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  1. Beverly Woolf
  2. https://www.cs.umass.edu/faculty/directory/woolf_beverly
  3. Research Professor
  4. Presenter’s NSFRESOURCECENTERS
  5. University of Massachusetts Amherst
  1. Danielle Allessio
  2. http://people.umass.edu/~allessio/index.html
  3. Graduate Student College of Education
  4. Presenter’s NSFRESOURCECENTERS
  5. University of Massachusetts Amherst
  1. Ivon Arroyo
  2. http://www.wpi.edu/academics/facultydir/ia2.html
  3. Assistant Professor
  4. Presenter’s NSFRESOURCECENTERS
  5. Worcester Polytechnic Institute

Impact of Adaptive Interventions on Student Affect, Performance, and Learning

NSF Awards: 1324825

2015 (see original presentation & discussion)

Grades 6-8

This research address how an online instructional tutor can respond to student emotion in online contexts. What to do when students are frustrated or anxious, bored or disappointed? We are evaluating the hypothesis that tailored emotional interventions can positively impact students’ emotions, attitude, and performance during learning. Our team has created MathSpring, an intelligent tutor, and has built models to detect student emotion in real-time, as students use the tutor, with and without sensors of physiological activity. We are measuring the impact of pedagogical treatments, such as reports of student progress and providing a choice to students about future curriculum. We intend to identify concrete principles about how a system might respond to student emotion as it occurs and to contribute to a theory of affect in interactive learning environments. In our past work, we integrated gendered and ethnic learning companions (male and female, White, Hispanic and African American), which offer advice and encouragement by talking to students. Learning companions can gesture and train attributions for “success/failure” – for example, the idea that intelligence is malleable, perseverance and practice are needed to learn, making mistakes is an essential part of learning and that temporary failure is not due to a lack of innate ability. In sum, we target students’ emotion in-situ in online learning, are gathering fine-grained data on emotions during learning, and will assess the impact of specific affective interventions on a moment-to-moment basis.

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