Cognitive biases in user experience and spreadsheet programming

Abstract The impact of cognitive biases, particularly biased self-assessment, on learning outcomes and decision-making in higher education is of great significance. This study delves into the confluence of cognitive biases and user experience in spreadsheet programming as a crucial IT skill across various academic disciplines. Through a quantitative analysis, we investigate whether structured learning in spreadsheet programming can counteract self-assessment biases among higher education students. Specifically, our focus is on scrutinizing the accuracy of self-assessment in Excel proficiency among professional STEM students at the University of Debrecen, Hungary, by comparing traditional written and digital assessments. Our findings reveal that while high-achieving students tend to exhibit more accurate self-assessments, many students have a pervasive tendency to overestimate their spreadsheet competencies. These results emphasize the necessity for educational strategies that acknowledge cognitive biases in self-assessment, with far-reaching implications for curriculum design and lean management in higher education, by integrating evidence-based approaches to enhance digital competencies. This study makes a valuable contribution to the broader dialogue on improving learning outcomes and user experience in spreadsheet programming. Additionally, the research provides valuable insights for educators and policymakers, advocating for pedagogical adjustments that can assist students in better evaluating their skills and knowledge, thereby promoting more precise self-assessment practices.