Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor

By Vincent A. W. M. M. Aleven, Bruce M. McLaren, Ido Roll, K. Koedinger. International Journal of Artificial Intelligence in Education, 2006

467 citations recorded by the source corpus.

The research reported in this paper focuses on the hypothesis that an intelligent tutoring system that provides guidance with respect to students' meta-cognitive abilities can help them to become better learners. Our strategy is to extend a Cognitive Tutor (Anderson, Corbett, Koedinger, & Pelletier, 1995) so that it not only helps students acquire domain-specific skills, but also develop better general help-seeking strategies. In developing the Help Tutor, we used the same Cognitive Tutor technology at the meta-cognitive level that has been proven to be very effective at the cognitive level. A key challenge is to develop a model of how students should use a Cognitive Tutor's help facilities. We created a preliminary model, implemented by 57 production rules that capture both effective and ineffective help-seeking behavior. As a first test of the model's efficacy, we used it off-line to evaluate students' help-seeking behavior in an existing data set of student-tutor interactions. We then refined the model based on the results of this analysis. Finally, we conducted a pilot study with the Help Tutor involving four students. During one session, we saw a statistically significant reduction in students' meta-cognitive error rate, as determined by the Help Tutor's model. These preliminary results inspire confidence as we gear up for a larger-scale controlled experiment to evaluate whether tutoring on help seeking has a positive effect on students' learning outcomes.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/005b078a8ea9346839b7ef19013e4ce6c251bab3

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Citations in the corpus

Why Students Engage in “Gaming the System” Behavior in Interactive Learning Environments (2008, background): “In recent years, there has been increasing evidence that students choose to useinteractivelearningenvironmentsinasurprisingvarietyofways(Wood& Wood, 1999; Aleven, McLaren, Roll, & Koedinger, 2004; Baker, Corbett, Koedinger, Wagner, et al., 2004; Mostow et al., 2002;Arroyo & Woolf, 2005; Stevens & So”
Focusing the Conceptual Lens on Metacognition, Self-regulation, and Self-regulated Learning (2008, background): “…was most salient with self-regulation and self-regulated learning, this issue occurred across all three constructs: metacognition and SR (e.g., Aleven et al. 2006; Marchand and Skinner 2007), metacognition and SRL (e.g., Nietfeld et al. 2005; Vandergrift 2005), and SR and SRL (e.g., van…”
Examining self-efficacy during learning: variability and relations to behavior, performance, and learning (2015, background): “Aleven et al. (2006) have proposed a formal model which catalogs the metacognitive monitoring and control processes involved when learners make a decision to venture answers or seek help.”
A review of recent advances in learner and skill modeling in intelligent learning environments (2012, background): “Aleven et al. (2006) developed a meta-cognitive model which unified several different constructs related to help-seeking and help avoidance behavior in a single model.”
AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back (2012, background): “…have been developed for mathematically well-formed topics, including algebra, geometry, programming languages, that is, the Cognitive Tutors, [Aleven et al. 2006; Anderson et al. 1995; Corbett 2002; Koedinger and Corbett 2006], physics such as AutoTutor, Andes, Atlas,and Why/Atlas…”
Exploring the Assistance Dilemma in Experiments with Cognitive Tutors (2007, background): “, algebra or geometry) but also with respect to students’ help-seeking skills (Aleven et al., 2006; Baker, Corbett, Koedinger, Evenson, Roll, et al., 2006; Roll, Aleven, McLaren, Ryu, Baker, et al. 2006).”
A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)? (2021, background): “Thereafter, designers often undertake a cognitive task analysis (Clark & Estes, 1996) to consider the nature of the learning task and the implicit cognitive (and often metacognitive; e.g., Aleven et al., 2006) processes employed by students.”
Handbook of Research on Learning and Instruction (2011, background): “Thus, children were taught to move based on elite, adult movement models rather than developmentally appropriate patterns such as those that Roberton (1982) described.”
A meta-analysis of the effectiveness of intelligent tutoring systems on college students' academic learning (2014, unknown): “In recent years, researchers have developed ITS for teaching specific skills, such as metacognitive skills (Aleven et al., 2006; Conati & VanLehn, 2000), which are seemingly domain independent.”