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Ethan Prihar
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2020 – today
- 2024
- [c18]Allison Wang, Ethan Prihar, Aaron Haim, Neil T. Heffernan:
Can Large Language Models Generate Middle School Mathematics Explanations Better Than Human Teachers? AIED Companion (2) 2024: 242-250 - [c17]Ethan Prihar, Bahar Radmehr, Tanja Käser:
An Interpretable Approach to Identify Performance Indicators Within Unstructured Learning Environments. AIED Companion (1) 2024: 356-363 - 2023
- [c16]Ethan Prihar, Morgan P. Lee, Mia Hopman, Adam Tauman Kalai, Sofia Vempala, Allison Wang, Gabriel Wickline, Aly Murray, Neil T. Heffernan:
Comparing Different Approaches to Generating Mathematics Explanations Using Large Language Models. AIED (Posters/Late Breaking Results/...) 2023: 290-295 - [c15]Ethan Prihar, Kirk Vanacore, Adam Sales, Neil T. Heffernan:
Effective Evaluation of Online Learning Interventions with Surrogate Measures. EDM 2023 - [c14]Ethan Prihar, Aaron Haim, Tracy Jia Shen, Adam Sales, Dongwon Lee, Xintao Wu, Neil T. Heffernan:
Investigating the Impact of Skill-Related Videos on Online Learning. L@S 2023: 4-13 - [c13]Ethan Prihar, Adam Sales, Neil T. Heffernan:
A Bandit You Can Trust. UMAP 2023: 106-115 - 2022
- [c12]Aaron Haim, Ethan Prihar, Neil T. Heffernan:
Toward Improving Effectiveness of Crowdsourced, On-Demand Assistance from Educators in Online Learning Platforms. AIED (2) 2022: 29-34 - [c11]Anthony F. Botelho, Ethan Prihar, Neil T. Heffernan:
Deep Learning or Deep Ignorance? Comparing Untrained Recurrent Models in Educational Contexts. AIED (1) 2022: 281-293 - [c10]Adam Sales, Ethan Prihar, Johann Gagnon-Bartsch, Ashish Gurung, Neil T. Heffernan:
More Powerful A/B Testing Using Auxiliary Data and Deep Learning. AIED (2) 2022: 524-527 - [c9]Ethan Prihar, Alexander Moore, Neil T. Heffernan:
Identifying Explanations Within Student-Tutor Chat Logs. EDM 2022 - [c8]Ethan Prihar, Manaal Syed, Korinn Ostrow, Stacy T. Shaw, Adam Sales, Neil T. Heffernan:
Exploring Common Trends in Online Educational Experiments. EDM 2022 - [c7]Ethan Prihar, Aaron Haim, Adam Sales, Neil T. Heffernan:
Automatic Interpretable Personalized Learning. L@S 2022: 1-11 - 2021
- [c6]Ethan Prihar, Alexander Moore, Neil T. Heffernan:
Identifying Struggling Students by Comparing Online Tutor Clickstreams. AIED (2) 2021: 290-295 - [c5]Jia Tracy Shen, Michiharu Yamashita, Ethan Prihar, Neil T. Heffernan, Xintao Wu, Sean McGrew, Dongwon Lee:
Classifying Math Knowledge Components via Task-Adaptive Pre-Trained BERT. AIED (1) 2021: 408-419 - [c4]Ethan Prihar, Neil T. Heffernan:
A Novel Algorithm for Aggregating Crowdsourced Opinions. EDM 2021 - [c3]Adam Sales, Ethan Prihar, Neil T. Heffernan, John Pane:
Estimating the Intelligent Tutor Effects on Specific Posttest Problems. EDM 2021 - [c2]Ethan Prihar, Thanaporn Patikorn, Anthony Botelho, Adam Sales, Neil T. Heffernan:
Toward Personalizing Students' Education with Crowdsourced Tutoring. L@S 2021: 37-45 - [i2]Jia Tracy Shen, Michiharu Yamashita, Ethan Prihar, Neil T. Heffernan, Xintao Wu, Sean McGrew, Dongwon Lee:
Classifying Math KCs via Task-Adaptive Pre-Trained BERT. CoRR abs/2105.11343 (2021) - [i1]Jia Tracy Shen, Michiharu Yamashita, Ethan Prihar, Neil T. Heffernan, Xintao Wu, Dongwon Lee:
MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education. CoRR abs/2106.07340 (2021)
2010 – 2019
- 2017
- [c1]Scott Barton, Ethan Prihar, Paulo Carvalho:
Cyther: a human-playable, self-tuning robotic zither. NIME 2017: 319-324
Coauthor Index
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last updated on 2024-08-03 20:15 CEST by the dblp team
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