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Title: | Investigating the learning approaches of students in nursing education |
Authors: | Alsayed, Sharifah Alshammari, Farhan Pasay-an, Eddieson Dator, Wireen Leila |
Keywords: | Comparativee-correlational Deep approach Demographic profiles Learning approaches Nursing students Surface approach |
Issue Date: | 2021 |
Publisher: | Journal of Taibah University Medical Sciences |
Series/Report no.: | Original Article;43-49 |
Abstract: | Objectives: This study investigates the differences between nursing students’ surface and deep approaches to learning across their demographic profiles. Further, this study explores the association between the participants’ ages, year levels, and learning approaches. Methods: From December 2019 to February 2020, we used a quantitative-comparative- correlational study design at the University of Hail KSA. A survey-based questionnaire was used to collect data from 349 randomly selected nursing students. One-way analysis of variance (ANOVA) and t-tests were used to examine the difference between the surface learning and the deep learning approaches of the participants across their profiles. Pearson’s correlation coefficient was used to determine the relationship between participants’ learning approaches, ages, and year levels. Results: A significant difference in the deep learning approach was noted for age (F (3, 345) ¼ 35.71; p ¼ 0.01] and program type (bridging/regular) [t (347) ¼ 8.81, p ¼ 0.01]. A moderately positive significant correlation was found between age and both deep (r ¼ 0.47, n ¼ 349, p ¼ 0.01) and surface (r ¼ 0.45, n ¼ 349, p ¼ 0.01) learning approaches. Conclusion: This study shows that nursing students use both surface and deep learning approaches alike and are able to capitalise on either learning style. Both learning approaches are important and valuable in nursing education. The age of the student is correlated with the learning approach. Older students have higher scores for both deep and surface learning approaches. Academia must develop creative learning environments that can encourage students to use both approaches and to advance the transition to deep learning. |
URI: | http://localhost:8080/xmlui/handle/123456789/6744 |
ISSN: | 1658-3612 |
Appears in Collections: | Vol 16 No 1 (2021) |
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