Dynamic Representation and Interpretation in a Multiagent 3D Tutoring System
In this paper we present an intelligent tutoring system which aims at decreasing students’ dropout rate by offering the possibility of a personalized follow-up. We address the specific problem of the evolution of the large amount of data to be processed a
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Abstract In this paper we present an intelligent tutoring system which aims at decreasing students’ dropout rate by offering the possibility of a personalized followup. We address the specific problem of the evolution of the large amount of data to be processed and interpreted in an intelligent tutoring system. In this regard we detail the architecture and experimental results of our decision support system used as the core of the intelligent tutor—which could be applied to a variety of teaching fields. The first part presents an overview of the characteristics of intelligent tutors, the chosen data organization—composed of a composite factual semantic feature descriptive representation associated to a multiagent system—and two examples used to illustrate the architecture of our prototype. The second and last part describes all the components of the prototype: student interface, dynamic representation layer, characterization, and interpretation layers. First, for the student interface, the system shows our 3D virtual campus named GE3D to be connected to the intelligent tutor. Then we explain how the agents of the first layer represent the evolution of the situation being analyzed. Next, we specify the use of the characterization layer to cluster the agents of representation layer and to compute compound parameters. Finally, we expose how—using compound parameters—the third layer can measure similarity between current target case and past cases to constitute an interpretation of cases according to a case-based reasoning paradigm.
P. Person ⋅ T. Galinho ⋅ H. Boukachour ⋅ F. Lecroq ⋅ J. Grieu (✉) LITIS University of Le Havre, 25 Rue Philippe Lebon, BP 1123, 76063 Le Havre, France e-mail: [email protected] P. Person e-mail: [email protected] T. Galinho e-mail: [email protected] H. Boukachour e-mail: [email protected] F. Lecroq e-mail: [email protected] © Springer International Publishing Switzerland 2017 V. Sgurev et al. (eds.), Recent Contributions in Intelligent Systems, Studies in Computational Intelligence 657, DOI 10.1007/978-3-319-41438-6_12
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1 Introduction Intelligent tutoring systems (ITS) might be one adequate answer toward helping decrease students’ dropout rate by offering personalized follow-up either in blended or distance learning courses [20]. Quoting Hafner [8], an ITS: “is educational software containing an artificial intelligence component. The software tracks students’ work, tailoring feedback, and hints along the way. By collecting information on a particular student’s performance, the software can make inferences about strengths and weaknesses, and can suggest additional work.” On the one hand, thanks to information feedback given by the decision support system we have conceived, our system lets the ITS deal with students who learn easily. On the other hand, the ITS spots students with most difficulties and calls on a human tutor for help. Thus the latter has more time to focus on students more likely to abandon
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