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Conrad Borchers is a PhD candidate at the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University, School of Computer Science. His research advances intelligent systems that support learner persistence, assessment, and educational outcomes through human-centered design and learning analytics. He holds an MSc in Social Data Science from the University of Oxford and a BSc in Psychology from the University of Tübingen.

Topic: Adaptive learning systems are widely used, yet many students struggle to sustain the effort required to benefit from them. As AI tools become more capable, increased automation can further challenge students’ self-regulation. Despite this, most online learning systems still rarely explicitly model or adapt to student effort in ways that meaningfully support persistence. This work explores alternative approaches in both K-12 and higher education contexts. In K-12 classrooms, fine-grained log data and classroom context are used to model student effort and inform adaptive weekly goal-setting. Students working with data-driven goals sustained higher effort, improved math proficiency by up to 40% relative to standard practice, and were up to 80% more likely to meet their goals than peers assigned fixed goals. In higher education, enrollment records and LMS clickstream data are used to model workload more accurately than traditional credit-hour measures. These models enable institution-level analyses of course success and persistence, highlighting how student characteristics and course design shape effort over time and informing decisions about study programs and student well-being. I close by sketching work that focuses on designing persistence-support tools in partnership with educators and leveraging multimodal classroom data to better understand effort regulation and long-term self-regulation.

For further reading on the topic: 

• https://doi.org/10.1007/978-3-031-98459-4_4

• https://doi.org/10.5281/zenodo.14907388