Pedagogical Innovation in Teaching Data Analysis in Engineering Through Project-Based Collaborative Learning and Design Thinking

This study addresses the challenge of teaching data analysis and introductory machine learning in higher education, specifically targeting Systems Engineering students with limited prior experience in these areas. An innovative teaching approach was introduced in the Elective II: Data Analysis course at the University of La Guajira, Colombia. The methodology consisted of three main stages: (1) designing the pedagogical innovation and reviewing the literature, (2) implementing innovative teaching and learning strategies, and (3) evaluating the results of this teaching sequence once it has been implemented. Students collaborated in teams to analyze real mining datasets sourced from Colombia’s open data portal, utilizing tools such as Google Colab, Power BI, and Tableau, while applying machine learning algorithms such as K-means and a priori. Incorporating Design Thinking shifted the focus onto student needs, fostering iterative and creative problem-solving. The results demonstrated increased student engagement, alongside the development of analytical skills and data-driven decision-making capabilities. This innovative educational model not only strengthened technical competencies but also offered meaningful opportunities for students to apply their knowledge in authentic contexts, highlighting the effectiveness of student-centered and creative teaching methods in engineering education.