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Digital transformation in education has created the need to train teachers and improve their digital competencies. However, training processes are often ineffective due to the lack of personalization in the proposals and the variety in teacher profiles. Given this, this article addresses the development of a web tool designed to diagnose teachers’ digital competencies and provide personalized recommendations for MOOCs to support the development of their skills. To achieve this, a system that uses Natural Language Processing (NLP) techniques and a content-based recommendation model is proposed. Course data is obtained through web scraping and processed with NLP techniques, using an advanced language model (BERT) to generate vector representations of the courses and competencies. The web system allows teachers to self-assess their competencies, visualize the results, and receive personalized recommendations for MOOCs. The implementation of this system has proven effective in identifying teachers’ areas for improvement and suggesting relevant courses for their professional development. The results suggest that the use of this system can be an effective solution for training teachers in digital competencies.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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