A Word Embeddings Based Clustering Approach for Collaborative Learning Group Formation

Today, collaborative learning has become quite central as a method for learning, and over the past decades, a large number of studies have demonstrated the benefits from various theoretical and methodological perspectives. This study proposes a novel approach that utilises Natural Language Processing(NLP) methods, particularly pre-trained word embeddings, to automatically create homogeneous or heterogeneous groups of students in terms of knowledge and knowledge gaps expressed in assessments. The two different ways of creating groups serve two different pedagogical purposes: (1) homogeneous group formation based on students’ knowledge can support and make teachers’ pedagogical activities such as feedback provision more time efficient, and (2) the heterogeneous groups can support and enhance collaborative learning. We evaluate the performance of the proposed approach through experiments with a dataset from a university course in programming didactics.