Stat-Knowlab. Assessment and Learning of Statistics with Competence-based Knowledge Space Theory
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Stat-Knowlab. Assessment and Learning of Statistics with Competence-based Knowledge Space Theory Debora de Chiusole1
· Luca Stefanutti1 · Pasquale Anselmi1 · Egidio Robusto1
Accepted: 28 September 2020 © International Artificial Intelligence in Education Society 2020
Abstract An intelligent tutoring system for learning basic statistics, called Stat-Knowlab, is presented and analyzed. The algorithms implemented in the system are based on the competence-based knowledge space theory, a mathematical theory developed for the formative assessment of knowledge and learning. The system’s architecture consists of the two assessment and learning modules that interact with each other in a continuous exchange of information about the current knowledge state of a student. This allows the system to personalize the student’s learning, providing only with the learning objects that she is ready to learn. During the browsing of the system, several types of navigation data are recorded. In this work, we analyzed data from two studies that were aimed at examining the learning processes induced by the navigation of the system. The results of both studies highlighted that the system is useful for monitoring the student learning processes during a university course of basic statistics. Keywords Knowledge space theory · Competence-based knowledge space theory · Intelligent tutoring system · Stat-Knowlab · Learning process
Debora de Chiusole
[email protected] Luca Stefanutti [email protected] Pasquale Anselmi [email protected] Egidio Robusto [email protected] 1
Department of Philosophy, Sociology, Pedagogy, and Applied Psychology, University of Padua, Via Venezia, 14, 35131, Padova, Italy
International Journal of Artificial Intelligence in Education
Introduction In recent decades, technology has spread to almost all human activities, even in the educational area. Interactive boards, tablets, digital registers, textbooks and apps for learning are only some examples. Additionally there are intelligent tutoring systems (ITSs). An ITS is a computer system that aims at providing immediate and customized instructions or feedback to learners, usually without the intervention of a human teacher (Psotka et al. 1988). Although all ITSs share this fundamental characteristic, different types of ITSs were built by AIED researchers over the years. It is possible to classify ITSs using different criteria. Some of these criteria are: (a) the subject that they teach (e.g., mathematics, statistics, physics, reading, writing, economics, etc.); (b) the school level of the target students (e.g., K-12, college, university); (c) the student modeling process on which they are based, such as model-tracing (Roll et al. 2004), probabilistic modeling (Conati and VanLehn 1999; Conati et al. 2002), constraint-based modeling (Suraweera and Mitrovic 2002), etc.; (d) the underlying (cognitive) learning theories, such as the adaptive control of thought theory by Anderson et al. (1995) or the learning from performance errors theory by Oh
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