Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining
Many contemporary studies realized in the Learning Analytics research field provide substantial insights into the virtual learning environment stakeholders’ behaviour on single-course or small-scale level. They used different knowledge discovery techniques, including frequent patterns ana...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9343817/ |
| _version_ | 1828406882013806592 |
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| author | Martin Drlik Michal Munk Jan Skalka |
| author_facet | Martin Drlik Michal Munk Jan Skalka |
| author_sort | Martin Drlik |
| collection | DOAJ |
| description | Many contemporary studies realized in the Learning Analytics research field provide substantial insights into the virtual learning environment stakeholders’ behaviour on single-course or small-scale level. They used different knowledge discovery techniques, including frequent patterns analysis. However, there are only a few studies that have explored the stakeholders’ behaviour over a more extended period of several academic years in detail. This article contributes to filling in this gap and provides a novel approach to using homogeneous groups of frequent patterns for identifying the changes in stakeholders’ behaviour from the perspective of time. The novelty of this approach lies in fact, that even though the time variable is not directly involved, identification of homogeneous groups of frequent itemsets allows analysis and comparison of the stakeholders’ behavioral patterns and their changes over different observed periods. Found homogeneous groups of frequent itemsets, which conform minimal threshold of selected measures, showed, that it is possible to uncover the changes in stakeholders’ behaviour throughout the observed longer period. As a result, these homogenous groups of found frequent patterns allow a better understanding of the hidden changes in seasonality or trends in stakeholders’ behaviour over several academic years. This article discusses the possible implications of the results and proposed approach in the context of virtual learning environment management and educational content improvement. |
| first_indexed | 2024-12-10T11:18:08Z |
| format | Article |
| id | doaj.art-7e4a63764dcc411d91d716b5bf51a499 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T11:18:08Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7e4a63764dcc411d91d716b5bf51a4992022-12-22T01:51:05ZengIEEEIEEE Access2169-35362021-01-019237952381310.1109/ACCESS.2021.30561919343817Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns MiningMartin Drlik0https://orcid.org/0000-0002-5958-7147Michal Munk1https://orcid.org/0000-0002-9913-3596Jan Skalka2Department of Informatics, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Nitra, SlovakiaDepartment of Informatics, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Nitra, SlovakiaDepartment of Informatics, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Nitra, SlovakiaMany contemporary studies realized in the Learning Analytics research field provide substantial insights into the virtual learning environment stakeholders’ behaviour on single-course or small-scale level. They used different knowledge discovery techniques, including frequent patterns analysis. However, there are only a few studies that have explored the stakeholders’ behaviour over a more extended period of several academic years in detail. This article contributes to filling in this gap and provides a novel approach to using homogeneous groups of frequent patterns for identifying the changes in stakeholders’ behaviour from the perspective of time. The novelty of this approach lies in fact, that even though the time variable is not directly involved, identification of homogeneous groups of frequent itemsets allows analysis and comparison of the stakeholders’ behavioral patterns and their changes over different observed periods. Found homogeneous groups of frequent itemsets, which conform minimal threshold of selected measures, showed, that it is possible to uncover the changes in stakeholders’ behaviour throughout the observed longer period. As a result, these homogenous groups of found frequent patterns allow a better understanding of the hidden changes in seasonality or trends in stakeholders’ behaviour over several academic years. This article discusses the possible implications of the results and proposed approach in the context of virtual learning environment management and educational content improvement.https://ieeexplore.ieee.org/document/9343817/Association rule analysiscomputational and artificial intelligencelearning management systemspredictive models |
| spellingShingle | Martin Drlik Michal Munk Jan Skalka Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining IEEE Access Association rule analysis computational and artificial intelligence learning management systems predictive models |
| title | Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining |
| title_full | Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining |
| title_fullStr | Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining |
| title_full_unstemmed | Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining |
| title_short | Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining |
| title_sort | identification of changes in vle stakeholders x2019 behavior over time using frequent patterns mining |
| topic | Association rule analysis computational and artificial intelligence learning management systems predictive models |
| url | https://ieeexplore.ieee.org/document/9343817/ |
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