their silence tends to presage the student's confusion. It is reported that in providing feedback, human tutors are more flexible than educational software and use high bandwidth communication in guiding the students (Merrill et al, 1992).…”

Section: Immediate Feedbackmentioning

confidence: 99%

“…Our work is inspired by both 1) principles and dialogue strategies that are used by instructional systems in guiding student learning and 2) some of the general educational theories for learning and instruction (Chi, 2000;Clark and Haviland, 1977;Kearsley, 2005;Fox, 1993;de Koning et al, 2000;Merrill et al, 1992).…”

Section: Introductionmentioning

confidence: 99%

Incorporating tutoring principles into interactive knowledge acquisition

Kim¹,

Gil²

2007

International Journal of Human-Computer Studies

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This paper argues that interactive knowledge acquisition systems would benefit from a tighter and more thorough incorporation of tutoring and learning principles. Current acquisition systems learn from users in a passive manner, and could instead be designed to incorporate the proactive capabilities that one expects of a good student. We first describe our analysis of the literature on teacher-student interaction and present a compilation of tutoring and learning principles that are relevant to interactive knowledge acquisition systems. We then point out what tutoring and learning principles have been used to date in the acquisition literature, though unintentionally and implicitly, and discuss how a more thorough and explicit representation of these principles would help improve how computers learn from users. We present our design and an initial implementation of an acquisition dialogue system called SLICK that represents acquisition principles and goals explicitly and declaratively, making the system actively reason about various acquisition tasks and generate its interactions dynamically. Finally, we discuss promising directions in designing acquisition systems by structuring interactions with users according to tutoring and learning principles.

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Effective Tutoring Techniques: A Comparison of Human Tutors and Intelligent Tutoring Systems

Cited by 203 publications

References 37 publications

Fostering the Intelligent Novice: Learning From Errors With Metacognitive Tutoring

Fostering the Intelligent Novice: Learning From Errors With Metacognitive Tutoring

Evaluation of Automated Diagnosis and Instruction for Mathematics Problem‐solving

Incorporating tutoring principles into interactive knowledge acquisition

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