Probabilistic Skill Acquisition Model for Adaptive Task Sequencing in MITutor

• In Event: AERA Virtual Poster Session 9
  In Poster Session: Integrating Technology in Education

Abstract

The aim of this study is to examine a probabilistic approach to model skill acquisition in MITutor, an intelligent tutoring system that engages students in dialogue to practice motivational interviewing with virtual agents. The system relies on natural language processing to analyze responses as well as data-mined models to provide ongoing diagnosis, calibration, and fading of instruction. We trained the probabilistic student model from 1,961 responses made by 66 students to virtual agents and compared its performance to an additive factors model following a student-level cross-validation procedure. The probabilistic model outperformed additive factors by detecting student responses with 78.18% accuracy. We discuss the implications for adaptive task sequencing to optimize the amount of practice for skill acquisition.

Authors

• Kent Ellsworth

• Eric G. Poitras, Dalhousie University

• Zac Imel, The University of Utah

• Robert Zheng, University of Utah

• Derek Caperton

• Grin Lord

• Jake Van Epps, The University of Utah

• Michael Tanana, The University of Utah

• David Atkins, University of Washington