Automated Predictive Assessment from Unstructured Student Writing

Ming, N.C., & Ming, V.L. (2012a, September). Automated predictive assessment from unstructured student writing. Paper presented at the First International Conference on Data Analytics, Barcelona, Spain.


We investigated the validity of applying topic modeling to unstructured student writing from online class discussion forums to predict students’ final grades. Using only student discussion data from introductory courses in biology and economics, both probabilistic latent semantic analysis (pLSA) and hierarchical latent Dirichlet allocation (hLDA) produced significantly better than chance predictions which improved with additional data collected over the duration of the course. Hierarchical latent Dirichlet allocation yielded superior predictions, suggesting the feasibility of mining student data to derive conceptual hierarchies. Results indicate that topic modeling of student-generated text may offer useful formative assessment information about students’ conceptual knowledge.

View full paper here.


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