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ORCIDRenzhe Yu
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2020 – today
- 2024
[j1]Jinsook Lee
, Yann Hicke, Renzhe Yu, Christopher Brooks, René F. Kizilcec:
The life cycle of large language models in education: A framework for understanding sources of bias. Br. J. Educ. Technol. 55(5): 1982-2002 (2024)- [c16]Dominik Glandorf, Hye Rin Lee, Gabe Avakian Orona, Marina Pumptow, Renzhe Yu, Christian Fischer:
Temporal and Between-Group Variability in College Dropout Prediction. LAK 2024: 486-497 - [c15]Zhen Xu, Joseph Olson, Nicole Pochinki, Zhijian Zheng, Renzhe Yu:
Contexts Matter but How? Course-Level Correlates of Performance and Fairness Shift in Predictive Model Transfer. LAK 2024: 713-724 - [c14]Renzhe Yu, Hui Yang, Xiaoying Lin, Chengyuan Yao, Paul Burkander, Krystal Thomas, Jessica Mislevy:
Technology-Based Instructional Strategies Show Promise in Improving Self-Regulated Learning Skills at Broad-Access Postsecondary Institutions. L@S 2024: 408-411 - [i9]Dominik Glandorf, Hye Rin Lee, Gabe Avakian Orona, Marina Pumptow, Renzhe Yu, Christian Fischer:
Temporal and Between-Group Variability in College Dropout Prediction. CoRR abs/2401.06498 (2024) - [i8]Alireza Javadian Sabet, Sarah H. Bana, Renzhe Yu, Morgan R. Frank:
A national longitudinal dataset of skills taught in U.S. higher education curricula. CoRR abs/2404.13163 (2024) - [i7]Nicolás Acevedo, Carmen Cortez, Christopher Brooks, René F. Kizilcec, Renzhe Yu:
Fairness Hub Technical Briefs: Definition and Detection of Distribution Shift. CoRR abs/2405.14186 (2024) - [i6]Jinsook Lee, Yann Hicke, Renzhe Yu, Christopher Brooks, René F. Kizilcec:
The Life Cycle of Large Language Models: A Review of Biases in Education. CoRR abs/2407.11203 (2024) - 2023
- [c13]Harshita Chopra, Yiwen Lin, Mohammad Amin Samadi, Jacqueline G. Cavazos, Renzhe Yu, Spencer Jaquay, Nia Nixon:
Semantic Topic Chains for Modeling Temporality of Themes in Online Student Discussion Forums. EDM 2023 - [c12]Joshua Gardner, Renzhe Yu, Quan Nguyen, Christopher Brooks, René F. Kizilcec:
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity. FAccT 2023: 1664-1684 - [i5]Josh Gardner, Renzhe Yu, Quan Nguyen, Christopher Brooks, René F. Kizilcec:
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity. CoRR abs/2305.00927 (2023) - [i4]Jinsook Lee, Christopher Brooks, Renzhe Yu, René F. Kizilcec:
Fairness Hub Technical Briefs: AUC Gap. CoRR abs/2309.12371 (2023) - 2022
- [c11]Harshita Chopra, Yiwen Lin, Mohammad Amin Samadi, Jacqueline Guadalupe Cavazos, Renzhe Yu, Spencer Jaquay, Nia Nixon:
Modeling Student Discourse in Online Discussion Forums Using Semantic Similarity Based Topic Chains. AIED (2) 2022: 453-457 - [c10]Collin F. Lynch, Mirko Marras, Mykola Pechenizkiy, Anna N. Rafferty, Steven Ritter, Vinitra Swamy, Renzhe Yu:
FATED 2022: Fairness, Accountability, and Transparency in Educational Data. EDM 2022 - [c9]Sunil Sabnis, Renzhe Yu, René F. Kizilcec:
Large-Scale Student Data Reveal Sociodemographic Gaps in Procrastination Behavior. L@S 2022: 133-141 - [i3]Jia Cui, Yonghui Jin, Renzhe Yu, Martin Onyeka Okoye, Yang Li, Junyou Yang, Shunjiang Wang:
A Robust Approach for the Decomposition of High-Energy-Consuming Industrial Loads with Deep Learning. CoRR abs/2203.07075 (2022) - 2021
- [c8]Renzhe Yu, Hansol Lee, René F. Kizilcec:
Should College Dropout Prediction Models Include Protected Attributes? L@S 2021: 91-100 - [i2]Renzhe Yu, John Scott, Zachary A. Pardos:
Unsupervised Representations Predict Popularity of Peer-Shared Artifacts in an Online Learning Environment. CoRR abs/2103.00163 (2021) - [i1]Renzhe Yu, Hansol Lee, René F. Kizilcec:
Should College Dropout Prediction Models Include Protected Attributes? CoRR abs/2103.15237 (2021) - 2020
- [c7]Yiwen Lin, Renzhe Yu, Nia Dowell:
LIWCs the Same, Not the Same: Gendered Linguistic Signals of Performance and Experience in Online STEM Courses. AIED (1) 2020: 333-345 - [c6]Renzhe Yu, Qiujie Li, Christian Fischer, Shayan Doroudi, Di Xu:
Towards Accurate and Fair Prediction of College Success: Evaluating Different Sources of Student Data. EDM 2020 - [c5]Catherine Kung, Renzhe Yu:
Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success. L@S 2020: 413-416
2010 – 2019
- 2019
- [c4]Renzhe Yu, Zachary A. Pardos, John Scott:
Student Behavioral Embeddings and Their Relationship to Outcomes in a Collaborative Online Course. EDM (Workshops) 2019: 23-29 - [c3]Fernando Rodriguez, Renzhe Yu, Jihyun Park, Mariela Janet Rivas, Mark Warschauer, Brian K. Sato:
Utilizing Learning Analytics to Map Students' Self-Reported Study Strategies to Click Behaviors in STEM Courses. LAK 2019: 456-460 - 2018
- [c2]Jihyun Park, Renzhe Yu, Fernando Rodriguez, Rachel B. Baker, Padhraic Smyth, Mark Warschauer:
Understanding Student Procrastination via Mixture Models. EDM 2018 - [c1]Renzhe Yu, Daokun Jiang, Mark Warschauer:
Representing and predicting student navigational pathways in online college courses. L@S 2018: 44:1-44:4
Coauthor Index
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