Publication

Peer Reviewed Journal Articles

  1. Badjatia, N., Podell, J., Felix, R.B., Chen, L.K., Dalton, K., Wang, T.I., Yang, S. and Hu, P ^., 2025. Machine Learning Approaches to Prognostication in Traumatic Brain Injury. Current Neurology and Neuroscience Reports, 25(1), pp.1-12. 
  2. Chowdhury, S.H*., Chen, L.K., Hu, P., Badjatia, N. and Podell, J.E.^, 2025. Group-Based Trajectory Modeling Identifies Distinct Patterns of Sympathetic Hyperactivity Following Traumatic Brain Injury. Neurocritical Care, pp.1-11. 
  3. Jebeli, A*., Chen, L.K., Guerrerio, K *., Papparotto, S*., Berlin, L ^., & Jones Harden, B ^. (2024). Quantifying the Quality of Parent-Child Interaction Through Machine-Learning Based Audio and Video Analysis: Towards a Vision of AI-assisted Coaching Support for Social Workers. ACM Journal of Computing and Sustainable Societies, 2(1), Article 6, 21 pages. 
  4. Doran, K., Witmer, S., Yoon, K. L., Fischer, E. R., Ebangwese, A., Sharma*, S., Duggirala*, G. S. C., & Chen, L. K. (2024). Gauging the stress of long-term care nursing assistants using ecological momentary assessment, wearable sensors and end of day reconstruction. International Journal of Older People Nursing, 19, e12592. 
  5. Salasky VR, Chowdhury SH*, Chen L.K, Almeida E, Kong X*, Armahizer M, Pajoumand M, Schrank GM, Rabinowitz RP, Schwartzbauer G, Hu P, Badjatia N, Podell JE  ^. Overlapping Physiologic Signs of Sepsis and Paroxysmal Sympathetic Hyperactivity After Traumatic Brain Injury: Exploring A Clinical Conundrum. Neurocrit Care. 2023 Oct 26. doi: 10.1007/s12028-023-01862-7. Epub ahead of print. PMID: 37884690. 
  6. Chen, L.K.., Gillan, J., Decker, M., Eteffa, E., Marzan, A., Thai, J*., & Jewett, S. (2023). Embedding Digital Data Storytelling in Introductory Data Science Course: An Inter-Institutional Transdisciplinary Pilot Study. Journal of Problem Based Learning in Higher Education, 11(2), 126–152. 
  7. Ferguson, R. ., Khosravi, H., Kovanović, V., Viberg, O., Aggarwal, A., Brinkhuis, M., Buckingham Shum, S., Chen, L. K., Drachsler, H., Guerrero, V. A., Hanses, M., Hayward, C., Hicks, B., Jivet, I., Kitto, K., Kizilcec, R., Lodge, J. M., Manly, C. A., Matz, R. L., Meaney, M. J., Ochoa, X., Schuetze, B. A., Spruit, M., van Haastrecht, M., van Leeuwen, A., van Rijn, L., Tsai, Y.-S., Weidlich, J., Williamson, K., & Yan, V. X. (2023). Aligning the Goals of Learning Analytics with its Research Scholarship: An Open Peer Commentary Approach. Journal of Learning Analytics, 10(2), 14-50. Impact factor 3.9
  8. Sarkar S, Gaur M, Chen L.K., Garg M, Srivastava B. A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement. Front Artif Intell. 2023 Oct 12;6:1229805. doi: 10.3389/frai.2023.1229805. PMID: 37899961; PMCID: PMC10601652.
  9. Podell, J., Pergakis, M., Yang, S., Felix, R., Parikh, G., Chen, H., Chen, L.K., Miller, C., Hu, P. and Badjatia, N  ^., 2022. Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications. Neurocritical Care, pp.1-14. 
  10. Chen, L.K., Ramsey, J. and Dubrawski, A., 2021. Affect, Support, and Personal Factors: Multimodal Causal Models of One-on-one Coaching. Journal of Educational Data Mining, 13(3), pp.36-68. 
  11. Yoon JH, Mu L,  Chen L, Dubrawski A, Hravnak M, Pinsky MR, Clermont G. Predicting tachycardia as a surrogate for instability in the intensive care unit. Journal of clinical monitoring and computing. 2019 Feb 14:1-3.
  12. Bose E, Chen L, Clermont G, Dubrawski A, Pinsky MR, Ren D, Hoffman LA, Hravnak M. Risk for cardiorespiratory instability following transfer to a monitored step-down unit. Respiratory care. 2017 Apr 1;62(4):415-22.
  13. Chen L, Ogundele, O., Clermont, G., Hravnak, M., Pinsky, M.R. and Dubrawski, A.W., 2017. Dynamic and personalized risk forecast in step-down units. Implications for monitoring paradigms. Annals of the American Thoracic Society, 14(3), pp.384-391.
  14. Chen L, Dubrawski, A., Wang, D., Fiterau, M., Guillame-Bert, M., Bose, E., Kaynar, A.M., Wallace, D.J., Guttendorf, J., Clermont, G. and Pinsky, M.R., 2016. Using supervised machine learning to classify real alerts and artifact in online multi-signal vital sign monitoring data. Critical care medicine, 44(7), p.e456
  15. Hravnak M, Chen L, Dubrawski, A., Bose, E., Clermont, G. and Pinsky, M.R., 2016. Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data. Journal of clinical monitoring and computing, 30(6), pp.875-888.
  16. Hravnak M, Chen L, Dubrawski, A., Bose, E. and Pinsky, M.R., 2015. Temporal distribution of instability events in continuously monitored step-down unit patients: implications for rapid response systems. Resuscitation, 89, pp.99-105.
  17. Dubrawski A, Ostlund J, Chen L, Computationally efficient scoring of activity using demographics and connectivity of entities.  Artur Dubrawski, John K. Ostlund, Lujie Chen, Andrew W. Moore. Information Technology and Management  04/2012; 11(2):77-89.

Peer-Reviewed Conference Proceedings (recent)

  1. Guerrerio, K.M*., Chen, L.K., Berlin, L.J., & Harden, B.J. Causal Explanation of the Quality of Parent-Child Inter- actions with Multimodal Behavioral Features. In Proceedings of the 27th International Conference on Multimodal Interaction (ICMI ’25), October 13– 17, 2025, Canberra, ACT, Australia. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3716553.3750816
  2. Yarnall, L., Yang, H., Ouyang, S., & Chen, L. K. (2025, July). Laying Foundations for Scalable Coaching for Data Storytelling: A Evidence-Centered Design Approach. In Proceedings of the Twelfth ACM Conference on Learning@ Scale (pp. 227-231).
  3. Priyanka Rani, F. N. U*., Alomair, M*., Pan, S., & Chen, L. K. (2025, July). Systematically Identifying, Defining and Organizing Knowledge Components for Data Science Problem Solving through Human-LLM Collaboration. In Proceedings of the Twelfth ACM Conference on Learning@ Scale (pp. 341-345).
  4. Hosen, M. B *, Chen, L. K., Chowdhury*, S. H., Bosarge, E., Gong, N., Hung, W., … & Zha, S. (2025, July). Modeling Socially Constructed Knowledge Using Multimodal Machine Learning: A Case Study in K-12 AI Literacy Education Classroom. In the International Conference on Artificial Intelligence in Education (pp. 85-92). (AIED 2025) Cham: Springer Nature Switzerland.
  5. Alpeshkumar Javiya, P., Kleinsmith, A., Chen L.K, & Fritz, J. (2024, July). Parsing Post-deployment Students’ Feedback: Towards a Student-Centered Intelligent Monitoring System to Support Self-regulated Learning. In the International Conference on Artificial Intelligence in Education (pp. 139-150). Cham: Springer Nature Switzerland.
  6. Anthraper, N., Javiya, P., Iluru, S*., Chen, L.K., & Kleinsmith, A. (2024). PeerConnect: Co-Designing a Peer-Mentoring Support System with Computing Transfer Students. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’24). Association for Computing Machinery, New York, NY, USA, Article 262, 1–7.
  7. Hwang, K*., Wang, K*., Alomair, M*., Choa, F. S., & Chen, L. K. (2024, July). Towards automated multiple choice question generation and evaluation: aligning with Bloom’s taxonomy. In International Conference on Artificial Intelligence in Education (pp. 389-396). Cham: Springer Nature Switzerland. 
  8. Hwang, K*., Challagundla, S., Alomair, M*., Janssen, D., Morton, K., Chen, L. K., & Choa, F. S. (2024, June). Towards the acceleration of human learning capabilities through AI-assisted knowledge tree building. In Big Data VI: Learning, Analytics, and Applications (Vol. 13036, pp. 61-67). SPIE.
  9. Janeja, V.P., Sanchez, M., Khoo, Y.X., Vacano, C.V., & Chen, L.K. (2024). Adopting Foundational Data Science Curriculum with Diverse Institutional Contexts. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE 2024), Volume 1. Association for Computing Machinery, New York, NY, USA, 576–582.
  10. Mandalapu, V., Chen, L, Chen, Z, Gong, J., Student-centric Model of Login Patterns: A Case Study with Learning Management Systems. In Proceedings of the 14th International Conference on Educational Data Mining (EDM). June 2021.
  11. Chen L.  Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data.  In Proceedings of the 11th International Learning Analytics and Knowledge (LAK) Conference. March 2021
  12. Goswami M,  Chen, L, Dubrawski A. Discriminating Cognitive Disequilibrium and Flow in Problem Solving: A Semi-supervised Approach Using Involuntary Dynamic Behavioral Signals. In AAAI 2020 Special Track on AI for Social Impact.
  13. Chen, L, Gjekmarkaj E, Dubrawski A. Parent as a Companion for Solving Challenging Math Problems: Insights from Multi-modal Observational Data. June 2019, In Proceedings of the 12th International Conference on Educational Data Mining (EDM).
  14. Chen, L. Supporting Math Problem Solving Coaching for Young Students: A Case for Weak Learning Companion. In Proceedings of the 10th International Conference on Artificial Intelligence in Education (AIED). Young Researcher Track paper. June 2018.
  15. Chen L, Dubrawski A. Accelerated apprenticeship: Teaching data science problem solving skills at scale. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale 2018 Jun 26 (pp. 1-4).
  16. Chen L, Dubrawski A. Learning from Learning Curves: Discovery of Interpretable Learning Trajectory Groups. In Proceedings of the 7th International Learning Analytics and Knowledge (LAK) Conference. March 2017.
  17. Chen L, Li X, Xia Z, Song Z, Morency LP, Dubrawski A. Riding an emotional roller-coaster: A multimodal study of young child’s math problem solving activities. June 2016, In Proceedings of the 9th International Conference on Educational Data Mining (EDM). (Exemplary paper nomination)
  18. Chen L, Clermont G,  Hravnak M, Pinsky MR, Dubrawski A. A Framework for Visual Tracking of Risk and its Drivers in Monitoring Patients Susceptible for Cardiorespiratory Instability.  November 2016, The American Medical Informatics Association Annual Symposium.
  19. Chen L, Dubrawski A, Clermont G, Hravnak M, Pinsky MR. Modelling Risk of Cardio-Respiratory Instability as a Heterogeneous Process.  November 2015, The American Medical Informatics Association Annual Symposium. (2nd place student paper competition award, 3rd place for “Best of Student Papers in Knowledge Discovery and Data Mining” by Knowledge Discovery and Data Mining (KDDM) Working Group)
  20. Dubrawski A, Sarkar P, Chen L. Trade-offs between Agility and Reliability of Predictions in Dynamic Social Networks Used to Model Risk of Microbial Contamination of Food.  International Conference on Advances in Social Network Analysis and Mining, ASONAM, Athens, Greece, 20-22 July 2009  (Best paper award)

Peer Reviewed Workshop Paper

  1. Chen, L.K. & Ke, F, A Conceptual and Computational Framework for Modeling Assistance Dilemma: Optimizing FUN-Stration in Math Problem Solving, Advancing the Science of Teaching with Tutoring Data: A Collaborative Workshop with the National Tutoring Observatory, 2025 ACM Learning @ Scale Conference, Palermo, Italy
  2. Anthraper, N*., Javiya, P*., Iluru, S*. Chen, L. K., & Kleinsmith, A. Supporting Computing Transfer Students Through Near-Peer Mentoring: Insights from Co-Design Studies for a Peer-Mentoring Support System,  HCLA workshop: New Horizons in Human-Centered Learning Analytics and AI in Education In LAK’25: 15th Learning Analytics and Knowledge Conference.
  3. Sivakumar, N*., Chen, L.K., Papasani, P*., Majmundar, V*., Feng, J.H., Yarnall, L. and Gong, J., 2024, October. Show and Tell: Exploring Large Language Model’s Potential in Formative Educational Assessment of Data Stories. In 2024 IEEE VIS Workshop on Data Storytelling in an Era of Generative AI (GEN4DS) (pp. 13-19). IEEE.
  4. Alomair, M*., Pan, S., & Chen, L. K.. Large Language Models for Intelligent Coaching in Data Science Problem Solving: A Preliminary Investigation. Educational Data Mining ’24 Human-Centric eXplainable AI in Education and Leveraging Large Language Models for Next-Generation Educational Technologies Workshop Joint Proceedings, July 13, 2024, Atlanta, GA
  5. Hwang, K*., Challagundla, S., Alomair, M*., Chen, L.K., & Choa, F.-S. (2023). Towards AI-Assisted Multiple Choice Question Generation and Quality Evaluation at Scale: Aligning with Bloom’s Taxonomy. In NeurIPS Workshop for Generative AI for Education (GAIED): Advances, Opportunities, and Challenges, New Orleans, USA.
  6. Sharma S*, Chen L.K. Yoon L, Doran K, Duggirala C*, Fischer E, Witmer S, Ebangwese A. Measuring and Understanding Work-related Stress using Wearable and Ecological Momentary Assessment: Insights from a Pilot Study with Long-term Care Workers. April 2023. The ACM Conference on Human Factors in Computing Systems (CHI) 2023, Workgroup on Interactive Systems in Healthcare (WISH). 
  7. Chen L.K., Shah P,* Prabhu K*, Daughrity L, Shetty S*, Lopez Delgado M*, Hamidi F, Godwin K,  Lipsmeyer L. ABii at School:  Some Initial Findings from a Long-term In-school Field Study with a Commercial Robot-assisted Learning System. March 2023, The ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2023, Robot for Learning Workshop
  8. Mandalapu, V*., Gong, J., & Chen, L.  Do we need to go Deep? Knowledge Tracing with Big Data. AAAI Workshop on AI Education, 2021
  9. Chen L, Admoni H,  Dubrawski A. Toward A Companion Robot Fostering Perseverance in Math – A Pilot Study. March 2018, Human-Robot Interaction 2018, Robot for Learning Workshop

Conference/Poster Presentation (Juried/Refereed) – selected 

  1. Zha, S., Chen, L.K., Hung, W., Gong, N., Moore, P. and Klemetsrud, B., 2025, February. Predicting Students’ Interest from Small Group Conversational Characteristics: Insights from an AI Literacy Education with High School Students. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1677-1678).
  2. MacFerrin, M., Boyda, E., Young, K., Namayanja, J., Subramanian, A., Mokbel, M.F., Chen, L.K. and Janeja, V.P., 2025, February. Engaging K-12 Learners in Data Annotation for AI Climate Models. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1529-1530).
  3. Chen, L. K.  Personal Learning Analytics: Integrating Data Science Education to Improve Data Literacy and Self-Regulated Learning. In LAK’25: Proceedings of the 15th Learning Analytics and Knowledge Conference. (2025)
  4. Chen, L. K., Ngo, S*., Coulibaly, S*., Talwar, A*., Vyas, N*., & Konkobo, K*. Bumpy Journey: Using Learning Analytics to Understand Undergraduate Computer Science Gateway Courses Performance and Major Switch Decisions. In LAK’25: Proceedings of the 15th Learning Analytics and Knowledge Conference. (2025)
  5. Guerrerio, K. M*. Chen, L. K., Berlin, L., & Harden, B. J. (2025, April). Causal Explanation of Quality of Parent-Child Interactions with Multimodal Behavioral Features (Student Abstract). In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 28, pp. 29379-29381). Selected oral presentation
  6. Chen, L.K., & Thai, J.* (2024). Assessment-via-Teaching: Exploring an Alternative Assessment Strategy in Undergraduate Introductory Data Science Course. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE 2024), Volume 2. Association for Computing Machinery, New York, NY, USA, 1598–1599.
  7. Choi J*, Lipsmeyer L, Bigenho C, Chen L.K. Distraction Analytics: Understanding Students’ Distraction Patterns during Digital Learning.  Companion Proceedings 13th International Conference on Learning Analytics & Knowledge (LAK23)
  8. Alomair, M*., Pan, S., Chen L.K. The Role of Cognition and Metacognition in Data Science Problem Solving: Insights from a Field Study. Companion Proceedings 13th International Conference on Learning Analytics & Knowledge (LAK23). March 2023
  9. Kong, X*., Chowdhury, S.H*., Islam, M.F*., Podell, J.E., Hu, P., Badjatia, N., & Chen, L.K. (2023). Towards Continuous and Efficient Detection of Paroxysmal Sympathetic Hyperactivity (PSH) Episodes among Traumatic Brain Injury Patients: Exploring Human-in-the-loop Anomaly Detection Approach. AMIA Annual Symposium, November 2023.
  10. Alomair, M*., Pan, S., Chen L.K.  Modeling Metacognitive and Cognitive Processes in Data Science Problem Solving,  In Proceedings of the 37th of AAAI Conference on Artificial Intelligence, 2023. Washington DC
  11. Chowdhury S*, Podell, J. Hu, P, Badjatia, N., Chen, L. Heterogeneous Physiological Trends and their Implications for diagnosis of Paroxysmal Sympathetic Hyperactivity (PSH) among Traumatic Brain Injury Patients, National Capital Area TBI Research Symposium, 2023.
  12. Chowdhury S*,  Podell, J, Hu P, Badjatia N,  Chen L, Temporal Structure of Daily Paroxysmal Sympathetic Hyperactivity Assessment Measure (PSH-AM) among Neurocritical Patients with Traumatic Brain Injury, The American Medical Informatics Association Annual Symposium, 2022 
  13. Salvi R*, Islam F, Chowdhury* S,  Podell, J, Hu P, Badjatia N,  Chen L,  Nowcasting PSH-AM: Towards Real-time Assessment of Paroxysmal Sympathetic Hyperactivity using Continuous Vital Sign Measurements in Neurocritical Units, The American Medical Informatics Association Annual Symposium 2022.
  14. Chen L. K. Gerritsen D.  Building Interpretable Descriptors for Student Posture Analysis in a Physical Classroom. In Proceedings of the 14th International Conference on Educational Data Mining (EDM). June 2021.