Developing the competence of educational data analysis for future physics teachers

Introduction. Currently, projects and research in almost all spheres, including economics and education, are implemented in the context of analysing and managing big data. Understanding the core of data mining and its objectives serves as a criterion of future physics teachers’ professional efficiency and their competitive advantage in the labour market. The purpose of the study is to develop a methodological system for training future physics teachers towards the proper analysis of educational data, including targeted, substantive, procedural and diagnostic blocks. Methods. The development of the methodological system was implemented in reverse engineering logic, from formulating the envisaged results to designing the objectives and content. When creating the diagnostic toolkit, the structure of the competence targeted at educational data analysis was considered as a set of three components: cognitive (knowledge), activity-based (skill), and integrative (practical experience). The study involved 70 students of the Physics Faculty of Perm State Humanitarian and Pedagogical University (Russian Federation) majoring in “Pedagogical Education” (with two training profiles), where “physics” is one of the training profiles. The respondents were also represented by the participants of the qualifying stage of the V Regional Academic Competition for physics teachers who were tested remotely (110 pedagogues). Results. The semantic analysis made it possible to develop the structure of the educational data analysis competence and to prepare control and assessment materials for diagnosing the competence formation level. The process of testing the diagnostic toolkit revealed that physics teachers do not pay sufficient attention to educational analytics and interpretation of training data. The decomposition of the data analysis competence made it possible to design targeted, substantive and procedural blocks of the methodological system. The experimental work on the realisation of the methodological system demonstrated the development of specific cognitive and integrative components of the educational data analysis competence in future physics teachers. The sign test empirical value at 1% significance level is below the critical value for the cognitive component of the KEYWORDS competence (Gemp< Gcrit , 8<17) and is equal to the critical value for the integrative component (Gemp=Gcrit , 10=10). The overall positive shift in the activity-based component of the competence, derived in the course of the experiment, proved to be statistically unreliable (Gemp> Gcrit , 21>13). This once again proves the need to include the discipline “Methods of quantitative and qualitative data analysis” in training programmes for future physics teachers, which discipline was never delivered to the students who participated in the diagnostics. Conclusion. The methodological system forms a basis for designing a cycle of curriculum disciplines for the systemic formation of future physics teachers’ competences in the sphere of educational data analysis. The diagnostic block of the methodological system can be used to identify pedagogues’ professional shortfalls and build individual professional development educational trajectories.