engleski

Application of K-means clustering algorithm for analysis of LMS content transformations caused by the COVID-19 pandemic

In the first half of 2020, due to the Covid-19 pandemic, educational institutions worldwide had to close their doors to students ; learning in the classroom was not possible due to the growing virus infection. Depending on previous experience and infrastructure, institutions have more or less successfully switched to online teaching. This paper presents a method that, from the data in the report of the administrator of one of the most popular LMS systems, Moodle, can bring new knowledge about this transfer on the Moodle system. It uses the k-means algorithm, which is used to divide courses into clusters depending on the content available in each course on the LMS. To analyse this transformation, a comparison was made of the number and content of clusters from the data of the winter semester of the academic year 2019/2020, with the winter semester of the academic year 2020/2021.

course transformation ; COVID-19 ; LMS ; k-means ; Moodle

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