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Improving Artificial Teachers by Considering How People Learn and Forget

Authors:

Aurelien Nioche,

Pierre-Alexandre Murena,

Carlos de la Torre-Ortiz,

Antti OulasvirtaAuthors Info & Claims

IUI '21: Proceedings of the 26th International Conference on Intelligent User Interfaces

Pages 445 - 453

https://doi.org/10.1145/3397481.3450696

Published: 14 April 2021 Publication History

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Abstract

The paper presents a novel model-based method for intelligent tutoring, with particular emphasis on the problem of selecting teaching interventions in interaction with humans. Whereas previous work has focused on either personalization of teaching or optimization of teaching intervention sequences, the proposed individualized model-based planning approach represents convergence of these two lines of research. Model-based planning picks the best interventions via interactive learning of a user memory model’s parameters. The approach is novel in its use of a cognitive model that can account for several key individual- and material-specific characteristics related to recall/forgetting, along with a planning technique that considers users’ practice schedules. Taking a rule-based approach as a baseline, the authors evaluated the method’s benefits in a controlled study of artificial teaching in second-language vocabulary learning (N = 53).

References

[1]

John R Anderson. 1996. ACT: A simple theory of complex cognition.American Psychologist 51, 4 (1996), 355–365.

[2]

John R Anderson, C Franklin Boyle, and Brian J Reiser. 1985. Intelligent tutoring systems. Science 228, 4698 (1985), 456–462.

[3]

Richard C Atkinson. 1972. Optimizing the learning of a second-language vocabulary.Journal of Experimental Psychology 96, 1 (1972), 124–129.

[4]

Roland Aydin, Lars Klein, Arnaud Miribel, and Robert West. 2020. Broccoli: Sprinkling lightweight vocabulary learning into everyday information diets. In WWW ’20: Proceedings of the Web Conference 2020. 1344–1354.

Digital Library

[5]

Nicholas J Cepeda, Harold Pashler, Edward Vul, John T Wixted, and Doug Rohrer. 2006. Distributed practice in verbal recall tasks: A review and quantitative synthesis.Psychological Bulletin 132, 3 (2006), 354–380.

[6]

Benjamin Clement, Didier Roy, Pierre-Yves Oudeyer, and Manuel Lopes. 2015. Multi-armed bandits for intelligent tutoring systems. Journal of Educational Data Mining 7, 2 (2015), 20–48.

[7]

Hermann Ebbinghaus. 1885. Über das Gedächtnis: Untersuchungen zur experimentellen Psychologie. Duncker & Humblot.

[8]

Emilie Gerbier and Thomas C Toppino. 2015. The effect of distributed practice: Neuroscience, cognition, and education. Trends in Neuroscience and Education 4, 3 (2015), 49–59.

[9]

Andrew Heathcote, Scott Brown, and DJK Mewhort. 2000. The power law repealed: The case for an exponential law of practice. Psychonomic Bulletin & Review 7, 2 (2000), 185–207.

[10]

Andrew Howes, Xiuli Chen, Aditya Acharya, and Richard L Lewis. 2018. Interaction as an emergent property of a Partially Observable Markov Decision Process. In Computational Interaction. Oxford University Press, 287–310.

[11]

Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, and Adish Singla. 2019. Teaching multiple concepts to a forgetful learner. In Advances in Neural Information Processing Systems. 4048–4058.

[12]

Sean HK Kang. 2016. Spaced repetition promotes efficient and effective learning: Policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences 3, 1 (2016), 12–19.

[13]

Sebastian Leitner. 1972. So lernt man lernen: Der Weg zum Erfolg. Herder.

[14]

Robert V Lindsey, Jeffery D Shroyer, Harold Pashler, and Michael C Mozer. 2014. Improving students’ long-term knowledge retention through personalized review. Psychological Science 25, 3 (2014), 639–647.

[15]

Philip I Pavlik, Jr and John R Anderson. 2005. Practice and forgetting effects on vocabulary memory: An activation-based model of the spacing effect. Cognitive Science 29, 4 (2005), 559–586.

[16]

Philip I Jr Pavlik and John R Anderson. 2003. An ACT-R model of the spacing effect. In Proceedings of the Fifth International Conference on Cognitive Modeling. Universitats-Verlag Bamberg, Germany, 177–182.

[17]

Philip I Jr Pavlik and John R Anderson. 2008. Using a model to compute the optimal schedule of practice.Journal of Experimental Psychology: Applied 14, 2 (2008), 101–117.

[18]

Paul Pimsleur. 1967. A memory schedule. The Modern Language Journal 51, 2 (1967), 73–75.

[19]

Anna N Rafferty, Emma Brunskill, Thomas L Griffiths, and Patrick Shafto. 2016. Faster teaching via POMDP planning. Cognitive Science 40, 6 (2016), 1290–1332.

[20]

Siddharth Reddy, Igor Labutov, Siddhartha Banerjee, and Thorsten Joachims. 2016. Unbounded human learning: Optimal scheduling for spaced repetition. In KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1815–1824.

Digital Library

[21]

David C Rubin and Amy E Wenzel. 1996. One hundred years of forgetting: A quantitative description of retention.Psychological Review 103, 4 (1996), 734–760.

[22]

Burr Settles and Brendan Meeder. 2016. A trainable spaced repetition model for language learning. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1848–1858.

[23]

Behzad Tabibian, Utkarsh Upadhyay, Abir De, Ali Zarezade, Bernhard Schölkopf, and Manuel Gomez-Rodriguez. 2019. Enhancing human learning via spaced repetition optimization. Proceedings of the National Academy of Sciences 116, 10(2019), 3988–3993.

[24]

Matthew M Walsh, Kevin A Gluck, Glenn Gunzelmann, Tiffany Jastrzembski, and Michael Krusmark. 2018. Evaluating the theoretic adequacy and applied potential of computational models of the spacing effect. Cognitive Science 42(2018), 644–691.

[25]

Jacob Whitehill and Javier Movellan. 2017. Approximately optimal teaching of approximately optimal learners. IEEE Transactions on Learning Technologies 11, 2 (2017), 152–164.

[26]

PA Wozniak and Edward J Gorzelanczyk. 1994. Optimization of repetition spacing in the practice of learning. Acta Neurobiologiae Experimentalis 54 (1994), 59–62.

Cited By

Gao HMa B(2024)Online Learning Strategy Induction through Partially Observable Markov Decision Process-Based Cognitive Experience ModelElectronics10.3390/electronics1319385813:19(3858)Online publication date: 29-Sep-2024
https://doi.org/10.3390/electronics13193858
Chen QLiu SHuang KWang XMa XZhu JPeng Z(2024)RetAssist: Facilitating Vocabulary Learners with Generative Images in Story Retelling PracticesProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661581(2019-2036)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661581
Gori JFruchard BBailly G(2024)Model-based Evaluation of Recall-based Interaction TechniquesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642637(1-16)Online publication date: 11-May-2024
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Improving Artificial Teachers by Considering How People Learn and Forget

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cover image ACM Conferences

IUI '21: Proceedings of the 26th International Conference on Intelligent User Interfaces

April 2021

618 pages

ISBN:9781450380171

DOI:10.1145/3397481

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 April 2021

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Human Automata
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IUI '21

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IUI '21: 26th International Conference on Intelligent User Interfaces

April 14 - 17, 2021

TX, College Station, USA

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Gao HMa B(2024)Online Learning Strategy Induction through Partially Observable Markov Decision Process-Based Cognitive Experience ModelElectronics10.3390/electronics1319385813:19(3858)Online publication date: 29-Sep-2024
https://doi.org/10.3390/electronics13193858
Chen QLiu SHuang KWang XMa XZhu JPeng Z(2024)RetAssist: Facilitating Vocabulary Learners with Generative Images in Story Retelling PracticesProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661581(2019-2036)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661581
Gori JFruchard BBailly G(2024)Model-based Evaluation of Recall-based Interaction TechniquesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642637(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642637
Peng ZWang XHan QZhu JMa XQu H(2023)Storyfier: Exploring Vocabulary Learning Support with Text Generation ModelsProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology10.1145/3586183.3606786(1-16)Online publication date: 29-Oct-2023
https://dl.acm.org/doi/10.1145/3586183.3606786
Su JYe JNie LCao YChen Y(2023)Optimizing Spaced Repetition Schedule by Capturing the Dynamics of MemoryIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325172135:10(10085-10097)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3251721
Aushev APutkonen AClarté GChandramouli SAcerbi LKaski SHowes A(2023)Online Simulator-Based Experimental Design for Cognitive Model SelectionComputational Brain & Behavior10.1007/s42113-023-00180-76:4(719-737)Online publication date: 21-Sep-2023
https://doi.org/10.1007/s42113-023-00180-7
Gao HZeng YMa BPan Y(2023)Improving Knowledge Learning Through Modelling Students’ Practice-Based Cognitive ProcessesCognitive Computation10.1007/s12559-023-10201-z16:1(348-365)Online publication date: 29-Sep-2023
https://doi.org/10.1007/s12559-023-10201-z
Murena PAl-Ghossein M(2021)Inferring Case-Based Reasoners’ Knowledge to Enhance InteractivityCase-Based Reasoning Research and Development10.1007/978-3-030-86957-1_12(171-185)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-86957-1_12

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