As remote and hybrid learning environments become increasingly prevalent, the ability to detect, monitor, and predict student attention in online learning has grown in importance. While traditional classroom settings offer environmental and behavioral cues that support attentional regulation, these are often diminished or absent in online formats. This study investigates the potential of physiological signals as objective indicators of attention in simulated online learning contexts, drawing on data from fourteen participants across four experimental conditions. These conditions mimic varying degrees of distractions to evaluate how attentional states manifest in measurable physiological patterns. The analysis included features such as heart rate, body temperature, eye movements, head pose, and different limb motions, which were collected and extracted through a dual-modality approach, integrating both contact-based (sensors) and non-contact (camera/software) measurements. The purpose of this study is to determine how features differ between focused and distracted learning and among different types of distraction. The results identify statistically significant variations in several physiological indicators across conditions, supporting their relevance for attention modeling. This work contributes to the growing body of research advocating for non-invasive attention monitoring methods in educational settings and aims to provide the groundwork for the development of machine learning models capable of predicting attention states based on physiological data.