2023

• Yang, B., Nam, S., & Huang, Y. (2023). "Why My Essay Received a 4?": A Natural Language Processing Based Argumentative Essay Structure Analysis. In The 24th International Conference on Artificial Intelligence in Education. (PDF)

2022

• Nam, S., Jurgens, D., & Collins-Thompson, K. (2022). An Attention-Based Model for Predicting Contextual Informativeness and Curriculum Learning Applications. arXiv preprint arXiv:2204.09885. (PDF)

2020

• Nam, S., Bylinskii, Z., Tensmeyer, C., Wigington, C., Jain, R., & Sun, T. (2020). Using Behavioral Interactions from a Mobile Device to Classify the Reader's Prior Familiarity and Goal Conditions. arXiv preprint arXiv:2004.12016. (PDF) (Journal of Vision abstract)
• (Thesis) Nam, S. (2020). Understanding and Supporting Vocabulary Learners via Machine Learning on Behavioral and Linguistic Data (Doctoral dissertation). (PDF)

  The dissertation consolidates my research works with Dynamic Support of Contextual Vocabulary Acquisition for Reading (DSCoVAR) . DSCoVAR is an intelligent tutoring system designed to help middle school students to improve their literacy skills and develop some compelling new technology in the process. Each chapter illustrates how I used machine learning and natural language processing techniques on the following topics:

  • Classifying off-task behaviors in vocabulary learning with behavioral and linguistic signals.
  • Developing interpretable semantic scales to score open-ended responses by combining the semantic differential and word embedding models.
  • Using BERT-based model to find contextually informative sentences for vocabulary learning curriculum.
  • Applying instructive vocabulary learning curricula designed for human students to NLP models for more efficient curriculum learning.

2019

• Nam, S., & Samson, P. (2019). Integrating Students' Behavioral Signals and Academic Profiles in Early Warning System. In The 20th International Conference on Artificial Intelligence in Education. (PDF) (Supplements)

2017

• Nam, S., Frishkoff, G. A., & Collins-Thompson, K. (2017). Predicting Short- and Long-Term Vocabulary Learning via Semantic Features of Partial Word Knowledge. In Educational Data Mining. (PDF)
• Nam, S., Frishkoff, G. A., & Collins-Thompson, K. (2017). Predicting Students’ Disengaged Behaviors in an Online Meaning-Generation Task. In IEEE Transactions on Learning Technologies. (PDF)

2016

• Waddington, R. J., & Nam, S., Lonn, S., & Teasley, S. D. (2016). Improving early Warning Systems with Categorized Course Resource Usage. In Journal of Learning Analytics (pp. 263-290). (PDF)
• Nam, S. (2016). Predicting Off-task Behaviors in an Adaptive Vocabulary Learning System (Doctoral consortium proceeding). In Educational Data Mining. (PDF)
• Nam, S., Collins-Thompson, K., & Frishkoff, G. A. (2016). Modeling Off-task Behaviors in a Meaning-Generation Task. In Measurement in Digital Environments White Paper Series.
• Frishkoff, G. A., Collins-Thompson, K., Nam, S., Hodges, L., & Crossley, S. (2016). Dynamic Support of Contextual Vocabulary Acquisition for Reading (DSCoVAR): An intelligent tutor for contextual word learning. In Crossley, S. A., & McNamara, D. S. (Eds.), Adaptive Educational Technologies for Literacy Instruction. New York, NY: Routledge.
• Nam, S., Collins-Thompson, K., Frishkoff, G. A., Bhide, A., Muth, K. B., & Perfetti, C. (2016). Modeling Real-time Performance on a Meaning-Generation Task (Short paper). In AERA’16 Annual Meeting (PDF) .

2015

• Nam, S., Collins-Thompson, K., Frishkoff, G. A, Bhide, A., Muth, K. B., & Perfetti, C. (2015). Measuring Real- time Student Engagement in Contextual Word Learning (Conference abstract). Presented in Twenty-Second Annual Meeting of Society for the Scientific Study of Reading.

2014

• Oh, J., Nam, S., & Lee, J. (2014). Generating highlights automatically from text-reading behaviors on mobile devices (Works-in-progress). In CHI'14 Extended Abstracts on Human Factors in Computing Systems (pp. 2317-2322). ACM. (PDF)
• Nam, S., Lonn, S., Brown, T., Davis, C. S., & Koch, D. (2014). Customized course advising: investigating engineering student success with incoming profiles and patterns of concurrent course enrollment (Full paper). In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 16-25). ACM. (PDF)
• Waddington, R. J., & Nam, S. (2014). Practice exams make perfect: incorporating course resource use into an early warning system (Short paper). In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 188-192). ACM. (PDF)