Latent Feature Extraction for Process Data via Multidimensional Scaling

Abstract

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.

26 pages, 11 figures

Cited by in corpus (4)

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• Accurate Assessment via Process Data
• Item Response Theory -- A Statistical Framework for Educational and Psychological Measurement
• Analysis of Log Data from an International Online Educational Assessment System: A Multi-state Survival Modeling Approach to Reaction Time between and across Action Sequence