Academic Data Analytics

 

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Academic Data Analytics

Driving student, faculty, and staff success by putting honest data into the hands of higher education  leaders. Simultaneously, making this data and our methods easily accessible through guides, templates, and dashboards.

 

 

 

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Driving Student &
Faculty Success with Data

The Academic Data Analytics (ADA) unit is designed to provide data-driven decision-making into the hands of stakeholders at the University of Oregon, while building a body of research that can be generalized to other higher education institutions. The unit emphasizes research on student and faculty success in a variety of research areas and technical methods.

We envision the unit as one that pushes the boundaries of what is possible in higher education research, through the exploration of machine learning and other advanced methods of analysis. The goal is to use these techniques to more efficiently distribute resources (in predicting student success), determining what the main issues are (in using text analytics), and to uncover any issues that are a hindrance to student success (in focusing on a specific subgroup of students or their paths to graduation).

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Data for Diversity, Equity, & Inclusion

 

Combating inequity is a core priority and the unit will have a strong focus on issues surrounding diversity, equity, and inclusion (DEI). All research will begin to attend to these areas through the inclusion of variables such as gender, race, ethnicity, first generation status, and Pell eligibility status. Accordingly, all efforts to predict student success will remain closely focused on bias, fairness, and equity throughout the research process, and researchers will employ state-of-the-art algorithms to quantify and mitigate bias in all models and predictions. 

Wherever feasible, predictive models will be optimized to actively combat historical inequities, not just disregard them. Predictions will be disseminated only once they are deemed sufficiently free from all forms of algorithmic bias and unfairness. To support its diverse research initiatives, ADA will seek input from subject matter experts to ensure that staff thoroughly understand issues of bias and equity as they manifest in each area. Researchers will document the process of quantifying and mitigating bias in each analysis and publish this insight as a key part of study methodology. In so doing, ADA hopes that other researchers and practitioners may benefit from its work in this space, advancing equity not only at the University of Oregon, but at other higher education institutions. 

 

Our Process

Our aim is to have research products that are actionable, rapidly produced, and in formats that are easy to use for university stakeholders and higher education researchers alike. The unit will focus on four main technical areas: 

FOCUS
to fairly and equitably predict academic outcomes
to determine sentiment and themes in written feedback
to see student and employee trends in real time
to see how students advance through their majors

Our Research

 

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Understanding Transfer Students

One in five undergraduate students transfer into the University of Oregon and are comprised of community college students (with and without associate degrees), as well as four-year college transfers from both within and outside the state of Oregon. This heterogenous group deserves an in-depth exploration as to who these students are, what their aspirations look like, and how they explore and navigate their way towards a bachelor’s degree. The insights gathered from this line of research will not only benefit transfer students but also support first-time students entering the university with transfer credits.


 
Deb Morrison with students in Allen Hall

Predicting Student Success

How accurately can we predict second-term persistence for incoming students before they matriculate? The ability to forecast which students may need additional support can help allocate advising resources more efficiently from day one, improving outcomes for potentially vulnerable students. Using cutting-edge machine learning tools, we are developing a model to generate these predictions and ensure that they are fair and transparent, combatting historical inequities in student success. We hope that these predictions can improve outcomes at the University of Oregon, and we hope that sharing our tools and processes can help other institutions do the same.


Distinguished teaching awards

COMING SOON: Understanding Faculty Progression

Faculty are at the heart of any university, and the University of Oregon invests substantial time and money in recruiting faculty who can be successful in all aspects of university life. Their success, measured as retention and timely achievement of promotion over time, is critical to the quality and stability of both the faculty and the UO overall. There is no single path to promotion, and a variety of professional and personal factors can delay a faculty member’s progress toward promotion or cause them to leave the UO altogether. This area of research will initially investigate how long it takes tenured associate professors to become full professors and will assess whether certain subgroups have disproportionate outcomes in terms of leaving the UO or achieving promotion at an above-average length of time.

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Apply Our Research

 

Our Practitioner Guides summarize student perspectives on specific teaching practices using machine learning techniques and provide instructors with recommendations to better support student learning. The UO Student Experience Surveys produce over 100,000 student comments each year about student learning experiences. The Practitioner Guides make the information in these surveys accessible to faculty and administrators by summarizing student perspectives on specific teaching practices and providing instructors with recommendations to better support student learning. 

►  Understand our technical methods and download our code

ADA Updates


April 6, 2022 - Austin Hocker and Grant Crider-Phillips presented at the American Association of Collegiate Registrars and Admissions Officers conference about ADA's text analytics to examine inclusivity and accessibility.
April 5, 2022 - Nathan Greenstein presented at the American Association of Collegiate Registrars and Admissions Officers conference about ADA's methods on predicting student success and combatting inequity.
Mar 17, 2022 - Austin Hocker presented at the American Association of Colleges and Universities (AAC&U) Conference on Diversity, Equity, and Student Success in New Orleans, Louisiana on March 17th about how to improve inclusive and accessible pedagogy through student course evaluations using machine learning techniques.
April 1, 2022 - Nathan Greenstein presented the ADA team's current research on transfer students at the Council for the Study of Community Colleges (CSCC) conference in Tempe, AZ on March 31st. The handout includes information on the overall transfer landscape, our transfer student dashboard, and next steps on using machine learning to predict transfer student success.