Design and simulation of a predictive model for the evaluation of teachers' digital competence using Machine Learning techniques
DOI:
https://doi.org/10.21556/edutec.2024.89.3201Keywords:
teachers' digital competence, machine learning, artificial intelligence, adaptive learning, emerging technologyAbstract
Machine Learning (ML) is a field of artificial intelligence that uses techniques to make predictions from massive data. Teachers’ Digital Competence (TDC) commonly refers to teachers' skills and abilities in digital systems and their application in teaching and learning processes. TDC research is important for institutions, since student learning, trajectory, direction, and behavior depend on its evaluation. TDC in Colombia is based on 5 elements: Communicative, management, investigative, pedagogy and technology, and each of them is measured at three levels: exploratory, integrative, and innovative. The research questions are: (1) What kind of results can we expect from CDD prediction with ML techniques? (2) What ML techniques are effective in predicting TDC? (3) What are the advantages of predicting TDC with ML techniques? The methodology aims to design a prediction model of TDC in Colombia through the application of 9 ML techniques using Orange Data Mining software. The results show the high effectiveness of intelligent techniques to predict TDC. The model demonstrates that it is feedbackable, scalable and allows proposing personalized learning itineraries.
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