2015 — 2018 |
Holland, Donal Walsh, Conor [⬀] Gajos, Krzysztof |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Iuse: a Pedagogical Framework For Undergraduate Project-Based Engineering Design Courses
This Improving Undergraduate STEM Education (IUSE) project will develop a framework to guide the design, evaluation, and improvement of project-based engineering design courses. Engineering courses involving design projects are increasingly of interest to educators setting curricula for undergraduate engineering programs. Design projects improve students' problem solving abilities. However, much variation exists in how projects are implemented and existing guidelines are limited. This is due, in part, to the non-traditional and open-ended nature of these courses when compared to typical engineering courses. Currently guidelines are also hindered by a lack of research aimed at aligning educational practice with learning theories and results from research on the cognitive processes involved in design. The research will result in a general framework and data collection tools intended to guide engineering schools in making improvements in undergraduate project-based design courses. Results of this project will help improve the ability of engineering students to design innovative systems and processes to meet consumer needs. Continued innovation in science and engineering is vital to national security, economic growth, and achieving a sustainable society.
The project will develop a framework that addresses three problematic aspects of engineering design projects: the need for process-related, context-specific coaching to help students acquire the procedural knowledge required for successful design; a lack of access to detailed documentation of prior designs, which students need to demonstrate an ability to transfer knowledge; and a related lack of access to engineering communities of practice, with whom students must engage to learn both the technical and social aspects of design knowledge. To define a general pedagogical framework for engineering design, it is necessary to gather data from large numbers of students and educators in a variety of courses and contexts. To achieve this, new methods and online instruments will be developed for evaluating the efficacy of different learning environments. The research embedded in this project will yield insights into the processes followed by novice designers and the types of coaching required to improve learners' procedural knowledge, the ways in which learners interact with prior designs and engineering communities of practice, and the effects these interactions have upon their design knowledge. This latter contribution is especially important given the increased availability of open source technology and related online communities of practice. The project includes efforts to disseminate the results of this work to the broader engineering education community.
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2016 — 2020 |
Nakayama, Ken [⬀] Gajos, Krzysztof Enos, Ryan (co-PI) [⬀] Li, Na |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: From Knowledge Consumers to Knowledge Producers: a Scalable Experiential Learning Approach For Psychology and Related Disciplines
STEM education provides both technical training and the development of cognitive skills, such as designing experiments, testing hypotheses, and analyzing data. While traditional STEM training is essential for developing a highly skilled technical workforce, the cognitive skills developed through this training are beneficial in almost every type of career. To provide cognitive skills training to undergraduates in psychology, who typically do not receive this type of education, this project will develop a computer program, named TELLab, that allows psychology students to design experiments and gather data using the internet. Using this program, students will have the opportunity to experience first hand the challenges of doing science, learning skills and concepts, and most importantly, formulating and solving problems of personal interest to them.
To this end, the proposal has three broadly defined goals: (1) Up-scaling and maturation of TELLab, so that it can handle hundreds of thousands of users, in countless numbers of classrooms, with students at all levels - all at the same time, (2) evaluation of TELLlab-based pedagogy in diverse settings, including evaluating STEM competencies in non-STEM students, particularly those in undergraduate psychology classes, and (3) advancing the effort to create an open source community of faculty and students across the nation to develop and sustain collective expertise. Through a collaborative partnership across a variety of institutions, TELLab modules will be developed and deployed in a variety of psychology courses. These modules will allow students to design experiments and test hypotheses, providing immersion in the cognitive skills that are at the core of STEM education. Course instructors and participating students will be evaluated to identify and assess the factors that influence student experiences and learning.
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2021 — 2025 |
Gajos, Krzysztof Doshi-Velez, Finale (co-PI) [⬀] Glassman, Elena |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Hcc: Medium: Improving Human-Ai Collaboration On Decision-Making Tasks
From loan approval to disease diagnosis, there are many situations in which human decisions are being assisted by artificial intelligence (AI). For example, a clinical decision support system might suggest a possible diagnosis or highlight a potential medication interaction based on elements of the patient's history that the human doctor might have otherwise missed. It was expected that by combining the complementary strengths of people and AI systems (human+AI), the quality of the decisions made in such settings would be better than that of either people or machines alone. Unfortunately human+AI systems have not lived up to this promise: Even with explainable AI, human+AI systems often perform worse than either alone. Recent work shows that users of AI decision-support often have a superficial understanding of the AI. This leads to inappropriate levels of trust swinging from ignoring the AI to over-reliance. This project will create human+AI systems that perform better than either alone. The research team will develop and test specific tools and techniques that will be valuable for creating effective human+AI decision systems across many domains.
The project will explore three ways of improving AI-based decision support. Humans typically engage AI systems heuristically, while successful interaction calls for an analytical approach by the human partner. Only then can the human appropriately combine their knowledge with the AI recommendation and its explanation. To encourage more analytic engagement, the project will design and test (a) adaptive cognitive forcing functions: cognitive interventions that guide the human to pay closer attention the AI's information (applied only when most valuable to avoid frustrating the user), and (b) intelligent contrasts: methods that ground the AI's information as a contrast to what the human is likely to do. The latter will spark the human user's curiosity about why the AI may be recommending something different than the human. The last thrust involves building systems to help users understand the AI in the context of the data that power it, enabling a more global understanding of when the AI is likely to be useful. This project will explore specific versions of each approach described above applied to clinical treatment decision and to nutrition planning. The research results will enhance our understanding of how to create better human+AI teams.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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