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Teaching Multiple Concepts to Forgetful Learners

Hunziker, Anette and Chen, Yuxin and Mac Aodha, Oisin and Gomez Rodriguez, Manuel and Krause, Andreas and Perona, Pietro and Yue, Yisong and Singla, Adish (2019) Teaching Multiple Concepts to Forgetful Learners. In: 33rd Conference on Neural Information Processing Systems. , Neural Information Processing Systems Foundation, Inc., Art. No. 8659.

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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.

Item Type:Book Section
Related URLs:
URLURL TypeDescription Paper
Mac Aodha, Oisin0000-0002-5787-5073
Krause, Andreas0000-0001-7260-9673
Perona, Pietro0000-0002-7583-5809
Yue, Yisong0000-0001-9127-1989
Additional Information:© 2020 Neural Information Processing Systems Foundation, Inc. This work was done when Yuxin Chen and Oisin Mac Aodha were at Caltech. This work was supported in part by NSF Award #1645832, Northrop Grumman, Bloomberg, AWS Research Credits, Google as part of the Visipedia project, and a Swiss NSF Early Mobility Postdoctoral Fellowship.
Funding AgencyGrant Number
Northrop Grumman CorporationUNSPECIFIED
Amazon Web ServicesUNSPECIFIED
Swiss National Science Foundation (SNSF)UNSPECIFIED
Record Number:CaltechAUTHORS:20180613-133348044
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87072
Deposited By: Caroline Murphy
Deposited On:13 Jun 2018 20:50
Last Modified:02 Jun 2023 00:41

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