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Related Experiment Video

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Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data

Pratiyush Guleria1, Manu Sood2

  • 1National Institute of Electronics and Information Technology (NIELIT), Shimla, Himachal Pradesh India.

Education and Information Technologies
|July 25, 2022
PubMed
Summary

This study introduces a machine learning (ML) framework for career counseling, integrating Explainable AI (XAI) to guide student career decisions. Naive Bayes models achieved high prediction accuracy, outperforming other ML techniques for career placement analysis.

Keywords:
Artificial intelligenceCareer counselingEducational data miningExplainable AIMachine learning

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Data Science

Background:

  • Machine learning (ML) and Explainable AI (XAI) offer advanced capabilities for analyzing complex data.
  • Educational frameworks can leverage ML/XAI to provide intelligent support for students' career development.
  • Current career counseling methods can be enhanced by data-driven insights into academic and employability factors.

Purpose of the Study:

  • To propose an ML and AI-powered framework for student career counseling.
  • To analyze educational datasets for factors influencing career placement and growth.
  • To integrate expert system functionalities for decision support in career choices.

Main Methods:

  • Development of a framework integrating ML and XAI for educational analysis.
  • Utilization of ML-based White Box and Black Box models.
  • Training models on an educational dataset encompassing academic and employability attributes.

Main Results:

  • Naive Bayes models demonstrated superior performance in prediction tasks.
  • Achieved a Recall score of 91.2% and an F-Measure score of 90.7% using Naive Bayes.
  • Outperformed Logistic Regression, Decision Tree, SVM, KNN, and Ensemble models in the study.

Conclusions:

  • The proposed framework effectively utilizes ML and AI for career counseling.
  • Naive Bayes proved to be the most effective model for predicting student career outcomes within the study's dataset.
  • The framework provides valuable decision support for students' career planning and placement.