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Predicting open education competency level: A machine learning approach.

Gerardo Ibarra-Vazquez1, María Soledad Ramírez-Montoya2, Mariana Buenestado-Fernández3

  • 1School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico.

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Summary
This summary is machine-generated.

Machine learning models effectively predict open education competency levels using student perceptions of knowledge, skills, and values. Decision Trees and Random Forests accurately classified competence based on these insights.

Keywords:
Competency levelEducational innovationHigher educationMachine learningOpen education

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Area of Science:

  • Educational Technology
  • Data Science
  • Machine Learning

Background:

  • Assessing open education competencies is crucial for effective learning.
  • Student perceptions offer valuable insights into their own competency levels.
  • Existing methods for competency assessment may not fully leverage data-driven approaches.

Purpose of the Study:

  • To investigate the feasibility of building machine learning models to predict open education competency.
  • To determine if student perceptions of knowledge, skills, and attitudes can serve as features for these models.
  • To classify students' open education competency levels using derived decision rules.

Main Methods:

  • Quantitative research approach analyzing data from 326 students across 26 countries via the eOpen instrument.
  • Application of Decision Trees and Random Forests machine learning models.
  • Derivation of decision rules from student perceptions to predict competence levels and analysis of prediction errors for bias.

Main Results:

  • Student perceptions of knowledge, skills, and attitudes/values related to open education provided satisfactory data for model building.
  • Machine learning models successfully predicted participants' competency levels.
  • Decision trees provided interpretable rules for competency prediction.

Conclusions:

  • Student perceptions are reliable predictors of open education competency.
  • Machine learning, specifically Decision Trees and Random Forests, can be effectively applied to classify competency levels.
  • The study validates the hypothesis that student-based data can inform accurate competency assessments in open education.