Teaching Multiple Concepts to a Forgetful Learner

By Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Kumar Singla. 2018

40 citations recorded by the source corpus.

How can we help a forgetful learner learn multiple concepts within a limited time frame? While there have been extensive studies in designing optimal schedules for teaching a single concept given a learner's memory model, existing approaches for teaching multiple concepts are typically based on heuristic scheduling techniques without theoretical guarantees. In this paper, we look at the problem from the perspective of discrete optimization and introduce a novel algorithmic framework for teaching multiple concepts with strong performance guarantees. Our framework is both generic, allowing the design of teaching schedules for different memory models, and also interactive, allowing the teacher to adapt the schedule to the underlying forgetting mechanisms of the learner. Furthermore, for a well-known memory model, we are able to identify a regime of model parameters where our framework is guaranteed to achieve high performance. We perform extensive evaluations using simulations along with real user studies in two concrete applications: (i) an educational app for online vocabulary teaching; and (ii) an app for teaching novices how to recognize animal species from images. Our results demonstrate the effectiveness of our algorithm compared to popular heuristic approaches.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, positive): “…has been shown to speed up the training of neural networks, while also leading to better generalization (Bengio et al., 2009; Graves, Bellemare, Menick, Munos, & Kavukcuoglu, 2017; Hunziker et al., 2019; Zhou & Bilmes, 2018; Fan, Tian, Qin, Li, & Liu, 2018; Achille, Rovere, & Soatto, 2018).”