Machine and Deep Learning as Perceived by Academic Teaching Staff at Faculty of Nursing in Assuit University | ||||
| Egyptian Journal of Health Care | ||||
| Volume 16, Issue 1, March 2025, Page 749-764 PDF (484.88 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ejhc.2025.420756 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Hanaa Mohamed Ahmed1; Samah Mohamed Abd Allah2; Hanan Abd Allah Abozeid3; Martha Melek Labieb4 | ||||
| 1Assistant Professor of Nursing Administration, Faculty of Nursing- Assuit University, Egypt. | ||||
| 2Professor of Nursing Administration, Faculty of Nursing- Assuit University, Egypt. | ||||
| 3Assistant Professor of Gerontogical Nursing, Faculty of Nursing- Assuit University, Egypt. | ||||
| 4Lecturer of Gerontogical Nursing, Faculty of Nursing- Assuit University, Egypt. | ||||
| Abstract | ||||
| This study investigated the perception of machine and deep learning among academic teaching staff at Assiut University's Faculty of Nursing. Defining machine learning as a subset of artificial intelligence and deep learning as multi-layered neural networks, the study employed a descriptive cross-sectional design. A convenience sample of teaching and assistant teaching staff completed questionnaires about their personal and job characteristics, as well as their understanding of machine and deep learning. The results showed a significant difference in perception: The vast majority of assistant teaching staff reported satisfactory understanding, compared to more than half of teaching staff reporting unsatisfactory understanding (P=0.001). The study concluded that assistant teaching staff demonstrated a significantly higher level of satisfaction and understanding of machine and deep learning compared to teaching staff, with statistically significant differences across all measured dimensions. The researchers recommended implementing educational programs for professors and assistant professors to improve their knowledge of these technologies, and for educational institutions to use this information to inform hiring, training, and evaluation practices, as well as the application of these technologies in the teaching process. | ||||
| Keywords | ||||
| Academic Teaching Staff; Perceived; & Machine and Deep Learning | ||||
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