Digital Transformation of Education - 2021 Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2021). Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks. ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I, Vol. 12748, pp. 356–368. https://doi.org/10.1007/978-3-030-78292-4_29 Ju, S., Zhou, G., Abdelshiheed, M., Barnes, T., & Chi, M. (2021). Evaluating Critical Reinforcement Learning Framework in the Field. ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I, Vol. 12748, pp. 215–227. https://doi.org/10.1007/978-3-030-78292-4_18 Isvik, A., Catete, V., Bell, D., Gransbury, I., & Barnes, T. (2021). Infusing Computing: Moving a Service Oriented Internship Program Online. IEEE STCBP RESPECT CONFERENCE: 2021 RESEARCH ON EQUITY AND SUSTAINED PARTICIPATION IN ENGINEERING, COMPUTING, AND TECHNOLOGY (RESPECT), pp. 199–203. https://doi.org/10.1109/RESPECT51740.2021.9620644 Isvik, A., Catete, V., Elmore, E., & Barnes, T. (2021). Examining Equity in Computing-Infused Lessons Made by Novices. IEEE STCBP RESPECT CONFERENCE: 2021 RESEARCH ON EQUITY AND SUSTAINED PARTICIPATION IN ENGINEERING, COMPUTING, AND TECHNOLOGY (RESPECT), pp. 157–161. https://doi.org/10.1109/RESPECT51740.2021.9620700 Ju, S., Kim, Y. J., Ausin, M. S., Mayorga, M. E., & Chi, M. (2021). To Reduce Healthcare Workload: Identify Critical Sepsis Progression Moments through Deep Reinforcement Learning. 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp. 1640–1646. https://doi.org/10.1109/BigData52589.2021.9671407 Ausin, M. S., Azizsoltani, H., Ju, S., Kim, Y. J., & Chi, M. (2021). InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem. 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp. 1337–1348. https://doi.org/10.1109/BigData52589.2021.9671827 Kim, Y. J., Ausin, M. S., & Chi, M. (2021). Multi-Temporal Abstraction with Time-Aware Deep Q-Learning for Septic Shock Prevention. 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp. 1657–1663. https://doi.org/10.1109/BigData52589.2021.9671662 Broll, B., Ledeczi, A., Stein, G., Jean, D., Brady, C., Grover, S., … Barnes, T. (2021). Removing the Walls Around Visual Educational Programming Environments. 2021 IEEE SYMPOSIUM ON VISUAL LANGUAGES AND HUMAN-CENTRIC COMPUTING (VL/HCC 2021). https://doi.org/10.1109/VL/HCC51201.2021.9576399 Akintunde, R. O., Limke, A., Barnes, T., Heckman, S., & Lynch, C. (2021). PEDI - Piazza Explorer Dashboard for Intervention. 2021 IEEE SYMPOSIUM ON VISUAL LANGUAGES AND HUMAN-CENTRIC COMPUTING (VL/HCC 2021). https://doi.org/10.1109/VL/HCC51201.2021.9576443 Dong, Y., Shabrina, P., Marwan, S., & Barnes, T. (2021). You Really Need Help: Exploring Expert Reasons for Intervention During Block-based Programming Assignments. ICER 2021: PROCEEDINGS OF THE 17TH ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH, pp. 334–346. https://doi.org/10.1145/3446871.3469764 Milliken, A., Catete, V., Limke, A., Gransbury, I., Chipman, H., Dong, Y., & Barnes, T. (2021). Exploring and Influencing Teacher Grading for Block-based Programs through Rubrics and the GradeSnap Tool. ICER 2021: PROCEEDINGS OF THE 17TH ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH, pp. 101–114. https://doi.org/10.1145/3446871.3469762 Sharma, E., Davis, L., Ivy, J., & Chi, M. (2021). Data to Donations: Towards In-Kind Food Donation Prediction across Two Coasts. 2021 IEEE GLOBAL HUMANITARIAN TECHNOLOGY CONFERENCE (GHTC), pp. 281–288. https://doi.org/10.1109/GHTC53159.2021.9612484 Eseryel, U. Y., Jiang, D., & Eseryel, D. (2021). NEW FINDINGS ON STUDENT MULTITASKING WITH MOBILE DEVICES AND STUDENT SUCCESS. JOURNAL OF INFORMATION TECHNOLOGY EDUCATION-INNOVATIONS IN PRACTICE, 20, 21–35. https://doi.org/10.28945/4723 Khoshnevisan, F., & Chi, M. (2021). Unifying Domain Adaptation and Domain Generalization for Robust Prediction Across Minority Racial Groups. MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, Vol. 12975, pp. 521–537. https://doi.org/10.1007/978-3-030-86486-6_32 Wiedbusch, M. D., Kite, V., Yang, Xi, Park, S., Chi, M., Taub, M., & Azevedo, R. (2021). A Theoretical and Evidence-Based Conceptual Design of MetaDash: An Intelligent Teacher Dashboard to Support Teachers' Decision Making and Students' Self-Regulated Learning. FRONTIERS IN EDUCATION, 6. https://doi.org/10.3389/feduc.2021.570229 Zhou, G., Azizsoltani, H., Ausin, M. S., Barnes, T., & Chi, M. (2021, August 16). Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION. https://doi.org/10.1007/s40593-021-00269-9 Mulvey, K. L., Joy, A., Caslin, M., Orcutt, D., Eseryel, D., & Katti, M. (2021, June 22). Forests After Florence: an informal community-engaged STEM research project promotes STEM identity in disaster-impacted students. RESEARCH IN SCIENCE & TECHNOLOGICAL EDUCATION, Vol. 6. https://doi.org/10.1080/02635143.2021.1944077 Cody, C., Maniktala, M., Lytle, N., Chi, M., & Barnes, T. (2021, May 21). The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION. https://doi.org/10.1007/s40593-021-00237-3 Maniktala, M., Cody, C., Barnes, T., & Chi, M. (2021, March). Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor (September, 10.1007/s40593-020-00213-3, 2020). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 31, pp. 154–155. https://doi.org/10.1007/s40593-020-00232-0 Geden, M., Emerson, A., Carpenter, D., Rowe, J., Azevedo, R., & Lester, J. (2021). Predictive Student Modeling in Game-Based Learning Environments with Word Embedding Representations of Reflection. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, 31(1), 1–23. https://doi.org/10.1007/s40593-020-00220-4