Emerging Research-Empirical Research--An Integrated Model of Cognitive and Affective Scaffolding for Intelligent Tutoring Systems

One-on-one human tutoring is remarkably effective. Seminal studies have shown that tutoring is significantly more effective than group instruction and may provide unparalleled opportunities for learning. A central, unanswered research question is, 'How do expert tutors provide effective cognitive and motivational support over the course of long-term tutorial interactions to improve learning?' With a curricular focus of college-level computer science education, this project will see the design and evaluation of a computer-based intelligent tutoring system, JavaTutor, which leverages artificial intelligence to provide both cognitive and motivational support. The project will be conducted at North Carolina State University in conjunction with three partner institutions: Meredith College, Shaw University, and St. Augustine's College.

The project has three major thrusts. First, the research team will conduct a semester-long observational study of cognitive and affective tutorial support provided by expert human tutors interacting with students in a fully-instrumented online tutoring environment. The environment will log all tutorial conversations, problem-solving traces, and affective data streams including physiological signals, posture, and facial expressions. Second, the research team will develop an empirically grounded, integrated model of cognitive and affective scaffolding using machine learning techniques including hidden Markov modeling. Third, they will validate the integrated model of cognitive and affective scaffolding in a semester-long experiment with the JavaTutor intelligent tutoring system. Four versions of the JavaTutor system will be deployed and compared. It is hypothesized that over the course of a semester, the version with an integrated model of cognitive and motivational scaffolding will outperform each of the other models on both cognitive and affective student outcomes and yield differential effects across learner groups, accruing particularly significant benefit to low-performing and female students.

The products of this project include findings and technologies that will inform the future development of intelligent tutoring systems. By promoting rich learning interactions through integrated cognitive and motivational scaffolding, the project will create new learning environment technologies that promote high levels of achievement and find broad application in STEM education. It is anticipated that the resulting intelligent tutoring system technologies will serve as a foundation for the next generation of educational software that both complements and expands the impact of classroom teachers. The impact should be significant given the effectiveness of human tutoring and the potential power of these new technologies to support learning.