Abstract
Learning in groups allows students to develop academic and social competencies but requires the presence of a human teacher that is actively guiding the group. In this paper we combine data-mining and visualization tools to support teachers’ understanding of learners’ activities in an inquiry based learning environment. We use supervised learning to recognize salient states of activity in the group’s work, such as reaching a solution to a problem, exhibiting idleness, or experiencing technical challenges. These “critical” moments are visualized to teachers in real time, allowing them to monitor several groups in parallel and to intervene when necessary to guide the group. We embedded this technology in a new system, called SAGLET, which augments existing collaborative educational software and was evaluated empirically in real classrooms. We show that the recognition capabilities of SAGLET are compatible with that of a human domain expert. Teachers were able to use the system successfully to make intervention decisions in groups when deemed necessary, without overwhelming them with information. Our results demonstrate how AI can be used to augment existing educational environments to support the “teacher in the group”, and to scale up the benefits of group learning to the actual classroom.
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Notes
- 1.
The original language of operation for SAGLET was Hebrew, and all of the examples shown in this paper are translations.
- 2.
Random forest achieved superior results to two alternative models, a Multi-Layer Perceptron, and a Gaussian Naive Bayes model.
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Acknowledgements
Thanks very much to the Math Forum for making it possible for us to use the development version of the VMT software. Thanks to Roy Fairstein for developing the NLP and visualization interfaces to SAGLET. This work was funded in part thanks to the Kamin fund by the Israeli Ministry of Trade and Industry.
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Segal, A. et al. (2017). Keeping the Teacher in the Loop: Technologies for Monitoring Group Learning in Real-Time. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_6
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