Publications

• Closser, A. H., Sales, A., & Botelho, A. F. (2024) Should We Account for Classrooms? Analyzing Online Experimental Data with Student-level Randomization. Journal of Educational Technology Research and Development. doi: https://doi.org/10.1007/s11423-023-10325-x

[pdf pending]

• Gagnon-Bartsch, J. A., Sales, A. C., Wu, E., Botelho, A. F., Erickson, J. A., Miratrix, L. W., & Heffernan, N. T. (2023). Precise Unbiased Estimation in Randomized Experiments Using Auxiliary Observational Data. Journal of Causal Inference, 11(1), 20220011

[pdf]

• Botelho, A. F., Baral, S., Erickson, J. A., Benachamardi, P., & Heffernan, N. T. (2023). Leveraging Natural Language Processing to Support Automated Assessment and Feedback for Student Open Responses in Mathematics. Journal of Computer Assisted Learning, 39, 823-840. doi: https://doi.org/10.1111/jcal.12793

[pdf]

• Lee, J. E., Chan, J. Y. C, Botelho, A. F., & Ottmar E. R. (2022). Does Slow and Steady Win the Race?: Clustering Patterns of Students’ Behaviors in an Interactive Online Mathematics Game. Journal of Educational Technology Research and Development. doi: https://doi.org/10.1007/s11423-022-10138-4

[pdf] 

• Closser, A. H., Erickson, J. A., Smith, H., Varatharaj, A., & Botelho, A. F. (2021). Blending Learning Analytics and Embodied Design to Model Students’ Comprehension of Measurement Using Their Actions, Speech, and Gestures.  In Abrahamson, D., Worsley, M., Pardos, Z., Ou, L. (Eds.) International Journal of Child-Computer Interaction (IJCCI) Special Issue on Learning Analytics of Embodied Design: Enhancing Synergy. doi: https://doi.org/10.1016/j.ijcci.2021.100391

[pdf] 

• Botelho, A. F., Varatharaj, A., Patikorn, T., Doherty, D., Adjei, S. A., Beck, J. E. (2019). Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep Learning. Journal of IEEE Transactions on Learning Technologies. Volume 12(2), pp. 158-170. doi: https://doi.org/10.1109/TLT.2019.2912162

[pdf] [journal page]

• Lee, J. E.,, Ottmar, E. R., Vanacore, K., Egorova, A., Botelho, A. F., & Gurung, A. (2024, Accepted). Investigations into the Effects of Hint Reading Time and Hint Type in a Digital Mathematics Game through Response Time Decomposition. In Proceedings of the2024 International Conference on Learning Sciences. Accepted.

[pdf pending]

• Gurung, A., Baral S., Vanacore, K.P., McReynolds, A.A., Kreisberg, H., Botelho, A.F., Shaw, S.T., & Heffernan, N.T. (2023). Identification, Exploration, and Remediation: Can Teacher Predict Common Wrong Answers? In Proceedings of the 13th International Conference on Learning Analytics and Knowledge, 399-410. ACM.

[pdf]

• Gurung, A., Botelho, A.F., Thompson, R., Sales, A.C., Baral, S., & Heffernan, N.T. (2022, November). Considerate, Unfair, or Just Fatigued? Examining Factors that Impact Teacher Grading. In Proceedings of the 30th International Conference on Computers in Education, 197-206.

[pdf]

• Botelho, A.F., Prihar, E., & Heffernan, N.T. (2022, July). Deep Learning or Deep Ignorance? Comparing Untrained Recurrent Models in Educational Contexts. In Proceedings of the 23rd International Conference on Artificial Intelligence in Education, 281-293. Springer, Cham.

[20% Acceptance Rate][pdf]

• Baral, S., Botelho, A.F., Erickson, J.A., Benachamardi, P., & Heffernan, N.T. (2021, June). Improving Automated Scoring of Student Open Responses in Mathematics. In Proceedings of the 14th International Conference on Educational Data Mining, 130-138.

[22% Acceptance Rate][Nominated for Best Paper Award][pdf]

• Prihar, E., Patikorn, T., Botelho, A.F., Sales, A.C., & Heffernan, N.T. (2021, June). Towards Personalizing Students' Education with Crowdsourced Tutoring. In Proceedings of the Eighth International Conference on Learning @ Scale, 37-45. ACM.

[pdf]

• Gurung, A., Botelho, A. F., & Heffernan, N. T. (2021, April). Examining Student Effort on Help through Response Time Decomposition. In Proceedings of the 11th International Conference on Learning Analytics and Knowledge, 292-301. ACM.

[pdf]

• Karumbaiah, S., Lan, A., Nagpal, S., Baker, R. S., Botelho, A. F., & Heffernan, N. T. (2021, April). Using Past Data to Warm Start Active Machine Learning: Does Context Matter?. In Proceedings of the 11th International Conference on Learning Analytics and Knowledge, 151-160. ACM.

[Nominated for Best Paper Award][pdf]

• Varatharaj, A., Botelho, A. F., Lu, X., Alphonsus, A. G., & Heffernan, N. T. (2020, July). Supporting Teacher Assessment in Chinese Language Learning Using Textual and Tonal Features. In Proceedings of the 21st International Conference on Artificial Intelligence in Education, 562-573. Springer, Cham.

[pdf]

• Erickson, J., Botelho, A. F., McAteer, S., Varatharaj, A., & Heffernan, N. T. (2020, March). The Automated Grading of Student Open Responses in Mathematics. In Proceedings of the 10th International Conference on Learning Analytics and Knowledge, 615-624. ACM.

[pdf]

• Botelho, A. F., Varatharaj, A., VanInwegen, E., & Heffernan, N. T. (2019, March). Refusing to Try: Characterizing Early Stopout on Student Assignments. In Proceedings of the 9th International Conference on Learning Analytics and Knowledge, 391-400. ACM.

[pdf]

• Karumbaiah, S, Andres, J.M.A, Botelho, A. F., Baker, R. S., & Ocumpaugh, J. (2018, November). The Implications of a Subtle Difference in the Calculation of Affect Dynamics. In Proceedings of the 26th International Conference on Computers in Education, 29-38.

[Nominated for Best Paper Award] [pdf]

• Botelho, A. F., Baker, R. S., Ocumpaugh, J., & Heffernan, N. T. (2018, July). Studying Affect Dynamics and Chronometry Using Sensor-Free Detectors. In Proceedings of the 11th International Conference on Educational Data Mining, 157-166. 

[16% Acceptance Rate] [Won Best Student Paper Award] [Nominated for Best Paper Award] [pdf]

• Botelho, A. F., Baker, R. S., & Heffernan, N. T. (2017, June). Improving Sensor-Free Affect Detection Using Deep Learning. In Proceedings of the 2017 International Conference on Artificial Intelligence in Education, 40-51. Springer, Cham.

[pdf]

• Botelho, A., Wan, H., & Heffernan, N. (2015, March). The prediction of student first response using prerequisite skills. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale, 39-45. ACM.

[25% acceptance rate] [pdf]

• Li, H., Lee, S., Botelho, A. F., (2024, Accepted). This Paper Was Written with the Help of ChatGPT: Exploring the Consequences of AI-Driven Academic Writing on Scholarly Practices. In Proceedings of the Seventheenth International Conference on Educational Data Mining. Accepted. 

[pdf pending]

• Li, H., Zhang, S., Lee, S., Lee, J. E., Zhong, Z., Weitnauer, E., & Botelho, A. F. (2024, Accepted). Math in Motion: Analyzing Real-Time Student Collaboration in Computer-Supported Learning Environments. In Proceedings of the Seventheenth International Conference on Educational Data Mining. Accepted. 

[pdf pending]

• Lee, M.P., Croteau, E., Gurung, A., Botelho, A.F., & Heffernan, N.T. (2023). Knowledge Tracing Over Time: A Longitudinal Analysis. In Proceedings of the Sixteenth International Conference on Educational Data Mining, 296-301.

[Nominated for Best Short Paper Award][pdf]

• Baral, S, Botelho, A.F., Santhanam, A., Gurung, A., Cheng, L., & Heffernan, N.T. (2023). Auto-scoring Student Responses with Images in Mathematics. In Proceedings of the Sixteenth International Conference on Educational Data Mining, 362-369.

[pdf]

• Botelho, A.F., Adjei, S.A., Bahel, V., & Baker, R.S. (2022). Exploring Relationships Between Temporal Patterns of Affect and Student Learning. In Proceedings of the 30th International Conference on Computers in Education, 139-144.

[pdf]

• Baral, S., Seetharaman, K., Botelho, A.F., Wang, A., Heineman, G., & Heffernan, N.T. (2022, July). Enhancing Auto-Scoring of Student Open-Responses in the Presence of Mathematical Terms and Expressions. In Proceedings of the 23rd International Conference on Artificial Intelligence in Education, 685-690. Springer, Cham.

[pdf]

• Sales, A., Botelho, A. F., Patikorn, T., & Heffernan, N. T. (2018, July). Using Big Data to Sharpen Design-Based Inference in A/B Tests. In Proceedings of the Eleventh International Conference on Educational Data Mining, 479-485.

[pdf]

• Adjei, S. A., Botelho, A. F., & Heffernan, N. T. (2017, March). Sequencing Content in an Adaptive Testing System: The Role of Choice. In Proceedings of the Seventh International Conference on Learning Analytics and Knowledge. (10.1145/3027385.3027412)

[pdf]

• Adjei, S. A., Botelho, A. F., & Heffernan, N. T. (2016, April). Predicting student performance on post-requisite skills using prerequisite skill data: an alternative method for refining prerequisite skill structures. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, 469-473. ACM.

[pdf]

• Botelho, A. F., Adjei, S. A., & Heffernan, N. T. (2016, June). Modeling Interactions Across Skills: A Method to Construct and Compare Models Predicting the Existence of Skill Relationships. In Proceedings of the Ninth International Conference on Educational Data Mining, 292-297. ACM

[pdf]

• Cheng, L., Prihar, E., Baral, S., Gurung, A., Botelho, A. F., Haim, A., Heffernan, C., Patikorn, T., Sales, A., & Heffernan, N. T. (In Press). Authoring Tools for Crowdsourcing from Teachers to Enhance Intelligent Tutoring Systems. In Design Recommendations for Intelligent Tutoring Systems: Volume 11 – Intelligent Tutoring System Applications for Professional Career Education. In Press.

[pdf pending]

• Botelho, A. F. & Heffernan, N. T. (2019). Crowdsourcing Feedback to Support Teachers and Students. In Sinatra, A.M., Graesser, A.C., Hu, X., Brawner, K., and Rus, V. (Eds.). (2019). Design Recommendations for Intelligent Tutoring Systems: Volume 7 - Self-Improving Systems. Orlando, FL: U.S. Army Research Laboratory. Pages 101-108. ISBN 978-0-9977257-7-3. Available at: https://gifttutoring.org/documents/.

[pdf]

• Botelho, A. (2019). Characterizing Productive Perseverance Using Sensor-Free Detectors of Student Knowledge, Behavior, and Affect. Retrieved from https://digitalcommons.wpi.edu/etd-dissertations/523

[pdf]

• Kao, Y. C. & Botelho, A. F. (2024, Accepted). Be Back in 5 Minutes: Exploring Correlations Between Short Breaks with Student Performance. In Proceedings of the Seventheenth International Conference on Educational Data Mining. Accepted. 

[pdf pending]

• Li, H. & Botelho, A. F. (2024, Accepted). Fine-Tuning Large Language Models for Data Augmentation to Detect At-Risk Students in Online Learning Communities. In Proceedings of the2024 International Conference on Learning Sciences. Accepted.

[pdf pending]

• Baral, S., Botelho, A.F., Santhanam, A., Gurung, A., Erickson, J.A., & Heffernan, N.T. (2023, July). Investigating Patterns of Tone and Sentiment in Teacher Written Feedback Messages. In Proceedings of the 24th International Conference on Artificial Intelligence in Education, 341-346. Cham: Springer Nature Switzerland.

[pdf]

• Gorgun, G. & Botelho, A.F. (2023, July). Enhancing the Automatic Identification of Common Math Misconceptions Using Natural Language Processing. In Proceedings of the 24th International Conference on Artificial Intelligence in Education, 302-307. Cham: Springer Nature Switzerland.

[pdf]

• Rivera-Bergollo, R., Baral, S., Botelho, A.F., & Heffernan, N.T. (2022, July). Leveraging Auxiliary Data from Similar Problems to Improve Automatic Open Response Scoring. In Proceedings of the 15th International Conference on Educational Data Mining, 679-683. doi: 10.5281/zenodo.6853119

[pdf]

• Erickson, J.A., Botelho, A.F., Peng, Z., Huang, R., Kasal, M.V., & Heffernan, N.T. (2021, June). Is it Fair? Automated Open Response Grading. In Proceedings of the 14th International Conference on Educational Data Mining, 682-687.

[pdf]

• Harrison, A., Smith, H., Botelho, A. F., Ottmar, E., & Arroyo, I. (2020, June). For good measure: Identifying student measurement estimation strategies through actions, language, and gesture. In Proceedings of the 2020 International Conference of the Learning Sciences (CLS), pp. 869-870. 

[pdf]

• Lan, A. S., Botelho, A. F., Karumbaiah, S., Baker, R. S., & Heffernan, N. T. (2020, March). Accurate and Interpretable Sensor-free Affect Detectors via Monotonic Neural Networks. In Proceedings of the 10th International Conference on Learning Analytics and Knowledge. ACM

[pdf]

• Varatharaj, A., Botelho, A. F., Lu, X., & Heffernan, N. T. (2019, July). Hao Fayin: Developing Automated Audio Assessment Tools for a Chinese Language Course. In Proceedings of the Twelfth International Conference on Educational Data Mining, 663-666.

[pdf]

• Botelho, A. F., Baker, R. S., & Heffernan, N. T. (2019, July). Machine-Learned or Expert-Engineered Features? Exploring Feature Engineering Methods in Detectors of Student Behavior and Affect. In Proceedings of the Twelfth International Conference on Educational Data Mining, 508-511.

[pdf]

• Hulse, T., Harrison, A., Ostrow, K. S., Botelho, A. F., & Heffernan, N. T. (2018, July). Starters and Finishers: Predicting Next Assignment Completion From Student Behavior During Math Problem Solving. In Proceedings of the Eleventh International Conference on Educational Data Mining, 525-528.

[pdf]

• Harrison, A, Wixon, N., Botelho, A. F., Arroyo, I. (2018, July). Sensor-Free Predictive Models of Affect in an Online Learning Environment. In Proceedings of the Eleventh International Conference on Educational Data Mining, 634-637.

[pdf]

• Yin, B., Botelho, A. F., Patikorn, T., Heffernan, N. T., & Zou, J. (2017, June). Causal Forest vs. Naïve Causal Forest in Detecting Personalization: An Empirical Study in ASSISTments. In Proceedings of the Tenth International Conference on Educational Data Mining, 388-389.

[pdf]

• Zhang, L., Xiong, X., Zhao, S., Botelho, A., & Heffernan, N. T. (2017, April). Incorporating Rich Features into Deep Knowledge Tracing. In Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, 169-172. ACM.

[pdf]

• Yin, B., Patikorn, T., Botelho, A. F., & Heffernan, N. T. (2017, April). Observing Personalizations in Learning: Identifying Heterogeneous Treatment Effects Using Causal Trees. In Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, 299-302. ACM.

[pdf]

• Zhao, S., Zhang, Y., Xiong, X., Botelho, A., & Heffernan, N. (2017, April). A Memory-Augmented Neural Model for Automated Grading. In Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, 189-192. ACM.

[pdf] [extended pdf]

• Patikorn, T., Selent, D., Heffernan, N. T., Yin, B., Botelho, A. (2016, October) ASSISTments Dataset for a Data Mining Competition to Improve Personalized Learning. In The Conference on Digital Experimentation (CODE) 2016, MIT, Cambridge, MA

[pdf] [conference page] [conference program]

• Williams, J. J., Botelho, A., Sales, A., Heffernan, N., & Lang, C. (2016, June). Discovering ‘Tough Love’ Interventions Despite Dropout. In Proceedings of the Ninth International Conference on Educational Data Mining, 650-651. ACM

[pdf]

• Botelho, A. F., Adjei, S. A., Wan, H., & Heffernan, N. T. (2015, June).  Predicting Student Aptitude Using Performance History. In Proceedings of the Eighth International Conference on Educational Data Mining, 622-623. ACM

[pdf]

• Botelho., A.F., Yun-Chen Chan, J., Trac, C., Closser, A.H., Smith, H., Drzewiecki, K.C., & Ottmar, E.R. (2021, July). State vs. Trait: Examining Gaming the System in the Context of Math Perception Tasks. In The 2021 Cognitive Science (CogSci) Conference, Vienna, Italy (Virtually)

[poster] 

• Closser, A.H., Lotero, E., & Botelho., A.F. (2021, June). Following up: Accounting for Classroom IDs in Student-Level RCTs. In The 2021 International Conference on Educational Data Mining Workshop on Causal Inference, Paris, France (Virtually)

[link] 

• Botelho, A.F. (2020, July). Open Questions: What Are the Right Causal Questions Surrounding the Events of COVID-19? In the 13th International Conference on Educational Data Mining (EDM) Workshop on Causal Inference in EDM, Virtual Workshop

[link] 

• Botelho, A. F., Erickson, J. A., Alphonsus, A. G., & Heffernan, N. T. (2020, March) Providing Directed Feedback Through QUICK-Comments. In the 10th International Conference on Learning Analytics and Knowledge (LAK) Workshop on Learning Analytic Services to Support Personalized Learning and Assessment at Scale, Online Workshop. 

[link] [slides]

• Botelho, A. F., Sales, A. C., Patikorn, T., & Heffernan, N. T. (2019, April). The ASSISTments TestBed: Opportunities and Challenges of Experimentation in Online Learning Platforms. In the 9th International Conference on Learning Analytics and Knowledge (LAK) Workshop on Learning Analytic Services to Support Personalized Learning and Assessment at Scale, Tempe, AZ.

[abstract] [link] [slides]

• Sales, A. Botelho, A. F., Wu, E., Gagnon-Bartsch, J., Miratrix, L., Patikorn, T., & Heffernan, N. T. (2018, October). Combining Residualization Methods to Better Estimate Treatment Effects in Randomized Controlled Trials. In The Conference on Digital Experimentation (CODE) 2018, MIT, Cambridge, MA.

[abstract] [link]

• Botelho, A. F. (2018, July). Observing Persistence and Mental Effort in the Presence of Failure. In the 11th International Conference on Educational Data Mining, Buffalo, NY.

[doctoral consortium paper]

• Sales, A., Botelho, A. F., Patikorn, T., & Heffernan, N. T. (2018, May). Deep Learning, Auxiliary Data, and Randomization: Analyzing Experiments Run Within a Computerized Math Tutor. In The 2018 Atlantic Causal Inference Conference, Pittsburgh, PA.

[abstract] [link]

• Adjei, S. A., Botelho, A. F., Patikorn, T., Yin, B., & Beck, J. E. (2017, June). Using "Snapshots" of Student Performance to Model Wheel Spinning. In the Tenth International Conference on Educational Data Mining, Wuhan, Hubei, China.

[workshop paper]

• Botelho, A. F. & Heffernan, N. T. (2016, May). Sharing Data Across Experiments. In The 2016 Atlantic Causal Inference Conference, New York City, NY.

[link]