Learning Trajectories Toward Energy Understanding

Energy literacy consists of two main components: energy knowledge and the ability to apply this knowledge, i.e. energy understanding. Developing energy understanding requires students to integrate energy ideas, such as energy transformation, into a well-integrated energy knowledge network. Research indicates, however, that students rarely develop such well-integrated knowledge during K-12 education. Individualized learning approaches can support this development through appropriate and student-centered learning opportunities. Investigating the individual development of energy understanding requires fine-grained longitudinal data. As part of this dissertation, such data were collected in the ALICE project (Analyzing Learning for Individualized Competence Development in Mathematics and Science Education). A ten-week, digitally implemented instructional unit was developed; the digital implementation enabled the analysis of students' artifacts and the subsequent reconstruction of learning trajectories. Across four publications, this dissertation examines how an instructional unit can be both engaging and used for assessment. Approaches for reconstructing and analyzing learning trajectories were evaluated. The trajectories were also characterized based on affective and metacognitive variables to inform future individualized feedback, and analyzed regarding students' ability to apply their integrated energy knowledge. The publications demonstrate that an engaging instructional unit developed with the ECD4LPA framework can also serve as an assessment tool. The evaluated reconstruction approaches showed promising results, particularly in identifying (un-)productive trajectories. Characterizing trajectories via affective and metacognitive variables suggests that comprehensive, individual AI-assisted feedback is feasible. The importance of well-integrated energy knowledge was also shown for applying energy, e.g. in socio-scientific argumentation. Overall, the publications illustrate the potential of Learning Progression Analytics - combining Learning Analytics with research on energy understanding - to systematically foster energy literacy.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Christian-Albrechts-Universität zu Kiel

Year

2026

Author dc:creator

• Wyrwich, Tobias

Contributors dc:contributor

• Kubsch, Marcus
• Neumann, Knut

• learning trajectories
• energy understanding
• knowledge networks
• machine learning
• learning progression analytics
• digital learning environment

Repository record source_url

https://macau.uni-kiel.de/receive/macau_mods_00008354

OAI identifier oai:identifier

oai:macau.uni-kiel.de:macau_mods_00008354