A Sentence-Embedding-Based Dashboard to Support Teacher Analysis of Learner Concept Maps

Abstract

Concept mapping is a valuable method to represent a domain of knowledge, also with the aim of supporting educational needs. Students are called upon to construct their own knowledge through a meaningful learning process, linking new concepts to concepts they have already learned, i.e., connecting new knowledge to knowledge they already possess. Moreover, the particular graphic form of a concept map makes it easy for the teacher to construct and interpret both. Consequently, for an educator, the ability to assess concept maps offered by students, facilitated by an automated system, can prove invaluable. This becomes even more apparent in educational settings where there is a large number of students, such as in Massive Open Online Courses. Here, we propose two new measures devised to evaluate the similarity between concept maps based on two deep-learning embedding models: InferSent and Universal Sentence Encoder. An experimental evaluation with a sample of teachers confirms the validity of one such deep-learning model as the baseline of the new similarity measure. Subsequently, we present a proof-of-concept dashboard where the measures are used to encode a concept map in a 2D space point, with the aim of helping teachers monitor students’ concept-mapping activity.