An ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysis
Abstract Academic data predictions are significantly important for improving the overall education system's effectiveness by providing early identification of weak students and personalized learning strategies. This paper proposes a deep learning model for the identification of weak and strong...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-11-01
|
| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-024-06274-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846171646914723840 |
|---|---|
| author | Harjinder Kaur Tarandeep Kaur Vivek Bhardwaj Mukesh Kumar |
| author_facet | Harjinder Kaur Tarandeep Kaur Vivek Bhardwaj Mukesh Kumar |
| author_sort | Harjinder Kaur |
| collection | DOAJ |
| description | Abstract Academic data predictions are significantly important for improving the overall education system's effectiveness by providing early identification of weak students and personalized learning strategies. This paper proposes a deep learning model for the identification of weak and strong students using ensemble learning and multiparametric analysis. It combines several machine learning algorithms, including Naive Bayes, Support Vector Machines, Multi-Layer Perceptron, and Logistic Regression using an ensemble learning approach to enhance the model’s performance. Additionally, a custom 1D Convolutional Neural Network (CNN) is designed for classification. It utilizes multiparametric analysis to identify weak and strong students considering various parameters such as age, academic performance, location, and online learning behavior. The evaluation results indicate the performance of the proposed model has been improved in comparison to MLA FIS, SHMM, and DRL by 16.5%, 5.5%, and 2.4%, in terms of precision, 16.4%, 6.5%, and 3.5 % in terms of accuracy and 10.4%, 2.5% and 6.5% in terms of recall. These improvisations described that the model is efficient for multidomain feature extraction, ensemble classification, and high-variance feature selection, which result in a deeper understanding of student performance. |
| format | Article |
| id | doaj-art-14964149c097443d9a5d465c0970e27c |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-14964149c097443d9a5d465c0970e27c2024-11-10T12:38:33ZengSpringerDiscover Applied Sciences3004-92612024-11-0161111410.1007/s42452-024-06274-6An ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysisHarjinder Kaur0Tarandeep Kaur1Vivek Bhardwaj2Mukesh Kumar3School of Computer Applications, Lovely Professional UniversitySchool of Computer Applications, Lovely Professional UniversitySchool of Computer Science and Engineering, Manipal University JaipurDepartment of Computer Applications, Chandigarh School of Business, Chandigarh Group of CollegesAbstract Academic data predictions are significantly important for improving the overall education system's effectiveness by providing early identification of weak students and personalized learning strategies. This paper proposes a deep learning model for the identification of weak and strong students using ensemble learning and multiparametric analysis. It combines several machine learning algorithms, including Naive Bayes, Support Vector Machines, Multi-Layer Perceptron, and Logistic Regression using an ensemble learning approach to enhance the model’s performance. Additionally, a custom 1D Convolutional Neural Network (CNN) is designed for classification. It utilizes multiparametric analysis to identify weak and strong students considering various parameters such as age, academic performance, location, and online learning behavior. The evaluation results indicate the performance of the proposed model has been improved in comparison to MLA FIS, SHMM, and DRL by 16.5%, 5.5%, and 2.4%, in terms of precision, 16.4%, 6.5%, and 3.5 % in terms of accuracy and 10.4%, 2.5% and 6.5% in terms of recall. These improvisations described that the model is efficient for multidomain feature extraction, ensemble classification, and high-variance feature selection, which result in a deeper understanding of student performance.https://doi.org/10.1007/s42452-024-06274-6Ensemble deep learningMultiparametric analysisStudent identificationAcademic performanceCustom 1D CNNOnline learning scenarios |
| spellingShingle | Harjinder Kaur Tarandeep Kaur Vivek Bhardwaj Mukesh Kumar An ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysis Discover Applied Sciences Ensemble deep learning Multiparametric analysis Student identification Academic performance Custom 1D CNN Online learning scenarios |
| title | An ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysis |
| title_full | An ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysis |
| title_fullStr | An ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysis |
| title_full_unstemmed | An ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysis |
| title_short | An ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysis |
| title_sort | ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysis |
| topic | Ensemble deep learning Multiparametric analysis Student identification Academic performance Custom 1D CNN Online learning scenarios |
| url | https://doi.org/10.1007/s42452-024-06274-6 |
| work_keys_str_mv | AT harjinderkaur anensembledeeplearningmodelforclassificationofstudentsasweakandstronglearnersviamultiparametricanalysis AT tarandeepkaur anensembledeeplearningmodelforclassificationofstudentsasweakandstronglearnersviamultiparametricanalysis AT vivekbhardwaj anensembledeeplearningmodelforclassificationofstudentsasweakandstronglearnersviamultiparametricanalysis AT mukeshkumar anensembledeeplearningmodelforclassificationofstudentsasweakandstronglearnersviamultiparametricanalysis AT harjinderkaur ensembledeeplearningmodelforclassificationofstudentsasweakandstronglearnersviamultiparametricanalysis AT tarandeepkaur ensembledeeplearningmodelforclassificationofstudentsasweakandstronglearnersviamultiparametricanalysis AT vivekbhardwaj ensembledeeplearningmodelforclassificationofstudentsasweakandstronglearnersviamultiparametricanalysis AT mukeshkumar ensembledeeplearningmodelforclassificationofstudentsasweakandstronglearnersviamultiparametricanalysis |