My Work
Google Scholar page here.
CONFERENCE FULL PAPERS
Andres, J.M.A.L., Baker, R.S., Hutt, S.J., Mills, C., Zhang, J., Rhodes, S., & DePiro, A., (in press).
Anxiety, achievement, and self-regulated learning in CueThink. To appear in the Proceedings
of the International Society of the Learning Sciences.
Zhang, J., Andres, J.M.A.L., Hutt, S., Baker, R.S., Ocumpaugh, J., Mills, C., Brooks, J., Sethuraman, S.,
Young, T. (2022) Detecting SMART Model Cognitive Operations in Mathematical Problem-Solving
Process. Proceedings of the International Conference on Educational Data Mining.
[Nominated for Best Paper Award]
Andres, J. M. A. L., Hutt, S., Ocumpaugh, J., Baker, R.S., Nasiar, N., & Porter, C. (2021). How
anxiety affects affect: A quantitative ethnographic investigation using affect detectors and
data targeted interviews. To appear in Proceedings of the 3rd International
Conference on Quantitative Ethnography.
Baker, R. S., Nasiar, N., Ocumpaugh, J. L., Hutt, S., Andres, J. M. A. L., Slater, S., Schofield, M.,
Moore, A., Paquette, L., Munshi, A. & Biswas, G. (2021, June). Affect-Targeted Interviews for
Understanding Student Frustration. In the International Conference on Artificial Intelligence in
Education (pp. 52-63). Springer, Cham.
Ocumpaugh, J., Hutt, S., Andres, J. M. A. L., Baker, R. S., Biswas, G., Bosch, N., Paquette, L. &
Munshi, A. (2021). Using Qualitative Data from Targeted Interviews to Inform Rapid AIED
Development. In Proceedings of the 29th International Conference on Computers
in Education.
Karumbaiah, S., Baker, R. B., Ocumpaugh, J., & Andres, J. M. A. L. (2021). A Re-Analysis and
Synthesis of Data on Affect Dynamics in Learning. IEEE Transactions on Affective
Computing.
Hutt, S., Ocumpaugh, J., Andres, J. M. A. L., Munshi, A., Bosch, N., Baker, R.S., Zhang, Y., Paquette, L., Slater, 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 Annual Meeting of the Cognitive Science Society (Vol. 43, No. 43).
Hutt, S., Ocumpaugh, J., Andres, J. M. A. 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 International Conference on Educational Data Mining.
Paquette, L., Ocumpaugh, J., Li, Z., Andres, J. M. A. L., & Baker, R. (2020). Who's Learning? Using Demographics in EDM Research.
Journal of Educational Data Mining, 12(3), 1-30.
Andres, J.M.A.L., Ocumpaugh, J. L., Baker, R. S., Slater, S., Paquette, L., Jiang, Y., Bosch, N., Munshi, A., Moore, A., & Biswas, G. (2019).
Affect Sequences and Learning in Betty’s Brain. Proceedings of the 9th International Learning Analytics and Knowledge
Conference, 383-390.
Karumbaiah, S., Andres, J.M.A.L., Botelho, A. F., Baker, R. S., & Ocumpaugh, J. S. (2018). The Implications of a Subtle Difference in the
Calculation of Affect Dynamics. In 26th International Conference for Computers in Education. [Nominated for Best Paper Award]
Jiang, Y., Bosch, N., Baker, R.S., Paquette, L., Ocumpaugh, J., Andres, J.M.A.L., Moore, A.L. and Biswas, G., (2018, June). Expert
Feature-Engineering vs. Deep Neural Networks: Which Is Better for Sensor-Free Affect Detection?. In International Conference on
Artificial Intelligence in Education (pp. 198-211). Springer, Cham. [Best Student Paper] [Nominated for Best Paper Award]
Palaoag, T. D., Rodrigo, M. M. T., Andres, J. M. L., Andres, J.M.A.L., & Beck, J. E. (2016, June). Wheel-Spinning in a Game-Based
Learning Environment for Physics. In International Conference on Intelligent Tutoring Systems (pp. 234-239). Springer
International Publishing.
Andres, J.M.A.L., Andres, J.M.L., Rodrigo, M.M.T., Beck, J.B.B. & Baker, R.S. (2015). An investigation of eureka and the affective states
surrounding eureka moments. In 23rd International Conference for Computers in Education (submitted June 2015). Poster.
Scopus-indexed.
JOURNAL PAPERS
Karumbaiah, S., Baker, R.S., Ocumpaugh, J., Andres, J.M.A.L. (in press) A Re-Analysis and Synthesis of Data on Affect Dynamics in Learning.
To appear in IEEE Transactions on Affective Computing.
Paquette, L., Ocumpaugh, J., Li, Z., Andres, J. M. A. L., & Baker, R. (2020). Who's Learning? Using Demographics in EDM Research.
Journal of Educational Data Mining, 12(3), 1-30.
Richey, J.E., Andres-Bray, J.M.L., Mogessie, M., Scruggs, R., Andres, J.M.A.L., Star, J.R., Baker, R.S., McLaren, B.M. (2019). More
Confusion and Frustration, Better Learning: The Impact of Erroneous Examples. Computers and Education.
BOOK CHAPTERS
Hutt, S., Baker, R.S., Ocumpaugh, J., Munshi, A., Andres, J.M.A.L., Karumbaiah, S., Slater, S., Biswas, G., Paquette, L., Bosch, N., van Velsen, M.
(in press) Quick Red Fox: An App Supporting a New Paradigm in Qualitative Research on AIED for STEM.
To appear in Ouyang, F., Jiao, P., McLaren, B.M., Alavi, A.H. (Eds.) Artificial Intelligence in STEM Education:
The Paradigmatic Shifts in Research, Education, and Technology.
Baker, R.S., Ocumpaugh, J.L., Andres, J.M.A.L. (2020). BROMP Quantitative Field Observations: A Review. In R. Feldman (Ed.) Learning
Science: Theory, Research, and Practice. New York, NY: McGraw-Hill.
Andres, J.M.A.L., Baker, R.S., Hutt, S.J., Mills, C., Zhang, J., Rhodes, S., & DePiro, A., (in press).
Anxiety, achievement, and self-regulated learning in CueThink. To appear in the Proceedings
of the International Society of the Learning Sciences.
Zhang, J., Andres, J.M.A.L., Hutt, S., Baker, R.S., Ocumpaugh, J., Mills, C., Brooks, J., Sethuraman, S.,
Young, T. (2022) Detecting SMART Model Cognitive Operations in Mathematical Problem-Solving
Process. Proceedings of the International Conference on Educational Data Mining.
[Nominated for Best Paper Award]
Andres, J. M. A. L., Hutt, S., Ocumpaugh, J., Baker, R.S., Nasiar, N., & Porter, C. (2021). How
anxiety affects affect: A quantitative ethnographic investigation using affect detectors and
data targeted interviews. To appear in Proceedings of the 3rd International
Conference on Quantitative Ethnography.
Baker, R. S., Nasiar, N., Ocumpaugh, J. L., Hutt, S., Andres, J. M. A. L., Slater, S., Schofield, M.,
Moore, A., Paquette, L., Munshi, A. & Biswas, G. (2021, June). Affect-Targeted Interviews for
Understanding Student Frustration. In the International Conference on Artificial Intelligence in
Education (pp. 52-63). Springer, Cham.
Ocumpaugh, J., Hutt, S., Andres, J. M. A. L., Baker, R. S., Biswas, G., Bosch, N., Paquette, L. &
Munshi, A. (2021). Using Qualitative Data from Targeted Interviews to Inform Rapid AIED
Development. In Proceedings of the 29th International Conference on Computers
in Education.
Karumbaiah, S., Baker, R. B., Ocumpaugh, J., & Andres, J. M. A. L. (2021). A Re-Analysis and
Synthesis of Data on Affect Dynamics in Learning. IEEE Transactions on Affective
Computing.
Hutt, S., Ocumpaugh, J., Andres, J. M. A. L., Munshi, A., Bosch, N., Baker, R.S., Zhang, Y., Paquette, L., Slater, 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 Annual Meeting of the Cognitive Science Society (Vol. 43, No. 43).
Hutt, S., Ocumpaugh, J., Andres, J. M. A. 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 International Conference on Educational Data Mining.
Paquette, L., Ocumpaugh, J., Li, Z., Andres, J. M. A. L., & Baker, R. (2020). Who's Learning? Using Demographics in EDM Research.
Journal of Educational Data Mining, 12(3), 1-30.
Andres, J.M.A.L., Ocumpaugh, J. L., Baker, R. S., Slater, S., Paquette, L., Jiang, Y., Bosch, N., Munshi, A., Moore, A., & Biswas, G. (2019).
Affect Sequences and Learning in Betty’s Brain. Proceedings of the 9th International Learning Analytics and Knowledge
Conference, 383-390.
Karumbaiah, S., Andres, J.M.A.L., Botelho, A. F., Baker, R. S., & Ocumpaugh, J. S. (2018). The Implications of a Subtle Difference in the
Calculation of Affect Dynamics. In 26th International Conference for Computers in Education. [Nominated for Best Paper Award]
Jiang, Y., Bosch, N., Baker, R.S., Paquette, L., Ocumpaugh, J., Andres, J.M.A.L., Moore, A.L. and Biswas, G., (2018, June). Expert
Feature-Engineering vs. Deep Neural Networks: Which Is Better for Sensor-Free Affect Detection?. In International Conference on
Artificial Intelligence in Education (pp. 198-211). Springer, Cham. [Best Student Paper] [Nominated for Best Paper Award]
Palaoag, T. D., Rodrigo, M. M. T., Andres, J. M. L., Andres, J.M.A.L., & Beck, J. E. (2016, June). Wheel-Spinning in a Game-Based
Learning Environment for Physics. In International Conference on Intelligent Tutoring Systems (pp. 234-239). Springer
International Publishing.
Andres, J.M.A.L., Andres, J.M.L., Rodrigo, M.M.T., Beck, J.B.B. & Baker, R.S. (2015). An investigation of eureka and the affective states
surrounding eureka moments. In 23rd International Conference for Computers in Education (submitted June 2015). Poster.
Scopus-indexed.
JOURNAL PAPERS
Karumbaiah, S., Baker, R.S., Ocumpaugh, J., Andres, J.M.A.L. (in press) A Re-Analysis and Synthesis of Data on Affect Dynamics in Learning.
To appear in IEEE Transactions on Affective Computing.
Paquette, L., Ocumpaugh, J., Li, Z., Andres, J. M. A. L., & Baker, R. (2020). Who's Learning? Using Demographics in EDM Research.
Journal of Educational Data Mining, 12(3), 1-30.
Richey, J.E., Andres-Bray, J.M.L., Mogessie, M., Scruggs, R., Andres, J.M.A.L., Star, J.R., Baker, R.S., McLaren, B.M. (2019). More
Confusion and Frustration, Better Learning: The Impact of Erroneous Examples. Computers and Education.
BOOK CHAPTERS
Hutt, S., Baker, R.S., Ocumpaugh, J., Munshi, A., Andres, J.M.A.L., Karumbaiah, S., Slater, S., Biswas, G., Paquette, L., Bosch, N., van Velsen, M.
(in press) Quick Red Fox: An App Supporting a New Paradigm in Qualitative Research on AIED for STEM.
To appear in Ouyang, F., Jiao, P., McLaren, B.M., Alavi, A.H. (Eds.) Artificial Intelligence in STEM Education:
The Paradigmatic Shifts in Research, Education, and Technology.
Baker, R.S., Ocumpaugh, J.L., Andres, J.M.A.L. (2020). BROMP Quantitative Field Observations: A Review. In R. Feldman (Ed.) Learning
Science: Theory, Research, and Practice. New York, NY: McGraw-Hill.