“Towards an understanding of affect and knowledge from student interaction with an intelligent tutoring system,”

Flow: the psychology of optimal experience

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Optimal Experience

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Knowledge tracing: Modeling the acquisition of procedural knowledge

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Optimal experience: Psychological studies of flow in consciousness.

Csíkszentmihályi, M., & Csikszentmihalyi, I. S. (1988) · Man · Cited by 1,699 · see citing works

Boredom in achievement settings: Exploring control–value antecedents and performance outcomes of a neglected emotion.

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Student engagement in high school classrooms from the perspective of flow theory

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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

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An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion

Kort, B. W., Reilly, R., & Picard, R. W. (2002) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2001.943850 · Cited by 751 · see citing works

Handbook of Positive Psychology in Schools

Gilman, R. (2009) · Routledge eBooks · doi:10.4324/9780203884089 · Cited by 749 · see citing works

Intelligent Tutoring Systems

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Handbook of Positive Psychology in Schools

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Children who do well in school: Individual differences in perceived competence and autonomy in above-average children.

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Affect and learning: An exploratory look into the role of affect in learning with AutoTutor

Craig, S., Graesser, A., Sullins, J., & Gholson, B. (2004) · Journal of Educational Media · doi:10.1080/1358165042000283101 · Cited by 630 · see citing works

Empirically Building and Evaluating a Probabilistic Model of User Affect

Conati, C., & Maclaren, H. (2009) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-009-9062-8 · Cited by 328 · see citing works

Automatic detection of learner’s affect from conversational cues

D’Mello, S. K., Craig, S. D., Witherspoon, A. M., McDaniel, B., & Graesser, A. C. (2007) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-007-9037-6 · Cited by 317 · see citing works

User Modeling, Adaptation and Personalization

Ricci⋆, F., Bontcheva, K., Conlan, O., & Lawless, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20267-9 · Cited by 262 · see citing works

Academic boredom in under- and over-challenging situations

Acee, T. W., Kim, H., Kim, H. J., Kim, J.-I., Chu, H.-N. R., Kim, M., Cho, Y., & Wicker, F. W. (2010) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2009.08.002 · Cited by 237 · see citing works

Affective states and state tests

Pardos, Z. A., Baker, R. S., Pedro, M. O. C. Z. S., Gowda, S. M., & Gowda, S. M. (2013) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2460296.2460320 · Cited by 160 · see citing works

Flow experiences in information technology use

Pilke, E. M. (2004) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2004.01.004 · Cited by 159 · see citing works

Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra.

Baker, R. S., Gowda, S. M., Wixon, M., Kalka, J., Wagner, A. Z., Salvi, A., Aleven, V., Kusbit, G. W., Ocumpaugh, J. L., & Rossi, L. M. (2012) · Cited by 154 · see citing works

Flow in Schools: Cultivating Engaged Learners and Optimal Learning Environments

(2009) · Routledge eBooks · doi:10.4324/9780203884089-20 · Cited by 148 · see citing works

Coarse-grained detection of student frustration in an introductory programming course

Rodrigo, M. M. T., & Baker, R. S. (2009) · doi:10.1145/1584322.1584332 · Cited by 129 · see citing works

Modeling Learner Affect with Theoretically Grounded Dynamic Bayesian Networks

Sabourin, J., Mott, B. W., & Lester, J. C. (2011) · Affective Computing and Intelligent Interaction · doi:10.1007/978-3-642-24600-5_32 · Cited by 121 · see citing works

Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor

Baker, R. S., Corbett, A. T., Gowda, S. M., Wagner, A. Z., MacLaren, B. A., Kauffman, L. R., Mitchell, A. P., & Giguere, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13470-8_7 · Cited by 119 · see citing works

Affective and behavioral predictors of novice programmer achievement

Rodrigo, M. M. T., Baker, R. S., Jadud, M. C., Amarra, A. C. M., Dy, T. T., Espejo-Lahoz, M. B. V., Lim, S. A. L., Pascua, S. A. M. S., Sugay, J. O., & Tabanao, E. S. (2009) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/1562877.1562929 · Cited by 101 · see citing works

Viewing Student Affect and Learning through Classroom Observation and Physical Sensors

Dragon, T., Arroyo, I., Woolf, B. P., Burleson, W., el Kaliouby, R., & Eydgahi, H. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69132-7_8 · Cited by 81 · see citing works

Affective and behavioral predictors of novice programmer achievement

Rodrigo, M. M. T., Baker, R. S., Jadud, M. C., Amarra, A. C. M., Dy, T. T., Espejo-Lahoz, M. B. V., Lim, S. A. L., Pascua, S. A. M. S., Sugay, J. O., & Tabanao, E. S. (2009) · ACM SIGCSE Bulletin · doi:10.1145/1595496.1562929 · Cited by 79 · see citing works

Proteus: A Lecturer-Friendly Adaptive Tutoring System.

Sessink, O. D. T., Beeftink, H. H., TRAMPER, J., & Hårtog, R. J. M. (2007) · Socio-Environmental Systems Modeling · Cited by 17 · see citing works

Blending Assessment and Instructional Assisting

Razzaq, L. M., Feng, M., Nuzzo-Jones, G., et al. (2005) · Cited by 16 · see citing works

The FaCT System: A New Tool Linking Cognitive Science with Educators

Pavlik, P. I., Presson, N., Dozzi, G., et al. (2007) · eScholarship (California Digital Library) · Cited by 6 · see citing works

Optimizing Paired-Associate Learning

Pavlik, P. I., & Anderson, J. R. (2004) · PsycEXTRA Dataset · doi:10.1037/e537052012-558 · Cited by 1 · see citing works

The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching

Heffernan, N. T., & Heffernan, C. L. (2014) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-014-0024-x · Cited by 616 · see citing works

Out of the laboratory and into the classroom: the future of artificial intelligence in education

Schiff, D. (2021) · AI & SOCIETY · doi:10.1007/s00146-020-01033-8 · Cited by 334 · see citing works

Proactive and reactive engagement of artificial intelligence methods for education: a review

Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works

The Many Faces of Boredom: A Negative Emotion That Is So Common, It Is Simply Accepted, or Brushed Off

Macklem, G. L. (2014) · SpringerBriefs in psychology · doi:10.1007/978-3-319-13120-7_3 · Cited by 1 · see citing works

A Boring Overview

Mills, C., & McGrath, C. (2020) · Frontiers for Young Minds · doi:10.3389/frym.2020.558229 · Cited by 3 · see citing works

“Like water after days in the desert”

Elpidorou, A. (2020) · doi:10.1093/oso/9780190912963.003.0005

Teacher Related Causes of Boredom

Nkwogu, F. U., & Ubah, J. N. (2019) · doi:10.7176/jep/10-3-03

Causes of boredom: The person, the situation, or both?

Mercer-Lynn, K. B., Bar, R. J., & Eastwood, J. D. (2013) · Personality and Individual Differences · doi:10.1016/j.paid.2013.08.034 · Cited by 169 · see citing works