Research Workshop: Machine Learning as Method: Possibilities for Higher Education Research
Dr. Pedro Rosselló González Puerto Rico Convention Center
100 Convention BoulevardSan Juan, San Juan 00907
Puerto Rico
This workshop will introduce participants to machine learning (ML) as a methodological approach. ML is a set of computational methods that train algorithms to detect patterns and predict outcomes from large datasets. We will review common models, including decision tree, random forest, gradient boosting, support vector machine, and neural networks.
Participants are not required to come with software, but may download R/R Studio in advance of this workshop to follow along with the software demonstration. Participants will be asked to read one ML paper in advance of the workshop.
Hailu, M. F., & Ross, L. (2024, November). Predictive modeling of employability pathways in East African higher education: A machine learning study. Paper presented at the annual meeting of the Association for the Study of Higher Education (ASHE) conference, Minneapolis, MN.
Who should attend:
- Entry: Little to no experience with this topic
- Moderate: Some experience with this topic
Registration Options
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Registration Options
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Price |
|---|---|
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Presenters
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FREE |
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Current ASHE Student Member
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$25.00
Price will increase to $35.00 on
10/16/26
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Current ASHE Member
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$35.00
Price will increase to $45.00 on
10/16/26
|
|
Non-ASHE Member (Guest or Expired Member)
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$45.00
Price will increase to $55.00 on
10/16/26
|
Product Add-ons
| Add-ons | Price |
|---|---|
| Donation to the ASHE Graduate Student Travel Fund View Product | FREE Donation |
Agenda
| November 4 | |
| 2:00 PM - 2:20 PM | Introductions |
| 2:20 PM - 3:00 PM | Brainstorming Activity: Predicting Outcomes with Big Data Sets |
| 3:00 PM - 3:30 PM | Overview of Machine Learning |
| 3:30 PM - 3:45 PM | Break |
| 3:45 PM - 4:10 PM | R Software Demonstration |
| 4:10 PM - 4:40 PM | Group Critique of Case of Hailu & Ross (2024) ML Paper |
| 4:40 PM - 5:00 PM | Closing Reflections and Networking |
Dr. Lydia Ross is an Assistant Professor in the Mary Lou Fulton College for Teaching & Learning Innovation at Arizona State University. She uses quantitative methodologies to understand issues of equity, access, and inclusion in higher education. She is an expert in working with large administrative data sets, including census data, educational data, and geographic data. In 2024, Ross was selected as a Quantitative Research Methods in STEM Education fellow (NSF-funded project at the University of Maryland).
Dr. Meseret F. Hailu is an Assistant Professor of Higher Education at the Louise McBee Institute of Higher Education, University of Georgia (UGA). She studies how higher education helps people mobilize skills, credentials, and networks to enhance their quality of life. Geographically, she focuses her work in East Africa and the United States. In 2024, Hailu participated in the Learning Analytics in STEM Education Research Institute at North Carolina State University, where she was trained on machine learning methods.
Event Information
- An opportunity to share how the event can be more accessible for you is provided in the registration form. For questions about accessibility, please reach out to the ASHE Staff at office@ashe.ws.
- All ASHE Professional Development events encourage participants to engage throughout the event in various ways. To provide an environment that is conducive to learning and engagement and to provide a safe space (to the extent possible), Professional Development events are not recorded.
- Attendees will review and agree to the ASHE Events Code of Conduct (https://www.ashe.ws/code-of-conduct) and Payment Policies (https://www.ashe.ws/payment-policies) during registration.