Human-tutor Coaching Technology (HTCT): Automated Discourse Analytics in a Coached Tutoring Model
Abstract
High-dosage tutoring has become an effective strategy for bolstering K-12 academic performance and combating education declines accelerated by the COVID-19 pandemic. To achieve high-dosage tutoring at scale, tutoring programs often rely on paraprofessional tutors - recruited tutors with college degrees who lack formal training in education - however, these tutors may require consistent and targeted feedback from instructional coaches for improvement. Accordingly, we developed a human-tutor coaching technology (HTCT) system to automatically extract discourse analytics pertaining to accountable talk moves (or academically productive talk) from tutoring sessions and provide feedback visualizations to coaches to aid their coaching sessions with tutors. We deployed HTCT in a user study using a virtual tutoring platform with 11 real coaches, 40 tutors, and their students to investigate coaches' usage patterns with HTCT, perceptions of its utility, and changes in tutors' talk. Overall, we found that coaches had positive perceptions of the system. We also observed an increase in accountable talk from tutors whose coaches used HTCT compared to tutors whose coaches did not. We discuss implications for AI-based applications which offer coaches a promising way to provide personalized, automated, and data-driven feedback to scale high-dosage tutoring.
Publication Title
ACM International Conference Proceeding Series
Recommended Citation
Booth, B., Jacobs, J., Bush, J., Milne, B., Fischaber, T., & Dmello, S. (2024). Human-tutor Coaching Technology (HTCT): Automated Discourse Analytics in a Coached Tutoring Model. ACM International Conference Proceeding Series, 725-735. https://doi.org/10.1145/3636555.3636937
