Leveraging PSO-MLP for intelligent assessment of student learning in remote environments: a multimodal approach
The rapid advancement of artificial intelligence (AI) has catalyzed transformative changes in education, particularly in mobile and online learning environments. While existing deep learning models struggle to efficiently integrate the complexity of remote education data and optimize model performan...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-08-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-3121.pdf |
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| author | Jing Wang Muhammad Asif |
| author_facet | Jing Wang Muhammad Asif |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | The rapid advancement of artificial intelligence (AI) has catalyzed transformative changes in education, particularly in mobile and online learning environments. While existing deep learning models struggle to efficiently integrate the complexity of remote education data and optimize model performance, this article proposes an intelligent evaluation method for students’ learning states based on multimodal data. First, the joint characteristics of the pre-class mental status survey information and the health big data of teachers and students in the online teaching process constitute input data. Then, the multilayer perceptron (MLP) is used to intelligently identify the students’ status and classify their enthusiasm for the class. Finally, the particle swarm optimization (PSO) model is used to optimize the model and improve the overall recognition rate. Compared to traditional methods, the PSO-MLP model with combined multimodal data performs well, achieving an accuracy of 0.891. It provides an operational, technical solution for the education system, provides a new AI foundation for personalized teaching and student health management by accurately assessing students’ learning status, and helps to improve the effectiveness and efficiency of remote education. |
| format | Article |
| id | doaj-art-d591d50b708f4a97a1b4f2865701d795 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-d591d50b708f4a97a1b4f2865701d7952025-08-20T15:05:07ZengPeerJ Inc.PeerJ Computer Science2376-59922025-08-0111e312110.7717/peerj-cs.3121Leveraging PSO-MLP for intelligent assessment of student learning in remote environments: a multimodal approachJing Wang0Muhammad Asif1College of Education Science, Xin Jiang Normal University, Urumqi, Xinjiang, ChinaDepartment of Computer Science, National Textile University, Faisalabad, PakistanThe rapid advancement of artificial intelligence (AI) has catalyzed transformative changes in education, particularly in mobile and online learning environments. While existing deep learning models struggle to efficiently integrate the complexity of remote education data and optimize model performance, this article proposes an intelligent evaluation method for students’ learning states based on multimodal data. First, the joint characteristics of the pre-class mental status survey information and the health big data of teachers and students in the online teaching process constitute input data. Then, the multilayer perceptron (MLP) is used to intelligently identify the students’ status and classify their enthusiasm for the class. Finally, the particle swarm optimization (PSO) model is used to optimize the model and improve the overall recognition rate. Compared to traditional methods, the PSO-MLP model with combined multimodal data performs well, achieving an accuracy of 0.891. It provides an operational, technical solution for the education system, provides a new AI foundation for personalized teaching and student health management by accurately assessing students’ learning status, and helps to improve the effectiveness and efficiency of remote education.https://peerj.com/articles/cs-3121.pdfRemote educationMobile educational intelligent systemDeep learningPSOMLP |
| spellingShingle | Jing Wang Muhammad Asif Leveraging PSO-MLP for intelligent assessment of student learning in remote environments: a multimodal approach PeerJ Computer Science Remote education Mobile educational intelligent system Deep learning PSO MLP |
| title | Leveraging PSO-MLP for intelligent assessment of student learning in remote environments: a multimodal approach |
| title_full | Leveraging PSO-MLP for intelligent assessment of student learning in remote environments: a multimodal approach |
| title_fullStr | Leveraging PSO-MLP for intelligent assessment of student learning in remote environments: a multimodal approach |
| title_full_unstemmed | Leveraging PSO-MLP for intelligent assessment of student learning in remote environments: a multimodal approach |
| title_short | Leveraging PSO-MLP for intelligent assessment of student learning in remote environments: a multimodal approach |
| title_sort | leveraging pso mlp for intelligent assessment of student learning in remote environments a multimodal approach |
| topic | Remote education Mobile educational intelligent system Deep learning PSO MLP |
| url | https://peerj.com/articles/cs-3121.pdf |
| work_keys_str_mv | AT jingwang leveragingpsomlpforintelligentassessmentofstudentlearninginremoteenvironmentsamultimodalapproach AT muhammadasif leveragingpsomlpforintelligentassessmentofstudentlearninginremoteenvironmentsamultimodalapproach |