As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative research, approaches and resources for use in a variety of settings. In this exploratory Change Makers project, the Concord Consortium will develop, test, and evaluate a citizen science program that leverages innovative technology, such that youth engage directly with energy issues through scientific inquiry. The project will create the Infrared Street View, a citizen science program that aims to produce a thermal version of Google's Street View using an affordable infrared camera attached to a smartphone. The infrared camera serves as a high-throughput data acquisition instrument that collects thousands of temperature data points each time a picture is taken. Youth will collect massive geotagged thermal data that have considerable scientific and educational value for visualizing energy usage and improving energy efficiency at all levels. The Infrared Street View program will provide a Web-based platform for youth and anyone interested in energy efficiency to view and analyze the aggregated data to identify possible energy losses. By sharing their scientific findings with stakeholders, youth will make changes to the way energy is being used. The project will start with school, public, and commercial buildings in selected areas where performing thermal scan of the buildings and publishing their thermal images for educational and research purposes are permitted by school leaders, town officials, and property owners. In collaboration with high schools and out-of-school programs in Massachusetts, this project will conduct pilot-tests with approximately 200 students.
To contribute to advancing learning, the study will probe three research questions: 1) Under what circumstances can technology bridge out-of-school and classroom science learning and improve learning on both sides? 2) To what extent can unobtrusive assessment based on data mining support research and evaluation of student learning in out-of-school settings? and 3) To what extent can instructional intelligence built into the app used in the program help students learn in out-of-school programs and improve the quality of data they contribute to the citizen science project? Data sources for investigating these questions include students' interaction data with the app logged behind the scenes and the images they have taken, as well as results based on traditional assessments from a small number of participants. Throughout the project, staff will widely disseminate project products and findings through the Internet, science fairs, conferences, publications, and partner networks. An eight-member Advisory Board consisting of cleantech experts, science educators, and educational researchers will oversee and evaluate this project.
As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative resources for use in a variety of settings. This project will develop and study a cyber-enhanced informal learning environment to improve observational practices and classification skills among citizen scientists. The project will focus on the taxonomic identification skills needed by volunteers to provide high-quality data for water quality monitoring of local streams, lakes, estuaries, wetlands, and ground water resources. To make the task of identifying freshwater insects easier and more engaging, the project will develop an innovative educational resource, the Macroinvertebrate Identification Training Environment, that will use zoomable high-resolution images, interactive media, and annotations of diagnostic features to improve perceptual skills. The goal is to increase the confidence and accuracy of volunteers engaged in identification tasks, while also increasing the reliability and quality of the data they are generating for purposes of scientific research and conservation efforts. This interdisciplinary design research and development project will use networked gigapixel image technology to create a visual environment where users can move seamlessly from full panoramic views of macroinvertebrates to extreme close-ups, with embedded text, images, graphics, audio, and video at various locations and zoom levels. This system will be developed in concert with a cognitive apprenticeship training model designed through a series of design studies. The design studies will be conducted over a two-year period and will include examination of the distinguishing features of various biomonitoring programs, reviews of existing training materials and strategies, expert performance analysis of professional entomologists, and development of user interface features. Project developers will collaborate with five regional volunteer biomonitoring organizations to engage a diverse set of volunteers in the design process, including rural populations, older adults, urban youth, and the trainers who support them. The project work will consist of four integrated strands of activity: design-based learning research, creation of an entomological teaching collection, cyberplatform development, and the external evaluation of the training system. The resulting Macroinvertebrate Identification Training Environment will be evaluated in terms of its impacts on volunteer accuracy, confidence, and engagement in taxonomic classification activities related to macroinvertebrates. The impacts of the learning system on trainers and volunteer biomonitoring organizations will also be examined.