Using machine learning to examine preservice teachers' perceptions of their digital competence

Abstract

This research paper's aim was to investigate both the pre-service teachers' perceptions of their digital competence and if gender, type of the bachelor's degree and age made the opinions different or not. To do so, the clustering analysis method was employed to analyze the areas and items of the digital competence questionnaires used as a data collection technique. The study group included 291 participants who are now teacher-trainees in Draa-Tafilalte Regional Teacher-Training Center in Errachidia and Ouarzazate in the south-east of Morocco. In so doing, a number of results attained and basically confirmed that the level of the teacher-trainees' digital competence was low or weak and that the three parameters (the type of the bachelor's degree, gender and age) played a significant role in shaping different opinions on their digital competence. This paper ended with some major implications that were drawn from the findings of this study in our bid to highlight some needs of the pre-service teachers that should be met in their training courses and related activities to develop their digital competence in teaching and learning process.