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There has been an increasingly popular trend in Universities for curriculumtransformation to make teaching more interactive and suitable for onlinecourses. An increase in the popularity of online courses would result in anincrease in the number of course-related queries for academics. This, coupledwith the fact that if lectures were delivered in a video on demand format,there would be no fixed time where the majority of students could askquestions. When questions are asked in a lecture there is a negligible chanceof having similar questions repeatedly, but asynchronously this is more likely.In order to reduce the time spent on answering each individual question,clustering them is an ideal choice. There are different unsupervised models fitfor text clustering, of which the Latent Dirichlet Allocation model is the mostcommonly used. We use the Hierarchical Dirichlet Process to determine anoptimal topic number input for our LDA model runs. Due to the probabilisticnature of these topic models, the outputs of them vary for different runs. Thegeneral trend we found is that not all the topics were being used forclustering on the first run of the LDA model, which results in a less effectiveclustering. To tackle probabilistic output, we recursively use the LDA model onthe effective topics being used until we obtain an efficiency ratio of 1.Through our experimental results we also establish a reasoning on how Zeno'sparadox is avoided.

Unification of HDP and LDA Models for Optimal Topic Clustering of Subject Specific Question Banks