Concept maps for formative assessment: Creation and implementation of an automatic and intelligent evaluation method

Formative assessment is about providing and using feedback and
diagnostic information. On this basis, further learning or further teaching should
be adaptive and, in the best case, optimized. However, this aspect is difficult to
implement in reality, as teachers work with a large number of students and the
whole process of formative assessment, especially the evaluation of student
performance takes a lot of time. To address this problem, this paper presents an
approach in which student performance is collected through a concept map and
quickly evaluated using Machine Learning techniques. For this purpose, a
concept map on the topic of mechanics was developed and used in 14 physics
classes in Germany. After the student maps were analysed by two human raters
on the basis of a four-level feedback scheme, a supervised Machine Learning
algorithm was trained on the data. The results show a very good agreement
between the human and Machine Learning evaluation. Based on these results, an
embedding in everyday school life is conceivable, especially as support for
teachers. In this way, the teacher can use and interpret the automatic evaluation
and use it in the classroom.