A multi‐strategy machine learning student modeling for intelligent tutoring systems

2013 ◽  
Vol 31 (2) ◽  
pp. 274-293 ◽  
Author(s):  
Mu‐Jung Huang ◽  
Heien‐Kun Chiang ◽  
Pei‐Fen Wu ◽  
Yu‐Jung Hsieh
Keyword(s):  
Machine Learning ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Student Modeling ◽  
Tutoring Systems

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 A tutorial on machine learning for 
interactive pedagogical systems

2018 ◽  
Vol 5 (3) ◽  
pp. 79-112
Author(s):  
Francisco S Melo ◽  
Samuel Mascarenhas ◽  
Ana Paiva
Keyword(s):  
Machine Learning ◽  
Intelligent Tutoring Systems ◽  
Serious Games ◽  
Intelligent Tutoring ◽  
Short Introduction ◽  
Tutoring Systems ◽  
Key Concepts

This paper provides a short introduction to the field of machine learning for interactive pedagogical systems. Departing from different examples encountered in interactive pedagogical systems—such as intelligent tutoring systems or serious games—we go over several representative families of methods in machine learning, introducing key concepts in this field. We discuss common challenges in machine learning and how current methods address such challenges. Conversely, by anchoring our presentation on actual interactive pedagogical systems, highlight how machine learning can benefit the development of such systems.

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.

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.

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