Professor Ryan Baker’s Seminar: “Educational Data Mining: Predict the Future, Change the Future”

February 15, 2013 –  Professor Ryan Baker (Teacher’s College) kicked off the Spring 2013 seminar series with his talk, “Educational Data Mining: Predict the Future, Change the Future,” at the CUNY Graduate Center. If you missed the talk, be sure to catch the video here: And if you enjoyed the talk, then do not forget to check out the Initiative’s other events!

In the talk, Professor Baker discussed educational research of online courses and educational software that tracks student performance. Educational data mining has proved useful in detecting student behaviors, such as frustration or not paying attention. Leveraging the substantial amount of student data gathered, researchers can model and predict certain student behaviors. Because data mining can be such a powerful tool for prediction, it can also be an important asset in educational interventions in the future. By modeling and predicting students’ off task behavior, for instance, educators can steer students back to their studies and improve academic performance in the long run.

Be sure to check out the entirety of Professor Baker’s talk if you have not done so already! Included below are the citations, listed by slides courtesy of Professor Baker. (In order to maximize your video streaming experience, you can change the viewing modes of the video. This will allow you to rotate between the PowerPoint slide and the recorded talk.)

Citations by slides:

–        36: Koedinger et al. (2008; 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 Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.d. (Eds.) Handbook of Educational Data Mining. Boca Raton, FL: CRC Press, pp. 43-56.

–        45: Baker & Yacef (2009)

  • 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

–        49: Corbett & Anderson (1995)

  • Corbett, A.T., & Anderson, J.R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 253-278.

–        49: Sao Pedro et al. (2012)

  • Sao Pedro, M.A., Baker, R.S.J.d., Gobert, J., Montalvo, O. Nakama, A. (in press) Leveraging Machine-Learned Detectors of Systematic Inquiry Behavior to Estimate and Predict Transfer of Inquiry Skill. To appear in User Modeling and User-Adapted Interaction.

–        56: Razzaq et al. (2005)

  • Razzaq, L., Feng, M., Nuzzo-Jones, G., Heffernan, N. T., Koedinger, K., Junker, B., … & Rasmussen, K. (2005, May). Blending Assessment and Instructional Assisting, Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology, 555-562.

–        56: Mendicino et al. (2009)

  • Mendicino, M., Razzaq, L., & Heffernan, N. T. (2009). A comparison of traditional homework to computer-supported homework. Journal of Research on Computing in Education41(3), 331-358.

–        68: Baker et al. (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.

–        94: Baker (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.

–        94: Cetintas et al. (2009)

  • Cetintas, S., Si, L., Xin, Y. P., Hord, C., & Zhang, D. (2009). Learning to Identify Students’ Off-task Behavior in Intelligent Tutoring Systems. Proceedings of the 14th International Conference on Artificial Intelligence in Education, 701-703.

–        95: D’Mello et al. (2008)

  • D’Mello, S. K., Craig, S. D., Witherspoon, A., Mcdaniel, B., & Graesser, A. (2008). Automatic detection of learner’s affect from conversational cues. User Modeling and User-Adapted Interaction18(1), 45-80.

–        95: Sabourin et al. (2011)

  • Sabourin, J., Rowe, J., Mott, B., & Lester, J. (2011). When off-task is on-task: the affective role of off-task behavior in narrative-centered learning environments, Artificial Intelligence in Education, 534-536.


–        95: Baker et al. (2012)

  • Baker, R.S.J.d., Gowda, S.M., Wixon, M., Kalka, J., Wagner, A.Z., Salvi, A., Aleven, V., Kusbit, G., Ocumpaugh, J., Rossi, L. (2012) Sensor-free automated detection of affect in a Cognitive Tutor for Algebra. Proceedings of the 5th International Conference on Educational Data Mining, 126-133.

–        100: Baker, Gowda, & Corbett (2011)

  • Baker, R.S.J.d., Gowda, S., Corbett, A.T. (2011) Towards predicting future transfer of learning.Proceedings of 15th International Conference on Artificial Intelligence in Education, 23-30.

–        100: Hershkovitz et al. (in preparation)

  • Hershkovitz, A., Baker, R.S.J.d., Gobert, J., Kauffman-Rogoff, Z., Wixon, M. (accepted) Student Attributes, Affective States, and Engagement in Science Inquiry Microworlds. To be presented at The European Association for Research on Learning and Instruction (EARLI) SIG 20 Conference.

–        101: Arnold (2010)

  • Arnold, K. E. (2010). Signals: Applying Academic Analytics. Educause Quarterly33(1), n1.

–        101: Ming & Ming (2012)

–        102: Dekker et al. (2009)

  • Dekker, G.W., Pechenizkiy, M., & Vleeshouwers, J.M. (2009). Predicting students drop out: A case study, Proceedings of the 2nd International Conference on Educational Data Mining, EDM, 9, 41-50.

–        102: Kovacic (2010)

  • Kovačić, Z. J. (2011). Early Prediction of Student Success: Mining Students Enrolment Data. Proceedings of Informing Science & IT Education Conference (InSITE), 647-665.

–        102: Marquez-Vera et al. (2012)

  • Márquez-Vera, C., Cano, A., Romero, C., & Ventura, S. (2012). Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Applied Intelligence, 1-16.

–        108: Baker & Gowda (2010)

–        115: Baker et al. (2006)

  • Baker, R.S.J.d., Corbett, A.T., Koedinger, K.R., Evenson, E., Roll, I., Wagner, A.Z., Naim, M., Raspat, J., Baker, D.J., Beck, J. (2006) Adapting to When Students Game an Intelligent Tutoring System. Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 392-401.

–        115: Rodrigo et al. (2011)

  • Rodrigo, M.M.T., Baker, R.S.J.d., Agapito, J., Nabos, J., Repalam, M.C., Reyes, S.S., & San Pedro, M.O.C. (2012). The Effects of an Interactive Software Agent on Student Affective Dynamics while Using; an Intelligent Tutoring System. Affective Computing, IEEE Transactions on3(2), 224-236.