Nicholas V. Mudrick

Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island

Game-based learning environments (GBLEs) have been touted as the solution for failing educational outcomes. In this study, we address some of these major issues by using multi-level modeling with data from eye movements and log files to examine the cognitive and metacognitive self-regulatory processes used by 50 college students as they read books and completed the associated in-game assessments (concept matrices) while playing the Crystal Island game-based learning environment. Results revealed that participants who read fewer books in total, but read each of them more frequently, and who had low proportions of fixations on books and concept matrices exhibited the strongest performance. Results stress the importance of assessing quality vs. quantity during gameplay, such that it is important to read books in-depth (i.e., quality), compared to reading books once (i.e., quantity). Implications for these findings involve designing adaptive GBLEs that scaffold participants based on their trace data, such that we can model efficient behaviors that lead to successful performance.

Interdisciplinary Research Methods Used to Investigate Emotions with Advanced Learning Technologies

This chapter provides a synthesis of several research methods used by interdisciplinary researchers to investigate emotions in advanced learning technologies. More specifically, the authors: (1) critique self-report measures used to investigate emotions; (2) briefly describe Scherer’s (2009) model as particularly relevant for investigating emotions due to its complex appraisal system; (3) describe three process-oriented methods (electrodermal activity, facial expressions, and eye-tracking) currently used by interdisciplinary researchers to detect, identify, and classify affective states during learning and briefly highlight strengths and weaknesses of each method; and (4) present major conceptual, methodological, and analytical issues related to investigating emotions during learning with advanced learning technologies.

Using Data Visualizations to Foster Emotion Regulation During Self-Regulated Learning with Advanced Learning Technologies

Emotions play a critical role during learning and problem solving with advanced learning technologies. However, learners typically do not accurately monitor and regulate their emotions and may therefore not learn as much, disengage from the task, and not optimize their learning of the instructional material. Despite their importance, relatively few attempts have been made to understand learners’ emotional monitoring and regulation during learning with advanced learning technologies by using data visualizations of their own (and others’) cognitive, affective, metacognitive, and motivational (CAMM) self-regulated learning (SRL) processes to potentially foster their emotion regulation during learning with advanced learning technologies. We present a theoretically-based and empirically-driven conceptual framework that addresses emotion regulation by proposing the use of visualizations of one’s and others’ CAMM-SRL multichannel data (e.g., cognitive strategy use, metacognitive monitoring accuracy, facial expressions of emotions, physiological arousal, eye-movement behaviors, etc.) to facilitate learners’ monitoring and regulation of their emotions during learning with advanced learning technologies. We use examples from several of our laboratory and classroom studies to illustrate a possible mapping between theoretical assumptions, emotion-regulation strategies, and the types of data visualizations that can be used to enhance and scaffold learners’ emotion regulation, including key processes such as emotion flexibility, emotion adaptivity, and emotion efficacy. We conclude with future directions that can lead to a systematic interdisciplinary research agenda that addresses outstanding emotion regulation-related issues by integrating models, theories, methods, and analytical techniques for the areas of cognitive, learning, and affective sciences, human computer interaction, data visualization, big data, data mining, data science, learning analytics, open learner models, and SRL.

The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents During Learning with MetaTutor

In this paper, we investigate the relationship between students’ (N = 28) individual differences and visual attention to pedagogical agents (PAs) during learning with MetaTutor, a hypermedia-based intelligent tutoring systems. We used eye tracking to capture visual attention to the PAs, and our results reveal specific visual attention-related metrics (e.g., fixation rate, longest fixations) that are significantly influenced by learning depending on student achievement goals. Specifically, performance-oriented students learned more with a long longest fixation and a high fixation rate on the PAs, whereas mastery-oriented students learned less with a high fixation rate on the PAs. Our findings contribute to understanding how to design PAs that can better adapt to student achievement goals and visual attention to the PA.

Impact of Individual Differences on Affective Reactions to Pedagogical Agents Scaffolding

Students’ emotions are known to influence learning and motivation while working with agent-based learning environments (ABLEs). However, there is limited understanding of how Pedagogical Agents (PAs) impact different students’ emotions, what those emotions are, and whether this is modulated by students’ individual differences (e.g., personality, goal orientation). Such understanding could be used to devise intelligent PAs that can recognize and adapt to students’ relevant individual differences in order to enhance their experience with learning environments. In this paper, we investigate the relationship between individual differences and students’ affective reactions to four intelligent PAs available in MetaTutor, a hypermedia-based intelligent tutoring system. We show that achievement goals and personality traits can significantly modulate students’ affective reactions to the PAs. These findings suggest that students may benefit from personalized PAs that could adapt to their motivational goals and personality.

Development of a Human Factors Methods Blog for an Audience of Game Developers

We, as human factors practitioners and researchers, often must describe what we study, how we do it, and why it matters. As a part of those efforts, we developed a blog introducing human factors methods and their applications for an audience of game developers. Each method is discussed and illustrated using examples applied to games. We also describe our goals for the blog’s style and structure, and ideas for further refinement.

How Do Different Levels of AU4 Impact Metacognitive Monitoring During Learning with Intelligent Tutoring Systems

We investigated how college students’ (n = 40) different levels of action unit 4 (AU4: brow lowerer), metacognitive monitoring process use and pre-test score were associated with metacognitive monitoring accuracy during learning with a hypermedia-based ITS. Results revealed that participants with high pre-test scores had the highest accuracy scores with low levels of AU4 and use of more metacognitive monitoring processes, whereas participants with low pre-test scores had higher accuracy scores with high levels of AU4 and use of more metacognitive monitoring processes. Implications include designing adaptive ITSs that provide different types of scaffolding based on levels of prior knowledge, use of metacognitive monitoring processes, and emotional expressivity keeping in mind that levels of emotions change over time, and therefore must be monitored to provide effective scaffolding during learning.

How Are Students’ Emotions Associated with the Accuracy of Their Note Taking and Summarizing During Learning with ITSs?

The goal of this study was to examine 38 undergraduate and graduate students’ note taking and summarizing, and the relationship between emotions, the accuracy of those notes and summaries, and proportional learning gain, during learning with MetaTutor, an ITS that fosters self-regulated learning while learning complex science topics. Results revealed that students expressed both positive (i.e., joy, surprise) and negative (i.e., confusion, frustration, anger, and contempt) emotions during note taking and summarizing, and that these emotions correlated with each other, as well as with proportional learning gain and accuracy of their notes and summaries. Specifically, contempt during note taking was positively correlated with proportional learning gain; note taking accuracy was negatively correlated with proportional learning gain; and confusion during summarizing was positively correlated with summary accuracy. These results reveal the importance of investigating specific self-regulated learning processes, such as taking notes or making summaries, with future research aimed at examining the differences and similarities between different cognitive and metacognitive processes and how they interact with different emotions similarly or differently during learning. Implications of these findings move us toward developing adaptive ITSs that foster self-regulated science learning, with specific scaffolding based on each individual student’s learning needs.

Identifying How Metacognitive Judgments Influence Student Performance During Learning with MetaTutorIVH

Students need to accurately monitor and judge the difficulty of learning materials to effectively self-regulate their learning with advanced learning technologies such as intelligent tutoring systems (ITSs), including MetaTutorIVH. However, there is a paucity of research examining how metacognitive monitoring processes such as ease of learning (EOLs) judgments can be used to provide adaptive scaffolding and predict student performance during learning ITSs. In this paper, we report on a study investigating how students’ EOL judgments can influence their performance and significantly predict their learning outcomes during learning with MetaTutorIVH, an ITS for human physiology. The results have important design implications for incorporating different types of metacognitive judgements in student models to support metacognition and foster learning of complex ITSs.

The Role of Negative Emotions and Emotion Regulation on Self-Regulated Learning with MetaTutor

Self-regulated learning (SRL) and emotion regulation have been studied as separate constructs which impact students’ learning with intelligent tutoring systems (ITSs). There is a general assumption that students who are proficient in enacting cognitive and metacognitive SRL processes during learning with ITSs are also proficient emotion regulators. In this paper, we investigated the relationship between metacognitive and cognitive SRL processes and emotion regulation by examining students’ self-perceived emotion regulation strategies and comparing the differences between their (1) mean self-reported negative emotions, (2) proportional learning gains (PLGs), and the frequency of (3) metacognitive and (4) cognitive strategy use as they interacted with MetaTutor, an ITS designed to teach students about the circulatory system. Students were classified into groups based on self-perceived emotion regulation strategies and results showed students who perceived themselves as using adaptive emotion regulation strategies reported less negative emotions. Although no significant differences were found between the groups’ learning outcomes, there were significant differences between the groups’ frequency use of cognitive and metacognitive processes throughout the task. Our results emphasize the need to better understand how real-time emotion regulation strategies relate to SRL processes during learning with ITSs and can be used to enhance learning outcomes by encouraging adaptive emotion regulation strategies as well as increased frequencies of metacognitive and cognitive SRL processes.