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.

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.

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.

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.

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.

Emotions play a critical role during learning and problem solving with advanced learning technologies (ALTs). Despite their importance, relatively few attempts have been made to understand learners' emotional monitoring and regulation 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 (ER). We present a theoretically based and empirically driven conceptual framework that addresses ER by proposing the use of visualizations of one's own and others' CAMM SRL multichannel data to facilitate learners' monitoring and regulation of emotions during learning with ALTs. We use an example with eye-tracking data to illustrate the mapping between theoretical assumptions, ER strategies, and the types of data visualizations that can enhance learners' ER, including key processes such as emotion flexibility, emotion adaptivity, and emotion efficacy. We conclude with future directions leading to a systematic interdisciplinary research agenda that addresses outstanding ER-related issues by integrating models, theories, methods, and analytical techniques for the cognitive, learning, and affective sciences; human computer interaction (HCI); data visualization; big data; data mining; and SRL.

Self-regulated learning (SRL) is a process that highly fluctuates as students actively deploy their metacognitive and cognitive processes during learning. In this paper, we apply an extension of latent profiling, latent transition analysis (LTA), which investigates the longitudinal development of students' SRL latent class memberships over time. We will briefly review the theoretical foundations of SRL and discuss the value of using LTA to investigate this multidimensional concept. This study is based on college students (n = 75) learning about the human circulatory system while using MetaTutor, an intelligent tutoring system that adaptively supports SRL and targets specific metacognitive SRL processes including judgment of learning (JOL) and content evaluation (CE). Preliminary results identify transitional probabilities of SRL profiles from four distinct events associated with the use of SRL.

Recent research on self-regulated learning (SRL) includes multichannel data, such as eye-tracking, to measure the deployment of key cognitive and metacognitive SRL processes during learning with adaptive hypermedia systems. In this study we investigated how 147 college students’ proportional learning gains (PLGs), proportion of time spent on areas of interest (AOIs), and frequency of fixations on AOI-pairs, differed based on their prior knowledge of the overall science content, and of specific content related to sub-goals, as they learned with MetaTutor. Results indicated that students with low prior sub-goal knowledge had significantly higher PLGs, and spent a significantly larger proportion of time fixating on diagrams compared to students with high prior sub-goal knowledge. In addition, students with low prior knowledge had significantly higher frequencies of fixations on some AOI-pairs, compared to students with high prior knowledge. The results have implications for using eye-tracking (and other process data) to understand the behavioral patterns associated with underlying cognitive and metacognitive SRL processes and provide real-time adaptive instruction, to ensure effective learning. © Springer International Publishing Switzerland 2016.

This study examined the proportional learning gains attained by 165 college students as they learned about the human circulatory system over two sessions with the intelligent tutoring system, MetaTutor. Results indicated that learners in the prompt and feedback condition, which were afforded the full capabilities of the four pedagogical agents (PAs), attained significantly greater proportional learning gains than learners in the control condition who did not receive the same scaffolding. In addition, we also found that the amount of time spent with each PA produced different types of impacts on the learners, with Sam the Strategizer having the most influence on proportional learning gains. Lastly, results from the revised Agent Persona Inventory (API), administered following the learning session with MetaTutor, revealed key findings regarding learners’ overall retrospective affective reactions towards each individual PA. These results have implications for the design of future PAs capable of offering real-time and adaptive pedagogical instruction within Intelligent Tutoring Systems (ITSs). © Springer International Publishing Switzerland 2016.

In this study we tested whether external regulation provided by artificial pedagogical agents (PAs) was effective in facilitating learners' self-regulated learning (SRL) and can therefore foster complex learning with a hypermedia-based intelligent tutoring system. One hundred twenty (N = 120) college students learned about the human circulatory system with MetaTutor during a 2-hour session under one of two conditions: adaptive scaffolding (AS) or a control (C) condition. The AS condition received timely prompts from four PAs to deploy various cognitive and metacognitive SRL processes, and received immediate directive feedback concerning the deployment of the processes. By contrast, the C condition learned without assistance from the PAs. Results indicated that those in the AS condition gained significantly more knowledge about the science topic than those in the C condition. In addition, log-file data provided evidence of the effectiveness of the PAs' scaffolding and feedback in facilitating learners' (in the AS condition) metacognitive monitoring and regulation during learning. We discuss implications for the design of external regulation by PAs necessary to accurately detect, track, model, and foster learners' SRL by providing more accurate and intelligent prompting, scaffolding, and feedback regarding SRL processes.