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【预告】统计学院系列学术报告(2017年第7期) 发布时间:2017-06-22 浏览次数:

报告题目:Model learning in Latent Class analysis: A higher-order hidden Markov model with covariates

报告时间:2017622 9:00

报告地点:统计学院办公楼1104

主讲人简介: Dr. Shiyu Wang(王诗宇) is an Assistant Professor from Quantitative Methodology Program, Department of Educational Psychology at University of Georgia.  She is also the adjunct Assistant Professor from Department of Statistics at UGA. Before moving to this position, she got her PhD in statistics from University of Illinois at Urbana-Champaign. Dr Wang’s primary research interests lie broadly in multivariate latent variable methodology for the measurement of complex traits and behaviors with application to educational and psychological assessment. Her current research interests are centered on two areas: statistical inference in latent variable modeling and adaptive testing, including item response theory, cognitive diagnostic modelling, computerized adaptive testing and multistage adaptive testing.

报告摘要:Cognitive diagnostic model (CDM) is a type of restricted latent class model which can generate a fine breakdown of the skills or attributes possessed by students that affect performance. The application of the traditional CDMs mainly focuses on providing classification of skills or attributes at a given point in time.  However, in the context of education students usually learn and master content over time. In this talk, we introduce a family of learning models which integrates the CDM framework with a higher-order, hidden Markov model (HO-HMM) for attribute transitions. This new modelling framework allows us to model transitions from non-mastery to mastery of an attribute as a function of student covariates. A Bayesian formulation is adopted to estimate parameters from the proposed learning model. We apply this model to analyze students’ learning trajectory in a computer-based 3-D spatial skills cognitive diagnostic assessment with learning tools. The spatial reasoning example indicates the benefits of practice, and the value of knowing some of the attributes, on the probability of making a transition to a master of a fixed attribute. It also revealed significant heterogeneity in individual learning rates. Students’ demographic variables, such as country, gender, major, are incorporated in the proposed model to better evaluate the designed intervention and investigate the impact of the social factors.

 




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