Increased access to mobile phones and other internet capable devices has created new opportunities for adult learner audiences to engage in community science programs, which rely on volunteers to provide a large amount of data for analysis. But the lack of scalability of volunteer training is a limitation on community science programs. Volunteers may come to the activity with varying degrees of expertise and different personal goals with respect to the activity. This project seeks to apply explainable artificial intelligence to the challenge of personalizing training for adult citizen scientists. The approach will be developed in the context of the Native Bee Watch (NBW) biodiversity monitoring project that began in 2016 at Colorado State University. NBW trains volunteers to identify and monitor native bees and other pollinators, and educates volunteers about principles and practices from entomology and other fields of ecology and biology. This project will (1) create an online learning environment that will provide adaptive feedback to volunteers to support their self-directed learning, (2) perform controlled user studies to refine the performance of the system, and (3) perform a longitudinal study on the effectiveness of the system for helping volunteers. As a result of helping the volunteers acquire skills and STEM knowledge, the volunteers should, in turn, produce higher-quality observations that can improve the scientific analyses based on the data, and be more likely to continue contributing over time.
The project's research questions explore how to structure curricula that can support learners with a wide variety of prior expertise, how to develop algorithms that can estimate the current expertise of a learner while minimizing intrusive assessment tests, how to extend explainable AI to provide feedback tailored to individual learners, how to provide customized suggestions to help learners make better use of the tutoring system to manage and support their learning and citizen science participation, and how the learner expertise estimates can be used to identify areas where the tutoring system may be in need of improved accuracy. Data will be collected via surveys, interviews, observations, focus groups, usage metrics, and performance data. Analyses include thematic analysis of qualitative data and descriptive and comparative statistics. The intellectual merit of the project lies in the exploration of both the pedagogical and algorithmic aspects of supporting adult informal science learning - adults are an under-studied population in informal learning, requiring different approaches to learner modeling and the provision of different kinds of automated feedback than is common in traditional classroom-based intelligent tutoring systems. Because the system is designed to embrace self-driven informal learning, it contains several novel approaches: it will model learners' use of software features to help them manage their mastery of using the software to attain their own (possibly idiosyncratic) learning objectives; and it will be transparent about how its estimate of the learner's skill level was reached and provide learners with the opportunity to better calibrate the estimate - a novel form of dialogue-based evaluation that both respects the agency of the informal learner and improves the system's functionality (both its estimates of learner skill, and the accuracy of the vision model). With respect to broader impacts, while some elements of the developed system will be highly specific to the current domain (pollinator identification), by creating a scalable training system, the user base of the NBW community science project can be radically extended, expanding both the number of adult learners acquiring STEM skills and the amount of quality data being gathered across a wider geographic region. The general approach can in turn be adapted to new citizen science programs, providing a template for how such programs can refactor the fundamental way the program interacts with volunteers, potentially changing the landscape for informal adult science education. Results from this work will be disseminated to a range of educational research, citizen science practitioner, and artificial intelligence venues, enhancing the literatures in all areas.
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