Environmental Data-Driven Inquiry and Exploration (EDDIE) modules engage students in analysis of data collected by networks of environmental sensors, which are used to study various natural phenomena, such as nutrient loading, climate change, and stream discharge. We compared two approaches to EDDIE module implementation in an undergraduate time-series analysis course. Course goals were to use high-frequency and long-term environmental datasets to improve quantitative literacy, develop data manipulation and analysis skills, construct scientific knowledge about natural phenomena, highlight the inherent variability in real data, and develop informed views about the nature of science (NOS). In both instructional treatments, students explored data and developed skills through a scaffolded in-class analysis and then solved more complex problems in homework assignments. In Treatment 1, engage and explore lesson phases involved discussion of instructor-prepared plots using the think--pair--share method. Conversely, in Treatment 2's engage and explore lesson phases, students prepared graphs and completed activities in a computer lab, which required more guidance in data manipulation and thus contained less structured discussion of data analysis and interpretation. We administered a pre/postquestionnaire to compare learning gains between the two treatments in quantitative literacy, statistical reasoning, nature-of-science (NOS) understanding, and understanding of seismological concepts. Results indicate that EDDIE modules are sufficiently flexible to be effective in both learning environments. Our results indicate that students reacted similarly to both instructional treatments, suggesting that EDDIE modules are flexible enough platforms to achieve measurable learning gains in a variety of pedagogical environments.