Abstract:

Analytics has been in focus on education in the recent years. The majority of the work has been on study and learning analytics where the actions and the results come from students. The data is collected in the learning environment and analyzed to track the learning and the progress. Based on this, for example, needs for intervention from the teacher for individual students can be identified. In addition to the study and the learning analytics, other areas of analytics such as environmental, biometric, and behavioral have also been identified.

However, there are still additional related areas of analytics applicable in education. For example, while current analytics focus on the actions of the student, they focus less on the actions of the teacher and the interactivity between the student and the teacher. For example, the workflow and teacher's activities related process and teaching analytics have not been studied in educational setting to the same extent as study, learning, environmental, biometric, and behavioral analytics.

In this sense, it can be argued that the current focus on analytics in education is mainly on the low hanging fruits (quick benefits) at the expense of the whole. Also, compared with other domains, the teaching process is typically not that well - or even at all - defined, which may be one of the reasons behind the current focus. Therefore, there is a need for a clearer picture of the education related analytics such as for a holistic framework describing the taxonomy of education analytics and providing insight on the scope and applicability of different analytic areas.

In this paper, we present a model for education analytics with a taxonomy of different areas of related analytics. In addition, we discuss the scope and applicability of the different areas. Our focus in this paper is on minimum viable analytics that should be implemented first, but we also present ideas for the next steps after achieving the minimum viable level. We base this part on existing literature of different areas of analytics from which we form a synthesis, the education analytics model, as a result.

Moreover, for the minimum viable analytics, we present an ongoing case study in K12 level schools where developing education analytics is a part of digitalization of education and its processes in a developing economy. The case is described, and early case experiences and observations are analyzed providing a practical example on applying the education analytics model when taking the first steps towards building a minimum viable education analytics. The case also illustrates the importance of the minimum viable approach, since the case was started just before the COVID-19 pandemic that resulted in a disruption where the digitalization overall needed to be implemented very quickly due to the education moving into remote work and distance learning.

Keywords:

Digitalization, education analytics, learning analytics, developing economy, K12 level schools, case study.