A Socratic AI chatbot that detects when students ‘black-box’ a chemical mechanism, asserting an outcome without explaining the underlying causal process, and prompts them toward more complete mechanistic explanations.
Measuring and modelling emotions as nonlinear dynamical systems with a game-based learning environment
Measuring SRL processing using mixed-multimodal methods with emerging technologies
A multi-study research program examining how task design, misleading graphics, and feedback shape epistemic emotions, motivation, and graph-reading literacy
Published in British Journal of Educational Technology, 2020
Recommended citation: Emerson, A., Cloude, E. B., Lester, J., & Azevedo, R. (2020). Multimodal learning analytics for game-based learning. British Journal of Educational Technology, 51(5), 1505-1526. https://ecloude.github.io/files/mutlimodal-learning-analytics-for-game-based-learning.pdf
Published in International Journal of Artificial Intelligence in Education, 2020
Recommended citation: Dever, D. A., Azevedo, R., Cloude, E. B., & Wiedbusch, M. (2020). The impact of autonomy and types of informational text presentations in game-based environments on learning: Converging multi-channel processes data and learning outcomes. International Journal of Artificial Intelligence in Education, 30(4), 581-615. https://ecloude.github.io/files/autonomy-and-information-text-presentation-during-game-based-learning.pdf
Published in Frontiers in Education, 2020
Recommended citation: Cloude, E. B., Dever, D. A., Wiedbusch, M. D., & Azevedo, R. (2020, November). Quantifying scientific thinking using multichannel data with crystal island: Implications for individualized game-learning analytics. In Frontiers in Education (Vol. 5, p. 572546). Frontiers Media SA. http://ecloude.github.io/files/quantifying-scientific-reasoning-during-game-based-learning.pdf
Published in Journal of Learning Analytics, 2021
Recommended citation: Cloude, E. B., Carpenter, D., Dever, D. A., Lester, J., & Azevedo, R. (2021). Game-based learning analytics for supporting adolescents’ reflection. Journal of Learning Analytics, 8(2), 51-71. http://ecloude.github.io/files/game-based-learning-analytics.pdf
Published in Learning & Instruction, 2021
Recommended citation: Taub, M., Azevedo, R., Rajendran, R., Cloude, E. B., Biswas, G., & Price, M. J. (2021). How are students’ emotions related to the accuracy of their use of cognitive and metacognitive processes during learning with a hypermedia-based intelligent tutoring system? Learning and Instruction, 72, 101200. http://ecloude.github.io/files/emotions-and-cognitive-and-metacognitive-accuracy.pdf
Published in Discourse Processes, 2021
Recommended citation: Dever, D. A., Wiedbusch, M. D., Cloude, E. B., Lester, J., & Azevedo, R. (2022). Emotions and the comprehension of single versus multiple texts during game-based learning. Discourse Processes, 59(1-2), 94-115. http://ecloude.github.io/files/emotions-and-comprehension.pdf
Published in Frontiers in Psychology, 2022
Recommended citation: Dever, D. A., Amon, M. J., Vrzakova, H., Wiedbusch, M. D., Cloude, E. B., & Azevedo, R. (2022). Capturing Sequences of learners' self-regulatory interactions with instructional material during game-based learning using auto-recurrence quantification analysis. Frontiers in Psychology. http://ecloude.github.io/files/SRL-as-a-complex-system.pdf
Published in Frontiers in Psychology, 2022
Recommended citation: Azevedo, R., Bouchet, F., Duffy, M., Harley, J., Taub, M., Trevors, G., Cloude, E. B., Dever, D. A., Wiedbusch, M. D., Wortha, F., & Cerezo, R. (2022). Lessons learned and future directions of MetaTutor: Leveraging multichannel data to scaffold self-regulated learning with an intelligent tutoring system. Frontiers in Psychology. http://ecloude.github.io/files/Metatutor.pdf
Published in IEEE Transactions on Affective Computing, 2022
Recommended citation: Cloude, E. B., Dever, D. A., Hahs-Vaughn, D. L., Emerson, A. J., Azevedo, R., & Lester, J. (2022). Affective dynamics and cognition during game-based learning. IEEE Transactions on Affective Computing, 13(4), 1705-1717. http://ecloude.github.io/files/TAK2022.pdf
Published in International Conference on Computers in Education, 2023
Recommended citation: Andres, J. M. Alexandra, Cloude, E. B., Baker, R. S., & Lee, S. (2023). Investigating Cognitive Biases in Self-Explanation Behaviors during Game-based Learning about Mathematics. In Proceedings of ICCE’23: The 31sth International Conference on Computers in Education (ICCE 2023). Asia-Pacific Society for Computers in Education (APSCE). http://ecloude.github.io/files/cognitive-bias-during-self-explanations.pdf
Published in International Conference on Knowledge and Learning Analytics (LAK), 2024
Recommended citation: Cloude, E. B., Munshi, A., Andres, J. M. A., Ocumpaugh, J., Baker, R. S., & Biswas, G. (2024). Exploring confusion and frustration as a non-linear dynamical systems. Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 1-12). ACM. Presented during March 18–22, 2024, Kyoto, Japan https://ecloude.github.io/files/lak24-32.pdf
Published in Games and Learning Alliance, 2025
Recommended citation: Cloude, E. B., Kormann, E., Steiner, M., Lindstedt, A., Kiili, K., & Ninaus, M. (2024). The role of feedback type and task performance on concurrent emotions and interest during game-based learning. In International Conference on Games and Learning Alliance (pp. 101-111). Springer Nature Switzerland. https://ecloude.github.io/files/Cloude et al. (2024) the effect of feedback and task type.pdf
Published in British Journal of Educational Technology, 2026
Recommended citation: Cloude, Huber, Wei, Esmanhoto, Dindar, Ninaus, amp& Killi. (In Press). A Balance between Stability and Flexibility: Adaptive Patterns of Self-regulated Learning Processes during Game-based Learning. British Journal of Educational Technology. Paper is currently in production.
Published:
Abstract: Emotional experiences have a significant impact on learning about complex topics. Yet, challenges exist because emotions are typically operationalized as end products, excluding if, how, and when emotions change during learning and their relation to metacognition and performance with advanced learning technologies such as intelligent tutoring systems (ITSs). In this paper, we addressed these challenges by capturing and analyzing 117 college students’ concurrent and self-reported emotions at 3 time points during learning with MetaTutor, an ITS. Analyses revealed negative relationships between increases in boredom, metacognitive monitoring accuracy, and performance. We also found that if confusion persisted over time during learning, it was detrimental to performance. These findings provide implications for designing affect-sensitive ITSs which foster emotion-regulation and metacognitive monitoring based on changes in emotions during learning to optimize performance.
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Abstract: In the realm of education, affect has long been acknowledged as a significant factor that impacts learning. Represented by cognitive structures in the mind, affect is described as a mood, feeling, or emotion, which transmits information about the world we experience and compels us to act and make decisions. Research finds that an inability (or ability) to regulate affect (e.g., confusion or frustration) can greatly impact how an individual learns with educational technologies (e.g., intelligent tutoring systems, game-based learning environments, MOOCs). Yet, there are significant theoretical, methodological, and analytical challenges impeding our understanding on how to best identify (and intervene) if and when affect becomes detrimental during learning with educational technologies.
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Abstract: Learning is a highly individual process of change that emerges from multiple interacting components (e.g. cognitive, social) that occur at varying levels (e.g., individual, group) and timescales (e.g. micro, meso, macro) in constantly changing environments. Due to its complexity, the theoretical assumptions that describe learning are difficult to computationally model, and many existing methodologies are limited by conventional statistics that do not adhere to these assumptions. In recent years, the learning analytics community has explored the potential of complex dynamical systems for modeling and analyzing learning processes. Complex dynamical systems (CDS) approach refers to theoretical views, largely from physics and biology, that preserve the complexity of learning and could be potentially useful in studying socio-/ technical-/ material-/ symbolic systems that learn.