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Evaluating Neural Networks Architectures for Competency Prediction from Process Data Using PISA Computer-Based

Huan Kuang1

  • 1Department of Educational Psychology and Learning Systems, Florida State University, Tallahassee, FL 32306, USA.

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|April 27, 2026
PubMed
Summary

Recurrent neural networks effectively predict student performance using computer-based assessment process data. Combining interaction sequences with expert features enhances accuracy and efficiency in educational testing.

Keywords:
GRULSTMPISARNNaction sequencescomputational psychometricsdeep learningexpert-engineered featuresneural networksprocess data

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Area of Science:

  • Educational Measurement
  • Computer Science

Background:

  • Computer-based assessments generate valuable process data from examinee interactions.
  • Analyzing this data can reveal insights into student proficiency and test performance.

Purpose of the Study:

  • To apply recurrent neural networks (RNNs) to predict item-level correctness and assessment-level latent proficiency using process data.
  • To examine the impact of features, architectural complexity, and variability on model performance.

Main Methods:

  • Utilized process data from the U.S. PISA 2012 computer-based mathematics assessment sample.
  • Applied various RNN architectures (standard RNN, GRU, LSTM) and analyzed expert-engineered features, architectural complexity, action variability, and score variability.

Main Results:

  • Item-level models achieved good predictive performance (AUC ≈ 0.80).
  • Moderate correlations were found between latent proficiency predictions and actual scores.
  • Expert features improved item-level model efficiency and assessment-level model performance, especially with low action variability.

Conclusions:

  • Simple neural network architectures are sufficient for modeling process data with limited action variability.
  • Combining action sequences with expert-engineered features enhances accuracy, efficiency, and interpretability in educational assessments.