Engaging Secondary Students in Data Science Learning Through a Mathematical Logic Approach: Insights from Surveys and Log Data

Data science education is becoming increasingly important in both K–12 and higher education. However, due to its interdisciplinary complexity, providing a high-quality and accessible open curriculum for K–12 students remains a challenge. Although many data science curricula have been developed for higher education, their topics and prerequisite knowledge often make them inappropriate for K–12 learners. As a critical pathway for preparing students with relevant skills, data science education for K–12 should be built on a rigorous foundation that integrates mathematics, statistics and programming. In this study, we designed and developed a logic-based data science curriculum aimed at engaging secondary school students through online learning and the use of student-relevant examples. We implemented a one-week pilot version consisting of six lessons and recruited 110 self-selected participants. To investigate student engagement and learning behaviors, we collected pre-surveys, end-of-lesson surveys, quiz responses, post-surveys, and learning management system (LMS) activity logs. Findings suggest that: (1) students generally engaged with the logic-based data science curriculum; (2) students from diverse academic and cultural backgrounds reported broadly similar experiences with the curriculum; and (3) entropy-based behavioral measures helped describe students’ LMS activity patterns, although average entropy was not significantly associated with quiz performance and the negative association between entropy CoV (coefficient of variation) and quiz performance was on the border of significance. This study offers preliminary empirical insights into the design, development and implementation of data science education for K–12 learners.