Renzhe Yu

According to our database, Renzhe Yu authored at least 24 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

The life cycle of large language models in education: A framework for understanding sources of bias.

Jinsook Lee, Yann Hicke, Renzhe Yu, Christopher Brooks, René F. Kizilcec

Br. J. Educ. Technol., September, 2024

Whose ChatGPT? Unveiling Real-World Educational Inequalities Introduced by Large Language Models.

Renzhe Yu, Zhen Xu, Sky CH-Wang, Richard Arum

CoRR, 2024

The Life Cycle of Large Language Models: A Review of Biases in Education.

Jinsook Lee, Yann Hicke, Renzhe Yu, Christopher Brooks, René F. Kizilcec

CoRR, 2024

Fairness Hub Technical Briefs: Definition and Detection of Distribution Shift.

Nicolás Acevedo, Carmen Cortez, Christopher Brooks, René F. Kizilcec, Renzhe Yu

CoRR, 2024

A national longitudinal dataset of skills taught in U.S. higher education curricula.

Alireza Javadian Sabet, Sarah H. Bana, Renzhe Yu, Morgan R. Frank

CoRR, 2024

Technology-Based Instructional Strategies Show Promise in Improving Self-Regulated Learning Skills at Broad-Access Postsecondary Institutions.

Renzhe Yu, Hui Yang, Xiaoying Lin, Chengyuan Yao, Paul Burkander, Krystal Thomas, Jessica Mislevy

Proceedings of the Eleventh ACM Conference on Learning @ Scale, 2024

Contexts Matter but How? Course-Level Correlates of Performance and Fairness Shift in Predictive Model Transfer.

Zhen Xu, Joseph Olson, Nicole Pochinki, Zhijian Zheng, Renzhe Yu

Proceedings of the 14th Learning Analytics and Knowledge Conference, 2024

Temporal and Between-Group Variability in College Dropout Prediction.

Dominik Glandorf, Hye Rin Lee, Gabe Avakian Orona, Marina Pumptow, Renzhe Yu, Christian Fischer

Proceedings of the 14th Learning Analytics and Knowledge Conference, 2024

2023

Fairness Hub Technical Briefs: AUC Gap.

Jinsook Lee, Christopher Brooks, Renzhe Yu, René F. Kizilcec

CoRR, 2023

Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity.

Joshua Gardner, Renzhe Yu, Quan Nguyen, Christopher Brooks, René F. Kizilcec

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Semantic Topic Chains for Modeling Temporality of Themes in Online Student Discussion Forums.

Harshita Chopra, Yiwen Lin, Mohammad Amin Samadi, Jacqueline G. Cavazos, Renzhe Yu, Spencer Jaquay, Nia Nixon

Proceedings of the 16th International Conference on Educational Data Mining, 2023

2022

A Robust Approach for the Decomposition of High-Energy-Consuming Industrial Loads with Deep Learning.

Jia Cui, Yonghui Jin, Renzhe Yu, Martin Onyeka Okoye, Yang Li, Junyou Yang, Shunjiang Wang

CoRR, 2022

Large-Scale Student Data Reveal Sociodemographic Gaps in Procrastination Behavior.

Sunil Sabnis, Renzhe Yu, René F. Kizilcec

Proceedings of the L@S'22: Ninth ACM Conference on Learning @ Scale, New York City, NY, USA, June 1, 2022

FATED 2022: Fairness, Accountability, and Transparency in Educational Data.

Collin F. Lynch, Mirko Marras, Mykola Pechenizkiy, Anna N. Rafferty, Steven Ritter, Vinitra Swamy, Renzhe Yu

Proceedings of the 15th International Conference on Educational Data Mining, 2022

Modeling Student Discourse in Online Discussion Forums Using Semantic Similarity Based Topic Chains.

Harshita Chopra, Yiwen Lin, Mohammad Amin Samadi, Jacqueline Guadalupe Cavazos, Renzhe Yu, Spencer Jaquay, Nia Nixon

Proceedings of the Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners' and Doctoral Consortium, 2022

2021

Unsupervised Representations Predict Popularity of Peer-Shared Artifacts in an Online Learning Environment.

Renzhe Yu, John Scott, Zachary A. Pardos

CoRR, 2021

Should College Dropout Prediction Models Include Protected Attributes?

Renzhe Yu, Hansol Lee, René F. Kizilcec

Proceedings of the L@S'21: Eighth ACM Conference on Learning @ Scale, 2021

2020

Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success.

Catherine Kung, Renzhe Yu

Proceedings of the L@S'20: Seventh ACM Conference on Learning @ Scale, 2020

Towards Accurate and Fair Prediction of College Success: Evaluating Different Sources of Student Data.

Renzhe Yu, Qiujie Li, Christian Fischer, Shayan Doroudi, Di Xu

Proceedings of the 13th International Conference on Educational Data Mining, 2020

LIWCs the Same, Not the Same: Gendered Linguistic Signals of Performance and Experience in Online STEM Courses.

Yiwen Lin, Renzhe Yu, Nia Dowell

Proceedings of the Artificial Intelligence in Education - 21st International Conference, 2020

2019

Utilizing Learning Analytics to Map Students' Self-Reported Study Strategies to Click Behaviors in STEM Courses.

Fernando Rodriguez, Renzhe Yu, Jihyun Park, Mariela Janet Rivas, Mark Warschauer, Brian K. Sato

Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 2019

Student Behavioral Embeddings and Their Relationship to Outcomes in a Collaborative Online Course.

Renzhe Yu, Zachary A. Pardos, John Scott

Proceedings of the Joint Proceedings of the Workshops of the 12th International Conference on Educational Data Mining co-located with the 12th International Conference on Educational Data Mining, 2019

2018

Representing and predicting student navigational pathways in online college courses.

Renzhe Yu, Daokun Jiang, Mark Warschauer

Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 2018

Understanding Student Procrastination via Mixture Models.

Jihyun Park, Renzhe Yu, Fernando Rodriguez, Rachel B. Baker, Padhraic Smyth, Mark Warschauer

Proceedings of the 11th International Conference on Educational Data Mining, 2018