335 citations · 535 across the 88 of their papers we have counts for
9 papers · 1 filter
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics
Zichao Wang, Yi Gu, Andrew Lan +1
We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estim…
An Estimation and Analysis Framework for the Rasch Model
Andrew S. Lan, Mung Chiang, Christoph Studer
The Rasch model is widely used for item response analysis in applications ranging from recommender systems to psychology, education, and finance. While a number of estimators have…
Linearized Binary Regression
Andrew S. Lan, Mung Chiang, Christoph Studer
Probit regression was first proposed by Bliss in 1934 to study mortality rates of insects. Since then, an extensive body of work has analyzed and used probit or related binary regr…
Data-Mining Textual Responses to Uncover Misconception Patterns
Joshua J. Michalenko, Andrew S. Lan, Richard G. Baraniuk
An important, yet largely unstudied, problem in student data analysis is to detect misconceptions from students' responses to open-response questions. Misconception detection enabl…
Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions
Andrew S. Lan, Divyanshu Vats, Andrew E. Waters +1
While computer and communication technologies have provided effective means to scale up many aspects of education, the submission and grading of assessments such as homework assign…
Quantized Matrix Completion for Personalized Learning
Andrew S. Lan, Christoph Studer, Richard G. Baraniuk
The recently proposed SPARse Factor Analysis (SPARFA) framework for personalized learning performs factor analysis on ordinal or binary-valued (e.g., correct/incorrect) graded lear…
Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics
Andrew S. Lan, Christoph Studer, Andrew E. Waters +1
Machine learning offers novel ways and means to design personalized learning systems wherein each student's educational experience is customized in real time depending on their bac…
Time-varying Learning and Content Analytics via Sparse Factor Analysis
Andrew S. Lan, Christoph Studer, Richard G. Baraniuk
We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based…
Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data
Andrew S. Lan, Christoph Studer, Andrew E. Waters +1
Modern machine learning methods are critical to the development of large-scale personalized learning systems that cater directly to the needs of individual learners. The recently d…