Teaching Multiple Concepts to a Forgetful Learner
This addresses the challenge of optimizing teaching schedules for forgetful learners in educational and training applications, though it is incremental as it builds on existing memory model studies.
The paper tackles the problem of teaching multiple concepts to a forgetful learner within a limited time frame by introducing a novel algorithmic framework with strong performance guarantees, and evaluations in vocabulary and animal recognition apps show its effectiveness compared to heuristic approaches.
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.