دورية أكاديمية
Information Undergraduate and Non-Information Undergraduate on an Artificial Intelligence Learning Platform: An Artificial Intelligence Assessment Model Using PLS-SEM Analysis
| العنوان: | Information Undergraduate and Non-Information Undergraduate on an Artificial Intelligence Learning Platform: An Artificial Intelligence Assessment Model Using PLS-SEM Analysis |
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| اللغة: | English |
| المؤلفون: | Hua-Xu Zhong, Jui-Hung Chang, Chin-Feng Lai, Pei-Wen Chen, Shang-Hsuan Ku, Shih-Yeh Chen |
| المصدر: | Education and Information Technologies. 2024 29(4):4371-4400. |
| 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.com/ |
| Peer Reviewed: | Y |
| Page Count: | 30 |
| تاريخ النشر: | 2024 |
| نوع الوثيقة: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Undergraduate Students, Artificial Intelligence, Programming, STEM Education, Computation, Thinking Skills, Cognitive Style, Independent Study, Information Technology, Information Systems |
| DOI: | 10.1007/s10639-023-11961-9 |
| تدمد: | 1360-2357 1573-7608 |
| مستخلص: | Artificial intelligence (AI) education is becoming an advanced learning trend in programming education. However, AI subjects can be difficult to understand because they require high programming skills and complex knowledge. This makes it challenging to determine how different departments of students are affected by them. This study draws on research in programming education and STEM education to explore the different factors that affect students in AI learning. Therefore, the purpose of this study is to investigate the impact of AI learning platforms on information undergraduate and non-information undergraduate by using a research model. The course was implemented for 65 students in the information undergraduate group and 39 students in the non-information undergraduate group. The findings showed that the two groups had different learning effects under different variables. Students with different cognitive styles may use different skills to positively influence self-regulated learning. This study provides important evidence to understand the learning impact of artificial intelligence among university students from different disciplines. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| رقم الأكسشن: | EJ1416349 |
| قاعدة البيانات: | ERIC |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1360-2357 1573-7608 |
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| DOI: | 10.1007/s10639-023-11961-9 |