Handbook of Learning Analytics

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Baker, R., & Ocumpaugh, J. (2015). Interaction-based affect detection in educational software. In R. Calvo, S. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective computing (pp. 233–245). New York: Oxford University Press.

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Blanchard, N., Donnelly, P., Olney, A. M., Samei, B., Ward, B., Sun, X., . . . D’Mello, S. K. (2016). Identifying teacher questions using automatic speech recognition in live classrooms. Proceedings of the 17th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL 2016) (pp. 191–201). Association for Computational Linguistics.

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Bosch, N., Chen, H., Baker, R., Shute, V., & D’Mello, S. K. (2015). Accuracy vs. availability heuristic in multimodal affect detection in the wild. Proceedings of the 17th ACM International Conference on Multimodal Interaction (ICMI 2015). New York: ACM.

Bosch, N., & D’Mello, S. K. (in press). The affective experience of novice computer programmers. International Journal of Artificial Intelligence in Education.

Bosch, N., D’Mello, S., Baker, R., Ocumpaugh, J., & Shute, V. (2016). Using video to automatically detect learner affect in computer-enabled classrooms. ACM Transactions on Interactive Intelligent Systems, 6(2), doi:10.1145/2946837.

Calvo, R. A., & D’Mello, S. K. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18–37. doi:10.1109/T-AFFC.2010.1

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D’Mello, S. K. (2016). On the influence of an iterative affect annotation approach on inter-observer and self-observer reliability. IEEE Transactions on Affective Computing, 7(2), 136–149.

D’Mello, S. K., Blanchard, N., Baker, R., Ocumpaugh, J., & Brawner, K. (2014). I feel your pain: A selective review of affect-sensitive instructional strategies. In R. Sottilare, A. Graesser, X. Hu, & B. Goldberg (Eds.), Design recommendations for adaptive intelligent tutoring systems: Adaptive instructional strategies (Vol. 2, pp. 35–48). Orlando, FL: US Army Research Laboratory.

D’Mello, S., Craig, S., Sullins, J., & Graesser, A. (2006). Predicting affective states expressed through an emote-aloud procedure from AutoTutor’s mixed-initiative dialogue. International Journal of Artificial Intelligence in Education, 16(1), 3–28.

D’Mello, S., & Graesser, A. (2012a). AutoTutor and Affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems, 2(4), 23:22–23:39.

D’Mello, S., & Graesser, A. (2012b). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157. doi:10.1016/j.learninstruc.2011.10.001

D’Mello, S., & Graesser, A. (2012c). Malleability of students’ perceptions of an affect-sensitive tutor and its influence on learning. In G. Youngblood & P. McCarthy (Eds.), Proceedings of the 25th Florida Artificial Intelligence Research Society Conference (pp. 432–437). Menlo Park, CA: AAAI Press.

D’Mello, S., & Graesser, A. (2014a). Inducing and tracking confusion and cognitive disequilibrium with breakdown scenarios. Acta Psychologica, 151, 106–116.

D’Mello, S. K., & Graesser, A. C. (2014b). Confusion. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 289–310). New York: Routledge.

D’Mello, S. K., & Kory, J. (2015). A review and meta-analysis of multimodal affect detection systems. ACM Computing Surveys, 47(3), 43:41–43:46.

D’Mello, S., Lehman, B., & Person, N. (2010). Monitoring affect states during effortful problem solving activities. International Journal of Artificial Intelligence in Education, 20(4), 361–389.

D’Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., . . . Graesser, A. (2010). A time for emoting: When affect-sensitivity is and isn’t effective at promoting deep learning. In J. Kay & V. Aleven (Eds.), Proceedings of the 10th International Conference on Intelligent Tutoring Systems (pp. 245–254). Berlin/Heidelberg: Springer.

D’Mello, S., & Mills, C. (2014). Emotions while writing about emotional and non-emotional topics. Motivation and Emotion, 38(1), 140–156.

D’Mello, S., Taylor, R. S., & Graesser, A. (2007). Monitoring affective trajectories during complex learning. In D. McNamara & J. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 203–208). Austin, TX: Cognitive Science Society.

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Dillon, J., Bosch, N., Chetlur, M., Wanigasekara, N., Ambrose, G. A., Sengupta, B., & D’Mello, S. K. (2016). Student emotion, co-occurrence, and dropout in a MOOC context. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016) (pp. 353–357). International Educational Data Mining Society.

Donnelly, P., Blanchard, N., Samei, B., Olney, A. M., Sun, X., Ward, B., . . . D’Mello, S. K. (2016a). Automatic teacher modeling from live classroom audio. In L. Aroyo, S. D’Mello, J. Vassileva, & J. Blustein (Eds.), Proceedings of the 2016 ACM International Conference on User Modeling, Adaptation, & Personalization (ACM UMAP 2016) (pp. 45–53). New York: ACM.

Donnelly, P., Blanchard, N., Samei, B., Olney, A. M., Sun, X., Ward, B., . . . D’Mello, S. K. (2016b). Multi-sensor modeling of teacher instructional segments in live classrooms. Proceedings of the 18th ACM International Conference on Multimodal Interaction (ICMI 2016). New York: ACM.

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Fredrickson, B., & Branigan, C. (2005). Positive emotions broaden the scope of attention and thought-action repertoires. Cognition & Emotion, 19(3), 313–332. doi:10.1080/02699930441000238

Galla, B. M., Plummer, B. D., White, R. E., Meketon, D., D’Mello, S. K., & Duckworth, A. L. (2014). The academic diligence task (ADT): Assessing individual differences in effort on tedious but important schoolwork. Contemporary Educational Psychology, 39(4), 314–325.

Graesser, A., Chipman, P., Haynes, B., & Olney, A. (2005). AutoTutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions on Education, 48(4), 612–618. doi:10.1109/TE.2005.856149

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Hess, U. (2003). Now you see it, now you don’t — the confusing case of confusion as an emotion: Commentary on Rozin and Cohen (2003). Emotion, 3(1), 76–80.

Isen, A. (2008). Some ways in which positive affect influences decision making and problem solving. In M. Lewis, J. Haviland-Jones, & L. Barrett (Eds.), Handbook of emotions (3rd ed., pp. 548–573). New York: Guilford.

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Jayaprakash, S. M., Moody, E. W., Lauría, E. J., Regan, J. R., & Baron, J. D. (2014). Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics, 1(1), 6–47.

Kai, S., Paquette, L., Baker, R., Bosch, N., D’Mello, S., Ocumpaugh, J., . . . Ventura, M. (2015). Comparison of face-based and interaction-based affect detectors in physics playground. In C. Romero, M. Pechenizkiy, J. Boticario, & O. Santos (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015) (pp. 77–84). International Educational Data Mining Society.

Kory, J., D’Mello, S. K., & Olney, A. (2015). Motion tracker: Camera-based monitoring of bodily movements using motion silhouettes. PloS ONE, 10(6), doi:10.1371/journal.pone.0130293.

Lench, H. C., Bench, S. W., & Flores, S. A. (2013). Searching for evidence, not a war: Reply to Lindquist, Siegel, Quigley, and Barrett (2013). Psychological Bulletin, 113(1), 264–268.

Lewis, M. D. (2005). Bridging emotion theory and neurobiology through dynamic systems modeling. Behavioral and Brain Sciences, 28(2), 169–245.

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Pardos, Z., Baker, R. S. J. d., San Pedro, M. O. C. Z., & Gowda, S. M. (2013). Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. In D. Suthers, K. Verbert, E. Duval, & X. Ochoa (Eds.), Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (LAK ’13), 8–12 April 2013, Leuven, Belgium (pp. 117–124). New York: ACM.

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Porayska-Pomsta, K., Mavrikis, M., D’Mello, S. K., Conati, C., & Baker, R. (2013). Knowledge elicitation methods for affect modelling in education. International Journal of Artificial Intelligence in Education, 22, 107–140.

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San Pedro, M., Baker, R. S., Bowers, A. J., & Heffernan, N. T. (2013). Predicting college enrollment from student interaction with an intelligent tutoring system in middle school. In S. D’Mello, R. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013) (pp. 177–184). International Educational Data Mining Society.

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