Physiological sensing technologies are becoming increasingly accessible, prompting educators and researchers to explore hands-on approaches for introducing K–12 students to physiological computing. While prior efforts have engaged students using sensor-based activities, few empirical studies have examined how outcomes differ between using real-time physiological hardware and pre-recorded biosignal data. This paper presents findings from a study in which students engaged in two sets of activities: one activity using a consumer-grade hardware physiological sensor (i.e., OpenBCI Ganglion) and the other using pre-recorded physiological data (i.e. muscle activity) to build simple applications. Our observations revealed similar outcomes and student engagement across both approaches. Notably, students reported increases in self-efficacy and confidence regardless of whether they worked with real-time or pre-recorded data. These findings suggest that hardware-free implementations may offer similar benefits when teaching time-series data and signal processing concepts. We discuss the implications of these findings and reflect on the benefits and constraints of incorporating physiological sensors in high school computing classrooms.