Ryan Baker

Research Interests

Learning analytics Learning Sciences Learning, motivation and emotion Machine learning

Prof Ryan Baker

Professor of AI in Education

School of Education

College of Education, Behavioural and Social Sciences

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

Additional webpage with list of publications, past courses taught, and resourceshttps://learninganalytics.upenn.edu/ryanbaker/index.html

Date Position Institution name
2025 - ongoing Professor of Artificial Intelligence and Education University of South Australia
2025 - ongoing Adjunct Professor University of Pennsylvania

Date Institution name Country Title
2000 - 2005 Carnegie Mellon University United States PhD
2000 - 2005 Carnegie Mellon University United States MS
1996 - 2000 Brown University United States ScB

Date Title Institution Country
2016 - 2016 Innovation and Entrepreneurship Columbia University United States

Year Citation
2026 Zambrano, A. F., Wei, Z., Ocumpaugh, J., Barany, A., Baker, R. S., Liu, X., . . . Ginger, J. (2026). Exploring Player Archetypes in a Minecraft-Based Learning Environment. International Journal of Serious Games, 13(1), 19-40.
DOI Scopus1 WoS1
2025 Zambrano, A. F., Singhal, S., Pankiewicz, M., Baker, R. S., Porter, C., & Liu, X. (2025). De-identifying student personally identifying information in discussion forum posts with large language models. Information and Learning Science, 126(5-6), 401-424.
DOI Scopus2
2025 Borchers, C., Zhang, J., Fleischer, H., Schanze, S., Aleven, V., & Baker, R. S. (2025). Large Language Models Generalize SRL Prediction to New Languages Within But Not Between Domains. Journal of Educational Data Mining, 17(2), 24-54.
DOI Scopus1
2025 Baker, R. S., Richey, J. E., Zhang, J., Karumbaiah, S., Andres-Bray, J. M., Nguyen, H. A., . . . McLaren, B. M. (2025). Gaming the system mediates the relationship between gender and learning outcomes in a digital learning game. Instructional Science, 53(2), 203-238.
DOI Scopus7
2025 Liu, X., Zambrano, A. F., Baker, R. S., Barany, A., Ocumpaugh, J., Zhang, J., . . . Wei, Z. (2025). Qualitative Coding with GPT-4: where it Works Better. Journal Of Learning Analytics, 12(1), 169-185.
DOI Scopus16
2025 Baker, R. S., Cloude, E., Andres, J. M. A. L., & Wei, Z. (2025). Confrustion Constellation: a New Way of Looking at Confusion and Frustration. Cognitive Science, 49(1, article no. e70035), 1-30.
DOI Scopus9
2025 Kaliisa, R., Baker, R. S., Wasson, B., & Prinsloo, P. (2025). Coming but Uneven Storm. Journal of Learning Analytics, 12(2), 1-18.
DOI
2025 Barany, A., Baker, R. S., Katz, A., & Lin, J. (2025). LAK 25 Workshop: LLMs for Qual Research (LLM-QUAL). Ceur Workshop Proceedings, 3995, 177-178.
2024 Gouveia, C., Baker, R. S., Wimer, S., Granville, P., & Babcock, B. (2024). Predictive Validity of the HESI Radiography Exit Exam and Best Practices for ARRT Certification Exam Success. Radiologic Technology, 95(6), 402-414.
2024 Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024). Cultural bias and cultural alignment of large language models. Pnas Nexus, 3(9, article no. 346), 1-9.
DOI Scopus178
2024 Ocumpaugh, J., Roscoe, R. D., Baker, R. S., Hutt, S., & Aguilar, S. J. (2024). Toward Asset-based Instruction and Assessment in Artificial Intelligence in Education. International Journal Of Artificial Intelligence In Education, 34(4), 1559-1598.
DOI Scopus38
2024 Baker, R. (2024). Foreword. Learning Analytics Methods and Tutorials A Practical Guide Using R, vii.
2024 Baker, R. S., Hutt, S., Bosch, N., Ocumpaugh, J., Biswas, G., Paquette, L., . . . Munshi, A. (2024). Detector-driven classroom interviewing: focusing qualitative researcher time by selecting cases in situ. Educational Technology Research and Development, 72(5), 2841-2863.
DOI Scopus6
2024 Hutt, S., Wong, A., Papoutsaki, A., Baker, R. S., Gold, J. I., & Mills, C. (2024). Webcam-based eye tracking to detect mind wandering and comprehension errors. Behavior Research Methods, 56(1), 1-17.
DOI Scopus49 Europe PMC10
2023 Munshi, A., Biswas, G., Baker, R., Ocumpaugh, J., Hutt, S., & Paquette, L. (2023). Analysing adaptive scaffolds that help students develop self-regulated learning behaviours. Journal of Computer Assisted Learning, 39(2), 351-368.
DOI Scopus65
2023 Belitz, C., Ocumpaugh, J., Ritter, S., Baker, R. S., Fancsali, S. E., & Bosch, N. (2023). Constructing categories: Moving beyond protected classes in algorithmic fairness. Journal of the Association for Information Science and Technology, 74(6), 663-668.
DOI Scopus12
2023 Nicolay, B., Krieger, F., Kuhn, J. T., Graesser, A. C., Ifenthaler, D., Baker, R., & Greiff, S. (2023). Unsuccessful and successful complex problem solvers – A log file analysis of complex problem solving strategies across multiple tasks. Intelligence, 101, 101793.
DOI Scopus8
2023 Baker, R. S. (2023). AI and self-regulated learning theory: What could be on the horizon?. Computers in Human Behavior, 147, 107849.
DOI Scopus1
2023 Ruiperez-Valiente, J. A., Kim, Y. J., Baker, R. S., Martinez, P. A., & Lin, G. C. (2023). The Affordances of Multivariate Elo-Based Learner Modeling in Game-Based Assessment. IEEE Transactions on Learning Technologies, 16(2), 152-165.
DOI Scopus10 WoS9
2023 Zhang, Y., Paquette, L., Baker, R. S., Bosch, N., Ocumpaugh, J., & Biswas, G. (2023). How are feelings of difficulty and familiarity linked to learning behaviors and gains in a complex science learning task?. European Journal Of Psychology Of Education, 38(2), 777-800.
DOI Scopus1
2023 Scruggs, R., Baker, R. S., Pavlik Jr, P. I., McLaren, B. M., & Liu, Z. (2023). How well do contemporary knowledge tracing algorithms predict the knowledge carried out of a digital learning game?. Etrandd-Educational Technology Research And Development, 71(3), 901-918.
DOI Scopus7
2023 Riley, T. A., Gouveia, C., Baker, R. S., Ruiz, K., & San Pedro, M. O. Z. (2023). Supporting student success on the practical nurse (PN) licensure exam: The Health Education Systems Incorporated (HESI) PN Exit Exam Study. Nurse Education Today, 121, 105669.
DOI Scopus4 Europe PMC2
2023 Baker, R. S., Esbenshade, L., Vitale, J., & Karumbaiah, S. (2023). Using Demographic Data as Predictor Variables: a Questionable Choice. Journal of Educational Data Mining, 15(2), 22-52.
DOI Scopus32
2023 Karumbaiah, S., Baker, R. S., Ocumpaugh, J., & Andres, J. M. A. L. (2023). A Re-Analysis and Synthesis of Data on Affect Dynamics in Learning. IEEE Transactions on Affective Computing, 14(2), 1696-1710.
DOI Scopus13
2022 Baker, R. S., Nasiar, N., Gong, W., & Porter, C. (2022). The impacts of learning analytics and A/B testing research: a case study in differential scientometrics. International Journal of Stem Education, 9(1).
DOI Scopus3
2022 Rebolledo-Mendez, G., Huerta-Pacheco, N. S., Baker, R. S., & du Boulay, B. (2022). Meta-Affective Behaviour within an Intelligent Tutoring System for Mathematics. International Journal of Artificial Intelligence in Education, 32(1), 174-195.
DOI Scopus32
2022 Hutt, S., Baker, R. S., Ashenafi, M. M., Andres Bray, J. M., & Brooks, C. (2022). Controlled outputs, full data: a privacy-protecting infrastructure for MOOC data. British Journal Of Educational Technology, 53(4), 756-775.
DOI Scopus19
2022 Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal Of Artificial Intelligence In Education, 32(4), 1052-1092.
DOI Scopus457
2022 Karumbaiah, S., Ocumpaugh, J., & Baker, R. S. (2022). Context Matters: Differing Implications of Motivation and Help-Seeking in Educational Technology. International Journal of Artificial Intelligence in Education, 32(3), 685-724.
DOI Scopus18
2022 Zhang, J., Baker, R. S., Mills, C., Young, T., Ma, J., Andres, A. L., . . . Sethuaman, S. (2022). Using Machine Learning to Detect SMART Model Cognitive Operations in Mathematical Problem-Solving Process. Journal of Educational Data Mining, 14(3), 76-108.
DOI Scopus19
2022 Rahimi, S., Shute, V. J., Fulwider, C., Bainbridge, K., Kuba, R., Yang, X., . . . D'Mello, S. K. (2022). Timing of learning supports in educational games can impact students’ outcomes. Computers and Education, 190, 104600.
DOI Scopus26
2022 Bainbridge, K., Shute, V., Rahimi, S., Liu, Z., Slater, S., Baker, R. S., & D'Mello, S. K. (2022). Does embedding learning supports enhance transfer during game-based learning?. Learning and Instruction, 77, 101547.
DOI Scopus24
2022 Zhang, Y., Paquette, L., Bosch, N., Ocumpaugh, J., Biswas, G., Hutt, S., & Baker, R. S. (2022). The evolution of metacognitive strategy use in an open-ended learning environment: Do prior domain knowledge and motivation play a role?. Contemporary Educational Psychology, 69, 102064.
DOI Scopus14
2022 Shah, M., Fuller, B., Gouveia, C., Mee, C. L., Baker, R. S., & San Pedro, M. O. Z. (2022). NCLEX-RN readiness: HESI Exit Exam validity and nursing program policies. Journal of Professional Nursing, 39, 131-138.
DOI Scopus22 Europe PMC7
2022 Kang, J., Baker, R., Feng, Z., Na, C., Granville, P., & Feldon, D. F. (2022). Detecting threshold concepts through Bayesian knowledge tracing: examining research skill development in biological sciences at the doctoral level. Instructional Science, 50(3), 475-497.
DOI Scopus6
2022 Nawaz, S., Srivastava, N., Yu, J. H., Khan, A. A., Kennedy, G., Bailey, J., & Baker, R. S. (2022). How Difficult is the Task for you? Modelling and Analysis of Students' Task Difficulty Sequences in a Simulation-Based POE Environment. International Journal of Artificial Intelligence in Education, 32(2), 233-262.
DOI Scopus13
2021 Rowe, E., Almeda, M. V., Asbell-Clarke, J., Scruggs, R., Baker, R., Bardar, E., & Gasca, S. (2021). Assessing implicit computational thinking in Zoombinis puzzle gameplay. Computers in Human Behavior, 120, 106707.
DOI Scopus68
2021 Asbell-Clarke, J., Rowe, E., Almeda, V., Edwards, T., Bardar, E., Gasca, S., . . . Scruggs, R. (2021). The development of students’ computational thinking practices in elementary- and middle-school classes using the learning game, Zoombinis. Computers in Human Behavior, 115, 106587.
DOI Scopus72
2021 Molenaar, I., Horvers, A., & Baker, R. S. (2021). What can moment-by-moment learning curves tell about students’ self-regulated learning?. Learning and Instruction, 72, 101206.
DOI Scopus60
2021 Baker, R. S., Gaševic, D., & Karumbaiah, S. (2021). Four paradigms in learning analytics: why paradigm convergence matters. Computers and Education Artificial Intelligence, 2(100021), 1-9.
DOI Scopus31
2021 Zhang, Y., Paquette, L., Baker, R. S., Ocumpaugh, J., Bosch, N., Biswas, G., & Munshi, A. (2021). Can strategic behaviour facilitate confusion resolution? The interplay between confusion and metacognitive strategies in Betty's brain. Journal of Learning Analytics, 8(3), 28-44.
DOI Scopus10
2021 Baker, R. S., Boser, U., & Snow, E. L. (2021). Learning engineering: a view on where the field is at, where it's going, and the research needed. Technology, Mind, And Behavior, 3(1), 1-23.
DOI Scopus11
2021 Godwin, K. E., Seltman, H., Almeda, M., Davis Skerbetz, M., Kai, S., Baker, R. S., & Fisher, A. V. (2021). The elusive relationship between time on-task and learning: not simply an issue of measurement. Educational Psychology, 41(4), 502-519.
DOI Scopus45
2021 Mojarad, S., Baker, R. S., Essa, A., & Stalzer, S. (2021). Replicating Studying Adaptive Learning Efficacy using Propensity Score Matching and Inverse Probability of Treatment Weighting. Journal of Interactive Learning Research, 32(3), 169-203.
Scopus2
2020 Baker, R. S., Berning, A. W., Gowda, S. M., Zhang, S., & Hawn, A. (2020). Predicting K-12 Dropout. Journal of Education for Students Placed at Risk, 25(1), 28-54.
DOI Scopus21
2020 Paquette, L., Ocumpaugh, J., Li, Z., Andres, A., & Baker, R. (2020). Who's learning? Using demographics in EDM research. Journal of Educational Data Mining, 12(3), 1-30.
DOI Scopus60
2020 Crossley, S. A., Karumbaiah, S., Ocumpaugh, J., Labrum, M. J., & Baker, R. S. (2020). Predicting math identity through language and click-stream patterns in a blended learning mathematics program for elementary students. Journal of Learning Analytics, 7(1), 19-37.
DOI Scopus20
2020 Almeda, M. V., & Baker, R. S. (2020). Predicting student participation in STEM careers: The role of affect and engagement during middle school. Journal of Educational Data Mining, 12(2), 33-47.
DOI Scopus43
2020 Owen, V. E., & Baker, R. S. (2020). Fueling Prediction of Player Decisions: Foundations of Feature Engineering for Optimized Behavior Modeling in Serious Games. Technology Knowledge and Learning, 25(2), 225-250.
DOI Scopus22
2020 Patikorn, T., Baker, R. S., & Heffernan, N. T. (2020). ASSISTments longitudinal data mining competition special issue: A preface. Journal of Educational Data Mining, 12(2), I-XI.
DOI Scopus11
2020 Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., . . . Warschauer, M. (2020). Mining Big Data in Education: Affordances and Challenges. Review of Research in Education, 44(1), 130-160.
DOI Scopus359
2019 Richey, J. E., Andres-Bray, J. M. L., Mogessie, M., Scruggs, R., Andres, J. M. A. L., Star, J. R., . . . McLaren, B. M. (2019). More confusion and frustration, better learning: The impact of erroneous examples. Computers and Education, 139, 173-190.
DOI Scopus73
2019 Slater, S., & Baker, R. (2019). Forecasting future student mastery. Distance Education, 40(3), 380-394.
DOI Scopus11
2019 Paquette, L., & Baker, R. S. (2019). Comparing machine learning to knowledge engineering for student behavior modeling: a case study in gaming the system. Interactive Learning Environments, 27(5-6), 585-597.
DOI Scopus51
2019 Baker, R. S. (2019). Challenges for the Future of Educational Data Mining: The Baker Learning Analytics Prizes. Journal of Educational Data Mining, 11(1), 1-17.
DOI Scopus111
2018 Wang, Y., & Baker, R. (2018). Grit and intention: Why do learners complete MOOCs?. International Review of Research in Open and Distributed Learning, 19(3), 20-42.
DOI Scopus66
2018 Reid, J. R., & Baker, R. S. (2018). Designing and testing an educational innovation. Pediatric Radiology, 48(10), 1406-1409.
DOI Scopus7 Europe PMC3
2018 Slater, S., & Baker, R. S. (2018). Degree of error in Bayesian knowledge tracing estimates from differences in sample sizes. Behaviormetrika, 45(2), 475-493.
DOI Scopus14
2018 Baker, R., Wang, F., Ma, Z., Ma, W., & Zheng, S. (2018). Studying the effectiveness of an online language learning platform in China. Journal of Interactive Learning Research, 29(1), 5-24.
Scopus12
2018 Salmeron-Majadas, S., Baker, R. S., Santos, O. C., & Boticario, J. G. (2018). A Machine Learning Approach to Leverage Individual Keyboard and Mouse Interaction Behavior from Multiple Users in Real-World Learning Scenarios. IEEE Access, 6, 39154-39179.
DOI Scopus33
2018 Sottilare, R. A., Baker, R. S., Graesser, A. C., & Lester, J. C. (2018). Special Issue on the Generalized Intelligent Framework for Tutoring (GIFT): Creating a Stable and Flexible Platform for Innovations in AIED Research. International Journal of Artificial Intelligence in Education, 28(2), 139-151.
DOI Scopus26
2018 DeFalco, J. A., Rowe, J. P., Paquette, L., Georgoulas-Sherry, V., Brawner, K., Mott, B. W., . . . Lester, J. C. (2018). Detecting and Addressing Frustration in a Serious Game for Military Training. International Journal of Artificial Intelligence in Education, 28(2), 152-193.
DOI Scopus105
2018 Jiang, Y., Clarke-Midura, J., Keller, B., Baker, R. S., Paquette, L., & Ocumpaugh, J. (2018). Note-taking and science inquiry in an open-ended learning environment. Contemporary Educational Psychology, 55, 12-29.
DOI Scopus18
2018 Almeda, M. V., Zuech, J., Utz, C., Higgins, G., Reynolds, R., & Baker, R. S. (2018). Comparing the factors that predict completion and grades among for-credit and open/mooc students in online learning. Online Learning Journal, 22(1), 1-18.
DOI Scopus26
2017 Rowe, E., Asbell-Clarke, J., Baker, R. S., Eagle, M., Hicks, A. G., Barnes, T. M., . . . Edwards, T. (2017). Assessing implicit science learning in digital games. Computers in Human Behavior, 76, 617-630.
DOI Scopus56
2017 André, E., Baker, R., Hu, X., Rodrigo, M. M. T., & du Boulay, B. (2017). Preface. Lecture Notes in Computer Science, 10331 LNAI, V-VI.
2017 San Pedro, M. O. Z., Baker, R. S., & Heffernan, N. T. (2017). An Integrated Look at Middle School Engagement and Learning in Digital Environments as Precursors to College Attendance. Technology Knowledge and Learning, 22(3), 243-270.
DOI Scopus21
2017 Almeda, V., Baker, R., & Corbett, A. (2017). Help avoidance: When students should seek help, and the consequences of failing to do so. Teachers College Record, 119(3).
Scopus12
2017 Slater, S., Joksimovic, S., Kovanovic, V., Baker, R. S., & Gasevic, D. (2017). Tools for educational data mining: a review. Journal of educational and behavioral statistics, 42(1), 85-106.
DOI Scopus180 WoS114
2016 Baker, R. S. (2016). Stupid Tutoring Systems, Intelligent Humans. International Journal of Artificial Intelligence in Education, 26(2), 600-614.
DOI Scopus309
2016 Ocumpaugh, J., Pedro, M. O. S., Lai, H. Y., Baker, R. S., & Borgen, F. (2016). Erratum to: Middle School Engagement with Mathematics Software and Later Interest and Self-Efficacy for STEM Careers (Journal of Science Education and Technology, (2016), 25, 6, (877-887), 10.1007/s10956-016-9637-1). Journal of Science Education and Technology, 25(6), 888.
DOI
2016 Bosch, N., D'Mello, S. K., Ocumpaugh, J., Baker, R. S., & Shute, V. (2016). Using video to automatically detect learner affect in computer-enabled classrooms. ACM Transactions on Interactive Intelligent Systems, 6(2), 1-26.
DOI Scopus101
2016 Baker, R. S., Clarke-Midura, J., & Ocumpaugh, J. (2016). Towards general models of effective science inquiry in virtual performance assessments. Journal of Computer Assisted Learning, 32(3), 267-280.
DOI Scopus56
2016 Ocumpaugh, J., San Pedro, M. O., Lai, H. Y., Baker, R. S., & Borgen, F. (2016). Middle School Engagement with Mathematics Software and Later Interest and Self-Efficacy for STEM Careers. Journal of Science Education and Technology, 25(6), 877-887.
DOI Scopus25
2016 Godwin, K. E., Almeda, M. V., Seltman, H., Kai, S., Skerbetz, M. D., Baker, R. S., & Fisher, A. V. (2016). Off-task behavior in elementary school children. Learning and Instruction, 44, 128-143.
DOI Scopus97
2015 Ogan, A., Walker, E., Baker, R., Rodrigo, M. M. T., Soriano, J. C., & Castro, M. J. (2015). Towards understanding how to assess help-seeking behavior across cultures. International Journal of Artificial Intelligence in Education, 25(2), 229-248.
DOI Scopus52
2015 Shute, V. J., D'Mello, S., Baker, R., Cho, K., Bosch, N., Ocumpaugh, J., . . . Almeda, V. (2015). Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Computers and Education, 86, 224-235.
DOI Scopus100
2015 Mulqueeny, K., Kostyuk, V., Baker, R. S., & Ocumpaugh, J. (2015). Incorporating effective e-learning principles to improve student engagement in middle-school mathematics. International Journal of Stem Education, 2(1).
DOI Scopus35
2015 Gobert, J. D., Baker, R. S., & Wixon, M. B. (2015). Operationalizing and Detecting Disengagement Within Online Science Microworlds. Educational Psychologist, 50(1), 43-57.
DOI Scopus98
2014 Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology Knowledge and Learning, 19(1-2), 205-220.
DOI Scopus207
2014 San Pedro, M. O. Z., Baker, R. S. J. D., & Rodrigo, M. M. T. (2014). Carelessness and affect in an intelligent tutoring system for mathematics. International Journal of Artificial Intelligence in Education, 24(2), 189-210.
DOI Scopus26
2014 Baker, R. S. (2014). Educational data mining: An advance for intelligent systems in education. IEEE Intelligent Systems, 29(3), 78-82.
DOI Scopus106
2014 Roll, I., Baker, R. S. J. D., Aleven, V., & Koedinger, K. R. (2014). On the Benefits of Seeking (and Avoiding) Help in Online Problem-Solving Environments. Journal of the Learning Sciences, 23(4), 537-560.
DOI Scopus102
2014 Miller, W. L., Baker, R. S., & Rossi, L. M. (2014). Unifying computer-based assessment across conceptual instruction, problem-solving, and digital games. Technology Knowledge and Learning, 19(1-2), 165-181.
DOI Scopus4
2014 Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., & Heffernan, C. (2014). Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45(3), 487-501.
DOI Scopus128
2013 Porayska-Pomsta, K., Mavrikis, M., D'Mello, S., Conati, C., & Baker, R. S. J. D. (2013). Knowledge elicitation methods for affect modelling in education. International Journal of Artificial Intelligence in Education, 22(3), 107-140.
DOI Scopus80
2013 Gowda, S. M., Baker, R. S., Corbett, A. T., & Rossi, L. M. (2013). Towards automatically detecting whether student learning is shallow. International Journal of Artificial Intelligence in Education, 23(1-4), 50-70.
DOI Scopus13
2013 Rodrigo, M. M. T., Baker, R. S. J. D., & Rossi, L. (2013). Student off-task behavior in computer- based learning in the Philippines: Comparison to prior research in the USA. Teachers College Record, 115(10), 1-27.
DOI Scopus30
2013 Gobert, J. D., Sao Pedro, M., Raziuddin, J., & Baker, R. S. (2013). From Log Files to Assessment Metrics: Measuring Students' Science Inquiry Skills Using Educational Data Mining. Journal of the Learning Sciences, 22(4), 521-563.
DOI Scopus194
2013 Baker, R. S. J. D., Corbett, A. T., & Gowda, S. M. (2013). Generalizing automated detection of the robustness of student learning in an intelligent tutor for genetics. Journal of Educational Psychology, 105(4), 946-956.
DOI Scopus5
2013 Goldin, I., Martin, T., Baker, R., Aleven, V., & Barnes, T. (2013). Preface. Ceur Workshop Proceedings, 1009, ii.
2013 Koedinger, K. R., Brunskill, E., Baker, R. S. J. D., McLaughlin, E. A., & Stamper, J. (2013). New potentials for data-driven intelligent tutoring system development and optimization. AI Magazine, 34(3), 37-41.
DOI Scopus139
2013 Baker, R. S., Hershkovitz, A., Rossi, L. M., Goldstein, A. B., & Gowda, S. M. (2013). Predicting Robust Learning With the Visual Form of the Moment-by-Moment Learning Curve. Journal of the Learning Sciences, 22(4), 639-666.
DOI Scopus31
2012 Rodrigo, M. M. T., Baker, R. S. J. D., Agapito, J., Nabos, J., Repalam, M. C., Reyes, S. S., & San Pedro, M. O. C. Z. (2012). The effects of an interactive software agent on student affective dynamics while using an intelligent tutoring system. IEEE Transactions on Affective Computing, 3(2), 224-236.
DOI Scopus57
2012 Desmarais, M. C., & Baker, R. S. J. D. (2012). A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User Adapted Interaction, 22(1-2), 9-38.
DOI Scopus366
2011 Raza Abidi, S. S., Raza Abidi, S., Baker, R., Bontcheva, K., Bui, H., Bunt, A., . . . Rivera, D. Z. (2011). Acknowledgment to reviewers. User Modeling and User Adapted Interaction, 21(4-5), 513.
DOI
2010 Baker, R. S. J. D., Merceron, A., & Pavlik, P. I. (2010). Preface. Educational Data Mining 2010 3rd International Conference on Educational Data Mining.
2010 Baker, R. S. J. D. (2010). Mining data for student models. Studies in Computational Intelligence, 308, 323-337.
DOI Scopus22
2010 Baker, R. S. J. D., D'Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (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.
DOI Scopus776
2008 Baker, R. S. J. D., Corbett, A. T., Roll, I., & Koedinger, K. R. (2008). Developing a generalizable detector of when students game the system. User Modeling and User Adapted Interaction, 18(3), 287-314.
DOI Scopus161
2008 Baker, R. S. J. D., & Beck, J. E. (2008). Preface. Educational Data Mining 2008 1st International Conference on Educational Data Mining Proceedings, 2.
2008 Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., & Koedinger, K. (2008). Why students engage in "gaming the system" behavior in interactive learning environments. Journal of Interactive Learning Research, 19(2), 185-224.
Scopus261
2007 Baker, R. S. J. D., Corbett, A. T., & Koedinger, K. R. (2007). The difficulty factors approach to the design of lessons in intelligent tutor curricula. International Journal of Artificial Intelligence in Education, 17(4), 341-369.
Scopus23
2004 Baker, R. S., Corbett, A. T., & Koedinger, K. R. (2004). Detecting Student Misuse of Intelligent Tutoring Systems. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 3220, 531-540.
DOI Scopus246
2004 Baker, R. S., Wagner, A. Z., Corbett, A. T., & Koedinger, K. R. (2004). The social role of technical personnel in the deployment of intelligent tutoring systems. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 3220, 785-787.
DOI
2004 Roll, I., Baker, R. S., Aleven, V., & Koedinger, K. R. (2004). A Metacognitive ACT-R Model of Students' Learning Strategies in Intelligent Tutoring Systems. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 3220, 854-856.
DOI Scopus5
2001 Tamassiayz, R., Goodrich, M. T., Vismara, L., Handy, M., Shubina, G., Cohen, R., . . . Brandes, U. (2001). JDSL: The data structures library in Java. Dr Dobb S Journal, 26(4), 21.
Scopus3
1999 Baker, R. S., Boilen, M., Goodrich, M. T., Tamassia, R., & Stibel, B. A. (1999). Testers and visualizers for teaching data structures. SIGCSE Bulletin Association for Computing Machinery Special Interest Group on Computer Science Education, 31(1), 261-265.
DOI Scopus22

Year Citation
2010 Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. J. D. (2010). Handbook of educational data mining. C. Romero, S. Ventura, M. Pechenizkiy, & R. S. J. D. Baker (Eds.), CRC Press.
DOI Scopus205

Year Citation
2025 Liu, X., Pankiewicz, M., Gupta, T., Huang, Z., & Baker, R. S. (2025). A Step Towards Adaptive Online: Learning: Exploring the Role of GPT as Virtual Teaching Assistants in Online Education. In Future of Learning with Large Language Models Applications and Research in Education (pp. 149-166). CRC Press.
DOI
2025 Deho, O. B., Joksimovic, S., Vieira, M., & Baker, R. (2025). Beyond Predictive Accuracy: Fairness and Bias in Predicting Test Anxiety. In A. I. Cristea (Ed.), Event/exhibition information: 26th International Conference, AIED 2025, Italy, 22/07/2025-26/07/2025
Source details - Title: Artificial Intelligence in Education (Vol. 15878 LNAI, pp. 247-262). Switzerland: Springer.
DOI
2025 Rodrigues, L., Zambrano, A. F., Pankiewicz, M., Barany, A., & Baker, R. (2025). Usage patterns and performance gains in gamified online judges: a data-driven analysis informed by cognitive psychology in CS1. In A. I. Cristea (Ed.), Event/exhibition information: 26th International Conference, AIED 2025, Palermo, Italy, 22/07/2025-26/07/2025
Source details - Title: Artificial Intelligence in Education: 26th International Conference, AIED 2025 (Vol. 15882 LNAI, pp. 99-106). US: Springer.
DOI
2025 Kizilcec, R. F., Baker, R. S., Brooks, C., Geathers, J., Hicke, Y., Moore, S., & Wu, B. (2025). Applications of generative AI to support teaching and learning in higher education: a half-day workshop. In A. I. Cristea (Ed.), Event/exhibition information: 26th International Conference, AIED 2025, Palermo, Italy, 22/07/2025-26/07/2025
Source details - Title: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and WideAIED (Vol. 2592 CCIS, pp. 267-274). Switzerland: Springer.
DOI
2025 Ocumpaugh, J., Paquette, L., Barany, A., & Baker, R. (2025). Full-day tutorial: conducting in-the-moment Data-Driven Classroom Interviews (DDCI) during digital learning using Quick Red Fox (QRF). In A. I. Cristea (Ed.), Event/exhibition information: 26th International Conference, AIED 2025, Palermo, Italy, Palermo, Italy, 22/07/2025-26/07/2025
Source details - Title: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and WideAIED (Vol. 2592 CCIS, pp. 278-283). Switzerland: Springer.
DOI
2025 Belitz, C., Lee, H. J., Nasiar, N., Fancsali, S. E., Stinar, F., Almoubayyed, H., . . . Bosch, N. (2025). Exploring student identity in adaptive learning systems through qualitative data. In A. I. Cristea (Ed.), Event/exhibition information: 26th International Conference, AIED 2025, Palermo, Italy, 22/07/2025-26/07/2025
Source details - Title: Artificial Intelligence in Education: 26th International Conference, AIED 2025 (Vol. 15881 LNAI, pp. 356-363). Switzerland: Springer.
DOI
2025 Ocumpaugh, J., Roscoe, R. D., Baker, R. S., Hutt, S., & Aguilar, S. J. (2025). ASSET-BASED PERSONALIZED LEARNING. In Handbook of Personalized Learning (pp. 437-450). Routledge.
DOI Scopus1
2024 Liu, X., Gurung, A., Baker, R. S., & Barany, A. (2024). Understanding the impact of observer effects on student affect. In A. Kim (Ed.), Event/exhibition information: 6th International Conference, ICQE 2024, Philadelphia, US, 03/11/2024-07/11/2024
Source details - Title: Advances in Quantitative Ethnography (Vol. 2279 CCIS, pp. 79-94). US: Springer.
DOI Scopus3
2024 Pankiewicz, M., Zambrano, A. F., Barany, A., & Baker, R. S. (2024). How we code code: leveraging GPT and ordered networks for understanding introductory programming education. In Y. J. Kim (Ed.), Event/exhibition information: 6th International Conference, ICQE 2024, Philadelphia, US, 03/11/2024-07/11/2024
Source details - Title: Advances in Quantitative Ethnography (Vol. 2279 CCIS, pp. 225-240). US: Springer.
DOI Scopus3
2024 Liu, X., Zambrano, A., Barany, A., Ocumpaugh, J., Ginger, J., Gadbury, M., . . . Baker, R. S. (2024). Investigating learner interest and observation patterns in a minecraft virtual astronomy environment. In Y. J. Kim (Ed.), Event/exhibition information: 6th International Conference, ICQE 2024, Philadelphia, US, 03/11/2024-07/11/2024
Source details - Title: Advances in Quantitative Ethnography (Vol. 2279 CCIS, pp. 19-34). US: Springer.
DOI Scopus3
2024 Swiecki, Z., Baker, R. S., Järvelä, S., & Shaffer, D. W. (2024). In Conversation: Baker, Järvelä, & Williamson Shaffer – The Relationship Between Computational Methods and Theory in Learning Analytics. In Theory Informing and Arising from Learning Analytics (pp. 175-186). Springer Nature Switzerland.
DOI Scopus1
2023 van Stee, E. G., Heath, T., Baker, R. S., Andres, J. M. A. L., & Ocumpaugh, J. (2023). Help seekers vs. Help accepters: understanding student engagement with a mentor agent. In N. Wang (Ed.), Event/exhibition information: 24th International Conference on Artificial Intelligence in Education (AIED), Tokyo, Japan, 03/07/2023-07/07/2023
Source details - Title: Artificial Intelligence In Education, Aied 2023 (Vol. 13916, pp. 139-150). US: Springer.
DOI Scopus2
2023 Scianna, J., Liu, X., Slater, S., & Baker, R. S. (2023). A case for (Inter)Action: the role of log eata in QE. In G. Irgens (Ed.), Event/exhibition information: 5th International Conference, ICQE 2023, Melbourne, Australia, 08/10/2023-12/10/2023
Source details - Title: Advances in Quantitative Ethnography (Vol. 1895 CCIS, pp. 395-408). US: Springer.
DOI Scopus8
2023 Liu, X., Hussein, B., Barany, A., Baker, R. S., & Chen, B. (2023). Decoding player behavior: analyzing reasons for player quitting using log data from puzzle game Baba is you. In G. Irgens (Ed.), Event/exhibition information: 5th International Conference, ICQE 2023, Melbourne, Australia, 08/10/2023-12/10/2023
Source details - Title: Advances in Quantitative Ethnography (Vol. 1895 CCIS, pp. 34-48). US: Springer.
DOI Scopus7
2022 Wang, K., Ma, Z., Baker, R. S., & Li, Y. (2022). Iterative Refinement of an AIS Rewards System. In R. A. Sottilare (Ed.), Event/exhibition information: 4th International Conference on Adaptive Instructional Systems Held as Part of the 24th International Conference on Human-Computer Interaction (HCII), online, 26/06/2022 -01 /06/2022
Source details - Title: Adaptive Instructional Systems, Ais 2022 (Vol. 13332 LNCS, pp. 113-125). US: Springer.
DOI Scopus2
2022 Baker, R. S., & Siemens, G. (2022). Learning Analytics and Educational Data Mining. In Cambridge Handbook of the Learning Sciences (pp. 259-278). Cambridge University Press.
DOI Scopus7
2022 Hutt, S., Baker, R. S., Ocumpaugh, J., Munshi, A., Andres, J. M. A. L., Karumbaiah, S., . . . van Velsen, M. (2022). Quick red fox: An app supporting a new paradigm in qualitative research on AIED for STEM. In Artificial Intelligence in Stem Education the Paradigmatic Shifts in Research Education and Technology (pp. 319-332). CRC Press.
DOI Scopus6
2021 Richey, J. E., Zhang, J., Das, R., Andres Bray, J. M., Scruggs, R., Mogessie, M., . . . McLaren, B. M. (2021). Gaming and confrustion explain learning advantages for a math digital learning game. In I. Roll (Ed.), Event/exhibition information: 22nd International Conference on Artificial Intelligence in Education (AIED) - Mind the Gap - AIED for Equity and Inclusion, online, 14/06/2021-18/06/2021
Source details - Title: Artificial Intelligence In Education (aied 2021), Pt I (Vol. 12748 LNAI, pp. 342-355). US: Springer.
DOI Scopus31
2021 San Pedro, M. O. Z., & Baker, R. S. (2021). Knowledge Inference Models Used in Adaptive Learning. In Methodology of Educational Measurement and Assessment (pp. 61-77). Springer International Publishing.
DOI Scopus1
2020 Wang, Y., Kai, S., & Baker, R. S. (2020). Early detection of wheel-spinning in ASSISTments. In I. I. Bittencourt (Ed.), Event/exhibition information: 21st International Conference on Artificial Intelligence in Education (AIED), online, 06/07/2020-10/07/2020
Source details - Title: Artificial Intelligence In Education (aied 2020), Pt I (Vol. 12163 LNAI, pp. 574-585). US: Springer.
DOI Scopus5
2019 Richey, J. E., McLaren, B. M., Andres Bray, M., Mogessie, M., Scruggs, R., Baker, R., & Star, J. (2019). Confrustion in learning from erroneous examples: does type of prompted self-explanation make a difference?. In S. Isotani (Ed.), Event/exhibition information: 20th International Conference on Artificial Intelligence in Education (AIED), Chicago, Il, 25/06/2019-29/06/2019
Source details - Title: Artificial Intelligence In Education (aied 2019), Pt I (Vol. 11625 LNAI, pp. 445-457). US: Springer.
DOI Scopus7
2018 Jiang, Y., Clarke-Midura, J., Baker, R. S., Paquette, L., & Keller, B. (2018). How immersive virtual environments foster self-regulated learning. In Digital Technologies and Instructional Design for Personalized Learning (pp. 28-54). IGI Global.
DOI Scopus14
2017 Biswas, G., Baker, R. S., & Paquette, L. (2017). DATA MINING METHODS FOR ASSESSING SELF-REGULATED LEARNING. In Handbook of Self Regulation of Learning and Performance Second Edition (pp. 388-403). Routledge.
DOI Scopus33
2016 Baker, R. S., Martin, T., & Rossi, L. M. (2016). Educational Data Mining and Learning Analytics. In Handbook of Cognition and Assessment (pp. 379-396). John Wiley & Sons, Inc..
DOI Scopus87
2016 Baker, R. S., Wang, Y., Paquette, L., Aleven, V., Popescu, O., Sewall, J., . . . Bergner, Y. (2016). Educational data mining: A MOOC experience. In Data Mining and Learning Analytics Applications in Educational Research (pp. 55-66). Wiley.
DOI Scopus5
2015 Rowe, E., Asbell-Clarke, J., & Baker, R. S. (2015). Serious games analytics to measure implicit science learning. In Serious Games Analytics Methodologies for Performance Measurement Assessment and Improvement (pp. 343-360). Springer International Publishing.
DOI Scopus38
2014 Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning Analytics from Research to Practice (pp. 61-75). Springer New York.
DOI Scopus608
2014 Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. In R. K. Sawyer (Ed.), Source details - Title: The Cambridge handbook of the learning sciences (2nd ed. ed., pp. 253-272). UK: Cambridge University Press.
DOI Scopus316
2010 Baker, R. S. J. D. (2010). Data mining. In International Encyclopedia of Education (pp. 112-118).
DOI Scopus264
2010 Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. J. D. (2010). Introduction. In Handbook of Educational Data Mining (pp. 1-6).
DOI Scopus26
2010 Koedinger, K. R., Baker, R. S. J. D., Cunningham, K., Skogsholm, A., Leber, B., & Stamper, J. (2010). A data repository for the EDM community: The PSLC datashop. In Handbook of Educational Data Mining (pp. 43-56).
DOI Scopus382
2009 Baker, R. S. J. D. (2009). Data Mining. In International Encyclopedia of Education Third Edition (pp. 112-118). Elsevier.
DOI Scopus3

Year Citation
2026 Pankiewicz, M., & Baker, R. S. (2026). Enhancing Student Focus and Problem-Solving with Real-Time LLM Feedback on Compiler Errors. In Lecture Notes in Computer Science Vol. 16063 LNCS (pp. 412-426). Springer Nature Switzerland.
DOI
2026 Zambrano, A. F., Ocumpaugh, J., Baker, R. S., & Vandenberg, J. (2026). Better to Be Confused or Frustrated Than Bored: Analyzing Affect Dynamics Across Player Archetypes. In Communications in Computer and Information Science Vol. 2677 CCIS (pp. 384-399). Springer Nature Switzerland.
DOI
2026 Karimov, A., Saarela, M., Liu, X., Wei, Z., Zambrano, A. F., Barany, A., . . . Kärkkäinen, T. (2026). ChatGPT-Assisted Codebook Design for Learning Analytics Datasets in Multiple Languages: A Case Study. In Communications in Computer and Information Science Vol. 2677 CCIS (pp. 177-192). Springer Nature Switzerland.
DOI
2026 Liu, X., Zhou, Y., Ocumpaugh, J., Barany, A., Zambrano, A. F., Wei, Z., . . . Giordano, C. (2026). Not All Who Wander Are Lost: Trailblazing Trajectories in a Minecraft-Based Learning Environment. In Communications in Computer and Information Science Vol. 2677 CCIS (pp. 336-352). Springer Nature Switzerland.
DOI
2026 Lin, J., Rao, J., Zhao, S. Y., Wang, Y., Gurung, A., Barany, A., . . . Koedinger, K. R. (2026). Automatic Large Language Models Creation of Interactive Learning Lessons. In Lecture Notes in Computer Science Vol. 16063 LNCS (pp. 259-274). Springer Nature Switzerland.
DOI
2026 Liu, X., Wei, Z., Barany, A., Ocumpaugh, J., Baker, R. S., Zambrano, A. F., . . . Giordano, C. (2026). Exploring Differences Between Hybrid GPT-Human and Human-Created Qualitative Codebooks in an Educational Game. In Communications in Computer and Information Science Vol. 2677 CCIS (pp. 193-208). Springer Nature Switzerland.
DOI
2026 Liu, X., Scianna, J., Metcalf, S. J., Wei, Z., Baker, R. S., Barany, A., . . . Gagnon, D. J. (2026). Modeling Player Progression in an Educational Game Using Ordered Networks. In Communications in Computer and Information Science Vol. 2677 CCIS (pp. 369-383). Springer Nature Switzerland.
DOI
2026 Wei, Z., Barany, A., Ocumpaugh, J., Liu, X., Zambrano, A. F., Baker, R. S., & Giordano, C. (2026). Let Me Explain: Linking Situational Interest to Student Response in Interviews. In Communications in Computer and Information Science Vol. 2677 CCIS (pp. 353-368). Springer Nature Switzerland.
DOI
2025 Li, Y., Nguyen, A., Baker, R., Cukurova, M., Gašević, D., Yang, K., . . . Yan, L. (2025). Generative AI for Learning Analytics (GenAI-LA): Evidence of Impacts on Human Learning. In Ceur Workshop Proceedings Vol. 3994.
2025 Gurung, A., Lin, J., Huang, Z., Borchers, C., Baker, R. S., Aleven, V., & Koedinger, K. R. (2025). Starting Seatwork Earlier as a Valid Measure of Student Engagement. In Proceedings of the International Conference on Educational Data Mining (pp. 303-316).
DOI
2025 Stinar, F., Lee, H., Belitz, C., Nasiar, N., Fancsali, S. E., Ritter, S., . . . Bosch, N. (2025). Fairness of Bayesian Knowledge Tracing for Math Learners of Different Reading Ability. In Proceedings of the International Conference on Educational Data Mining (pp. 170-181).
DOI
2025 Zambrano, A. F., Ocumpaugh, J., Baker, R. S., Vanacore, K., Esiason, J., & Vandenberg, J. (2025). The Half-Life of Epistemic Emotions: How Motivation Influences Affective Chronometry. In Proceedings of the International Conference on Educational Data Mining (pp. 317-327).
DOI Scopus1
2025 Zhang, J., Vanacore, K., Baker, R. S., Ch, N., Mills, C., & Henkel, O. (2025). How Much Mastery is Enough Mastery? The Relationship between Mastery in a Lesson and the Performance on the Subsequent Lesson. In Proceedings of the International Conference on Educational Data Mining (pp. 427-433).
DOI Scopus1
2025 Zhang, J., Baker, R. S., Srivastava, N., Ocumpaugh, J., Mills, C., & Mclaren, B. M. (2025). Carelessness Detection using Performance Factor Analysis: A New Operationalization with Unexpectedly Different Relationship to Learning. In 2025 7th International Conference on Computer Science and Technologies in Education Cste 2025 (pp. 302-311). IEEE.
DOI
2025 Pankiewicz, M., Shi, Y., & Baker, R. S. (2025). srcML-DKT: Enhancing Deep Knowledge Tracing with Robust Code Representations from srcML. In Proceedings of the International Conference on Educational Data Mining (pp. 541-548).
DOI
2025 Baker, R., Mills, C., & Choi, J. (2025). Difficulty of Achieving High Precision with Low Base Rates for High-Stakes Intervention. In Fifteenth International Conference On Learning Analytics and Knowledge, Lak 2025 (pp. 790-796). US: ACM.
DOI Scopus1
2025 Baker, R., & Hutt, S. (2025). MORF: a Post-Mortem. In Fifteenth International Conference On Learning Analytics and Knowledge, Lak 2025 (pp. 797-802). US: ACM.
DOI Scopus1
2025 Borchers, C., & Baker, R. S. (2025). ABROCA Distributions For Algorithmic Bias Assessment: considerations Around Interpretation. In Fifteenth International Conference On Learning Analytics and Knowledge, Lak 2025 (pp. 837-843). US: ACM.
DOI Scopus1
2025 Dai, Y., Vanacore, K., Baker, R., & Slater, S. (2025). Understanding MOOC stopout patterns: course and assessment-level insights. In L@s 2025 Proceedings of the 12th ACM Conference on Learning @ Scale (pp. 300-304). US: ACM.
DOI
2025 Shah, M., Pankiewicz, M., Baker, R. S., Chi, J., Xin, Y., Shah, H., & Fonseca, D. (2025). Students' use of an LLM-Powered virtual teaching assistant for recommending educational applications of games. In J. L. Plass (Ed.), Serious Games, Jcsg 2024 Vol. 15259 LNCS (pp. 19-24). US: Springer.
DOI Scopus3
2025 Liu, X., Slater, S., Swanson, L., Metcalf, S. J., Gagnon, D. J., & Baker, R. S. (2025). Identifying When and Why Students Choose to Quit Jobs in a Science Exploration Game. In J. L. Plass (Ed.), Serious Games, Jcsg 2024 Vol. 15259 (pp. 56-69). US: Springer.
DOI
2025 Karimov, A., Saarela, M., Aliyev, S., & Baker, R. S. (2025). Ethical Considerations and Student Perceptions of Engagement Data in Learning Analytics. In T. X. Bui (Ed.), Proceedings of the Annual Hawaii International Conference on System Sciences (pp. 4757-4766). HI: HICSS.
DOI Scopus4 WoS2
2025 Scarlatos, A., Baker, R. S., & Lan, A. (2025). Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs. In Fifteenth International Conference On Learning Analytics and Knowledge, Lak 2025 (pp. 249-259). US: ACM.
DOI Scopus10
2025 Liu, X., Wei, Z., Baker, R. S., Metcalf, S. J., Zhang, J., Barany, A., . . . Gagnon, D. J. (2025). Integrating large language models and machine learning to detect struggle in educational games. In A. I. Cristea (Ed.), Artificial Intelligence in Education Vol. 15881 LNAI (pp. 398-405). US: Springer.
DOI Scopus1
2025 Mehta, S., Srivastava, N., Liu, X., Vanacore, K., & Baker, R. S. (2025). Do MOOC conversations matter? investigating the role of social presence and course-relevant discussion in career advancement. In L@s 2025 Proceedings of the 12th ACM Conference on Learning @ Scale (pp. 236-240). US: ACM.
DOI
2024 Borchers, C., Zhang, J., Baker, R. S., & Aleven, V. (2024). Using think-aloud data to understand relations between self-regulation cycle characteristics and student performance in intelligent tutoring systems. In Fourteenth International Conference On Learning Analytics and Knowledge, Lak 2024 (pp. 529-539). US: ACM.
DOI Scopus15
2024 Cloude, E. B., Kumar, P., Baker, R. S., & Fouh, E. (2024). Novice programmers inaccurately monitor the quality of their work and their peers′ work in an introductory computer science course. In Fourteenth International Conference On Learning Analytics and Knowledge, Lak 2024 (pp. 35-45). US: ACM.
DOI Scopus3
2024 Zambrano, A. F., & Baker, R. S. (2024). Long-term prediction from topic-level knowledge and engagement in mathematics learning. In Fourteenth International Conference On Learning Analytics and Knowledge, Lak 2024 (pp. 66-77). US: ACM.
DOI Scopus4
2024 Zambrano, A. F., Zhang, J., & Baker, R. S. (2024). Investigating algorithmic bias on bayesian knowledge tracing and carelessness detectors. In Fourteenth International Conference On Learning Analytics and Knowledge, Lak 2024 (pp. 349-359). US: ACM.
DOI Scopus13
2024 Belitz, C., Lee, H. J., Nasiar, N., Fancsali, S. E., Ritter, S., Almoubayyed, H., . . . Bosch, N. (2024). Hierarchical dependencies in classroom settings influence algorithmic bias metrics. In Fourteenth International Conference On Learning Analytics and Knowledge, Lak 2024 (pp. 210-218). US: ACM.
DOI Scopus5
2024 Hutt, S., Depiro, A., Wang, J., Rhodes, S., Baker, R. S., Hieb, G., . . . Mills, C. (2024). Feedback on Feedback: comparing Classic Natural Language Processing and Generative AI to Evaluate Peer Feedback. In Fourteenth International Conference On Learning Analytics and Knowledge, Lak 2024 (pp. 55-65). US: ACM.
DOI Scopus25
2024 Cloude, E. B., Munshi, A., Andres, J. M. A., Ocumpaugh, J., Baker, R. S., & Biswas, G. (2024). Exploring confusion and frustration as non-linear dynamical systems. In Fourteenth International Conference On Learning Analytics and Knowledge, Lak 2024 (pp. 241-252). US: ACM.
DOI Scopus8
2024 Zambrano, A. F., Pankiewicz, M., Barany, A., & Baker, R. S. (2024). Ordered network analysis in CS education: unveiling patterns of success and struggle in automated programming assessment. In Proceedings Of The 2024 Conference Innovation And Technology In Computer Science Education, Vol 1, Iticse 2024 Vol. 1 (pp. 443-449). US: ACM.
DOI Scopus3
2024 Lee, F. M., Baker, R. S., Tomar, P. S., Kumari, K., Liang, Q., & Wei, Z. (2024). Videos for Parents and Child Performance. In Proceedings Of The Eleventh Acm Conference On Learning@scale, L@s 2024 (pp. 265-268). US: ACM.
DOI Scopus1
2024 Shah, M., Baker, R. S., Granville, P., & Sharp, K. (2024). Examining student engagement in online learning platforms for promoting exam readiness and success in undergraduate nursing education. In Proceedings Of The Eleventh Acm Conference On Learning@scale, L@s 2024 (pp. 443-445). US: ACM.
DOI Scopus1
2024 Cloude, E. B., Zhang, J., Baker, R. S., & Fouh, E. (2024). Procrastination vs. active delay: how students prepare to code in introductory programming. In Proceedings Of The 55th Acm Technical Symposium On Computer Science Education, Sigcse 2024, Vol. 1 Vol. 1 (pp. 214-220). US: ACM.
DOI Scopus3
2024 Pankiewicz, M., & Baker, R. S. (2024). Navigating compiler errors with AI assistance - a study of GPT hints in an introductory programming course. In Proceedings Of The 2024 Conference Innovation And Technology In Computer Science Education, Vol 1, Iticse 2024 Vol. 1 (pp. 94-100). US: ACM.
DOI Scopus15
2024 Svabensky, V., Pankiewicz, M., Zhang, J., Cloude, E. B., Baker, R. S., & Fouh, E. (2024). Comparison of three programming error measures for explaining variability in CS1 grades. In Proceedings Of The 2024 Conference Innovation And Technology In Computer Science Education, Vol 1, Iticse 2024 Vol. 1 (pp. 87-93). US: ACM.
DOI Scopus1
2024 Zambrano, A. F., Baker, R. S., Mehta, S., & Barany, A. (2024). Epistemic association rule networks: incorporating association rule mining into the quantitative ethnography toolbox. In Y. J. Kim (Ed.), Advances in Quantitative Ethnography Vol. 2278 CCIS (pp. 3-17). US: Springer.
DOI Scopus1
2024 Liu, X., Zhang, J., Barany, A., Pankiewicz, M., & Baker, R. S. (2024). Assessing the potential and limits of large language models in qualitative coding. In Y. J. Kim (Ed.), Advances in Quantitative Ethnography (pp. 80-103). US: Springer.
DOI
2024 Nasiar, N., Baker, R. S., Andres, J. M. A. L., & Srivastava, N. (2024). Same Learning Platform, Different Types of Research: A National-Level Analysis. In Proceedings of the International Conference on Educational Data Mining (pp. 814-820).
DOI
2024 Esbenshade, L., Vitale, J., & Baker, R. S. (2024). Non-Overlapping Leave Future Out Validation (NOLFO): Implications for Graduation Prediction. In Proceedings of the International Conference on Educational Data Mining (pp. 602-609).
DOI
2024 Zhang, J., Borchers, C., Aleven, V., & Baker, R. S. (2024). Using Large Language Models to Detect Self-Regulated Learning in Think-Aloud Protocols. In Proceedings of the International Conference on Educational Data Mining (pp. 157-168).
DOI Scopus13
2024 Baker, R. S., Hutt, S., Brooks, C. A., Srivastava, N., & Mills, C. (2024). Open Science and Educational Data Mining: Which Practices Matter Most?. In Proceedings of the International Conference on Educational Data Mining (pp. 279-287).
DOI Scopus2
2024 Zambrano, A. F., Baker, R. S., Baral, S., Heffernan, N. T., & Lan, A. (2024). From Reaction to Anticipation: Predicting Future Affect. In Proceedings of the International Conference on Educational Data Mining (pp. 566-574).
DOI Scopus3
2024 Singhal, S., Zambrano, A. F., Pankiewicz, M., Liu, X., Porter, C., & Baker, R. S. (2024). De-Identifying Student Personally Identifying Information with GPT-4. In Proceedings of the International Conference on Educational Data Mining (pp. 559-565).
DOI Scopus11
2024 Barany, A., Nasiar, N., Porter, C., Zambrano, A. F., Andres, A. L., Bright, D., . . . Baker, R. S. (2024). ChatGPT for Education Research: Exploring the Potential of Large Language Models for Qualitative Codebook Development. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 14830 LNAI (pp. 134-149). Springer Nature Switzerland.
DOI Scopus35
2024 Švábenský, V., Tkáčik, K., Birdwell, A., Weiss, R., Baker, R. S., Čeleda, P., . . . Chattopadhyay, A. (2024). Detecting Unsuccessful Students in Cybersecurity Exercises in Two Different Learning Environments. In Proceedings Frontiers in Education Conference Fie (pp. 1-9). IEEE.
DOI Scopus2
2024 Richey, J. E., Nguyen, H. A., Mehrvarz, M., Else-Quest, N., Arroyo, I., Baker, R. S., . . . McLaren, B. M. (2024). Understanding Gender Effects in Game-Based Learning: The Role of Self-Explanation. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 14829 LNAI (pp. 206-219). Springer Nature Switzerland.
DOI Scopus1
2024 Švábenský, V., Verger, M., Rodrigo, M. M. T., Monterozo, C. J. G., Baker, R. S., Saavedra, M. Z. N. L., . . . Shimada, A. (2024). Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students. In Proceedings of the International Conference on Educational Data Mining (pp. 744-751).
DOI Scopus3
2023 Andres, A. L. J. M., Baker, R. S., Hutt, S. J., Mills, C., Zhang, J., Rhodes, S., & DePiro, A. (2023). Anxiety, Achievement, and Self-Regulated Learning in CueThink. In Proceedings of International Conference of the Learning Sciences Icls (pp. 258-265).
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2023 Gonzalez, H., Li, J., Jin, H., Ren, J., Zhang, H., Akinyele, A., . . . Callison-Burch, C. (2023). Automatically Generated Summaries of Video Lectures May Enhance Students’ Learning Experience. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 382-393).
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2023 Ritter, S., Heffernan, N., Williams, J. J., Lomas, D., Bicknell, K., Roschelle, J., . . . Murphy, A. (2023). Fourth Annual Workshop on A/B Testing and Platform-Enabled Learning Research. In L@s 2023 Proceedings of the 10th ACM Conference on Learning @ Scale (pp. 254-256).
DOI Scopus1
2023 Švábenský, V., Baker, R. S., Zambrano, A., Zou, Y., & Slater, S. (2023). Towards Generalizable Detection of Urgency of Discussion Forum Posts. In Proceedings of the International Conference on Educational Data Mining (pp. 302-309).
DOI Scopus2
2023 Zambrano, A. F., Liu, X., Barany, A., Baker, R. S., Kim, J., & Nasiar, N. (2023). From nCoder to ChatGPT: From Automated Coding to Refining Human Coding. In Communications in Computer and Information Science Vol. 1895 CCIS (pp. 470-485). Springer Nature Switzerland.
DOI Scopus39
2023 Zhang, J., Baker, R. S., Andres, J. M. A., Hutt, S., & Sethuraman, S. (2023). Automated Multi-Dimensional Analysis of Peer Feedback in Middle School Mathematics. In Proceedings of International Conference of the Learning Sciences Icls Vol. 2023-June (pp. 221-224).
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2023 Nawaz, S., Mayle, K., Martens, G., Stein, R., & Baker, R. S. (2023). Question Dosage in MOOCs: An Empirical Investigation. In Ascilite 2023 Conference Proceeding 40th International Conference on Innovation Practice and Research in the Use of Educational Technologies in Tertiary Education (pp. 630-634). Open Access Publishing Association.
DOI Scopus1
2023 Nasiar, N., Baker, R. S., Zou, Y., Zhang, J., & Hutt, S. (2023). Modeling Problem-Solving Strategy Invention (PSSI) Behavior in an Online Math Environment. In Communications in Computer and Information Science Vol. 1831 CCIS (pp. 453-459). Springer Nature Switzerland.
DOI Scopus3
2023 Pankiewicz, M., Baker, R., & Ocumpaugh, J. (2023). Using Intelligent Tutoring on the First Steps of Learning to Program: Affective and Learning Outcomes. In Communications in Computer and Information Science Vol. 1831 CCIS (pp. 593-598). Springer Nature Switzerland.
DOI Scopus4
2023 Hutt, S., Das, S., & Baker, R. S. (2023). The Right To Be Forgotten and Educational Data Mining: Challenges and Paths Forward. In Proceedings of the International Conference on Educational Data Mining (pp. 251-259).
DOI Scopus10
2023 Andres, J. M. A., Cloude, E. B., Baker, R. S., & Lee, S. (2023). Investigating Cognitive Biases in Self-Explanation Behaviors during Game-based Learning about Mathematics. In 31st International Conference on Computers in Education Icce 2023 Proceedings Vol. 1 (pp. 637-642).
Scopus1
2023 Maier, C., Slavin, I., Baker, R. S., & Stalzer, S. (2023). Studying Memory Decay and Spacing within Knowledge Tracing. In 31st International Conference on Computers in Education Icce 2023 Proceedings Vol. 1 (pp. 24-33).
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2023 Zhang, J., Baker, R. S., & Farmer, T. (2023). No benefit for high-dosage time management interventions in online courses. In Proceedings Of The Tenth Acm Conference On Learning @ Scale, L@s 2023 (pp. 302-305). US: ACM.
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2023 Andres Bray, J. M., Hutt, S., & Baker, R. S. (2023). Exploring cross-country prediction model generalizability in MOOCs. In Proceedings Of The Tenth Acm Conference On Learning @ Scale, L@s 2023 (pp. 183-193). US: ACM.
DOI Scopus1
2023 Liu, X., Slater, S., Andres, J. M. A. L., Swanson, L., Scianna, J., Gagnon, D., & Baker, R. S. (2023). Struggling to Detect Struggle in Students Playing a Science Exploration Game. In Chi Play 2023 Companion Proceedings of the Annual Symposium on Computer Human Interaction in Play (pp. 83-88). US: ACM.
DOI Scopus8
2023 Cloude, E. B., Baker, R. S., & Fouh, E. (2023). Online help-seeking occurring in multiple computer-mediated conversations affects grades in an introductory programming course. In Thirteenth International Conference On Learning Analytics and Knowledge, Lak2023 (pp. 378-387). US: ACM.
DOI Scopus10
2023 Wong, A. Y., Bryck, R. L., Baker, R. S., Hutt, S., & Mills, C. (2023). Using a webcam based eye-tracker to understand students' thought patterns and reading behaviors in neurodivergent classrooms. In Thirteenth International Conference On Learning Analytics and Knowledge, Lak2023 (pp. 453-463). ACM: US.
DOI Scopus15
2023 Pankiewicz, M., & Baker, R. S. (2023). Large Language Models (GPT) for automating feedback on programming assignments. In 31st International Conference on Computers in Education Icce 2023 Proceedings Vol. 1 (pp. 68-77).
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2023 Cloude, E. B., Baker, R. S., & Pankiewicz, M. (2023). Measuring Self-regulated Learning Processes in Computer Science Education. In 31st International Conference on Computers in Education Icce 2023 Proceedings Vol. 1 (pp. 406-408).
2023 Zambrano, A. F., Baker, R. S., & Lan, A. S. (2023). Active Learning for a Classroom Observer who Can't Time Travel. In 2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos Aciiw 2023 (pp. 1-8). IEEE.
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2022 Karumbaiah, S., Baker, R., Tao, Y., & Liu, Z. (2022). How does Students' Affect in Virtual Learning Relate to Their Outcomes? A Systematic Review Challenging the Positive-Negative Dichotomy. In ACM International Conference Proceeding Series (pp. 24-33). ACM.
DOI Scopus18
2022 Baker, R., Hutt, S., Mogessie, M., & Valayaputtar, H. (2022). Research Using the MOOC Replication Framework and E-TRIALS. In Proceedings of 2022 IEEE Learning with Moocs Lwmoocs 2022 (pp. 131-136). IEEE.
DOI Scopus4
2022 Andres, J. M. A. L., Hutt, S., Ocumpaugh, J., & Baker, R. S. (2022). Investigating How Achievement Goals Influence Student Behavior in Computer Based Learning. In 30th International Conference on Computers in Education Conference Icce 2022 Proceedings Vol. 1 (pp. 95-100).
2022 Slater, S., Baker, R., Shute, V., & Bowers, A. (2022). Engagement-Based Player Typologies Describe Game-Based Learning Outcomes. In Lecture Notes in Computer Science Vol. 13356 LNCS (pp. 325-328). Springer International Publishing.
DOI Scopus1
2022 Botelho, A. F., Adjei, S. A., Bahel, V., & Baker, R. S. (2022). Exploring Relationships Between Temporal Patterns of Affect and Student Learning. In 30th International Conference on Computers in Education Conference Icce 2022 Proceedings Vol. 1 (pp. 83-88).
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2022 Nasiar, N., Baker, R. S., Li, J., & Gong, W. (2022). How do A/B Testing and Secondary Data Analysis on AIED Systems Influence Future Research?. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 13355 LNCS (pp. 115-126). Springer International Publishing.
DOI Scopus1
2022 Li, Y., Zou, X., Ma, Z., & Baker, R. S. (2022). A Multi-pronged Redesign to Reduce Gaming the System. In Lecture Notes in Computer Science Vol. 13356 LNCS (pp. 334-337). Springer International Publishing.
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2022 Levin, N., Baker, R. S., Nasiar, N., Fancsali, S., & Hutt, S. (2022). Evaluating Gaming Detector Model Robustness Over Time. In Proceedings of the International Conference on Educational Data Mining.
DOI Scopus10
2022 Zhang, J., Cunningham, T., Iyer, R., Baker, R., & Fouh, E. (2022). Exploring the Impact of Voluntary Practice and Procrastination in an Introductory Programming Course. In SIGCSE 2022 Proceedings of the 53rd ACM Technical Symposium on Computer Science Education Vol. 1 (pp. 356-361). ACM.
DOI Scopus15
2022 Andres, J. M. A. L., Hutt, S., Ocumpaugh, J., Baker, R. S., Nasiar, N., & Porter, C. (2022). How Anxiety Affects Affect: A Quantitative Ethnographic Investigation Using Affect Detectors and Data-Targeted Interviews. In Communications in Computer and Information Science Vol. 1522 CCIS (pp. 268-283). Springer International Publishing.
DOI Scopus10
2022 Slater, S., Baker, R. S., Gagnon, D., Harpstead, E., Andres, J. M. A. L., & Swanson, L. (2022). Changing Students' Perceptions of a History Exploration Game Using Different Scripts. In 30th International Conference on Computers in Education Conference Icce 2022 Proceedings Vol. 1 (pp. 499-504).
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2022 Zhang, J., Juliana, J. M., Hutt, S., Baker, R. S., Ocumpaugh, J., Mills, C., . . . Young, T. (2022). Detecting SMART Model Cognitive Operations in Mathematical Problem-Solving Process. In Proceedings of the International Conference on Educational Data Mining.
DOI Scopus6
2022 Karumbaiah, S., Zhang, J., Baker, R. S., Scruggs, R., Cade, W., Clements, M., & Lin, S. (2022). Using Neural Network-Based Knowledge Tracing for a Learning System with Unreliable Skill Tags. In Proceedings of the 15th International Conference on Educational Data Mining Edm 2022.
DOI Scopus1
2022 Ritter, S., Heffernan, N., Williams, J. J., Lomas, D., Motz, B., Basu Mallick, D., . . . Baker, R. (2022). Third Annual Workshop on A/B Testing and Platform-Enabled Learning Research. In L@s 2022 Proceedings of the 9th ACM Conference on Learning @ Scale (pp. 252-254). ACM.
DOI Scopus2
2021 Zhou, Y., Andres-Bray, J. M., Hutt, S., Ostrow, K., & Baker, R. S. (2021). A Comparison of Hints vs. Scaffolding in a MOOC with Adult Learners. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 12749 LNAI (pp. 427-432). Springer International Publishing.
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2021 Hutt, S., Ocumpaugh, J., Andres, M. A. J. L., Bosch, N., Paquette, L., Biswas, G., & Baker, R. S. (2021). Investigating SMART Models of Self-Regulation and their Impact on Learning. In Proceedings of the 14th International Conference on Educational Data Mining Edm 2021 (pp. 580-587).
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2021 Ocumpaugh, J., Hutt, S., Andres, J. M. A. L., Baker, R. S., Biswas, G., Bosch, N., . . . Munshi, A. (2021). Using Qualitative Data from Targeted Interviews to Inform Rapid AIED Development. In 29th International Conference on Computers in Education Conference Icce 2021 Proceedings Vol. 1 (pp. 69-74).
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2021 Baker, R. S., McLaren, B. M., Hutt, S., Richey, J. E., Rowe, E., Almeda, M. V., . . . Andres, J. M. A. L. (2021). Towards Sharing Student Models Across Learning Systems. In Lecture Notes in Computer Science Vol. 12749 LNAI (pp. 60-65). Springer International Publishing.
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2021 Baker, R. S., Nasiar, N., Ocumpaugh, J. L., Hutt, S., Andres, J. M. A. L., Slater, S., . . . Biswas, G. (2021). Affect-Targeted Interviews for Understanding Student Frustration. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 12748 LNAI (pp. 52-63). Springer International Publishing.
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2021 Hutt, S., Ocumpaugh, J., Andres, J. M. A. L., Munshi, A., Bosch, N., Baker, R. S., . . . Biswas, G. (2021). Who’s Stopping You? – Using Microanalysis to Explore the Impact of Science Anxiety on Self-Regulated Learning Operations. In Proceedings of the 43rd Annual Meeting of the Cognitive Science Society Comparative Cognition Animal Minds Cogsci 2021 (pp. 1409-1415).
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2021 Karumbaiah, S., Lan, A., Nagpal, S., Baker, R. S., Botelho, A., & Heffernan, N. (2021). Using past data to warm start active machine learning: Does context matter?. In ACM International Conference Proceeding Series (pp. 151-160). ACM.
DOI Scopus15
2021 Bosch, N., Zhang, Y., Paquette, L., Baker, R. S., Ocumpaugh, J., & Biswas, G. (2021). Students' verbalized metacognition during computerized learning. In Conference on Human Factors in Computing Systems Proceedings (pp. 1-12). ACM.
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2021 Paquette, L., Grant, T., Zhang, Y., Biswas, G., & Baker, R. (2021). Using Epistemic Networks to Analyze Self-regulated Learning in an Open-Ended Problem-Solving Environment. In Communications in Computer and Information Science Vol. 1312 (pp. 185-201). Springer International Publishing.
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2021 Salehian Kia, F., Hatala, M., Baker, R. S., & Teasley, S. D. (2021). Measuring students' self-regulatory phases in LMS with behavior and real-time self report. In ACM International Conference Proceeding Series (pp. 259-268). ACM.
DOI Scopus32
2021 Das, R., Zhang, J., Baker, R. S., & Scruggs, R. (2021). A New Interpretation of Knowledge Tracing Models' Predictive Performance in Terms of the Cold Start Problem. In Ceur Workshop Proceedings Vol. 3051.
2021 Adjei, S. A., Baker, R. S., & Bahel, V. (2021). Seven-Year Longitudinal Implications of Wheel Spinning and Productive Persistence. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 12748 LNAI (pp. 16-28). Springer International Publishing.
DOI Scopus15
2021 Maier, C., Baker, R. S., & Stalzer, S. (2021). Challenges to Applying Performance Factor Analysis to Existing Learning Systems. In 29th International Conference on Computers in Education Conference Icce 2021 Proceedings Vol. 1 (pp. 57-62).
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2021 Zhang, J., Das, R., Baker, R. S., & Scruggs, R. (2021). Knowledge Tracing Models’ Predictive Performance when a Student Starts a Skill. In Proceedings of the 14th International Conference on Educational Data Mining Edm 2021 (pp. 625-629).
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2021 Fouh, E., Lee, W., & Baker, R. S. (2021). Nudging students to reduce procrastination in office hours and forums. In Proceedings of the International Conference on Information Visualisation Vol. 2021-July (pp. 248-254). IEEE.
DOI Scopus7
2021 Bahel, V., Adjei, S. A., & Baker, R. S. (2021). Transferring an existing gaming detection model to different system using semi-supervised approach. In Ceur Workshop Proceedings Vol. 3051.
2021 Karumbaiah, S., & Baker, R. S. (2021). Studying Affect Dynamics Using Epistemic Networks. In Communications in Computer and Information Science Vol. 1312 (pp. 362-374). Springer International Publishing.
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2020 Agarwal, D., Baker, R. S., & Muraleedharan, A. (2020). Dynamic knowledge tracing through data driven recency weights. In Proceedings of the 13th International Conference on Educational Data Mining Edm 2020 (pp. 725-729).
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2020 Slater, S., Baker, R. S., & Wang, Y. (2020). Iterative Feature Engineering Through Text Replays of Model Errors. In Proceedings of the 13th International Conference on Educational Data Mining Edm 2020 (pp. 503-508).
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2020 Shah, M., Snow, E., Baker, R. S., & Gouveia, C. (2020). Learning scientists in academia and industry: Building bridges and expanding the potential of our community. In Computer Supported Collaborative Learning Conference Cscl Vol. 2 (pp. 697-700).
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2020 Agnihotri, L., Baker, R. S., & Stalzer, S. (2020). A Procrastination Index for Online Learning Based on Assignment Start Time. In Proceedings of the 13th International Conference on Educational Data Mining Edm 2020 (pp. 550-554).
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2020 Molenaar, I., Horvers, A., Dijkstra, R., & Baker, R. S. (2020). Personalized visualizations to promote young learners' SRL: The learning path app. In ACM International Conference Proceeding Series (pp. 330-339). ACM.
DOI Scopus49
2020 Mogessie, M., Elizabeth Richey, J., McLaren, B. M., Andres-Bray, J. M. L., & Baker, R. S. (2020). Confrustion and Gaming While Learning with Erroneous Examples in a Decimals Game. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 12164 LNAI (pp. 208-213). Springer International Publishing.
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2020 Ocumpaugh, J., Baker, R. S., Karumbaiah, S., Crossley, S. A., & Labrum, M. (2020). Affective sequences and student actions within reasoning mind. In Lecture Notes in Computer Science Vol. 12163 LNAI (pp. 437-447). Springer International Publishing.
DOI Scopus2
2020 Baker, R., Ma, W., Zhao, Y., Wang, S., & Ma, Z. (2020). The Results of Implementing Zone of Proximal Development on Learning Outcomes. In Proceedings of the 13th International Conference on Educational Data Mining Edm 2020 (pp. 749-753).
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2020 Henderson, N., Rowe, J., Paquette, L., Baker, R. S., & Lester, J. (2020). Improving affect detection in game-based learning with multimodal data fusion. In Lecture Notes in Computer Science Vol. 12163 LNAI (pp. 228-239). Springer International Publishing.
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2020 Tywoniw, R., Crossley, S. A., Ocumpaugh, J., Karumbaiah, S., & Baker, R. (2020). Relationships Between Math Performance and Human Judgments of Motivational Constructs in an Online Math Tutoring System. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 12164 LNAI (pp. 329-333). Springer International Publishing.
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2020 Nawaz, S., Srivastava, N., Yu, J. H., Baker, R. S., Kennedy, G., & Bailey, J. (2020). Analysis of task difficulty sequences in a simulation-based POE environment. In Lecture Notes in Computer Science Vol. 12163 LNAI (pp. 423-436). Springer International Publishing.
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2020 Baker, R. S., Al Yammahi, A., El Sebaaly, J., Nadaf, A., Kapp, T., & Adjei, S. (2020). Can computer-based learning environments mitigate large class size?. In Icce 2020 28th International Conference on Computers in Education Proceedings Vol. 1 (pp. 622-627).
2020 Zhang, Y., Bosch, N., Paquette, L., Munshi, A., Baker, R. S., Biswas, G., & Ocumpaugh, J. (2020). The relationship between confusion and metacognitive strategies in Betty's Brain. In ACM International Conference Proceeding Series (pp. 276-284). ACM.
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2020 Munshi, A., Mishra, S., Zhang, N., Paquette, L., Ocumpaugh, J., Baker, R., & Biswas, G. (2020). Modeling the relationships between basic and achievement emotions in computer-based learning environments. In Lecture Notes in Computer Science Vol. 12163 LNAI (pp. 411-422). Springer International Publishing.
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2020 Jiang, Y., Almeda, M. V., Kai, S., Baker, R. S., Ostrow, K., Inventado, P. S., & Scupelli, P. (2020). Single template vs. Multiple templates: Examining the effects of problem format on performance. In Computer Supported Collaborative Learning Conference Cscl Vol. 2 (pp. 1015-1022).
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2020 Scruggs, R., Baker, R. S., & McLaren, B. M. (2020). Extending deep knowledge tracing: Inferring interpretable knowledge and predicting post-system performance. In Icce 2020 28th International Conference on Computers in Education Proceedings Vol. 1 (pp. 195-204).
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2019 Elizabeth Owen, V., Roy, M. H., Thai, K. P., Burnett, V., Jacobs, D., Keylor, E., & Baker, R. S. (2019). Detecting wheel-spinning and productive persistence in educational games. In Edm 2019 Proceedings of the 12th International Conference on Educational Data Mining (pp. 378-383).
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2019 Andres, J. M. A. L., Paquette, L., Ocumpaugh, J., Jiang, Y., Baker, R. S., Karumbaiah, S., . . . Biswas, G. (2019). Affect sequences and learning in Betty's brain. In ACM International Conference Proceeding Series (pp. 383-390). ACM.
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2019 Karumbaiah, S., Baker, R. S., & Ocumpaugh, J. (2019). The case of self-transitions in affective dynamics. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 11625 LNAI (pp. 172-181). Springer International Publishing.
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2019 Henderson, N. L., Rowe, J. P., Mott, B. W., Brawner, K., Baker, R., & Lester, J. C. (2019). 4D affect detection: Improving frustration detection in game-based learning with posture-based temporal data fusion. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 11625 LNAI (pp. 144-156). Springer International Publishing.
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2019 Aslan, S., Okur, E., Alyuz, N., Esme, A. A., & Baker, R. S. (2019). Human expert labeling process: Valence-arousal labeling for students’ affective states. In Advances in Intelligent Systems and Computing Vol. 804 (pp. 53-61). Springer International Publishing.
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2019 Yang, T. Y., Baker, R. S., Studer, C., Heffernan, N., & Lan, A. S. (2019). Active learning for student affect detection. In Edm 2019 Proceedings of the 12th International Conference on Educational Data Mining (pp. 208-217).
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2019 Crossley, S., Karumbaiah, S., Ocumpaugh, J., Labrum, M. J., & Baker, R. S. (2019). Predicting math success in an online tutoring system using language data and click-stream variables: A longitudinal analysis. In Openaccess Series in Informatics Vol. 70.
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2019 Gardner, J., Yang, Y., Baker, R. S., & Brooks, C. (2019). Modeling and experimental design for MOOC dropout prediction: A replication perspective. In Edm 2019 Proceedings of the 12th International Conference on Educational Data Mining (pp. 49-58).
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2019 Karumbaiah, S., Ocumpaugh, J., & Baker, R. S. (2019). The influence of school demographics on the relationship between students' help-seeking behavior and performance and motivational measures. In Edm 2019 Proceedings of the 12th International Conference on Educational Data Mining (pp. 99-108).
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2019 Anderson, H., Boodhwani, A., & Baker, R. S. (2019). Assessing the fairness of graduation predictions. In Edm 2019 Proceedings of the 12th International Conference on Educational Data Mining (pp. 488-491).
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2019 Karumbaiah, S., Baker, R. S., Barany, A., & Shute, V. (2019). Using Epistemic Networks with Automated Codes to Understand Why Players Quit Levels in a Learning Game. In Communications in Computer and Information Science Vol. 1112 (pp. 106-116). Springer International Publishing.
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2019 Zou, X., Ma, W., Ma, Z., & Baker, R. S. (2019). Towards helping teachers select optimal content for students. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 11626 LNAI (pp. 413-417). Springer International Publishing.
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2019 Andres-Bray, J. M. L., Ocumpaugh, J. L., & Baker, R. S. (2019). Hello? Who is posting, who is answering, and who is succeeding in Massive Open Online Courses. In Edm 2019 Proceedings of the 12th International Conference on Educational Data Mining (pp. 492-495).
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2019 Gardner, J., Brooks, C., & Baker, R. (2019). Evaluating the fairness of predictive student models through slicing analysis. In ACM International Conference Proceeding Series (pp. 225-234). ACM.
DOI Scopus173
2019 Coleman, C., Baker, R. S., & Stephenson, S. (2019). A better cold-start for early prediction of student at-risk status in new school districts. In Edm 2019 Proceedings of the 12th International Conference on Educational Data Mining (pp. 732-737).
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2019 Raamadhurai, S., Baker, R. S., & Poduval, V. (2019). Curio smartchat : A system for natural language question answering for self-paced k-12 learning. In Acl 2019 Innovative Use of Nlp for Building Educational Applications Bea 2019 Proceedings of the 14th Workshop (pp. 336-342).
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2019 Botelho, A. F., Baker, R. S., & Heffernan, N. T. (2019). Machine-learned or expert-engineered features? Exploring feature engineering methods in detectors of student behavior and affect. In Edm 2019 Proceedings of the 12th International Conference on Educational Data Mining (pp. 508-511).
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2019 Molenaar, I., Horvers, A., & Baker, R. S. (2019). Towards hybrid human-system regulation: Understanding children' SRL support needs in blended classrooms. In ACM International Conference Proceeding Series (pp. 471-480). ACM.
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2018 Aghababyan, A., Lewkow, N., & Baker, R. S. (2018). Enhancing the clustering of student performance using the variation in confidence. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10858 LNCS (pp. 274-279). Springer International Publishing.
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2018 Karumbaiah, S., Baker, R. S., & Shute, V. (2018). Predicting quitting in students playing a learning game. In Proceedings of the 11th International Conference on Educational Data Mining Edm 2018.
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2018 Paquette, L., Baker, R. S., & Moskal, M. (2018). A system-general model for the detection of gaming the system behavior in CTAT and LearnSphere. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10948 LNAI (pp. 257-260). Springer International Publishing.
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2018 Andres, J. M. L., Baker, R. S., Gašević, D., Siemens, G., Crossley, S. A., & Joksimović, S. (2018). Studying MOOC completion at scale using the MOOC replication framework. In LAK 2018 Proceedings of the 8th International conference on learning analytics and knowledge (pp. 71-78). US: ACM Press.
DOI Scopus32 WoS23
2018 Botelho, A. F., Baker, R. S., Ocumpaugh, J., & Heffernan, N. T. (2018). Studying affect dynamics and chronometry using sensor-free detectors. In Proceedings of the 11th International Conference on Educational Data Mining Edm 2018.
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2018 Baker, R. S., Gowda, S. M., & Salamin, E. (2018). Modeling the learning that takes place between online assessments. In Icce 2018 26th International Conference on Computers in Education Main Conference Proceedings (pp. 21-28).
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2018 Agarwal, D., Babel, N., & Baker, R. S. (2018). Contextual derivation of stable BKT parameters for analyzing content efficacy. In Proceedings of the 11th International Conference on Educational Data Mining Edm 2018.
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2018 Kostyuk, V., Victoria Almeda, M., & Baker, R. S. (2018). Correlating affect and behavior in reasoning mind with state test achievement. In ACM International Conference Proceeding Series (pp. 26-30). ACM.
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2018 Gardner, J., Brooks, C., Andres, J. M., & Baker, R. (2018). Replicating MOOC predictive models at scale. In Proceedings of the 5th Annual ACM Conference on Learning at Scale L at S 2018 (pp. 1-10). ACM.
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2018 Gardner, J., Brooks, C., Andres, J. M., & Baker, R. S. (2018). MORF: A Framework for Predictive Modeling and Replication at Scale with Privacy-Restricted MOOC Data. In Proceedings 2018 IEEE International Conference on Big Data Big Data 2018 (pp. 3235-3244). IEEE.
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2018 Mojarad, S., Essa, A., Mojarad, S., & Baker, R. S. (2018). Data-driven learner profiling based on clustering student behaviors: Learning consistency, pace and effort. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10858 LNCS (pp. 130-139). Springer International Publishing.
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2018 Aslan, S., Okur, E., Alyuz, N., Arslan Esme, A., & Baker, R. S. (2018). Towards human affect modeling: A comparative analysis of discrete affect and valence-arousal labeling. In Communications in Computer and Information Science Vol. 851 (pp. 372-379). Springer International Publishing.
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2018 Karumbaiah, S., Andres, J. M. A. L., Botelho, A. F., Baker, R. S., & Ocumpaugh, J. (2018). The implications of a subtle difference in the calculation of affect dynamics. In Icce 2018 26th International Conference on Computers in Education Main Conference Proceedings (pp. 29-38).
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2018 Okur, E., Aslan, S., Alyuz, N., Arslan Esme, A., & Baker, R. S. (2018). Role of socio-cultural differences in labeling students’ affective states. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10947 LNAI (pp. 367-380). Springer International Publishing.
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2018 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?. In Proceedings of International Conference of the Learning Sciences Icls Vol. 3 (pp. 1385-1386).
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2018 Rowe, E., Asbell-Clarke, J., Baker, R., Gasca, S., Bardar, E., & Scruggs, R. (2018). Labeling implicit computational thinking in pizza pass gameplay. In Conference on Human Factors in Computing Systems Proceedings Vol. 2018-April (pp. 1-6). ACM.
DOI Scopus9
2018 Munshi, A., Rajendran, R., Ocumpaugh, J., Biswas, G., Baker, R. S., & Paquette, L. (2018). Modeling learners' cognitive and affective states to scaffold srl in open-ended learning environments. In Umap 2018 Proceedings of the 26th Conference on User Modeling Adaptation and Personalization (pp. 131-138). ACM.
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2018 Crossley, S., Ocumpaugh, J., Labrum, M., Bradfield, F., Dascalu, M., & Baker, R. S. (2018). Modeling math identity and math success through sentiment analysis and linguistic features. In Proceedings of the 11th International Conference on Educational Data Mining Edm 2018.
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2018 Aleven, V., Sewall, J., Andres, J. M., Sottilare, R., Long, R., & Baker, R. (2018). Towards Adapting to Learners at Scale: Integrating MOOC and intelligent tutoring frameworks. In Proceedings of the 5th Annual ACM Conference on Learning at Scale L at S 2018 (pp. 1-4). ACM.
DOI Scopus18
2018 Jiang, Y., Bosch, N., Baker, R. S., Paquette, L., Ocumpaugh, J., Andres, J. M. A. L., . . . Biswas, G. (2018). Expert feature-engineering vs. Deep neural networks: Which is better for sensor-free affect detection?. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10947 LNAI (pp. 198-211). Springer International Publishing.
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2018 Slater, S., Ocumpaugh, J., Baker, R., Li, J., & Labrum, M. (2018). Identifying changes in math identity through adaptive learning systems use. In Icce 2018 26th International Conference on Computers in Education Main Conference Proceedings (pp. 71-76).
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2017 Kai, S., Andres, J. M. L., Paquette, L., Baker, R. S., Molnar, K., Watkins, H., & Moore, M. (2017). Predicting student retention from behavior in an online orientation course. In Proceedings of the 10th International Conference on Educational Data Mining Edm 2017 (pp. 250-255).
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2017 Eagle, M., Corbett, A., Stamper, J., McLaren, B. M., Baker, R., Wagner, A., . . . Mitchell, A. (2017). Exploring learner model differences between students. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10331 LNAI (pp. 494-497). Springer International Publishing.
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2017 Ocumpaugh, J., Andres, J. M., Baker, R., DeFalco, J., Paquette, L., Rowe, J., . . . Sottilare, R. (2017). Affect dynamics in military trainees using vMedic: From engaged concentration to boredom to confusion. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10331 LNAI (pp. 238-249). Springer International Publishing.
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2017 Slater, S., Ocumpaugh, J., Baker, R., Almeda, M. V., Allen, L., & Heffernan, N. (2017). Using natural language processing tools to develop complex models of student engagement. In 2017 7th International Conference on Affective Computing and Intelligent Interaction Acii 2017 Vol. 2018-January (pp. 542-547). IEEE.
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2017 Xie, J., Mojarad, S., Shubeck, K., Essa, A., Baker, R. S., & Hu, X. (2017). Student learning strategies and behaviors to predict success in an online adaptive mathematics tutoring system. In Proceedings of the 10th International Conference on Educational Data Mining Edm 2017 (pp. 460-465).
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2017 Ocumpaugh, J., Baker, R. S., Pedro, M. O. C. Z. S., Hawn, M. A., Heffernan, C., Heffernan, N., & Slater, S. A. (2017). Guidance counselor reports of the ASSISTments college Prediction model (acpm). In ACM International Conference Proceeding Series (pp. 479-488). ACM.
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2017 Kai, S., Almeda, M. V., Baker, R. S., Shechtman, N., Heffernan, C., & Heffernan, N. (2017). Modeling wheel-spinning and productive persistence in skill builders. In Proceedings of the 10th International Conference on Educational Data Mining Edm 2017 (pp. 5).
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2017 Riedesel, M. A., Zimmerman, N., Baker, R., Titchener, T., & Cooper, J. (2017). Using a model for learning and memory to simulate learner response in spaced practice. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10331 LNAI (pp. 644-649). Springer International Publishing.
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2017 Baker, R., Beck, J. E., Chi, M., Heffernan, N. T., & Mozer, M. (2017). Workshop on deep learning with educational data. In Proceedings of the 10th International Conference on Educational Data Mining Edm 2017 (pp. 474).
2017 Slater, S., Baker, R., Almeda, M. V., Bowers, A., & Heffernan, N. (2017). Using correlational topic modeling for automated topic identification in intelligent tutoring systems. In ACM International Conference Proceeding Series (pp. 393-397). ACM.
DOI Scopus5
2017 Crossley, S. A., Dascalu, M., McNamara, D. S., Baker, R., & Trausan-Matu, S. (2017). Predicting success in massive open online courses (Moocs) using cohesion network analysis. In Computer Supported Collaborative Learning Conference Cscl Vol. 1 (pp. 103-110).
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2017 Andres, J. M. L., Baker, R. S., Siemens, G., Spann, C. A., Gašević, D., & Crossley, S. (2017). Studying MOOC completion at scale using the MOOC replication framework. In Proceedings of the 10th International Conference on Educational Data Mining Edm 2017 (pp. 338-339).
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2017 Aghababyan, A., Lewkow, N., & Baker, R. (2017). Exploring the asymmetry of metacognition. In ACM International Conference Proceeding Series (pp. 115-119). ACM.
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2017 Botelho, A. F., Baker, R. S., & Heffernan, N. T. (2017). Improving sensor-free affect detection using deep learning. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10331 LNAI (pp. 40-51). Springer International Publishing.
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2017 Agnihotri, L., Essa, A., & Baker, R. (2017). Impact of student choice of content adoption delay on course outcomes. In ACM International Conference Proceeding Series (pp. 16-20). ACM.
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2017 Wang, Y., Baker, R. S., & Paquette, L. (2017). Behavioral predictors of MOOC post-course development. In Ceur Workshop Proceedings Vol. 1967 (pp. 100-111).
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2017 Brooks, C., Baker, R., & Andres, J. M. L. (2017). Infrastructure for replication in learning analytics. In Ceur Workshop Proceedings Vol. 1915.
2017 Zimmerman, N. L., & Baker, R. S. (2017). Mining Knowledge Components from many untagged questions. In ACM International Conference Proceeding Series (pp. 566-567). ACM.
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2017 Paquette, L., & Baker, R. S. (2017). Variations of gaming behaviors across populations of students and across learning environments. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10331 LNAI (pp. 274-286). Springer International Publishing.
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2016 Owen, E. V., Anton, G., & Baker, R. (2016). Modeling user exploration and boundary testing in digital learning games. In Umap 2016 Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization (pp. 301-302). ACM.
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2016 Crossley, S., Mcnamara, D. S., Paquette, L., Baker, R. S., & Dascalu, M. (2016). Combining click-Stream data with NLP tools to better understand MOOC completion. In ACM International Conference Proceeding Series Vol. 25-29-April-2016 (pp. 6-14). ACM.
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2016 Aleven, V., Baker, R., Wang, Y., Sewall, J., & Popescu, O. (2016). Bringing non-programmer authoring of intelligent tutors to MOOCs. In L@s 2016 Proceedings of the 3rd 2016 ACM Conference on Learning at Scale (pp. 313-316). ACM.
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2016 Malkiewich, L., Baker, R. S., Shute, V., Kai, S., & Paquette, L. (2016). Classifying behavior to elucidate elegant problem solving in an educational game. In Proceedings of the 9th International Conference on Educational Data Mining Edm 2016 (pp. 448-453).
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2016 Ma, Y., Agnihotri, L., Baker, R., & Mojarad, S. (2016). Effect of student ability and question difficulty on duration. In Proceedings of the 9th International Conference on Educational Data Mining Edm 2016 (pp. 135-142).
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2016 Slater, S., Baker, R., Ocumpaugh, J., Inventado, P., Scupelli, P., & Heffernan, N. (2016). Semantic features of math problems: Relationships to student learning and engagement. In Proceedings of the 9th International Conference on Educational Data Mining Edm 2016 (pp. 223-230).
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2016 Godwin, K. E., Seltman, H., Almeda, M. V. Q., Kai, S., Baker, R. S., & Fisher, A. V. (2016). The Variable Relationship Between On-Task Behavior and Learning. In Proceedings of the 38th Annual Meeting of the Cognitive Science Society Cogsci 2016 (pp. 812-817).
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2016 Zhu, M., Bergner, Y., Zhang, Y., Baker, R., Wang, Y., & Paquette, L. (2016). Longitudinal engagement, performance, and social connectivity: A MOOC case study using exponential random graph models. In ACM International Conference Proceeding Series Vol. 25-29-April-2016 (pp. 223-230). ACM.
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2016 Inventado, P. S., Scupelli, P., Van Inwegen, E. G., Ostrow, K. S., Heffernan, N., Ocumpaugh, J., . . . Almeda, M. (2016). Hint availability slows completion times in summer work. In Proceedings of the 9th International Conference on Educational Data Mining Edm 2016 (pp. 388-393).
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2016 Eagle, M., Corbett, A., Stamper, J., McLaren, B. M., Baker, R., Wagner, A., . . . Mitchell, A. (2016). Predicting individual differences for learner modeling in intelligent tutors from previous learner activities. In Umap 2016 Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization (pp. 55-63). ACM.
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2016 Liu, Z., Brown, R., Lynch, C. F., Barnes, T., Baker, R., Bergner, Y., & McNamara, D. (2016). MOOC learner behaviors by country and culture; An exploratory analysis. In Proceedings of the 9th International Conference on Educational Data Mining Edm 2016 (pp. 127-134).
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2016 Bosch, N., D'Mello, S. K., Baker, R. S., Ocumpaugh, J., Shute, V., Ventura, M., . . . Zhao, W. (2016). Detecting student emotions in computer-enabled classrooms. In Ijcai International Joint Conference on Artificial Intelligence Vol. 2016-January (pp. 4125-4129).
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2015 Bosch, N., D'Mello, S., Baker, R., Ocumpaugh, J., Shute, V., Ventura, M., . . . Zhao, W. (2015). Automatic detection of learning-centered affective states in the wild. In International Conference on Intelligent User Interfaces Proceedings IUI Vol. 2015-January (pp. 379-388). ACM.
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2015 Brown, R., Lynch, C., Wang, Y., Eagle, M., Albert, J., Barnes, T., . . . McNamara, D. (2015). Communities of performance & communities of preference. In Ceur Workshop Proceedings Vol. 1446.
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2015 Miller, W. L., Baker, R. S., Labrum, M. J., Petsche, K., Liu, Y. H., & Wagner, A. Z. (2015). Automated detection of proactive remediation by teachers in reasoning mind classrooms. In ACM International Conference Proceeding Series Vol. 16-20-March-2015 (pp. 290-294). ACM.
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2015 Ocumpaugh, J., Baker, R. S., Rodrigo, M. M., Salvi, A., Van Velsen, M., Aghababyan, A., & Martin, T. (2015). HART: The human affect recording tool. In SIGDOC 2015 Proceedings of the 33rd Annual International Conference on the Design of Communication (pp. 1-6). ACM.
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2015 Andres, J. M. L., Rodrigo, M. M. T., Baker, R. S., Paquette, L., Shute, V. J., & Ventura, M. (2015). Analyzing student action sequences and affect while playing Physics Playground. In Ceur Workshop Proceedings Vol. 1432 (pp. 24-33).
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2015 Andres, J. M. L., Rodrigo, M. M. T., Baker, R. S., Paquette, L., Shute, V. J., & Ventura, M. (2015). Analyzing student action sequences and affect while playing physics playground. In Ceur Workshop Proceedings Vol. 1446.
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2015 Bosch, N., Chen, H., Baker, R., Shute, V., & D'mello, S. (2015). Accuracy vs. Availability heuristic in multimodal affect detection in the wild. In Icmi 2015 Proceedings of the 2015 ACM International Conference on Multimodal Interaction (pp. 267-274). ACM.
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2015 Andres, J. M. A. L., Andresa, J. M. L., Rodrigo, M. M. T., Baker, R. S., & Beck, J. B. (2015). An investigation of eureka and the affective states surrounding eureka moments. In Proceedings of the 23rd International Conference on Computers in Education Icce 2015 (pp. 103-105).
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2015 Mulqueeny, K., Mingle, L. A., Kostyuk, V., Baker, R. S., & Ocumpaugh, J. (2015). Improving engagement in an E-learning environment. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 9112 (pp. 730-733). Springer International Publishing.
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2015 Bosch, N., D’Mello, S., Baker, R., Ocumpaugh, J., & Shute, V. (2015). Temporal generalizability of face-based affect detection in noisy classroom environments. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 9112 (pp. 44-53). Springer International Publishing.
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2015 Jiang, Y., Baker, R. S., Paquette, L., Pedro, M. S., & Heffernan, N. T. (2015). Learning, moment-by-moment and over the long term. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 9112 (pp. 654-657). Springer International Publishing.
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2015 Paquette, L., Baker, R. S., de Carvalho, A., & Ocumpaugh, J. (2015). Cross-system transfer of machine learned and knowledge engineered models of gaming the system. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 9146 (pp. 189-194). Springer International Publishing.
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2015 Pedro, M. O. Z. S., Baker, R. S., Heffernan, N. T., & Ocumpaugh, J. L. (2015). Exploring college major choice and middle school student behavior, affect and learning: What happens to students who game the system?. In ACM International Conference Proceeding Series Vol. 16-20-March-2015 (pp. 36-40). ACM.
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2015 Moore, G. R., Baker, R. S., & Gowda, S. M. (2015). The Antecedents of Moments of Learning. In Proceedings of the 37th Annual Meeting of the Cognitive Science Society Cogsci 2015 (pp. 1631-1636).
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2015 Rosé, C. P., Ferschke, O., Tomar, G., Yang, D., Howley, I., Aleven, V., . . . Baker, R. (2015). Challenges and opportunities of dual-layer MOOCs: Reflections from an edX deployment study. In Computer Supported Collaborative Learning Conference Cscl Vol. 2 (pp. 848-851).
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2015 Kovanović, V., Gašević, D., Dawson, S., Joksimović, S., Baker, R. S., & Hatala, M. (2015). Penetrating the black box of time-on-task estimation. In ACM International Conference Proceeding Series Vol. 16-20-March-2015 (pp. 184-193). US: ACM.
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2015 Aleven, V., Sewall, J., Popescu, O., Xhakaj, F., Chand, D., Baker, R., . . . Gasevic, D. (2015). The beginning of a beautiful friendship? Intelligent tutoring systems and MOOCs. In C. Conati (Ed.), Artificial Intelligence in Education: 17th International Conference, AIED 2015 - Proceedings Vol. 9112 (pp. 525-528). Switzerland: Springer.
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2014 Ocumpaugh, J., Baker, R. S., Kamarainen, A. M., & Metcalf, S. J. (2014). Modifying field observation methods on the fly: Creative Metanarrative and Disgust in an environmental MUVE. In Ceur Workshop Proceedings Vol. 1181 (pp. 49-54).
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2014 Almeda, M. V., Scupelli, P., Baker, R. S., Weber, M., & Fisher, A. (2014). Clustering of design decisions in classroom visual displays. In ACM International Conference Proceeding Series (pp. 44-48). ACM.
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2014 Baker, R. S., Ocumpaugh, J., Gowda, S. M., Kamarainen, A. M., & Metcalf, S. J. (2014). Extending log-based affect detection to a multi-user virtual environment for science. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 8538 (pp. 290-300). Springer International Publishing.
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2014 Paquette, L., de Carvalho, A. M. J. A., & Baker, R. S. (2014). Towards Understanding Expert Coding of Student Disengagement in Online Learning. In Proceedings of the 36th Annual Meeting of the Cognitive Science Society Cogsci 2014 (pp. 1126-1131).
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2014 Pedro, M. S., Jiang, Y., Paquette, L., Baker, R. S., & Gobert, J. (2014). Identifying transfer of inquiry skills across physical science simulations using educational data mining. In Proceedings of International Conference of the Learning Sciences Icls Vol. 1 (pp. 222-229).
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2014 Paquette, L., Baker, R. S. J. D., Sao Pedro, M. A., Gobert, J. D., Rossi, L., Nakama, A., & Kauffman-Rogoff, Z. (2014). Sensor-free affect detection for a simulation-based science inquiry learning environment. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 8474 LNCS (pp. 1-10). Springer International Publishing.
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2014 Hawkins, W. J., Heffernan, N. T., & Baker, R. S. J. D. (2014). Learning bayesian knowledge tracing parameters with a knowledge heuristic and empirical probabilities. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 8474 LNCS (pp. 150-155). Springer International Publishing.
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2014 Andres, J. M. L., Rodrigo, M. M. T., Sugay, J. O., Baker, R. S., Paquette, L., Shute, V. J., . . . Small, M. (2014). An exploratory analysis of confusion among students using Newton's playground. In Proceedings of the 22nd International Conference on Computers in Education Icce 2014 (pp. 65-70).
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2013 Buckingham Shum, S., Hawksey, M., Baker, R. S. J. D., Jeffery, N., Behrens, J. T., & Pea, R. (2013). Educational data scientists: A scarce breed. In ACM International Conference Proceeding Series (pp. 278-281). ACM.
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2013 Corbett, A., MacLaren, B., Wagner, A., Kauffman, L., Mitchell, A., & Baker, R. S. J. D. (2013). Enhancing Robust Learning Through Problem Solving in the Genetics Cognitive Tutor. In Cooperative Minds Social Interaction and Group Dynamics Proceedings of the 35th Annual Meeting of the Cognitive Science Society Cogsci 2013 (pp. 2094-2099).
2013 Goldin, I. M., Martin, T., Baker, R., Aleven, V., & Barnes, T. (2013). Formative feedback in interactive learning environments. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7926 LNAI (pp. 946). Springer Berlin Heidelberg.
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2013 Corbett, A., MacLaren, B., Wagner, A., Kauffman, L., Mitchell, A., & Baker, R. S. J. D. (2013). Differential impact of learning activities designed to support robust learning in the genetics cognitive tutor. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7926 LNAI (pp. 319-328). Springer Berlin Heidelberg.
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2013 Hershkovitz, A., Baker, R. S. J. D., Moore, G. R., Rossi, L. M., & Van Velsen, M. (2013). The interplay between affect and engagement in classrooms using AIED software. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7926 LNAI (pp. 587-590). Springer Berlin Heidelberg.
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2013 Ocumpaugh, J., Baker, R. S. J. D., Gaudino, S., Labrum, M. J., & Dezendorf, T. (2013). Field observations of engagement in reasoning mind. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7926 LNAI (pp. 624-627). Springer Berlin Heidelberg.
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2013 Hawkins, W., Heffernan, N., Wang, Y., & Baker, R. S. J. D. (2013). Extending the assistance model: Analyzing the use of assistance over time. In Proceedings of the 6th International Conference on Educational Data Mining Edm 2013.
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2013 Pardos, Z. A., Baker, R. S. J. D., San Pedro, M. O. C. Z., Gowda, S. M., & Gowda, S. M. (2013). Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. In ACM International Conference Proceeding Series (pp. 117-124). ACM.
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2013 Doddannara, L. S., Gowda, S. M., Baker, R. S. J. D., Gowda, S. M., & De Carvalho, A. M. J. B. (2013). Exploring the relationships between design, students' affective states, and disengaged behaviors within an its. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7926 LNAI (pp. 31-40). Springer Berlin Heidelberg.
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2013 Godwin, K. E., Almeda, M. V., Petroccia, M., Baker, R. S., & Fisher, A. V. (2013). Classroom activities and off-task behavior in elementary school children. In Cooperative Minds Social Interaction and Group Dynamics Proceedings of the 35th Annual Meeting of the Cognitive Science Society Cogsci 2013 (pp. 2428-2433).
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2013 Sao Pedro, M. A., Baker, R. S. J. D., & Gobert, J. D. (2013). What different kinds of stratification can reveal about the generalizability of data-mined skill assessment models. In ACM International Conference Proceeding Series (pp. 190-194). ACM.
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2013 Liu, Z., Baker, R. S. J. D., Pataranutaporn, V., & Ocumpaugh, J. (2013). Sequences of frustration and confusion, and learning. In Proceedings of the 6th International Conference on Educational Data Mining Edm 2013.
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2013 San Pedro, M. O. Z., Baker, R. S. J. D., Bowers, A. J., & Heffernan, N. T. (2013). Predicting college enrollment from student interaction with an intelligent tutoring system in middle school. In Proceedings of the 6th International Conference on Educational Data Mining Edm 2013.
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2013 San Pedro, M. O. Z., Baker, R. S. J. D., Gowda, S. M., & Heffernan, N. T. (2013). Towards an understanding of affect and knowledge from student interaction with an intelligent tutoring system. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7926 LNAI (pp. 41-50). Springer Berlin Heidelberg.
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2013 Hershkovitz, A., Baker, R. S. J. D., Gowda, S. M., & Corbett, A. T. (2013). Predicting future learning better using quantitative analysis of moment-by-moment learning. In Proceedings of the 6th International Conference on Educational Data Mining Edm 2013.
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2013 Hawkins, W., Heffernan, N., & Baker, R. S. J. D. (2013). Which is more responsible for boredom in intelligent tutoring systems: Students (trait) or problems (state)?. In Proceedings 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction Acii 2013 (pp. 618-623). IEEE.
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2013 DeFalco, J. A., & Baker, R. S. J. D. (2013). Detection and transition analysis of engagement and affect in a simulation-based combat medic training environment. In Ceur Workshop Proceedings Vol. 1009 (pp. 88-94).
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2013 Sao Pedro, M. A., Baker, R. S. J. D., & Gobert, J. D. (2013). Incorporating scaffolding and tutor context into Bayesian knowledge tracing to predict inquiry skill acquisition. In Proceedings of the 6th International Conference on Educational Data Mining Edm 2013.
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2013 Baker, R. S. J. D., & Clarke-Midura, J. (2013). Predicting successful inquiry learning in a virtual performance assessment for science. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7899 LNCS (pp. 203-214). Springer Berlin Heidelberg.
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2012 Sao Pedro, M. A., Baker, R. S. J. D., & Gobert, J. D. (2012). Improving construct validity yields better models of systematic inquiry, even with less information. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7379 LNCS (pp. 249-260). Springer Berlin Heidelberg.
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2012 Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: towards communication and collaboration. In S. Shum, D. Gasevic, & R. Ferguson (Eds.), LAK 2012 Proceedings of the 2nd International conference on learning analytics and knowledge (pp. 252-254). US: ACM Press.
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2012 Hershkovitz, A., Baker, R. S. J. D., Gobert, J., & Nakama, A. (2012). A data-driven path model of student attributes, affect, and engagement in a computer-based science inquiry microworld. In 10th International Conference of the Learning Sciences the Future of Learning Icls 2012 Proceedings Vol. 1 (pp. 167-174).
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2012 Wixon, M., Baker, R. S. J. D., Gobert, J. D., Ocumpaugh, J., & Bachmann, M. (2012). WTF? Detecting students who are conducting inquiry without thinking fastidiously. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7379 LNCS (pp. 286-296). Springer Berlin Heidelberg.
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2012 Baker, R. S. J. D., Gowda, S. M., Corbett, A. T., & Ocumpaugh, J. (2012). Towards automatically detecting whether student learning is shallow. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7315 LNCS (pp. 444-453). Springer Berlin Heidelberg.
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2012 Roberge, D., Rojas, A., & Baker, R. (2012). Does the length of time off-task matter?. In ACM International Conference Proceeding Series (pp. 234-237). ACM.
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2012 Roll, I., Aleven, V., Koedinger, K. R., Berland, M., Martin, T., Benton, T., . . . Pea, R. (2012). Building (Timely) bridges between learning analytics, educational data mining and core learning sciences perspectives. In 10th International Conference of the Learning Sciences the Future of Learning Icls 2012 Proceedings Vol. 2 (pp. 134-141).
2012 Ogan, A., Walker, E., Baker, R. S. J. D., Rebolledo-Mendez, G., Castro, M. J., Laurentino, T., & De Carvalho, A. (2012). Collaboration in cognitive tutor use in Latin America: Field study and design recommendations. In Conference on Human Factors in Computing Systems Proceedings (pp. 1381-1390). ACM.
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2012 Gowda, S. M., Pardos, Z. A., & Baker, R. S. J. D. (2012). Content learning analysis using the moment-by-moment learning detector. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7315 LNCS (pp. 434-443). Springer Berlin Heidelberg.
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2012 Baker, R. S. J. D., Gowda, S. M., Wixon, M., Kalka, J., Wagner, A. Z., Salvi, A., . . . Rossi, L. (2012). Towards sensor-free affect detection in cognitive tutor algebra. In Proceedings of the 5th International Conference on Educational Data Mining Edm 2012.
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2012 Soriano, J. C. A., Rodrigo, M. M. T., Baker, R. S. J. D., Ogan, A., Walker, E., Castro, M. J., . . . Belmontez, R. (2012). A cross-cultural comparison of effective help-seeking behavior among students using an ITS for math. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7315 LNCS (pp. 636-637). Springer Berlin Heidelberg.
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2011 Corbett, A., MacLaren, B., Wagner, A., Kauffman, L., Mitchell, A., Baker, R. S. J. D., & Gowda, S. M. (2011). Preparing Students for Effective Explaining of Worked Examples in the Genetics Cognitive Tutor. In Expanding the Space of Cognitive Science Proceedings of the 33rd Annual Meeting of the Cognitive Science Society Cogsci 2011 (pp. 1476-1481).
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2011 Baker, R. S. J. D., Moore, G. R., Wagner, A. Z., Kalka, J., Salvi, A., Karabinos, M., . . . Yaron, D. (2011). The dynamics between student affect and behavior occurring outside of educational software. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6974 LNCS (pp. 14-24). Springer Berlin Heidelberg.
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2011 Stamper, J. C., Koedinger, K. R., Baker, R. S. J. D., Skogsholm, A., Leber, B., Demi, S., . . . Spencer, D. (2011). Managing the educational dataset lifecycle with datashop. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6738 LNAI (pp. 557-559). Springer Berlin Heidelberg.
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2011 Stamper, J. C., Koedinger, K. R., Baker, R. S. J. D., Skogsholm, A., Leber, B., Demi, S., & Yu, S. (2011). DataShop: A data repository and analysis service for the learning science community (interactive event). In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6738 LNAI (pp. 628). Springer Berlin Heidelberg.
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2011 Walker, E., Ogan, A., Baker, R. S. J. D., De Carvalho, A., Laurentino, T., Rebolledo-Mendez, G., & Castro, M. J. (2011). Observations of collaboration in cognitive tutor use in latin america. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6738 LNAI (pp. 575-577). Springer Berlin Heidelberg.
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2011 Nooraei, B., Pardos, Z. A., Heffernan, N., & Baker, R. S. J. D. (2011). Less is more: Improving the speed and prediction power of knowledge tracing by using less data. In Edm 2011 Proceedings of the 4th International Conference on Educational Data Mining (pp. 101-109).
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2011 Lee, D. M. C., Rodrigo, M. M. T., Baker, R. S. J. D., Sugay, J. O., & Coronel, A. (2011). Exploring the relationship between novice programmer confusion and achievement. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6974 LNCS (pp. 175-184). Springer Berlin Heidelberg.
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2011 Gowda, S. M., Rowe, J. P., Baker, R. S. J. D., Chi, M., & Koedinger, K. R. (2011). Improving models of slipping, guessing, and moment-by-moment learning with estimates of skill difficulty. In Edm 2011 Proceedings of the 4th International Conference on Educational Data Mining (pp. 199-208).
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2011 Hershkovitz, A., Wixon, M., Baker, R. S. J. D., Gobert, J., & Sao Pedro, M. (2011). Carelessness and goal orientation in a science microworld. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6738 LNAI (pp. 462-465). Springer Berlin Heidelberg.
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2011 Baker, R. S. J. D., Pardos, Z. A., Gowda, S. M., Nooraei, B. B., & Heffernan, N. T. (2011). Ensembling predictions of student knowledge within intelligent tutoring systems. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6787 LNCS (pp. 13-24). Springer Berlin Heidelberg.
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2011 Pardos, Z. A., Gowda, S. M., Baker, R. S. J. D., & Heffernan, N. T. (2011). Ensembling predictions of student post-test scores for an intelligent tutoring system. In Edm 2011 Proceedings of the 4th International Conference on Educational Data Mining (pp. 189-198).
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2011 Hershkovitz, A., Baker, R. S. J. D., Gobert, J., & Wixon, M. (2011). Goal orientation and changes of carelessness over consecutive trials in science inquiry. In Edm 2011 Proceedings of the 4th International Conference on Educational Data Mining (pp. 315-316).
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2011 San Pedro, M. O. C. Z., Baker, R. S. J. D., & Rodrigo, M. M. T. (2011). Detecting carelessness through contextual estimation of slip probabilities among students using an intelligent tutor for mathematics. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6738 LNAI (pp. 304-311). Springer Berlin Heidelberg.
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2011 Baker, R. S. J. D., Gowda, S. M., & Corbett, A. T. (2011). Automatically detecting a student's preparation for future learning: Help use is key. In Edm 2011 Proceedings of the 4th International Conference on Educational Data Mining (pp. 179-188).
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2011 San Pedro, M. O. C. Z., Rodrigo, M. M. T., & Baker, R. S. J. D. (2011). The relationship between carelessness and affect in a cognitive tutor. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6974 LNCS (pp. 306-315). Springer Berlin Heidelberg.
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2011 Baker, R. S. J. D., Gowda, S. M., & Corbett, A. T. (2011). Towards predicting future transfer of learning. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6738 LNAI (pp. 23-30). Springer Berlin Heidelberg.
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2011 Baker, R. S. J. D., Goldstein, A. B., & Heffernan, N. T. (2011). Detecting learning moment-by-moment. In International Journal of Artificial Intelligence in Education Vol. 21 (pp. 5-25). IOS Press.
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2010 Baker, R. S. J. D., Goldstein, A. B., & Heffernan, N. T. (2010). Detecting the moment of learning. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6094 LNCS (pp. 25-34). Springer Berlin Heidelberg.
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2010 Baker, R. S. J. D., Mitrović, A., & Mathews, M. (2010). Detecting gaming the system in constraint-based tutors. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6075 LNCS (pp. 267-278). Springer Berlin Heidelberg.
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2010 Giguere, S., Beck, J., & Baker, R. (2010). Analyzing student gaming with Bayesian networks. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6095 LNCS (pp. 321-323). Springer Berlin Heidelberg.
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2010 Baker, R. S. J. D., & Gowda, S. M. (2010). An analysis of the differences in the frequency of students' disengagement in urban, rural, and suburban high schools. In Educational Data Mining 2010 3rd International Conference on Educational Data Mining (pp. 11-20).
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2010 Gobert, J. D., Montalvo, O., Toto, E., Pedro, M. S., & Baker, R. S. J. D. (2010). The science assistments project: Scaffolding scientific inquiry skills. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6095 LNCS (pp. 445). Springer Berlin Heidelberg.
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2010 Montalvo, O., Baker, R. S. J. D., Pedro, M. A. S., Nakama, A., & Gobert, J. D. (2010). Identifying students' inquiry planning using machine learning. In Educational Data Mining 2010 3rd International Conference on Educational Data Mining (pp. 141-150).
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2010 Sao Pedro, M. A., Baker, R. S. J. D., Montalvo, O., Nakama, A., & Gobert, J. D. (2010). Using text replay tagging to produce detectors of systematic experimentation behavior patterns. In Educational Data Mining 2010 3rd International Conference on Educational Data Mining (pp. 181-190).
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2010 Stamper, J., Koedinger, K., Baker, R. S. J. D., Skogsholm, A., Leber, B., Rankin, J., & Demi, S. (2010). PSLC DataShop: A data analysis service for the learning science community. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6095 LNCS (pp. 455). Springer Berlin Heidelberg.
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2010 Rodrigo, M. M. T., Baker, R. S. J. D., Agapito, J., Nabos, J., Repalam, M. C., & Reyes, S. S. (2010). Comparing disengaged behavior within a cognitive tutor in the USA and Philippines. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6095 LNCS (pp. 263-265). Springer Berlin Heidelberg.
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2010 Baker, R. S. J. D., Corbett, A. T., Gowda, S. M., Wagner, A. Z., MacLaren, B. A., Kauffman, L. R., . . . Giguere, S. (2010). Contextual slip and prediction of student performance after use of an intelligent tutor. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 6075 LNCS (pp. 52-63). Springer Berlin Heidelberg.
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2010 Baker, R. S. J. D., Gobert, J. D., Van Joolingen, W., Azevedo, R., Roll, I., São Pedro, M., . . . Greesser, A. (2010). Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors. In Learning in the Disciplines Icls 2010 Conference Proceedings 9th International Conference of the Learning Sciences Vol. 2 (pp. 45-52).
2010 Rodrigo, M. M. T., Baker, R. S. J. D., & Nabos, J. Q. (2010). The relationships between sequences of affective states and learner achievement. In Proceedings of the 18th International Conference on Computers in Education Enhancing and Sustaining New Knowledge Through the Use of Digital Technology in Education Icce 2010 (pp. 56-60).
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2009 Rodrigo, M. M. T., Baker, R. S., Jadud, M. C., Amarra, A. C. M., Dy, T., Espejo-Lahoz, M. B. V., . . . Tabanao, E. S. (2009). Affective and behavioral predictors of novice programmer achievement. In Proceedings of the Conference on Integrating Technology into Computer Science Education Iticse (pp. 156-160). ACM.
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2009 Baker, R. S. J. D., De Carvalho, A. M. J. B., Raspat, J., Aleven, V., Corbett, A. T., & Koedinger, K. R. (2009). Educational software features that encourage and discourage "gaming the system". In Frontiers in Artificial Intelligence and Applications Vol. 200 (pp. 475-482). IOS Press.
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2009 Prata, D. N., Baker, R. S. J. D., Costa, E. D. B., Rosé, C. P., Cui, Y., & De Carvalho, A. M. J. B. (2009). Detecting and understanding the impact of cognitive and interpersonal conflict in computer supported collaborative learning environments. In Edm 09 Educational Data Mining 2009 2nd International Conference on Educational Data Mining (pp. 131-140).
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2009 Rodrigo, M. M. T., & Baker, R. S. J. D. (2009). Coarse-grained detection of student frustration in an introductory programming course. In Icer 09 Proceedings of the 2009 ACM Workshop on International Computing Education Research (pp. 75-79). ACM.
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2009 Cocea, M., Hershkovitz, A., & Baker, R. S. J. D. (2009). The impact of off-task and gaming behaviors on learning: Immediate or aggregate. In Frontiers in Artificial Intelligence and Applications Vol. 200 (pp. 507-514). IOS Press.
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2009 Baker, R. S. J. D. (2009). Differences between intelligent tutor lessons, and the choice to go off-task. In Edm 09 Educational Data Mining 2009 2nd International Conference on Educational Data Mining (pp. 11-20).
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2008 Baker, R. S. J. D., Corbett, A. T., & Aleven, V. (2008). Improving contextual models of guessing and slipping with a truncated training set. In Educational Data Mining 2008 1st International Conference on Educational Data Mining Proceedings (pp. 67-76).
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2008 Baker, R. S. J. D., & De Carvalho, A. M. J. A. (2008). Labeling student behavior faster and more precisely with text replays. In Educational Data Mining 2008 1st International Conference on Educational Data Mining Proceedings (pp. 38-47).
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2008 Baker, R. S. J. D., Corbett, A. T., & Aleven, V. (2008). More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 5091 LNCS (pp. 406-415). Springer Berlin Heidelberg.
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2008 Rodrigo, M. M. T., Anglo, E. A., Sugay, J. O., & Baker, R. S. J. D. (2008). Use of unsupervised clustering to characterize learner behaviors and affective states while using an intelligent tutoring system. In Proceedings Icce 2008 16th International Conference on Computers in Education (pp. 49-56).
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2008 Rodrigo, M. M. T., Baker, R. S. J. D., D'Mello, S., Gonzalez, M. C. T., Lagud, M. C. V., Lim, S. A. L., . . . Viehland, N. J. B. (2008). Comparing learners' affect while using an intelligent tutoring system and a simulation problem solving game. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 5091 LNCS (pp. 40-49). Springer Berlin Heidelberg.
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2008 Rodrigo, M. M. T., Rebolledo-Mendez, G., Baker, R. S. J. D., Du Boulay, B., Sugay, J. O., Lim, S. A. L., . . . Luckin, R. (2008). The effects of motivational modeling on affect in an intelligent tutoring system. In Proceedings Icce 2008 16th International Conference on Computers in Education (pp. 57-64).
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2007 Rodrigo, M. M. T., Baker, R. S. J. D., Lagud, M. C. V., Lim, S. A. L., Macapanpan, A. F., Pascua, S. A. M. S., . . . Viehland, N. J. B. (2007). Affect and Usage Choices in Simulation Problem-Solving Environments. In Frontiers in Artificial Intelligence and Applications Vol. 158 (pp. 145-152).
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2007 Baker, R. S. J. D., Habgood, M. P. J., Ainsworth, S. E., & Corbett, A. T. (2007). Modeling the acquisition of fluent skill in educational action games. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 4511 LNCS (pp. 17-26). Springer Berlin Heidelberg.
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2007 Baker, R. S. J. D., Rodrigo, M. M. T., & Xolocotzin, U. E. (2007). The dynamics of affective transitions in simulation problem-solving environments. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 4738 LNCS (pp. 666-677). Springer Berlin Heidelberg.
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2007 Baker, R. S. J. D. (2007). Modeling and understanding students' off-task behavior in intelligent tutoring systems. In Conference on Human Factors in Computing Systems Proceedings (pp. 1059-1068). ACM.
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2006 Baker, R. S. J. D., Corbett, A. T., Koedinger, K. R., & Roll, I. (2006). Generalizing detection of gaming the system across a tutoring curriculum. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 4053 LNCS (pp. 402-411). Springer Berlin Heidelberg.
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2006 Baker, R. S. J. D., Corbett, A. T., Koedinger, K. R., Evenson, S., Roll, I., Wagner, A. Z., . . . Beck, J. E. (2006). Adapting to when students game an intelligent tutoring system. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 4053 LNCS (pp. 392-401). Springer Berlin Heidelberg.
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2006 Roll, I., Aleven, V., McLaren, B. M., Ryu, E., Baker, R. S. J. D., & Koedinger, K. R. (2006). The help tutor: Does metacognitive feedback improve students' help-seeking actions, skills and learning?. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 4053 LNCS (pp. 360-369). Springer Berlin Heidelberg.
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2005 Roll, I., Baker, R. S., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2005). Modeling students' metacognitive errors in two intelligent tutoring systems. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 3538 LNAI (pp. 367-376). Springer Berlin Heidelberg.
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2005 Baker, R. S., Corbett, A. T., Koedinger, K. R., & Roll, I. (2005). Detecting when students game the system, across tutor subjects and classroom cohorts. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 3538 LNAI (pp. 220-224). Springer Berlin Heidelberg.
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2005 Baker, R. S., Roll, I., Corbett, A. T., & Koedinger, K. R. (2005). Do Performance Goals Lead Students to Game the System?. In Frontiers in Artificial Intelligence and Applications Vol. 125 (pp. 57-64).
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2005 Fogarty, J., Baker, R. S., & Hudson, S. E. (2005). Case studies in the use of ROC curve analysis for sensor-based estimates in human computer interaction. In Proceedings Graphics Interface (pp. 129-136).
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2004 Baker, R. S., Corbett, A. T., Koedinger, K. R., & Wagner, A. Z. (2004). Off-task behavior in the cognitive tutor classroom: When students "game the system". In Conference on Human Factors in Computing Systems Proceedings (pp. 383-390).
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2004 Beck, J., Baker, R., Corbett, A., Kay, J., Litman, D., Mitrovic, T., & Ritter, S. (2004). Workshop on analyzing student-tutor interaction logs to improve educational outcomes. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 3220 (pp. 909).
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2003 Dabbish, L. A., & Baker, R. S. (2003). Administrative assistants as interruption mediators. In Conference on Human Factors in Computing Systems Proceedings (pp. 1020-1021). ACM Press.
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1999 Baker, R. S., Boilen, M., Goodrich, M. T., Tamassia, R., & Stibel, B. A. (1999). Testers and visualizers for teaching data structures. In SIGCSE 1999 Proceedings of the 13th SIGCSE Technical Symposium on Computer Science Education (pp. 261-265). ACM.
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  • EdPsych FY25 UoSA - University of Minnesota, I-University of Minnesota, 30/04/2025 - 31/01/2027

Date Role Research Topic Program Degree Type Student Load Student Name
2026 Co-Supervisor 111126 - Developing learner profile of university students: Assessing and monitoring the development of critical skills and competencies during their tertiary learning Doctor of Philosophy Doctorate Full Time Vimukthini Jayalath

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