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Evaluation of Student Performance through a Multidimensional Finite Mixture IRT Model.

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  • 1a Department of Economics , University of Perugia (IT).

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|September 28, 2017
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Summary

This study introduces a new method to evaluate student academic performance, even when exams are not attempted. The item response theory model accounts for missing exam data to provide a more accurate assessment of student progress.

Keywords:
Educationlatent class modelnonignorable missing responsesordinal responseswithin-item multidimensionality

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Academic Analytics

Background:

  • In the Italian academic system, students can either take exams immediately or postpone them, leading to missing data for postponed exams.
  • Traditional student performance evaluations often overlook non-attempted exams, potentially skewing results.

Purpose of the Study:

  • To develop a novel approach for evaluating student performance that incorporates non-attempted exams.
  • To provide a more comprehensive and accurate assessment of a student's academic journey.

Main Methods:

  • Utilized an item response theory (IRT) model with two discrete latent variables: student performance and exam selection priority.
  • Employed a within-item multidimensionality approach to handle non-ignorable missing observations, where attempted exams inform performance measurement.
  • Incorporated individual covariates into the structural part of the model.

Main Results:

  • The proposed IRT model effectively evaluates student performance by accounting for both attempted and non-attempted exams.
  • The model demonstrates that non-attempted exams provide valuable information for performance assessment.
  • The inclusion of covariates allows for the analysis of factors influencing student performance and exam selection.

Conclusions:

  • The developed IRT model offers a robust framework for assessing student academic performance in systems with flexible exam scheduling.
  • This approach provides a more nuanced understanding of student progress by integrating data from non-attempted exams.
  • The methodology has implications for educational policy and student support strategies.