VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics

TL;DR

VarFA is a scalable variational inference framework for factor analysis in educational data mining, providing efficient uncertainty estimation to improve adaptive testing and other applications.

Contribution

It introduces a general variational inference approach for factor analysis models, enabling efficient Bayesian uncertainty estimation on large datasets.

Findings

Effective on synthetic data

Demonstrates scalability to large datasets

Provides meaningful uncertainty estimates

Abstract

We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors. Such uncertainty information is useful, for example, for an adaptive testing scenario, where additional tests can be administered if the model is not quite certain about a students' skill level estimation. Traditional Bayesian inference methods that produce such uncertainty information are computationally expensive and do not scale to large data sets. VarFA utilizes variational inference which makes it possible to efficiently perform Bayesian inference even on very large data sets. We use the sparse factor analysis model as a case study and demonstrate the efficacy of VarFA on both synthetic and real data sets. VarFA is also very general and can be applied to a wide array of factor analysis models.