Patrick Donnelly, Ph.D. - US grants

Women are underrepresented in STEM units in Dayton academic institutions, as they are nationwide. Four institutions in the Dayton region with diverse histories, missions and demographics form the LEADER consortium for the purpose of Launching Equity in the Academy across the Dayton Entrepreneurial Region. The institutions include a public doctoral university (Wright State University, host institution), a private Catholic institution (University of Dayton), a minority-serving public institution (Central State University) and a federal graduate institution (Air Force Institute of Technology). All are located in close proximity and collaborate routinely on STEM initiatives. They also share a commitment to regional STEM education, pipeline, and economic development, and recognition that inclusiveness, including directed efforts to recruit and support women in STEM, is a necessary component of that mission. Our ADVANCE collaborative will address these issues through a unique combination of inter-institutional coordination and approaches drawn from social and organizational psychology to improve climate and thereby transform the individual participating institutions.

Intellectual Merit: The underrepresentation of women among academic STEM faculties reflects gender disparities in recruitment, support, and promotion. Underlying the persistence of these problems are features of institutional climate that are rooted in the often nonconscious attitudes and behaviors of individuals. Thus, progress toward gender equity in the STEM academy requires transformation of institutional structures and processes, and transformation of climate. The LEADER consortium will implement models of social/organizational psychology based on gender schemas, persuasion theory, and social contracts, to transform institutional climate in support of STEM women. We will facilitate implementation of strategies proven in prior ADVANCE initiatives to enhance recruitment, retention and advancement of tenure-track STEM women. Implementation of these initiatives within a framework of inter-institutional accountability and administrative architecture (the LEADER Consortium) will catalyze transformation of climate within institutions, thus creating a sustainable women-friendly STEM culture within a region built upon a legacy of STEM innovation.
The specific aims of LEADER are: (a) to conduct a comparative analysis of climate for STEM women across the institutions and thereby identify best practices related to recruitment, retention, and advancement; (b) to initiate gender schema education and a campaign based on persuasion theory that will promote new norms of expectation and thereby facilitate implementation of those best practices; and (c) to implement social contracts across the consortium that promote transparency and accountability for transformation of climate, leading to recruitment, promotion and success of STEM women.
Implementation and Management: Social science research will be undertaken by a social psychologist and a philosopher working in the area of moral psychology and gender theory. Initially climate will be compared across the institutions to inform climate initiatives. At the unit and institutional levels, chairs and faculty equity advisors will implement proposed initiatives with the assistance of a centralized LEADER administrative office. Accountability for achieving benchmarks in recruitment and advancement of women will be centrally monitored using accepted metrics, formative and summative evaluation, and continuous improvement under the direction of the LEADER Council (composed of representatives from each institution) and with external oversight from an Advisory Board.

Broader Impacts: The inter-institutional collaboration and accountability should significantly increase retention and advancement of women in the STEM academy. More broadly, our ADVANCE program is designed to promote equity and that model can be applied to diverse target populations. The consortium includes an HBCU (Central State) and an institution committed to accessibility for the disabled (Wright State); as such, this project should promote significant gains in these two demographic groups within the community of STEM women. Our selection of the acronym "LEADER" recognizes this transferability; advancement of STEM women in the Dayton region today will provide leadership, by example, for efforts toward equity within the academy.

This project will use multiple sources of middle school classroom data to give feedback and assessment information to teachers so that their teaching ability is enhanced. The data includes anonymized student performance data (grades and standardized test results) and anonymized existing audio recordings of classroom discussions between students and teachers. The audio data will be used to analyze the student-teacher discussions for effectiveness of the student-teacher discussions in student learning. As the effectiveness measures are developed, feedback for instructional improvement will be provided to the teachers in a design cycle for continuous improvement. The technological innovations are in the analysis of the student-teacher discussions, in natural language understanding of student-teacher discussions, and in machine learning to classify effective from non-effective student-teacher discussions.

This project will advance cyber-enabled, teacher analytics as a new genre of technology that provides automated feedback on teacher performance with the goal of improving teaching effectiveness and student achievement. The exemplary implementation will autonomously analyze audio from real-world English and language arts classes for indicators of effective discourse to enable a new paradigm of datadriven reflective practice. The project emphasizes six theoretical dimensions of discourse linked to student achievement growth: goal clarity, disciplinary concepts, and strategy use for teacher-led discourse, and challenge, connection, and elaborated feedback for transactional discourse. The innovation aims to help teachers develop expertise on these dimensions and will be developed and tested in 9th grade classrooms in Western Pennsylvania. The team will first generate initial insights on how teacher discourse predicts student achievement via a re-analysis of large volumes (128 hours) of existing classroom audio. Next, they will design and iteratively refine hardware/software interfaces for efficient,flexible, scalable audio data collection by teachers. The data will be used to computationally model dimensions of effective discourse by combining linguistic, discursive, acoustic, and contextual analysis ofaudio with supervised and semi-supervised deep recurrent neural networks. The model-based estimates will be incorporated into an interactive analytic/visualization platform to promote data-driven reflective practice. After refinement via design studies, the impact of the innovation on instructional improvement and student literacy outcomes will be evaluated in a randomized control trial. Finally, generalizable insights will be identified at every stage of the project to promote transferability to future cyber-enabled,teacher-analytics technologies.

This proposal requests acquisition of a swept-source optical coherence tomography (OCT) imaging system and a deep learning workstation (DLW) at California State University, Chico (CSU, Chico). The proposed OCT imaging system and DLW will enable studies on the application of OCT imaging in dentistry to detect early carious lesions, micro-fractures, pulpal inflammation, early dysplastic changes in oral malignancies and signatures of other dental diseases. Acquisition of the OCT imaging system and DLW will significantly enhance current research programs and enable new research directions at CSU, Chico. These instruments will also support the established collaborative research with faculty at the Stony Brook University School of Dental Medicine. Approximately 500 students from the Departments of Electrical and Computer Engineering, Computer Science, and Mechanical and Mechatronic Engineering and Sustainable Manufacturing will be among the major users of the OCT system and DLW and will be available for use by the broader CSU, Chico research community. CSU, Chico is a minority-serving institution with a large proportion of students from underrepresented and underserved groups, including veterans, and Hispanics. This cutting-edge OCT system and DLW will provide these students with hands-on experience. The OCT system and DLW will be used to (1) increase involvement of undergraduate students in research, (2) promote active learning and skills development, (3) train upper-division undergraduate and graduate students on state-of-the-art imaging and algorithm development techniques, and (4) stimulate collaborations with faculty and students across CSU, Chico and from other institutions of higher learning.

The proposed swept-source optical coherence tomography (OCT) imaging system and deep learning workstation (DLW) will stimulate interdisciplinary research projects in healthcare and industrial nondestructive testing at CSU, Chico. OCT is a noninvasive optical imaging modality based on low-coherence interferometry that utilizes non-ionizing near-infrared laser to obtain images with 1-10 micrometer resolution. Currently, the major biomedical application of OCT is in ophthalmology. Many other applications of OCT are under investigation as researchers take advantage of the ability to rapidly acquire images noninvasively. Machine learning and deep learning techniques can be used to supplement the OCT images to more accurately identify diseased and damaged tissue. The following research projects at CSU, Chico will utilize the OCT imaging system and DLW: (1) the development of a deep learning model, namely convolutional neural networks (CNN), and quantitative analysis of OCT images for early dental caries detection; (2) the investigation of various deep learning optimization methods and their performances with OCT images to minimize back-propagating errors; (3) the development of signal and image processing algorithms to extract meaningful features from OCT data for image classification; (4) the design of compact and low-cost fiber optic based probe for noninvasive OCT imaging of occlusal caries; and (5) the use of deep learning to analyze and model non-speech sounds, such as music or industrial noise, as well as automatic analysis and classification of musical timbre. The OCT imaging system and DLW will help catalyze interdisciplinary efforts between engineering, science, and agriculture faculty, and develop an undergraduate and graduate research and teaching laboratory at CSU, Chico.

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.