Educational Resources for Geoethical Aspects of Water Management

Google Earth Geoscience Video Library (GEGVL): Organizing Geoscience Videos in a Google Earth Environment to Support Fieldwork Teaching Methodology in Earth Science

Fieldwork teaching methodology (FTM) and active learning are effective strategies for geoscience education. However, traditional field trips require significant resources, time, physical abilities, and the expertise of teachers. In this study, we provide a supplementary virtual field trip experience by showing how different kinds of geoscience videos can be spatially organized into one digital interactive virtual environment. Here, we present the Google Earth Geoscience Video Library (GEGVL) which uses Google Earth and location-specific videos about Earth events, to create a virtual field-based learning experience. Using Google Earth, GEGVL organizes field-based videos by location and links pertinent non-field-based videos, and allows users to roam the globe in search of geoscientific videos that are pertinent to them or their students. Currently, GEGVL contains 150 videos organized into ten different geoscience disciplines: Plate Tectonics, Minerals, Structural Geology, Metamorphism, Magmatism, Hydrology, Environmental Science, Sedimentology, Paleontology, and Paleomagnetism. Despite stability challenges with Google Earth integration, results of user surveys among lower-division undergraduates show that the design logic of GEGVL is a promising virtual field-based learning organizer for increasing students’ interest in and helping them learn about Earth sciences.

A Virtual Reality and Online Learning Immersion Experience Evaluation Model Based on SVM and Wearable Recordings

The increasing development in the field of biosensing technologies makes it feasible to monitor students’ physiological signals in natural learning scenarios. With the rise of mobile learning, educators are attaching greater importance to the learning immersion experience of students, especially with the global background of COVID-19. However, traditional methods, such as questionnaires and scales, to evaluate the learning immersion experience are greatly influenced by individuals’ subjective factors. Herein, our research aims to explore the relationship and mechanism between human physiological recordings and learning immersion experiences to eliminate subjectivity as much as possible. We collected electroencephalogram and photoplethysmographic signals, as well as self-reports on the immersive experience of thirty-seven college students during virtual reality and online learning to form the fundamental feature set. Then, we proposed an evaluation model based on a support vector machine and got a precision accuracy of 89.72%. Our research results provide evidence supporting the possibility of predicting students’ learning immersion experience by their EEGs and PPGs.