Publications

* Student author
+ Equal contribution or alphabetical order
** Corresponding author

Journal and Conference Papers

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

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

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

Should college dropout prediction models include protected attributes? [link][paper]
Yu; Lee; Kizilcec
ACM Conference on Learning @ Scale (L@S), 2021
Best Paper Nomination

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

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

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

Quasi-experimental evidence of a school equalization reform on housing prices in Beijing [link][paper]
Ha; **Yu
Chinese Education & Society, 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

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

Workshop and Short Papers

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

Interpretable models do not compromise accuracy or fairness in predicting college success [link][paper]
*Kung; Yu
ACM Conference on Learning @ Scale (L@S), 2020

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

Representing and predicting student navigational pathways in online college courses [link][paper]
Yu; Jiang; Warschauer
ACM Conference on Learning @ Scale (L@S), 2018

Book Chapters and Reports

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

How universities can mind the skills gap [link][report]
Lastra-Anadon; Das; Varshney; Raghavan; Yu
Center for the Governance of Change, IE University, 2021

Preprints

Is big data better? LMS data and predictive analytic performance in postsecondary education [link]
+Bird; +Castleman; +Song; +Yu
EdWorkingPapers, 2022

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)