Affective Learning Companions
Affective Learning Companions (ALC)
Affective Learning Companions (ALC), are real-time, multi-modal automated characters and software agents that sense and respond to learners’ affective cues to sustain motivation in the face of difficulty and failure.
This research is grounded in educational and social psychology, as well as cognitive science. ALC research integrates physiological sensors and virtual computing environments to create rich pedagogical and social relationships that support meta-cognitive strategies of affective self-awareness known to benefit learning processes.
This field of work is beginning to articulate the potential of intelligent agents and smart-products to form rich relationships between users. A set of low-cost affective sensors, including posture chairs, pressure mice, skin conductors, heart rate bracelets and facial expression cameras are being developed into Affective Tool Kits and evaluated in classrooms, museums, work and domestic settings.
Affective Tool Kits
Affective Tool Kits combined with communities that openly share developments and prototypes to exchange complimentary expertise are utilized in many of the research group’s other projects. Affective Tool Kits and Ubiquitous Design Environments work together to support communities of lead- and end-users that provide tools and expertise in the fields of human computer interaction. The ownership of these development tools and the skills to use them will enable users to develop their own tools for self-awareness, to have their own ability to create and distribute mentors, peers, colleagues, and teammates. Some examples of affective tools are low cost affective sensors like, a posture chair, a pressure mouse, a skin conductance and heart rate bracelet and a facial expression camera.
Arroyo, I., Burleson, W., Tai, M., Muldner, K., Woolf B. (Accepted) Gender Differences In the Use and Benefit of Advanced Learning Technologies for Mathematics. Journal of Educational Psychology, Special Issue on Advanced Learning Technologies, Aleven, V. and Beal, C. guest editors.
VanLehn, K., Burleson, W., Chavez Echeangary, H., Christopherson, R., Gonzales Sanchez J., HidalgoPontet, Y., Muldner, K., Zhang, L., (2011) The Affective Meta-Tutoring Project: How to MotivateStudents to Use Effective Meta-Cognitive Strategies, Proceedings of the 19th International Conferenceon Computers in Education. Chiang Mai, Thailand: Asia-Pacific Society for Computers in Education,November 2011.
VanLehn, K., Burleson, W., Chavez Echeangary, H., Christopherson, R., Gonzales Sanchez J., HidalgoPontet, Y., Muldner, K., Zhang, L. (2011) The Level Up Procedure: How to Measure Learning GainsWithout Pre- and Post-testing, Proceedings of the 19th International Conference on Computers inEducation. Chiang Mai, Thailand: Asia-Pacific Society for Computers in Education, November 2011.
Arroyo, I., Cooper, D., Burleson, W., Woolf, B., Muldner, K., Christopherson, R. (2009) Emotion sensors go to school, Proceedings of 14th International Conference on Artificial Intelligence in Education, Brighton, England, July 2009. Overall Best Paper Award.
In the News
NSF 1109142: REU: Personalized Learning: Strategies to Respond to Distress and Promote Success (06/12-5/13) $15,781.
NSF Computing Innovation Fellows Postdoctoral Mentor Award (5/12-11/12) $73,344.
NSF Award 11049425: Personalized Learning: Strategies to Respond to Distress and Promote Success (09/11-09/14) $600,000.
NSF Computing Innovation Fellows Postdoctoral Mentor Award (5/11-5/12) $225,000.
NSF Award 0910221: Deeper Modeling via Affective Meta-Tutoring (9/09-9/12) $900,000.
NSF Award 0931237: Preparing for College: Using Technology to Support Students with Learning Disabilities in Mathematics (9/09-9/10) $120,668.
NSF Award 0705883: “HCC: Collaborative Research (U-Mass Amherst): Affective Learning Companions: Modeling and Supporting Emotion During Learning (10/07 – 10/10) $304,465.