Semantic knowledge networks as identification of learning problems for postgraduate Biostatistics

Abstract

The cognitive relationship that the student makes about statistical problems in postgraduate studies in the health area is stimulated by triggering questions, meaningful materials and brainstorming directed by a self-aware and critical teacher of the forms of teaching practices. The study analyzes the process of learning the subject of biostatistics in a meaningful-learning technique that, through semantic networks of knowledge, intrinsically discovers what determines the problems for the understanding and appropriation of new knowledge. The use of semantic networks allows the responses to learning problems to be graphically represented through categories that identify those errors that limit learning. The objective of the study is to detect learning problems in biostatistics subjects at postgraduate levels in the health area of ​​the Juarez University of the State of Durango, Mexico. A qualitative methodology was used with a critical interpretive approach to action-research. The results were the obtaining of semantic networks of knowledge on recorded audios and focal interviews with graduate students during the years 2019 to 2023 2023 (before, during and after the SARS-COV 2 pandemic) using the Atlas.ti software, the results revealed several problems, such as the lack of contextualization of the subject in students' everyday situations, the lack of interdisciplinarity and the lack of significant materials for learning. It is concluded that semantic knowledge networks identify areas of opportunity in understanding the teaching and learning process for biostatistics topics.