Selected Refereed Journal and Conference Papers

(iCODE affiliates in bold) 

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.

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.  

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.

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).

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. 

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. 

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.

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.

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.

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.

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.

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).

Tamang, L.J., Alshaikh, Z., Khayi, N.A., Oli,P. and Rus, V. 2021. A Comparative Study of Free Self-Explanations and Socratic Tutoring Explanations for Source Code Comprehension. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE '21). Association for Computing Machinery, New York, NY, USA, 219–225. 

Banjade, R., Oli, P. Tamang, L.J.  Chapagain, J. and Rus, V. 2021. “Domain Model Discovery from Textbooks for Computer Programming Intelligent Tutors”. The International FLAIRS Conference Proceedings 34 (April).

Bernacki, M. L., Vosicka, L., Utz, J. C., & Warren, C. B. (2021). Effects of digital learning skill training on the academic performance of undergraduates in science and mathematics. Journal of Educational Psychology, 113(6), 1107–1125.

Bernacki, M. L., Greene, J., & Lobczowski, N. G. (2021). A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)?. Educational Psychology Review. Published.

Bernacki, M., Crompton, H., & Greene, J. (2020). Towards convergence of mobile and psychological theories of learning. Contemporary Educational Psychology, 60, 101828.

Trevors G, Kendeou P. The effects of positive and negative emotional text content on knowledge revision. Q J Exp Psychol (Hove). 2020 Sep;73(9):1326-1339. doi: 10.1177/1747021820913816. Epub 2020 Apr 21. PMID: 32312183.

Bernacki, M., Greene, J., & Crompton, H. (2020). Mobile technology, learning, and achievement: Advances in understanding and measuring the role of mobile technology in education. Contemporary Educational Psychology, 60, 101827.

Amy Cook, Steven Dow, and Jessica Hammer. 2020. Designing Interactive Scaffolds to Encourage Reflection on Peer Feedback. In Proceedings of the 2020 ACM Designing Interactive Systems Conference (DIS '20). Association for Computing Machinery, New York, NY, USA, 1143–1153.

Matthew L. Bernacki, Michelle M. Chavez, P. Merlin Uesbeck, Predicting achievement and providing support before STEM majors begin to fail, Computers & Education, Volume 158, 2020, 103999, ISSN 0360-1315,

Crompton, H., Bernacki, M., & Greene, J. (2020). Psychological Foundations of Emerging Technologies for Teaching and Learning in Higher Education. Current Opinion in Psychology, 36, 101–105.

McMaster, K., Kendeou, P., Bresina, B. C., Slater, S., Wagner, K., White, M. J., Butterfuss, R., Kim, J., & Umana, C. (2019). Developing an interactive software application to support young children’s inference-making. L1-Educational Studies in Language and Literature, 19(4), 1–30.

Zachari Swiecki, Andrew R. Ruis, Dipesh Gautam, Vasile Rus, David Williamson Shaffer: Understanding when students are active-in-thinking through modeling-in-context. BJET 50(5): 2346-2364 (2019).

Ait-Khayi, N. and Rus, V. (2019). BI-GRU Capsule Networks for Student Answers Assessment, In Proceedings of The 2019 KDD Workshop on Deep Learning for Education (DL4Ed) in conjunction with the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), August 4-8, 2019, Anchorage, Alaska, USA

Rus, V., Brusilovskoy, P, Fleming, S., Tamang, L., Risha, Z., Akhuseyinoglu, K., Pineda, J.B., Ait-Khayi, N., & Alshaikh, Z. (2019). An Intelligent Tutoring System for Source Code Comprehension, Demo Session, The 20th International Conference on Artificial Intelligence in Education, June 25-29, Chicago, IL, USA.

Graesser, A.C., Hu, X., Rus, V., & Cai, Z. (2018). AutoTutor and other Conversation-based Learning and Assessment Environments, Chapter for Handbook of Automated Scoring: Theory into Practice, edited by Andre Rupp, Duanli Yan, and Peter Foltz

Maharjan, N., & Rus. V. (2018). A Tutorial Markov Analysis of Effective Human Tutorial Sessions, Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pages 30–34, Melbourne, Australia, July 19, 2018.

Gautam, D. & Rus, V. (2018). Long Short Term Memory based Models for Negation Handling in Tutorial Dialogues, Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference (FLAIRS 2018), Melbourne Beach, FL, USA May 21-23, 2018.

Rus, V., Olney, A.M., Foltz, P., Hu, X. (2017). Automated Assessment of Learner-Generated Natural Language Responses, In R. Sottilare, A. Graesser, X. Hu, & G. Goodwin (Eds.), Design Recommendations for Intelligent Tutoring Systems: Assessment Methods (Vol. 5, pp. 155–170). Orlando, FL: U.S. Army Research Laboratory.

Maharjan, N., Banjade, R., Rus, V. (2017). Automated Assessment of Open-ended Student Answers in Tutorial Dialogues Using Gaussian Mixture Models, Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2017), San Marco Island, Florida, USA May 16-18, 2017. [NOMINATED FOR BEST STUDENT PAPER AWARD]

Rus, V., Maharjan, N., Tamang, L.J., Yudelson, M., Berman, S., Fancsali, S.E., Ritter, S. (2017). An Analysis of Human Tutors’ Actions In Tutorial Dialogues, Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2017), San Marco Island, Florida, USA May 16-18, 2017.

Rus, V., Banjade, R., Maharjan, N., Morrison, D., Ritter, S., and Yudelson, M. (2016). Preliminary Results on Dialogue Act Classification in Chatbased Online Tutorial Dialogues, Proceedings of the 9th International Conference on Educational Data Mining, Raleigh, NC, June 29 - July 2, 2016.

Van Den Broek, P., & Kendeou, P. (2015). Building coherence in web-based and other non-traditional reading environments: Cognitive opportunities and challenges. In R. Spiro (Ed.), Reading at a Crossroads?: Disjunctures and Continuities in Current Conceptions and Practices (pp. 104-114). Taylor and Francis Inc.. 

 Rus, V., Niraula, N., & Banjade, R. (2015). DeepTutor: An Effective, Online Intelligent Tutoring System That Promotes Deep Learning, The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15). January 25–30, 2015, Austin, Texas, USA.

 Samei, B., Rus, V., Nye, B., & Morrison, D. (2015). Hierarchical Dialogue Act Classification in Online Tutoring Sessions, The Eighth International Conference on Educational Data Mining (EDM 2015), Madrid, Spain. June 26-29, 2015.

 Rus, V., Graesser, A., Niraula, N., & Banjade, R. (2015). DeepTutor: Integrating Learning Progressions In Unsupervised After-school Online Intelligent Tutoring, The 17th International Conference on Artificial Intelligence in Education (AIED 2015), June 22-26, 2015, Madrid, Spain.

 Rus, V., Maharjan, N., & Banjade, R. (2015). Unsupervised Discovery of Tutorial Dialogue Modes in Human-to-Human Tutorial Data, The 3rd Generalized Intelligent Tutoring Framework Symposium, June 17-18, Orlando, FL. [PDF]

Rus, V. & Stefanescu, D. (2015). Towards Non-Intrusive Assessment in Dialogue-based Intelligent Tutoring Systems, The 2nd International Conference on Smart Learning Environments, September 23-25, Sinaia, Romania. [BEST PAPER AWARD] [PDF]

Banjade, R., Niraula, N., Maharjan, N., Rus, V., Stefanescu, D., Lintean, M., & Gautam, D. (2015). NeRoSim: A System for Measuring and Interpreting Semantic Textual Similarity, Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 164-171, Denver, Colorado, June 4-5, 2015. [WINNER OF SEMEVAL COMPETITION] [PDF]

Papadopoulos, T., Kendeou, P. & Shiakalli, M. (2014). Reading comprehension tests and poor readers: How test processing demands result in different profiles. L’Année psychologique, 114, 725-752. 

Rus, V., Graesser, A.C., & Conley, M. (2014). The DENDROGRAM Model of Instruction, Design Recommendations for Adaptive Intelligent Tutoring Systems: Adaptive Instructional Strategies (Volume 2), (Eds. Sottilare, R.), Army Research Lab. [PDF]

Niraula, N. & Rus, V. (2014). A Machine Learning Approach to Anaphora Resolution in Dialogue based Intelligent Tutoring Systems, In Proceedings of 15th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2014), April 6-12, Kathmandu, Nepal. [PDF]

Rus, V., Stefanescu, D., Baggett, W., Niraula, N., Franceschetti, D., & Graesser, A.C. (2014). Macro-adaptation in Conversational Intelligent Tutoring Matters, The 12th International Conference on Intelligent Tutoring Systems, June 5-9, Honolulu, Hawaii. [PDF]

LeBeau, B., Harwell, M., Monson, D., Dupuis, D., Medhanie, A., & Post, T. (2012). Student and high-school characteristics related to completing a science, technology, engineering, or mathematics (STEM) major in college. Research in Science & Technological Education, 30, 17-28. 

Ming, L. Calvo, R., & Rus, V. (2012). G-Asks: An Intelligent Automatic Generation System for Academic Writing Support, Special Issue on Question Generation of the Journal of Dialogue and Discourse, Vol. 3., No. 2, pp. 101-124. [PDF]

Cook, A., Boyce, A.K., Gadwal, C., & Barnes, T. (2013). Effective practices in-game tutorial systems. International Conference on Foundations of Digital Games.

Rus, V., D'Mello, S., Hu, X., & Graesser, A.C. (2013). Recent Advances in Conversational Intelligent Tutoring Systems, AI Magazine, 34,(3):42-54. [PDF]

Rus, V., Baggett, W., Gire, E., Franceschetti, D., Conley, M., Graesser, A.C. (2013). Towards Learner Models based on Learning Progressions in DeepTutor, Learner Models (Eds. Sottilare, R.), Army Research Lab. [PDF]

Rus, V., Niraula, N., Lintean, M., Banjade, R., Stefanescu, D., & Baggett, W. (2013). Recommendations for the Generalized Intelligent Framework for Tutoring based on the Development of the DeepTutor Tutoring Service, Workshop on Generalized Intelligent Framework for Tutoring (GIFT), The 16th International Conference on Artificial Intelligence in Education (AIED 2013), July 9-13, Memphis, TN. [PDF]

Rus, V., Linean, M., Cai, Z., Graesser, A.C., Witherspoon, A., & Azevedo, R. (2011). Computational Aspects of The Intelligent Tutoring System MetaTutor, In Applied Natural Language Processing, Phillip McCarthy and Chutima Boonthum-Denecke (Eds.), IGI Global, 2011. [PDF]

Lintean, M., Rus, V., & Azevedo, R. (2011). Automatic Detection of Student Mental Models during Prior Knowledge Activation in MetaTutor, International Journal of Artificial Intelligence in Education, 21(3), pp. 169-190. [local copy - PDF]

Rus. V. (2010). Language Processing Challenges and Solutions in Intelligent Tutoring Systems with Natural Language Interaction, in Forascu, C. & Tufis, D., Multilinguality and Interoperability in Language Processing, Romanian Academy Press, ISBN: 978-973-27-1972-5. [PDF]

Rus, V., Lintean, M., & Azevedo, R., (2010). Computational Aspects of The Intelligent Tutoring System MetaTutor. Proceedings of the 23st International Florida Artificial Intelligence Research Society Conference. Daytona Beach, FL. [PDF] 

Kendeou, P., van den Broek, P., White, M. J., & Lynch, J. S. (2009). Predicting reading comprehension in early elementary school: The independent contributions of oral language and decoding skills. Journal of Educational Psychology, 101(4), 765–778.


Graesser, A. C., D'Mello, S., & Person, N. K. (2009). Metaknowledge in tutoring. In D. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education. Mahwah, NJ: Taylor & Francis.

Rus, V. (2009). What next in knowledge representation? Knowledge Engineering: Principles and Techniques. Cluj-Napoca, Romania. Babes-Bolyai University Press.

Graesser, A. C., Rus., V., DMello, S., & Jackson, G. T. (2008). AutoTutor: Learning through natural language dialogue that adapts to the cognitive and affective states of the learner. In D. H. Robinson & G. Schraw (Eds.), Current perspectives on cognition, learning and instruction: Recent innovations in educational technology that facilitate student learning (pp. 95125). Information Age Publishing.