Valuing the Invaluable: An Investigation of Outdoor Recreation Behavior, Perceived Values of Ecosystem Services, and Biophysical Conditions on Channel Islands National Park

Impacts on parks and protected areas are modifying ecosystems that provide benefits to sustain human health and well-being. Compelling evidence of ecological and economic values has been gathered to better understand the implications of these changing social-ecological conditions; however, social values have received considerably less attention. There is a strong need to integrate disciplinary perspectives on the value concept and illustrate the full value of nature experienced through outdoor recreation activities. My dissertation drew from theoretical frameworks in psychology, economics, and ecology to better understand the multiple values of Channel Islands National Park (CINP), California, U.S. Specifically, I examined “held” value orientations, “assigned” values of ecosystem services, and ecological values of the CINP. In first of three papers, I tested the value-belief-norm (VBN) theory of environmentalism to determine the psychological processes driving low-impact behavior among outdoor recreationists. I observed that behavioral engagement was more strongly related to biospheric-altruistic held values than egoistic concerns. Also, moral norm activation was a direct antecedent to behaviors that minimized the spread of invasive species, degradation of archeological artifacts, and overfishing in marine protected areas. In the second paper, I investigated how environmental worldview shaped the spatial dynamics of assigned values for ecosystem services on Santa Cruz Island within the CINP. Using Public Participation Geographic Information Systems methods, I found that held value orientations (i.e., biocentrism, anthropocentrism) manifested different values ascribed to marine and terrestrial environments. In the third paper, I compared assigned biodiversity values to spatially-explicit measures of ecosystem structure and function using a Social Values for Ecosystem Services (SolVES) mapping application and Maximum Entropy modeling. My results showed that distance to features relevant for park management, carbon sequestration, species richness, elevation, vegetation density, and several categories of land cover predicted the locations and intensity of preferences for biodiversity on Santa Cruz

High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

Analyzing Learners Behavior in MOOCs: An Examination of Performance and Motivation Using a Data-Driven Approach

Massive Open Online Courses (MOOCs) have been experiencing increasing use and popularity in highly ranked universities in recent years. The opportunity of accessing high quality courseware content within such platforms, while eliminating the burden of educational, financial and geographical obstacles has led to a rapid growth in participant numbers. The increasing number and diversity of participating learners has opened up new horizons to the research community for the investigation of effective learning environments. Learning Analytics has been used to investigate the impact of engagement on student performance. However, extensive literature review indicates that there is little research on the impact of MOOCs, particularly in analyzing the link between behavioral engagement and motivation as predictors of learning outcomes. In this study, we consider a dataset, which originates from online courses provided by Harvard University and Massachusetts Institute of Technology, delivered through the edX platform [1]. Two sets of empirical experiments are conducted using both statistical and machine learning techniques. Statistical methods are used to examine the association between engagement level and performance, including the consideration of learner educational backgrounds. The results indicate a significant gap between success and failure outcome learner groups, where successful learners are found to read and watch course material to a higher degree. Machine learning algorithms are used to automatically detect learners who are lacking in motivation at an early time in the course, thus providing instructors with insight in regards to student withdrawal

Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals

This paper introduces a neural network and natural language processing approach to predict the outcome of crowdfunding startup pitches using text, speech, and video metadata in 20,188 crowdfunding campaigns. Our study emphasizes the need to understand crowdfunding from an investor’s perspective. Linguistic styles in crowdfunding campaigns that aim to trigger excitement or are aimed at inclusiveness are better predictors of campaign success than firm-level determinants. At the contrary, higher uncertainty perceptions about the state of product development may substantially reduce evaluations of new products and reduce purchasing intentions among potential funders. Our findings emphasize that positive psychological language is salient in environments where objective information is scarce and where investment preferences are taste based. Employing enthusiastic language or showing the product in action may capture an individual’s attention. Using all technology and design-related crowdfunding campaigns launched on Kickstarter, our study underscores the need to align potential consumers’ expectations with the visualization and presentation of the crowdfunding campaign