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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TL;DR: The formalism that was developed for the representation of the instructional knowledge, the interpretation engine that can generate instructional processes based on the knowledge in the knowledge base, and the actual content of theknowledge base are described.
Abstract: The instructional competence of an Intelligent Tutoring System lies in its instructional model. Such a model has been approached in the ITS field from a theoretical and from a computational point of view. GTE approaches the instructional model from an epistemological point of view by making it reflect the instructional knowledge and expertise that underlies human teaching. The underlying assumption is that such knowledge and expertise has a generic nature, and that it can be modelled. The central component of the GTE architecture is therefore a large generic instructional knowledge base that is capable of dynamically generating a huge variety of instructional plans. It enables to flexibly adapt the teaching performance to the requirements of the individual teaching context. In this paper we describe the formalism that was developed for the representation of the instructional knowledge, the interpretation engine that can generate instructional processes based on the knowledge in the knowledge base, and the actual content of the knowledge base. It illustrates the feasibility of the assumption that was made, and the impact this may have on authoring instructional strategies.
42 citations
TL;DR: The authors investigated the role of fidelity in a game-based, virtual learning environment as well as feedback delivered by an intelligent tutoring system, and found large gains in learning across conditions.
Abstract: In the context of practicing intercultural communication skills, we investigated the role of fidelity in a game-based, virtual learning environment as well as the role of feedback delivered by an intelligent tutoring system. In 2 experiments, we compared variations on the game interface, use of the tutoring system, and the form of the feedback. Our findings suggest that for learning basic intercultural communicative skills, a 3-dimensional (3-D) interface with animation and sound produced equivalent learning to a more static 2-D interface. However, learners took significantly longer to analyze and respond to the actions of animated virtual humans, suggesting a deeper engagement. We found large gains in learning across conditions. There was no differential effect with the tutor engaged, but it was found to have a positive impact on learner success in a transfer task. This difference was most pronounced when the feedback was delivered in a more general form versus a concrete style.
42 citations
16 Mar 2015
TL;DR: The authors used natural language processing tools to build models of students' comprehension ability from the linguistic properties of their self-explanations and found that the linguistic indices were predictive of reading comprehension ability, over and above the current system algorithms.
Abstract: This study builds upon previous work aimed at developing a student model of reading comprehension ability within the intelligent tutoring system, iSTART. Currently, the system evaluates students' self-explanation performance using a local, sentence-level algorithm and does not adapt content based on reading ability. The current study leverages natural language processing tools to build models of students' comprehension ability from the linguistic properties of their self-explanations. Students (n = 126) interacted with iSTART across eight training sessions where they self-explained target sentences from complex science texts. Coh-Metrix was then used to calculate the linguistic properties of their aggregated self-explanations. The results of this study indicated that the linguistic indices were predictive of students' reading comprehension ability, over and above the current system algorithms. These results suggest that natural language processing techniques can inform stealth assessments and ultimately improve student models within intelligent tutoring systems.
42 citations
02 Jun 2002
TL;DR: The Geometry Explanation Tutor, a dialogue system that helps students, through a restricted form of dialogue, to construct general explanations of problem-solving steps in their own words, suggests that the techniques used are up to the task.
Abstract: Previous studies have shown that self-explanation is an effective metacognitive strategy and can be supported effectively by intelligent tutoring systems. It is plausible however that students may learn even more effectively when stating explanations in their own words and when receiving tutoring focused on their explanations. We are developing the Geometry Explanation Tutor in order to test this hypothesis. This system helps students, through a restricted form of dialogue, to construct general explanations of problem-solving steps in their own words. We conducted a pilot study in which the tutor was used for two class periods in a junior high school. The data from this study suggest that the techniques that we chose to implement the dialogue system, namely a knowledge-based approach to natural language understanding and classification of student explanations, are up to the task. There are a number of ways in which the system could be improved within the current architecture.
42 citations
01 Jan 1999
TL;DR: The CIRCSIM-Tutor intelligent tutoring system as mentioned in this paper was built on the basis of numerous studies of transcripts of expert human tutors (professors) teaching first year medical students.
Abstract: The CIRCSIM-Tutor intelligent tutoring system project has been built on the basis of numerous studies of transcripts of expert human tutors (professors) teaching first year medical students. We also have transcripts of novice tutors (second year medical students) teaching the same material to medical students at the same level. In this paper we identify measurable differences in the teaching styles between the novices and experts. Examples of tutoring of identical topics were isolated from the novice- and expert-tutored transcripts and various dialogue acts were counted. The primary result is that expert tutors are more likely than novice tutors to query students for information as opposed to informing them directly.
42 citations