Neighbourhood Analysis to Foster Meaningful Learning Using Concept Mapping in Science Education.
Year: 2014
Type: article
Abstract: One critical aspect that hinders the systematic use of concept mapping in everyday classrooms is the difficulty of providing high-quality feedback to students so as to keep improving and revising their concept maps (Cmaps). The development of an innovative way to analyse, at a glance, students' Cmaps is presented to allow a diagnostic assessment of student conceptual learning. This paper proposes Neighbourhood Analysis (NeAn) as an innovative way to foster meaningful learning through the identification of Limited or Inappropriate Propositional Hierarchies (LIPHs) in Cmaps. The instructional strategy that underlies NeAn involves the selection of a compulsory concept (CC), which must be a threshold concept addressing the Cmap's focal question. The goal is to estimate students' understanding of the subject, in order to detect the presence (or absence) of LIPHs. We analysed 69 Cmaps from a higher education setting and found that 175 propositions were related to the CC. This subset of all propositions was enough for instructors to provide specific high- quality feedback to their students, even under normal teaching conditions. NeAn is a straightforward way to identify LIPHs allowing instructors to make diagnostic assessments of students' conceptual outcomes during the learning process, and to track the effects of their instructional options. NeAn can be meaningfully applied for assessing science students at secondary level and above, with special potential applicability across science subjects.
Source: Science education international
Institution: Universidade de São Paulo
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W2434855229