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Mining sequential patterns with flexible constraints from MOOC data
Online learning is playing an increasingly important role in education. Massive open online course (MOOC) platforms are among the most important tools in online learning, and record historical learning data from an extremely large number of learners. To enhance the learning experience, a promising a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8940599/ https://www.ncbi.nlm.nih.gov/pubmed/35340983 http://dx.doi.org/10.1007/s10489-021-03122-7 |
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author | Song, Wei Ye, Wei Fournier-Viger, Philippe |
author_facet | Song, Wei Ye, Wei Fournier-Viger, Philippe |
author_sort | Song, Wei |
collection | PubMed |
description | Online learning is playing an increasingly important role in education. Massive open online course (MOOC) platforms are among the most important tools in online learning, and record historical learning data from an extremely large number of learners. To enhance the learning experience, a promising approach is to apply sequential pattern mining (SPM) to discover useful knowledge in these data. In this paper, mining sequential patterns (SPs) with flexible constraints in MOOC enrollment data is proposed, which follows that research approach. Three constraints are proposed: the length constraint, discreteness constraint, and validity constraint. They are used to describe the effect of the length of enrollment sequences, variance of enrollment dates, and enrollment moments, respectively. To improve the mining efficiency, the three constraints are pushed into the support, which is the most typical parameter in SPM, to form a new parameter called support with flexible constraints (SFC). SFC is proved to satisfy the downward closure property, and two algorithms are proposed to discover SPs with flexible constraints. They traverse the search space in a breadth-first and depth-first manner. The experimental results demonstrate that the proposed algorithms effectively reduce the number of patterns, with comparable performance to classical SPM algorithms. |
format | Online Article Text |
id | pubmed-8940599 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-89405992022-03-23 Mining sequential patterns with flexible constraints from MOOC data Song, Wei Ye, Wei Fournier-Viger, Philippe Appl Intell (Dordr) Article Online learning is playing an increasingly important role in education. Massive open online course (MOOC) platforms are among the most important tools in online learning, and record historical learning data from an extremely large number of learners. To enhance the learning experience, a promising approach is to apply sequential pattern mining (SPM) to discover useful knowledge in these data. In this paper, mining sequential patterns (SPs) with flexible constraints in MOOC enrollment data is proposed, which follows that research approach. Three constraints are proposed: the length constraint, discreteness constraint, and validity constraint. They are used to describe the effect of the length of enrollment sequences, variance of enrollment dates, and enrollment moments, respectively. To improve the mining efficiency, the three constraints are pushed into the support, which is the most typical parameter in SPM, to form a new parameter called support with flexible constraints (SFC). SFC is proved to satisfy the downward closure property, and two algorithms are proposed to discover SPs with flexible constraints. They traverse the search space in a breadth-first and depth-first manner. The experimental results demonstrate that the proposed algorithms effectively reduce the number of patterns, with comparable performance to classical SPM algorithms. Springer US 2022-03-23 2022 /pmc/articles/PMC8940599/ /pubmed/35340983 http://dx.doi.org/10.1007/s10489-021-03122-7 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 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 Song, Wei Ye, Wei Fournier-Viger, Philippe Mining sequential patterns with flexible constraints from MOOC data |
title | Mining sequential patterns with flexible constraints from MOOC data |
title_full | Mining sequential patterns with flexible constraints from MOOC data |
title_fullStr | Mining sequential patterns with flexible constraints from MOOC data |
title_full_unstemmed | Mining sequential patterns with flexible constraints from MOOC data |
title_short | Mining sequential patterns with flexible constraints from MOOC data |
title_sort | mining sequential patterns with flexible constraints from mooc data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8940599/ https://www.ncbi.nlm.nih.gov/pubmed/35340983 http://dx.doi.org/10.1007/s10489-021-03122-7 |
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