Learner Data Institute Workshop Proceedings

Rus, V., Fancsali, S.E. (Organizers; 2021). Proceedings of The Second Workshop of The Learner Data Institute: Big Data, Research Challenges, & Science Convergence in Educational Data Science. In T.W. Price & S. San Pedro (Eds.) In Joint Proceedings of the Workshops at the International Conference on Educational Data Mining 2021 co-located with 14th International Conference on Educational Data Mining (EDM 2021). CEUR Workshop Proceedings Volume 3051.

Papers from The First Workshop of the Learner Data Institute: Big Data, Research Challenges, & Science Converge in Educational Data Science are available at the LDI@EDM '20 website.

Peer-Reviewed Articles & Conference Proceedings

Al Farabi K.M., Sarkhel S., Dey S., & Venugopal D. (2020). Fine-Grained Explanations Using Markov Logic. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, C. Robardet. (Eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science, Vol. 11907. Springer, Cham. (pp. 614-629). https://doi.org/10.1007/978-3-030-46147-8_37

Alshaikh, Z., Tamang, L., & Rus, V. (2020). Experiments with a Socratic Intelligent Tutoring System for Source Code Understanding. In R. Barták & E. Bell (Eds.) Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference. FLAIRS-33 2020. (pp. 457-460). Palo Alto, CA: AAAI Press.

Ait-Khayi, N., & Rus, V. (2020). Attention Based Transformer for Student Answers Assessment. In R. Barták & E. Bell (Eds.) Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference. FLAIRS-33 2020. (pp. 3-8). Palo Alto, CA: AAAI Press.

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

Cai Z., Siebert-Evenstone A., Eagan B., Shaffer D.W., Hu X., & Graesser A.C. (2019). nCoder+: A Semantic Tool for Improving Recall of nCoder Coding. In: Eagan B., Misfeldt M., Siebert-Evenstone A. (Eds.) Advances in Quantitative Ethnography (ICQE 2019). Communications in Computer and Information Science, Vol. 1112. Springer, Cham. https://doi.org/10.1007/978-3-030-33232-7_4

Chen, S, Fang, Y., Shi, G., Sabatini, J., Greenberg, D., Frijters, J., & Graesser, A.C. (2021). Automated disengagement tracking within an intelligent tutoring system. Frontiers in Artificial Intelligence, 3, 1-16.

DeFalco J.A., Hampton A.J. (2020). Dewey’s Ethics of Moral Principles and Deliberation: Extending IEEE’s Ethics Initiative for Adaptive Instructional Systems. In R. Sottilare & J. Schwarz (Eds.) Adaptive Instructional Systems. HCII 2020. Lecture Notes in Computer Science, Vol 12214. Springer, Cham. (pp. 45-54). https://doi.org/10.1007/978-3-030-50788-6_4

Deng, L-Y, Bowman, D., Yang, C-C, and Lu, Henry H-S. (submitted Annual Modeling and Simulation Conference, 2021). Extending RC4 to Construct Secure Random Number Generators.

Deng, L-Y, Yang, C-C, Bowman, D., Lin, D.K.J., and Lu, Henry H-S. (submitted JASA, 2021). Big Data Model Building using Dimension Reduction and Sample Selection.

Elswick, S. (2021). "Minecraft™: Just a Game or a Conduit to Enhance Social-Emotional Learning?" Education and Information Technologies. (Submitted for review 5/4/2021).

Fancsali, S.E., Holstein, K., Sandbothe, M., Ritter, S., McLaren, B.M., Aleven, V. (2020). Towards Practical Detection of Unproductive Struggle. In I. Bitencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millan (Eds.) Proceedings of the 21st International Conference on Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, Vol. 12164. Springer, Cham. (pp. 92-97). https://doi.org/10.1007/978-3-030-52240-7_17

Fancsali, S.E., Li, H., Sandbothe, M., Ritter, S. (2021). Targeting Design-Loop Adaptivity. Proceedings of the Fourteenth International Conference on Educational Data Mining (EDM 2021).

Fancsali, S.E., Pavelko, M., Fisher, J., Wheeler, L., Ritter, S. (2021). Scaffolds and Nudges: A Case Study in Learning Engineering Design Improvements. Proceedings of the 22nd International Conference on Artificial Intelligence in Education. AIED 2021.

Fisher, J., Fancsali, S.E., Lewis, A., Fisher, V., Hausmann, R.G.M., Pavelko, M., Finocchi, S.B., Ritter, S. (2020). LiveHint: Intelligent Digital Support for Analog Learning Experiences. In S. Sosnovsky, P. Brusilovsky, R. Baraniuk, & A. Lan (Eds.) Proceedings of the Second International Workshop on Intelligent Textbooks 2020 (co-located with AIED 2020). CEUR Workshop Proceedings, Vol. 2674. Sun SITE Central Europe. (pp. 80-89). http://ceur-ws.org/Vol-2674/paper08.pdf

Forsyth, C.M., Graesser, A.C., & Millis, K. (2020). Predicting learning in a multi-component serious game. Technology, Knowledge, and Learning, 25, 251-277.

Graesser, A.C., & Li, H. (in press). Intelligent tutoring systems and conversational agents. In R. Tierney, F. Rizvi, K. Ercikan, and G. Smith (Eds.), International Encyclopedia of Education, edition 4. Elsevier.

Graesser, A.C., Sabatini, J., & Li, H. (under revision). Educational psychology is evolving to accommodate technology, multiple disciplines, and 21^st century skills. Annual Review of Psychology.

Graesser, A.C., Greiff, S., Stadler, M., & Shubeck, K.T. (2020). Collaboration in the 21^st century: The theory, assessment, and teaching of collaborative problem solving. Computers in Human Behavior, 104, Article 106134.

Graesser, A.C. (2020). Emotions are the experiential glue of learning environments in the 21^st century. Learning and Instruction, 70, 101212.

Green, A., Cockroft, J.L., Kaufman, R.A., McCullers, J.A., Arnold, S.R., Utility of Induced Sputum in Assessing Bacterial Etiology for Community-Acquired Pneumonia in Hospitalized Children, Journal of the Pediatric Infectious Diseases Society, 2022, piac014, https://doi.org/10.1093/jpids/piac014

Harrell-Williams, L. M., Mueller., C., Fancsali, S., Ritter, S., Zhang, X. & Venugopal, D. (2022). Exploring Stability in Multilevel Achievement-Goal Profile Membership in Mathematics Learning in an Intelligent Tutoring System. Paper to be presented at the American Educational Research Association Annual Meeting, San Diego, CA.

Hu, X., Cai, Z., Hampton, A.J., Cockroft, J.L., Graesser, A.C., Copland, C., Folsom-Kovarik, J.T. (2019). Capturing AIS Behavior using xAPI-like Statements. In R. Sottilare & J. Schwarz (Eds.) Adaptive Instructional Systems. HCII 2019. Lecture Notes in Computer Science, Vol. 11597. Springer, Cham. (pp. 204-216).

Islam, M.M., Sarkhel, S., and Venugopal, D. (2020). Augmenting Deep Learning with Relational Knowledge from Markov Logic Networks. IEEE Conference on Big Data (IEEE BigData). https://ieeexplore.ieee.org/document/9378055. DOI: 10.1109/BigData50022.2020.9378055

Li, Y., Schoenfeld, diSessa, A.A., Graesser, A.C., Benson, L.C., English, L.D., & Duschl, R.A. (2020). Design and design thinking in STEM education. International Journal of STEM Education, 2, 93-104.

Li, Y., Schoenfeld, A.H., diSessa, A.A., Graesser, A.C., Benson, L.C., English, L.D., Duschl, R.A. (2020). On computational thinking and STEM education. Journal of STEM Education Research, 2, 147-166.

Li, H., & Graesser, A.C. (in press). The impact of conversational agents’ language on summary writing. Journal on Research on Technology and Education.

Lippert, A., Shubeck, K., Morgan, B., Hampton, A., & Graesser, A. (2020). Multiple agent designs in conversational intelligent systems. Technology, Knowledge, and Learning, 25, 443-463.

Long, Z. Luo, D., Kiu, K., Gao, H., Qu, J., and Hu, X. Raising Academic Performance in Socio-cognitive Conflict Learning Through Gamification. In Bittencourt et al. (Eds.): AIED 2020, LNAI 12164, pp. 180-184, 2020. https://doi.org/10.1007/978-3-030-52240-7_33.

Mitra, N., and Morshed, B.I. (2022). Automatic Detection of Situational Contexts Using AI from Minimal Sensor Modality”, IEEE Intl. Conf. On Electro/Information Technology (EIT), Minnesota State University, MN, USA, May 15-19, 2022.

Olney, A. M., & Fleming, S. D. (2019). A Cognitive Load Perspective on the Design of Blocks Languages for Data Science. 2019 IEEE Blocks and Beyond Workshop (B&B), (pp. 95–97). https://olney.ai/category/2019/12/26/blocksbeyond.html

Pasiah, B. P., Deng, L-Y, Bowman, D., and Yang, C-C. (submitted Annual Modeling and Simulation Conference, 2021). Searching For Large-Order Multiple Recursive Generators.

Pavlik Jr, P. I., & Eglington, L. G. (2021). LKT: Logistic Knowledge Tracing: R package version 1.0.

Pavlik, P. I., Eglington, L. G., & Harrell-Williams, L. M. (2021). Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling. IEEE Transactions on Learning Technologies, 1-1. doi:10.1109/TLT.2021.3128569

Pavlik Jr., P. I., Cao, M., & Eglington, L. (2019). Mathematically Modeling the Optimal Desirable Difficulty. Proceedings of the 60th Annual Meeting of the Psychonomic Society, Montreal, Canada. (Poster)

Pavlik Jr., P. I., & Eglington, L. (2021). Modeling the EdNet Dataset with Logistic Regression. 35th AAAI Conference on Artificial Intelligence, Imagining Post-COVID Education with AI Workshop (pp. 1-5).

Pavlik Jr., P. I., Olney, A. M., Banker, A., Eglington, E. & Yarbro, J. (2020). The Mobile Fact and Concept Textbook System (MoFaCTS). In S. Sosnovsky, P. Brusilovsky, R. Baraniuk, & A. Lan (Eds.) Proceedings of the Second International Workshop on Intelligent Textbooks 2020 (co-located with AIED 2020). CEUR Workshop Proceedings, Vol. 2674. Sun SITE Central Europe. (pp. 35-49). http://ceur-ws.org/Vol-2674/paper04.pdf

Ritter, S., Harrell-Williams, L., Mueller., C., Fancsali, S., Zhang, X. & Venugopal, D. (2021). Designing a study for assessing change in in-the-moment motivation profiles in an algebra intelligent tutoring system. Poster presented at Association of Psychological Science (APS) Annual Convention, Virtual Conference.

Robson, R. Hu, X., Goodell, J., Jay, M., and Redd, B. Empowering Education with AI Technology – IEEE LTSC. In Bittencourt et al. (Eds.): AIED 2020, LNAI 12164, pp. 420-423, 2020. https://doi.org/10.1007/978-3-030-52240-7.

Rus, V., Olney, A., & Graesser, A.C. (under revision). Interactions with learners in natural language. In B. du Boulay, A. Mitrovic, and K. Yacef (Eds)., Handbook of Artificial Intelligence in Education.

Shakya, A., Rus, V., Venugopal, D. (2021). Student Strategy Prediction using Neuro-Symbolic Approach. Proceedings of the Fourteenth International Conference on Educational Data Mining (EDM 2021). [pdf] [code]

Shi, G., Wang, L., Zhang, L., Shubeck, K., Peng, S., Hu, X., & Graesser, A. C. (2021). The adaptive features of an intelligent tutoring system for adult literacy. In Proceedings of the Third International Conference, AIS 2021, Held as Part of the 23rd HCI International Conference, Washington DC, USA.

Sinatra, A., Graesser, A.C., Hu, X., Goldberg, B., & Hampton, A. (2020). Design Recommendations for Intelligent Tutoring Systems: Data Visualization (Vol.8). Orlando, FL: Army Research Laboratory.

Stadler, M., Graesser, A., & Fischer, F. (in press). Editorial: Transdisciplinary research on teaching and teachers – chances and challenges. Frontiers in Psychology.

Stadler, M., Shubeck, K.T., Greiff, S., & Graesser, A.C. (2020). Some critical reflections on the special issue on collaboration in the 21^st century. Computers in Human Behavior, 104, Article 106135.

Tamang, L., Alshaikh, Z., Ait-Khayi, N., & Rus, V. (2020). The Effects of Open Self-Explanation Prompting During Source Code Comprehension. In R. Barták & E. Bell (Eds.) Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference. FLAIRS-33 2020. (pp. 451-456). Palo Alto, CA: AAAI Press.

Tawfik, A.A., Graesser, A.C., & Love, J. (2020). Supporting project-based learning through the Virtual Internship Author. Technology, Knowledge, and Learning, 25, 433-442.

Tawfik, A.A., Graesser, A.C., Gatewood, J. & Gishbaugher, J. (2020). Role of questions in inquiry-based instruction: towards a design taxonomy for question-asking and implications for design. Educational Technology Research and Development, 68, 653-678. 10.1007/s11423-020-09738-9.

Book Chapters

Graesser, A.C., and Hampton, A.J. Introduction to General Applications of Data Visualization. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 13-14) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Harrison, L.T., and Hampton, A.J. Walk Me Through This: Utilizing Kinesthetic Effect in Data Storytelling. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 41-46) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Swiecki, Z., Misfeldt, M., Hu, X., and Shaffer, D.W. Visualizing Team Processes using Epistemic Network Analysis Affordances for Researchers, Educators, and Teams. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 53-60) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Zapata-Rivera, D., Graesser, A.C., Kay, J., Hu, X., and Ososky, S.J. Visualization Implications for the Validity of Intelligent Tutoring Systems. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 61-68) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Goldberg, B. and Hu, X. Introduction to Data Visualization in Specific Domains and Applications. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 75-76) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Stevens, R., Mullins, R., Hu, X., Zapata-Rivera, D., and Galloway, T. Visualizing the Momentary Neurodynamics of Team Uncertainty. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 89-100) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Misfeldt, M., Swiecki, Z., Zapata-Rivera, D., and Hu, X. Pedagogical Use Scenarios for Data Visualizations: Prepare, Conduct and Evaluate. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 101-108) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Sottilare, R.A., Folsom-Kovarik, J., Cockroft, J.L., and Hampton, A.J. Using Digital Twins to Visualize Performance During Adaptive Instruction of Maintenance Tasks. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 109-116) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Hu, X., Rus, V., Cockroft, J.L., and Zhang, L. Visualizing Intelligent Tutoring System Data in Real Time. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 117-124) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Kay, J., Rus, V., Zapata-Rivera, D., and Durlach, P. Open Learner Model Visualizations for Contexts where Learners Think Fast or Slow. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 125-136) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Rus, V., Swiecki, Z., Cockroft, J.L., and Hu, X. Visualization Of Sequential Interactions in Adaptive Instructional Systems. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 149-154) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Goldberg, B., Hoffman, M., and Graesser, A.C. Adding a Human to the Adaptive Instructional System Loop: Integrating GIFT and Battle Space Visualization. In Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. (pp. 191-203) A.M. Sinatra, A.C. Graesser, X. Hu, Goldberg, B., and A.J. Hampton (Eds.). (2020). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9977257-8-0. Available at: https://gifttutoring.org/documents/

Hu, X., Cai, Zhiqiang, & Olney, A. M. (2019). Semantic Representation and Analysis (SRA) and its Application in Conversion-Based Intelligent Tutoring Systems (CbITS). In R. Feldman (Ed.) Learning Science: Theory, Research, and Practice (pp. 103–126). McGraw-Hill Education. https://olney.ai/category/2019/11/01/husra.html

Braasch, J.,L.G., & Graesser, A.C. (2020). Avoiding and overcoming misinformation on the internet. In R.J. Sternberg and D. Halpern (Eds.), Critical Thinking in Psychology (vol. 2, pp. 125-151). Cambridge: Cambridge University Press.

Graesser, A.C. (2020). Learning science principles and technologies with agents that promote deep learning. In R.S. Feldman (ed.), Learning science: Theory, research, and practice (pp. 2-33). New York: McGraw-Hill.

Graesser, A.C., Hu, X., Rus, V., & Cai, Z. (2020). Conversation-based learning and assessment environments. In D. Yan, A. Rupp, and P. Foltz (Eds.)., Handbook of automated scoring: Theory into practice (pp. 383-402). New York: CRC Press/Taylor and Francis.

Morgan, B.A., Hogan, A.M., Hampton, D., Lippert, A.& Graesser, A.C. (2020). The need for personalized learning and the potential of intelligent tutoring systems. In P. van Meter, A. List, & D. Lombardin and P. Kendeou (Eds), Handbook of learning from multiple representations and perspectives (pp. 495-512). New York: Routledge.

D’Mello, S.K., & Graesser, A.C. (in press). Intelligent tutoring systems: How computers achieve learning gains that rival human tutors. In P. Shutz and K.R. Muis (Eds), Handbook of Educational Psychology, vol. 4. Washington, D.C.: American Psychological Association.

Graesser, A.C., & Windsor, L. (in press). Text and discourse with humans and computers. In R. Boyd and M. Dehghani (Eds.), The atlas of language analysis in psychology. Guilford Press.

Selected Talks

Pavlik Jr, P. I. (2019). Instructional Engineering for Personalized Adaptive Practice Systems. Keynote Talk @ EdCrunch 2019. Moscow, Russia.