Publications

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INVITE Exclusive

• Jaldi, C. D., Ilkou, E., Schroeder, N., & Shimizu, C. (2024). Education in the era of Neurosymbolic AI. Journal of Web Semantics, 100857.
• Zhang, S., Palaguachi, C., Pitera, M., Jaldi, C. D., Schroeder, N. L., Botelho, A. F., & Gladstone, J. R. (2024). Semi-automating the Scoping Review Process: Is it Worthwhile? A Methodological Evaluation. Educational Psychology Review, 36(4), 1-35. 
• Mannekote, A., Davies, A., Pinto, J. D., Zhang, S., Olds, D., Schroeder, N. L., Lehman, B., Zapata-Rivera, D., Zhai, C. (2024). Large Language Models for Whole-Learner Support: Opportunities and Challenges. Frontiers in Artificial Intelligence, 7, 1460364.
• Zhang, S., Jaldi, C., Schroeder, N. L., López, A. A., Gladstone, J. R., & Heidig, S. (2024). Pedagogical agent design for K-12 education: A systematic review. Computers & Education. 105165. 
• Barrett, J. (2023). Thinking Critically: Classroom Activities to Examine Ethics in Computing. In ACM EngageCSEdu. ACM, New York, NY, USA.
• Bhavya, S.S., Xiong, J., Zhai. C. (2024). AnaDE1.0: A Novel Data Set for Benchmarking Analogy Detection and Extraction. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL).
• Bhavya, S.S., Zhou, Y., Sehgal, S., Bhat, S., Zhai, C. (2024). Analego: Let’s build analogies together! Demo presentation at the AAAI 2024 Workshop on AI for Education (AI4Ed). 2024.
• Forsyth, C.M., Zapata-Rivera, D., Graf, A., & Jiang, Y. (2024) Complex Conversations: LLMs vs. Knowledge Engineered Conversation-based Assessment. In The Proceedings of the International Conference on Educational Data Mining. 862-867.
• Zapata-Rivera, D., Forsyth, C. M., Graf, A., & Jiang, Y. (2024). Designing and Evaluating Evidence-Centered Design based Conversations for Assessment with LLMs. Proceedings of EDM 2024 Workshop: Leveraging Large Language Models for Next Generation Educational Technologies.
• Zhang, S., Jaldi, C., Schroeder, N. L., & Gladstone, J. R. (2024). Pedagogical agents in K-12 education: A scoping review. Journal of Research on Technology in Education, 1–28. 
• ChengXiang Zhai, LiveDataLab: A Cloud-based Open Lab for Integrating Big Data Research, Education, and Applications, In Proceedings of 2024 IEEE International Conference on Big Data, Washington DC, Dec. 15-18, 2024, pp. 8820-8824.

Works Partially Supported by INVITE

• Hemmat, A., Davies, A., Lamb, T., Yuan, J., Torr, P., Khakzar, A., & Pinto, F. (2024). Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language Models. In Thirty-eighth Conference on Neural Information Processing Systems.
• Schroeder, N. L., Davis, R. O., & Yang, E. (2024). Designing and Learning With Pedagogical Agents: An Umbrella Review. Journal of Educational Computing Research, 07356331241288476.  
• Zapata-Rivera, D., Arslan, B. (2024). Learner Modeling Interpretability and Explainability in Intelligent Adaptive Systems. In: Santoianni, F., Giannini, G., Ciasullo, A. (eds) Mind, Body, and Digital Brains. Integrated Science, vol 20. Springer, Champp. 95–109.
• Lehman, B., Sparks, J. R., Zapata-Rivera, D., Steinberg, J., & Forsyth, C. (2024). A culturally enhanced framework of caring assessments for diverse learners. Practical Assessment, Research, and Evaluation, 29(1), 9.
• Sparks, J. R., Lehman, B., & Zapata-Rivera, D. (2024). Caring assessments: Challenges and opportunities. Frontiers in Education – Assessment, Testing, and Applied Measurement, 9:1216481.
• Mahajan, J., Hum, S., Henhapl, J., Yunus, D., Gadbury, M,. Brown, E., Ginger, J., Lane, H.C. (2024). MineObserver 2.0: A Deep Learning & In-Game Framework for Assessing Natural Language Descriptions of Minecraft Imagery. Association for the Advancement of Artificial Intelligence 2024 Conference, Vancouver.
• Gladstone, J. R., Tallberg, M., Boston Jaxon, J., & Cimpian, A. (2024). What makes a STEM role model motivating for young girls? The effects of the role model’s growth versus fixed mindsets about ability and interest. Journal of Experimental Child Psychology, 238, 105775.
• Ojha, V., West, L., & Lewis, C. M. (2024). Computing Self-Efficacy in Undergraduate Students: A Multi-Institutional and Intersectional Analysis. Proceedings of the 2024 ACM SIGCSE Technical Symposium on Computer Science Education.
• Zeng, Z., Cheng, K., Nanniyur, S., Zhou, J., & Bhat, S. (2023). IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP).
• Zeng, Z., and Bhat, S. (2023). Unified Representation for Non-compositional and Compositional Expression. In Findings of the Association for Computational Linguistics: EMNLP 2023.
• Zhou, J., Zeng, Z., Gong, H. & Bhat, S. (2023). Non-compositional Expression Generation Based on Curriculum Learning and Continual Learning. In Findings of the Association for Computational Linguistics: EMNLP 2023.
• Hum, S. Gadbury, M., Shipley, E., Lane, H.C., Ginger, J. (2024). Mars, Minecraft, and AI: A Deep Learning Approach to Improve Learning by Building. 25th International Conference on Artificial Intelligence in Education, Recife, Brazil, 2024.
• Beigman Klebanov, B., Weeks, J., Sinharay, S. (2024). Predicting student engagement in interactive reading.. In Proceedings of the 25th International Conference on Artificial Intelligence in Education: AIED 2024.
• Zhang, S.,  Earle-Randell, T. V., Shen, Q, Botelho, A. F., Israel, M., Boyer, K. E., Lynch, C. F., & Wiebe, E. (2024). Predicting and Analyzing Students’ Higher-Order Questions in Collaborative Problem-Solving. International Conference on Computers in Education. [Nominated for the Best Overall Paper Award]

Informing Literature

Some sample works that have contributed to the knowledge base INVITE is inspired by:

Learning STEM in INVITE platforms

• Fostering Balanced Contributions Among Children Through Dialogue Visualization
  Celepkolu, M., Galdo, A. C., & Boyer, K. E. (2022). IEEE Transactions on Learning Technologies.
• Triggering STEM interest with Minecraft in a Hybrid Summer Camp
  Lane, H. C., Gadbury, M., Ginger, J., Yi, S., Comins, N., Henhapl, J., & Rivera-Rogers, A. (2022). Technology, Mind, and Behavior.
• Mining for STEM Interest Behaviors in Minecraft
  Gadbury, M., & Lane, H. C. (2022). The 23th International Conference on Artificial Intelligence in Education (AIED).
• Would you? Could you? On a tablet? Analytics of Children’s eBook Reading
  Klebanov, B. B., Loukina, A., Madnani, N., Sabatini, J., & Lentini, J. (2019). The 9th International Conference on Learning Analytics & Knowledge (LAK).

Learner Modeling and Adaptive Educational Systems

• Enhancing Personalization by Integrating Top-down and Bottom-up Approaches to Learner Modeling
  Zapata-Rivera D., & Arslan, B. (2021). The 23rd HCI International Conference.
• Learner Modeling in the Context of Caring Assessments
  Zapata-Rivera, D., Lehman, B., & Sparks, J. R. (2020). The 22nd HCI International Conference.
• The Challenge of Noisy Classrooms: Speaker Detection During Elementary Students’ Collaborative Dialogue
  Ma Y, Wiggins J, Celepkolu M, Boyer K, Lynch C, Wiebe E. (2021). The 22nd International Conference on Artificial Intelligence in Education (AIED).
• Adaptive Educational Systems
  Shute, V. J., & Zapata-Rivera, D. (2012). Adaptive Technologies for Training and Education.
• Learning Intercultural Communication Skills with Virtual Humans
  Lane, H.C., Hays, M.J., Core, M.G., & Auerbach, D. (2013). Journal of Educational Psychology.
• Autonomous Agent that Provides Automated Feedback Improves Negotiation skills
  Monahan, S., Johnson, E., Lucas, G., Finch, J., & Gratch, J. (2018). The 19th International Conference on Artificial Intelligence in Education (AIED).

Learning Analytics and Educational Data Mining

• Modeling MOOC Student Behavior With Two-Layer Hidden Markov Models
  Geigle, C., & Zhai, C. (2017). The 4th Annual ACM Conference on Learning at Scale.
• A Sequential Decision Formulation of the Interface ard Model for Interactive IR
  Zhang, Y., & Zhai, C. (2016). The 39th International ACM SIGIR conference on Research and Development in Information Retrieval.
• Detecting Learning in Noisy Data: The Case of Oral Reading Fluency
  Klebanov, B. B., Loukina, A., Lockwood, J., Liceralde, V. R. T., Sabatini, J., Madnani, N., … & Lentini, J. (2020). The 10th International Conference on Learning Analytics & Knowledge.
• Exploring Emergent Features of Student Interaction within an Embodied Science Learning Simulation
  Kang, J., Lindgren, R., & Planey, J. (2018). Journal of Multimodal Technologies and Interaction.

assessment

• Assessing Collaborative Computing: Development of the Collaborative-Computing Observation Instrument (C-COI)
  Israel, M., Wherfel, Q. M., Shehab, S., Ramos, E. A., Metzger, A., & Reese, G. C. (2016). Computer Science Education.
• Culturally Responsive Evaluation
  Hood, S., Hopson, R. K., & Kirkhart, K. E. (2015). Handbook of Practical Program Evaluation.
• Hitting a High Note on Math Tests: Remembered Success Influences Test Preferences
  Finn, B., & Miele, D. B. (2016). Journal of Experimental Psychology: Learning, Memory, and Cognition.

MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING

• Knowledge-Driven Data Construction for Zero-Shot Evaluation in Commonsense Question Answering
  Ma, K., Ilievski, F., Francis, J., Bisk, Y., Nyberg, E., & Oltramari, A. (2021). The 35th AAAI Conference on Artificial Intelligence (AAAI).
• Illinimet: Illinois System for Metaphor Detection with Contextual and Linguistic Information
  Gong, H., Gupta, K., Jain, A., & Bhat, S. (2020). The 2nd Workshop on Figurative Language Processing.
• Idiomatic Expression Paraphrasing without Strong Supervision
  Zhou, J., Zeng, Z., Gong, H., & Bhat, S. (2022). The 36th AAAI Conference on Artificial Intelligence (AAAI).
• On Robustness and Regularization of Structural Support Vector Machines
  Torkamani, M. A., & Lowd, D. (2014). The 31st International Conference on Machine Learning.