Publications
Conference Full Papers
How to Open Science: Analyzing the Open Science Statement Compliance of the Learning @ Scale Conference
Haim, A., Gyurcsan, R., Baxter, C., Shaw, S. T., & Heffernan, N. T., III. (Accepted). How to Open Science: Analyzing the Open Science Statement Compliance of the Learning @ Scale Conference. To be presented at the 10th ACM Conference on Learning @ Scale (L@S ‘23), July 20-22, 2023, University of Copenhagen, Copenhagen, Denmark.
Associated Links
OSF Project: https://doi.org/10.17605/osf.io/pj3te
How to Open Science: Debugging Reproducibility within the Educational Data Mining Conference
Haim, A., Gyurcsan, R., Baxter, C., Shaw, S. T., & Heffernan, N. T., III. (Accepted). How to Open Science: Debugging Reproducibility within the Educational Data Mining Conference. To be presented at the 16th International Conference on Educational Data Mining (EDM ‘23), July 11-14, 2023, Indian Institute of Science Campus, Bengalaru, India.
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. Presented at 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., & 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
Poster Papers
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
How to Open Science: Promoting Principles and Reproducibility Practices within the Learning @ Scale Community
Haim, A., Shaw, S. T., & Heffernan, N. T., III. (Accepted). How to Open Science: Promoting Principles and Reproducibility Practices within the Learning @ Scale Community. To be presented at the 10th ACM Conference on Learning @ Scale (L@S ‘23), July 20-22, 2023, University of Copenhagen, Copenhagen, Denmark. https://lats2023-tutorial.howtoopenscience.com. Accessible at https://doi.org/10.17605/osf.io/jp6cq.
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
How to Open Science: Promoting Principles and Reproducibility Practices within the Educational Data Mining Community
Haim, A., Shaw, S. T., & Heffernan, N. T., III. (Accepted). How to Open Science: Promoting Principles and Reproducibility Practices within the Educational Data Mining Community. To be presented at the 16th International Conference on Educational Data Mining (EDM ‘23), July 11-14, 2023, Indian Institute of Science Campus, Bengalaru, India. https://edm2023-tutorial.howtoopenscience.com. Accessible at https://doi.org/10.17605/osf.io/gkuqv.
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
How to Open Science: Promoting Principles and Reproducibility Practices within the Artificial Intelligence in Education Community
Haim, A., Shaw, S. T., & Heffernan, N. T., III. (Accepted). How to Open Science: Promoting Principles and Reproducibility Practices within the Artificial Intelligence in Education Community. To be presented at the 24th International Conference on Artificial Intelligence in Education (AIED ‘23), July 3-7, 2023, Tokyo, Japan. https://aied2023-tutorial.howtoopenscience.com. Accessible at https://doi.org/10.17605/osf.io/yd9kr.
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., & 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., 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