In a recent study, we explored how students’ conceptual understanding develops throughout a digitally supported chemistry unit on chemical kinetics. Working with approximately 300 upper-secondary students, we combined automated scoring of students’ written responses and other learning artifacts with network analysis techniques to model the growth of individual knowledge structures over time. The results showed that transformer-based language models can reliably identify and score chemistry-related knowledge elements from classroom data, enabling the construction of longitudinal knowledge networks that reflect how students connect scientific concepts. Several network characteristics were associated with students’ posttest performance, suggesting that these representations capture meaningful aspects of learning progress. The findings highlight the potential of combining automated assessment and learning analytics to provide teachers with real-time insights into students’ developing understanding and to support adaptive instructional decisions in regular classroom settings.
Bernholt, S., Lossjew, J., & Gombert, S. (2026). Analyzing students’ conceptual understanding over the course of a teaching unit: Tracking changes in knowledge structures over time. Unterrichtswissenschaft. doi:10.1007/s42010-026-00244-0
Link: https://doi.org/10.1007/s42010-026-00244-0