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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26 Apr 2018
TL;DR: This work unpack the difficulties associated with data interpretation from those associated with warranting claims within the context of Inq-ITS (Inquiry Intelligent Tutoring System), a lightweight LMS, providing computer-based assessment and tutoring for science inquiry practices/skills.
Abstract: This chapter addresses students’ data interpretation, a key NGSS inquiry practice, with which students have several different types of difficulties. In this work, we unpack the difficulties associated with data interpretation from those associated with warranting claims. We do this within the context of Inq-ITS (Inquiry Intelligent Tutoring System), a lightweight LMS, providing computer-based assessment and tutoring for science inquiry practices/skills. We conducted a systematic analysis of a subset of our data to address whether our scaffolding is supporting students in the acquisition and transfer of these inquiry skills. We also describe an additional study, which used Bayesian Knowledge Tracing (Corbett and Anderson. User Model User-Adapt Interact 4(4):253–278, 1995), a computational approach allowing for the analysis of the fine-grained sub-skills underlying our practices of data interpretation and warranting claims.
18 citations
TL;DR: The intelligent tutoring system for real estate management developed by the authors is described and is applied in Vilnius Gediminas Technical University, Department of Construction Economics and Property Management.
Abstract: The review on the worldwide intelligent tutoring systems and their application possibilities is presented in the paper. The intelligent tutoring system for real estate management developed by the authors is described. This system is applied in Vilnius Gediminas Technical University, Department of Construction Economics and Property Management. Besides the common components ‐ student model, domain model, pedagogical model and graphical interface, the new developed system has testing model, decision support subsystem and database of computer learning systems. Domain model includes knowledge with the supplemental audio and video material for 63 modules being taught in Vilnius Gediminas Technical University. Student model enables to adapt to a learner needs and knowledge level. Decision support subsystem is used for all components of intelligent tutoring system giving them different level of intelligence. Database of computer learning systems enables using the following web‐based learning systems: co...
18 citations
TL;DR: Case studies of application of social media, game-based learning and various technology-enhanced learning tools in different courses at several Serbian institutions show that educational processes must be modernized and enhanced by technological progress.
18 citations
11 Jun 2018
TL;DR: AttentiveLearner2, a multimodal intelligent tutor running on unmodified smartphones, is proposed to supplement today’s clickstream-based learning analytics for MOOCs and detect 6 emotions in mobile MOOC learning reliably with high accuracy.
Abstract: Massive Open Online Courses (MOOCs) are a promising approach for scalable knowledge dissemination. However, they also face major challenges such as low engagement, low retention rate, and lack of personalization. We propose AttentiveLearner2, a multimodal intelligent tutor running on unmodified smartphones, to supplement today’s clickstream-based learning analytics for MOOCs. AttentiveLearner2 uses both the front and back cameras of a smartphone as two complementary and fine-grained feedback channels in real time: the back camera monitors learners’ photoplethysmography (PPG) signals and the front camera tracks their facial expressions during MOOC learning. AttentiveLearner2 implicitly infers learners’ affective and cognitive states during learning from their PPG signals and facial expressions. Through a 26-participant user study, we found that: (1) AttentiveLearner2 can detect 6 emotions in mobile MOOC learning reliably with high accuracy (average accuracy = 84.4%); (2) the detected emotions can predict learning outcomes (best R2 = 50.6%); and (3) it is feasible to track both PPG signals and facial expressions in real time in a scalable manner on today’s unmodified smartphones.
18 citations
TL;DR: It is found that students’ facial behaviors were powerful predictors of their cognitive engagement states and the SVM (Support Vector Machine) model demonstrated excellent capacity for distinguishing engaged and less engaged states when 17 informative facial features were added into the model.
Abstract: In the present paper, we used supervised machine learning algorithms to predict students' cognitive engagement states from their facial behaviors as 61 students solved a clinical reasoning problem in an intelligent tutoring system. We also examined how high and low performers differed in cognitive engagement levels when performing surface and deep learning behaviors. We found that students' facial behaviors were powerful predictors of their cognitive engagement states. In particular, we found that the SVM (Support Vector Machine) model demonstrated excellent capacity for distinguishing engaged and less engaged states when 17 informative facial features were added into the model. In addition, the results suggested that high performers did not differ significantly in the general level of cognitive engagement with low performers. There was also no difference in cognitive engagement levels between high and low performers when they performed shallow learning behaviors. However, high performers showed a significantly higher level of cognitive engagement than low performers when conducting deep learning behaviors. This study advances our understanding of how students regulate their engagement to succeed in problem-solving. This study also has significant methodological implications for the automated measurement of cognitive engagement.
18 citations