Jennifer Sabourin

Modeling learner affect with theoretically grounded dynamic bayesian networks

Evidence of the strong relationship between learning and emotion has fueled recent work in modeling affective states in intelligent tutoring systems. Many of these models are based on general models of affect without a specific focus on learner emotions. This paper presents work that investigates the benefits of using theoretical models of learner emotions to guide the development of Bayesian networks for prediction of student affect. Predictive models are empirically learned from data acquired from 260 students interacting with the game-based learning environment, CRYSTAL ISLAND. Results indicate the benefits of using theoretical models of learner emotions to inform predictive models. The most successful model, a dynamic Bayesian network, also highlights the importance of temporal information in predicting learner emotions. This work demonstrates the benefits of basing predictive models of learner emotions on theoretical foundations and has implications for how these models may be used to validate theoretical models of emotion.

Affect and Engagement in Game-BasedLearning Environments

The link between affect and student learning has been the subject of increasing attention in recent years. Affective states such as flow and curiosity tend to have positive correlations with learning while negative states such as boredom and frustration have the opposite effect. Student engagement and motivation have also been shown to be critical in improving learning gains with computer-based learning environments. Consequently, it is a design goal of many computer-based learning environments to encourage positive affect and engagement while students are learning. Game-based learning environments offer significant potential for increasing student engagement and motivation. However, it is unclear how affect and engagement interact with learning in game-based learning environments. This work presents an in-depth analysis of how these phenomena occur in the game-based learning environment, Crystal Island. The findings demonstrate that game-based learning environments can simultaneously support learning and promote positive affect and engagement.

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

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 self-regulated 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.

Affect Recognition and Expression in Narrative-Centered Learning Environments

While there are many open questions about the role of affect in learning, a key issue is determining how emotion impacts learning in immersive, technology-rich learning environments. We examine these issues within narrative-centered learning environments, which leverage narrative’s motivating features such as compelling plots, engaging characters, and fantastical settings. These environments offer a novel and rich setting for investigating affective reasoning in intelligent support systems that aim to improve student learning, motivation and engagement.

Discovering Behavior Patterns of Self-Regulated Learners in an Inquiry-Based Learning Environment

Inquiry-based learning has been proposed as a natural and authentic way for students to engage with science. Inquiry-based learning environments typically require students to guide their own learning and inquiry processes as they gather data, make and test hypotheses and draw conclusions. Some students are highly self-regulated learners and are able to guide and monitor their own learning activities effectively. Unfortunately, many students lack these skills and are consequently less successful in open-ended, inquiry-based environments. This work examines differences in inquiry behavior patterns in an open-ended, game-based learning environment, Crystal Island. Differential sequence mining is used to identify meaningful behavior patterns utilized by Low, Medium, and High self-regulated learners. Results indicate that self-regulated learners engage in more effective problem solving behaviors and demonstrate different patterns of use of the provided cognitive tools. The identified patterns help provide further insight into the role of SRL in inquiry-based learning and inform future approaches for scaffolding.

Utilizing Dynamic Bayes Nets to Improve Early Prediction Models of Self-regulated Learning

Student engagement and motivation during learning activities is tied to better learning behaviors and outcomes and has prompted the development of learner-guided environments. These systems attempt to personalize learning by allowing students to select their own tasks and activities. However, recent evidence suggests that not all students are equally capable of guiding their own learning. Some students are highly self-regulated learners and are able to select learning goals, identify appropriate tasks and activities to achieve these goals and monitor their progress resulting in improved learning and motivational benefits over traditional learning tasks. Students who lack these skills are markedly less successful in self-guided learning environments and require additional scaffolding to be able to navigate them successfully. Prior work has examined these phenomena within the learner-guided environment, Crystal Island, and identified the need for early prediction of students’ self-regulated learning abilities. This work builds upon these findings and presents a dynamic Bayesian approach that significantly improves the classification accuracy of student self-regulated learning skills.

Generalizing models of student affect in game-based learning environments

Evidence of the strong relationship between learning and emotion has fueled recent work in modeling affective states in intelligent tutoring systems. Many of these models are designed in ways that limit their ability to be deployed to a large audience of students by using expensive sensors or subjectdependent machine learning techniques. This paper presents work that investigates empirically derived Bayesian networks for prediction of student affect. Predictive models are empirically learned from data acquired from 260 students interacting with the game-based learning environment, CRYSTAL ISLAND. These models are then tested on data from a second identical study involving 140 students to examine issues of generalizability of learned predictive models of student affect. The findings suggest that predictive models of affect that are learned from empirical data may have significant dependencies on the populations on which they are trained, even when the populations themselves are very similar.