Project Activities
Structured Abstract
Setting
Sample
Research design and methods
Control condition
Key measures
Data analytic strategy
People and institutions involved
IES program contact(s)
Products and publications
Products: The major expected product is an intelligent mathematics tutor that will contain three major components. The first component is affect detection software that estimates the emotional state of the user. This dynamic estimation of the user's current state will help determine the appropriate difficulty level for ensuing math problems. The second component is a suite of interventions that has the potential to bring disengaged students-whether from frustration, boredom, or low self-confidence-back to an engaged state. Finally, the third component consists of teacher assessment tools which inform teachers about each student's progress and affect.
ERIC Citations: Find available citations in ERIC for this award here.
Book chapter
Arroyo, I., Cooper, D.G., Burleson, W., and Woolf, B.P. (2010). Bayesian Networks and Linear Regression Models of Students' Goals, Moods, and Emotions. In R.S.J.D. Baker, and K. Yacef (Eds.), Handbook of Educational Data Mining (pp. 323-338). New York: Routledge Press.
Cooper, D., Arroyo, I., and Woolf, B.P. (2011). Actionable Affective Processing for Automatic Tutor Intervention. In S. D'Mello, and R. Calvo (Eds.), Affect and Learning Technologies (pp. 127-140). New York: Springer Publishing.
Woolf, B. (2010). Affective Tutors: Automatic Detection of and Response to Student Emotion. In R. Nkambou, J. Bourdeau, and R. Mizoguchi (Eds.), Advances in Intelligent Tutoring Systems (pp. 207-227). Berlin-Heidelberg: Springer.
Woolf, B. (2010). Student Modelling. In R. Nkambou, J. Bourdeau, and R. Mizoguchi (Eds.), Advances in Intelligent Tutoring Systems (pp. 267-279). Berlin-Heidelberg: Springer.
Woolf, B.P., Arroyo, I., Muldner, K., Burleson, W., Cooper, D., Dolan, R., and Christopherson, R.M. (2010). The Effect of Motivational Learning Companions on Low-Achieving Students and Students With Learning Disabilities. In V. Aleven, J. Kay, and J. Mostow (Eds.), Intelligent Tutoring Systems(pp. 327-337). Pittsburg, PA.: Springer.
Journal article, monograph, or newsletter
Arroyo, I., Burleson, W., Tai, M., Muldner, K., and Woolf, B.P. (2013). Gender Differences in the Use and Benefit of Advanced Learning Technologies for Mathematics. Journal of Educational Psychology, 105(4): 957-969.
Arroyo, I., Woolf, B.P., and Burleson, W. (2011). Using an Intelligent Tutor and Math Fluency Training to Improve Math Performance. International Journal of Artificial Intelligence in Education, 21(1): 135-152.
Woolf, B.P., Burleson, W., Arroyo, I., Dragon, T., and Picard, R. (2009). Affect-Aware Tutors: Recognizing and Responding to Student Affect Emotional Intelligence for Computer Tutors, Special Issue on Modeling and Scaffolding Affective Experiences to Impact Learning. International Journal of Learning Technology, 4(3): 129-164.
Proceeding
Arroyo, I., Mehranian, H., and Woolf, B. (2010). Effort-Based Tutoring: An Empirical Approach to Intelligent Tutoring. In Proceedings of the Third International Conference on Educational Data Mining (pp. 1-10). Pittsburgh, PA.
Woolf, B. (2010). Social and Caring Tutors. In Proceedings of the International Conference on Intelligent Tutoring Systems (pp. 5-13). New York: Springer.
Supplemental information
When the algorithm detects student disengagement, digital intervention tools (e.g., progress visualization charts, self-referencing charts, an Advanced Emotional Notebook) will be deployed to help counteract this state. They will be designed to enable students to collect, reflect, display and share newly acquired math concepts and for students to assess and monitor their own learning.
Assessment reports of student performance will be generated so that teachers can adjust their instructional strategies and actively encourage students to assess their own learning. Teachers' use of the reports will be assessed via interviews and tracking their use of the reports' hyperlinks, which allow for in-depth examination of the data.
The feasibility of implementing the intervention in mathematics classrooms as well as the promise of the intervention for improving student mathematics outcomes will be examined by conducting 11 field studies, each including 40 students.
Questions about this project?
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