The deep analysis of Concept Maps (Cmaps) may reveal latent information that is not perceived from the simple reading of its propositional network. This paper proposes the Neighborhood Analysis (NeAn) as an innovative way to assess the Cmaps obtained in a classroom. The selection of a Compulsory Concept (CC) allows teachers to evaluate how the students relate it to other concepts, which are classified as Neighbor Concepts (NCs). The propositions established between the CC, and the NCs are enough to indicate the students' level of understanding on the mapped subject. Cmaps (n = 69) about climate change are the first set of empirical data that confirms the potential of NeAn. Dispersion was selected as CC in order to check whether students can relate this physical phenomenon with the global perspective of climate change. The patterns found from the NeAn suggest that, despite being exposed to the same didactic activities, some students could not use the CC properly. This may be explained from David Ausubel's learning theory, which stresses the critical role of prior knowledge in the assimilation process of new information.
climate change; compulsory concept; concept maps; dispersion; higher education; neighbor concept; neighborhood analysis; science teaching
