Topic
Intelligent tutoring system
About: Intelligent tutoring system is a research topic. Over the lifetime, 3472 publications have been published within this topic receiving 58217 citations. The topic is also known as: ITS.
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Proceedings Article•
18 Jul 1999
TL;DR: This paper isolated a set of hinting strategies from human tutoring transcripts and described a model for choosing among them based on domain knowledge, the type of error made by the student, the focus of the tutor's question, and the conversational history.
Abstract: Hinting is an important tutoring tactic in one-on-one tutoring, used when the tutor needs to respond to an unexpected answer from the student. To issue a follow-up hint that is pedagogically helpful and conversationally smooth, the tutor needs to suit the hinting strategy to the student's need while making the strategy fit the high level tutoring plan and the tutoring context. This paper describes a study of the hinting strategies in a corpus of human tutoring transcripts and the implementation of these strategies in a dialogue-based intelligent tutoring system, CIRcslM-Tutor v. 2. We isolated a set of hinting strategies from human tutoring transcripts. We describe our analysis of these strategies and a model for choosing among them based on domain knowledge, the type of error made by the student, the focus of the tutor's question, and the conversational history. We have tested our model with two classes totaling 74 medical students. Use of this extended model of hinting increases the percentage of questions that students are able to answer for themselves rather than needing to be told.
57 citations
14 Jun 2010
TL;DR: After receiving positive feedback from AutoTutor, learners mostly experienced ‘delight' while surprise was experienced after negative feedback, which indicated that tutor feedback and learner affect were related.
Abstract: We investigate how positive, neutral and negative feedback responses from an Intelligent Tutoring System (ITS) influences learners' affect and physiology. AutoTutor, an ITS with conversational dialogues, was used by learners (n=16) while their physiological signals (heart signal, facial muscle signal and skin conductivity) were recorded. Learners were asked to self-report the cognitive-affective states they experienced during their interactions with AutoTutor via a retrospective judgment protocol immediately after the tutorial session. Statistical analysis (Chi-square) indicated that tutor feedback and learner affect were related. The results revealed that after receiving positive feedback from AutoTutor, learners mostly experienced ‘delight' while surprise was experienced after negative feedback. We also classified physiological signals based on the tutor's feedback (Negative vs. Non-Negative) with a support vector machine (SVM) classifier. The classification accuracy, ranged from 42% to 84%, and was above the baseline for 10 learners.
57 citations
TL;DR: This paper investigated 65 students' evidence scores of emotions while they engaged in cognitive and metacognitive self-regulated learning processes as they learned about the circulatory system with MetaTutor, a hypermedia-based intelligent tutoring system.
56 citations
05 May 2012
TL;DR: Treating her as a partner, primarily through aligning oneself with Stacy using pronouns like you or the authors rather than she or it significantly correlates with student learning, as do playful face-threatening comments such as teasing, while elaborate explanations of Stacy's behavior in the third-person and formal tutoring statements reduce learning gains.
Abstract: Understanding how children perceive and interact with teachable agents (systems where children learn through teaching a synthetic character embedded in an intelligent tutoring system) can provide insight into the effects of so-cial interaction on learning with intelligent tutoring systems. We describe results from a think-aloud study where children were instructed to narrate their experience teaching Stacy, an agent who can learn to solve linear equations with the student's help. We found treating her as a partner, primarily through aligning oneself with Stacy using pronouns like you or we rather than she or it significantly correlates with student learning, as do playful face-threatening comments such as teasing, while elaborate explanations of Stacy's behavior in the third-person and formal tutoring statements reduce learning gains. Additionally, we found that the agent's mistakes were a significant predictor for students shifting away from alignment with the agent.
56 citations
TL;DR: The COMET intelligent tutoring system for collaborative medical problem-based learning (PBL) as discussed by the authors uses Bayesian networks to model individual student knowledge and activity, as well as that of the group.
Abstract: Today a great many medical schools have turned to a problem-based learning (PBL) approach to teaching as an alternative to traditional didactic medical education to teach clinical-reasoning skills at the early stages of medical education While PBL has many strengths, effective PBL tutoring is time-intensive and requires the tutor to provide a high degree of personal attention to the students, which is difficult in the current academic environment of increasing demands on faculty time This paper describes the student modeling approach used in the COMET intelligent tutoring system for collaborative medical PBL To generate appropriate tutorial actions, COMET uses a model of each student's clinical reasoning for the problem domain In addition, since problem solving in group PBL is a collaborative process, COMET uses a group model that enables it to do things like focus the group discussion, promote collaboration, and suggest peer helpers Bayesian networks are used to model individual student knowledge and activity, as well as that of the group The validity of the modeling approach has been tested with student models in the areas of head injury, stroke, and heart attack Receiver operating characteristic (ROC) curve analysis shows that the models are highly accurate in predicting individual student actions Comparison with human tutors shows that the focus of group activity determined by the model agrees with that suggested by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0774, Kappa = 0823)
56 citations