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 Cognitive Tutor Algebra is extended with a reciprocal peer tutoring activity designed to increase conceptual learning, and peer tutors learned when they reflected on tutee problem-solving actions and tutees learned when the tutor's help was responsive to those actions.
Abstract: Intelligent tutoring systems have been successful at increasing student mathematics learning, but may be further improved with the addition of collaborative activities. We have extended the Cognitive Tutor Algebra, a successful intelligent tutoring system for individual learning, with a reciprocal peer tutoring activity designed to increase conceptual learning. While using our peer tutoring environment, students take on tutor and tutee roles, and engage in both problem-solving actions and dialogue. In a classroom study, we randomly assigned 62 participants to three conditions (adaptive assistance to peer tutoring, fixed assistance to peer tutoring, and individual learning). All conditions yielded significant learning gains, but there were no differences between conditions in final outcomes. There were significant process differences, however. We assessed student interaction using problem-solving information logged by the intelligent tutoring system and collaborative dialogue captured in a chat window. Our analysis integrated these multiple data sources in order to better understand how collaborative dialogue and problem-solving actions might lead to conceptual learning. This rich data sheds light on how students benefitted from the reciprocal peer tutoring activity: Peer tutors learned when they reflected on tutee problem-solving actions, and tutees learned when the tutor's help was responsive to those actions.
37 citations
TL;DR: An approach to the debugging of student Prolog programs in a Prolog Intelligent Tutoring System (PITS) that detects errors in them and proposes the corrections necessary to fix them, and demonstrates the use of a heuristic bestfirst search of the program decomposition space.
Abstract: This paper describes an approach to the debugging of student Prolog programs in a Prolog Intelligent Tutoring System (PITS) that detects errors in them and proposes the corrections necessary to fix them It proposes the use of multiple sources of expertise to analyse Prolog programs for tutoring purposes, and the collaboration of these sources for understanding student programs, detection of bugs and suggesting fixes to buggy student programs It also demonstrates the use of a heuristic bestfirst search of the program decomposition space to parse a Prolog program into a hierarchical structure of predicate definitions, clauses, subgoals, arguments and terms This article illustrates the merits of an algorithm-based approach supplemented by multiple sources of expertise for program debugging by showing that APROPOS2 is an effective, realistic and useful tool for program debugging It then highlights the difficulties inherent in debugging Prolog programs and discusses the limitations of our algorithm-based approach
37 citations
TL;DR: This paper presents an intelligent tutoring approach for training Portuguese control center operators in tasks like incident analysis and diagnosis, and service restoration of power systems.
Abstract: The activity of control center operators is important to guarantee the effective performance of power systems. Operators' actions are crucial to deal with incidents, especially severe faults like blackouts. In this paper, we present an intelligent tutoring approach for training Portuguese control center operators in tasks like incident analysis and diagnosis, and service restoration of power systems. Intelligent tutoring system (ITS) approach is used in the training of the operators, having into account context awareness and the unobtrusive integration in the working environment. Several artificial intelligence techniques were criteriously used and combined together to obtain an effective intelligent tutoring environment, namely multiagent systems, neural networks, constraint-based modeling, intelligent planning, knowledge representation, expert systems, user modeling, and intelligent user interfaces.
37 citations
TL;DR: The students who use DragonBox solved many more problems and enjoyed the experience more, but the students who used Lynnette performed significantly better on the post-test, showing that intuitions about what works, educationally, can be fallible.
Abstract: Educational games and intelligent tutoring systems (ITS) both support learning by doing, although often in different ways. The current classroom experiment compared a popular commercial game for equation solving, DragonBox and a research-based ITS, Lynnette with respect to desirable educational outcomes. The 190 participating 7th and 8th grade students were randomly assigned to work with either system for 5 class periods. We measured out-of-system transfer of learning with a paper and pencil pre- and post-test of students’ equation-solving skill. We measured enjoyment and accuracy of self-assessment with a questionnaire. The students who used DragonBox solved many more problems and enjoyed the experience more, but the students who used Lynnette performed significantly better on the post-test. Our analysis of the design features of both systems suggests possible explanations and spurs ideas for how the strengths of the two systems might be combined. The study shows that intuitions about what works, educationally, can be fallible. Therefore, there is no substitute for rigorous empirical evaluation of educational technologies.
37 citations
TL;DR: Recent attempts to incorporate human-like conversational behaviors into the dialog moves delivered by an animated pedagogical agent that simulates human tutors, including AutoTutor, an intelligent tutoring system are described.
Abstract: This paper describes our recent attempts to incorporate human-like conversational behaviors into the dialog moves delivered by an animated pedagogical agent that simulates human tutors. We first present a brief overview of the modules comprising AutoTutor, an intelligent tutoring system. The second section describes a set of conversational behaviors that are being incorporated into AutoTutor. The behaviors of interest involve variations in intonation, head movements, arm and hand movements, facial expressions, eye blinking, gaze direction, and back-channel feedback. The final section presents a recent empirical study concerned with back-channel feedback events during human-to-human tutoring sessions. The back-channel feedback events emitted by tutors are mostly positive (63%), mostly verbal (77%), and immediately follow speech-act boundaries or noun-phrase boundaries (83%). Tutors also deliver back-channelevents at a very high rate when students are emitting dialog, about 13 events per minute. Conversely, 88% of students' back-channel feedback events are head nods, and they occur at unbounded locations (63%).
36 citations