TIDES - Using Bayesian Networks for Student Modeling

Danine, A.; Lefebvre, Bernard; Mayers, André

doi:10.1109/icalt.2006.1652614

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…Hence, probabilistic and approximate techniques such as Bayesian networks or fuzzy modeling are needed. An example is found in the TIDES ITS, proposed in [52], which uses a Bayesian network to model student behavior based on the bugs that the student commits.…”

Section: Intelligent Tutoring Systemsmentioning

confidence: 99%

A Classification of Artificial Intelligence Systems for Mathematics Education

Vaerenbergh¹,

Pérez-Suay²

2021

Preprint

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This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in contemporary digital tools for Mathematics Education (ME). It is aimed at researchers in AI and Machine Learning (ML), for whom we shed some light on the specific technologies that are being used in educational applications; and at researchers in ME, for whom we clarify: i) what the possibilities of the current AI technologies are, ii) what is still out of reach and iii) what is to be expected in the near future. We start our analysis by establishing a highlevel taxonomy of AI tools that are found as components in digital ME applications. Then, we describe in detail how these AI tools, and in particular ML, are being used in two key applications, specifically AI-based calculators and intelligent tutoring systems. We finish the chapter with a discussion about student modeling systems and their relationship to artificial general intelligence.

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Section: Intelligent Tutoring Systemsmentioning

confidence: 99%

A Classification of Artificial Intelligence Systems for Mathematics Education

Vaerenbergh¹,

Pérez-Suay²

2021

Preprint

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“…In a similar manner, Graf [54] adopted a datadriven approach for the identification of learning styles using BNs and also a literature based approach for finding out the effectiveness of suggested concept with the use of one or the other approach for the identification of learning styles. Furthermore, Danine and Mayers [55] designed and developed an intelligent tutoring system that exploits BNs. The BNs were employed for the evaluation of students' knowledge, for the perception of their strategies, for predicting their actions while solving a problem and for making a highly reliable diagnosis.…”

Section: ) Mining Students' Learning Styles and Affective States Usimentioning

confidence: 99%

Automatic Student Modelling for Detection of Learning Styles and Affective States in Web Based Learning Management Systems

et al. 2019

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In traditional learning environments, it is easy for a teacher to get an accurate and deep understanding about how students are learning and undertaking tasks. This results in teacher understanding about each student's learning preferences and behavior, which exhibits, for instance, students' learning styles and affective states. On the other hand, identification of learning styles and affective states in webbased learning environments is quite challenging. The existing approaches for the identification of learning styles such as questionnaire is not without limitations. Similarly, affective states identification approaches mentioned in the literature indicate several shortcomings. This paper proposes an automatic approach for the identification of learning styles and affective states in web-based Learning Management Systems (LMSs). The unique feature of this approach is that it is generic in nature. Using this approach, the students learning styles and affective states are calculated automatically from their learning preferences and behavior within a course. Evaluation of this approach was performed by following a study with 81 students. The results of the study were then compared with the learning styles and affective states questionnaires, which demonstrate that the suggested approach is more appropriate for the identification of learning styles and affective states. Therefore, using this approach, a tool (AsLim) has been developed and can be used by the teachers for the identification of learning styles and affective states of their students.

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“…Knowledge exploration is an important process for adopting knowledge with information systems, whereas graphical representation of the knowledge can help to optimize the learning process and reduce the cognitive overload [14]. For evaluating the system with ground-truth data, we are using the Freebase data base [8] extended with an own schema. Freebase contains open linked data, which can easily be mapped to a formal data specification, whereas Domains are the highest concepts (of an ontology), Types inherit from Domains, the individuals are called Topics.…”

Section: A Semadapt Application Examplementioning

confidence: 99%

“…This research area disposes of a comprehensive pool of methods, systems and algorithms for recognizing and analyzing user related information. With these methods Adaptive User Interfaces (AUIs) facilitate the handling with complex information and support users during their work process [8]. Different existing systems e.g.…”

Section: Introductionmentioning

confidence: 99%

A Reference Model for Adaptive Visualization Systems

Nazemi

Stab

Kuijper

2011

Human-Computer Interaction. Design and Development Approaches

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One key issue of both Information Visualization as well as Adaptive User Interfaces is information overload. While both disciplines have already devised well performing algorithms, methods and applications, a real merging has not taken place yet. Only a few attempts bring the surplus values of both disciplines together, whereas a fine-grained investigation of visualization parameterization is not investigated. Today's systems focus either on the adaptation of visualization types or the parameterization of visualizations. This paper presents a reference Model for Adaptive Visualization Systems (MAVS) that allows the adaptation of both the visualization type and the visualization parameterization. Based on this model, a framework for the adaptive visualization of semantics data will be derived. A use case describing the interaction with an "adaptive visualization cockpit" covering different visualization metaphors concludes the paper.

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TIDES - Using Bayesian Networks for Student Modeling

Cited by 7 publications

References 3 publications

A Classification of Artificial Intelligence Systems for Mathematics Education

A Classification of Artificial Intelligence Systems for Mathematics Education

Automatic Student Modelling for Detection of Learning Styles and Affective States in Web Based Learning Management Systems

A Reference Model for Adaptive Visualization Systems

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