2023

Mailagaha Kumbure Mahinda; Stoklasa Jan; Luukka Pasi; Tarkiainen Anssi; Jantunen Ari

To efficiently manage and (re)design the education events, it is necessary to determine what their effect on student’s learning is. Even though there are tools to reflect the level of knowledge of students on the output, the usability of such information for course design and optimization is limited. This paper presents an open-access set of tools that enable the analysis of the effects of learning events on the cognitive structures of their participants represented by cognitive maps. This unsupervised learning-outcome assessment approach operationalizes learning as changes in cognitive diversity, presence and strengths of causal relationships, etc., or robustness thereof.