Discovering action insights from large-scale assessment log data using machine learning
Abstract This study introduces a novel machine learning algorithm that combines natural language processing techniques, such as Word2Vec and Doc2Vec, with neural networks to identify and validate significant actions within human action sequences. Using the 2012 Program for the International Assessme...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-14802-6 |
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| _version_ | 1849226269377429504 |
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| author | Minyoung Yun Minjeong Jeon Heyoung Yang |
| author_facet | Minyoung Yun Minjeong Jeon Heyoung Yang |
| author_sort | Minyoung Yun |
| collection | DOAJ |
| description | Abstract This study introduces a novel machine learning algorithm that combines natural language processing techniques, such as Word2Vec and Doc2Vec, with neural networks to identify and validate significant actions within human action sequences. Using the 2012 Program for the International Assessment of Adult Competencies dataset, the algorithm visualizes and analyzes action sequences in a 2D vector space to uncover high-impact behaviors that influence performance. The methodology, validated across two problem sets (“Party Invitation” and “Club Membership”), successfully distinguishes performance groups by focusing on critical actions, leading to enhanced classification accuracy (up to 94.6%) and clustering coherence (silhouette score of 0.491). This approach demonstrates potential applications in personalized education, healthcare diagnostics, and consumer behavior prediction, advancing the understanding of human behavior through digital footprints. |
| format | Article |
| id | doaj-art-4a19e2acd24d4a3593afb2ecc43a93b4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4a19e2acd24d4a3593afb2ecc43a93b42025-08-24T11:26:43ZengNature PortfolioScientific Reports2045-23222025-08-0115111310.1038/s41598-025-14802-6Discovering action insights from large-scale assessment log data using machine learningMinyoung Yun0Minjeong Jeon1Heyoung Yang2Laboratory PIMM, Arts et Métieres Paris TechSchool of Education and Information Studies, University of CaliforniaCenter for Future Technology Analysis, Korea Institute of Science and Technology InformationAbstract This study introduces a novel machine learning algorithm that combines natural language processing techniques, such as Word2Vec and Doc2Vec, with neural networks to identify and validate significant actions within human action sequences. Using the 2012 Program for the International Assessment of Adult Competencies dataset, the algorithm visualizes and analyzes action sequences in a 2D vector space to uncover high-impact behaviors that influence performance. The methodology, validated across two problem sets (“Party Invitation” and “Club Membership”), successfully distinguishes performance groups by focusing on critical actions, leading to enhanced classification accuracy (up to 94.6%) and clustering coherence (silhouette score of 0.491). This approach demonstrates potential applications in personalized education, healthcare diagnostics, and consumer behavior prediction, advancing the understanding of human behavior through digital footprints.https://doi.org/10.1038/s41598-025-14802-6Human action sequenceMeaningful actionsMachine learningNatural language processingPIAAC log data |
| spellingShingle | Minyoung Yun Minjeong Jeon Heyoung Yang Discovering action insights from large-scale assessment log data using machine learning Scientific Reports Human action sequence Meaningful actions Machine learning Natural language processing PIAAC log data |
| title | Discovering action insights from large-scale assessment log data using machine learning |
| title_full | Discovering action insights from large-scale assessment log data using machine learning |
| title_fullStr | Discovering action insights from large-scale assessment log data using machine learning |
| title_full_unstemmed | Discovering action insights from large-scale assessment log data using machine learning |
| title_short | Discovering action insights from large-scale assessment log data using machine learning |
| title_sort | discovering action insights from large scale assessment log data using machine learning |
| topic | Human action sequence Meaningful actions Machine learning Natural language processing PIAAC log data |
| url | https://doi.org/10.1038/s41598-025-14802-6 |
| work_keys_str_mv | AT minyoungyun discoveringactioninsightsfromlargescaleassessmentlogdatausingmachinelearning AT minjeongjeon discoveringactioninsightsfromlargescaleassessmentlogdatausingmachinelearning AT heyoungyang discoveringactioninsightsfromlargescaleassessmentlogdatausingmachinelearning |