Srovnání různých přístupů k procesu výuky pomocí analýzy dat z Moodle

Abstract

The paper builds on previous work that dealt with the automatic generator of parameterized tasks and tests. In these works, the author focused mainly on the issue of automatic generation of parameterized tasks in the field of data science. A key problem in creating tasks of this type is the generation of source data and especially their storage and subsequent access in various systems and formats. This contribution is an innovation of previously used procedures and an extension of the possibilities of working with synthetic data files. The innovative data storage system uses cloud storage for its work and thus simplifies the work of the generator user when generating tasks that also contain the statistics data stored in the data file. No knowledge of cloud technologies is required to generate these tasks. In this solution we can work with the statistics tasks containing data files not only in format of selected LMS (for example LMS Moodle), but we can work with these tasks published in PDF format, where the data is represented by a link to the cloud storage. The contribution is more technical and contains the procedures and codes needed to work with cloud storage in both directions – data storage and data retrieval. These procedures also include instructions that allow you to create and use cloud storage. An integral part of the solution is also the design of the administration system of stored data and their periodic cleaning