Fostering Scientific Skill Development Through Interactive Classification of Celestial Bodies

Developing scientific literacy in primary education can be challenging. Teachers often lack the time, scientific domain knowledge, and capacity to provide individualised support and guidance during hands-on science activities. As a result, students may struggle to interpret observations and draw accurate conclusions. To address this, we designed a feedback-enhanced lesson and investigated students’ performance and engagement with optional feedback while developing classification skills using the Solar System as context. Developed within the Minds-On application, the lesson combines hands-on activities with a knowledge-based artificial intelligence approach in which encoded domain knowledge enables automated checking of students’ classifications and error-triggered feedback. Students classify celestial bodies by shape, composition, and orbit using a three-level classification tree. A classroom evaluation with 164 students (ages 10–12), using student-performance and interaction-log data, showed that most successfully engaged with the tasks and navigated their increasing complexity. Performance and interaction data indicated that task difficulty peaked at Level 2, while error rates and engagement decreased at Level 3. Although feedback was available to 89 students following an error, only 25 consulted it at least once, and use was fragmented across classification levels. The small and inconsistent feedback-user subgroup therefore does not allow strong conclusions about feedback effectiveness but provides insight into patterns of feedback uptake and interaction.