On the assessment and reliability of political and ideological education in colleges using deep learning methods
The reliability and effectiveness of teaching outcomes are reliant upon the accurate evaluation of ideological and political (IAP) education in colleges. This study focuses on predicting assessment scores to evaluate student performance, identify areas of vulnerability, and implement targeted interv...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825001413 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859412357349376 |
|---|---|
| author | Yongsheng Ma Xianhui Sun Aiqun Ma |
| author_facet | Yongsheng Ma Xianhui Sun Aiqun Ma |
| author_sort | Yongsheng Ma |
| collection | DOAJ |
| description | The reliability and effectiveness of teaching outcomes are reliant upon the accurate evaluation of ideological and political (IAP) education in colleges. This study focuses on predicting assessment scores to evaluate student performance, identify areas of vulnerability, and implement targeted interventions. Sophisticated deep learning techniques including artificial neural networks (ANN), convolutional neural networks (CNN), and support vector machines (SVM) were utilized to enhance the reliability of these evaluations. The results demonstrated clear distinctions between the training and test errors for the models. The ANN exhibited the highest errors, with a training RMSE (root mean squares error) of 14.13 and test RMSE of 13.55, indicating weak generalization. The CNN showed substantial improvement, with a training RMSE of 9.31 and test RMSE of 9.32, reflecting moderate but consistent performance. However, the SVM emerged as the most reliable model, achieving the lowest prediction errors: training RMSE of 7.68 and test RMSE of 8.0, with minimal discrepancies between training and test results. These findings provide valuable insights for instructors and policymakers to refine curriculum delivery, monitor student outcomes, and address educational disparities effectively. By adopting robust models like the SVM, institutions can ensure reliable predictions, fostering a more inclusive and outcome-oriented education system. |
| format | Article |
| id | doaj-art-9461735ce5c34c3598283aadf87cc808 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-9461735ce5c34c3598283aadf87cc8082025-02-11T04:33:37ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119511517On the assessment and reliability of political and ideological education in colleges using deep learning methodsYongsheng Ma0Xianhui Sun1Aiqun Ma2Chemistry & Chemical Engineering and Environment Engineering College, Weifang University, Weifang 261061, Shandong, China; Corresponding author.Food and Drug College, Weifang Vocational College, Weifang 262737, Shandong, ChinaSchool of Marxism, Weifang University, Weifang 261061, Shandong, ChinaThe reliability and effectiveness of teaching outcomes are reliant upon the accurate evaluation of ideological and political (IAP) education in colleges. This study focuses on predicting assessment scores to evaluate student performance, identify areas of vulnerability, and implement targeted interventions. Sophisticated deep learning techniques including artificial neural networks (ANN), convolutional neural networks (CNN), and support vector machines (SVM) were utilized to enhance the reliability of these evaluations. The results demonstrated clear distinctions between the training and test errors for the models. The ANN exhibited the highest errors, with a training RMSE (root mean squares error) of 14.13 and test RMSE of 13.55, indicating weak generalization. The CNN showed substantial improvement, with a training RMSE of 9.31 and test RMSE of 9.32, reflecting moderate but consistent performance. However, the SVM emerged as the most reliable model, achieving the lowest prediction errors: training RMSE of 7.68 and test RMSE of 8.0, with minimal discrepancies between training and test results. These findings provide valuable insights for instructors and policymakers to refine curriculum delivery, monitor student outcomes, and address educational disparities effectively. By adopting robust models like the SVM, institutions can ensure reliable predictions, fostering a more inclusive and outcome-oriented education system.http://www.sciencedirect.com/science/article/pii/S1110016825001413AssessmentReliabilityEducationMachine learningDeep learningPolicymaking |
| spellingShingle | Yongsheng Ma Xianhui Sun Aiqun Ma On the assessment and reliability of political and ideological education in colleges using deep learning methods Alexandria Engineering Journal Assessment Reliability Education Machine learning Deep learning Policymaking |
| title | On the assessment and reliability of political and ideological education in colleges using deep learning methods |
| title_full | On the assessment and reliability of political and ideological education in colleges using deep learning methods |
| title_fullStr | On the assessment and reliability of political and ideological education in colleges using deep learning methods |
| title_full_unstemmed | On the assessment and reliability of political and ideological education in colleges using deep learning methods |
| title_short | On the assessment and reliability of political and ideological education in colleges using deep learning methods |
| title_sort | on the assessment and reliability of political and ideological education in colleges using deep learning methods |
| topic | Assessment Reliability Education Machine learning Deep learning Policymaking |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825001413 |
| work_keys_str_mv | AT yongshengma ontheassessmentandreliabilityofpoliticalandideologicaleducationincollegesusingdeeplearningmethods AT xianhuisun ontheassessmentandreliabilityofpoliticalandideologicaleducationincollegesusingdeeplearningmethods AT aiqunma ontheassessmentandreliabilityofpoliticalandideologicaleducationincollegesusingdeeplearningmethods |