Analyzing open-ended learner reflections can provide deep insights into students’ knowledge construction processes, yet these unstructured texts remain challenging to process at scale. In this work, we propose a context-aware graph-based approach to reveal knowledge construction patterns in learner reflections. By transforming reflections into Personal Knowledge Graphs (PKGs) with the assistance of large language models (LLMs), we extract motifs, regularly appearing substructures in graphs, to capture common patterns in how learners organize and connect knowledge. The experiments demonstrate that our approach effectively transforms learner reflections into interpretable motifs while preserving contextual relationships. Through clustering and regression analysis, we confirm correlations between motif structures and learning outcomes. Moreover, motif-based representations enable superior performance in both grade prediction and at-risk identification tasks compared to baseline approaches. This work emphasizes the potential of motif mining and analysis for understanding and supporting learning processes through reflection analysis.