Bayesian Networks in Intelligent Tutoring Systems as an Assessment of Student Performance using Student Modeling

2018 ◽  
Author(s):  
Roselie B. Alday
Keyword(s):  
Bayesian Networks ◽  
Student Performance ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Student Modeling ◽  
Tutoring Systems

Using Bayesian Networks for Student Modeling

2008 ◽  
pp. 97-113 ◽  
Author(s):  
Chao-Lin Liu
Keyword(s):  
Bayesian Networks ◽  
Graphical Models ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Influence Diagrams ◽  
Student Modeling ◽  
Learning Needs ◽  
Tutoring Systems ◽  
Test Items ◽  
Educational Applications

This chapter purveys an account of Bayesian networks-related technologies for modeling students in intelligent tutoring systems. Uncertainty exists ubiquitously when we infer students’ internal status, for example, learning needs and emotion, from their external behavior, for example, responses to test items and explorative actions. Bayesian networks offer a mathematically sound mechanism for representing and reasoning about students under uncertainty. This chapter consists of five sections, and commences with a brief overview of intelligent tutoring systems, emphasizing the needs for uncertain reasoning. A succinct survey of Bayesian networks for student modeling is provided in Bayesian Networks, and we go through an example of applying Bayesian networks and mutual information to item selection in computerized adaptive testing in Applications to Student Models. We then touch upon influence diagrams and dynamic Bayesian networks for educational applications in More Graphical Models, and wrap up the chapter with an outlook and discussion for this research direction.

Using Bayesian Networks for Student Modeling

2011 ◽  
pp. 283-311 ◽  
Author(s):  
Chao-Lin Liu
Keyword(s):  
Bayesian Networks ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Uncertain Reasoning ◽  
Student Modeling ◽  
Learning Needs ◽  
Tutoring Systems ◽  
Test Items ◽  
External Behavior

This chapter purveys an account of Bayesian networks-related technologies for modeling students in intelligent tutoring systems. Uncertainty exists ubiquitously when we infer students’ internal status, for example, learning needs and emotion, from their external behavior, for example, responses to test items and explorative actions. Bayesian networks offer a mathematically sound mechanism for representing and reasoning about students under uncertainty. This chapter consists of five sections, and commences with a brief overview of intelligent tutoring systems, emphasizing the needs for uncertain reasoning. A succinct survey of Bayesian networks for student modeling is provided in Bayesian Networks, and we go through an example of applying Bayesian networks and mutual

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

scholarly journals Conceptual clustering: a new approach to student modeling in Intelligent Tutoring Systems

2018 ◽  
pp. 70-76 ◽  
Author(s):  
Yunia Reyes-González ◽  
◽  
Natalia Martínez-Sánchez ◽  
Adolfo Díaz-Sardiñas ◽  
Marisol de la Caridad Patterson-Peña ◽  
...  
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Student Modeling ◽  
Conceptual Clustering ◽  
New Approach ◽  
Tutoring Systems

scholarly journals Multi-Relational and Social-Influence Model for Predicting Student Performance in Intelligent Tutoring Systems (ITS)

2020 ◽  
Vol 9 (3) ◽  
pp. 2058-2066
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Matrix Factorization ◽  
Intelligent Tutoring Systems ◽  
Mean Squared Error ◽  
Mutual Influence ◽  
Intelligent Tutoring ◽  
Essential Elements ◽  
Tutoring Systems ◽  
Predicting Student Performance

Recent studies have shown that Matrix Factorization (MF) method, deriving from recommendation systems, can predict student performance as part of Intelligent Tutoring Systems (ITS). In order to improve the accuracy of this method, we hypothesize that taking into account the mutual influence effect in the relations of student groups would be a major asset. This criterion, coupled with those of the different relationships between the students, the tasks and the skills, would thus be essential elements for a better performance prediction in order to make personalized recommendations in the ITS. This paper proposes an approach for Predicting Student Performance (PSP) that integrates not only friendship relationships such as workgroup relationships, but also mutual influence values into the Weighted Multi-Relational Matrix Factorization method. By applying the Root Mean Squared Error (RMSE) metric to our model, experimental results from KDD Challenge 2010 database show that this approach allows to refine student performance prediction accuracy.

Student Modeling in an Intelligent Tutoring System

2011 ◽  
pp. 208-236 ◽  
Author(s):  
Mingyu Feng ◽  
Neil Heffernan ◽  
Kenneth Koedinger
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Student Modeling ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Fine Grained ◽  
Knowledge States ◽  
Complex Models ◽  
Cognitively Diagnostic

Student modeling and cognitively diagnostic assessment are important issues that need to be addressed for the development and successful application of intelligent tutoring systems (its). Its needs the construction of complex models to represent the skills that students are using and their knowledge states, and practitioners want cognitively diagnostic information at a finer grained level. This chapter reviews our effort on modeling student’s knowledge in the ASSISTment project. Intelligent tutors have been mainly used to teach students. In the ASSISTment project, we have emphasized using the intelligent tutoring system as an assessment system that provides instructional assistance during the test. Usually it is believed that assessment get harder if students are allowed to learn during the test, as its then like try to hit a moving target. So our results are surprising that by providing tutoring to students while they are assessed we actually prove the assessment of students’ knowledge. Additionally, in this article, we present encouraging results about a fine-grained skill model with that system that is able to predict state test scores. We conclude that using intelligent tutoring systems to do assessment seems like a reasonable way of dealing with the dilemma that every minute spent testing students takes time away from instruction.

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