Abstract

Statistics teaching in Sahel region universities and professional training institutions often relies on a theoretical approach centred on mathematical formulas and statistical software. Although this approach enables learners to acquire technical knowledge, it does not always foster a thorough understanding of data, its context, or the purpose of the statistical methods being used. In professional training programmes, the practical application of statistical knowledge generally takes place during tutorials, using datasets that may not reflect the realities of the learners’ fields of study, or during internships. This separation between theory and practice can lead learners to focus on the mechanical application of formulas at the expense of interpreting results and assessing their relevance to the situations under investigation. As a result, a gap may emerge between the information contained in the data, the statistical results obtained, and their interpretation.
To address these limitations, we have been developing a data-driven pedagogical approach for several years, placing data understanding at the centre of the learning process. This approach starts with a real-world situation, encourages learners to explore the data and formulate relevant questions, and then guides them in identifying and applying statistical methods suited to the objectives of the analysis. It aims to make statistical tools more meaningful, strengthen learners’ analytical and interpretative skills, and facilitate the transfer of knowledge to real-world professional contexts.
This paper presents the principles of this approach, its implementation, and the outcomes of our efforts to improve learners’ conceptual understanding, statistical reasoning, and autonomy.