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Stealth assessment of self-regulative behaviors within a game-based environment

Description
Students' ability to regulate and control their behaviors during learning has been shown to be a critical skill for academic success. However, researchers often struggle with ways to capture the nuances of this ability, often solely relying on self-report measures. This thesis proposal employs a novel approach to investigating variations…

Students' ability to regulate and control their behaviors during learning has been shown to be a critical skill for academic success. However, researchers often struggle with ways to capture the nuances of this ability, often solely relying on self-report measures. This thesis proposal employs a novel approach to investigating variations in students' ability to self-regulate by using process data from the game-based Intelligent Tutoring System (ITS) iSTART-ME. This approach affords a nuanced examination of how students' regulate their interactions with game-based features at both a coarse-grained and fine-grain levels and the ultimate impact that those behaviors have on in-system performance and learning outcomes (i.e., self-explanation quality). This thesis is comprised of two submitted manuscripts that examined how a group of 40 high school students chose to engage with game-based features and how those interactions influenced their target skill performance. Findings suggest that in-system log data has the potential to provide stealth assessments of students' self-regulation while learning.
ContributorsSnow, Erica L (Author) / McNamara, Danielle S. (Thesis advisor) / Glenburg, Arthur M (Committee member) / Duran, Nicholas (Committee member) / Arizona State University (Publisher)
Created2014
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Promoting self-regulation and metacognition through the use of online trace data within a game-based environment

Description
Computer-based environments provide a window into the complex and multifaceted learning process. These systems often collect a vast amount of information concerning how users choose to engage and behave within the interface (i.e., click streams, language input, and choices). Researchers have begun to use this information to gain a deeper…

Computer-based environments provide a window into the complex and multifaceted learning process. These systems often collect a vast amount of information concerning how users choose to engage and behave within the interface (i.e., click streams, language input, and choices). Researchers have begun to use this information to gain a deeper understanding of users’ cognition, attitudes, and abilities. This dissertation is comprised of two published articles that describe how post-hoc and real-time analyses of trace data provides fine-grained details about how users regulate, process, and approach various learning tasks within computer-based environments. This work aims to go beyond simply understanding users’ skills and abilities, and instead focuses on understanding how users approach various tasks and subsequently using this information in real-time to enhance and personalize the user’s learning experience.
ContributorsSnow, Erica L (Author) / McNamara, Danielle S. (Thesis advisor) / Connor, Carol (Committee member) / Winne, Phillip (Committee member) / Duran, Nicholas (Committee member) / Arizona State University (Publisher)
Created2015