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This paper explores the suitability of using automatically discovered topicsfrom MOOC discussion forums for modelling students' academic abilities. TheRasch model from psychometrics is a popular generative probabilistic model thatrelates latent student skill, latent item difficulty, and observed student-itemresponses within a principled, unified framework. According to scholarlyeducational theory, discovered topics can be regarded as appropriatemeasurement items if (1) students' participation across the discovered topicsis well fit by the Rasch model, and if (2) the topics are interpretable tosubject-matter experts as being educationally meaningful. Such Rasch-scaledtopics, with associated difficulty levels, could be of potential benefit tocurriculum refinement, student assessment and personalised feedback. Thetechnical challenge that remains, is to discover meaningful topics thatsimultaneously achieve good statistical fit with the Rasch model. To addressthis challenge, we combine the Rasch model with non-negative matrixfactorisation based topic modelling, jointly fitting both models. Wedemonstrate the suitability of our approach with quantitative experiments ondata from three Coursera MOOCs, and with qualitative survey results on topicinterpretability on a Discrete Optimisation MOOC.

TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs