@article{wiedbusch_kite_yang_park_chi_taub_azevedo_2021, title={A Theoretical and Evidence-Based Conceptual Design of MetaDash: An Intelligent Teacher Dashboard to Support Teachers' Decision Making and Students’ Self-Regulated Learning}, volume={6}, ISSN={2504-284X}, url={http://dx.doi.org/10.3389/feduc.2021.570229}, DOI={10.3389/feduc.2021.570229}, abstractNote={Teachers’ ability to self-regulate their own learning is closely related to their competency to enhance self-regulated learning (SRL) in their students. Accordingly, there is emerging research for the design of teacher dashboards that empower instructors by providing access to quantifiable evidence of student performance and SRL processes. Typically, they capture evidence of student learning and performance to be visualized through activity traces (e.g., bar charts showing correct and incorrect response rates, etc.) and SRL data (e.g., eye-tracking on content, log files capturing feature selection, etc.) in order to provide teachers with monitoring and instructional tools. Critics of the current research on dashboards used in conjunction with advanced learning technologies (ALTs) such as simulations, intelligent tutoring systems, and serious games, argue that the state of the field is immature and has 1) focused only on exploratory or proof-of-concept projects, 2) investigated data visualizations of performance metrics or simplistic learning behaviors, and 3) neglected most theoretical aspects of SRL including teachers’ general lack of understanding their’s students’ SRL. Additionally, the work is mostly anecdotal, lacks methodological rigor, and does not collect critical process data (e.g. frequency, duration, timing, or fluctuations of cognitive, affective, metacognitive, and motivational (CAMM) SRL processes) during learning with ALTs used in the classroom. No known research in the areas of learning analytics, teacher dashboards, or teachers’ perceptions of students’ SRL and CAMM engagement has systematically and simultaneously examined the deployment, temporal unfolding, regulation, and impact of all these key processes during complex learning. In this manuscript, we 1) review the current state of ALTs designed using SRL theoretical frameworks and the current state of teacher dashboard design and research, 2) report the important design features and elements within intelligent dashboards that provide teachers with real-time data visualizations of their students’ SRL processes and engagement while using ALTs in classrooms, as revealed from the analysis of surveys and focus groups with teachers, and 3) propose a conceptual system design for integrating reinforcement learning into a teacher dashboard to help guide the utilization of multimodal data collected on students’ and teachers’ CAMM SRL processes during complex learning.}, journal={Frontiers in Education}, publisher={Frontiers Media SA}, author={Wiedbusch, Megan D. and Kite, Vance and Yang, Xi and Park, Soonhye and Chi, Min and Taub, Michelle and Azevedo, Roger}, year={2021}, month={Feb} }
 @article{yang_kim_taub_azevedo_chi_2020, title={PRIME: Block-Wise Missingness Handling for Multi-modalities in Intelligent Tutoring Systems}, volume={11962}, ISBN={["978-3-030-37733-5"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-37734-2_6}, abstractNote={Block-wise missingness in multimodal data poses a challenging barrier for the analysis over it, which is quite common in practical scenarios such as the multimedia intelligent tutoring systems (ITSs). In this work, we collected data from 194 undergraduates via a biology ITS which involves three modalities: student-system logfiles, facial expressions, and eye tracking. However, only 32 out of the 194 students had all three modalities and 83% of them were missing the facial expression data, eye tracking data, or both. To handle such a block-wise missing problem, we propose a Progressively Refined Imputation for Multi-modalities by auto-Encoder (PRIME), which trains the model based on single, pairwise, and entire modalities for imputation in a progressive manner, and therefore enables us to maximally utilize all the available data. We have evaluated PRIME against single-modality log-only (without missingness handling) and five state-of-the-art missing data handling methods on one important yet challenging student modeling task: to predict students’ learning gains. Our results show that using multimodal data as a result of missing data handling yields better prediction performance than using logfiles only, and PRIME outperforms other baseline methods for both learning gain prediction and data reconstruction tasks.}, journal={MULTIMEDIA MODELING (MMM 2020), PT II}, author={Yang, Xi and Kim, Yeo-Jin and Taub, Michelle and Azevedo, Roger and Chi, Min}, year={2020}, pages={63–75} }
 @article{taub_azevedo_2019, title={How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System?}, volume={29}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-018-0165-4}, number={1}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Taub, Michelle and Azevedo, Roger}, year={2019}, month={Mar}, pages={1–28} }
 @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{cloude_taub_lester_azevedo_2019, title={The Role of Achievement Goal Orientation on Metacognitive Process Use in Game-Based Learning}, volume={11626}, ISBN={["978-3-030-23206-1"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-23207-8_7}, abstractNote={To examine relations between achievement goal orientation—a construct of motivation, metacognition and learning, multiple data channels were collected from 58 students while problem solving in a game-based learning environment. Results suggest students with different goal orientations use metacognitive processes differently but found no differences in learning. Findings have implications for measuring motivation using multiple data channels to design adaptive game-based learning environments.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II}, author={Cloude, Elizabeth B. and Taub, Michelle and Lester, James and Azevedo, Roger}, year={2019}, pages={36–40} }
 @article{harley_taub_azevedo_bouchet_2018, title={"Let's set up some subgoals": Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance}, volume={11}, number={1}, journal={IEEE Transactions on Learning Technologies}, author={Harley, J. M. and Taub, M. and Azevedo, R. and Bouchet, F.}, year={2018}, pages={54–66} }
 @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_mudrick_rajendran_dong_biswas_azevedo_2018, title={How Are Students' Emotions Associated with the Accuracy of Their Note Taking and Summarizing During Learning with ITSs?}, volume={10858}, ISBN={["978-3-319-91463-3"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-319-91464-0_23}, abstractNote={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.}, journal={INTELLIGENT TUTORING SYSTEMS, ITS 2018}, author={Taub, Michelle and Mudrick, Nicholas V. and Rajendran, Ramkumar and Dong, Yi and Biswas, Gautam and Azevedo, Roger}, year={2018}, pages={233–242} }
 @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} }
 @article{cloude_taub_azevedo_2018, title={Investigating the Role of Goal Orientation: Metacognitive and Cognitive Strategy Use and Learning with Intelligent Tutoring Systems}, volume={10858}, ISBN={["978-3-319-91463-3"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-319-91464-0_5}, abstractNote={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.}, journal={INTELLIGENT TUTORING SYSTEMS, ITS 2018}, author={Cloude, Elizabeth B. and Taub, Michelle and Azevedo, Roger}, year={2018}, pages={44–53} }
 @article{price_mudrick_taub_azevedo_2018, title={The Role of Negative Emotions and Emotion Regulation on Self-Regulated Learning with MetaTutor}, volume={10858}, ISBN={["978-3-319-91463-3"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-319-91464-0_17}, abstractNote={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.}, journal={INTELLIGENT TUTORING SYSTEMS, ITS 2018}, author={Price, Megan J. and Mudrick, Nicholas V. and Taub, Michelle and Azevedo, Roger}, year={2018}, pages={170–179} }
 @article{taub_azevedo_bradbury_millar_lester_2018, title={Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment}, volume={54}, ISSN={["0959-4752"]}, DOI={10.1016/j.learninstruc.2017.08.005}, abstractNote={The goal of this study was to assess how metacognitive monitoring and scientific reasoning impacted the efficiency of game completion during learning with Crystal Island, a game-based learning environment that fosters self-regulated learning and scientific reasoning by having participants solve the mystery of what illness impacted inhabitants of the island. We conducted sequential pattern mining and differential sequence mining on 64 undergraduate participants’ hypothesis testing behavior. Patterns were coded based on the relevancy of what items were being tested for, and the items themselves. Results revealed that participants who were more efficient at solving the mystery tested significantly fewer partially-relevant and irrelevant items than less efficient participants. Additionally, more efficient participants had fewer sequences of testing items overall, and significantly lower instance support values of the PartiallyRelevant--Relevant to Relevant--Relevant and PartiallyRelevant--PartiallyRelevant to Relevant--Partially Relevant sequences compared to less efficient participants. These findings have implications for designing adaptive GBLEs that scaffold participants based on in-game behaviors.}, journal={LEARNING AND INSTRUCTION}, author={Taub, Michelle and Azevedo, Roger and Bradbury, Amanda E. and Millar, Garrett C. and Lester, James}, year={2018}, month={Apr}, pages={93–103} }
 @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{taub_azevedo_2016, title={Using eye-tracking to determine the impact of prior knowledge on self-regulated learning with an adaptive hypermedia-learning environment}, volume={0684}, booktitle={Intelligent tutoring systems, its 2016}, author={Taub, M. and Azevedo, R.}, year={2016}, pages={34–47} }
 @inproceedings{taub_azevedo_2016, title={Using multi-channel data to assess, understand, and support affect and metacognition with intelligent tutoring systems}, volume={0684}, booktitle={Intelligent tutoring systems, its 2016}, author={Taub, M. and Azevedo, R.}, year={2016}, pages={543–544} }
 @article{taub_azevedo_bouchet_khosravifar_2014, title={Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners' levels of prior knowledge in hypermedia-learning environments?}, volume={39}, journal={Computers in Human Behavior}, author={Taub, M. and Azevedo, R. and Bouchet, F. and Khosravifar, B.}, year={2014}, pages={356–367} }
 @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} }