Godwin, K. E., Almeda, M. V., Seltman, H., Kai, S., Skerbetz, M. D., Baker, R. S., Fisher, A.V. (in press). Off-task behavior in elementary school children. Learning and Instruction.
Bosch, N., D’Mello, S. K., Ocumpaugh, J., Baker, R. S., & Shute, V. (in press). Using video to automatically detect learner affect in computer-enabled classrooms. ACM Transactions on Interactive Intelligent Systems.
Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence and Education, 26(2), 600–614.
Baker, R., Clarke-Midura, J., & Ocumpaugh, J. (2016). Toward general models of effective science inquiry in virtual performance assessments. Journal of Computer Assisted Learning, 32(3), 267–280.
Kovanovic, V., Gasevic, D., Dawson, S., Joksimovic, S., Baker, R. S., & Hatala, M. (2015). Does time-on-task estimation matter? Implications on validity of learning analytics findings. Journal of Learning Analytics, 2(3), 81–110.
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(15).
Shute, V. J., D'Mello, S., Baker, R., Cho, K., Bosch, N., Ocumpaugh, J., Ventura, M., & Almeda, V. (2015). Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Computers & Education, 86, 224–235.
Comer, D., Baker, R., & Wang, Y. (2015). Negativity in massive online open courses: Impacts on learning and teaching. InSight: A Journal of Scholarly Teaching, 10.
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.
Gobert, J. D., Baker, R. S., & Wixon, M. B. (2015). Operationalizing and detecting disengagement within online science microworlds. Educational Psychologist, 50(1), 43–57.
Wang, Y., & Baker, R. (2015). Content or platform: Why do students complete MOOCs? MERLOT Journal of Online Learning and Teaching, 11(1), 17–30.
Wang, Y. E., Paquette, L., & Baker, R. (2014). A longitudinal study on learner career advancement in MOOCs. Journal of Learning Analytics, 1(3), 203–206.
Roll, I., Baker, R. S. Jd., 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.
Baker, R. S., Corbett, A.T. (2014). Assessment of robust learning with educational data mining. Research & Practice in Assessment, 9, 38–50.
Baker, R. S. (2014). Educational data mining: An advance for intelligent systems in education. IEEE Intelligent Systems, 29 (3), 78–82.
Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge, and Learning, 19, 205–220.
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, 165–181.
Pardos, Z. A., Baker, R. S., San Pedro, M. O. C. Z., Gowda, S. M., & Gowda, S.M. (2014). Affective states and state tests: Investigating how affect and engagement during the school year predict end of year learning outcomes. Journal of Learning Analytics, 1 (1), 107–128.
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.
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, 189–210.
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.
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.
Porayska-Pomsta, K., Mavrikis, M., D’Mello, S., Conati, C., & Baker, R. S. J. d. (2013). Knowledge elicitation methods for affect modeling in education. International Journal of Artificial Intelligence in Education, 22(3), 107–140.
Baker, R. S. J. d., 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.
Gobert, J. D., Sao Pedro, M., Raziuddin, J., & Baker, R. (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.
Gowda, S. M., Baker, R. S. J. d., Corbett, A. T., & Rossi, L. M. (2013). Towards automatically detecting whether student learning is shallow. International Journal of Artificial Intelligence in Education, 23(1), 50–70.
Hershkovitz, A., Baker, R. S. J. d., Gobert, J., Wixon, M., & Sao Pedro, M. (2013). Discovery with models: A case study on carelessness in computer-based science inquiry. American Behavioral Scientist, 57(10), 1479–1498.
Winne, P. H., & Baker, R. S. J. d. (2013). The potentials of educational data mining for researching metacognition, motivation, and self-regulated learning. Journal of Educational Data Mining, 5(1), 1–8.
Sao Pedro, M. A., Baker, R. S. J. d., Gobert, J., Montalvo, O., & Nakama, A. (2013). Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill. User Modeling and User-Adapted Interaction, 23(1), 1–39.
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), 27–41.
Gobert, J. D., Sao Pedro, M. A., Baker, R. S. J. d., Toto, E., & Montalvo, O. (2012). Leveraging educational data mining for real-time performance assessment of scientific inquiry skills within Microworlds. Journal of Educational Data Mining, 4(1), 111–143.
Rodrigo, M. M. T., Baker, R. S. J. d., Agapito, J., Nabos, J., Repalam, M. C., Reyes, S. S., & San Pedro, M. 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.
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.
Baker, R. S. J. d., Goldstein, A. B., & Heffernan, N. T. (2011). Detecting learning moment-by-moment. International Journal of Artificial Intelligence in Education, 21 (1–2), 5–25.
Pardos, Z. A., Baker, R. S. J. d., Gowda, S. M., & Heffernan, N. T. (2011). The sum is greater than the parts: Ensembling models of student knowledge in educational software. SIGKDD Explorations, 13(2), 37–44.
Rodrigo, M. M. T., & Baker, R. S. J. d. (2011). Comparing learners’ affect while using an intelligent tutor and an educational game. Research and Practice in Technology Enhanced Learning, 6(1), 43–66.
Baker, R. S. J. d., Isotani, S., & de Carvalho, A. (2011). Mineração de dados educacionais: Oportunidades para o Brasil. Revista Brasileira de Informática na Educação, 19(2), 3–13.
Baker, D. J., Baker, R. S. J. d., & Uhing, B. (2011). Content of instruction for transition-age youth with disabilities: A brief report. National Association for the Dually Diagnosed (NADD) Bulletin, 14(5), 89–94.
Baker, R. S. J. d. (2011). Gaming the system: A retrospective look. Philippine Computing Journal, 6(2), 9–13.
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.
Baker, R. S. J. d., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.
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.
Baker, R. S. J. d., Walonoski, J. A., Heffernan, N. T., Roll, I., Corbett, A. T., & Koedinger, K. R. (2008). Why students engage in “gaming the system” behavior in interactive learning environments. Journal of Interactive Learning Research, 19(2), 185–224.
Baker, R. S. J. d., Corbett, A. T., & Koedinger, K. R. (2007). The difficulty factors approach to the design of intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 17(4), 341–369.
Baker, R. S., Corbett, A. T., & Koedinger, K. R. (2006). Responding to problem behaviors in cognitive tutors: Towards educational systems which support all students. National Association for the Dually Diagnosed (NADD) Bulletin, 9(4), 70–75.
Tamassia, R., Goodrich, M. T., Vismara, L., Handy, M., Cohen, R., Hudson, B., Baker, R. S., Gelfand, N., Shubina, G., & Brandes, U. (2001). JDSL: The Data Structures Library in Java. Dr. Dobb's Journal and Sourcebook, April 2001, 21–33.
Book Chapters
Baker, R. S., & Inventado, P. S. (in press). Educational data mining and learning analytics: Potentials and possibilities for online distance education. In G. Veletsianos (Ed.), Emerging technologies in distance education. Edmonton, Alberta: Athabasca University Press.
Baker, R. S., Wang, Y., Paquette, L., Aleven, V., Popescu, O., Sewall, J., Rose, C., Tomar, G., Ferschke, O., Zhang, J., Cennamo, M., Ogden, S., Condit, T., Diaz, J., Crossley, S., McNamara, D., Comer, D., Lynch, C., Brown, R., Barnes, T., & Bergner, Y. (in press). A MOOC on educational data mining. In O. Zaiane, S. ElAtia, & D. Ipperciel, (Eds.), Data mining and learning analytics in educational research. Hoboken, NJ: Wiley-Blackwell.
Baker, R. (2016). Using learning analytics in personalized learning. In M. Murphy, S. Redding, & J. Twyman (Eds.), Handbook on personalized learning for states, districts, and schools (pp. 165–174).
San Pedro, M. O. Z., & Baker, R. S. (2016). Adaptive learning. In M. McCarthy (Ed.), The Cambridge guide to blended learning for language technologies (pp. 234–247). New York: Cambridge University Press.
Rowe, E., Asbell-Clarke, J., & Baker, R. S. (2015). Serious games analysis to measure implicit science learning. In C. S. Loh, Y. Sheng, & D. Ifenthaler (Eds.), Serious games analytics: Methodologies for performance measurement, assessment, and improvement (pp. 343–362). Berlin, Germany: Springer.
Baker, R. S. J. d., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice. Berlin, Germany: Springer.
Baker, R. S. J. d., & Ocumpaugh, J. (2014). Interaction-based affect detection in educational software. In R. A. Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective computing. Oxford, UK: Oxford University Press.
Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (2nd ed., pp. 253–274). New York: Cambridge University Press.
DeFalco, J. A., Baker, R. S., & D'Mello, S. K. (2014). Addressing behavioral disengagement in online learning. In R. Sottilare, A. Graesser, X. Hu, & B. Goldberg (Eds.), Design recommendations for intelligent tutoring systems, volume 2: Instructional management (pp. 49–56). Orlando, FL: U.S. Army Research Laboratory.
D'Mello, S., Blanchard, N., Baker, R., Ocumpaugh, J., & Brawner, K. (2014). I feel your pain: A selective review of affect-sensitive instructional strategies. In R. Sottilare, A. Graesser, X. Hu, & B. Goldberg (Eds.), Design recommendations for intelligent tutoring systems, volume 2: Instructional management (pp. 35–48). Orlando, FL: U.S. Army Research Laboratory.
Baker, R. S. J. d. (2013). Learning, schooling, and data analytics. In M. Murphy, S. Redding, & J. Twyman (Eds.), Handbook on innovations in learning (pp.179–190). Philadelphia, PA: Center on Innovations in Learning.
Baker, R. S. J. d., & Rossi, L. M. (2013). Assessing the disengaged behavior of learners. In R. Sottilare, A. Graesser, X. Hu, & H. Holden (Eds.), Design recommendations for intelligent tutoring systems, volume 1: Learner modeling (pp. 155–166). Orlando, FL: U.S. Army Research Lab.
Baker, R. S. J. d., Corbett, A. T., Roll, I., Koedinger, K.R., Aleven, V., Cocea, M., Hershkovitz, A., de Carvalho, A. M. J. B., Mitrovic, A., & Mathews, M. (2013). Modeling and studying gaming the system with educational data mining. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 97–116). New York, NY: Springer.
Rodrigo, M. M. T., & Baker, R. S. J. d. (2011). Comparing the incidence and persistence of learners’ affect during interactions with different educational software packages. In R. A. Calvo & S. D’Mello (Eds.), New perspectives on affect and learning technologies (pp. 183–202). New York, NY: Springer.
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 C. Romero, S. Ventura, M. Pechenizkiy, & R.S.J.d. Baker (Eds.), Handbook of educational data mining (pp. 43–56). Boca Raton, FL: CRC Press.
Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. J. d. (2010). Introduction. In C. Romero, S. Ventura, M. Pechenizkiy, & R. S. J. d. Baker (Eds.), Handbook of educational data mining (pp. 1–8). Boca Raton, FL: CRC Press.
Baker, R. S. J. d. (2010). Mining data for student models. In R. Nkmabou, R. Mizoguchi, & J. Bourdeau (Eds.), Advances in intelligent tutoring systems (pp. 323–338). Secaucus, NJ: Springer.
Baker, R. S. J. d. (2010). Data mining for education. In B. McGaw, P. Peterson, E. Baker (Eds.), International encyclopedia of education, Volume 7 (3rd ed., pp. 112–118). Oxford, UK: Elsevier.
Koedinger, K., Aleven, V., Roll, I., & Baker, R. (2009). In vivo experiments on whether supporting metacognition in intelligent tutoring systems yields robust learning. In A. Graesser & D. Hacker (Eds.), Handbook of metacognition in education (pp. 383–412).