Teaching Multiple Concepts to a Forgetful Learner
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Abstract (arXiv)
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.
Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 May 2018.
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Citing papers per year: 2019: 2; 2020: 0; 2021: 7; 2022: 4; 2023: 2; 2024: 1; 2025: 1.
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- 2021Replay in Deep Learning: Current Approaches and Missing Biological Elementscited by 71
- 2021Teaching Humans When To Defer to a Classifier via Exemplarscited by 29
- 2019Interactive Teaching Algorithms for Inverse Reinforcement Learningcited by 24
- 2021Counterfactual Temporal Point Processescited by 20
- 2019Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraintscited by 18
- 2021Teaching an Active Learner with Contrastive Examplescited by 12
- 2021Iterative Teaching by Label Synthesiscited by 10
- 2021Curriculum Design for Teaching via Demonstrations: Theory and Applicationscited by 9
- 2023Nonparametric Iterative Machine Teachingcited by 9
- 2022From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Taskscited by 8
- 2022Iterative Teaching by Data Hallucinationcited by 8
- 2023Nonparametric Teaching for Multiple Learnerscited by 7
5 more
- 2025Do Your Best and Get Enough Rest for Continual Learningcited by 5
- 2021Improving Artificial Teachers by Considering How People Learn and Forgetcited by 4
- 2024KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Studentscited by 2
- 2022Visual Knowledge Tracingcited by 1
- 2022One-shot Machine Teaching: Cost Very Few Examples to Converge Fastercited by 0
- 2025Do Your Best and Get Enough Rest for Continual Learning24 Mar 2025
- 2024KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students19 Feb 2024
- 2023Nonparametric Teaching for Multiple Learners17 Nov 2023
- 2023Nonparametric Iterative Machine Teaching5 Jun 2023
- 2022One-shot Machine Teaching: Cost Very Few Examples to Converge Faster13 Dec 2022
- 2022Iterative Teaching by Data Hallucination31 Oct 2022
- 2022Visual Knowledge Tracing20 Jul 2022
- 2022From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Tasks3 May 2022
- 2021Teaching Humans When To Defer to a Classifier via Exemplars22 Nov 2021
- 2021Counterfactual Temporal Point Processes15 Nov 2021
- 2021Teaching an Active Learner with Contrastive Examples28 Oct 2021
- 2021Iterative Teaching by Label Synthesis27 Oct 2021
5 more
- 2021Curriculum Design for Teaching via Demonstrations: Theory and Applications8 Jun 2021
- 2021Replay in Deep Learning: Current Approaches and Missing Biological Elements1 Apr 2021
- 2021Improving Artificial Teachers by Considering How People Learn and Forget8 Feb 2021
- 2019Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints2 Jun 2019
- 2019Interactive Teaching Algorithms for Inverse Reinforcement Learning28 May 2019
- 2021Replay in Deep Learning: Current Approaches and Missing Biological Elementscited by 71
- 2021Teaching Humans When To Defer to a Classifier via Exemplarscited by 29
- 2021Counterfactual Temporal Point Processescited by 20
- 2021Teaching an Active Learner with Contrastive Examplescited by 12
- 2021Iterative Teaching by Label Synthesiscited by 10
- 2023Nonparametric Iterative Machine Teachingcited by 9
- 2022From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Taskscited by 8
- 2022Iterative Teaching by Data Hallucinationcited by 8
- 2023Nonparametric Teaching for Multiple Learnerscited by 7
- 2025Do Your Best and Get Enough Rest for Continual Learningcited by 5
- 2021Improving Artificial Teachers by Considering How People Learn and Forgetcited by 4
- 2024KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Studentscited by 2
12 most cited of 14 All 14 (JSON)
- 2025Do Your Best and Get Enough Rest for Continual Learning24 Mar 2025
- 2024KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students19 Feb 2024
- 2023Nonparametric Teaching for Multiple Learners17 Nov 2023
- 2023Nonparametric Iterative Machine Teaching5 Jun 2023
- 2022One-shot Machine Teaching: Cost Very Few Examples to Converge Faster13 Dec 2022
- 2022Iterative Teaching by Data Hallucination31 Oct 2022
- 2022Visual Knowledge Tracing20 Jul 2022
- 2022From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Tasks3 May 2022
- 2021Teaching Humans When To Defer to a Classifier via Exemplars22 Nov 2021
- 2021Counterfactual Temporal Point Processes15 Nov 2021
- 2021Teaching an Active Learner with Contrastive Examples28 Oct 2021
- 2021Iterative Teaching by Label Synthesis27 Oct 2021
12 newest of 14 All 14 (JSON)