* Student author
+ Equal contribution or alphabetical order
** Corresponding author
Contexts matter but how? Course-level correlates of performance and fairness shift in predictive model transfer [link][paper]
+*Xu; +*Olson; *Pochinki; *Zheng; Yu
International Conference on Learning Analytics & Knowledge (LAK), 2024
Temporal and between-group variability in college dropout prediction [link][paper]
*Glandorf; Lee; Orona; Pumptow; +Yu; +Fischer
International Conference on Learning Analytics & Knowledge (LAK), 2024
A national longitudinal dataset of skills taught in U.S. higher education curricula [link]
*Sabet; Bana; Yu; Frank
ArXiv, 2024
Semantic topic chains for modeling temporality of themes in online student discussion forums [link][paper]
*Chopra; Lin; Samadi; Cavazos; Yu; Jaquay; Nixon
International Conference on Educational Data Mining (EDM), 2023
Best Paper Nominee
Cross-institutional transfer learning for educational models: Implications for model performance, fairness, and equity [link][paper]
Gardner; Yu; Nguyen; Brooks; Kizilcec
ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2023
What can digital trace data tell us about postsecondary students academic success? An overview of the literature and an illustrative example [link][chapter]
von Keyserlingk; Lauermann; Yu; Rubach; Arum
Jahrbuch der Schulentwicklung (Band 23), 2023
Salient syllabi: Examining design characteristics of science online courses in higher education [link][paper]
Fischer; McPartlan; Orona; Yu; Xu; Warschauer
PLOS One, 2022
Risk and protective factors of college students’ psychological well-being during the COVID-19 pandemic: Emotional stability, mental health, and household resources [link][paper]
Moeller; von Keyserlingk; Spengler; Gaspard; Lee; Yamaguchi-Pedroza; Yu; Fischer; Arum
AERA Open, 2022
Large-scale student data reveal sociodemographic gaps in procrastination behavior [link][paper]
*Sabnis; Yu; Kizilcec
ACM Conference on Learning @ Scale (L@S), 2022
Best Undergraduate Paper
Is big data better? LMS data and predictive analytic performance in postsecondary education [link]
+Bird; +Castleman; +Song; +Yu
EdWorkingPapers, 2022
Opening the black box: User-log analyses of children’s e-Book reading and associations with word knowledge [link][paper]
Umarji; Day; Xu; Zargar; Yu; Connor
Reading and Writing, 2021
Should college dropout prediction models include protected attributes? [link][paper]
Yu; Lee; Kizilcec
ACM Conference on Learning @ Scale (L@S), 2021
Best Paper Nomination
A research framework for understanding education-occupation alignment with NLP techniques [link][paper]
Yu; Das; Gurajada; Varshney; Raghavan; Lastra-Anadon
NLP for Positive Impact (ACL Workshop), 2021
Construction of weighted course co-enrollment network [paper]
Li; Yu
Using Network Science in Learning Analytics: Building Bridges towards a Common Agenda (LAK Workshop), 2021
How universities can mind the skills gap [link][report]
Lastra-Anadon; Das; Varshney; Raghavan; Yu
Center for the Governance of Change, IE University, 2021
Unsupervised representations predict popularity of peer-shared artifacts in online learning environment [link][video]
Yu; Scott; Pardos
ArXiv, 2021
Best Paper Honorable Mention (AERA Conference on Educational Data Science)
The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: Opening the black box of learning processes [link][paper]
Baker; Xu; Park; Yu; Li; Cung; Fischer; Rodriguez; Warschauer; Smyth
International Journal of Educational Technology in Higher Education, 2020
Mining big data in education: Affordances and challenges [link][paper]
Fischer; Pardos; Baker; Williams; Smyth; Yu; Slater; Baker; Warschauer
Review of Research in Education, 2020
Towards accurate and fair prediction of college success: Evaluating different sources of student data [link][paper][video]
Yu; Li; Fischer; Doroudi; Xu
International Conference on Educational Data Mining (EDM), 2020
LIWCs the same, not the same: Gendered linguistic signals of performance and experience in online STEM courses [link][paper]
Lin; Yu; Dowell
International Conference on Artificial Intelligence in Education (AIED), 2020
Interpretable models do not compromise accuracy or fairness in predicting college success [link][paper]
*Kung; Yu
ACM Conference on Learning @ Scale (L@S), 2020
Quasi-experimental evidence of a school equalization reform on housing prices in Beijing [link][paper]
Ha; **Yu
Chinese Education & Society, 2019
Utilizing learning analytics to map students’ self-reported study strategies to click behaviors in stem courses [link][paper]
Rodriguez; Yu; Park; Rivas; Warschauer; Sato
International Conference on Learning Analytics & Knowledge (LAK), 2019
Student behavioral embeddings and their relationship to outcomes in a collaborative online course [paper]
Yu; Pardos; Scott
Learning Analytics: Building Bridges Between the Education and the Computing Communities (EDM Workshop), 2019
Deconstructing the evolution of collaborative learning networks [link][paper]
Yu
Connectivism: Using Learning Analytics to Operationalize A Research Agenda (LAK Workshop), 2019
Understanding student procrastination via mixture models [link][paper]
Park; Yu; Rodriguez; Baker; Smyth; Warschauer
International Conference on Educational Data Mining (EDM), 2018
Best Paper Award
Representing and predicting student navigational pathways in online college courses [link][paper]
Yu; Jiang; Warschauer
ACM Conference on Learning @ Scale (L@S), 2018
How much is an improved school worth? Evidence from the comprehensive reform in compulsory education in Beijing
Ha; **Yu
Peking University Education Review, 2017
Outstanding Research Award (Ministry of Education of China)
A new research on the capitalization of school quality in housing prices: An empirical study based on repeated cross-sectional data in Beijing
Ha; Wu; Yu
Education & Economy, 2015