Two presentations were selected for the awards this year.
First Place Winner
Presentation Topic: Analyzing qualitative data using text classification
Presenter: Dr. Kelly Lin, University of Hawaii
Abstract: Qualitative data yields invaluable insights for institutional researchers, offering a depth of understanding that quantitative data alone may struggle to provide. Despite its advantages, the reluctance among researchers to analyze qualitative data persists due to the time-intensive nature of the analysis process. This presentation aims to delve into text classification, a machine learning method designed to swiftly categorize narrative responses into topics. This methodology was employed to effectively analyze the Common Data Set Feedback Survey.
Participants will gain insights into the merits and drawbacks of text classification techniques using AI. The presentation will focus on the rationale behind implementing these techniques, emphasizing efficiency in classifying survey responses into topics without delving into mathematical intricacies. Additionally, strategies for adept data cleaning will be discussed, contributing to a comprehensive understanding of the applied methodologies.
Second Place Winner
Presentation Topic: A Deep Dive into U.S. News Best Engineering Graduate Program Ranking
Presenter: Dr. Jennifer Wu, Penn State University
Abstract: This presentation summarizes an in-dept analysis of the U.S. News best engineering graduate school ranking data. The speaker takes the audience on a deep dive into the methodology, interpretation, and application of ranking. The speaker shares insights on how to understand the ranking methodology and gain access to ranking data to examine the ranking results. The presentation also includes a demonstration of a Power BI dashboard that allows users to explore ranking results under different scenarios. With a few clicks, the dashboard shows the impact on ranking of changes in any ranking indicator, reducing the turn-around time from months to hours. It buys leadership time and helps set and focus on realistic goals. Additionally, with six years of data, it becomes a powerful benchmarking tool to track growth and identify gaps with peers. It gives users freedom to customize peer groups, e.g., BigTen+, Top 25 ranked, Top 20 largest in student size, etc. The presentation concludes with practical tips on helping institutions understand and use rankings data. Although this presentation uses the engineering school as a case study, the knowledge, methods, and practice shared can be helpful to decode other college or institutional ranking results.