My ultimate research goal is to develop intelligent and adaptive learning technologies that enhance the accessibility, inclusivity, and overall quality of digital education, making the benefits of self-regulated learning (SRL) skills accessible to learners globally. Achieving this goal requires studying SRL comprehensively, as a process with four underlying components: cognition, affect, metacognition, and motivation (CAMM) during learning. Studying how CAMM processes unfold and give rise to SRL requires collecting fine-grained and continuous data streams that transpire during learning activities with learning software.
Because of this, I have selected a curated list of my publications, which are organized by two process-oriented approaches with diverse learning software: Multimodal Learning Analytics and Mixed-multimodal Methods. The first involves the majority of my completed (PhD) research, involving multimodal data streams (solely relying on quantitative CAMM measures). The second approach involves my active and future work, involving mixed-multimodal methods (merging quantitaive and qualitative CAMM measures). Much of the mixed-multimodal methods work is on-going and in data-collection and analysis phases of the research cycle.
You can also find my articles on my Google Scholar profile.
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. (In press). Exploring confusion and frustration as a non-linear dynamical systems. Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 1-12). ACM. To be presented during March 18–22, 2024, Kyoto, Japan https://ecloude.github.io/files/lak24-32.pdf