@article{mudrick_azevedo_taub_2019, title={Integrating metacognitive judgments and eye movements using sequential pattern mining to understand processes underlying multimedia learning}, volume={96}, ISSN={["1873-7692"]}, DOI={10.1016/j.chb.2018.06.028}, abstractNote={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.}, journal={COMPUTERS IN HUMAN BEHAVIOR}, author={Mudrick, Nicholas V. and Azevedo, Roger and Taub, Michelle}, year={2019}, month={Jul}, pages={223–234} }
 @article{sinclair_jang_azevedo_lau_taub_mudrick_2018, title={Changes in Emotion and Their Relationship with Learning Gains in the Context of MetaTutor}, volume={10858}, ISBN={["978-3-319-91463-3"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-319-91464-0_20}, abstractNote={Positive academic emotions are generally associated with positive learning experiences, while the opposite is true for negative emotions. This study examined changes in learners’ emotional profiles as they participated in MetaTutor, a computer-based learning environment designed to foster self-regulated learning via study of the human circulatory system. Latent transition analysis was employed to determine distinct, parsimonious emotional profiles over time. Learners are shown to move systematically among three profiles (positive, bored/frustrated, and moderate) in fairly predictable patterns. Of these, boredom is the most pressing concern given the relatively small chance of moving from boredom to a different emotional profile. Students’ learning gains were also significant predictors of emotional transitions. The findings suggest the need for timely intervention for learners who are on the verge of negative emotional trajectories, and the complex relationship between learning gains and emotions. In addition, latent transition analysis is demonstrated as a potentially useful technique for analyzing and utilizing multivariate panel data.}, journal={INTELLIGENT TUTORING SYSTEMS, ITS 2018}, author={Sinclair, Jeanne and Jang, Eunice Eunhee and Azevedo, Roger and Lau, Clarissa and Taub, Michelle and Mudrick, Nicholas V.}, year={2018}, pages={202–211} }
 @article{taub_azevedo_mudrick_2018, title={How Do Different Levels of AU4 Impact Metacognitive Monitoring During Learning with Intelligent Tutoring Systems?}, volume={10858}, ISBN={["978-3-319-91463-3"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-319-91464-0_22}, abstractNote={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.}, journal={INTELLIGENT TUTORING SYSTEMS, ITS 2018}, author={Taub, Michelle and Azevedo, Roger and Mudrick, Nicholas V.}, year={2018}, pages={223–232} }
 @inproceedings{mudrick_sawyer_price_lester_roberts_azevedo_2018, title={Identifying How Metacognitive Judgments Influence Student Performance During Learning with MetaTutorIVH}, volume={10858}, ISBN={0}, DOI={10.1007/978-3-319-91464-0_14}, abstractNote={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.}, booktitle={INTELLIGENT TUTORING SYSTEMS, ITS 2018}, author={Mudrick, Nicholas V. and Sawyer, Robert and Price, Megan J. and Lester, James and Roberts, Candice and Azevedo, Roger}, year={2018}, pages={140–149} }
 @inproceedings{zhong_qin_yang_chen_mudrick_taub_azevedo_lobaton_2017, title={Emotion recognition with facial expressions and physiological signals}, url={http://dx.doi.org/10.1109/ssci.2017.8285365}, DOI={10.1109/ssci.2017.8285365}, abstractNote={This paper proposes a temporal information preserving multi-modal emotion recognition framework based on physiological and facial expression data streams. The performance of each component is evaluated and compared individually and after data fusion. Specifically, we compared the effect of different views of cameras on facial expressions for emotion recognition, and combined these views to achieve better performance. A Temporal Information Preserving Framework (TIPF) is proposed to more accurately model the relationships between emotional and physiological states over time. Additionally, different fusion strategies are compared when combining information from different time periods and modalities. The experiments show that, TIPF significantly improves the emotion recognition performance when physiological signals are used and the best performance is achieved when fusing facial expressions and physiological data.}, booktitle={2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher={IEEE}, author={Zhong, Boxuan and Qin, Z. K. and Yang, S. and Chen, J. Y. and Mudrick, N. and Taub, M. and Azevedo, R. and Lobaton, E.}, year={2017}, pages={1170–1177} }
 @article{lalle_taub_mudrick_conati_azevedo_2017, title={The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents During Learning with MetaTutor}, volume={10331}, ISBN={["978-3-319-61424-3"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-319-61425-0_13}, abstractNote={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.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2017}, author={Lalle, Sebastien and Taub, Michelle and Mudrick, Nicholas V. and Conati, Cristina and Azevedo, Roger}, year={2017}, pages={149–161} }
 @inproceedings{mudrick_taub_azevedo_rowe_lester_2017, title={Toward affect-sensitive virtual human tutors: The influence of facial expressions on learning and emotion}, DOI={10.1109/acii.2017.8273598}, abstractNote={Affective support can play a central role in adaptive learning environments. Although virtual human tutors hold significant promise for providing affective support, a key open question is how a tutor's facial expressions can influence learners' performance. In this paper, we report on a study to examine the influence of a human tutor agent's facial expressions on learners' performance and emotions during learning. Results from the study suggest that learners' performance is significantly better when a human tutor agent facially expresses emotions that are congruent with the content relevancy. Results also suggest that learners facially express significantly more confusion when the human tutor agent provides incongruent facial expressions. These results can inform the design of virtual humans as pedagogical agents can inform the design of virtual humans as pedagogical agents and designing intelligent learner-agent interactions.}, booktitle={International conference on affective computing and intelligent}, author={Mudrick, N. V. and Taub, M. and Azevedo, R. and Rowe, J. and Lester, J.}, year={2017}, pages={184–189} }
 @article{taub_mudrick_azevedo_millar_rowe_lester_2017, title={Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with CRYSTAL ISLAND}, volume={76}, ISSN={["1873-7692"]}, DOI={10.1016/j.chb.2017.01.038}, abstractNote={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.}, journal={COMPUTERS IN HUMAN BEHAVIOR}, author={Taub, Michelle and Mudrick, Nicholas V. and Azevedo, Roger and Millar, Garrett C. and Rowe, Jonathan and Lester, James}, year={2017}, month={Nov}, pages={641–655} }
 @article{azevedo_martin_taub_mudrick_millar_grafsgaard_2016, title={Are Pedagogical Agents' External Regulation Effective in Fostering Learning with Intelligent Tutoring Systems?}, volume={9684}, ISBN={["978-3-319-39582-1"]}, ISSN={["0302-9743"]}, DOI={10.1007/978-3-319-39583-8_19}, abstractNote={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.}, journal={INTELLIGENT TUTORING SYSTEMS, ITS 2016}, author={Azevedo, Roger and Martin, Seth A. and Taub, Michelle and Mudrick, Nicholas V. and Millar, Garrett C. and Grafsgaard, Joseph F.}, year={2016}, pages={197–207} }
 @article{taub_mudrick_azevedo_millar_rowe_lester_2016, title={Using Multi-level Modeling with Eye-Tracking Data to Predict Metacognitive Monitoring and Self-regulated Learning with CRYSTAL ISLAND}, volume={9684}, ISBN={["978-3-319-39582-1"]}, ISSN={["0302-9743"]}, DOI={10.1007/978-3-319-39583-8_24}, abstractNote={Studies investigating the effectiveness of game-based learning environments (GBLEs) have reported the effectiveness of these environments on learning and retention. However, there is limited research on using eye-tracking data to investigate metacognitive monitoring with GBLEs. We report on a study that investigated how college students’ eye tracking behavior (n = 25) predicted performance on embedded assessments within the Crystal Island GBLE. Results revealed that the number of books, proportion of fixations on book and article content, and proportion of fixations on concept matrices—embedded assessments associated with each in-game book and article—significantly predicted the number of concept matrix attempts. These findings suggest that participants strategized when reading book and article content and completing assessments, which led to better performance. Implications for designing adaptive GBLEs include adapting to individual student needs based on eye-tracking behavior in order to foster efficient completion of in-game embedded assessments.}, journal={INTELLIGENT TUTORING SYSTEMS, ITS 2016}, author={Taub, Michelle and Mudrick, Nicholas V. and Azevedo, Roger and Millar, Garrett C. and Rowe, Jonathan and Lester, James}, year={2016}, pages={240–246} }
 @inproceedings{mudrick_taub_azevedo_rowe_lester, title={Toward affect-sensitive virtual human tutors: The influence of facial expressions on learning and emotion}, booktitle={International conference on affective computing and intelligent}, author={Mudrick, N. V. and Taub, M. and Azevedo, R. and Rowe, J. and Lester, J.}, pages={184–189} }
 @inproceedings{taub_mudrick_azevedo_millar_rowe_lester, title={Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with CRYSTAL ISLAND}, volume={0684}, booktitle={Intelligent tutoring systems, its 2016}, author={Taub, M. and Mudrick, N. V. and Azevedo, R. and Millar, G. C. and Rowe, J. and Lester, J.}, pages={240–246} }
 @inproceedings{taub_mudrick_azevedo_millar_rowe_lester, title={Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with crystal island}, volume={9684}, booktitle={Intelligent tutoring systems, its 2016}, author={Taub, M. and Mudrick, N. V. and Azevedo, R. and Millar, G. C. and Rowe, J. and Lester, J.}, pages={240–246} }