Abstract
This paper describes an initial pilot study of Rimac, a natural-language tutoring system for physics. Rimac uses a student model to guide decisions about what content to discuss next during reflective dialogues that are initiated after students solve quantitative physics problems, and how much support to provide during these discussions—that is, domain contingent scaffolding and instructional contingent scaffolding, respectively. The pilot study compared an experimental and control version of Rimac. The experimental version uses students’ responses to pretest items to initialize the student model and dynamically updates the model based on students’ responses to tutor questions during reflective dialogues. It then decides what and how to discuss the next question based on the model predictions. The control version initializes its student model based on students’ pretest performance but does not update the model further and assigns students to a fixed line of reasoning level based on the student model predictions. We hypothesized that students who used the experimental version of Rimac would achieve higher learning gains than students who used the control version. Although we did not find a significant difference in learning between conditions, the experimental group took significantly less time to complete the pilot study dialogues than did the control group. That is, the experimental condition led to more efficient learning, for both low and high prior knowledge level learners. We discuss this finding and describe future work to improve the tutor’s potential to support student learning.
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Notes
- 1.
Time on task did not include the time students spent solving the problems on paper. Additionally, any inactivity longer than three minutes while a student worked on the system was not counted towards the time on task estimate since it could be indicative that the student had taken a break from the learning activity.
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Acknowledgments
We thank Sarah Birmingham, Dennis Lusetich and Scott Silliman. This research was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A150155 to the University of Pittsburgh. The opinions are those of the authors and do not represent views of the Institute or the U.S. Department of Education.
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Albacete, P., Jordan, P., Katz, S., Chounta, IA., McLaren, B.M. (2019). The Impact of Student Model Updates on Contingent Scaffolding in a Natural-Language Tutoring System. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_4
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