Utilizing Learning Analytics in Radiology: A Pilot Study of an e-Learning Platform in Medical Student Education.

RATIONALE AND OBJECTIVES: Learning analytics is a rapidly advancing scientific field that enables data-driven insights and personalized learning experiences. However, traditional methods for teaching and assessing radiology skills do not provide the data needed to leverage this technology in radiology education.

MATERIALS AND METHODS: In this paper, we implemented rapmed.net, an interactive radiology e-learning platform designed to utilize learning analytics tools in radiology education. Second-year medical students' pattern recognition skills were evaluated using time to solve a case, dice score, and consensus score, while their interpretation abilities were assessed through multiple-choice questions (MCQs). Assessments were conducted before and after a pulmonary radiology block to examine the learning progress.

RESULTS: Our results show that a comprehensive assessment of students' radiological skills using consensus maps, dice scores, time metrics, and MCQs revealed shortcomings traditional MCQs would not have detected. Learning analytics tools allow for a better understanding of students' radiology skills and pave the way for a data-driven educational approach in radiology.

CONCLUSION: As one of the most important skills for physicians across all disciplines, improving radiology education will contribute to better healthcare outcomes.