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A latent topic model with Markov transition for process data
British Journal of Mathematical and Statistical Psychology ( IF 2.388 ) Pub Date : 2020-01-08 , DOI: 10.1111/bmsp.12197
Haochen Xu; Guanhua Fang; Zhiliang Ying

We propose a latent topic model with a Markov transition for process data, which consists of time‐stamped events recorded in a log file. Such data are becoming more widely available in computer‐based educational assessment with complex problem‐solving items. The proposed model can be viewed as an extension of the hierarchical Bayesian topic model with a hidden Markov structure to accommodate the underlying evolution of an examinee's latent state. Using topic transition probabilities along with response times enables us to capture examinees' learning trajectories, making clustering/classification more efficient. A forward‐backward variational expectation‐maximization (FB‐VEM) algorithm is developed to tackle the challenging computational problem. Useful theoretical properties are established under certain asymptotic regimes. The proposed method is applied to a complex problem‐solving item in the 2012 version of the Programme for International Student Assessment (PISA).
更新日期:2020-01-08

 

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