Reflections on and Exploration of Academic Early Warning Management and Support for Students in Colleges and Universities
In this paper, the feature increment can be regarded as a learning mapping function, and a non-equilibrium incremental learning (WILS) method for the academic warning is proposed, and the academic warning model of the non-equilibrium incremental learning method is constructed. The learning factor is...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
|
| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.01327 |
| _version_ | 1797340603552366592 |
|---|---|
| author | Feng Junli Lian Xiaojie |
| author_facet | Feng Junli Lian Xiaojie |
| author_sort | Feng Junli |
| collection | DOAJ |
| description | In this paper, the feature increment can be regarded as a learning mapping function, and a non-equilibrium incremental learning (WILS) method for the academic warning is proposed, and the academic warning model of the non-equilibrium incremental learning method is constructed. The learning factor is regulated by introducing the Focal loss function, and the learned knowledge is integrated into the Focal loss as the final loss function. Finally, the three-dimensional indicators of social characteristics, personal characteristics, and student behavior were used to explore the influencing factors of academic performance and academic support strategies were explored in this way. The results show that the average value of the accuracy of the academic early warning model is 0.857, and the F1-Measure is 0.891, which indicates that the model can reasonably and efficiently provide prior warning of students’ learning situations and behavioral performance. This paper proposes countermeasure suggestions for managing academic early warning and academic support work, which enhances the purpose of talent cultivation quality. |
| first_indexed | 2024-03-08T10:05:34Z |
| format | Article |
| id | doaj.art-3aad688c94164dabbec3dec3047ffb9a |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:05:34Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-3aad688c94164dabbec3dec3047ffb9a2024-01-29T08:52:41ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.01327Reflections on and Exploration of Academic Early Warning Management and Support for Students in Colleges and UniversitiesFeng Junli0Lian Xiaojie11International College, Krirk University, Bangkok, 10220, Thailand.1International College, Krirk University, Bangkok, 10220, Thailand.In this paper, the feature increment can be regarded as a learning mapping function, and a non-equilibrium incremental learning (WILS) method for the academic warning is proposed, and the academic warning model of the non-equilibrium incremental learning method is constructed. The learning factor is regulated by introducing the Focal loss function, and the learned knowledge is integrated into the Focal loss as the final loss function. Finally, the three-dimensional indicators of social characteristics, personal characteristics, and student behavior were used to explore the influencing factors of academic performance and academic support strategies were explored in this way. The results show that the average value of the accuracy of the academic early warning model is 0.857, and the F1-Measure is 0.891, which indicates that the model can reasonably and efficiently provide prior warning of students’ learning situations and behavioral performance. This paper proposes countermeasure suggestions for managing academic early warning and academic support work, which enhances the purpose of talent cultivation quality.https://doi.org/10.2478/amns.2023.2.01327characteristic incrementmapping functionunbalanced incrementalacademic warning modelacademic support90b50 |
| spellingShingle | Feng Junli Lian Xiaojie Reflections on and Exploration of Academic Early Warning Management and Support for Students in Colleges and Universities Applied Mathematics and Nonlinear Sciences characteristic increment mapping function unbalanced incremental academic warning model academic support 90b50 |
| title | Reflections on and Exploration of Academic Early Warning Management and Support for Students in Colleges and Universities |
| title_full | Reflections on and Exploration of Academic Early Warning Management and Support for Students in Colleges and Universities |
| title_fullStr | Reflections on and Exploration of Academic Early Warning Management and Support for Students in Colleges and Universities |
| title_full_unstemmed | Reflections on and Exploration of Academic Early Warning Management and Support for Students in Colleges and Universities |
| title_short | Reflections on and Exploration of Academic Early Warning Management and Support for Students in Colleges and Universities |
| title_sort | reflections on and exploration of academic early warning management and support for students in colleges and universities |
| topic | characteristic increment mapping function unbalanced incremental academic warning model academic support 90b50 |
| url | https://doi.org/10.2478/amns.2023.2.01327 |
| work_keys_str_mv | AT fengjunli reflectionsonandexplorationofacademicearlywarningmanagementandsupportforstudentsincollegesanduniversities AT lianxiaojie reflectionsonandexplorationofacademicearlywarningmanagementandsupportforstudentsincollegesanduniversities |