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Assessing Digital Competence Through Teacher Training in Early Education Teachers

Abstract:

Training programs on digital competences for teachers are often carried out without a thorough diagnosis, so it is difficult to accurately identify which digital competences need to be developed in teachers. Best-case scenario, questionnaires are proposed as the only diagnostic tool, and the answers are often based on the subjectivity of respondents which inevitably led to contradictions as revealed by the literature. In this study, a machine learning algorithm has been used to determine the teachers’ needs in terms of detecting which and how much they need to improve their digital competences. 181 teachers took a 4-week-virtual course containing two modules (Content Creation and School Management) to assess specific teacher competences. Results showed that the type of question used in the evaluations influences the academic performance of the participants. Data were analyzed based on 1) three types of questions (matching, multiple choice and true/false) and 2) scores obtained by the participants at the end of the course. Firstly, matching questions reflect a greater distribution in the results, which indicates that this type of question may be more challenging, followed by the multiple-choice questions. While true or false questions should not be included as they leave too much room for chance. Secondly, performance level reflects that demographic information (e.g. age, type of institution, and level at which they teach) is associated with final performance of digital competences. This type of analytics can provide a much more detailed overview in terms of detecting strengths and weaknesses in the development of digital competences of teachers.