Typology of schools operating in the Moscow Electronic School system based on the analysis of network indicators

Abstract

Network analysis methods are actively used to research the behavior of digital repository users who utilize and create digital objects. At the same time, the research into the collective behavior of a group of participants who are members of the same school is much less common. The library of the Moscow Electronic School is a rather complicated system with multiple roles offered to users. The actors of the repository are teachers, students, parents, and publishers – anyone performing any actions with the objects. In this study, the school is seen as an actor performing actions with objects – lesson scenarios within the Moscow Electronic School repository of digital objects. Within the study, the authors compare the sociograms of schools that unite teachers and the scenarios created by the teachers and divide schools into factions based on network indicators in sociograms. The main method of presenting and analyzing data is network analysis and sociogram creation. The authors identify two types of networks: the network of single participant’s relationships and the network of relationships of teachers from a single school. The authors not only describe the data structure in the Moscow Electronic School system that records the digital trace of every individual and collective user but also create a digital map that reflects the dynamics of actions in the Moscow Electronic School system and identify the indicators that characterize the common activity of key participants. Moreover, the authors identify graph factions for schools that characterize the degree of interaction between teachers: disconnected groups, sparse graphs, crystallization centers, dense graphs.

Data farming for virtual school laboratories

“…Например, данные электронного журнала российского вики-проекта Lepisi.org постоянно доступны для визуализации в виде социограмм, представляющих отношения участников и статей, которые они совместно редактируют [14]. Этот же подход ис-DIGITAL EDUCATIONAL ENVIRONMENT пользуется для визуализации отношений между учителями и учениками в Московской электронной школе [15]; -данные интернета вещей (remote and embedded networked sensing)развитие технологий интернета вещей позволяет получать данные, генерируемые устройствами, подключенными к Сети. Так, проект с участием школьников, реализованный Concord Consortium, показал применимость формируемых таким образом датасетов в преподавании школьного курса биологии [16; 17];…”