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An important problem in many fields is the analysis of counts data to extract meaningful latent components. Methods like Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) have been proposed for this purpose. However, they are limited in the number of components they can extract and lack an explicit provision to control the “expressiveness” of the extracted comp...

We investigated the validity of applying topic modeling to unstructured student text data 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 pred...

The purpose of this study is to examine different formats of comment data to predict student performance. Having students write comment data after every lesson can reflect students’ learning attitudes, tendencies and learning activities involved with the lesson. In this research, Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (pLSA) are employed to predict student ...

Probabilistic Latent Semantic Indexing (PLSI) is a statistical technique for automatic document indexing. A novel method is proposed for updating PLSI when new documents arrive. The proposed method adds incrementally the words of any new document in the term-document matrix and derives the updating equations for the probability of terms given the class (i.e. latent) variables and the probabilit...