The thesis is about the language of science, specifically biology, from the perspective of relational content analysis of biology textbooks and students’ representations. In this exploratory study, we identified gaps and problems in the nature and usage of predicates used for connecting concepts and objects in sentences. To address these gaps, we designed and developed a method of re-representation with iterative refinements focusing on the semantics of predicates, inspired by semantic network-based knowledge representation, concept mapping, representational redescription, and systemic functional linguistics as theoretical frameworks. The method was validated by using well-defined predicates from published open biomedical ontologies and library of generic concepts. The method was
used for modelling biology language in terms of using predicates for describing structures, processes, variable properties, experimental procedures, and explanations. This resulted in curating a Reference Set of predicates i.e. relations, attributes. This Set was used for the relational content analysis of cell biology passages from six textbooks as well as students’ representations. From this exploratory study, our findings are interesting for unraveling the nature of biology knowledge. We also conducted feasibility, efficacy studies to demonstrate how the method may be used by suggesting a constrained concept mapping method derived from the work. We performed a proximity study to demonstrate how the method can be used as an indicator of expertise. The thesis highlights the importance of
developing rigor by reducing ambiguities, and concludes by drawing implications of the work for research in biology education and science communication in general.