Abstract:

Demand of professional competences in the field of digital skills (either transversal or specific) is growing within biomedical sciences. Indeed, acquiring such knowledge would enhance the opportunity of students to enter the labor market. Therefore, there is an urgent need of including digital competences in the academic curricula during undergraduate and postgraduate degree studies. Specifically, in the framework of life sciences (biology, biochemistry, biotechnology, medicine and related biomedical areas), software for the analysis of images obtained from histological/histopathological samples have become crucial and indispensable tools. However, the students do not usually receive any training about how to deal with these specific tasks, and there is no coordination among different subjects concerning this topic. As a result, students are unaware of the most important and suitable software for their professional profile, and are unfamiliar with these procedures. For this reason, we will include the use of the free software Image J for image analysis, and EXCEL for the subsequent data processing and graphing, in our histology classes.

Our aim is to assess the students’ self-perception concerning digital skills through self-evaluation using a pre-test/post-test design. Moreover, we propose a methodology based on a classification in different levels that would somehow certify the extent of acquisition of specific digital competences. Such levels would be equivalent to the “common European framework for language competences”. Once the student has satisfactorily succeeded specific courses will be granted with a digital badge, indicating the acquired level in each specific digital competence. For example, the course “Organology’’ (human microscopy anatomy), in the second semester of the 2nd year of Biochemistry, would grant an A1 level in Image J software, and database treatment of the generated data would grant a A2 level in Excel.

Keywords:

Digital skills badge, Vertical and horizontal coordination, Image analysis.