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Register a new userLatent Feature Extraction for Process Data via Multidimensional Scaling
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Added by
Xueying Tang
Publication date
2019
fields
Mathematical Statistics
and research's language is
English
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Computer-based interactive items have become prevalent in recent educational assessments. In such items, the entire human-computer interactive process is recorded in a log file and is known as the response process. This paper aims at extracting useful information from response processes. In particular, we consider an exploratory latent variable analysis for process data. Latent variables are extracted through a multidimensional scaling framework and can be empirically proved to contain more information than classic binary responses in terms of out-of-sample prediction of many variables.
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Accurate assessment of students ability is the key task of a test. Assessments based on final responses are the standard. As the infrastructure advances, substantially more information is observed. One of such instances is the process data that is collected by computer-based interactive items, which contain a students detailed interactive processes. In this paper, we show both theoretically and empirically that appropriately including such information in the assessment will substantially improve relevant assessment precision. The precision is measured empirically by out-of-sample test reliability.
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