Integrating Philosophy of Science Into Research Skills and Scientific Writing Education: The Data (Do Not) Speak for Themselves

Abstract

This dissertation aimed to provide insights into what constitutes effective training for biomedical bachelor’s students to become critical and independent academics. Specifically, this training should address the changing landscape of science, where data are increasingly large and complex, knowledge within a discipline expands exponentially, and the variety of data analysis techniques has become too extensive to cover in a degree program. To achieve this objective, this dissertation addressed the following research question with an educational design research approach: How can we prepare biomedical bachelor’s students for conducting rigorous research with increasingly complex and large datasets? Through an exploration of current approaches to biomedical data science education, I identified four dimensions of learning to conduct rigorous research. I concluded that current educational designs for data science learning primarily focus on the two lower dimensions of learning to conduct research (applying techniques and making choices based on flowcharts) and that they do so in non-integrated manner. This narrow focus on technical aspects risks fostering questionable research practices due to insufficient understanding of the implications behind methodological and data analysis choices (third dimension). To address this, I proposed an educational design for data science learning that is focused on statistical literacy and integrating the first three dimensions of learning to conduct scientific research. In addition, by integrating data science learning with courses focused on biomedical content, students’ motivation for rigorous research practices can be increased by showing the relevance of good data science understanding for biomedical sciences. An additional benefit of this integration throughout the bachelor’s program is that it creates spaced moments of repetition and a setting that is beneficial to scaffolded learning, which also seems to increase students’ motivation for data science learning and to keep their cognitive load germane. I further argued that greater emphasis should be placed on the fourth dimension of learning to conduct research—understanding the nature of science (NOS). Students might hold uninformed or unproductive views of NOS that could hinder learning to conduct rigorous research. Addressing these unproductive views might be more effective than focusing solely on technical aspects of conducting research. Students learn much about science by engaging with scientific inquiry and by being trained to become scientists in Dutch life sciences curricula. However, views of NOS rarely become more informed and sometimes even become less informed through engagement with scientific inquiry alone. Our research indicated that there are discrepancies between biomedical bachelor’s students’ explicit views of NOS and the epistemological notions conveyed through their writing practices. In addition, the students seemed to struggle with making sense of some epistemological ideas—such as the inferential nature of science—especially when they contrasted with the epistemological notions conveyed through scientific discourse. To better prepare biomedical bachelor’s students for conducting rigorous research, I argued that educational interventions should integrate all four dimensions of learning. These interventions should include explicit reflections on NOS and address discrepancies between epistemological notions conveyed through scientific language use and commonly accepted views of NOS.