Poster(v): DELT-Predicting At-Risk Learners with Explainable AI

• In Event: Gallery Walk Showcase
  In Showcase Session: Gallery 19 - Emerging Trends in Research and Practice Across Contexts

Short Description

This in-progress research explores the efficacy of explainable artificial intelligence (XAI) as an early warning system (EWS) for predicting at-risk online learners in higher education. The XAI model integrates genetic programming (GP) with theory-guided data science (TGDS), such as Activity Theory and Fink’s Holistic View of Active Learning. XAI research incorporating instructional theories can provide informative and meaningful feedback for guiding learner-centric instruction intervention.

Authors

• Presenter: Ngocvan Bui, Sam Houston State University

• Presenter: Donggil Song, Sam Houston State University