Publications
Conference Full Papers
Gurung, A., Lee, M. P., Baral, S., Vanacore, K. P., McReynolds, A. A., Kreisberg, H., Heffernan, C., Haim, A., Smearsoll, N., Sales, A. C., & Heffernan, N. T. (2022, July 22). How Common are Common Wrong Answers? Crowdsourcing Remediation at Scale. In Proceedings of the Tenth ACM Conference on Learning @ Scale (L@S '23). Association for Computing Machinery, New York, NY, USA, 70–80. https://doi.org/10.1145/3573051.3593390
Associated Links
Authors
Morgan P. Lee
Sami Baral
Kirk P. Vanacore
Andrew A. McReynolds
Hilary Kreisberg
Christina Heffernan
Aaron Haim (me)
Nathan Smearsoll
Prihar, E., Haim, A., Shen, T., Sales, A. C., Lee, D., Wu, X., & Heffernan, N.. (2023, July 22). Investigating the Impact of Skill-Related Videos on Online Learning. In Proceedings of the Tenth ACM Conference on Learning @ Scale (L@S '23). Association for Computing Machinery, New York, NY, USA, 4–13. https://doi.org/10.1145/3573051.3593376
Associated Links
OSF Project: https://doi.org/10.17605/osf.io/cxkzf
Haim, A., Gyurcsan, R., Baxter, C., Shaw, S. T., & Heffernan, N. T., III. (2023, July 21). How to Open Science: Analyzing the Open Science Statement Compliance of the Learning @ Scale Conference. In Proceedings of the Tenth ACM Conference on Learning @ Scale (L@S '23). Association for Computing Machinery, New York, NY, USA, 174–182. https://doi.org/10.1145/3573051.3596166
Associated Links
OSF Project: https://doi.org/10.17605/osf.io/pj3te
Haim, A., Gyurcsan, R., Baxter, C., Shaw, S. T., & Heffernan, N. T., III. (2023, July 11). How to Open Science: Debugging Reproducibility within the Educational Data Mining Conference. Proceedings of the 16th International Conference on Educational Data Mining (EDM ‘23), 114–124, July 11-14, 2023, Indian Institute of Science Campus, Bengalaru, India. https://doi.org/10.5281/zenodo.8115651
Associated Links
OSF Project: https://doi.org/10.17605/osf.io/unhyp
Haim, A., Shaw, S. T., & Heffernan, N. T., III. (2023, March 16). How to Open Science: A Principle and Reproducibility Review of the Learning Analytics and Knowledge Conference. In Proceedings of the 13th International Learning Analytics and Knowledge Conference (LAK ‘23), March 13-17, 2023, Arlington, Texas, USA. https://doi.org/10.1145/3576050.3576071
Awards and Honors
Best Full Paper Nominee
Honorable Mention
Associated Links
OSF Project: https://doi.org/10.17605/osf.io/74bzs
GitHub: https://github.com/ahaim5357/10.17605-osf.io-74bzs/tree/lak2023
Prihar, E., Haim, A., Sales, A. C., & Heffernan, N. (2022, June). Automatic Interpretable Personalized Learning. In Proceedings of the Ninth ACM Conference on Learning @ Scale (L@S ’22), June 1–3, 2022, New York City, NY, USA. 1-11. https://doi.org/10.1145/3491140.3528267
Awards and Honors
Best Paper Nominee
Best Dataset
Associated Links
OSF Project: https://doi.org/10.17605/osf.io/9pgv5
Book Chapters
Cheng, L., Prihar, E., Baral, S., Gurung, A., Botelho, A. T., Haim, A., Heffernan, C., Patikorn, T., Sales, A., & Heffernan, N. T. (2023). Authoring Tools for Crowdsourcing from Teachers to Enhance Intelligent Tutoring Systems. In Sinatra, A.M., Graesser, A.C., Hu, X., Townsend, L.N. and Rus, V. (Eds.), Design Recommendations for Intelligent Tutoring Systems: Volume 11 - Professional Career Education, Orlando, FL: US Army Combat Capabilities Development Command - Soldier Center. 115-125. ISBN 978-0-9977258-5-8. Available at: https://gifttutoring.org/documents/167
Authors
Li Cheng
Sami Baral
Aaron Haim (me)
Cristina Heffernan
Poster Papers
Wang, A., Prihar, E., Haim, A., Heffernan, N. (2024). Can Large Language Models Generate Middle School Mathematics Explanations Better Than Human Teachers?. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024. Communications in Computer and Information Science, vol 2151. Springer, Cham. https://doi.org/10.1007/978-3-031-64312-5_29
Associated Links
OSF Project: https://doi.org/10.17605/osf.io/f8w9p
Haim A., & Heffernan, N. (2022, July). Student Perception on the Effectiveness of On-Demand Assistance in Online Learning Platforms. Proceedings of the 15th International Conference on Educational Data Mining (EDM '22), July 24-27, 2022, Durham, England, UK. 734–737. https://doi.org/10.5281/zenodo.6853053
Associated Links
OSF Project: https://doi.org/10.17605/osf.io/f8w9p
Workshops and Tutorials
Haim, A., Hutt, S., Shaw, S. T., & Heffernan, N. T., III. (2024, July 14). Promoting Open Science in Educational Data Mining: An Interactive Tutorial on Licensing, Data, and Containers. In Proceedings of the 17th International Conference on Educational Data Mining (EDM '24), 1017-1020, July 14-17, 2024, Atlanta, Georgia, USA. https://doi.org/10.5281/zenodo.12730037
Associated Links
Website: https://edm2024-tutorial.howtoopenscience.com
OSF Project: https://osf.io/thsgx/
GitHub: https://github.com/HowToOpenScience/edm2024-tutorial/tree/main
Haim, A., Hutt, S., Shaw, S.T., Heffernan, N.T. (2024). Promoting Open Science in Artificial Intelligence: An Interactive Tutorial on Licensing, Data, and Containers. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024. Communications in Computer and Information Science, vol 2151. Springer, Cham. https://doi.org/10.1007/978-3-031-64312-5_56
Associated Links
Website: https://aied2024-tutorial.howtoopenscience.com
OSF Project: https://osf.io/bmq9t/
GitHub: https://github.com/HowToOpenScience/aied2024-tutorial/tree/main
Haim, A., Shaw, S. T., & Heffernan, N. T., III. (2023, July 20). How to Open Science: Promoting Principles and Reproducibility Practices within the Learning @ Scale Community. In Proceedings of the Tenth ACM Conference on Learning @ Scale (L@S '23). Association for Computing Machinery, New York, NY, USA, 248–250. https://doi.org/10.1145/3573051.3593398
Associated Links
Website: https://lats2023-tutorial.howtoopenscience.com
OSF Project: https://doi.org/10.17605/osf.io/jp6cq
GitHub: https://github.com/HowToOpenScience/lats2023-tutorial/tree/main
Haim, A., Shaw, S. T., & Heffernan, N. T., III. (2023, July 14). How to Open Science: Promoting Principles and Reproducibility Practices within the Educational Data Mining Community. Presented at the 16th International Conference on Educational Data Mining (EDM ‘23), 582–584, July 11-14, 2023, Indian Institute of Science Campus, Bengalaru, India. https://doi.org/10.5281/zenodo.8115776
Associated Links
Website: https://edm2023-tutorial.howtoopenscience.com
OSF Project: https://doi.org/10.17605/osf.io/gkuqv
GitHub: https://github.com/HowToOpenScience/edm2023-tutorial/tree/main
Haim, A., Shaw, S. T., & Heffernan, N. T., III. (2023, July 3). How to Open Science: Promoting Principles and Reproducibility Practices Within the Artificial Intelligence in Education Community. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_11
Associated Links
Website: https://aied2023-tutorial.howtoopenscience.com
OSF Project: https://doi.org/10.17605/osf.io/yd9kr
GitHub: https://github.com/HowToOpenScience/aied2023-tutorial/tree/main
Haim, A., Shaw, S. T., & Heffernan, N. T., III. (2023, March 13-14). How to Open Science: Promoting Principles and Reproducibility Practices within the Learning Analytics Community. Presented at the 13th International Learning Analytics and Knowledge Conference (LAK '23), March 13-17, 2023, Arlington, Texas, USA. https://lak2023-tutorial.howtoopenscience.com. Accessible at https://doi.org/10.17605/osf.io/kyxba.
Associated Links
Website: https://lak2023-tutorial.howtoopenscience.com
OSF Project: https://doi.org/10.17605/osf.io/kyxba
GitHub: https://github.com/HowToOpenScience/lak2023-tutorial/tree/main
Haim, A., Shaw, S. T., & Heffernan, N. T., III. (2023, March 10). Understanding Licenses, Terms of Use/Service, and Private Policies. Presented at the Unconference 2023: Open Scholarship Practices in Education Research (Unconference ‘23), March 9-10, 2023. Accessible at https://osf.io/f784k/
Associated Links
OSF Project: https://doi.org/10.17605/osf.io/kurfc
YouTube: https://www.youtube.com/watch?v=b87OOplZfPU&list=PLChfyH8TVDGl5JUEUxM5ehg6WdkEN-Lxd&index=18
Extended Abstracts
Haim, A., Prihar, E., Shaw, S. T., Sales, A. C., & Heffernan, N. T., III. (2022, October 20). Expansion on Exploring Common Trends in Online Educational Experiments. Presented at the 2022 Conference on Digital Experimentation @ MIT (CODE@MIT '22), October 20-21, 2022, Boston, MA, US. Accessible at https://doi.org/10.17605/osf.io/m2jqe
Associated Links
OSF Project: https://doi.org/10.17605/osf.io/m2jqe
Doctoral Consortiums
Haim, A., Worden, E., Heffernan, N.T. (2024). The Effectiveness of AI Generated, On-Demand Assistance Within Online Learning Platforms. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024. Communications in Computer and Information Science, vol 2151. Springer, Cham. https://doi.org/10.1007/978-3-031-64312-5_45
Haim, A., Prihar, E., Heffernan, N.T. (2022, July). Toward Improving Effectiveness of Crowdsourced, On-Demand Assistance from Educators in Online Learning Platforms. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_5
Associated Links
OSF Project: https://doi.org/10.17605/osf.io/zcbjx
GitHub: https://github.com/ahaim5357/10.17605-osf.io-zcbjx/tree/xprize