Applying Artificial Intelligence to Student Success
Concurrent Session 7
This session will demonstrate how artificial intelligence can be used to help improve student success through learning pathway analysis and personality dimensionalization. Case studies demonstrating the use of AI and potential applications within the institutional context and implications will be the focus of the presentation.
To date, most learning analytics models and services are predicated upon student course history, demographics, aspirations (i.e. Job Role and Industry), and a potential set of courses that the student will need to complete to graduate. Though the methodologies for analysis can vary significantly, we still see a considerable amount of variance not accounted for in predicting student retention at both the course and program level. This session examines how the incorporation of cognitive-affective personality analysis, using artificial intelligence, can be used to improve model accuracy through the analysis of student work products to expose multiple dimensions of personality. This dimensional analysis is then combined with traditional data points such as those previously discussed to boost predictive accuracy.
As compelling as the results from utilization of this methodology are, the results are even greater when paired with other AI tools that help optimize completion planning and pathway execution. In this case, the presentation will segue into a discussion of how the LinkedIn SDK was utilized to mine professions and self-identified skills for each profession. These were then correlated with course materials for all courses at an extremely large university (over 100,000 students) using AI to conduct semantic analysis and correlation between skills and course content. The product of this analysis was individualized course maps for each student that would allow them to understand what skills were needed in their selected field. From this point the results of the personality dimensionalization analysis was used to assess where each student would likely encounter problems, allowing the institution to provide preemptive services to support students at these junctures.
Aside from the large institution implementation discussed above, the presentation will utilize case studies from nine other universities, where these techniques were used to improve persistence, guide career choices, strengthen student commitment, and aid in the advising process. Directions for future development and ecosystem enhancement, including the provisioning of Blockchain to provide immutable, verified records associated with the process, will be discussed. The presenters will encourage the audience to pose questions related to the implications of personality analysis as it relates to contemporary notions of self-determination in academia and the weight that this and similar tools should be given in the advising process.