Emotions in Intelligent Tutoring Systems (ITS) are often modeled as single affective states, however there is evidence that emotions co-occur during learning, with implications for affect-aware ITS that need to have a comprehensive understanding of a student's affective state to react accordingly. In this paper we broaden the evidence that emotions co-occur in an educational context, and present a first attempt to predict these co-occurrences from data, using the MetaTutor ITS as a test-bed. We show that boredom+frustration, as well as curiosity+anxiety, frequently co-occur in MetaTutor, and that we can predict when these emotions co-occur significantly better than a baseline using eye-tracking and interaction data. These findings provide a first step toward building affect-aware ITS that can adapt to these complex co-occurring affective states.

The goal of this study was to investigate 65 students' evidence scores of emotions while they engaged in cognitive and metacognitive self-regulated learning processes as they learned about the circulatory system with MetaTutor, a hypermedia-based intelligent tutoring system. We coded for the accuracy of detecting students' cognitive and metacognitive processes, and examined how the computed scores related to mean evidence scores of emotions and overall learning. Results indicated that mean evidence score of surprise negatively predicted the accuracy of making a metacognitive judgment, and mean evidence score of frustration positively predicted the accuracy of taking notes, a cognitive learning strategy. These results have implications for understanding the beneficial role of negative emotions during learning with advanced learning technologies. Future directions include providing students with feedback about the benefits of both positive and negative emotions during learning and how to regulate specific emotions to ensure the most effective learning experience with advanced learning technologies.

65 undergraduate students from a North American university interacted with MetaTutorIVH, an agent-based multimedia learning environment that fosters self-regulated learning (SRL) strategy use (e.g., metacognition) while presenting information on several human body systems. Participants completed a self-paced task to study the influence of relevant content and an agents' expressed emotions on metacognitive judgments, gaze behaviors, and science learning. The goal of this study was to examine eye gaze behavior and metacognitive process use relative to their perceived relevancy of content based on discrepancies between multimedia materials and artificial agent's facial expressions. Results indicate an increase in overall fixation duration on multimedia content (e.g., text, diagram) when the text was perceived as less relevant, specifically revealing longer time spent fixating on text when the text was perceived as less relevant compared text and diagrams that were perceived as relevant. Further analyses reveal an increase in diagram fixation duration when the text was judged as being less relevant, but when compared to instances where the text and diagrams were both seen as only somewhat relevant. Across trials, there was no indication of the actual relevancy of content influencing the gaze behaviors of participants.

The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students' (N = 194, with a subset of n = 30 for eye tracking and sequence mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the sequences of engaging in cognitive and metacognitive self-regulated learning processes during learning with MetaTutor, an Intelligent Tutoring System that teaches students about the human circulatory system. Results revealed that there were no significant differences in fixations on single areas of interest by the prior knowledge group students were assigned to; however there were significant differences in fixations on pairs of areas of interest, as evidenced by eye-tracking data. Furthermore, there were significant differences in sequential patterns of engaging in cognitive and metacognitive self-regulated learning processes by students' prior knowledge group, as evidenced from log-file data. Specifically, students with high prior knowledge engaged in processes containing cognitive strategies and metacognitive strategies whereas students with low prior knowledge did not. These results have implications for designing adaptive intelligent tutoring systems that provide individualized scaffolding and feedback based on individual differences, such as levels of prior knowledge.

Metacomprehension is key to successful learning of complex topics when using multimedia materials. The goal of this study was to determine if eye-movement dyads could be: (1) identified by sequence mining techniques, and (2) aligned with self-reported metacognitive judgments during learning with multimedia materials that contain conceptual discrepancies designed to interfere with participants' metacomprehension. Thirty-two undergraduate students' metacognitive judgments were examined with RM-MANOVAs, and sequential pattern mining and differential sequence mining were conducted on their eye movements as they learned with complex multimedia materials. Additionally, we distinguished between event- (i.e., if participants looked at specific areas of the content) and duration-based (i.e., if participants looked at areas of interest [AOIs] for a medium or long amount of time) eye-movement dyads to assess if qualitative and quantitative differences existed in their eye-movement behaviors. For content with text and graph discrepancies, results indicated participants' metacognitive judgments were lower and less accurate, and more fixation dyads were found between the text and graph. Furthermore, specific dyads of different length (i.e., long fixations on the graph to medium fixations on the text) fixations may align with lowered and inaccurate metacognitive judgments for content with text and graph discrepancies. This study begins to address how to identify behavioral indices of metacomprehension processes during multimedia learning.

Cognitive, affective, metacognitive, and motivational (CAMM) processes are critical components of self-regulated learning (SRL) essential for learning and problem solving. Currently, ITSs are designed to foster cognitive, affective, and metacognitive (CAM) strategies and processes, presenting major gaps in the research since motivation is a key component of SRL and influences the remaining CAM processes. In our study, students interacted with MetaTutor, a hypermedia-based ITS, to investigate how 190 undergraduate students' proportional learning gain (PLG) related to sub-goals set, cognitive strategy use and metacognitive processes differed based on self-reported achievement goal orientation. Results indicated differences between approach, avoidance, and students who adopted both approach and avoidance goal orientations, but no differences between mastery, performance and students who adopted both mastery and performance goal orientations on PLG for content related to sub-goal 1. Conversely, no differences were found between goal orientation groups on PLG for sub-goal 2, revealing possible changes in goal orientation following sub-goal 1. Analyses indicated no differences between goal orientation groups on metacognitive processes and cognitive strategy use. Thus, we suggest turning away from self-report data, where future studies aim to incorporate multi-channel data over durations of tasks as students interact with ITSs to measure motivation and its tendency to fluctuate in real-time. Implications for using multiple data channels to measure motivation could contribute to adaptive ITS design based on all CAMM processes.

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.

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.

Self-regulated learning (SRL) involves learners’ ability to monitor and regulate their cognitive, affective, metacognitive, and motivational (CAMM2) processes and plays a critical role in learning about challenging domains while using advanced learning technologies (ALTs). Additionally, emerging empirical evidence indicates that CAM processes play an important role in learning and problem solving as well as self-regulation with ALTs. However, capturing CAM processes during learning with ALTs poses several major conceptual, theoretical, methodological, and analytical challenges. For example, researchers currently measure CAM SRL processes using several online trace methodologies, such as concurrent think-alouds, eye tracking, log files, physiological sensors, and so forth. While these methods have the potential to advance current SRL frameworks, models, and theories, they still pose serious challenges (e.g., temporal alignment of data channels, lack of analytical techniques, and accuracy of inferences made from individual channels and across data channels) that currently plague the field. Our chapter focuses on understanding and reasoning about real-time CAM processes to foster self-regulation with ALTs. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

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.