Publications

Papers by year of publication: 

2024

• Akpanoko, C. E., Ashwin, T. S., Cordell, G., & Biswas, G. (2024). Investigating the Relations between Students’ Affective States and the Coherence in their Activities in Open-Ended Learning Environments. https://educationaldatamining.org/edm2024/proceedings/2024.EDM-short-papers.51/2024.EDM-short-papers.51.pdf
• Akpanoko, C. E., & Biswas, G. (2024). The Interplay of Affective States and Cognitive Processes in an Open-Ended Learning Environment: A Case Study. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 873-880. International Society of the Learning Sciences. https://repository.isls.org/bitstream/1/11182/1/ICLS2024_873-880.pdf
• Ashwin, T.S., Biswas, G. (2024). Identifying and Mitigating Algorithmic Bias in Student Emotional Analysis. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. AIED 2024. Lecture Notes in Computer Science, vol 14830. Springer, Cham. https://doi.org/10.1007/978-3-031-64299-9_7, Nominated for Best Paper
• Cloude, E. B., Munshi, A., Andres, J. A., Ocumpaugh, J., Baker, R. S., & Biswas, G. (2024, March). Exploring Confusion and Frustration as Non-linear Dynamical Systems. 14th International Learning and Knowledge Conference (LAK 24), Kyoto, Japan, March 18-22, 2024.
• Cohn, C., Snyder, C., Montenegro, J., Biswas, G. (2024). Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse Analysis. In: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024. Communications in Computer and Information Science. https://doi.org/10.1007/978-3-031-64312-5_2.
• Cohn, C., Snyder, C., Fonteles, J. H., TS, A., Montenegro, J., & Biswas, G. (2024). A multimodal approach to support teacher, researcher and AI collaboration in STEM+ C learning environments. British Journal of Educational Technology, 00, 1–26. https://doi.org/10.1111/bjet.13518
• Cohn, C., Hutchins, N., Le, T., & Biswas, G. (2024). A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students’ Formative Assessment Responses in Science. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23182-23190. https://doi.org/10.1609/aaai.v38i21.30364
• Eduardo Davalos, Yike Zhang, Ashwin T. S., Joyce H. Fonteles, Umesh Timalsina, and Gautam Biswas. 3D Gaze Tracking for Studying Collaborative Interactions in Mixed-Reality Environments. In INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (ICMI Companion ’24), November 4–8, 2024, San Jose, Costa Rica. ACM, New York, NY, USA, 9 pages. In press.
• Fonteles, J., Davalos, E., Ashwin, T. S., Zhang, Y., Zhou, M., Ayalon, E., Lane, A., Steinberg, S., Anton, G., Danish, J., Enyedy, N., & Biswas, G. (2024). A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments. In Lecture Notes in Computer Science (pp. 3–16). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64299-9_1
• Fonteles, J. H., Akpanoko, C. E., Wisniewski, P. J., & Biswas, G. (2024). Promoting Equitable Learning Outcomes for Underserved Students in Open-Ended Learning Environments. In Proceedings of the 23rd Annual ACM Interaction Design and Children Conference. IDC ’24: Interaction Design and Children. ACM. https://doi.org/10.1145/3628516.3655753
• Lester, J., Bansal, M., Biswas, G., Hmelo-Silver, C., Roschelle, J., Rowe, J. (2023, in press). The AI Institute for Engaged Learning. AI Magazine. AAAI.
• Snyder, C., Hutchins, N.M., Cohn, C., Fonteles, J.H., & Biswas, G. (2024). Analyzing Students Collaborative Problem-Solving Behaviors in Synergistic STEM+C Learning. The 14th International Learning Analytics and Knowledge Conference, Kyoto, Japan, March 18-22, 2024.
• Snyder, C., Wen, C., Hutchins, N. M., Vatral, C., Liu, C., & Biswas, G. (2024). Investigating Collaborative Problem Solving Behaviors during STEM+C Learning in Groups with Different Prior Knowledge Distributions. In Clarke-Midura, J., Kollar, I., Gu, X., & D’Angelo, C. (Eds.), Proceedings of the 17th International Conference on Computer-Supported Collaborative Learning – CSCL 2024 (pp. 107-114). International Society of the Learning Sciences. https://doi.org/10.22318/cscl2024.231933. Outstanding Long Paper
• Vatral, C., Biswas, G., & Goldberg, B. (2024, in press). A Theoretical Framework for Performance Analysis in Competency-Based Experiential Learning Environments. Technologies to support Training, Assessment, and Selection, James Ferraro and Phillip Mangos, editors. Taylor & Francis.
• Zhou, M., Fonteles, J., Danish, J., Davalos, E., Steinberg, S., Biswas, G., Enyedy, N.: Exploring artificial intelligence supported interaction analysis. In: Proceedings of the 18th International Conference of the Learning Sciences – ICLS 2024. pp. 2327–2328. International Society of the Learning Sciences, NY, USA (2024). https://repository.isls.org/bitstream/1/10999/1/ICLS2024_2327-2328.pdf

2023

• Cochran, K., Cohn, C., and Hastings, P. (2023). Improving NLP model performance on small educational data sets using self-augmentation. Proceedings of the 15th International Conference on Computer Supported Education, Prague, Czech Republic. (pp. 70-78).
• Cochran, K., Cohn, C., Rouet, J. F., & Hastings, P. (2023, June). Improving Automated Evaluation of Student Text Responses Using GPT-3.5 for Text Data Augmentation. In International Conference on Artificial Intelligence in Education (pp. 217-228). Cham: Springer Nature Switzerland. Nominated for Best Student Paper
• Davalos, E., Timalsina, U., Zhang, Y., Wu, J., Fonteles, J. H., & Biswas, G. (2023, December). ChimeraPy: A Scientific Distributed Streaming Framework for Real-time Multimodal Data Retrieval and Processing. In 2023 IEEE International Conference on Big Data (BigData) (pp. 201-206). IEEE.
• Davalos, E., Vatral, C., Cohn, C., Fonteles, J.H., Biswas, G., Mohammed, N., Lee, M.J., and Levin, D.T. (2023). Identifying Gaze Behavior Evolution via Temporal Fully-Weighted Scanpath Graphs. In LAK23: 13th International Learning Analytics and Knowledge Conference (LAK 2023), March 13–17, 2023, Arlington, TX, USA. ACM, New York, NY, USA, (pp. 476-487). https://doi.org/10.1145/3576050.3576117
• Hutchins, N.M., Basu, S., Rachmatullah, A., McElhaney, K, Alozie, N., Mohammed, N., & Biswas, G.  A technology-enhanced learning environment integrating science, computing, and engineering for upper-elementary grades. Proceedings of the 17th International Conference of the Learning Sciences – ICLS2023. Montreal, Canada: International Society of the Learning Sciences. (pp. 55-59).
• Hutchins, N. M., & Biswas, G. (2023). Co-designing teacher support technology for problem-based learning in middle school science. British Journal of Educational Technology, 00, 1– 21. https://doi.org/10.1111/bjet.13363
• Hutchins, N.M., Biswas, G. (2023). Catalyzing teachers’ evidence-based responses to students’ problem-based learning in STEM. Proceedings of the 17th International Conference of the Learning Sciences – ICLS2023. Montreal, Canada: International Society of the Learning Sciences (pp. 154-161).
• Hutchins, N.M., Biswas, G. (2023). Using Teacher Dashboards to Customize Lesson Plans for a Problem-Based, Middle School STEM Curriculum. In LAK23: 13th International Learning Analytics and Knowledge Conference (LAK 2023), Arlington, TX, USA. (pp. 324-332).
• McElhaney, K., Basu, S., McBride, E., Hutchins, N.M., & Biswas, G. (accepted). Design and Implementation of a Week-long, High School Curriculum Unit Integrating Physics and Computational Modeling. Proceedings of the 17th International Conference of the Learning Sciences – ICLS2023. Montreal, Canada: International Society of the Learning Sciences (pp. 497-504).
• Munshi, A., Biswas, G., Baker, R., Ocumpaugh, J., Hutt, S., & Paquette, L. (2023). Analysing adaptive scaffolds that help students develop self-regulated learning behaviours. Journal of Computer Assisted Learning, 39(2), 351–368.
• Vatral, C., Cohn, C., Davalos, E., Biswas, G., Lee, M., Levin, D., Hall, E., Holt, J.E. (2023). A Tale of Two Nurses: Studying Groupwork in Nurse Training by Analyzing Taskwork Roles, Social Interactions, and Self-Efficacy. 2023 International Conference on Computer Supported Collaborative Learning (CSCL), June 10-15, 2023, Montréal, QC, Canada (pp. 217-220).
• Vatral, C., Lee, M., Cohn, C., Davalos, E., Levin, D., and Biswas, G. (2023). Prediction of Students’ Self-confidence Using Multimodal Features in an Experiential Nurse Training Environment. International Conference on Artificial Intelligence in Education. Cham: Springer Nature Switzerland (pp. 266-271).

2022

• Basu, S., McElhaney, K., Rachmatullah, A., Hutchins, N.M., Biswas, G., & Chiu, J. (2022). Promoting Computational Thinking Through Science-Engineering Integration Using Computational Modeling. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.). Proceedings of the 16th International Conference of the Learning Sciences – ICLS 2022. Hiroshima, Japan: International Society of the Learning Sciences.
• Biswas, G. & Hutchins, N.M. (2022). Towards a Deeper Understanding of K-12 Students’ CT and Engineering Design Processes. Artificial Intelligence in STEM Education: The Paradigmatic Shifts in Research, Education, and Technology. Ouyang, F., Jiao, P., McLaren, B., and Alavi, A., editors. Auerbach/CRC Press, pages 15–38.
• Cochran, K., Cohn, C., Hutchins, N., Biswas, G., Hastings, P. (2022). Improving Automated Evaluation of Formative Assessments with Text Data Augmentation. 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_32
• Hutchins, N. (2022). Co-Designing Teaching Augmentation Tools to Support The Integration of Problem-Based Learning in K-12 Science. [Doctoral dissertation, Vanderbilt University]
• Hutt, S., Baker, R. S., Ocumpaugh, J., Munshi, A., Andres, J., Karumbaiah, S., Slater, S., Biswas, G., Paquette, L., Bosch, N., et al. (2022). Quick Red Fox: An app supporting a new paradigm in qualitative research on AIED for STEM. Artificial Intelligence in STEM Education: The Paradigmatic Shifts in Research, Education, and Technology. Ouyang, F., Jiao, P., McLaren, B., and Alavi, A., editors. Auerbach/CRC Press, pages 319–332.
• Quinde-Zlibut, J., Munshi, A., Biswas, G., & Cassio, C. (in review, preprint available) Identifying and Describing Subtypes of Spontaneous Empathic Facial Expression Production in Autistic Adults. Journal of Neurodevelopmental Disorders.
• Snyder, C., Wen, C. T., & Biswas, G. (2022). Assessing Students’ Knowledge Co-construction Behaviors in a Collaborative Computational Modeling Environment. In International Conference on Artificial Intelligence in Education (pp. 515-519). Springer, Cham.
• Snyder, C., Hutchins, N.M., Biswas, G., Narasimham, G., Emara, M., & Yett, B. (2022). Instructor facilitation of STEM+CT discourse: engaging, prompting, and guiding students’ computational modeling in physics. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.). Proceedings of the 16th International Conference of the Learning Sciences – ICLS2022. Hiroshima, Japan: International Society of the Learning Sciences.
• Snyder, C., Narasimham, G., Hutchins, N.M., Biswas, G., Yett, B. (2022). Examining how prior knowledge impacts students’ discussions and knowledge construction during computational model building. To appear in Proceedings of the American Educational Research Association Annual Meeting.
  
• Vatral, C., Biswas, G., Cohn, C., Davalos, E., & Mohammed, N. (2022). Using the DiCoT framework for integrated multimodal analysis in mixed-reality training environments. Frontiers in Artificial Intelligence, 5, 941825. https://doi.org/10.3389/frai.2022.941825. PMID: 35937140.
• Vatral, C., Biswas, G., Goldberg, B.S. (2022). Multimodal Learning Analytics Using Hierarchical Models for Analyzing Team Performance. In Proceedings of the 15th International Conference on Computer Supported Collaborative Learning (CSCL) (pp. 403-406). International Society of the Learning Sciences.
• Vatral, C., Mohammed, N., Biswas, G., Goldberg, B.S. (2022). Moving Beyond Training Doctrine to Explainable Evaluations of Teamwork using Distributed Cognition. In Proceedings of the 10th Annual Generalized Intelligent Framework for Tutoring Users Symposium (GIFTSym10) (pp. 127-137). US Army Combat Capabilities Development Command – Soldier Center. (ISBN 13: 978-0-9977258-2-7).
• Vatral, C., Mohammed, N., Biswas, G., Goldberg, B.S. (2022). Automated Assessment of Team Performance Using Multimodal Bayesian Learning Analytics. To appear in Proceedings of the 2022 Interservice/Industry Training, Simulation and Education Conference (I/ITSEC). National Training and Simulation Association. Won Best Paper in the Human Performance Analysis and Engineering Subcommittee
• Zlibut, J. Q., Munshi, A., Biswas, G., & Cascio, C. (2022). Identifying and describing subtypes of spontaneous empathic facial expression production in autistic adults. Journal of Neurodevelopmental Disorders,14.

2021

• Baker, R.S., Nasiar, N., Ocumpaugh, J.L., Hutt, S., Andres, J.M.A.L., Slater, S., Schofield, M., Moore, A., Paquette, L., Munshi, A., Biswas, G. (2021) Affect-Targeted Interviews for Understanding Student Frustration. Proceedings of the International Conference on Artificial Intelligence and Education. Won Best Paper Award
• Emara, M., Hutchins, N.M., Grover, S., Snyder, C., & Biswas, G. (2021). Examining Students’ Regulation of Collaborative, Computational, Problem-Solving Processes in Open-Ended Learning Environments. Journal of Learning Analytics, 8(1), 49-74.
• Hutchins, N.M., Basu, S., McElhaney, K., Chiu, J., Fick, S., Zhang, N., & Biswas, G. (2021). Coherence across conceptual and computational representations of students’ scientific models. In E. de Vries, J. Ahn, & Y. Hod (Eds.), 15th International Conference of the Learning Sciences – ICLS 2021 (pp. 330-337). International Society of the Learning Sciences.
• Hutchins, N.M., Snyder, C., Emara, M., Grover, S., & Biswas, G. (2021). Analyzing debugging processes during collaborative, computational modeling in science. In C. Hmelo-Silver, B. de Wever, & J. Oshima (Eds.), 14th International Conference on Computer-Supported Collaborative Learning – CSCL 2021 (pp. 221–224). International Society of the Learning Sciences.
• Hutt, S., Ocumpaugh J., Andres, J.M.A.L., Munshi, A., Bosch, N., Baker, R.S., Zhang, Y., Paquette, L., Slater, S., Biswas, G. (in press) Who’s Stopping You? – Using Microanalysis to Explore the Impact of Science Anxiety on Self Regulated Learning Operations. To appear in Proceedings of the 42nd Annual Meeting of the Cognitive Science Society.
• Ocumpaugh, J., Hutt, S., Andres, J.M.A.L., Baker, R.S., Biswas, G., Bosch, N., Paquette, L., Munshi, A. (in press) Using Qualitative Data from Targeted Interviews to Inform Rapid AIED Development. To appear in Proceedings of the 29th International Conference on Computers in Education.
• Vatral, C. Mohammed, N., Biswas, G., Goldberg, B.G. (2021). GIFT External Assessment Engine for Analyzing Individual and Team Performance for Dismounted Battle Drills. Proceedings of the Ninth Annual Generalized Intelligent Framework for Tutoring Users Symposium (GIFTSym9) (pp. 107-127). US Army Combat Capabilities Development Command – Soldier Center (ISBN 13: 978-0-9977257-9-7)
• Vatral, C. Mohammed, N., Biswas, G. (2021). A Machine Learning-Based External Assessment Engine for GIFT to Support Team Training in Dismounted Battle Drill Operations. Proceedings of the Challenges and Advances in Team Tutoring Workshop held in conjunction with the 22nd International Conference on Artificial Intelligence in Education (AIED 2021), 17-25.
• Zhang, N., Biswas, G., & Hutchins, N.M. (2021). Measuring and Analyzing Students’ Strategic Learning Behaviors in Open-Ended Learning Environments. International Journal of Artificial Intelligence in Education.
• Zhang, Y., Paquette, L., Baker, R., Ocumpaugh, J., Bosch, N., Biswas, G., & Munshi, A. (2021). Can strategic behavior facilitate confusion resolution? In the Journal of Learning Analytics.

2020

• Conlin, L., Hutchins, N.M., Grover, S., & Biswas, G. (2020). “Doing Physics” And “Doing Code”: Students’ Framing During Computational Modeling in Physics. In Proceedings of the International Conference of the Learning Sciences (ICLS), Nashville, TN, USA.
• Emara, M., Grover, S., Hutchins, N.M., Biswas, G., & Snyder, C. (2020). Examining Students’ Debugging and Regulation Processes During Collaborative Computational Modeling in Science. In Proceedings of the International Conference of the Learning Sciences (ICLS), Nashville, TN, USA.
• Hutchins, N.M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., … & McElhaney, K. (2020). C2STEM: a System for Synergistic Learning of Physics and Computational Thinking. Journal of Science Education and Technology, 29(1), 83-100.Technology. https://doi.org/10.1007/s10956-019-09804-9
• Hutchins, N.M., Biswas, G., Wolf, R., Chin, D., Grover, S., & Blair, K. (2020). Computational thinking in support of learning and transfer. In Proceedings of the International Conference of the Learning Sciences (ICLS), Nashville, TN, USA.
• Hutchins, N.M., Biswas, G., Zhang, N., Snyder, C., Lédeczi, Á., & Maróti, M. (2020). Domain-Specific Modeling Languages in Computer-Based Learning Environments: a Systematic Approach to Support Science Learning through Computational Modeling. International Journal of Artificial Intelligence in Education, 30, 537–580. https://doi.org/10.1007/s40593-020-00209-z
• Kafai, Y., Biswas, G., Hutchins, N.M., Snyder, C., Brennan, K., Haduong, P., et al. (2020). Turning Bugs into Learning Opportunities: Understanding Debugging Processes, Perspectives, and Pedagogies. In Proceedings of the International Conference of the Learning Sciences (ICLS), Nashville, TN, USA. [symposium]
• McElhaney, K.W., Zhang, N., Basu, S., McBride, E., Biswas, G., & Chiu, J.L. (2020). Using Computational Modeling to Integrate Science and Engineering Curricular Activities. In Proceedings of the International Conference of the Learning Sciences (ICLS), Nashville, TN, USA. Nominated for Best Paper
• Munshi, A., Mishra, S., Zhang, N., Paquette, L., Ocumpaugh, J., Baker, R., & Biswas, G. (2020). Modeling the Relationships Between Basic and Achievement Emotions in Computer-Based Learning Environments. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Springer, Cham
• Sharma, K., Mishra, S., Papamitsiou, Z., Munshi, A., De, B. K., Biswas, G., & Giannakos, M. (2020). Towards Obtaining Facial Proxies for Gaze behaviour in TEL. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 5 (pp. 2621-2622). Nashville, Tennessee: International Society of the Learning Sciences.
• Snyder C., Hutchins N.M., Biswas G., Emara M., Yett B., & Mishra S. (2020). Understanding Collaborative Question Posing During Computational Modeling in Science. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12164. Springer, Cham.
• Snyder, C., Hutchins, N.M., Biswas, G., Mishra, S., & Emara, M. (2020). Exploring Synergistic Learning Processes through Collaborative Learner-to-Learner Questioning. In Proceedings of the International Conference of the Learning Sciences (ICLS), Nashville, TN, USA.
• Yett, B., Hutchins, N.M., Snyder, C., Zhang, N., Mishra, S., & Biswas, G. (2020). Evaluating Student Learning in a Synchronous, Collaborative Programming Environment Through Log-Based Analysis of Projects. Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part II, 12164, 352–357.
• Yett, B., Hutchins, N.M., Stein, G., Zare, H., Snyder, C., Biswas, G., Metelko, M., & Ledeczi, A.  (2020). A Hands-On Cybersecurity Curriculum Using a Robotics Platform. In Proceedings of the Special Interest Group on Computer Science Education (SIGCSE) Annual Meeting, Portland, USA (pp.1040–1046).
• Yett, B., Snyder, C., Zhang, N., Hutchins, N.M., Mishra, S., & Biswas, G. (2020). Using Log and Discourse Analysis to Improve Understanding of Collaborative Programming. In Proceedings of the International Conference on Computers in Education (ICCE). Best Student Paper Award
• Yett, B., Snyder, C., Hutchins, N.M., & Biswas, G. (2020). Exploring the Relationship Between Collaborative Discourse, Programming Actions, and Cybersecurity and Computational Thinking Knowledge. In Proceedings of IEEE TALE.
• Zhang N., Biswas G., McElhaney K.W., Basu S., McBride E., Chiu J.L. (2020). Studying the Interactions Between Science, Engineering, and Computational Thinking in a Learning-by-Modeling Environment. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Springer, Cham.
• Zhang, Y., Paquette, L., Baker, R.S., Ocumpaugh, J., Bosch, N., Munshi, A., & Biswas, G. (2020). The relationship between confusion and metacognitive strategies in Betty’s Brain. In Proceedings of the 10th International Learning Analytics and Knowledge (LAK) Conference, Frankfurt, Germany.

2019

• Andres, A., Ocumpaugh, J., Baker, R.S., Slater, S., Paquette, L., Jiang, Y., Bosch, N., Munshi, A., Moore, A., & Biswas, G. (2019). Affect Sequences and Learning in Betty’s Brain. In Proceedings of the 9th International Learning Analytics and Knowledge (LAK) Conference, Tempe, Arizona.
• Biswas, G., Hutchins, N., Lédeczi, Á., Grover, S., Basu, S. (2019). Integrating Computational Modeling in K-12 STEM Classrooms. In Proceedings of the Special Interest Group on Computer Science Education (SIGCSE) Annual Meeting, Minneapolis, USA (p. 1288).
• Biswas, G., Rajendran, R., Mohammed, N., Goldberg, B.S., Sottilare, R.A., Brawner, K., and Hoffman, M., “Multilevel Learner Modeling in Training Environments for Complex Decision Making,” IEEE Transactions on Learning Technologies, 2019 (Preprint)
• Biswas, G., Rajendran, R., & Munshi, A. (2019). Multi Modal Data Analysis of Students’ SRL Behaviors in Open Ended Learning Environments. In Proceedings of the American Educational Research Association 2019 Symposium on Multimodal Data during Learning with Advanced Learning Technologies.
• Dorsey, C., Haavind, S., Hutchins, N., & Levin, M. (2019). Computational Thinking in STEM from Preschool to High School: Research and Practice. In Proceedings of International Society for Technology in Education, Philadelphia, PA, USA.
• Grover, S., Hutchins, N., & Biswas, G. (2019) Examining Synergistic Learning of Physics and Computational Thinking through Collaborative Problem Solving in Computational Modeling. In Proceedings of the American Educational Research Association Annual Meeting, Toronto, Canada.
• Hutchins, N., Biswas, G., Grover, S., Basu, S., & Snyder, C. (2019). A systematic approach for analyzing students’ computational modeling processes in C2STEM. In Proceedings of the 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA (pp. 116-121).
• Hutchins, N., Shi, C., & Biswas, G. (2019) A High School Computational Modeling Approach to Studying the Effects of Climate Change on Coral Reefs. In Proceedings of the American Educational Research Association Annual Meeting, Toronto, Canada.
• Lédeczi, Á., Maróti, M., Zare, H., Yett, B., Hutchins, N., Broll, B., Völgyesi, P., Smith, M.B., Darrah, T., Metelko, M., Koutsoukos, X., & Biswas, G. (2019) Teaching Cybersecurity with Networked Robots. In Proceedings of the Special Interest Group on Computer Science Education (SIGCSE) Annual Meeting, Minneapolis, USA (pp. 885–891).
• Mishra, S., Biswas, G., Mohammed, N., Goldberg, B. S. (2019). Learner Modeling of Cognitive and Psychomotor Processes for Dismounted Battle Drills. In Proceedings of the 7th Annual Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium (GIFTSym7), Orlando, Memphis, USA.
• Mishra, S., Munshi, A., Rushdy, M., & Biswas, G. (2019). LASAT: Learning Activity Sequence Analysis Tool. In Technology-Enhanced & Evidence-Based Education & Learning (TEEL) Workshop at the 9th International Learning Analytics and Knowledge (LAK) Conference, Tempe, Arizona, USA.
• Mishra, S. (2019). Learner Modeling of Cognitive & Metacognitive Processes for Complex Collaborative Learning Tasks. In the International conference on Computer Supported Collaborative Learning, Lyon France.
• Munshi, A., & Biswas, G. (2019). Personalization in OELEs: Developing A Data-Driven Framework to Model and Scaffold SRL Processes. In Proceedings of the 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, pp. 354-358.
• Snyder, C., Hutchins, N., Biswas, G., Emara, M., Grover, S., Conlin, L. (2019). Analyzing Students’ Synergistic Learning Processes in Physics and CT by Collaborative Discourse Analysis. In Proceedings of the International Conference on Computer Supported Collaborative Learning, Lyon, France (pp. 360-367).
• Snyder, C., Hutchins, N., Biswas, G., & Grover, S. (2019). Understanding Students’ Model Building Strategies through Discourse Analysis. In Proceedings of the 20th International Conference on Artificial Intelligence in Education (AIED 2019), pp. 263-268, Chicago, USA.
• Taub, M., Azevedo, R., Rajendran, R., Cloude, E.B., Biswas, G., & Price, M.J. (2019). How are students’ emotions related to the accuracy of cognitive and metacognitive processes during learning with an intelligent tutoring system? Learning and Instruction.
• Zhang, N., Biswas, G., Chiu, J.L., & McElhaney, K.W. (2019).Analyzing Students’ Design Solutions in an NGSS-Aligned Earth Sciences Curriculum. In Proceedings of the 20th International Conference on Artificial Intelligence in Education (AIED), Chicago, pp. 532-543. Best Student Paper Award
• Zhang, N., & Biswas, G. (2019). Defining and Assessing Students’ Computational Thinking in a Learning by Modeling Environment. In Computational Thinking Education (pp. 203-221). Springer, Singapore.

2018

• Basu, S., McElhaney, K., Grover, S., Harris, C., & Biswas, G. (2018) A Principled Approach to Designing Assessments That Integrate Science and Computational Thinking. In Proceedings of the 13th International Conference of the Learning Sciences (ICLS), London, England, Volume 1, pp. 384-391.
• Basu, S., McElhaney, K., Grover, S., Harris, C., & Biswas, G. (2018) Designing Assessments to Measure Integrated Proficiency With Concepts and Practices in Science and Computational Thinking . In Proceedings of the American Educational Research Association Annual Meeting, New York City, NY.
• Biswas, G., Baker, R. S., & Paquette, L. (2018). Data mining methods for assessing self-regulated learning. In D. H. Schunk & J. A. Greene (Eds.), Educational psychology handbook series. Handbook of self-regulation of learning and performance (p. 388–403). Routledge/Taylor & Francis Group.
• Darrah, T., Hutchins, N., & Biswas, G. (2018). Design and Development of a Low-Cost Open-Source Robotics Education Platform. In Proceedings of the 50th International Symposium on Robotics, Munich, Germany (pp. 107-110).
• Emara, M., Rajendran, R., and Biswas, G., Do Students Learning Behaviors Differ when they Collaborate in Open-Ended Learning Environments?, 21st ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) 2018, US
• Hutchins, N., Biswas, G., Conlin, L., Emara, M., Grover, S., Basu, S., & McElhaney, K. (2018) Studying Synergistic Learning of Physics and Computational Thinking in a Learning by Modeling Environment. In Yang, J. C. et al. (Eds.). In Proceedings of the 26th International Conference on Computers in Education (ICCE), Manila, Philippines (pp. 153-162). Best Student Paper Award
• Hutchins, N. Developing Learning Environments that Support Synergistic Learning of STEM+C. (2018) In DSC Proceedings of the 26th International Conference on Computers in Education (ICCE), Manila, Philippines (pp. 25-28).
• Hutchins, N., Biswas, G., Maroti, M., Ledeczi, A., & Broll, B. (2018). A design-based approach to a classroom-centered OELE. In Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED), London, England (pp. 155-159).
• Hutchins, N., Darrah, T., Zare, H., & Biswas, G. (2018). A DSML for a Robotics Environment to Support Synergistic Learning of CT and Geometry. Kong, S. C., Sheldon, J., & Li, K. Y.. (Eds.). Conference Proceedings of International Conference on Computational Thinking Education 2018, Hong Kong (pp. 77-82).
• Munshi, A., Rajendran, R., Moore, A., Ocumpaugh, J., & Biswas, G. (2018). Studying the Interactions between Components of Self-Regulated Learning in Open Ended Learning Environments. In Proceedings of the 13th International Conference of the Learning Sciences (ICLS), London, England, pp. 1691-1692.
• Munshi, A., Rajendran, R., Ocumpaugh, J., Biswas, G., Baker, R. S., & Paquette, L. (2018). Modeling Learners Cognitive and Affective States to Scaffold SRL in Open Ended Learning Environments. In Proceedings of the International Conference on User Modelling, Adaptation and Personalization (UMAP), Singapore, pp. 131-138.
• Rajendran, R., Munshi, A., Emara, M., & Biswas, G. (2018) A Temporal Model of Learner Behaviors in OELEs Using Process Mining. In Yang, J. C. et al. (Eds.). In Proceedings of the 26th International Conference on Computers in Education (ICCE), Manila, Philippines, pp. 276-285. Best Technical Design Paper Award
• Rajendran, R., Kumar, A., Carter, K. E., Levin, D. T., & Biswas, G. (2018). Predicting Learning by Analyzing Eye-Gaze Data of Reading Behavior. In Proceedings of the 11th International Conference on Educational Data Mining (EDM), Buffalo, USA, pp. 455-461.
• Taub, M., Mudrick, N. V., Rajendran, R., Dong, Y., Biswas, G., & Azevedo, R. (2018). How are students emotions associated with the accuracy of their note taking and summarizing during learning with ITSs? In Proceedings of the 14th International Conference on Intelligent Tutoring Systems (ITS), Montreal, Canada, pp. 233-242.
• Zhang, N., & Biswas, G. (2018).Understanding Students’ Problem-Solving Strategies in a Synergistic Learning-by-Modeling Environment. In Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED), London, England, pp. 405-410.

2017

• Basu, S., Biswas, G., Kinnebrew, J.S. (2017). Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment. User Modeling and User-Adapted Interaction, 27(1), 5-53.
• Dong, Y., & Biswas, G. (2017). An Extended Learner Modeling Method to Assess Students’ Learning Behaviors. In Proceedings of the 10th International Conference on Educational Data Mining (pp. 302-305). Wuhan, China.
• Emara, M., Tscholl, M., Dong, Y., & Biswas, G. (2017). Analyzing Students’ Collaborative Regulation Behaviors in a Classroom-Integrated Open Ended Learning Environment. In Proceedings of the International Conference on Computer-Supported Collaborative Learning. Philadelphia, PA. 319-326
• Hasan, A., & Biswas, G. (2017). Domain specific modeling language design to support synergistic learning of STEM and computational thinking. Kong, S. C., Sheldon, J., & Li, K. Y.. (Eds.). Conference Proceedings of International Conference on Computational Thinking Education 2017. Hong Kong: The Education University of Hong Kong, 28-33.
• Hutchins, N., Zhang, N., & Biswas, G. (2017). The Role Gender Differences in Computational Thinking Confidence Levels Plays in STEM Applications. Kong, S. C., Sheldon, J., & Li, K. Y.. (Eds.). Conference Proceedings of International Conference on Computational Thinking Education 2017. Hong Kong: The Education University of Hong Kong (pp. 33-38).
• Kinnebrew, J., Segedy, J.R. & Biswas, G. (2017). Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments. IEEE Transactions on Learning Technologies, 10(2), 140-153.
• Kinnebrew, J.S., Killingsworth, S.S., Clark, D.B., Biswas, G., Sengupta, P., Minstrell, J., Martinez-Garza, M. & Krinks, K. (2017). Contextual Markup and Mining in Digital Games for Science Learning: Connecting Player Behaviors to Learning Goals. IEEE Transactions on Learning Technologies, 10(1), 93-103.
• Rajendran, R., Mohammed, N., Biswas, G., Goldberg, B. S., & Sottilare, R. A. (2017). Multi-level User Modeling in GIFT to Support Complex Learning Tasks. In Proceedings of the 5th Annual Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium (GIFTSym5).
• Zhang, N., Biswas, G., & Dong, Y. (2017). Characterizing Students’ Learning Behaviors Using Unsupervised Learning Methods. In André E., Baker R., Hu X., Rodrigo M., du Boulay B. (Eds.), Artificial Intelligence in Education. AIED 2017. (pp. 430-441). Wuhan, China: Lecture Notes in Computer Science, vol 10331. Springer, Cham.
• Zhang, N. & Biswas, G. (2017). Assessing Students’ Computational Thinking in a Learning by Modeling Environment. Kong, S. C., Sheldon, J., & Li, K. Y.. (Eds.). Conference Proceedings of International Conference on Computational Thinking Education 2017. Hong Kong: The Education University of Hong Kong, 11-16.

2016

• Basu, S. (2016). Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments (Doctoral dissertation). Department of EECS, Vanderbilt University, Nashville, TN.
• Basu, S. & Biswas, G. (2016). Providing adaptive scaffolds and measuring their effectiveness in open ended learning environments. In 12th International Conference of the Learning Sciences (pp. 554-561). Singapore.
• Basu, S., Biswas, G. & Kinnebrew, J.S. (2016). Using multiple representations to simultaneously learn computational thinking and middle school science. In Thirtieth AAAI conference on Artificial Intelligence (pp. 3705-3711). Phoenix, Arizona, USA.
• Basu, S., Biswas, G., Sengupta, P., Dickes, A., Kinnebrew, J. S., & Clark, D. (2016). Identifying middle school students’ challenges in computational thinking-based science learning. Research and Practice in Technology Enhanced Learning, 11(1), 1-35.
• Biswas, G., Segedy, J.R., & Bunchongchit, K. (2016). From Design to Implementation to Practice – A Learning by Teaching System: Betty’s Brain. International Journal of Artificial Intelligence in Education, 26(1), 350-364.
• Clark, D. B., Virk, S., Sengupta, P., Brady, C., Martinez-Garza, M., Krinks, K., Killingsworth, S., Kinnebrew, J., Biswas, G., Barnes, J., Minstrell, J., Nelson, B., Slack, K., & D’Angelo, C. (2016). SURGE’s evolution deeper into formal representations: The siren’s call of popular game-play mechanics. International Journal of Designs for Learning, 7(1), 107-146.
• Dong, Y., Kinnebrew, J., Biswas, G. (2016). Comparison of Selection Criteria for Multi-Feature Hierarchical Activity Mining in Open Ended Learning Environments. In Proceedings of the 9th International Conference on Educational Data Mining (pp. 591-592). Raleigh, North Carolina.
• Gauch, B. & Biswas, G. (2016). Behavior Changes Across Time and Between Populations in Open-Ended Learning Environments. In 13th International Conference on Intelligent Tutoring Systems (pp. 187-196). Zagreb, Croatia.
• Rajendran, R., & Biswas, G. (2016). Modeling Learners’ Metacognitive Skills in Open Ended Learning Environments. In Chen, W. et al. (Ed.), Proceedings of the 24th International Conference on Computers in Education. India: Asia-Pacific Society for Computers in Education.
• Tscholl, M., Biswas, G., Goldberg, B. S., & Sottilare, R. A (2016). Detecting Metacognitive Strategies through Performance Analyses in Open-Ended Learning Environment. In Chen, W. et al. (Ed.), Proceedings of the 24th International Conference on Computers in Education. India: Asia-Pacific Society for Computers in Education.
• Tscholl, M., Biswas, G., Goldberg, B. S., & Sottilare, R. A. (2016). Automated Detection of Cognitive and Metacognitive Strategies for Learner Modeling in GIFT. In Proceedings of the 4th Annual GIFT Users Symposium (GIFTSym4) (pp. 15 – 25).
• Tscholl, M., Rajendran, R., Biswas, G., Goldberg, B. S., & Sottilare, R. A (2016). Data Collection in Open Ended Learning Environment for Learning Analytics. In Chen, W. et al. (Ed.), Workshop Proceedings of the 24th International Conference on Computers in Education. India: Asia-Pacific Society for Computers in Education.

2015

• Basu, S., Biswas, G., Kinnebrew, J., & Rafi, T. (2015). Relations between modeling behavior and learning in a Computational Thinking based science learning environment. In Ogata, H. et al. (Ed.), Proceedings of the 23rd International Conference on Computers in Education (pp. 184-189). China: Asia-Pacific Society for Computers in Education.
• Basu, S., Kinnebrew, J.S., Shekhar, S., Calgar, F., Rafi, T.H., Biswas, G., & Gokhale, A. (2015). Collaborative Problem Solving using a Cloud-based Infrastructure to Support High school STEM Education. In Proceedings of the 122nd ASEE Annual Conference & Exposition. Seattle, WA, USA.
• Basu, S., Sengupta, P., & Biswas, G. (2015). A scaffolding framework to support learning of emergent phenomena using multi-agent based simulation environments. Research in Science Education, 45(2), 293-324.
  DOI: 10.1007/s11165-014-9424-z
• Caglar, F., Shekhar S., Gokhale, A., Basu, S., Rafi, T., Kinnebrew, J. & Biswas, G. (2015). Cloud-hosted Simulation-as-a-Service for High School STEM Education. Simulation Modelling Practice and Theory, 58(2), 255-273. doi:10.1016/j.simpat.2015.06.006
• Kinnebrew, J.S., Gauch, B., Segedy, J.R., & Biswas, G. (2015). Studying Student use of Self-Regulated Learning Tools in an Open-Ended Learning Environment. In Proceedings of the 17th International Conference on Artificial Intelligence in Education. Madrid, Spain.
  Lecture Notes in Computer Science Volume 9112, 2015, pp 185-194
• Segedy, J. R., Kinnebrew, J. S., & Biswas, G. (2015). Demonstration: Using GIFT to Support Students’ Understanding of the UrbanSim Counter Insurgency Simulation. In Workshop on Developing a Generalized Intelligent Framework for Tutoring (GIFT): Informing Design through a Community of Practice (p. 54).
• Segedy, J. R., Kinnebrew, J. S., Goldberg, B. S., Sottilare, R. A., & Biswas, G. (2015). Designing Representations and Support for Metacognition in the Generalized Intelligent Framework for Tutoring. In Foundations of Augmented Cognition (pp. 663-674). Springer International Publishing.
• Segedy, J. R., Kinnebrew, J. S., Goldberg, B. S., Sottilare, R. A., & Biswas, G. (2015). Using GIFT to Model and Support Students’ Metacognition in the UrbanSim Open-Ended Learning Environment. In Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium (GIFTSym3) (p. 13).
• Segedy, J.R., Kinnebrew, J.S., & Biswas, G. (2015). Coherence Over Time: Understanding Day-to-Day Changes in Students’ Open-Ended Problem Solving Behaviors. In Proceedings of the 17th International Conference on Artificial Intelligence in Education. Madrid, Spain.
  Lecture Notes in Computer Science Volume 9112, 2015, pp 449-458
• Segedy, J.R., Kinnebrew, J.S., & Biswas, G. (2015). Using Coherence Analysis to Characterize Self-Regulated Learning Behaviours in Open-Ended Learning Environments. Journal of Learning Analytics, 2(1), 13-48.
• Segedy, J.R., Kinnebrew, J.S., Goldberg, B.S., Sottilare, R.A., & Biswas, G. (2015). Designing Representations and Support for Metacognition in the Generalized Intelligent Framework for Tutoring. In Proceedings of the 17th International Conference on Human-Computer Interaction. Los Angeles, CA, USA.
  In Foundations of Augmented Cognition (pp. 663-674). Springer International Publishing.
• Ye, C., Kinnebrew, J.S., Segedy, J.R., & Biswas, G. (2015). Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences. In Proceedings of the 8th International Conference of Educational Data Mining. Madrid, Spain.

2014

• Basu, S., Dukeman, A., Kinnebrew, J., Biswas, G., & Sengupta, P. (2014). Investigating student generated computational models of science. In Proceedings of the 11th International Conference of the Learning Sciences (pp. 1097-1101). Boulder, CO, USA.
• Basu, S., Kinnebrew, J., & Biswas, G. (2014). Assessing student performance in a computational-thinking based science learning environment. In Proceedings of the 12th International Conference on Intelligent Tutoring Systems (pp. 476-481). Honolulu, HI, USA: Springer International Publishing.
• Biswas, G., Kinnebrew, J.S., Segedy, J.R. (2014). Using a Cognitive/Metacognitive Task Model to analyze Students Learning Behaviors. In Proceedings of the 16th International Conference on Human-Computer Interaction. Creta Maris, Heraklion, Crete, Greece.
• Dukeman, A., Shekhar, S., Caglar, F., Gokhale, A., Biswas, G., & Kinnebrew, J.S. (2014). Analyzing Students’ Computational Models as they Learn in STEM Disciplines. In the 121st American Society for Engineering Education Annual Conference & Exposition. Indianapolis, IN.
• Kinnebrew, J. S., Mack, D. L., Biswas, G., & Chang, C. K. (2014). A Differential Approach for Identifying Important Student Learning Behavior Patterns with Evolving Usage over Time. In Pacific-Asia Conference on Knowledge Discovery and Data Mining: PAKDD-2014 (pp. 281-292). Springer, Switzerland.
• Kinnebrew, J.S., Segedy, J.R., & Biswas, G. (2014). Analyzing the Temporal Evolution of Students’ Behaviors in Open-Ended Learning Environments. Metacognition and Learning, 9(2), 187-215.
• Segedy, J.R. (2014). Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments (Doctoral dissertation). Department of EECS, Vanderbilt University, Nashville, TN.
• Segedy, J.R., Biswas, G., & Sulcer, B. (2014). A Model-Based Behavior Analysis Approach for Open-Ended Environments. Journal of Educational Technology & Society, 17(1), 272-282.
• Shekhar, S., Caglar, F., Dukeman, A., Hou, L., Gokhale, A., Kinnebrew, J.S., & Biswas, G. (2014). A Collaborative K-12 STEM Education Framework Using Traffic Flow as a Real-world Challenge Problem. In the 121st American Society for Engineering Education Annual Conference & Exposition. Indianapolis, IN.
• Ye, C., and Biswas, G. (2014). Early Prediction of Student Dropout and Performance in MOOCs using Higher Granularity Temporal Information. Journal of Learning Analytics, 1(3), 169-172.
• Ye, C., Kinnebrew, J.S., & Biswas, G. (2014). Mining and Identifying Relationships Among Sequential Patterns in Multi-Feature, Hierarchical Learning Activity Data. In Proceedings of the 7th International Conference on Educational Data Mining (pp. 389-390). London, UK.

2013

• Basu, S., & Biswas, G. (2013). A Computational Thinking Approach to Learning Middle School Science. In Doctoral Consortium Proceedings of the 16^th International Conference on Artificial Intelligence in Education (pp. 920-923). Memphis, TN, USA.
• Basu, S., Dickes, A., Kinnebrew, J.S., Sengupta, P., & Biswas, G. (2013). CTSiM: A Computational Thinking Environment for Learning Science through Simulation and Modeling. In Proceedings of the 5th International Conference on Computer Supported Education (pp. 369-378). Aachen, Germany.
• Biswas, G., Kinnebrew, J.S., & Mack, D.L.C. (2013). How do students’ learning behaviors evolve in scaffolded open-ended learning environments?. In Proceedings of the 21st International Conference on Computers in Education. Bali, Indonesia.
  * Recipient of the Best Paper Award
• Biswas, G., Kinnebrew, J.S., & Segedy, J.R. (2013). Analyzing Students’ Metacognitive Strategies in Open-Ended Learning Environments. In Proceedings of the 35th annual meeting of the Cognitive Science Society. Berlin, Germany.
• Biswas, G., Segedy, J.R., & Kinnebrew, J.S. (2013). Smart Open-Ended Learning Environments that Support Learners’ Cognitive and Metacognitive Processes. In A. Holzinger & G. Pasi (Eds.), Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data: Vol. 7947. Lecture Notes in Computer Science (pp. 303-310). Springer-Verlag Berlin Heidelberg.
• Dickes, A., Sengupta, P., Krishnan, G., & Basu, S. (2013). Thinking Like a Butterfly: Leveraging Students’ Embodied Intuitions in Elementary Ecology Classrooms. In the Annual Conference of the National Association of Research on Science Teaching (NARST 2013). Rio Grande, Puerto Rico.
• Dukeman, A., Caglar, F., Shekhar, S., Kinnebrew, J., Biswas, G., Fisher, D., & Gokhale, A. (2013). Teaching Computational Thinking Skills in C3STEM with Traffic Simulation. In A. Holzinger & G. Pasi (Eds.), Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data: Vol. 7947. Lecture Notes in Computer Science (pp. 350-357). Springer-Verlag Berlin Heidelberg.
• Gokhale, A., Biswas, G., Sarkar, N., Sastry, S., & Branicky, M. (2013). CPS Laboratory-as-a-Service: Enabling Technology for Readily Accessible and Scalable CPS Education. In Proceedings of the First Workshop on Cyber-Physical Systems Education (CPS-Ed 2013) at Cyber Physical Systems Week. Philadelphia, Pennsylvania, USA.
• Kinnebrew, J., Biswas, G., Sulcer, B., & Taylor, R. (2013). Investigating Self-Regulated Learning in Teachable Agent Environments. In R. Azevedo & V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies: Vol. 26. Springer International Handbooks of Education (pp. 451-470). New York: Springer.
• Kinnebrew, J.S., Loretz, K.M., & Biswas, G. (2013). A Contextualized, Differential Sequence Mining Method to Derive Students’ Learning Behavior Patterns. Journal of Educational Data Mining, 5(1), 190-219.
• Kinnebrew, J.S., Mack, D.L.C., & Biswas, G. (2013). Mining Temporally-Interesting Learning Behavior Patterns. In Proceedings of the 6th International Conference on Educational Data Mining. Memphis, TN, USA.
• Mendiburo, M., Williams, L., Segedy, J.R., & Hasselbring, T. (2013). Towards Automated Support for Small-Group Instruction: Using Data from an ITS to Automatically Group Students. In Proceedings of the Fall 2013 Conference of the Society for Research on Educational Effectiveness. Washington, D.C.. U.S.A..
• Mendiburo, M., Williams, L., Segedy, J.R., Wright, M., Biswas, G., & Hasselbring, T. (2013). An Investigation of the Effect of Competition on the Way Students Engage in Game-Based Deliberate Practice. In Proceedings of the 13th IEEE International Conference on Advanced Learning Technologies. Beijing, China.
• Roscoe, R.D., Segedy, J.R., Sulcer, B., Jeong, H., & Biswas, G. (2013). Shallow Strategy Development in a Teachable Agent Environment Designed to Support Self-Regulated Learning. Computers & Education, 62, 286-297.
• Segedy, J.R., Biswas, G., Blackstock, E.F., & Jenkins, A. (2013). Guided Skill Practice as an Adaptive Scaffolding Strategy in Open-Ended Learning Environments. In Proceedings of The 16th International Conference on Artificial Intelligence in Education. Memphis, TN, USA.
• Segedy, J.R., Kinnebrew, J.S., & Biswas, G. (2013). The Effect of Contextualized Conversational Feedback in a Complex Open-Ended Learning Environment. Educational Technology Research and Development, 61(1), 71-89.
• Segedy, J.R., Loretz, K.M., & Biswas, G. (2013). Model-Driven Assessment of Learners in an Open-Ended Learning Environment. In Proceedings of the Third International Conference on Learning Analytics and Knowledge(pp. 200-204). New York, NY: ACM.
• Segedy, J.R., Loretz, K.M., & Biswas, G. (2013). Suggest-Assert-Modify: A Taxonomy of Adaptive Scaffolds in Computer-Based Learning Environments. In Proceedings of the Workshop on Scaffolding in Open-Ended Learning Environments held at the 16th International Conference on Artificial Intelligence in Education. Memphis, TN.
• Sengupta, P., Kinnebrew, J.S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating Computational Thinking with K-12 Science Education Using Agent-based Computation: A Theoretical Framework. Education and Information Technologies, 18(2), 351-380.

2012

• Basu, S., Kinnebrew, J., Dickes, A., Farris, A.V., Sengupta, P., Winger, J., & Biswas, G. (2012). A Science Learning Environment using a Computational Thinking Approach. In Proceedings of the 20th International Conference on Computers in Education (pp. 722-729). Singapore.
  * Recipient of the Best Student Paper Award
• Biswas, G., Kinnebrew, J.S., & Segedy, J.R. (2012). Analyzing Student Learning and Metacognitive Processes in a Choice-Rich Science Learning Environment. In the 5th Biennial Meeting of the EARLI Special Interest Group on Metacognition. Milano, Italy.
• Biswas, G., Kinnebrew, J.S., & Segedy, J.R. (2012). Modeling student behaviors in an open-ended learning environment. In V. G. Duffy (Ed.), Advances in Applied Human Modeling and Simulation (pp. 202-211). San Francisco, CA, USA: CRC Press.
• Bouchet, F., Kinnebrew, J.S., Biswas, G., & Azevedo, R. (2012). Identifying Students’ Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning. In Proceedings of the 5th International Conference on Educational Data Mining. Chania, Greece.
• Kinnebrew, J.S., & Biswas, G. (2012). Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution. In Proceedings of the 5th International Conference on Educational Data Mining. Chania, Greece.
  * Recipient of the Best Paper Award
• Segedy, J. R., Kinnebrew, J. S., & Biswas, G. (2012). Supporting Student Learning using Conversational Agents in a Teachable Agent Environment. In Proceedings of the 10th International Conference of the Learning Sciences. Sydney, Australia.
• Segedy, J. R., Kinnebrew, J. S., & Biswas, G. (2012). Relating Student Performance to Action Outcomes and Context in a Complex, Choice-Rich Learning Environment. In Proceedings of the 11th International Conference on Intelligent Tutoring Systems. Chania, Greece.
• Sengupta, P., Kinnebrew, J.S., Biswas, G., & Clark, D. (2012). Integrating computational thinking with K-12 science education: A theoretical framework. In 4th International Conference on Computer Supported Education(pp. 40-49). Porto, Portugal.

2011

• Basu, S. & Biswas, G. (2011). A Scaffolding Framework to Support Learning in Multi-Agent Based Simulation Environments. In Doctoral Student Consortium Proceedings of the 19th International Conference on Computers in Education. Chiang Mai, Thailand.
• Basu, S., & Biswas, G. (2011). Multiple Representations to Support Learning of Complex Ecological Processes in Simulation Environments. In Proceedings of the 19th International Conference on Computers in Education. Chiang Mai, Thailand.
• Basu, S., Biswas, G., & Sengupta, P. (2011). Scaffolding to Support Learning of Ecology in Simulation Environments. In Susan Bull and Gautam Biswas (Eds.), Proceedings of The 15th International Conference on Artificial Intelligence in Education. Auckland, New Zealand.
• Kinnebrew, J.S., & Biswas, G. (2011). Modeling and Measuring Self-Regulated Learning in Teachable Agent Environments. Journal of e-Learning and Knowledge Society, 7(2), 19-35.
• Kinnebrew, J.S., & Biswas, G. (2011). Comparative Action Sequence Analysis with Hidden Markov Models and Sequence Mining. In The Knowledge Discovery in Educational Data Workshop at the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. San Diego, CA.
• Segedy, J.R., Kinnebrew, J.S., & Biswas, G. (2011). Investigating the Relationship Between Dialogue Responsiveness and Learning in a Teachable Agent Environment. In Susan Bull and Gautam Biswas (Eds.), Proceedings of The 15th International Conference on Artificial Intelligence in Education. Auckland, New Zealand.
• Segedy, J.R., Kinnebrew, J.S., & Biswas, G. (2011). Modeling Learner’s Cognitive and Metacognitive Strategies in an Open-Ended Learning Environment. In AAAI Fall Symposium on Advances in Cognitive Systems. Arlington, VA.

2010

• Biswas, G., & Sulcer, B. (2010). Visual exploratory data analysis methods to characterize student progress in intelligent learning environments. In 2010 International Conference on Technology for Education (T4E) (pp. 114-121). Mumbai.
• Biswas, G., Jeong, H., Kinnebrew, J., Sulcer, B., & Roscoe, R. (2010). Measuring Self-regulated Learning Skills through Social Interactions in a Teachable Agent Environment. Research and Practice in Technology-Enhanced Learning, 5(2), 123-152.
• Jeong, H., Biswas, G., Johnson, J., & Howard, L. (2010). Analysis of Productive Learning Behaviors in a Structured Inquiry Cycle Using Hidden Markov Models. In R.S.j.d. Baker, A. Merceron, & P.I. Pavlik Jr. (Eds.), Proceedings of the 3rd International Conference on Educational Data Mining. Pittsburgh.
• Kinnebrew, J., Biswas, G., & Sulcer, B. (2010). Measuring Self-regulated Learning Skills through Social Interactions in a Teachable Agent Environment. In The AAAI Fall Symposium on Cognitive and Metacognitive Educational Systems (MCES).
• Segedy, J., Sulcer, B., & Biswas, G. (2010). Are ILEs Ready for the Classroom? Bringing Teachers into the Feedback Loop. In Proceedings of the 10th International Conference on Intelligent Tutoring Systems (pp. 405-407). Pittsburgh.

2009

• Biswas, G., Roscoe, R., Jeong, H., & Sulcer, B. (2009). Promoting self-regulated learning skills in agent-based learning environments. In Proceedings of the 17th International Conference on Computers in Education. Hong Kong: Asia-Pacific Society for Computers in Education.
  * Recipient of the Best Paper Award
• Linn, J. G., Segedy, J., Jeong, H., Podgursky, B., & Biswas, G. (2009). Reconfigurable architecture for building intelligent learning environments. In Proceedings of the 14th International Conference on Artificial Intelligence in Education (pp. 115-120). Brighton, United Kingdom: IOS Press.
• Schwartz, D. L., Chase, C., Chin, D., Oppezzo, M., Kwong, H., Okita, S., Biswas, G., Roscoe, R.D., Jeong, H., & Wagster, J.D. (2009). Interactive Metacognition: Monitoring and Regulating a Teachable Agent. In D.J. Hacker, J. Dunlosky, & A.C. Graesser (Eds.), Handbook of Metacognition in Education (pp. 340-358). Routledge Press.

2008

• Jeong, H., & Biswas, G. (2008). Mining student behavior models in learning-by-teaching environments. In R. Baker, T. Barnes, & J. Beck (Eds.), Proceedings of the First International Conference on Educational Data Mining (pp. 127-136). Montreal, Canada.
• Jeong, H., Gupta, A., Roscoe, R., Wagster, J., Biswas, G., & Schwartz, D. (2008). Using hidden markov models to characterize student behaviors in learning-by-teaching environments. In Intelligent Tutoring Systems: Vol. 5091. Lecture Notes in Computer Science (pp. 614-625). Montreal, Canada: Springer.
• Leelawong, K., & Biswas, G. (2008). Designing learning by teaching agents: The Betty’s Brain system. International Journal of Artificial Intelligence in Education, 18(3), 181-208.
• Roscoe, R. D., Wagster, J., & Biswas, G. (2008). Using teachable agent feedback to support effective learning-by-teaching. In Proceedings of the 30th Annual Meeting of the Cognitive Science Society (pp. 2381-2386). Washington, DC.
• Wagster, J., Kwong, H., Segedy, J., Biswas, G., & Schwartz, D. (2008). Bringing CBLEs into classrooms: Experiences with the Betty’s Brain system. In Proceedings of the Eighth IEEE International Conference on Advanced Learning Technologies (pp. 252-256). Santander, Cantabria, Spain.

2007

• Schwartz, D. L., Blair, K. P., Biswas, G., Leelawong, K., & Davis, J. (2007). Animations of thought: Interactivity in the teachable agent paradigm. In R. Lowe & W. Schnotz (Eds.), Learning with Animation: Research and Implications for Design (pp. 114-140). UK: Cambrige University Press.
• Tan, J., & Biswas, G. (2007). Simulation-based game learning environments: Building and sustaining a fish tank. In Proceedings of the First IEEE International Workshop on Digital Game and Intelligent Toy Enhanced Learning(pp. 73-80). Jhongli, Taiwan.
  * Recipient of Best Student-Authored Paper Award
• Tan, J., Skirvin, N., Biswas, G., & Catley, K. (2007). Providing guidance and opportunities for self-assessment and transfer in a simulation environment for discovery learning. In Proceedings of the 29th Annual Meeting of the Cognitive Science Society (pp. 1539-1544). Nashville, TN.
• Tan, J., Wagster, J., Wu, Y., & Biswas, G. (2007). Effect of metacognitive support on student behaviors in learning by teaching environments. In Proceedings of the 13th International Conference on Artificial Intelligence in Education. Marina del Rey, CA: IOS Press.
• Wagster, J., Tan, J., Biswas, G., & Schwartz, D. (2007). How metacognitive feedback affects behavior in learning and transfer. In Proceedings of the 13th International Conference on Artificial Intelligence in Education. Marina del Rey, CA: IOS Press.
• Wagster, J., Tan, J., Wu, Y., Biswas, G., & Schwartz, D. (2007). Do learning by teaching environments with metacognitive support help students develop better learning behaviors?. In Proceedings of the 29th Annual Meeting of the Cognitive Science Society (pp. 695-700). Nashville, TN.

2006

• Blair, K., Schwartz, D., Biswas, G., & Leelawong, K. (2006). Pedagogical agents for learning by teaching: Teachable agents. Educational Technology & Society, Special Issue on Pedagogical Agents.
• Tan, J., & Biswas, G. (2006). The role of feedback in preparation for future learning: A case study in learning by teaching environments. In Intelligent Tutoring Systems: Vol. 4053. Lecture Notes in Computer Science (pp. 370-381). Jhongli, Taiwan: Springer.
• Tan, J., Biswas, G., & Schwartz, D. (2006). Feedback for metacognitive support in learning by teaching environments. In Proceedings of the 28th Annual Meeting of the Cognitive Science Society (pp. 828-833). Vancouver, Canada.

2005

• Biswas, G., Leelawong, K., Belynne, K., & Adebiyi, B. (2005). Case studies in learning by teaching behavioral differences in directed versus guided learning. In Proceedings of the 27th Annual Conference of the Cognitive Science Society (pp. 828-833). Stresa, Italy.
• Biswas, G., Leelawong, K., Schwartz, D., & Vye, N. (2005). Learning by teaching: A new agent paradigm for educational software. Applied Artificial Intelligence, 19, 363-392.
• Gupta, R., Wu, Y., & Biswas, G. (2005). Teaching about dynamic processes: A teachable agents approach. In Artificial Intelligence in Education – Supporting Learning through Intelligent and Socially Informed Technology (pp. 241-248). Amsterdam, The Netherlands: IOS Press.
• Katzlberger, T. (2005). Learning by teaching agents (Doctoral dissertation). Department of EECS, Vanderbilt University, Nashville, TN.
• Leelawong, K. (2005). Using the learning-by-teaching paradigm to design learning environments (Doctoral dissertation). Department of EECS, Vanderbilt University, Nashville, TN.
• Tan, J., Beers, C., Gupta, R., & Biswas, G. (2005). Computer games as intelligent learning environments: A river ecosystem adventure. In Artificial Intelligence in Education – Supporting Learning through Intelligent and Socially Informed Technology (pp. 646-653). Amsterdam: IOS Press.

2004

• Biswas, G., Leelawong, K., Belynne, K., Viswanath, K., Schwartz, D., & Davis, J. (2004). Developing learning by teaching environments that support self-regulated learning. In Intelligent Tutoring Systems: Vol. 3220. Lecture Notes in Computer Science (pp. 730-740). Maceió, Brazil: Springer.
• Biswas, G., Leelawong, K., Belynne, K., Viswanath, K., Vye, N., Schwartz, D., & Davis, J. (2004). Incorporating self regulated learning techniques into learning by teaching environments. In Proceedings of the 26th Annual Meeting of the Cognitive Science Society (pp. 120-125). Chicago: Erlbaum.
• Viswanath, K., Adebiyi, B., Leelawong, K., & Biswas, G. (2004). A multi-agent architecture implementation of learning by teaching systems. In Proceedings of the 4th IEEE International Conference on Advanced Learning Technologies (pp. 61-65). Joensuu, Finland.

2003

• Davis, J., Leelawong, K., Belynne, K., Bodenheimer, B., Biswas, G., Vye, N., & Bransford, J. (2003). Intelligent user interface design for teachable agent systems. In Proceedings of the 8th International Conference on Intelligent User Interfaces (pp. 26-33). Miami, FL: Association for Computing Machinery.
• Davis, J., Leelawong, K., Belynne, K., Bodenheimer, B., Biswas, G., Vye, N., & Bransford, J. (2003). Intelligent user interface design for teachable agent systems: A demonstration abstract. In Proceedings of the 8th International Conference on Intelligent User Interfaces (p. 320). Miami, FL: Association for Computing Machinery.
• Leelawong, K., Viswanath, K., Davis, J., Biswas, G., Vye, N., Belynne, K., & Bransford, J. (2003). Teachable agents: Learning by teaching environments for science domains. In Proceedings of the Fifteenth Annual Conference on Innovative Applications of Artificial Intelligence (pp. 109-116). Menlo Park, CA: AAAI Press.

2002

• Leelawong, K., Davis, J., Vye, N., Biswas, G., Schwartz, D., Belynne, T., Katzlberger, T., & Bransford, J. (2002). The effects of feedback in supporting learning by teaching in a teachable agent environment. In P. Bell, R. Stevens, & T. Satwicz (Eds.), Keeping Learning Complex: The Proceedings of the Fifth International Conference of the Learning Sciences (pp. 245-252). Mahwah, NJ: Erlbaum.

2001

• Biswas, G., Katzlberger, T., Bransford, J., & Schwartz, D. (2001). Extending intelligent learning environments with teachable agents to enhance learning. In J. D. Moore, C. L. Redfield, & W. L. Johnson (Eds.), Artificial Intelligence for Education 01 (pp. 389-397). Amsterdam: IOS Press.
• Biswas, G., Schwartz, D., & Bransford, J. (2001). Technology support for complex problem solving: From SAD environments to AI. In K.D. Forbus & P.J. Feltovich (Eds.), Smart Machines in Education (pp. 71-98). Menlo Park, CA: AAAI Press.
• Leelawong, K., Wang, Y, Biswas, G., Vye, N., Bransford, J., & Schwartz, D. (2001). Qualitative reasoning techniques to support learning by teaching: The teachable agents project. In Proceedings of the Fifteenth International Workshop on Qualitative Reasoning. San Antonio, TX: AAAI Press.

1999

• Brophy, S., Biswas, G., Katzlberger, T., Bransford, J., & Schwartz, D. (1999). Teachable agents: Combining insights from learning theory and computer science. In S.P. Lajoie & M. Vivet (Eds.), Artificial Intelligence in Education (pp. 21-28). Amsterdam: IOS Press.

1998

• Brophy, S., Schwartz, D., Biswas, G., & Bransford, J. (1998, August). Learning through programmable agents. Paper presented at Workshop on Pedagogical Agents (ITS ’98), San Antonio, TX.