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ProcData: An R Package for Process Data Analysis.

Xueying Tang1, Susu Zhang2, Zhi Wang3

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This summary is machine-generated.

This study introduces ProcData, an R package for analyzing process data from computer-based assessments. It offers tools to process, analyze, and extract features from response sequences, enhancing educational assessment.

Keywords:
autoencodermultidimensional scalingprocess data analysissequence model

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Area of Science:

  • Educational Measurement
  • Data Science
  • Psychometrics

Background:

  • Process data, recorded as timestamped action sequences in log files, capture detailed respondent problem-solving behaviors.
  • Analyzing process data offers rich insights to improve educational assessment accuracy and utility.

Purpose of the Study:

  • To introduce the R package ProcData, designed for the inspection, processing, and analysis of process data.
  • To provide tools for feature extraction and prediction using sequence models on process data.

Main Methods:

  • Development of an S3 class 'proc' for organizing process data.
  • Implementation of feature extraction methods to compress irregular response processes into numeric vectors.
  • Integration of functions for predictions from neural-network-based sequence models.

Main Results:

  • The ProcData package provides a comprehensive framework for handling process data.
  • Feature extraction methods enable efficient summarization of complex behavioral sequences.
  • The package includes a real-world dataset from the 2012 Programme for International Student Assessment for practical application.

Conclusions:

  • ProcData facilitates advanced analysis of educational assessment data.
  • The package supports the utilization of rich behavioral information for more accurate and insightful assessments.
  • It offers a valuable resource for researchers and practitioners in educational measurement and data science.