Titlebook: Intelligent Tutoring Systems; 17th International C Alexandra I. Cristea,Christos Troussas Conference proceedings 2021 Springer Nature Switz

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Intelligent Tutoring Systems978-3-030-80421-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Wide-Scale Automatic Analysis of 20 Years of ITS Research in-depth understanding of current studies within an area, they are limited by the number of studies which they take into account. Importantly, whilst publications in hot areas abound, it is not feasible for an individual or team to analyse a large volume of publications within a reasonable amount o

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Exploring the Barriers of Educational Innovationparticular, has been studied since the 1970s more systematically. Educational innovation, its adoption and implementation have been studied not only by various researchers, namely Fullan, Westera, Cohen and Ball, but different organizations, such as the Organization for Economic Cooperation and Deve

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A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCst. However, there is a well-known higher chance of dropout from MOOCs than from conventional off-line courses. Researchers have implemented extensive methods to explore the reasons behind learner attrition or lack of interest to apply timely interventions. The recent success of neural networks has r

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CompPrehension - Model-Based Intelligent Tutoring System on Comprehension Levelg a scaffolding system from lower- to higher-level cognitive skills. We designed and implemented an intelligent tutoring system CompPrehension aimed at the comprehension level of Bloom’s taxonomy that often gets neglected in favour of the higher levels. The system features plugin-based architecture,

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Learning Logical Reasoning : Improving the Student Model with a Data Driven Approachexts. Logic-Muse components were validated and argued by experts throughout the designing process (ITS researchers, logicians and reasoning psychologists). A Bayesian network with expert validation has been developed and used in a Bayesian Knowledge Tracing (BKT) process that allows the inference of

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Comparing Bayesian Knowledge Tracing Model Against Na?ve Mastery Modelollected by two programming tutors to compute BKT models for every concept covered by each tutor. The novelty of our model was that slip and guess parameters were computed for every problem presented by each tutor. Next, we used cross-validation to evaluate whether the resulting BKT model would have