Natural Language Processing Pipeline for Assessment Data: An R-Based Tutorial
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The article outlines the application of natural language processing (NLP) in analyzing written responses for educational assessments. It details essential steps for text preprocessing, feature extraction, and data analysis, preserving the integrity of examinees' syntax and semantics. An R-based example using Latent Dirichlet Allocation illustrates the methodology, serving as a practical guide for researchers and practitioners in the field.
Natural Language Processing Pipeline Enhances Educational Assessments
Natural language processing (NLP) is increasingly utilized in the analysis of textual responses in educational assessments. A recent paper outlines essential steps for implementing NLP techniques in educational measurement.
The focus of the paper is on cleaning and structuring examinees' written responses, which is vital for creating input data that facilitates the extraction of relevant features. The tutorial guides users through text preprocessing, feature extraction, and the analysis of data from constructed response items.
Among the practical applications is an R-based example employing Latent Dirichlet Allocation (LDA), demonstrating how NLP can be integrated into educational assessment workflows. This serves as a reference for those looking to enhance their analysis of textual data.
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📰 Original Source: https://doi.org/10.59863/sdyz2049
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