From Novice to Expert in Cloud Physics: a Graph-Based Analysis of Learner Understanding

TL;DR

This study tracks how learners' understanding of cloud physics develops from basic water cycle concepts to detailed microphysical processes, using graph-based network analysis to inform teaching strategies.

Contribution

It introduces a novel graph-based analytical approach to model and understand the evolution of conceptual knowledge in cloud physics education.

Findings

Learners progress from general water cycle concepts to detailed cloud microphysics.

Network analysis reveals key epistemological shifts during learning.

Expert triangulation confirms the validity of the knowledge evolution model.

Abstract

Understanding how learners conceptualise complex scientific systems remains a key challenge in geoscience education. We investigate the evolution of conceptual understanding of cloud physics among 153 learners, ranging from bachelor students to disciplinary experts and representing diverse academic backgrounds across STEM. To do so, we trace how knowledge structures evolve over time using metrics from a cross-sectional network analysis. The analysis characterises the quantitative and qualitative dimensions of the epistemological shift that learners experience as they mature in their understanding of the discipline. We show that in their description of the life-cycle of a cloud, they progressively transition from the general physics of the water cycle to detailed descriptions of cloud microphysical processes. A triangulation of data sources with a panel of experts complements and confirms the analysis. The results can assist lecturers in structuring their teaching towards higher levels of understanding and enable students to anticipate the key complexities and conceptual challenges in the field during their learning process. Furthermore, the generic nature of the analysis can be transferred to a wide range of disciplines.