In this paper, I examine the use of Epistemic Network Analysis (ENA) for modeling multimodal interactions in learning contexts and outlines three key considerations: (1) operationalization and segmentation of multimodal events, (2) modeling approaches, and (3) interpretations of shared ENA spaces that integrate codes across modalities. These considerations are illustrated through two empirical case studies involving speech, gestures, and digital traces. By offering methodological guidance for applying ENA to multimodal data, this work advances both Quantitative Ethnography and Multimodal Learning Analytics, refining analytic techniques and providing theoretical insights into learning as a temporally entangled multimodal process. Ultimately, it contributes to more accurate modeling and understanding of learning processes and improved instructional support in educational practices.