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Predicting Student Performance Using Machine Learning in fNIRS Data.

Amanda Yumi Ambriola Oku1, João Ricardo Sato1

  • 1Center of Mathematics, Computing and Cognition, Universidade Federal Do ABC, São Bernardo Do Campo, Brazil.

Frontiers in Human Neuroscience
|February 22, 2021
PubMed
Summary

Functional near-infrared spectroscopy (fNIRS) can monitor brain activity to distinguish student engagement levels during online learning. This study used fNIRS to predict quiz performance, identifying key brain regions for improved educational strategies.

Keywords:
educationfNIRSlogistic regressionmachine learningneuroscienceprefrontal cortexrandom forest

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Area of Science:

  • Neuroscience
  • Educational Technology
  • Machine Learning

Background:

  • Student engagement in online learning is a persistent challenge.
  • Interactivity tools like quizzes are used, but their effectiveness needs evaluation.
  • Functional near-infrared spectroscopy (fNIRS) offers a portable and non-invasive method to monitor brain activity.

Purpose of the Study:

  • To investigate student task involvement by analyzing brain activity during online quizzes.
  • To differentiate between high and low engagement levels based on neural data.
  • To identify brain regions and hemodynamic responses predictive of student performance.

Main Methods:

  • Collected fNIRS data from the prefrontal cortex (PFC) of 18 students during video lectures and quizzes.
  • Utilized supervised learning algorithms, including random forests and penalized logistic regression (GLMNET with LASSO).
  • Modeled oxyhemoglobin and deoxyhemoglobin concentrations to classify correct and incorrect quiz answers.

Main Results:

  • Random forest and penalized logistic regression models achieved ROC areas of 0.67 and 0.65, respectively.
  • Channels F4-F6 and AF3-AFz in the PFC were identified as most relevant for predicting student performance.
  • Model significance was validated using leave-one-subject-out cross-validation and permutation testing.

Conclusions:

  • fNIRS combined with machine learning can effectively monitor and predict student engagement and performance in online learning environments.
  • The identified brain regions offer insights into cognitive processes during learning.
  • This methodology can inform the development of more adaptive and effective online educational content and tools.