Cyther: a human-playable, self-tuning robotic zither. - Scott Barton; Ethan Prihar; Paulo Carvalho (NIME, 2017)

Identifying Struggling Students by Comparing Online Tutor Clickstreams. - Ethan Prihar; Alexander Moore; Neil T. Heffernan (AIED (2), 2021)

Classifying Math Knowledge Components via Task-Adaptive Pre-Trained BERT. - Jia Tracy Shen; Michiharu Yamashita; Ethan Prihar; Neil T. Heffernan; Xintao Wu; Sean McGrew; Dongwon Lee 001 (AIED (1), 2021)

A Novel Algorithm for Aggregating Crowdsourced Opinions. - Ethan Prihar; Neil T. Heffernan (EDM, 2021)

Estimating the Intelligent Tutor Effects on Specific Posttest Problems. - Adam Sales; Ethan Prihar; Neil T. Heffernan; John Pane (EDM, 2021)

Toward Personalizing Students' Education with Crowdsourced Tutoring. - Ethan Prihar; Thanaporn Patikorn; Anthony Botelho; Adam Sales; Neil T. Heffernan (L@S, 2021)

Classifying Math KCs via Task-Adaptive Pre-Trained BERT. - Jia Tracy Shen; Michiharu Yamashita; Ethan Prihar; Neil T. Heffernan; Xintao Wu; Sean McGrew; Dongwon Lee 001 (CoRR, 2021)

MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education. - Jia Tracy Shen; Michiharu Yamashita; Ethan Prihar; Neil T. Heffernan; Xintao Wu; Dongwon Lee 001 (CoRR, 2021)

Deep Learning or Deep Ignorance? Comparing Untrained Recurrent Models in Educational Contexts. - Anthony F. Botelho; Ethan Prihar; Neil T. Heffernan (AIED (1), 2022)

Toward Improving Effectiveness of Crowdsourced, On-Demand Assistance from Educators in Online Learning Platforms. - Aaron Haim; Ethan Prihar; Neil T. Heffernan (AIED (2), 2022)

More Powerful A/B Testing Using Auxiliary Data and Deep Learning. - Adam Sales; Ethan Prihar; Johann Gagnon-Bartsch; Ashish Gurung; Neil T. Heffernan (AIED (2), 2022)

Identifying Explanations Within Student-Tutor Chat Logs. - Ethan Prihar; Alexander Moore; Neil T. Heffernan (EDM, 2022)

Exploring Common Trends in Online Educational Experiments. - Ethan Prihar; Manaal Syed; Korinn Ostrow; Stacy T. Shaw; Adam Sales; Neil T. Heffernan (EDM, 2022)

Automatic Interpretable Personalized Learning. - Ethan Prihar; Aaron Haim; Adam Sales; Neil T. Heffernan (L@S, 2022)

Comparing Different Approaches to Generating Mathematics Explanations Using Large Language Models. - Ethan Prihar; Morgan P. Lee; Mia Hopman; Adam Tauman Kalai; Sofia Vempala; Allison Wang; Gabriel Wickline; Aly Murray; Neil T. Heffernan (AIED (Posters/Late Breaking Results/...), 2023)

Effective Evaluation of Online Learning Interventions with Surrogate Measures. - Ethan Prihar; Kirk Vanacore; Adam Sales; Neil T. Heffernan (EDM, 2023)

Investigating the Impact of Skill-Related Videos on Online Learning. - Ethan Prihar; Aaron Haim; Tracy Jia Shen; Adam Sales; Dongwon Lee 001; Xintao Wu; Neil T. Heffernan (L@S, 2023)

A Bandit You Can Trust. - Ethan Prihar; Adam Sales; Neil T. Heffernan (UMAP, 2023)

An Interpretable Approach to Identify Performance Indicators Within Unstructured Learning Environments. - Ethan Prihar; Bahar Radmehr; Tanja Käser (AIED Companion (1), 2024)

Can Large Language Models Generate Middle School Mathematics Explanations Better Than Human Teachers? - Allison Wang; Ethan Prihar; Aaron Haim; Neil T. Heffernan (AIED Companion (2), 2024)

The Effect of Different Support Strategies on Student Affect. - Julie Le Tallec; Ethan Prihar; Tanja Käser (LAK, 2025)