Principal Investigator: Vinod Lohani
CoPrincipal Investigator(s):
Organization: Virginia Polytechnic Institute and State University

Abstract:
This I-Corps L project will examine the potential of environmental monitoring modules informed by high frequency water and weather data from a small urban watershed for 9-12th-grade students. The Learning Enhanced Watershed Assessment System (LEWAS) is a real-time, high frequency (1-3 min.) water and weather monitoring system and its cyberlearning system is called the Online Watershed Learning System (OWLS). Through early 2016, the LEWAS/OWLS-based learning modules have been used in at least 17 different undergraduate university courses in engineering, sciences and industrial design, one graduate hydrology course across four universities in three countries and four first-year engineering courses across two community colleges (Virginia Western and John Tyler) in Virginia. The LEWAS/OWLS has also been used in: one high school course, an NSF-supported Chautauqua Professional Development Short Course Series and interactive touchscreen displays available to the public on the Virginia Tech campus.

This I-Corps L project builds upon the outcomes of prior successful NSF grants in which the investigators have demonstrated successful implementation of the LEWAS/OWLS-based learning modules into various courses. This research, using the theoretical framework of situated learning, has shown that use of the OWLS as a remote lab within hybrid instruction increases students’ learning of environmental monitoring concepts and motivation. The team proposes to investigate potential customer interest in incorporating the LEWAS/OWLS-based modules into 9-12 grade instruction. The expectation is that incorporation of these modules that combine the idea of understanding, evaluating, creating, and managing environmental (water quality and quantity and weather) data through computation will lead to wide-scale adoption of a cyberlearning approach for developing skills among 9-12th-grade students in environmental monitoring and computational thinking.