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Adaptive Forgetting Curves for Spaced Repetition Language Learning
The forgetting curve has been extensively explored by psychologists, educationalists and cognitive scientists alike. In the context of Intelligent Tutoring Systems, modelling the forgetting curve for each user and knowledge component (e.g. vocabulary word) should enable us to develop optimal revisio...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334729/ http://dx.doi.org/10.1007/978-3-030-52240-7_65 |
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author | Zaidi, Ahmed Caines, Andrew Moore, Russell Buttery, Paula Rice, Andrew |
author_facet | Zaidi, Ahmed Caines, Andrew Moore, Russell Buttery, Paula Rice, Andrew |
author_sort | Zaidi, Ahmed |
collection | PubMed |
description | The forgetting curve has been extensively explored by psychologists, educationalists and cognitive scientists alike. In the context of Intelligent Tutoring Systems, modelling the forgetting curve for each user and knowledge component (e.g. vocabulary word) should enable us to develop optimal revision strategies that counteract memory decay and ensure long-term retention. In this study we explore a variety of forgetting curve models incorporating psychological and linguistic features, and we use these models to predict the probability of word recall by learners of English as a second language. We evaluate the impact of the models and their features using data from an online vocabulary teaching platform and find that word complexity is a highly informative feature which may be successfully learned by a neural network model. |
format | Online Article Text |
id | pubmed-7334729 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73347292020-07-06 Adaptive Forgetting Curves for Spaced Repetition Language Learning Zaidi, Ahmed Caines, Andrew Moore, Russell Buttery, Paula Rice, Andrew Artificial Intelligence in Education Article The forgetting curve has been extensively explored by psychologists, educationalists and cognitive scientists alike. In the context of Intelligent Tutoring Systems, modelling the forgetting curve for each user and knowledge component (e.g. vocabulary word) should enable us to develop optimal revision strategies that counteract memory decay and ensure long-term retention. In this study we explore a variety of forgetting curve models incorporating psychological and linguistic features, and we use these models to predict the probability of word recall by learners of English as a second language. We evaluate the impact of the models and their features using data from an online vocabulary teaching platform and find that word complexity is a highly informative feature which may be successfully learned by a neural network model. 2020-06-10 /pmc/articles/PMC7334729/ http://dx.doi.org/10.1007/978-3-030-52240-7_65 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Zaidi, Ahmed Caines, Andrew Moore, Russell Buttery, Paula Rice, Andrew Adaptive Forgetting Curves for Spaced Repetition Language Learning |
title | Adaptive Forgetting Curves for Spaced Repetition Language Learning |
title_full | Adaptive Forgetting Curves for Spaced Repetition Language Learning |
title_fullStr | Adaptive Forgetting Curves for Spaced Repetition Language Learning |
title_full_unstemmed | Adaptive Forgetting Curves for Spaced Repetition Language Learning |
title_short | Adaptive Forgetting Curves for Spaced Repetition Language Learning |
title_sort | adaptive forgetting curves for spaced repetition language learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334729/ http://dx.doi.org/10.1007/978-3-030-52240-7_65 |
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