papers

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Most highly cited

  • Affect Detection: An Interdisciplinary Review of Models, Methods, and their Applications. [PDF]

  • Better to be frustrated than bored. [PDF]

  • Dynamics of affective states during complex learning [PDF]

  • Confusion can be beneficial for learning [PDF]

  • Towards an Affect-Sensitive AutoTutor. [PDF]

  • Boring but important: A self-transcendent purpose for learning fosters academic self-regulation [PDF]

  • Automatic Detection of Learner’s Affect from Conversational Cues. [PDF]

Journal Articles

In press

    • Southwell, R., Mills, C., Caruso, M., & D’Mello, S. K. A gaze-based predictive model of deep comprehension based on self-explanations. User-Modeling and User-Adapted Interaction. [PDF]

    • Rahimi, S., Shute, V. J., Fulwider, C., Bainbridge, K., Kuba, R., Yang, X., Smith, G., Baker, R., & D’Mello, S. K. (2022). Timing of learning supports in educational games can impact students’ outcomes. Computers & Education. [PDF]

    • Martinez, G., Grover, T., Mattingly, S., Mark, G., D’Mello, S. K., Aledavood, A., Akbar, F., Robles-Granda, P, & Striegel, A. Alignment between Heart rate Variability from Fitness Trackers and Perceived Stress: Perspectives from a Large-Scale In-Situ Longitudinal Study of Information Worker. JMIR mHealth and uHealth. [PDF]

    • Booth, B., Vrzakova., H., Mattingly, M., Martinez, G., Faust, L., & D’Mello, S. K. Toward Robust Stress Prediction in the Age of Wearables: Modeling Perceived Stress in Information Workers, IEEE Transactions on Affective Computing. [PDF]

    • Huggins‐Manley, A. C., Booth, B. M., & D'Mello, S. K. (in press). Toward Argument‐Based Fairness with an Application to AI‐Enhanced Educational Assessments. Journal of Educational Measurement. [PDF]

    • Amon, M., Mattingly, S., Necaise, A., Mark, G., Chawla, N., Dey, A., & D’Mello, S. K. (in press). Flexibility versus routineness in multimodal health indicators: A sensor-based longitudinal in situ study of information workers, ACM Transactions on Computing for Healthcare. [PDF]

    • Breideband, T., Martinez, G., Sukumar, P. T., Caruso, M., D’Mello, S. K., Striegel, A., & Mark, G. (2022). Sleep Patterns and Sleep Alignment in Remote Teams during COVID-19. Proceedings of the ACM on Human-Computer Interaction: Computer Supported Collaborative Work (ACM CSCW). [PDF]

    • D’Mello, S. K., Tay, L., & Southwell, R. Psychological Measurement in the Information Age: Machine-Learned Computational Models. Current Directions in Psychological Science. [PDF]

    • Sumer, O., Goldberg, P., D’Mello, S. K., Gerjets, P., Trautwein, U. & Kasneci, E. (in press). Multimodal Engagement Analysis from Facial Videos in the Classroom. IEEE Transactions on Affective Computing. [PDF]

2017-2022

    • Tay, L., Woo, S., Hickman, L., & D’Mello, S. K. (2022). A Conceptual Framework for Investigating Machine Learning Measurement Bias. Advances in Methods and Practices in Psychological Science (AMPPS). [PDF]

    • Breideband, T., Sukumar, P., Mark, G., Caruso, M., D’Mello, S. K., & Striegel, A. (2022). Home-Life and Work Rhythm Diversity in Distributed Teamwork: A Study with Information Workers during the COVID-19 Pandemic. Proceedings of the ACM: Computer Supported Collaborative Work. [PDF]

    • Bosch, N., & D’Mello, S. K. (2022). Can Computers Outperform Untrained Humans in Detecting User Zone-Outs from Video? ACM Transactions on Computer-Human Interaction (TOCHI). [PDF]

    • Dale, M., Godley, A., Capello, S., Donnelly, P., DMello, S., & Kelly, S. (2022). Toward the Automated Analysis of Teacher Talk in Secondary ELA Classrooms, Teaching and Teacher Education. [PDF]

    • Sun, C., Shute, V. J., Stewart, A. E. B., Beck-White, Q., Reinhardt, C. R., Duran, N., & D'Mello, S. K. (2022). The relationship between collaborative problem solving processes and objective outcomes in a game-based learning environment. Computers in Human Behavior. [PDF]

    • Bainbridge, K., Shute, V., Rahimi, A., Liu, Z., Slater, S., Baker, R., & D’Mello, S. K. (2022). Does Embedding Learning Supports Enhance Transfer During Game-Based Learning? A Case Study with Physics Playground. Learning & Instruction. [PDF] PRE PRINT DRAFT

    • Bainbridge, K., Smith, G., Shute, V., & D’Mello, S. K. (2022). Designing and Testing Affective Supports in an Educational Game, International Journal of Game-Based Learning, 12(1). [PDF]

    • McCarthy, K. S., Crossley, S. A., Meyers, K., Boser, U., Allen, L. K., Chaudhri, V. K., Collins-Thompson, K., D’Mello, S. K. , De Choudhury, M., Garg, K., Goel, A., Gosha, K., Heffernan, N., Hooper, M. A., Hyman, E., Jarratt, D. C., Khalil, D., Kizilcec, R. F., Litman, D., Malatinszky, A., Marks, K., McNamara, D. S., Menko, R., Palermo, C., Porcaro, D., Roscoe, R., Shapiro, S., Khanh-Phoung, T., Trumbore, A. M., White, C., Wong, W., Yang, D., & Zampieri, M. (in press). Toward more effective and equitable learning: Identifying barriers and solutions for the future of online education. Technology, Mind, & Behavior. [PDF]

    • D’Mello, S. K. & Gruber, J. (2021). Emotional Regularity: Associations with Personality, Health, and Occupational Outcomes. Cognition & Emotion. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Mills, C. (2021). Mind wandering during reading: A review of cognitive, behavioral, computational, and intervention research. Language and Linguistics Compass: Cognitive Science of Language. [PDF] PRE PRINT DRAFT

    • Martinez, G. J., Mattingly, S. M., Robles-Granda, P., Saha, K., Sirigiri, A., Young, J., Chawla, N. V., Choudhury, M. D., D'Mello, S. K., Mark, G., & Striegel, A. D. (2021). Predicting participant compliance: a large multi-modal longitudinal study using wearables, smartphones, and EMAs. JMIR mHealth and uHealth. [PDF]

    • Booth, B., Hickman, L., Subburaj, S. K., Tay, L., Woo, S., & D’Mello, S. K. (2021). Integrating Psychometrics and Computing Perspectives on Bias and Fairness in Affective Computing: A Case Study of Automated Video Interviews, IEEE Signal Processing Magazine [PDF] PRE PRINT DRAFT

    • Stewart, A., Keirn, Z., & D’Mello, S. K. (2021). Multimodal Modeling of Collaborative Problem Solving Facets in Triads. User Modeling and User-Adapted Interaction. [PDF] PRE PRINT DRAFT

    • Bosch, N., & D’Mello, S. K. (2021). Automatic Detection of Mind Wandering from Video in the Lab and in the Classroom, IEEE Transactions on Affective Computing. [PDF] PRE PRINT DRAFT

    • Mills, C., Gregg, J., Bixler, R., & D’Mello, S. K. (2021) .Eye-Mind Reader: An Intelligent Reading Interface that Promotes Long-term Comprehension by Detecting and Responding to Mind Wandering, Human-Computer Interaction. [PDF] PRE PRINT DRAFT

    • Forrin, N., Mills, C., D’Mello, S. K., Seli P., Risko, E., & Smilek D. (2021). TL;DR: Longer sections of text increase rates of unintentional mind-wandering. The Journal of Experimental Education. [PDF] PRE PRINT DRAFT

    • Robles-Granda, P., Lin, S., Wu, X., D'Mello, S., K., Martinez, G. J., Saha, K., Nies, K., Mark, G., Campbell, A. T., & De Choudhury, M., Anind D., Gregg, J. Grover, T., Mattingly, S., Mirjafari, S., Moskal, E. Striegel, A. & Chawla, Nitesh. (2021). Jointly predicting job performance, personality, cognitive ability, affect, and well-being. IEEE Computational Intelligence Magazine. [PDF] PRE PRINT DRAFT

    • Faust, L., Feldman, K., Lin, S., Mattingly, S., D'Mello, S. K. & Chawla, N. (2021) Examining Response to Negative Life Events through Fitness Tracker Data. Frontiers in Digital Health. [PDF] PRE PRINT DRAFT

    • Vrzakova, H., Amon, M. J., Rees, McKenzie, Faber, M., & D’Mello, S. K. (2020). Looking for a Deal? Social Visual Attention during Negotiations via Mixed Media Videoconferencing. Proceedings of the ACM: Human Computer Interaction, Computer Supported Collaborative Work (CSCW 2020). [PDF] PRE PRINT DRAFT

    • Faber, M., Krasich, K., Bixler, R., Brockmole, R. & D’Mello, S. K. (2020). The Eye-Mind Wandering Link: Identifying Gaze Indices of Mind Wandering Across Tasks. Journal of Experimental Psychology: Human Perception and Performance. [PDF] PRE PRINT DRAFT

    • Southwell, R., Gregg., J., Bixler, R., D’Mello, S. K. (2020). What Eye Movements Reveal about Comprehension during Naturalistic Reading of Long, Connected Texts. Cognitive Science. [PDF] PRE PRINT DRAFT

    • Gardner, M., Hutt, S., Kamentz, D. Duckworth, A., L., & D’Mello, S. K. (2020). How does high school extracurricular participation predict bachelor’s degree attainment? It’s complicated. Journal of Research on Adolescence. [PDF] PRE PRINT DRAFT

    • Zamarro, G., Nichols, M., Duckworth, A. L. & D’Mello, S. K. (2020). Validation of survey effort measures of grit and self-control in a sample of high school students. PLOS ONE [PDF] PRE PRINT DRAFT

    • Spann, C., Yu, A., Galla, B., Duckworth, A., & D’Mello, S. K. (2020). Is Academic Diligence Domain-Specific or Domain-General? An Investigation of the Math, Verbal, and Spatial Academic Diligence Tasks with Middle Schoolers. Learning & Individual Differences. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K., Southwell, R. & Gregg., J. (2020). Machine-Learned Computational Models can Enhance the Study of Text & Discourse: A Case Study Using Eye Tracking to Model Reading Comprehension. Discourse Processes. [PDF] PRE PRINT DRAFT

    • Sun, C., Shute, V., Stewart, A., Yonehiro, J., Duran, N., & D'Mello, S. K. (2020). Toward a Generalized Competency Model of Collaborative Problem Solving. Computers & Education, 143, 103672. [PDF] PRE PRINT DRAFT

    • Morshed, M. B., Saha, K., Li, R., D'Mello, S. K., De Choudhury, M., Abowd, G. D., & Plötz, T. (2019). Prediction of Mood Instability with Passive Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(3), 1-21. [PDF] PRE PRINT DRAFT

    • Amon, M. J., Vrzakova, H., & D’Mello, S. K. (2019) Beyond dyadic coordination: Multimodal behavioral irregularity in triads predicts facets of collaborative problem solving, Cognitive Science. [PDF] PRE PRINT DRAFT

    • Stewart, A., Vrzakova, H., Sun, C., Yonehiro, J., Stone, C., Duran, N., Shute, V., & D’Mello, S. K. (2019). I Say, You Say, We Say: Using Spoken Language to Model Socio-Cognitive Processes during Computer-Supported Collaborative Problem Solving. Proceedings of the ACM: Human Computer Interaction. 3, Computer Supported Collaborative Work (CSCW 2019). [PDF]

    • Huggins-Manley, A., Beal, C., D’Mello, S. K., Leite, W., & Cetin-Berber, D., Kim., D., & McNamara, D. (2019) A Commentary on Construct Validity when using Operational Virtual Learning Environment Data in Effectiveness Studies, Journal of Educational Effectiveness. [PDF] PRE PRINT DRAFT

    • Galla, B. M., Shulman, E. P., Plummer, B. D., Gardner, M., Hutt, S. J., Goyer, J. P., Finn, A. S., D’Mello, S. K., & Duckworth, A. L. (2019). Why high school grades are better predictors of on-time college graduation than are admissions test scores: The role of self-regulation and cognitive ability. American Educational Research Journal. [PDF] PRE PRINT DRAFT

    • Hutt, S., Krasich, K., Mills, C. Bosch, N., White, S., Brockmole, J., & D'Mello, S. K. (2019) Gaze-based Models of Mind Wandering in Classrooms. User Modeling & User-Adapted Interaction. [PDF] PRE PRINT DRAFT

    • Meindl, P., Yu, A., Galla, B., Quirk, A., Haeck, C., Goyer, P., Lejuez, C., D’Mello, S. K., & Duckworth, A. (2019).A Brief Behavioral Measure of Frustration Tolerance Predicts Academic Achievement Immediately and Two Years Later. Emotion. [PDF] PRE PRINT DRAFT

    • Spann, C., Shute, V. J. Rahimi, S., & D’Mello, S. K. (2019) The Productive Role of Cognitive Reappraisal in Regulating Affect during Game-Based Learning. Computers in Human Behavior, 100, 358-369. [PDF] PRE PRINT DRAFT

    • Mirjafari, S., Masaba, K., Grover, T., Wang, W., Audia, P., Campbell, A. T., Chawla, N. V., Swain, V. D., Choudhury, M. D., Dey, A. K., D'Mello, S. K., Gao, G., Gregg, J. M., Jagannath, K., Jiang, K., Lin, S., Liu, Q., Mark, G., Martinez, G. J., Mattingly, S. M., Moskal, E., Mulukutla, R., Nepal, S., Nies, K., Reddy, M. D., Robles-Granda, P., Saha, K., Sirigiri, A., & Striegel, A. (2019). Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 3(2). [PDF] PRE PRINT DRAFT

    • Mills, C., Wu., J. & D’Mello, S. K. (2019) Being Sad is not always Bad: The Influence of Affect on Expository Text Comprehension. Discourse Processes, 56(2), 99-116. [PDF] PRE PRINT DRAFT

    • Kelly, S., Olney, A., Donnelly, P., Nystrand, M., & D’Mello, S. K. (2018). Automatically Measuring Question Authenticity in Real-World Classrooms. Educational Researcher. [PDF] PRE PRINT DRAFT

    • Wiese, C., Tay, L., Duckworth, A., D’Mello, S. K., & Kuykendall, L. Hofmann , W., Baumeister , R., & Vohs, K. (2018). Too much of a good thing? Exploring the inverted-U relationship between self-control and happiness, Journal of Personality. [PDF] PRE PRINT DRAFT

    • Martin, L., Mills, C., D’Mello, S. K., & Risko, E., F. (2018). Re-watching Lectures as a Study Strategy and its Effect on Mind Wandering. Experimental Psychology [PDF] PRE PRINT DRAFT

    • Faber, M., & D’Mello, S. K. (2018). How the stimulus influences mind wandering in semantically-rich task contexts. Cognitive Research: Principles and Implications. [PDF] PRE PRINT DRAFT

    • Brom, C., Stárková, T., & D’Mello, S. K. (2017). How Effective is Emotional Design? A Meta-Analysis of Facial Anthropomorphisms and Pleasant Colors during Multimedia Learning. Educational Research Review. [PDF] PRE PRINT DRAFT

    • Krasich, K., McManus, B., Hutt, C., Faber, M., D’Mello, S. K., Brockmole, J. (2017) Gaze-Based Signatures of Mind Wandering During Real-World Scene Processing, Journal of Experimental Psychology: General [PDF] PRE PRINT DRAFT

    • Faber, M., Radvansky, G., & D’Mello, S. K. (2018) Driven to distraction: a lack of change gives rise to mind wandering. Cognition. [PDF] PRE PRINT DRAFT

    • Wilson, K., Martinez, M. Mills, C., D'Mello, S. K., Smilek, D., Risko, E. (2018). Instructor presence effect: Liking does not always lead to learning. Computers & Education. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K., Kappas, A., & Gratch, J. (2018). The Affective Computing Approach to Affect Measurement, Emotion Review. [PDF] PRE PRINT DRAFT

    • Faber, M., Bixler, R., & D’Mello, (2018). S. K. An Automated Behavioral Measure of Mind Wandering during Computerized Reading, Behavior Research Methods. [PDF] PRE PRINT DRAFT

    • Brom, C., Děchtěrenko, F., Frollová, N., Stárková, T., Bromová, E., D’Mello, S. K. (2017). Enjoyment or Involvement? Affective-Motivational Mediation during Learning from a Complex Computerized Simulation. Computers & Education, 114, 236-254 [PDF] PRE PRINT DRAFT

    • Mills, C., Graesser, A., Risko, E., D’Mello, S. K. (2017). Cognitive Coupling during Reading. Journal of Experimental Psychology: General, 146(6), 872-833. [PDF] PRE PRINT DRAFT

    • D'Mello, S., K., Dieterle, E. & Duckworth, A. (2017). Advanced, Analytic, Automated (AAA) Measurement of Engagement during Learning, Educational Psychologist, 52(2), 104-123. [PDF] PRE PRINT DRAFT + [PDF] ONLINE SUPPLEMENT

    • Faber, M., Mills, C., Kopp. K., & D’Mello, S. K. (2017). The effect of disfluency on mind wandering during text comprehension. Psychonomic Bulletin & Review, 24(3), 914-919. [PDF] PRE PRINT DRAFT

    • Monkaresi, H., Bosch, P. Calvo, R., & D'Mello, S. K. (2017). Automated Detection of Engagement using Video-Based Estimation of Facial Expressions and Heart Rate. IEEE Transactions on Affective Computing, 8(1), 15-28. [PDF] PRE PRINT DRAFT

    • Gu, Y., Wang, C., Bixler, R., & D’Mello, S. K. (2017). ETGraph: A Graph-Based Approach for Visual Analytics of Eye-Tracking Data, Computers & Graphics, 62(1), 1-14. [PDF] PRE PRINT DRAFT

    • Bosch, N., & D'Mello, S. K. (2017). The Affective Experience of Novice Computer Programmers. International Journal of Artificial Intelligence In Education, 27(1), 181-206. [PDF] PRE PRINT DRAFT

2012-2017

    • Phillips, N., Mills, C. D’Mello, S. K., & Risko, E. (2016) On the influence of Re-Reading on Mind Wandering. Quarterly Journal of Experimental Psychology, 69(1), 2338-2357. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. On the Influence of an Iterative Affect Annotation Approach on Inter-Observer and Self-Observer Reliability (2016). IEEE Transactions on Affective Computing. 7(2), 136-149. [PDF] PRE PRINT DRAFT

    • Bosch, N., D’Mello, S. K., Ocumpaugh, J., Baker, R., & Shute, V. (2016). Using video to automatically detect learner affect in computer-enabled classrooms. ACM Transactions on Interactive Intelligent Systems (TiiS), 6(2), 17.1-17.31. [PDF] PRE PRINT DRAFT

    • Morgan, B., & D’Mello, S. K. (2016). The Influence of Positive vs. Negative Affect on Multitasking. Acta Psychologica, 170, 10-18. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. (2016). Giving Eyesight to the Blind: Towards attention-aware AIED, International Journal of Artificial Intelligence in Education, 26(2), 645-659. [PDF] PRE PRINT DRAFT

    • Bixler, R. & D’Mello, S. K. (2016). Automatic Gaze-based User-independent Detection of Mind Wandering during Computerized Reading, User Modeling & User-Adapted Interaction, 26(1), 33-68. [PDF] PRE PRINT DRAFT

    • Kopp, K., Mills, S., & D’Mello, S. K. (2016). Mind Wandering during Film Comprehension: The Role of Prior Knowledge and Situational Interest, Psychonomic Bulletin & Review, 23(3), 842-848. [PDF] PRE PRINT DRAFT

    • Price, K., Meisinger, E., D’Mello, S. K. & Louwerse, M.(2016). The contributions of oral and silent reading fluency to reading comprehension, Reading Psychology, 37 (2), 167-201. [PDF] PRE PRINT DRAFT

    • Radvansky, G., D’Mello, S. K. Abbott, R., & Bixler, R. (2016). Predicting Individual Action Switching in Covert and Continuous Interactive Tasks Using the Fluid Events Model. Frontiers in Psychology: Cognition, 7:23. [PDF]

    • Kopp, K., & D’Mello, S. K. The Impact of Modality on Mind Wandering during Comprehension (2016). Applied Cognitive Psychology, 30, 29-40. [PDF] PRE PRINT DRAFT

    • Mills, C., D’Mello, S. K., & Kopp, K. (2015). The Influence of Consequence Value and Text Difficulty on Affect, Attention, and Learning while Reading Instructional Texts, Learning & Instruction, 40, 9-20. [PDF] PRE PRINT DRAFT

    • Shute, V. D'Mello, S. K., Baker, R. Cho, K., Bosch, N., Ocumpaugh, J. Ventura, M., Almeda, V. (2015). Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game, Computers & Education, 86, 224-235. [PDF] PRE PRINT DRAFT

    • Kory, J., D’Mello, S. K., & Olney, A. (2015). Motion Tracker: Camera-based Monitoring of Bodily Movements using Motion Silhouettes. PloS ONE, 10(6), 10.1371/journal.pone.0130293 [PDF] PRE PRINT DRAFT

    • Kopp, K., D’Mello, S. K., Mills, C. (2015). Influencing the Occurrence of Mind Wandering While Reading, Consciousness and Cognition, 34(1), 52-62. [PDF] PRE PRINT DRAFT

    • Radvansky, G., D’Mello, S. K., Abbott, R., Morgan, B., Fike, K., & Tamplin, A. (2015). The Fluid Events Model: Predicting Continuous Task Action Change. Quarterly Journal of Experimental Psychology, 68(10), 2051-2072. [PDF] PRE PRINT DRAFT

    • Koedinger, K. R., D’Mello, S. K., McLaughlin, E., Pardos, Z., & Rosé, C. P. (2015). Data mining and education, Wiley Interdisciplinary Reviews: Cognitive Science, 6(4), 333-353. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K., & Kory, J. (2015). A Review and Meta-Analysis of Multimodal Affect Detection Systems, ACM Computing Surveys,47(3), 43:1-43:46. [PDF] PRE PRINT DRAFT

    • Fulmer, S. M., D'Mello, S. K., Strain, A., Graesser, A. C. (2015). Interest-based text preference moderates the effect of text difficulty on engagement and learning. Contemporary Educational Psychology, 41(1), 98-110. [PDF] PRE PRINT DRAFT

    • AlZoubi, O., Fossati, D., D’Mello, S. K., & Calvo, R. (2015). Affect Detection from non-Stationary Physiological Data using Dynamic Ensemble Classifiers, Evolving Systems, 6(2), 79-92. [PDF] PRE PRINT DRAFT

    • Galla, B., Plummer, B., White, R., Meketon, D., D’Mello, S. K., & Duckworth, A. (2014). The Academic Diligence Task (ADT): Assessing Individual Differences in Effort on Tedious but Important Schoolwork, Contemporary Educational Psychology, 39(4), 314-325. [PDF] PRE PRINT DRAFT

    • Hulovatyy, Y., D’Mello, S. K., Calvo, R. A. & Milenkovic, T. (2014). Network analysis improves interpretation of affective physiological data, Journal of Complex Networks, 2(4), 614-636. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Graesser A. C. (2014). Confusion and its Dynamics during Device Comprehension with Breakdown Scenarios, Acta Psychologica, 151, 106-116. [PDF] PRE PRINT DRAFT

    • Strain, A., & D’Mello, S. K. (2014). Affect regulation during learning: The enhancing effect of cognitive reappraisal, Applied Cognitive Psychology, DOI: 10.1002/acp.3049 . [PDF] PRE PRINT DRAFT

    • Yeager, D., Henderson, M., Paunesku, D., Walton, G., D’Mello, S., Spitzer, B., Duckworth, A. (2014) Boring but Important: A Self-Transcendent Purpose for Learning Fosters Academic Self-Regulation, Journal of Personality & Social Psychology, 107(4), 559-580. [PDF] PRE PRINT DRAFT

    • Mills, C. & D’Mello, S. K. (2014). On the Validity of the Autobiographical Emotional Memory Task for Emotion Induction, PLOS One, 9(4): e95837. [PDF]

    • Franklin, S. & D’Mello, S. K., & Snaider, J., & Madl, T. (2014). LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning, IEEE Transactions on Autonomous Mental Development, 6(1), 19-41 [PDF] PRE PRINT DRAFT

    • D’Mello, S. K., Dowell, N. & Graesser, A. C. (2014). Unimodal and Multimodal Human Perception of Naturalistic Non-Basic Affective States during Human-Computer Interactions, IEEE Transactions on Affective Computing, 38(1), 140-156. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K., & Mills, C. (2014). Emotions while Writing about Emotional and Non-emotional Topics, Motivation and Emotion, 38(1), 140-156. [PDF] PRE PRINT DRAFT

    • D'Mello, S. K., Lehman, B. Pekrun, R., & Graesser, A. C. (2014). Confusion Can be Beneficial For Learning, Learning & Instruction, 29(1), 153-170. [PDF] PRE PRINT DRAFT

    • Rus, V., D’Mello, S. K., Hu, X., & Graesser, A. C. (2013). Recent advances in intelligent tutoring systems with conversational dialogue, AI Magazine, 34(3), 42-54. [PDF] PRE PRINT DRAFT

    • Morgan, B., D’Mello, S. K., Abbott, R., Radvansky, G., Haass, M., & Tamplin, A. (2013). Individual Differences in Multitasking Ability and Adaptability, Human Factors, 55(4), 776-788. [PDF] PRE PRINT DRAFT

    • D'Mello, S. K. (2013). A Selective Meta-analysis on the Relative Incidence of Discrete Affective States during Learning with Technology, Journal of Educational Psychology, 105(4), 1082-1099. [PDF] PRE PRINT DRAFT

    • 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. [PDF] PRE PRINT DRAFT

    • Feng, S., D’Mello, S. K., & Graesser, A. (2013). Mind wandering while reading easy and difficult texts, Psychonomic Bulletin & Review,20(1), 586-592. [PDF] PRE PRINT DRAFT

    • Lehman, B. A., D'Mello, S. K., Strain, A., Millis, C., Gross, M., Dobbins, A., Wallace, P., Millis, K., & Graesser, A. C. (2013). Inducing and tracking confusion with contradictions during complex learning, International Journal of Artificial Intelligence in Education, 22(2), 85-105. [PDF] PRE PRINT DRAFT

    • Strain, A., Azevedo, R., & D’Mello, S. K. (2013). Using a false biofeedback methodology to explore relationships between learners' affect, metacognition, and performance, Contemporary Educational Psychology, 38(1), 22-39. [PDF] PRE PRINT DRAFT

Before 2012

    • D’Mello, S. K. & Graesser, A. C. (2012). AutoTutor and Affective AutoTutor: Learning by Talking with Cognitively and Emotionally Intelligent Computers that Talk Back, ACM Transactions on Interactive Intelligent Systems, 2(4), 23:2-23:39. [PDF] PRE PRINT DRAFT

    • AlZoubi, O., D’Mello, S. K., & Calvo, R. A. (2012). Detecting naturalistic expressions of nonbasic affect using physiological signals. IEEE Transactions on Affective Computing, 3(3), 298-310, . [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Graesser, A. C. (2012). Language and Discourse are Powerful Signals of Student Emotions during Tutoring. IEEE Transactions on Learning Technologies, 5(4), 304-317. [PDF] PRE PRINT DRAFT

    • Calvo, R. A., & D’Mello, A. C. (2012). Frontiers of affect-aware learning technologies, IEEE Intelligent Systems, 27(6), 86-89 (invited article). [PDF] PRE PRINT DRAFT

    • Graesser, A. C. & D’Mello, S. K. (2012). Moment-to-Moment Emotions during Reading, The Reading Teacher, 66, 22-39 (invited article). [PDF] PRE PRINT DRAFT

    • Lehman, B., D’Mello, S. K., & Graesser, A. C. (2012). Confusion and Complex Learning during Interactions with Computer Learning Environments, The Internet and Higher Education, 15(3), 184-194. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K., Olney, A., Williams, C., Hays, P. (2012). Gaze Tutor: A Gaze-Reactive Intelligent Tutoring System, International Journal of Human-Computer Studies, 70(5), 377-398. [PDF] PRE PRINT DRAFT

    • Price, K., Meisinger, E., D’Mello, S. K. & Louwerse, M. (2012) Silent Reading Fluency with Underlining: A New Method of Assessment, Psychology in the Schools, 49(6), 606-618. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K., Dale, R. A., & Graesser, A. C. (2012). Disequilibrium in the Mind, Disharmony in the Body, Cognition & Emotion, 26(2), 362-374. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Graesser, A. C. (2012). Dynamics of Affective States during Complex Learning, Learning and Instruction, 22, 145-157. [PDF] PRE PRINT DRAFT

    • Olney, A., Dale, R., & D'Mello, S. (2012). The World Within Wikipedia: An Ecology of Mind, Information, 3(2), 229-255. [PDF] OPEN ACCESS

    • D’Mello, S., & Graesser, A.C. (2011). The Half-Life of Cognitive-Affective States during Complex Learning. Cognition and Emotion, 25(7), 1299-1308. [PDF] PRE PRINT DRAFT

    • Johnson, A., Azevedo, R., & D’Mello, S. K. (2011). The Temporal and Dynamic Nature of Regulatory Processes during Self and Externally-Regulated Hypermedia Learning, Cognition & Instruction, 29(4), 471-504. [PDF]

    • D’Mello, S. K., Dowell, N., & Graesser, A.C. (2011). Does It Really Matter Whether Students’ Contributions Are Spoken versus Typed in an Intelligent Tutoring System with Natural Language? Journal of Experimental psychology: Applied,17(1), 1-17. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Franklin, S. (2011). A Cognitive Model’s View of Animal Cognition, Current Zoology, 57(4), 499-512. [PDF] PRE PRINT DRAFT

    • D’Mello, S. & Franklin, S. (2011). Computational Modeling/Cognitive Robotics Can Complement Functional Modeling/Experimental Psychology, New Ideas in Psychology, 29, 217-227. (Special Issue on Cognitive Robotics and Theoretical Psychology). [PDF] PRE PRINT DRAFT

    • Graesser, A.C., D'Mello, S.K. & Strain, A. (2011). Computer agents that help students learning with intelligent strategies and emotional sensitivity. Philippine Computing Journal, Special Issue on Affective and Empathetic Computing, 6(2), 1-8 (invited article). [PDF]

    • D'Mello, S. K., King, B. G., Chipman, P. & Graesser, A. C. (2010). Towards Spoken Human-Computer Tutorial Dialogues. Human-Computer Interaction, 25(4), 289-323. [PDF] PRE PRINT DRAFT

    • 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. [Target Article] [PDF]

    • D’Mello, S., & Graesser, A.C. (2010). Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-adapted Interaction. 20 (2), 147-187. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K., Lehman, B. A., & Person, N. (2010). Monitoring Affect States During Effortful Problem Solving Activities. International Journal of Artificial Intelligence in Education, 20(4), 361-389. [PDF] PRE PRINT DRAFT

    • D’Mello, S., Olney, A., & Person, N. (2010). Mining Collaborative Patterns in Tutorial Dialogues. Journal of Educational Data Mining, 2(1), 1-37. [PDF] PRE PRINT DRAFT

    • Baker, R., D'Mello, S., Rodrigo, M., & Graesser, A. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68 (4), 223-241. [PDF] PRE PRINT DRAFT

    • Cornell, P., Herrin-Griffith, D., Keim, C., Petschonek, S., Sanders, A., D’Mello, S., Golden, T., & Shepherd, G. (2010). Transforming Nursing 1flow, Part 1: The Chaotic Nature of Nurse Activities. The Journal of Nursing Administration, 40(9), 366-373. [PDF] PRE PRINT DRAFT

    • D’Mello, S., Craig, S., & Graesser, A. (2009). Multi-Method Assessment of Affective Experience and Expression during Deep Learning, International Journal of Learning Technology, 4(3/4), 165-187. [PDF] PRE PRINT DRAFT

    • Franklin, S. P., D’Mello, S. K., Baars, B. J., & Ramamurthy, U. (2009). Evolutionary Pressures for Perceptual Stability and Self as Guides to Machine Consciousness. International Journal of Machine Consciousness, 1(1), 99-110. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Graesser, A. C. (2009). Automatic Detection of Learners’ Emotions from Gross Body Language, Applied Artificial Intelligence, 23(2), 123-150. [PDF] PRE PRINT DRAFT

    • Craig S., D’Mello S., Witherspoon A., and Graesser A, (2008). Emote-Aloud during Learning with AutoTutor: Applying the Facial Action Coding System to Cognitive-Affective States during Learning. Cognition and Emotion, 22(5), 777-788. [PDF] PRE PRINT DRAFT

    • Graesser, A. C., D'Mello, S. K., Craig, S. D., Witherspoon A., Sullins J., McDaniel B., Gholson, B., (2008). The Relationship between Affective States and Dialog Patterns during Interactions with AutoTutor. Journal of Interactive Learning Research, 19(2), 293-312. [PDF] PRE PRINT DRAFT

    • D'Mello, S. K., Craig, S.D., Witherspoon, A. W., McDaniel, B. T., and Graesser, A. C. (2008). Automatic Detection of Learner’s Affect from Conversational Cues. User Modeling and User-Adapted Interaction, 18(1-2), 45-80. [PDF] PRE PRINT DRAFT

    • D'Mello, S. K., Mathews, E., McCauley, L., and Markham J. (2008). Impact of Position and Orientation of RFID Tags on Real Time Asset Tracking in a Supply Chain. Journal of Theoretical and Applied Electronic Commerce Research, 3(1), 1-12. [PDF] PRE PRINT DRAFT

    • D'Mello, S. K., Picard, R. W., and Graesser, A. C. (2007) Towards an Affect-Sensitive AutoTutor. Special issue on Intelligent Educational Systems – IEEE Intelligent Systems, 22(4), 53-61. [PDF] PRE PRINT DRAFT

    • D'Mello, S. K., Craig, S.D., Sullins, J. and Graesser, A. C. (2006). Predicting Affective States expressed through an Emote-Aloud Procedure from AutoTutor's Mixed-Initiative Dialogue. International Journal of Artificial Intelligence in Education. 16, pp. 3-28. [PDF] [DRAFT]

Published Conference Proceedings (Strictly peer reviewed)

2017-2022

    • Hutt, S., & D'Mello, S. K. (2022). Evaluating Calibration-free Webcam-based Eye Tracking for Gaze-based User Modeling. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI'22). [PDF]

    • Moulder, R., Duran, N., & D’Mello, S. K. (2022). Assessing Multimodal Dynamics in Multi-Party Collaborative Interactions with Multi-Level Vector Autoregression. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI'22). [PDF]

    • Southwell, R., Pugh, S., Perkoff, M., Clevenger, C., Bush, J. B., Lieber, R., Ward, W., Foltz, P., & D’Mello, S. K. (2022). Challenges and Feasibility of Automatic Speech Recognition for Modeling Student Collaborative Discourse in Classrooms. In Proceeedings of the 15th International Educational Data Mining Conference (EDM 22). [PDF]

    • Caruso, M., Peacock, C. E., Southwell, R., Zhou, G., & D’Mello, S. K. (2022). Going Deep and Far: Gaze-Based Models Predict Multiple Depths of Comprehension During and One Week Following Reading. In Proceeedings of the 15th International Educational Data Mining Conference (EDM 22). [PDF]

    • Zhou, G., Moulder, R., Sun, C., & D’Mello, S. K. (2022). Investigating Temporal Dynamics Underlying Successful Collaborative Problem Solving Behaviors with Multilevel Vector Autoregression. In Proceeedings of the 15th International Educational Data Mining Conference (EDM 22). [PDF]

    • Abinito, A., Pugh, S., Peacock, C. E., & D'Mello, S. K. (2022). Eye to Eye: Gaze Patterns Predict Remote Collaborative Problem Solving Behaviors in Triads. In Proceedings of the 23rd International Conference on Artificial Intelligence in Education (AIED 2022). [PDF]

    • Zhou. G, & D'Mello, S. K. (2022). What do Students’ Interactions with Online Lecture Videos Reveal about their Learning? In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '22). ACM. [PDF]

    • Hutt, S., Stewart, A. E., Gregg, J., Mattingly, S., & D'Mello, S. K. (2022). Feasibility of Longitudinal Eye-Gaze Tracking in the Workplace. In Proceedings of the ACM Symposium on Eye Tracking Research (ETRA 22). [PDF]

    • Leite, W. L., Kuang, H., Shen, Z., Chakraborty, N., Michailidis, G., D'Mello, S., & Xing, W. (2022). Heterogeneity of Treatment Effects of a Video Recommendation System for Algebra. Proceedings of the Ninth ACM Conference on Learning@ Scale. [PDF]

    • Andrews-Todd, J., Steinberg, J., Pugh, S., D’Mello, S. K. (2022). Comparing Collaborative Problem Solving Profiles Derived from Human and Semi-automated Annotation. Proceedings of the International Conference of the Learning Sciences (ICLS 2022). [PDF]

    • Pugh, S., Rao, A., Stewart, A., & D’Mello, S. K. (2022). Do Speech-Based Collaboration Analytics Generalize Across Task Contexts? Proceedings of the 12th Learning Analytics & Knowledge Conference (LAK 2022). New York: ACM. [PDF]

    • Hunkins, N., Kelly, S., D’Mello, S. K. (2022). “Beautiful work, you’re rock stars!”: Teacher Analytics to Uncover Discourse that Supports or Undermines Student Motivation, Identity, and Belonging in Classrooms. Proceedings of the 12th Learning Analytics & Knowledge Conference (LAK 2022). New York: ACM. [PDF]

    • Leite, W. L., Roy, S., Chakraborty, N., Michailidis, G., Huggins-Manley, A. C., D’Mello, S. K., . . . Jing, Z. (2022). A novel video recommendation system for algebra: An effectiveness evaluation study. Proceedings of the 12th Learning Analytics & Knowledge Conference (LAK 2022). New York: ACM. [PDF]

    • Booth, B. M., Hickman, L., Subburaj, S. K., Tay, L., Woo, S. E., & D’Mello, S. K. (2021). Bias and Fairness in Multimodal Machine Learning: A Case Study of Automated Video Interviews. Proceedings of the 2021 International Conference on Multimodal Interaction (ICMI ’21). New York: ACM. [PDF]

    • Bixler, R., & S. K. D'Mello, S. (2021). Crossed Eyes: Domain Adaptation for Gaze-Based Mind Wandering Models. ACM Symposium on Eye Tracking Research and Applications (ETRA'21) (pp. 1-12). New York: ACM. [PDF]

    • Pugh, S. L., Subburaj, S. K., Rao, A. R., Stewart, A. E., Andrews-Todd, J., & D’Mello, S. K. (2021). Say What? Automatic Modeling of Collaborative Problem Solving Skills from Student Speech in the Wild. Proceedings of The 14th International Conference on Educational Data Mining (EDM21). [PDF]

    • Hutt, S., Krasich, K., Brockmole, J., & D'Mello, S. K. (2021). Breaking out of the Lab: Mitigating Mind Wandering with Gaze-Based Attention-Aware Technology in Classrooms. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2021). New York: ACM. [PDF]

    • Jensen, E., Pugh, S., & D'Mello, S. K. (2021). A Deep Transfer Learning Approach to Automated Teacher Discourse Feedback. Proceedings of the 11th Learning Analytics & Knowledge Conference (LAK 2021). New York: ACM. [PDF]

    • Jensen, E., Umada, T., Hunkins, N. C., Hutt, S., Huggins-Manley, A. C., & D'Mello, S. K. (2021). What You Do Predicts How You Do: Prospectively Modeling Student Quiz Performance Using Activity Features in an Online Learning Environment. Proceedings of the 11th Learning Analytics & Knowledge Conference (LAK 2021). New York: ACM. [PDF]

    • Lin, S., Faust, L., D'Mello, S. K., Martinez, G., Chawla, N. (2020). MBead: Semi-supervised Multilabel Behaviour Anomaly Detection on Multivariate Temporal Sensory Data. Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2020) (Short paper). [PDF]

    • Subburaj, S., Stewart, A., Rao, A., D’Mello, S. K. (2020). Multimodal, Multiparty Modeling of Collaborative Problem Solving Performance. Proceedings of the 22nd ACM International Conference on Multimodal Interaction (ICMI 2020). [PDF]

    • Stewart, A., Amon, M. J., Duran, N., & D'Mello, S. K. (2020). Beyond Team Makeup: Diversity in Teams Predicts Valued Outcomes in Computer-Mediated Collaborations. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2020). [PDF]

    • Jensen, E., Dale, M., Donnelly, P., Stone, C., Kelly, S., Godley, A., & D'Mello, S. K. (2020). Toward Automated Feedback on Teacher Discourse to Enhance Teacher Learning. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2020). [PDF]

    • Vrzakova, H., Amon, M. J., Stewart, A., Duran, N., & D'Mello, S. K. (2020). Focused or Stuck Together: Multimodal Patterns Reveal Triads’ Performance in Collaborative Problem Solving. Proceedings of the International Conference on Learning Analytics & Knowledge (LAK). [PDF] [Winner of the Best Full Paper Award]

    • TalkadSukumar, P., Martinez, G. J., Grover, T., Mark, G., D'Mello, S., Chawla, N. V., Mattingly, S. M., & Striegel, A. D. (2020). Characterizing Exploratory Behaviors on a Personal Visualization Interface Using Interaction Logs. Proceedings of the Eurographics and Eurovis (EGEV) Joint Conference: The Eurographics Association. (Short paper) [PDF]

    • Truong H, Bui N. Raghebi Z, Čeko M, Pham N, Nguyen P, Nguyen A, Kim T, Siegfried K, Stene E, Tvrdy T, Weinman L, Payne T, Burke D, Dinh T, D’Mello, S. K., Banaei-Kashani F, Wager T, Goldstein P, Vu T (2020). Painometry: Wearable system for automated, objective, and continuous quantification of pain. Proceedings of the 19th ACM International Conference on Mobile Systems, Applications, and Services. [PDF]

    • Eloy, L., Stewart, A., Amon, M., Reindhardt, C., Michaels, A., Sun, C., Shute, V., Duran, N., & D’Mello, S. K. (2019) Modeling Team-level Multimodal Dynamics during Multiparty Collaboration. Proceedings of the 21st ACM International Conference on Multimodal Interaction (ICMI 2019). [PDF] [Winner of the Best Student Paper Award]

    • Hutt, S., Gardner, M., Duckworth, A., & D’Mello, S. K. (2019). Evaluating Fairness and Generalizability in Models Predicting On-Time Graduation from College Applications. Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019) International Educational Data Mining Society. [PDF]

    • Stone, C., Donnelly, P., Dale, M., Capello, S., Kelly, S., Godley, A., & D’Mello, S. K. (2019). Utterance-level Modeling of Indicators of Engaging Classroom Discourse. Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019). International Educational Data Mining Society. [PDF]

    • Jensen, E., Hutt, S., & D’Mello, S. K. (2019). Generalizability of Sensor-Free Affect Detection Models in a Longitudinal Dataset of Tens of Thousands of Students. Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019). International Educational Data Mining Society. [PDF]

    • Mills, C., Bosch, N., Krasich, K., & D’Mello, S. K. (2019). Reducing Mind-Wandering during Vicarious Learning from an Intelligent Tutoring System. Proceedings of the 20th International Conference on Artificial Intelligence in Education (AIED'19). Springer. [PDF]

    • Hutt, S., Grafsgaard, J., & D'Mello, S. K. (2019). Time to Scale: Generalizable Affect Detection for Tens of Thousands of Students across An Entire Schoolyear. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2019). New York: ACM. [PDF]

    • Vrzakova, H., Amon, M. J., Stewart, A., & D'Mello, S. K. (2019). Dynamics of Visual Attention in Multiparty Collaborative Problem Solving using Multidimensional Recurrence Quantification Analysis. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2019). New York: ACM. [PDF]

    • Aslan, S., Alyuz, N., Tanriover, C., Mete, S. E., Okur, E., D’Mello, S. K., et al. (2019). Investigating the Impact of a Real-time, Multimodal Student Engagement Analytics Technology in Authentic Classrooms. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2019). . New York: ACM. [PDF]

    • Saha, K., Reddy, M. D., das Swain, V., Gregg, J. M., Grover, T., Lin, S., Martinez, G. J., Mattingly, S. M., Mirjafari, S., Mulukutla, R., Nies, K., Robles-Granda, P., Sirigiri, A., Whi Yoo, D., Audia, P., Campbell, A. T., Chawla, N., D’Mello, S. K., Dey, A. K., Jiang, K., Liu, Q., Mark, G., Moskal, E., Striegel, A., & De Choudhury, M. (2019). Imputing Missing Social Media Data Stream in Multisensor Studies of Human Behavior. In Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction (ACII 2019) (pp. 178-184). Washington DC: IEEE. [PDF]

    • Mattingly, S. M., Gregg, J., Audia, P., Baytaktaroglu, E., Campbell, A., Chawla, N., Swain, V. D., Choudhury, M. D., D’Mello, S. K., Dey, A. K., Gao, G., Jagannath, K., Jiang7, K., Lin, S., Liu8, Q., Mark, G., Martinez, G., Masaba, K., Mirjafari, S., Moskal, E., Mulukutla, R., Nies, K., Reddy, M., Robles, P., Saha, K., Sirigiri, A., & Striegel, A. (2019). The Tesserae Project: Experiences with Large-Scale, Longitudinal, In Situ, Multimodal Sensing of Information Workers. Case Study at the ACM CHI Conference on Human Factors in Computing Systems (CHI 2019). [PDF]

    • Saha, K., Bayraktaroglu, A. E., Campbell, A. T., Chawla, N. V., Choudhury, M. D., D’Mello, S. K., Dey, A. K., Gao, G., Gregg, J. M., Jagannath, K., Mark, G., Martinez, G. J., Mattingly, S. M., Moskal, E., Sirigiri, A., Striegel, A., & Yoo, D. W. (2019). Social Media as a Passive Sensor in Longitudinal Studies of Human Behavior andWellbeing. Case Study at the ACM CHI Conference on Human Factors in Computing Systems (CHI 2019). New York: ACM. [PDF]

    • Stone, C., Quirk, A., Gardener, M., Hutt, S., Duckworth, A. L., & D’Mello, S. K. (2019). Language as Thought: Using Natural Language Processing to Model Noncognitive Traits that Predict College Success. Proceedings of the 9th International Learning Analytics and Knowledge Conference (LAK'19). [PDF]

    • Stewart, A., Keirn, Z., & D’Mello, S. K. (2018). Multimodal Modeling of Coordination and Coregulation Patterns in Speech Rate during Triadic Collaborative Problem Solving. Proceedings of the 20th ACM International Conference on Multimodal Interaction (ACM ICMI’18). [PDF]

    • Cook, C., Olney, A., Kelly, S., & D'Mello, S. K. (2018). An Open Vocabulary Approach for Detecting Authentic Questions in Classroom Discourse. Proceedings of the 11th International Conference on Educational Data Mining (EDM 2018). International Educational Data Mining Society. [PDF] [Finalist for the Best Paper Award]

    • Stewart, A., & D’Mello, S. K. (2018). Connecting the Dots Towards Collaborative AIED: Linking Group Makeup to Process to Learning. Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED'18). [PDF]

    • Krasich, K., Hutt, S., Mills, C., Spann, C., Brockmole, J., & D’Mello, S. K. (2018). “Mind” TS: Testing a brief mindfulness intervention with an intelligent tutoring system. Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED'18). [talk] [PDF]

    • Karumbaiah, S., Rahimi, S., Baker, R. S., Shute, V., & D’Mello, S. K. (2018). Is Student Frustration in Learning Games More Associated with Game Mechanics or Conceptual Understanding? Proceedings of the 13th International Conference of the Learning Sciences (ICLS 2018). [poster] [PDF]

    • Grafsgaard, J., Duran, N., Randall, A., & D'Mello, S. K. (2018). Generative Models of Nonverbal Synchrony in Close Relationships. Proceedings of the 13th IEEE Conference on Automatic Face and Gesture Recognition (FG’18). Washington DC: IEEE. [PDF]

    • Hutt, S., Gardener, M., Kamentz, D., Duckworth, A., & D'Mello, S. K. (2018). Prospectively Predicting 4-Year College Graduation from Student Applications. In S. B. Shum, R. Ferguson. A. Merceron, & X. Ochoa (Eds.). Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK'18). New York: ACM. [PDF]

    • Hutt, S., Mills, C., Bosch, N., Krasich, K., Brockmole, J. R., & D'Mello, S. K. (2017). Out of the fr-"eye"-ing pan: Towards gaze-based models of attention during learning with technology in the classroom. In M., Bielikova, E. Herder, F. Cena, & M. Desmarais (Eds.). Proceedings of the 25th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2017) (pp. 94-103). ACM: New York. [PDF]

    • Stewart, A., Bosch, P., Chen, H., Donnelly, P. D'Mello, S. K. (2017). Face Forward: Detecting Mind Wandering from Video During Narrative Film Comprehension. In E. André, R. Baker, X., Hu, M. Rodrigo, & B. du Boulay (Eds). Proceedings of the 18th International Conference on Artificial Intelligence in Education (AIED 2017). Springer: Verlag (pp. 359-370). [PDF]

    • D'Mello, S. K., Mills, C., Bixler, R., & Bosch, N. (2017). Zone out no more: Mitigating mind wandering during computerized reading. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.). Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017). (pp. 8-15). International Educational Data Mining Society. [PDF] [Finalist for the Best Paper Award]

    • Stewart, A., Bosch, P., D'Mello, S. K. (2017). Generalizability of Face-Based Mind Wandering Detection Across Task Contexts. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.). Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017). (pp. 88-95). International Educational Data Mining Society. [PDF]

    • Hutt, S., Hardey, J., Bixler, R., Stewart, A., Risko, E., D’Mello, S. K. (2017). Gaze-based Detection of Mind Wandering during Lecture Viewing. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.). Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017). (pp. 226-231). International Educational Data Mining Society. [PDF]

    • Olney, A., Samei, B., Donnelly, P., & D’Mello, S. K. (2017). Assessing the Dialogic Properties of Classroom Discourse: Proportion Models for Imbalanced Classes. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.). Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017). (pp. 162-167). International Educational Data Mining Society. [PDF]

    • Olney, A., Hosman, E., Graesser, A., & D’Mello, S. K. (2017). Tracking Online Reading of College Students. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.). Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017). (pp. 406-407). International Educational Data Mining Society. [poster] [PDF]

    • Mills, C., Fridman, I., Soussou, W., Waghray, D., Olney, A. M., & D’Mello, S. K. (2017). Put Your Thinking Cap On: Detecting Cognitive Load using EEG during Learning. In I. Molenaar, X., Ochoa, & S. Dawson. (Eds.). Proceedings of the 7th International Learning Analytics and Knowledge Conference (LAK’17) (pp. 80-89). ACM: New York, NY. [PDF]

    • Donnelly, P. J., Blanchard, N., Olney, A. M., Kelly, S., Nystrand, M., & D'Mello, S. K. (2017). Words Matter: Automatic Detection of Questions in Classroom Discourse using Linguistics, Paralinguistics, and Context. In I. Molenaar, X., Ochoa, & S. Dawson. (Eds.). Proceedings of the 7th International Learning Analytics and Knowledge Conference (LAK’17) (pp. 218-227). ACM: New York, NY. [talk] [PDF]

    • Khan, S., Suendermann-Oeft, D., Evanini, D., Williamson, D., Paris, S., Qian, Y., Huang, Y., Bosch, P., D’Mello, S. K., Loukina, A., & Davis, L., (2017) MAP: Multimodal Assessment Platform for Interactive Communication Competency. In S. Shehata & J. P-L. Tan, J.P-L. (Eds.). Practitioner Track Proceedings of the 7th International Learning Analytics & Knowledge Conference (LAK17). SoLAR. [PDF]

2012-2017

    • Donnelly, P. J., Blanchard, N., Samei, B., Olney, A. M., Sun, X., Ward, B., Kelly, S., Nystrand, M., D'Mello, S. K. (2016). Multi-Sensor Modeling of Teacher Instructional Segments in Live Classrooms. Proceedings of the 18th ACM International Conference on Multimodal Interaction (ICMI 2016) (pp. 177-184). ACM: New York, NY. [poster] [PDF]

    • Blanchard, N., Donnelly, P. J., Olney, A. M., Samei, B., Ward, B., Sun, X., Kelly, S., Nystrand, M., 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. [poster] [PDF]

    • D'Mello, S. K., Kopp, K., Bixler, R., & Bosch, N. (2016). Attending to Attention: Detecting and Combating Mind Wandering during Computerized Reading. Extended Abstracts of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2016). New York: ACM. [poster] [PDF]

    • Donnelly, P. J., Blanchard, N., Samei, B., Olney, A. M., Sun, X., Ward, B., Kelly, S., Nystrand, M., & D’Mello, S. K. (2016). Automatic Teacher Modeling from Live Classroom Audio. Proceedings of the 24th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2016) (pp. 45-53). New York: ACM. [PDF] [Winner of an Outstanding Paper Award] [Finalist for the Best Paper Award]

    • Stewart, A., Bosch, P, Chen, H., Donnelly, P. J., & D’Mello, S. K. (2016). Where's Your Mind At? Video-Based Mind Wandering Detection During Film Viewing. Proceedings of the 24th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2016) (pp. 295-296). New York: ACM. [poster] [PDF]

    • Hutt, S., Mills, C., White, S., Donnelly, P. J. D’Mello, S. K. (2016). The Eyes Have It: Gaze-based Detection of Mind Wandering during Learning with an Intelligent Tutoring System. In T. Barnes, M. Chi, & M. Feng (Eds.) Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016) (pp. 86-93). International Educational Data Mining Society [PDF] [Exemplary paper - top 15% of submissions]

    • Mills, C., Bixler, R., Wang, X., D’Mello, S.K. (2016). Automatic gaze-based detection of mind wandering during film viewing. In T. Barnes, M. Chi, & M. Feng (Eds.) Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016) (pp. 30-37). International Educational Data Mining Society [PDF]

    • Blanchard, N., Donnelly, P. J., Olney, A. M., Samei, B., Ward, B., Sun, X., Kelly, S., Nystrand, M., D'Mello, S. K. (2016). Semi-Automatic Detection of Teacher Questions from Human-Transcripts of Audio in Live Classrooms. In T. Barnes, M. Chi, & M. Feng (Eds.) Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016) (pp. 288-291). International Educational Data Mining Society. [PDF]

    • 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. [PDF]

    • Bosch, N., D'Mello, S. K., Baker, R. S., Ocumpaugh, J., Shute, V. J., Ventura, M., Wang, L., & Zhao, W. (2016). Detecting student emotions in computer-enabled classrooms. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016) (pp. 4125-4129). Menlo Park, CA: AAAI Press. (Invited talk) [PDF]

    • Allen, L., Mills., C., Jacovina, M., Crossley, S., D’Mello, S. K., & McNamara, D. (2016). Investigating Boredom and Engagement during Writing Using Multiple Sources of Information: The Essay, The Writer, and Keystrokes. In S. Dawson, H., Drachsler, & C. Rosé (Eds.) Proceedings of the 6th International Conference on Learning Analytics and Knowledge. (LAK 2016) (pp. 114-123). ACM: NewYork, NY. [PDF]

    • Dillon, J., Ambrose, A. G., Wanigasekara, N., Chetlur, M., Dey, P., Sengupta, B., & D’Mello, S. K. (2016). Student Affect during Learning with a MOOC. In S. Dawson, H., Drachsler, & C. Rosé (Eds.) Proceedings of the 6th International Conference on Learning Analytics and Knowledge. (LAK 2016) (pp. 528-529). ACM: NewYork, NY. [poster] [PDF]

    • D’Mello, S. K., Olney, A. M, Blanchard, N., Sun, X., Ward, B., Samei, B., & Kelly, S. (2015). Multimodal Capture of Teacher-Student Interactions for Automated Dialogic Analysis in Live Classrooms. Proceedings of the 17th ACM International Conference on Multimodal Interaction (ICMI 2015) (Workshop on Multimodal Learning Analytics MLA’15)(pp. 557-566). New York, NY: ACM. [PDF]

    • Bosch, N., Chen, H., Baker, R., Shute, V., & D'Mello, S. K. (2015). Accuracy vs. Availability Heuristic in Multimodal Affect Detection in the Wild. In Proceedings of the 17th ACM International Conference on Multimodal Interaction (ICMI 2015) (pp. 557-566) . New York, NY: ACM. [poster] [PDF]

    • Bixler, R., Garrison, L., & D’Mello, S. K. (2015). Automatically Detecting Mind Wandering During Reading Using Gaze and Physiology. In Proceedings of the 17th ACM International Conference on Multimodal Interaction (ICMI 2015) (pp. 299-306). New York, NY: ACM. [poster] [PDF]

    • Bixler, R., & D’Mello, S. K. (2015). Automatic Gaze-Based Detection of Mind Wandering with Metacognitive Awareness. In F. Ricci, K. Bontcheva, O. Conlan, & S. Lawless (Eds.), Proceedings of the 23rd International Conference on User Modeling, Adaptation, and Personalization (UMAP 2015) (pp. 31- 43). Springer-Verlag: Berlin Heidelberg. [PDF]

    • Mills, C., D’Mello, S. (2015). Toward a Real-time (Day) Dreamcatcher: Detecting Mind Wandering Episodes During Online Reading. in press). In C. Romero, M. Pechenizkiy, J. Boticario, & O. Santos (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015) (pp. 69- 76). International Educational Data Mining Society. [PDF]

    • Blanchard, N, D’Mello, S, Olney, A M., & Nystrand, M. (2015). Automatic Classification of Question & Answer Discourse Segments from Teacher’s Speech in Classrooms. In C. Romero, M. Pechenizkiy, J. Boticario, & O. Santos (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015) (pp. 282- 288). International Educational Data Mining Society. [PDF]

    • Kai, S., Paquette, L., Baker, R., Bosch, N., D’Mello, S., Ocumpaugh, J., Shute, V., & 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. [PDF] [Winner of the Best Student Paper Award] [Nominated for the Best Paper Award]

    • Samei, B, Olney, A M., Kelly, S, Nystrand, M., D’Mello, S, Blanchard, N, & Graesser, A. (2015). Modeling Classroom Discourse: Do Models that Predict Dialogic Instruction Properties Generalize across Populations? In C. Romero, M. Pechenizkiy, J. Boticario, & O. Santos (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015) (pp. 444- 447). International Educational Data Mining Society. [PDF]

    • Chen, Y., Bosch, N., & D’Mello, S. (2015). Video-Based Affect Detection in Noninteractive Learning Environments. In C. Romero, M. Pechenizkiy, J. Boticario, & O. Santos (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015) (pp. 440- 443). International Educational Data Mining Society. [PDF]

    • Goedecke, P. J., Dong, D., Shi, G., Feng, S., Risko, E., Olney, A., D’Mello, S. K., & Graesser, A. C. (2015). Breaking Off Engagement: Readers’ Disengagement as a Function of Reader and Text Characteristics. In C. Romero, M. Pechenizkiy, J. Boticario, & O. Santos (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015) (pp. 448- 451). International Educational Data Mining Society. [PDF]

    • Bosch, N., D’Mello, S. K., Baker, R., Ocumpaugh, J., & Shute, V. (2015). Temporal Generalizability of Face-Based Affect Detection in Noisy Classroom Environments. In C. Conati, N. Heffernan, A. Mitrovic, & M. Felisa Verdejo (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015) (pp. 44- 53). Springer-Verlag: Berlin Heidelberg. [PDF] [Winner of the Best Paper Award]

    • Mills, C., D’Mello, S. K., Bosch, N., & Olney, A. (2015). Mind Wandering during Learning with an Intelligent Tutoring System. In C. Conati, N. Heffernan, A. Mitrovic, & M. Felisa Verdejo (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015) (pp. 267- 276). Springer-Verlag: Berlin Heidelber [PDF]

    • Blanchard, N., Brady, M., Olney, A., Glaus M., Sun, X., Nystrand, M., Samei, B., Kelly, S. & D’Mello, S. K. (2015). A Study of Automatic Speech Recognition in Noisy Classroom Environments for Automated Dialog Analysis. Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015). Springer-Verlag: Berlin Heidelberg. [PDF]

    • Bosch, N., D'Mello, S., Baker, R., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., & Zhao, W. (2015). Automatic Detection of Learning-Centered Affective States in the Wild. Proceedings of the 2015 ACM International Conference on Intelligent User Interfaces (IUI 2015). New York, NY: ACM. [PDF] [Honorable mention for the Best Paper Award]

    • Byrd, C., McNeil, N., D’Mello, S. K., Cook, S. (2014). Gesturing May Not Always Make Learning Last. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (Cogsci 2014) (pp. 1982-1987). Cognitive Science Society: Austin, TX. [poster] [PDF]

    • Bixler, R., & D'Mello, S. (2014). Toward Fully Automated Person-Independent Detection of Mind Wandering. In V. Dimitrova, T. Kuflik, D. Chin, F. Ricci, P. Dolog & G.-J. Houben (Eds.), Proceedings of the 22nd International Conference on User Modeling, Adaptation, and Personalization (pp. 37-48). Switzerland: Springer International Publishing. [talk] [Winner of the James Chen Best Student Paper Award] [PDF]

    • Rodeghero, P., McMillan, C., McBurney, P., Bosch, N., & D’Mello, S. K. (2014). Improving Automated Source Code Summarization via an Eye-Tracking Study of Programmers. In Proceedings of the 36th International Conference on Software Engineering (ICSE 2014) (pp. 390-401), ACM: New York, NY. [PDF] [Winner of the ACM Distinguished Paper award]

    • Samei, B., Olney, A., Kelly, S., Nystrand, M., D’Mello, S. K., Blanchard, N., Sun, X., Glaus, M., & Graesser, A. C. (2014). Domain Independent Assessment of Dialogic Properties of Classroom Discourse. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren, (Eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014) (pp. 233-236). International Educational Data Mining Society. [PDF]

    • Mintz, L., Stefanescu, D., Feng, S., D’Mello, S. K., & Graesser, A. C. (2014). Automatic assessment of student reading comprehension from short summaries. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren, (Eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014) (pp. 333-334). International Educational Data Mining Society. [poster] [PDF]

    • Kopp, K., Bixler, R., & D’Mello, S. K. (2014). Identifying Learning Conditions that Minimize Mind Wandering by Modeling Individual Attributes. In S. Trausan-Matu, K. E. Boyer, M. Crosby & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems (ITS 2014) (pp. 94-103). Switzerland: Springer International Publishing. [PDF]

    • Bosch, N., Chen, Y., & D'Mello, S. K. (2014). It’s Written On Your Face: Detecting Affective States from Facial Expressions while Learning Computer Programming. In S. Trausan-Matu, K. Boyer, M. Crosby & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems (ITS 2014) (pp. 39-44). Switzerland: Springer International Publishing. [PDF]

    • Mills, C., Bosch, N., Graesser, A., & D’Mello, S. K. (2014). To Quit or Not to Quit: Predicting Future Behavioral Disengagement from Reading Patterns. In S. Trausan-Matu, K. Boyer, M. Crosby & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems (ITS 2014) (pp. 19-28). Switzerland: Springer International Publishing. [PDF]

    • Blanchard, N., Bixler, R., & D’Mello, S. K. (2014). Automated Physiological-Based Detection of Mind Wandering During Learning. In S. Trausan-Matu, K. Boyer, M. Crosby & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems (ITS 2014) (pp. 55-60). Switzerland: Springer International Publishing. [PDF]

    • Bosch, N., & D'Mello, S. K. (2014). It Takes Two: Momentary Co-occurrence of Affective States during Computerized Learning. In S. Trausan-Matu, K. Boyer, M. Crosby & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems (ITS 2014) (pp. 638-639). Switzerland: Springer International Publishing. [poster] [PDF]

    • D’Mello, S. K. (2014). Emotional Rollercoasters: Day Differences in Affect Incidence during Learning. In W. Eberle & C. Boonthum-Denecke (Eds.) Proceedings of 27th Florida Artificial Intelligence Research Society Conference (FLAIRS 2014) (pp. 387-392). Menlo Park, CA: AAAI Press. [PDF]

    • Hulovatyy, Y., D’Mello, S. K., Calvo, R. A. & Milenkovic, T. (2013). Network analysis improves interpretation of affective physiological data. In K. Yetongnon, A. Dipanda, & R. Chbeir (Eds.) IEEE Proceedings of the 2nd International Workshop on Complex Networks and their Applications at the 9th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS) (pp. 470-477). IEEE Computer Society: Washington DC. [PDF]

    • AlZoubi, O., Fossati, D., D’Mello, S. K., & Calvo, R. (2013). Affect Detection and Classification from Non-Stationary Physiological Data. Proceedings of the 12th International Conference on Machine Learning and Applications (ICMLA'13) (pp. 240-245). IEEE: Washington, DC. [poster] [PDF]

    • Morgan, B., D’Mello, S. K., Abbott, R., Haass, H., Tamplin, A., & Radvansky, G. (2013). Performance-Based Adaptability Profiles in Multitasking. Proceedings of the Human Factors and Ergonomics Society 2013 Annual Meeting (pp. 843-847). SAGE: Thousand Oaks, CA. [PDF]

    • Morgan, B. & D’Mello, S. K. (2013). The Effect of Positive vs. Negative Emotion on Multitasking. Proceedings of the Human Factors and Ergonomics Society 2013 Annual Meeting (pp. 848-852). SAGE: Thousand Oaks, CA. [PDF]

    • Bosch, N., D’Mello, S. K., & Mills, C. (2013). What Emotions Do Novices Experience During their First Computer Programming Learning Session? In K. Yacef, C. Lane, J. Mostow, & P. Pavlik (Eds.) Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (pp. 11-20). Springer-Verlag:Berlin Heidelberg. [PDF]

    • Mills, C., D’Mello, S. K., Lehman, B., Bosch, N., Strain, A., & Graesser, A. (2013). What Makes Learning Fun? Exploring the Influence of Choice and Difficulty on Mind Wandering and Engagement during Learning. In K. Yacef, C. Lane, J. Mostow, & P. Pavlik (Eds.) Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (pp. 71-80). Springer-Verlag: Berlin Heidelberg. [PDF]

    • Lehman, B., D’Mello, S. K., & Graesser, A. (2013). Who Benefits from Confusion Induction during Learning? An Individual Differences Cluster Analysis. In K. Yacef, C. Lane, J. Mostow, & P. Pavlik (Eds.) Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (pp. 51-60). Springer-Verlag:Berlin Heidelberg. [PDF]

    • Bosch., N., & D’Mello, S. K. (2013). Programming with Your Heart on Your Sleeve: Analyzing the Affective States of Computer Programming Students. In K. Yacef, C. Lane, J. Mostow, & P. Pavlik (Eds.) Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (pp. 908-911). Springer-Verlag:Berlin Heidelberg. [PDF]

    • Mills, C. & D’Mello, S. K. (2013). Sorry, I Must Have Zoned Out: Tracking Mind Wandering Episodes in an Interactive Learning Environment. In K. Yacef, C. Lane, J. Mostow, & P. Pavlik (Eds.) Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (pp. 896-899). Springer-Verlag:Berlin Heidelberg. [PDF]

    • Bixler, R., & D’Mello, S. K. (2013). Towards Automated Detection and Regulation of Affective States During Academic Writing. In K. Yacef, C. Lane, J. Mostow, & P. Pavlik (Eds.) Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (pp. 904-907). Springer-Verlag:Berlin Heidelberg. [PDF]

    • D’Mello, S. K., Cobian, J., Hunter, M. (2013). Automatic Gaze-Based Detection of Mind Wandering during Reading. In S. K. D’Mello, R. A. Calvo, & A. Olney (Eds). Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013) (pp. 364-365). International Educational Data Mining Society. [PDF]

    • Vega, B., Feng, S., Lehman, B., Graesser, A., & D’Mello, S. (2013). Reading into the Text: Investigating the Influence of Text Complexity on Cognitive Engagement. In S. K. D’Mello, R. A. Calvo, & A. Olney (Eds). Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013) (pp. 296-299). International Educational Data Mining Society. [PDF]

    • D’Mello, S. K., & Calvo, R. A. (2013). Beyond the Basic Emotions: What Should Affective Computing Compute? In S. Brewster, S. Bødker, & W. Mackay (Eds.). Extended Abstracts of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2013) (pp. 2287-2294).New York: ACM. [PDF]

    • Mills, C. & D’Mello, S. K. (2013). Emotions During Writing about Socially-Charged Issues: Effects of the (Mis)Alignment of Personal Positions with Instructed Positions. Proceedings of 26th Florida Artificial Intelligence Research Society Conference. Menlo Park, CA: AAAI Press. [PDF] [Winner of the Best Student Paper Award] [Winner of the Best Overall Paper Award]

    • Kelly, K., Heffernan, N., D’Mello, S. K., Namais, J., Strain, A. (2013). Adding Teacher-Created Motivational Video to an ITS. Proceedings of 26th Florida Artificial Intelligence Research Society Conference (pp. 503-508). Menlo Park, CA: AAAI Press. [PDF]

    • Bixler, R., & D'Mello, S. (2013). Detecting boredom and engagement during writing with keystroke analysis, task appraisals, and stable traits. Proceedings of the 2013 International Conference on Intelligent User Interfaces (IUI 2013) (pp. 225-234). New York, NY:ACM. [PDF]

    • D’Mello, S. K., & Kory, J. (2012). Consistent but Modest: A Meta-Analysis on Unimodal and Multimodal Affect Detection Accuracies from 30 Studies. In L. P. Morency et al. (Eds.). Proceedings of the 14th ACM International Conference on Multimodal Interaction (pp. 31-38). New York:ACM. [PDF] [Winner of the Outstanding Paper Award]

    • Lehman, B., Mills, C., D’Mello, S. K., & Graesser, A. C. (2012). Automatic Evaluation of Learner Self-Explanations and Erroneous Responses for Dialogue-Based ITSs. In S. Cerri, W. Clancey, G. Papadourakis, & K. Panourgia (Eds.) Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 541-550). Berlin Heidelberg: Springer-Verlag. [PDF]

    • Lehman, B., D’Mello, S. K., Cade, W., & Person, N. (2012). How Do They Do It? Investigating Dialogue Moves within Dialogue Modes in Expert Human Tutoring. In S. Cerri, W. Clancey, G. Papadourakis, & K. Panourgia (Eds.) Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 557-562). Berlin Heidelberg: Springer-Verlag. [PDF]

    • Lehman, B., D’Mello, S. K., & Graesser, A. C. (2012). Interventions to Regulate Confusion during Learning. In S. Cerri, W. Clancey, G. Papadourakis, & K. Panourgia (Eds.) Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 576-578). Berlin Heidelberg: Springer-Verlag. [PDF] [Winner of the Young Researchers Track Award]

    • Mills, C. & D’Mello, S. K. (2012). Emotions During Writing on Topics that Align or Misalign with Personal Beliefs. In S. Cerri, W. Clancey, G. Papadourakis, & K. Panourgia (Eds.) Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 638-639). Berlin Heidelberg: Springer-Verlag. [poster] [PDF]

    • Olney, A., D’Mello, S. K., Person, N., Cade, W., Hays, P., Williams, C., Lehman, B., & Graesser, A. C. (2012). Guru: A Computer Tutor that Models Expert Human Tutors. In S. Cerri, W. Clancey, G. Papadourakis, & K. Panourgia (Eds.) Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 256-261). Berlin Heidelberg: Springer-Verlag. [PDF]

    • Strain, A., D’Mello, S. K., & Gross, M. (2012). How Do Learners Regulate their Emotions? In S. Cerri, W. Clancey, G. Papadourakis, & K. Panourgia (Eds.) Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 618-619). Berlin Heidelberg: Springer-Verlag. [poster] [PDF]

    • Strain, A., Azevedo, R., & D’Mello, S. K.. (2012). Exploring relationships between learners’ affective states, metacognitive processes, and learning outcomes. In S. Cerri, W. Clancey, G. Papadourakis, & K. Panourgia (Eds.) Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 59-64). Berlin Heidelberg: Springer-Verlag. [PDF]

    • D’Mello, S. K. & Graesser, A. C. (2012). Malleability of Students’ Perceptions of an Affect-Sensitive Tutor and its Influence on Learning. In G. Youngblood & P. McCarthy (Eds.) Proceedings of 25th Florida Artificial Intelligence Research Society Conference (pp. 432-437). Menlo Park, CA: AAAI Press. [PDF]

    • Person, N., Olney, A., D’Mello, S. K. & Lehman, B. (2012). Interactive Concept Maps and Learning Outcomes in Guru. In G. Youngblood & P. McCarthy (Eds.) Proceedings of 25th Florida Artificial Intelligence Research Society Conference (pp. 456-461). Menlo Park, CA: AAAI Press. [PDF]

Before 2012

    • AlZoubi, O., Hussain, M. S., D’Mello, S. K., & Calvo, R. A. (2011). Affective Modeling from Multichannel Physiology: Analysis of Day Differences. In S. K. D’Mello, A. Graesser, B. Schuller, B., & J. Martin (Eds. ). Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction (ACII 2011) (pp. 4-13). Berlin Heidelberg: Springer-Verlag. [PDF]

    • Morgan, B., D’Mello, S. K., Fike, K., Abbott, Haass, M., R., Tamplin, A., Radvansky, G., & Forsythe, C. (2011). Individual Differences in Multitasking Ability and Adaptability. Proceedings of the 55th Annual Meeting of the Human Factors and Ergonomics Society. [PDF]

    • D’Mello, S. K. (2011). Dynamical Emotions: Bodily Dynamics of Affect during Problem Solving. In C. Hölscher, T. F. Shipley, & L. Carlson (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, (pp. 402-407). Austin, TX: Cognitive Science Society. [PDF]

    • Morgan, B., D’Mello, S. K., Fielding, J., Fike, K., Tamplin A., Radvansky, G., Arnett, J., Abbott, R., & Graesser, A. C. (2011). Strategy Shifting in a Procedural-Motor Drawing Task. In C. Hölscher, T. F. Shipley, & L. Carlson (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, (pp. 2099-2104). Austin, TX: Cognitive Science Society. [poster] [PDF]

    • Lehman, B. A., D'Mello, S. K., Strain, A., Gross, M., Dobbins, A., Wallace, P., Millis, K., & Graesser, A. C. (2011). Inducing and tracking confusion with contradictions during critical thinking and scientific reasoning. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Proceedings of 15th International Conference on Artificial Intelligence in Education (pp. 171-178). Berlin: Springer-Verlag. [PDF] [Winner of the Best Paper Award]

    • Sazzad, M. S., AlZoubi, O., Calvo, R. A., & D’Mello, S. K. (2011). Affect Detection from Multichannel Physiology during Learning. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Proceedings of 15th International Conference on Artificial Intelligence in Education (pp. 131-138). Berlin: Springer-Verlag. [PDF]

    • Strain, A. C. & D’Mello, S. K. (2011). Emotion Regulation During Learning. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Proceedings of 15th International Conference on Artificial Intelligence in Education (pp. 566-568). Berlin: Springer-Verlag. [poster] [PDF]

    • Strain, A. C. & D’Mello, S. K. & Graesser, A. C. (2011). Training Emotion Regulation Strategies During Computerized Learning: A Method for Improving Learner Self-Regulation. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Proceedings of 15th International Conference on Artificial Intelligence in Education (pp. 616-618). Berlin: Springer-Verlag. [doctoral consortium poster] [PDF]

    • Dowell, N., D’Mello, S. K., Mills, C., & Graesser, A. C. (2011). Does Topic Matter? Topic Influences on Linguistic and Rubric-Based Evaluation of Writing. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Proceedings of 15th International Conference on Artificial Intelligence in Education (pp. 450-452). Berlin: Springer-Verlag. [poster] [PDF]

    • D’Mello, S. K. (2011). Patterns of Word Usage in Expert Tutoring Sessions: Verbosity vs. Quality. In P. McCarthy & C. Boonthum (Eds.), Proceedings of 24rd Florida Artificial Intelligence Research Society Conference (pp. 501-506). Menlo Park, CA: AAAI Press. [PDF]

    • Rasor, T., Olney, A., & D’Mello, S. K. (2011). Student Speech Act Classification using Machine Learning. Proceedings of 24rd Florida Artificial Intelligence Research Society Conference (pp. 275-280). Menlo Park, CA: AAAI Press. [PDF]

    • D'Mello, S., & Graesser, A. (2010). Modeling Cognitive-Affective Dynamics with Hidden Markov Models. In R. Catrambone & S. Ohlsson(Eds.), Proceedings of the 32nd Annual Cognitive Science Society (pp. 2721-2726). Austin, TX: Cognitive Science Society. [PDF]

    • D'Mello, S., Williams, C., Hays, P. & Olney, A. (2010). Individual Differences as Predictors of Learning and Engagement. In R. Catrambone & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Cognitive Science Society (pp. 308-313). Austin, TX: Cognitive Science Society. [PDF]

    • D'Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., et al. (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 10th International Conference on Intelligent Tutoring Systems (pp. 245-254). Springer:Berlin / Heidelberg. [PDF]

    • D'Mello, S., Hays, P., Williams, C., Cade, W., Brown, J., & Olney, A. (2010). Collaborative lecturing by human and computer tutors. In J. Kay & V. Aleven (Eds.), 10th International Conference on Intelligent Tutoring Systems (pp. 609-618). Springer:Berlin / Heidelberg. [PDF]

    • Lehman, B., D'Mello, S., & Person, N. (2010). The intricate dance between cognition and emotion during expert tutoring. In J. Kay & V. Aleven (Eds.), Proceedings of 10th International Conference on Intelligent Tutoring Systems. (pp. 433-442). Springer:Berlin / Heidelberg. [PDF]

    • Williams, C., & D'Mello, S. (2010). Predicting student knowledge levels from domain-independent function and content words In J. Kay & V. Aleven (Eds.), Proceedings of 10th International Conference on Intelligent Tutoring Systems. (pp. 494-503). Springer:Berlin / Heidelberg. [PDF]

    • Pour, P. A., Hussein, S., AlZoubi, O., D'Mello, S. K., & Calvo, R. (2010). The impact of system feedback on learners’ affective and physiological states. In J. Kay & V. Aleven (Eds.), Proceedings of 10th International Conference on Intelligent Tutoring Systems. (pp. 264-273). Springer:Berlin / Heidelberg. [PDF]

    • Olney, A. & D'Mello, S. K. (2010). Interactive Event: A DIY Pressure Sensitive Chair for Intelligent Tutoring Systems. In J. Kay & V. Aleven (Eds.), Proceedings of 10th International Conference on Intelligent Tutoring Systems (pp. 456). Springer:Berlin / Heidelberg. [demo] [PDF]

    • D'Mello, S., & Graesser, A. (2010). Mining Bodily Patterns of Affective Experience during Learning . In A. Merceron, P. Pavlik, and R. Baker (Eds.), Proceedings of the Third International Conference on Educational Data Mining (pp. 31-40). [PDF]

    • D'Mello, S. K., Lehman, B., & Person, N. (2010).Expert Tutors Feedback is Immediate, Direct, and Discriminating. Proceedings of the 23rd Florida Artificial Intelligence Research Society Conference (FLAIRS-23). (595-604). AAAI Press. [PDF] [Winner of the Best Paper award]

    • D’Mello, S. K., Dowell, N., & Graesser, A. C. (2009). Cohesion Relationships in Tutorial Dialogue as Predictors of Affective States. In V. Dimitrova, R. Mizoguchi, B. du Boulay, & A. Graesser (Eds.), Proceedings of 14th International Conference on Artificial Intelligence In Education.(pp. 9-16). Amsterdam: IOS Press. [PDF]

    • D’Mello, S. K., Person, N., & Lehman, B. A. (2009). Antecedent-Consequent Relationships and Cyclical Patterns between Affective States and Problem Solving Outcomes. In V. Dimitrova, R. Mizoguchi, B. du Boulay, & A. Graesser (Eds.), Proceedings of 14th International Conference on Artificial Intelligence In Education.(pp. 57-64). Amsterdam: IOS Press. [PDF]

    • Sullins, J., Jeon, M., D’Mello, S. K., & Graesser, A. C. (2009). The Relationship Between Modality and Metacognition While Interacting with AutoTutor. In V. Dimitrova, R. Mizoguchi, B. du Boulay, & A. Graesser (Eds.), Proceedings of 14th International Conference on Artificial Intelligence In Education.(pp. 674-676). Amsterdam: IOS Press. [poster] [PDF]

    • D’Mello, S. K., Craig, S. D., Fike, K., & Graesser, A. C. (2009). Responding to Learners’ Cognitive-Affective States with Supportive and Shakeup Dialogues. In Jacko, J.A. (Ed.) Human-Computer Interaction. Ambient, Ubiquitous and Intelligent Interaction (pp. 595-604). Berlin/Heidelberg: Springer. [PDF]

    • D'Mello, S. K., Taylor, R., Davidson, K., and Graesser, A. (2008). Self versus Teacher Judgments of Learner Emotions during a Tutoring Session with AutoTutor. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Proceedings of the Ninth International Conference on Intelligent Tutoring Systems (pp. 9-18). Berlin, Heidelberg: Springer-Verlag. [talk] [PDF]

    • Cade, W., Copeland, J. Person, N., and D'Mello, S. K. (2008). Dialogue Modes in Expert Tutoring. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Proceedings of the Ninth International Conference on Intelligent Tutoring Systems (pp. 470-479). Berlin, Heidelberg: Springer-Verlag. [PDF] [Nominated for Outstanding Paper Award].

    • Lehman, B. A., Matthews, M., D'Mello, S. K., and Person, N. (2008). What Are You Feeling? Investigating Student Affective States During Expert Human Tutoring Sessions. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Proceedings of the Ninth International Conference on Intelligent Tutoring Systems (pp. 50-59). Berlin, Heidelberg: Springer-Verlag. [PDF]

    • Witherspoon, A. Azevedo, R. and D'Mello, S. K. (2008). The Dynamics of Self-Regulatory Processes within Self- and Externally Regulated Learning Episodes. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Proceedings of the Ninth International Conference on Intelligent Tutoring Systems (pp. 260-269). Berlin, Heidelberg: Springer-Verlag. [PDF]

    • Rodrigo, M., Baker R., D’Mello, S. K. Gonzalez, C., Lagud, M., Lim, S., Macapanpan, A., Pascua, S., Santillano, S., Sugay, J., Tep, S., and Viehland, N. (2008). Comparing Learners’ Affect While Using an Intelligent Tutoring System and a Simulation Problem Solving Game. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Proceedings of the Ninth International Conference on Intelligent Tutoring Systems (pp. 40-49). Berlin, Heidelberg: Springer-Verlag. [PDF]

    • D'Mello, S. K., Choudhary, D., Chari, S., Markham J., and McCauley, L. (2007) RFID Tag Characterization in a GHz Transverse Electromagnetic Cell, IEEE/INFORMS International Conference on Service Operations and Logistics, and Informatics (SOLI 2007). [PDF]

    • D’Mello, S. K., Chipman, P., & Graesser, A. C. (2007). Posture as a predictor of learner’s affective engagement. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 905-910). Austin, TX: Cognitive Science Society. [poster] [PDF]

    • D’Mello, S. K., Taylor, R., & Graesser, A. C. (2007). Monitoring Affective Trajectories during Complex Learning. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 203-208). Austin, TX: Cognitive Science Society. [PDF]

    • Willits, J., D’Mello, S. K., Duran, N., & Olney, A. (2007). Distributional Statistics and Thematic Role Relationships. 29th Annual Meeting of the Cognitive Science Society. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 707-712). Austin, TX: Cognitive Science Society. [PDF]

    • McDaniel, B. T., D’Mello, S. K., King, B. G., Chipman, P., Tapp, K., & Graesser, A. C. (2007). Facial Features for Affective State Detection in Learning Environments. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 467-472). Austin, TX: Cognitive Science Society. [PDF]

    • D'Mello, S. K., and Graesser, A. C. (2007). Mind and Body: Dialogue and Posture for Affect Detection in Learning Environments. 13th International Conference on Artificial Intelligence in Education (AIED 2007). R. Luckin et al. (Eds), (pp 161-168). IOS Press. [PDF]

    • Graesser, A. C. Chipman, P., King, B., McDaniel, B., and D’Mello, S (2007). Emotions and Learning with AutoTutor. 13th International Conference on Artificial Intelligence in Education (AIED 2007). R. Luckin et al. (Eds), (pp 569-571). IOS Press. [poster] [PDF]

    • Kim, L., McCauley, L. T., Polkosky, M., D'Mello, S., Craig, S., and Nikiforova, B. (2007). Finding Information And Finding Spaces: A Case Study Of User-Testing An Intelligent Kiosk. The Second IASTED International Conference on Human-Computer Interaction (IASTED-HCI). [PDF]

    • D’Mello, S., & Graesser, A.C. (2006). Affect Detection from Human-Computer Dialogue with an Intelligent Tutoring System. J. Gratch et al. (Eds.): IVA 2006, LNAI 4133, pp. 54 – 67. Springer-Verlag Berlin Heidelberg 2006. [PDF]

    • McCauley, L., & D’Mello, S. (2006). MIKI: A Speech Enabled Intelligent Kiosk. J. Gratch et al. (Eds.): IVA 2006, LNAI 4133, pp. 132 – 144. Springer-Verlag Berlin Heidelberg 2006. [PDF]

    • Ramamurthy U., D'Mello S., and Franklin S. (2006). Realizing Forgetting in a Modified Sparse Distributed Memory System. Proceedings of the 28th Annual Meeting of the Cognitive Science Society, (pp. 1992-1997), Vancouver, Canada. [poster] [PDF]

    • Graesser, A.C., McDaniel, B., Chipman, P., Witherspoon, A., D’Mello, S., and Gholson, B. (2006). Detection of Emotions During Learning with AutoTutor. Proceedings of the 28th Annual Meeting of the Cognitive Science Society, (pp. 285-290),Vancouver, Canada. [PDF]

    • Ramamurthy, U., Baars, B., D'Mello, S. K., & Franklin, S. (2006). LIDA: A Working Model of Cognition. Proceedings of the 7th International Conference on Cognitive Modeling. Eds: Danilo Fum, Fabio Del Missier and Andrea Stocco; pp 244-249. Edizioni Goliardiche, Trieste, Italy. [poster] [PDF]

    • D'Mello, S. K., Craig, S. D., Witherspoon A., Sullins J., McDaniel B., Gholson, B., & Graesser, A. C.(2005). The relationship between affective states and dialog patterns during interactions with AutoTutor. (2005). Proceedings of the World Conference on E-learning in Corporate, Government, Health Care, and Higher Education. (pp. 2004-2011). Chesapeake, VA: Association for the Advancement of Computing in Education. [PDF] [Winner of the outstanding paper award)

    • D'Mello S., Ramamurthy U., and Franklin S. (2005). Encoding and Retrieval Efficiency of Episodic Data in a Modified Sparse Distributed Memory System. Proceedings of the 27th Annual Meeting of the Cognitive Science Society (pp. 571-576). Stresa, Italy. [PDF]

    • Ventura, M., D’Mello, S., & Graesser, A. (2005). A Computational Approach to Mental Models. Proceedings of the 27th Annual Meeting of the Cognitive Science Society (pp. 2289-2294). Stresa, Italy. [poster] [PDF]

    • McCauley, L., D'Mello, S., & Daily, S. (2005). Understanding Without Formality: Augmenting speech recognition to understand informal verbal commands. Paper presented at the ACM Southeast Conference, Kennesaw, GA. [PDF]

    • Craig S., D’Mello S., Gholson B., Witherspoon A., Sullins J., and Graesser A, (2004). Emotions during learning: The first steps toward an affect sensitive intelligent tutoring system. E-learn Association for the Advancement of Computing in Education, Norfolk, VA. [PDF]

    • Ramamurthy U., D'Mello S., and Franklin S. (2004). Modified Sparse Distributed Memory as Transient Episodic Memory for Cognitive Software Agents. IEEE International Conference on Systems, Man and Cybernetics - SMC2004 , Netherlands, October 10-13, 2004. [poster] [PDF]

edited books

    • D’Mello, S. K., Scherer, S., Georgiou, P., Worsley, M., Provost, M., & Soleymani, M. (Eds.) (2018). Proceedings of the 20th ACM International Conference on Multimodal Interaction (ICMI 2018). New York, NY:ACM. [LEARN MORE]

    • D'Mello, S. K., Morency, L-P, Valstar, M., & Yin, L. P. (Eds.) (2018). Proceedings of the 13th IEEE Conference on Automatic Face and Gesture Recognition (FG’18). Washington, DC: IEEE. [LEARN MORE]

    • Aroyo, L., D’Mello, S. K., Vassileva, J., & Blustein, J. (Eds. ) (2016). Proceedings of the 24th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2016). New York: ACM. [LEARN MORE]

    • Calvo, R. A., D’Mello, S. K., Gratch, J., & Kappas, A. (Eds.) (2015). The Oxford Handbook of Affective Computing. Oxford University Press: New York, NY. [LEARN MORE]

    • Nijholt, A., D’Mello, S. K., & Pantic, M. (Eds.) (2013). Proceedings of the 5th International Conference on Affective Computing and Intelligent Interaction (ACII 2013). IEEE: Washington, DC. [CONFERENCE WEBSITE]

    • D’Mello, S. K., Calvo, R. A., & Olney, A. (Eds.) (2013). Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013). International Educational Data Mining Society. [DOWNLOAD] [CONFERENCE WEBSITE]

    • D’Mello, S. K. & Graesser, A. C., Schuller, B., & Martin, J. (Eds.) (2011). Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction (ACII 2011) (Part 1: LNCS 6974; Part 2 LNCS 6975), Springer: Berlin Heidelberg. [GET PART 1 PROCEEDINGS] GET PART 2 PROCEEDINGS] [CONFERENCE WEBSITE]

    • Calvo, R. A. & D'Mello, S. K. (Eds.) (2011). New Perspectives on Affect and Learning Technologies. New York:Springer. [GET BOOK]

Book chapters

    • D’Mello, S. K. (2020). Multimodal Analytics for Automated Assessment. In D. Yan, A. A. Rupp, & P. Foltz (Eds.). Handbook of Automated Scoring: Theory into Practice. Chapman & Hall/CRC.

    • D'Mello, S. K. (2020). Big data in psychological research. In S. E. E. Woo, L. E. Tay & R. W. Proctor (Eds.), Big data in psychological research (pp. 203-226). Washington, DC: APA Books. [PDF] PRE PRINT DRAFT

    • D'Mello, S. K. (2019). What do we Think About When we Learn? In K. Millis, J. Magliano, D. Long & K. Wiemer (Eds.), Understanding Deep Learning, Educational Technologies and Deep Learning, and Assessing Deep Learning: Routledge/Taylor and Francis. [PDF] PRE PRINT DRAFT

    • Lane, H. C., & D’Mello, S. K. (2019). Uses of Physiological Monitoring in Intelligent Learning Environments. In T. D. Parsons, D. Cockerham, & L. Lin (Eds.). Mind, Brain and Technology: How People Learn in the Age of Emerging Technologies (pp. 67-86). Springer International Publishing. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. (2019). Gaze-based Attention-aware Cyberlearning Technologies. In T. D. Parsons, D. Cockerham, & L. Lin (Eds.). Mind, Brain and Technology: How People Learn in the Age of Emerging Technologies (pp. 87-106). Springer International Publishing. [PDF] PRE PRINT DRAFT

    • Gorman, J. C., D’Mello, S. K., Stevens, R. H., & Burke, C. S. (2018). Characteristics and Mechanisms of Team Effectiveness in Dynamic Environments. In R. Sottilare, C. G. Arthur, X. Hu & A. M. Sinatra (Eds.), Design Recommendations for Intelligent Tutoring Systems (Vol. 6, pp. 161-168). Orlando, FL: US Army Research Lab. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K., Bosch, N., & Chen, H. (2018). Multimodal, Multisensory Affect Detection. In S. Oviatt, P. Cohen & A. Krueger (Eds.). The Handbook of Multimodal-Multisensor Interfaces. ACM Books/Morgan Claypool. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. (2017). Emotional Learning Analytics. Handbook of Learning Analytics & Educational Data Mining. [PDF] PRE PRINT DRAFT

    • Olney, A. M., Kelly, S., Samei, B., Donnelly, P., & D’Mello, S. K. (2017). Assessing teacher questions in classrooms. In Sottilare et al. (Eds.) Design Recommendations for Intelligent Tutoring Systems: Volume 5- Assessment. Orlando, FL: U.S. Army Research Laboratory. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. (2016). Automated mental state detection for mental healthcare. In D. Luxton (Ed.). Artificial Intelligence in Behavioral and Mental Healthcare (pp. 117-131). Elsevier/Academic Press: San Diego, CA. [PDF] PRE PRINT DRAFT

    • Olney, A., D’Mello, S. K., Risko, E. F., & Graesser, A. C. (2017). Attention & Engagement in Educational Contexts: The Role of Task Demands in Structuring Goals that Guide Attention. In J. Fawcett, E. F. Risko & A. Kingstone (Eds.). The Handbook of Attention. MIT Press: Cambridge, MA. [PDF] PRE PRINT DRAFT

    • Calvo, R. A., D’Mello, S. K., Gratch, J., & Kappas, A. (2015). Introduction: A Guided Tour to the Handbook of Affective Computing. In Calvo, R. A., D’Mello, S. K., Gratch, J., & Kappas, A. (Eds.) The Oxford Handbook of Affective Computing (pp. 1-10). Oxford University Press: New York, NY. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Graesser, A. C. (2015). Feeling, Thinking, and Computing with Affect-Aware Learning Technologies. In Calvo, R. A., D’Mello, S. K., Gratch, J., & Kappas, A. (Eds.) The Oxford Handbook of Affective Computing (pp. 419-434). Oxford University Press: New York, NY. [PDF] PRE PRINT DRAFT

    • Kory, J., & D’Mello, S. K. (2015). Affect Elicitation for Affective Computing. In Calvo, R. A., D’Mello, S. K., Gratch, J., & Kappas, A. (Eds.) The Oxford Handbook of Affective Computing (pp. 371-383). Oxford University Press: New York, NY. [PDF] PRE PRINT DRAFT

    • Hussain, M. S., D’Mello, S. K., & Calvo, R. A. (2015). Research and Development Tools in Affective Computing. In Calvo, R. A., D’Mello, S. K., Gratch, J., & Kappas, A. (Eds.) The Oxford Handbook of Affective Computing (pp. 349-358). Oxford University Press: New York, NY. [PDF] PRE PRINT DRAFT

    • Graesser, A. C., Millis, K., D’Mello, S. K., & Hu, X. (2014). Conversational Agents Can Help Humans Identify Flaws in the Science Reported in Digital Media. In D. Rapp & J. Braasch (Eds.) Processing Inaccurate Information: Theoretical and Applied Perspectives from Cognitive Science and the Educational Sciences (pp. 139-158). MIT Press: Cambridge, MA. [PDF] PRE PRINT DRAFT

    • 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: Volume 2 – Instructional Management (pp. 35-48). U.S. Army Research Laboratory: Orlando, FL. [PDF] PRE PRINT DRAFT

    • DeFalco, J., Baker, R., & D’Mello, S. K. (2014). Addressing Behavioral Disengagement in Online Learning. In R. Sottilare, A. Graesser, X. Hu, & B. Goldberg (Eds.). Design Recommendations for Adaptive Intelligent Tutoring Systems: Volume 2 - Instructional Management (pp. 49-56). U.S. Army Research Laboratory: Orlando, FL. [PDF] PRE PRINT DRAFT

    • Graesser, A. C. & D’Mello, S. K. (2014). Emotions in Advanced Learning Technologies. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education: (pp. 473-493). Routledge: New York, NY. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Graesser, A. C. (2014). Confusion. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 289-310) : Routledge: New York, NY. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. (2013). Affective or Emotional Computing. In A. Runehov & L. Oviedo (Eds.). Encyclopedia of Sciences and Religions (pp. 29-32). Springer: Dordrecht. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Graesser, A. C. (2013). Design of Dialog-Based Intelligent Tutoring Systems to Simulate Human-to-Human Tutoring. In A. Neustein & J. Markowitz (Eds.) Where Humans Meet Machines: Innovative Solutions to Knotty Natural Language Problems (pp. 233-269). Springer Verlag: Heidelberg/New York. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K., Strain, A. C., Olney, A., & Graesser, A. C. (2013). Affect, Meta-affect, and Affect Regulation during Complex Learning. In R. Azevedo and V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies (pp. 669-681). Springer: New York. [PDF] PRE PRINT DRAFT

    • Graesser, A. & D’Mello, S. K. (2012). Emotions during the learning of difficult material. In B. Ross (Ed.), Psychology of Learning and Motivation (Vol. 57) (pp. 183-226). Elsevier. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. (2012). Affect Trajectories during Complex Learning. In N. Seel et al. (eds). Encyclopedia of the Sciences of Learning (p. 792). New York:Springer [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Graesser, A. C. (2012). Emotions during Learning with AutoTutor. In P. Durlach and A. Lesgold (Eds.), Adaptive Technologies for Training and Education (pp. 117-139). Cambridge, U.K.: Cambridge University Press. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Graesser, A. C. (2012). Text-Based Affect Detection in Intelligent Tutors. In P. McCarthy, C. Boonthum-Denecke, & T. Lamkin (Eds.), Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches (pp. 284-304). Hershey, PA: IGI Global. [PDF] PRE PRINT DRAFT

    • Graesser, A. C., D’Mello, S. K., Hu. X., Cai, Z., Olney, A., & Morgan, B. (2012). AutoTutor. In P. McCarthy and C. Boonthum-Denecke (Eds.), Applied Natural Language Processing: Identification, Investigation, and Resolution (pp. 169-187). Hershey, PA: IGI Global. [PDF] PRE PRINT DRAFT

    • Jeuniaux, P. Olney, A. & D'Mello, S. K. (2012). Practical Programming for NLP. In P. McCarthy and C. Boonthum-Denecke (Eds.), Applied Natural Language Processing: Identification, Investigation, and Resolution (pp. 122-156). Hershey, PA: IGI Global. [PDF] PRE PRINT DRAFT

    • Graesser, A. C. & D’Mello, S. K. (2011). Theoretical Perspectives on Affect and Deep Learning. In R. Calvo and S. D’Mello (Eds.). New Perspectives on Affect and Learning Technologies (pp. 11-22). New York: Springer. [PDF] PRE PRINT DRAFT

    • D’Mello, S., Lehman, B. & Graesser, A. C., (2011). A Motivationally Supportive Affect-Sensitive AutoTutor. In R. Calvo and S. D’Mello (Eds.). New Perspectives on Affect and Learning Technologies (pp. 113-126). New York: Springer. [PDF] PRE PRINT DRAFT

    • Calvo, R., and D’Mello, S. K. (2011). Introduction to Affect-Sensitive Learning Technologies. In R. A. Calvo and S. D'Mello (Eds), New Perspectives on Affect and Learning Technologies (pp. 3-10). New York: Springer. [PDF] PRE PRINT DRAFT

    • D’Mello, S. K. & Calvo, R. (2011). Significant Accomplishments, New Challenges, and New Perspectives. In R. A. Calvo and S. D'Mello (Eds), New Perspectives on Affect and Learning Technologies (pp. 255-272). New York: Springer. [PDF] PRE PRINT DRAFT

    • Graesser, A. C., D’Mello, S., Cade, W. (2011). Instruction based on tutoring. In R.E. Mayer and P.A. Alexander (Eds.), Handbook of Research on Learning and Instruction (pp. 408-426). New York Routledge Press. [PDF] PRE PRINT DRAFT

    • Mavrikis, M., D’Mello, S. K., Porayska-Pomsta, K., Cocea, M., and Graesser, A. C. (2011). Modeling Affect by Mining Students Interactions within Learning Environments. In Romero et al. (Eds.), Handbook of Educational Data Mining (pp. 231-244. Boca Raton, FL: CRC Press. [PDF] PRE PRINT DRAFT

    • Graesser, A. C., Lin, D., and D’Mello, S. K. (2010). Computer Learning Environments with Agents that Support Deep Comprehension and Collaborative Reasoning. In M.T. Banich and D. Caccamise (Eds.), Generalization of Knowledge (pp. 201-224). New York: Psychology Press. [PDF] PRE PRINT DRAFT

    • Graesser, A. C., D'Mello, S. K., and Person, N., (2009). Meta-knowledge in tutoring. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 361-412). Mahwah, NJ: Erlbaum. [PDF] PRE PRINT DRAFT

    • Graesser, A. C., Rus., V., D’Mello, S., K., and Jackson, G. T. (2008). AutoTutor: Learning through natural language dialogue that adapts to the cognitive and affective states of the learner. In D. H. Robinson & G. Schraw (Eds.), Current perspectives on cognition, learning and instruction: Recent innovations in educational technology that facilitate student learning (pp. 95–125). Information Age Publishing. [PDF] PRE PRINT DRAFT

    • McCauley, L., D’Mello, S. K., Kim, L., and Polkosky, M. (i2009). MIKI: A Case Study of an Intelligent Kiosk and Its Usability. In N. Magnenat-Thalmann et al. (Eds.): New Advances in Virtual Humans.: Arti. Intel., SCI 140 (pp. 153-176). Springer-Verlag Berlin Heidelberg. [PDF] PRE PRINT DRAFT

    • Person, N., D'Mello, S. K., and Olney, A. (2008). Toward Socially Intelligent Interviewing Systems. In M. Schober & F. Conrad (Eds.), Envisioning the Survey Interview of the Future (pp. 195-204), New York: Wiley. [PDF] PRE PRINT DRAFT

    • Negatu, A., D'Mello, S. K., and Franklin, S. (2007). Cognitively Inspired Anticipatory Adaptation and Associated Learning Mechanisms for Autonomous Agents. In Butz et al. (Eds.) Anticipatory Behavior in Adaptive Learning Systems (pp. 108-127), Berlin: Springer-Verlag. [PDF] PRE PRINT DRAFT

Editorials and Commentary

  • D’Mello, S. K. (2021). How to Unleash the Power of Collaborative Learning, Education Week (blog post).

  • Kelly, S., & D’Mello, S. K. (2017). Using Questions That 'Position Students as Meaning-Makers (blog post for Education Week)

  • Calvo, R. A., Peters, D., & D’Mello, S. K. (2015). When technologies manipulate our emotions - Implications of the Facebook emotions study, Communications of the ACM, 58 (11), 41-42.(peer-reviewed commentary). [PDF]

  • D’Mello, S. K., Pantic, M., & Nijholt, A. (2015). Introduction to the “Best of ACII 2013” Special Issue. IEEE Transactions on Affective Computing,6(2), 84-85 (editorial – not peer reviewed). [PDF] [Link to special issue]

Workshop papers (peer-reviewed)

    • Stewart, A.E.B, D'Mello, S.K. (2019). Towards Automated Real-Time Interventions to Improve Computer-Mediated Collaborative Problem Solving. Workshop on Mapping the "How" of Collaborative Action at the 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW'19). [PDF]

    • D’Mello, S. K., Stewart, A., Amon, M., Sun, C., Duran N., & Shute, V. (2019). Towards Dynamic Intelligent Support for Collaborative Problem Solving. Approaches and Challenges in Team Tutoring Workshop at the 20th International Conference on Artificial Intelligence in Education (AIED'19). [PDF]

    • Bixler, R., Kopp, K., & D’Mello, S. K. (2014). Evaluation of a Personalized Method for Proactive Mind Wandering Reduction. In M. Kravcik, O.C. Santos, J.G. Boticario (Eds.) Proceedings of the 4th International Workshop on Personalization Approaches in Learning Environments (PALE), held in conjunction with the 22nd International Conference on User Modeling, Adaptation, and Personalization (UMAP 2014), vol. 1181, pages 33-41. CEUR Workshop Proceedings. [PDF]

    • Bosch. N., & D’Mello, S. K. (2014). Co-occurring Affective States in Automated Computer Programming Education. In E. Walker, C-K. Looi (Eds.). Proceedings of the Workshop on AI-supported Education for Computer Science at the 12th International Conference on Intelligent Tutoring Systems (AIEDws 2014). [PDF]

    • Bosch. N., & D’Mello, S. K. (2013). Sequential Patterns of Affective States of Novice Programmers. In E. Walker, C-K. Looi (Eds.). Proceedings of the Workshop on AI-supported Education for Computer Science (AIEDCS) at the 16th International Conference on Artificial Intelligence in Education (AIEDws 2013). [PDF]

    • D’Mello, S. K., Jackson, G. T., Craig, S. D., Morgan, B., Chipman, P., White, H., Person, N., Kort, B., el Kaliouby, R., Picard, R., and Graesser, A. C. (2008). AutoTutor Detects and Responds to Learners Affective and Cognitive States. Workshop on Emotional and Cognitive issues in ITS held in conjunction with Ninth International Conference on Intelligent Tutoring Systems. [PDF]

    • Lehman, B., D'Mello, S. K., and Person, N. (2008). All Alone with your Emotions: An Analysis of Student Emotions during Effortful Problem Solving Activities. Workshop on Emotional and Cognitive issues in ITS held in conjunction with Ninth International Conference on Intelligent Tutoring Systems. [PDF]

    • Graesser, A. C., Millis, K., Chipman, P., Cai, Z., Wallace, P., Britt, A., Storey, J., Wiemer, K., Magliano, J., and D'Mello, S. (2008). A Demonstration of ITSs That Promote Scientific Inquiry Skills: Critical Thinking Tutor and ARIES, Demo Session at Ninth International Conference on Intelligent Tutoring Systems. [PDF]

    • D'Mello, S. K., King, B., Stolarski, M., Chipman, P., & Graesser, A. (2007). The Effects of Speech Recognition Errors on Learner’s Contributions Knowledge, Emotions, and Interaction Experience. Workshop on Speech and Language Technology in Education (SlaTE2007). [PDF]

    • Franklin, S., Ramamurthy, U., D’Mello, S. K., McCauley, T. L., Negatu, A., Silva, R. L., and Datla, V. (2007). LIDA: A Computational Model of Global Workspace Theory and Developmental Learning. AAAI Fall Symposium on AI and Consciousness: Theoretical Foundations and Current Approaches. [PDF]

    • D’Mello, S. K. and Franklin, S. (2007). Exploring the Complex Interplay between AI and Consciousness. AAAI Fall Symposium on AI and Consciousness: Theoretical Foundations and Current Approaches. [PDF]

    • Graesser, A. C., D’Mello, S. K., Chipman, P., King, B., and McDaniel, B. (2007). Exploring Relationships Between Affect and Learning with AutoTutor. Supplementary Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED 2007), (pp 16-23). [PDF]

    • Negatu, A., D'Mello, S. K., and Franklin, S. 2006. Cognitively Inspired Anticipation and Anticipatory Learning Mechanisms for Autonomous Agents. Proceedings of the Third Workshop on Anticipatory Behavior in Adaptive Learning Systems (ABiALS 2006), ed. M. V. Butz, O. Sigaud, G. Pezzulo, and G. Baldassarre (pp 97-108). Rome. Italy. [PDF]

    • D'Mello, S. K., Ramamurthy, U., Negatu, A., & Franklin, S. (2006). A Procedural Learning Mechanism for Novel Skill Acquisition. Proceeding of Workshop on Motor Development, part of AISB'06: Adaptation in Artificial and Biological Systems. Eds: Tim Kovacs and James A. R. Marshall) Vol 1, pp. 184-185. (Published by the Society for the Study of Artificial Intelligence and the Simulation of Behaviour, Bristol, UK). [PDF]

    • D'Mello, S. K., Franklin, S., Ramamurthy, U., & Baars, B. J. (2006). A Cognitive Science Based Machine Learning Architecture. AAAI Spring Symposia Technical Series, Stanford CA, USA. Technical Report SS-06-02 (pp. 40-45). AAAI Press. [PDF]

    • D’Mello, S. K., McCauley L. T., & Markham J. (2005). A Mechanism for Human - Robot Interaction through Informal Voice Commands. IEEE International Workshop on Robot and Human Interactive Communication (RO-MAN), Nashville, TN, August 13-15, 2005. [PDF]

    • D'Mello, S. K., Craig, S. D., Gholson, B., Franklin, S., Picard, R.,& Graesser, A. C.(2005). Integrating affect sensors in an intelligent tutoring system. In Affective Interactions: The Computer in the Affective Loop Workshop at 2005 International conference on Intelligent User Interfaces (pp. 7-13) New York: AMC Press. [PDF]