Publications

Selected Refereed Journal and Conference Papers

(iCODE affiliates in bold) 

Rus, V., Sinatra, A.M., Lintean, M. (Eds.). (in press). On The Role of Generative AI in Conversational Intelligent Tutoring Systems, In Design Recommendations for Intelligent Tutoring Systems: Volume 12 – Generative AI for Intelligent Tutoring Systems, Orlando, FL: US Army Combat Capabilities Development Command - Soldier Center.

Chapagain, J., Sajib, M.I., Prodan, R., Rus, V. (2024). A Study of LLM Generated Line-by-Line Explanations in the Context of Conversational Program Comprehension Tutoring Systems, In European Conference on Technology Enhanced Learning, 2024.

Lekshmi-Narayanan, A.B., Oli, P., Chapagain, J., Hassany, M., Banjade, R., Brusilovsky, P., Rus, V. (2024). Explaining Code Examples in Introductory Programming Courses: LLM vs Humans, In Proceedings of the AI for Education Workshop, AAAI 2024, Vancouver, Canada.

Oli, P., Banjade, R., Chapagain, J., Rus, V. (2024). Automated Assessment of Students' Code Comprehension using LLMs, Proceedings of the AI for Education Workshop, AAAI 2024, Vancouver, Canada

Oli, P., Banjade, R., Lekshmi-Narayanan, A.B., Brusilovsky, P., Rus, V. (2024). Exploring The Effectiveness of Reading vs. Tutoring For, Enhancing Code Comprehension For Novices. In Proceedings of ACM SAC Conference (SAC'24). ACM,Avila, Spain

Banjade, R. & Oli, P. & Sajib, M.H, Rus, V. (2024). Identifying Gaps in Students’ Explanations of Code Using LLMs. Proceedings of The 25th International Conference on Artificial Intelligence in Education; AIED 2024. 8-12 July; Recife, Brazil, 10.1007/978-3-031-64299-9_21.

Magar, A.T., Fancsali, S.E., Rus, V., Murphy, A., Ritter, S., Venugopal, D. (2024). Learning Representations for Math Strategies using BERT, ACM Learning @ Scale 2024.

Rus, V. (2024). Large Language Models and Their Implications for Conversational Tutors and GIFT. In Proceedings of the 12th Annual GIFT Users Symposium. Orlando, FL: US Army Combat Capabilities Development Command - Soldier Center. ISBN 978-0-9977258-6-5.

Sinatra, A.M., Graesser, A.C., Hu, X., Townsend, L.N. and Rus, V. (Eds.). (2023). Design Recommendations for Intelligent Tutoring Systems: Volume 11 - Professional Career Education. Orlando, FL: US Army Combat Capabilities Development Command - Soldier Center. ISBN 978-0-9977258-3-4.

Rus, V., Tamang, L, Oli, P., Banjade, R., Brusilovsky, P. (submitted) DeepCode: An Annotated Instructional CodeSet Based on Code Comprehension and Learning Theories To Foster The Development of Code Comprehension Adaptive Instructional Systems, ACM Transactions on Learning Engineering. 

Rus, V., Olney, A. M., & Graesser, A. C. (2023). Deeper learning through interactions with students in natural language. In B. du Boulay, A. Mitrovic, & K. Yacef (Eds.), Handbook of Artificial Intelligence in Education (pp. 250–272). Edward Elgar Publishing. https://doi.org/10.4337/9781800375413.00021.

Shakya, A., Rus, V. and Venugopal, D. (2023). Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data. Proceedings of the 16th International Conference on Educational Data Mining, 137—148, Bengaluru, India, July, 2023, https://doi.org/10.5281/zenodo.8115669.

Banjade, R. & Oli, P. & Rus, V. (2023). Automated Extraction of Domain Models from Textbook Indexes for Developing Intelligent Tutoring Systems. In Augmented Intelligence and Intelligent Tutoring Systems: 19th International Conference, ITS 2023, Corfu, Greece, June 2–5, 2023, Proceedings. Springer-Verlag, Berlin, Heidelberg,  https://doi.org/10.1007/978-3-031-32883-1_11.

Oli, P., Banjade, R., Chapagain, J., & Rus, V. (2023). The Behavior of Large Language Models When Prompted to Generate Code Explanations.  Proceedings of The workshop on Generative AI for Education (GAIED): Advances, Opportunities, and Challenges, The Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA, December 2023.

Oli, P., Banjade, R., Lekshmi-Narayanan, A.B., Chapagain, J., Tamang, L.J., Brusilovsky, P., and & Rus. V. Improving code comprehension through scaffolded self-explanations. In International Conference on Artificial Intelligence in Education, pages 478–483. Springer, 2023.

Chapagain, J., Risha, Z., Banjade, R., Oli, P., Tamang, L., Brusilovsky, P., & Rus, V. (2023). SelfCode: An Annotated Corpus and a Model for Automated Assessment of Self-Explanation During Source Code Comprehension. The International FLAIRS Conference Proceedings, 36(1). https://doi.org/10.32473/flairs.36.133385.

Chapagain, J., Tamang, L. ., Banjade, R., Oli, P., & Rus, V. (2022). Automated Assessment of Student Self-explanation During Source Code Comprehension. The International FLAIRS Conference Proceedings, 35. https://doi.org/10.32473/flairs.v35i.130540

Banjade, R., Oli, P., Tamang, L.J., & Rus, V. (2022). Preliminary Experiments with Transformer-based Approaches To Automatically Inferring Domain Models from Textbooks. Proceedings of the 15th International Conference on Educational Data Mining, 667–672. https://doi.org/10.5281/zenodo.6853051  

Bye, J. K., Harsch, R. M., & Varma, S. (2022). Decoding Fact Fluency and Strategy Flexibility in Solving One-Step Algebra Problems: An Individual Differences Analysis. Journal of Numerical Cognition, 8(2), 281-294. https://doi.org/10.5964/jnc.7093

Shakya, A., Rus, V., Fancsali, S., Ritter, S, Venugopal, D. (2022). NeTra: A Neuro-Symbolic System to Discover Strategies in Math Learning,  Proceedings of The Third Workshop of the Learner Data Institute, The 15th International Conference on Educational Data Mining (EDM 2022)

Arizmendi, C.J., Bernacki, M.L., Raković, M. et al. Predicting student outcomes using digital logs of learning behaviors: Review, current standards, and suggestions for future work. Behav Res (2022). https://doi.org/10.3758/s13428-022-01939-9

Tamang, L.J., Banjade, R., Chapagain, J., Rus, V. (2022). Automatic Question Generation for Scaffolding Self-explanations for Code Comprehension. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_77 

Rus, V., Brusilovsky, P., Tamang, L.J., Akhuseyinoglu, K., Fleming, S. (2022). DeepCode: An Annotated Set of Instructional Code Examples to Foster Deep Code Comprehension and Learning. In: Crossley, S., Popescu, E. (eds) Intelligent Tutoring Systems. ITS 2022. Lecture Notes in Computer Science, vol 13284. Springer, Cham. https://doi.org/10.1007/978-3-031-09680-8_4 

Alina Zaman, Vinhthuy Phan, and Amy Cook. 2022. Enabling In-Class Peer Feedback on Introductory Computer Science Coding Exercises. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2 (SIGCSE 2022). Association for Computing Machinery, New York, NY, USA, 1163. https://doi.org/10.1145/3478432.3499109

Amy Cook, Vinhthuy Phan, and Alistair Windsor. 2022. Improving TA Feedback on In-Class Coding Assignments for Introductory Computer Science. In Proceedings of the 27th ACM Conference on Innovation and Technology in Computer Science Education Vol. 1 (ITiCSE '22). Association for Computing Machinery, New York, NY, USA, 421–427. https://doi.org/10.1145/3502718.3524746

Raković, M., Bernacki, M., Greene, J., Plumley, R., Hogan, K., K. M., & Panter, A. T. (2022). Examining the critical role of evaluation and adaptation in reflective writing and self-regulated learning. Contemporary Educational Psychology, 68, 102027. https://doi.org/doi.org/10.1016/j.cedpsych.2021.102027

Amy Cook, Alina Zaman, Eric Hicks, Kriangsiri Malasri, and Vinhthuy Phan. 2022. Try That Again! How a Second Attempt on In-Class Coding Problems Benefits Students in CS1. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 1 (SIGCSE 2022), Vol. 1. Association for Computing Machinery, New York, NY, USA, 509–515. https://doi.org/10.1145/3478431.3499362

Tawfik, Andrew A. PhD; Bradford, Jacque EdD, DPT, MS; Gish-Lieberman, Jaclyn EdD; Gatewood, Jessica BA. Repeated Measures of Cognitive and Affective Learning Outcomes in Simulation Debriefing. Journal of Physical Therapy Education 36(2):p 133-138, June 2022. | DOI: 10.1097/JTE.0000000000000233

Stephen, J.S., & Tawfik, A.A. (2022). Self-Efficacy Sources and Impact on Readiness to Teach Online. Routledge. https://doi.org/10.4324/9781138609877-REE106-1

Tawfik, A.A., Gatewood, J., Gish-Lieberman, J.J. et al. Toward a Definition of Learning Experience Design. Tech Know Learn 27, 309–334 (2022). https://doi.org/10.1007/s10758-020-09482-2