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 main goal is to analyses learner facial expressions and show how Affective Computing could contribute for this interaction, being part of the complete student tracking (traceability) to monitor student behaviors during learning sessions.
Abstract: Affective Computing is a new Artificial Intelligence area that deals with the possibility of making computers able to recognize human emotions in different ways. This paper represents a study about the integration of this new area in the intelligent tutoring system. We argue that socially appropriate affective behaviors would provide a new dimension for collaborative learning systems. The main goal is to analyses learner facial expressions and show how Affective Computing could contribute for this interaction, being part of the complete student tracking (traceability) to monitor student behaviors during learning sessions.
149 citations
TL;DR: Examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference are provided.
Abstract: Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.
144 citations
TL;DR: Evidence is presented that this detector developed in 1 scientific domain can be used—with no modification or retraining—to effectively detect science inquiry skill in another scientific domain, density.
Abstract: We present a method for assessing science inquiry performance, specifically for the inquiry skill of designing and conducting experiments, using educational data mining on students' log data from online microworlds in the Inq-ITS system (Inquiry Intelligent Tutoring System; www.inq-its.org). In our approach, we use a 2-step process: First we use text replay tagging, a type of rapid protocol analysis in which categories are developed and, in turn, used to hand-score students' log data. In the second step, educational data mining is conducted using a combination of the text replay data and machine-distilled features of student interactions in order to produce an automated means of assessing the inquiry skill in question; this is referred to as a detector. Once this detector is appropriately validated, it can be applied to students' log files for auto-assessment and, in the future, to drive scaffolding in real time. Furthermore, we present evidence that this detector developed in 1 scientific domain, phase c...
144 citations
TL;DR: In this paper, the authors used an emote-aloud procedure in which participants were recorded as they verbalised their affective states while interacting with an intelligent tutoring system (AutoTutor).
Abstract: In an attempt to discover the facial action units for affective states that occur during complex learning, this study adopted an emote-aloud procedure in which participants were recorded as they verbalised their affective states while interacting with an intelligent tutoring system (AutoTutor). Participants’ facial expressions were coded by two expert raters using Ekman's Facial Action Coding System and analysed using association rule mining techniques. The two expert raters received an overall kappa that ranged between .76 and .84. The association rule mining analysis uncovered facial actions associated with confusion, frustration, and boredom. We discuss these rules and the prospects of enhancing AutoTutor with non-intrusive affect-sensitive capabilities.
140 citations
TL;DR: The hypothesis that a pedagogical agent incorporated in an ITS can enhance students' learning experience is confirmed and the hypothesis that the presence of the agent improves short-term learning effects was rejected.
Abstract: This paper describes the evaluation of the persona effect of a speech-driven anthropomorphic agent that has been embodied in the interface of an intelligent tutoring system (ITS). This agent is responsible for guiding the student in the environment and communicating the system's feedback messages. The agent was evaluated in terms of the effect that it could have on students' learning, behaviour and experience. The participants in the experiment were divided into two groups: half of them worked with a version of the ITS which embodied the agent and the rest worked with an agent-less version. The results from this study confirm the hypothesis that a pedagogical agent incorporated in an ITS can enhance students' learning experience. On the other hand, the hypothesis that the presence of the agent improves short-term learning effects was rejected.
139 citations