Improving the quality of Physical Education: a teaching behaviour-based study protocol using artificial intelligence
DOI:
https://doi.org/10.47197/retos.v72.116921Keywords:
circumplex approach, motivation, natural language processing, teaching behaviours, supervised machine learningAbstract
Introduction: Over the last few decades, the analysis of quality in the physical education (PE) context has received remarkable attention, self-determination theory being one of the most successful theoretical perspectives in explaining teacher-student interactions. The aim is to present an artificial intelligence-based study protocol to automatise the identification of teaching behaviours, enhancing educational research oriented to improving PE quality.
Method: Eight different teaching behaviours are classified using natural language processing techniques. A data set is generated containing transcriptions of voice recordings from numerous PE lesson extracts, coded by PE experts. These data are used to train different machine learning algorithms so that teaching behaviours can be automatically identified and labelled.
Results: Algorithms tested will be assessed through different metrics such as accuracy, precision, recall, and F1-score for each teaching behaviour to be predicted. The findings are believed to provide a promising tool to improve educational research, which will, in turn, favour the quality of PE teaching behaviours.
Discussion: The analysis of teaching behaviours and students’ outcomes has traditionally relied on self-reported questionnaires and external observation. While valid, these practices are highly time- and resource-consuming, acting as a barrier to sustaining certain projects aimed at improving educational practices. This study protocol seeks to overcome such limitations.
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