Modeling Learner Affect with Theoretically Grounded Dynamic Bayesian Networks

Sabourin, Jennifer; Mott, Bradford W.; Lester, James C.

doi:10.1007/978-3-642-24600-5_32

Cited by 85 publications

(65 citation statements)

References 13 publications

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“…This split implies that High SRL students are making mostly specific reflections, while Low SRL students are primarily un-reflective. Three groups were chosen to cover a larger gradient of SRL behaviors and because tertiary splits have been effective in other machine learning applications (Sabourin et al 2011a;). …”

Section: Srl Annotationmentioning

confidence: 99%

Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

Sabourin

Shores

Mott

et al. 2013

Int J Artif Intell Educ

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Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students' success in computer-based learning environments. Consequently, understanding students' self-regulated learning behavior has been the subject of increasing attention. Unfortunately, monitoring these behaviors in real-time has proven challenging. This paper presents an initial investigation into selfregulated learning in a game-based learning environment. Evidence of goal setting and monitoring behaviors is examined through students' text-based responses to update their 'status' in an in-game social network. Students are then classified into SRL-use categories. This article describes the methodology used to classify students and discusses analyses demonstrating the learning and gameplay behaviors across students in different SRL-use categories. Finally, machine learning models capable of predicting these classes early in students' interaction are presented.

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Section: Srl Annotationmentioning

confidence: 99%

Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

Sabourin

Shores

Mott

et al. 2013

Int J Artif Intell Educ

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“…D'Mello and colleagues achieved better than chance agreement to groundtruth labels provided by human video coders, distinguishing students' frustration, boredom, confusion, and flow from each other. Conati andMaclaren (2009) andSabourin, Mott, andLester (2011) used a combination of interaction data and questionnaire data to infer a range of affective states. More recent work by Baker and colleagues (2012) found that better agreement to ground-truth labels could be achieved by explicitly using data from automated detectors of student disengaged behaviors when predicting affect.…”

Section: The Difference Between Generic Model Parameters and The Inmentioning

confidence: 99%

New Potentials for Data‐Driven Intelligent Tutoring System Development and Optimization

et al. 2013

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Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.

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“…Crystal Island has served as a platform for investigating a range of artificial intelligence technologies for dynamically supporting students' learning experiences. This includes work on narrative-centered tutorial planning (Lee, Mott, and Lester 2012;Mott and Lester 2006), student knowledge modeling (Rowe and Lester 2010), student goal recognition (Ha et al 2011), and affect recognition models (Sabourin, Mott, and Lester 2011). The environment has also been the subject of extensive empirical investigations of student learning and presence , with results informing the design and revision of successive iterations of the system.…”

Section: The Crystal Island Learning Environmentmentioning

confidence: 99%

“…Elliot and Pekrun's model of learner emotions was used as a theoretical foundation for structuring a sensor-free affect detection model (Sabourin, Mott, and Lester 2011). This model was empirically learned from a corpus of student interaction data.…”

Section: Modeling Student Affectmentioning

confidence: 99%

“…A static Bayesian network (figure 5) was designed with the structure hand-crafted to include the relationships described within Elliot and Pekrun's model of learner emotions (Sabourin, Mott, and Lester 2011). Specifically the structure focused on representing the appraisal of learning and performance goals and how these goals were being met based on students' progress and activities in the game.…”

Section: Modeling Student Affectmentioning

confidence: 99%

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Serious Games Get Smart: Intelligent Game‐Based Learning Environments

Lester

Lee

et al. 2013

AI Magazine

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Intelligent game-based learning environments integrate commercial game technologies with AI methods from intelligent tutoring systems and intelligent narrative technologies. This article introduces the CRYSTAL ISLAND intelligent game-based learning environment, which has been under development in the authors’ laboratory for the past seven years. After presenting CRYSTAL ISLAND, the principal technical problems of intelligent game-based learning environments are discussed: narrative-centered tutorial planning, student affect recognition, student knowledge modeling, and student goal recognition. Solutions to these problems are illustrated with research conducted with the CRYSTAL ISLAND learning environment.

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Modeling Learner Affect with Theoretically Grounded Dynamic Bayesian Networks

Cited by 85 publications

References 13 publications

Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

New Potentials for Data‐Driven Intelligent Tutoring System Development and Optimization

Serious Games Get Smart: Intelligent Game‐Based Learning Environments

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