No abstract

One of the problems connected with the development of meta-subject skills in school students is the lack of publicly available educational materials with a focus on such skills. A possible solution is the formation of a collection of meta-subject materials stored in the library of learning scenarios of the Moscow Electronic School — now there are over 40,000 learning scenarios that have undergone moderation. This article presents the results of research that identified a cluster of teachers most inclined to create meta-subject scenarios and suggested recommendations for motivating teachers to create such scenarios. To achieve this purpose, a sample of authors of such scenarios published by the Moscow Electronic School were analyzed and clustered with the help of machine learning methods. As a result of this work, a gradient boosting algorithm was developed, which produced the best results. The clusters of users described as a result of the application of the algorithm followed five main behavior strategies in terms of the activity related to the creation of new scenarios. Teachers that are most likely to create meta-subject scenarios show interest in their colleagues’ scenarios not only in their subject but also in other academic disciplines taught at school, willingness to copy and customize them. To develop teachers’ readiness for the creation of meta-subject scenarios, it is recommended to conduct teacher training including their introduction to the best practices of developing such scenarios presented by the Moscow Electronic School. The research results are used in the development of a recommender system enabling easier search and navigation among the scenarios published by the Moscow Electronic School.

Problem and goal. Building statistical, mathematical, computational and research literacies in teaching school subjects is discussed in the article. The purpose is to develop a model for generating data for research experiments by students. Methodology. The Netlogo data generation and consecutive statistical data procession in CODAP and R programming language were used. Results. The generative approach helps students to work with data collected by agents, programmed by students themselves. In doing so, the student assumes the position of a researcher, who plans an experiment and analyses its results. Conclusion. The proposed approach of data generation and analysis allows to introduce the student to the contemporary culture of generating and sharing data.