This study aims to systematically evaluate the use of social network analysis (SNA) metrics to measure eye-tracking behavior to assess and predict student learning performance. We integrated 11 network metrics from published research and tested them on six eye-tracking datasets. Our preliminary results indicate that no consistent predictor variable can effectively predict student performance across different datasets. The number of nodes, edges, reciprocity, and entropy measures contribute differently to predicting students’ performance. This work deepens our understanding of how different SNA metrics relate to eye-tracking data and advances the methodological framework to predict learning outcomes.