دورية أكاديمية
Framework for Suggesting Corrective Actions to Help Students Intended at Risk of Low Performance Based on Experimental Study of College Students Using Explainable Machine Learning Model
| العنوان: | Framework for Suggesting Corrective Actions to Help Students Intended at Risk of Low Performance Based on Experimental Study of College Students Using Explainable Machine Learning Model |
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| اللغة: | English |
| المؤلفون: | Harsimran Singh (ORCID |
| المصدر: | Education and Information Technologies. 2024 29(7):7997-8034. |
| الإتاحة: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.comTest/ |
| تمت مراجعته من قبل الزملاء: | Y |
| Page Count: | 38 |
| تاريخ النشر: | 2024 |
| نوع الوثيقة: | Journal Articles Reports - Evaluative |
| Education Level: | Higher Education Postsecondary Education |
| الواصفات: | College Students, At Risk Students, Academic Achievement, Artificial Intelligence, Models, Identification, Technology Uses in Education, Prediction, Influences, Remedial Instruction, Dropout Prevention, Mental Health, Student Interests |
| DOI: | 10.1007/s10639-023-12072-1 |
| تدمد: | 1360-2357 1573-7608 |
| مستخلص: | Today, the main aim of educational institutes is to provide a high level of education to students, as career selection is one of the most important and quite difficult decisions for learners, so it is essential to examine students' capabilities and interests. Higher education institutions frequently face higher dropout rates, low academic achievement, and graduation delays. One potential answer to these issues is to better leverage student data stored in institutional databases and online learning platforms to forecast students' academic achievements early by using artificial intelligence and advanced computer algorithms. Several research projects have been launched with the goal of building systems that can predict student performance. However, a system that can forecast student performance and identify the various factors that directly impact it is required. The purpose of this research work is to create a model that correctly identifies students who are in danger of low performance, as well as to identify the factors that contribute to this phenomenon and suggesting the remedial actions so as to reduce dropout rate and low performance among students. The emphasis of this study is to explore various factors that may affect mental health which lead to low performance or loss of interest in studies. The developed model can accurately identify at-risk students with over 96.5% accuracy using Machine learning techniques. This study focuses extensively on various factors apart from academics, such as personal and family factors and their association with student performance. To increase the accuracy of performance predictions, the model combines explainable Machine learning techniques to outline the factors associated with poor performance and discusses a novel framework that will help to increase the accuracy of prediction of the established prediction system. This assists low-performing students in improving their academic metrics by executing corrective actions that address the issues. The proposed novel framework, with the help of a mapping table, will recommend corrective actions along with visualization using the heatmap technique which may help the students to perform better in exams, increase the institution's effectiveness, and improves any country's economic growth and stability. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| رقم الانضمام: | EJ1424560 |
| قاعدة البيانات: | ERIC |
| تدمد: | 1360-2357 1573-7608 |
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| DOI: | 10.1007/s10639-023-12072-1 |