Characterizing students’ energy learning trajectories

Enabling students to apply their energy knowledge to various everyday phenomena is one of the main goals of physics education. Understanding how and why some students achieve this goal and others not is crucial to adapt instruction in order to better support the majority of students. To achieve support, research suggests that it is not sufficient to solely focus on content knowledge, but also include affective and metacognitive variables. To better understand why some students are able to apply their energy understanding while others are not, we developed a ten-week-long instructional unit to collect fine-grained longitudinal data, not only on the energy understanding of students but also their affective and metacognitive characteristics. Using unsupervised machine learning, specifically a k-means longitudinal analysis, we were able to distinguish, from N = 165 students, three clusters based on students’ learning trajectories, represented by their energy knowledge network coherence. These three clusters were then analyzed on basis of affective and metacognitive variables. The analysis showed disparities in the accumulation of energy knowledge. These disparities were then be analyzed in greater detail by the trajectories of affective and metacognitive variables, mainly showing disparities in the perception of the instructional unit regarding emotions and cognitive load. These findings indicate that affective and metacognitive variables have an impact on the learning outcome of students, which can be used to design instructional units, that address the needs of all students.