The Assessment Methods and its Relationship to Learning Approaches of Nursing Students in Kerman University of Medical Sciences Iran

Background & Objective: There are two basic learning approaches in academic education deep and surface If educational assessment is designed based on deep understanding it will lead to deep learning This study aimed to investigate conventional methods of assessment in medical surgical courses and its relationship with learning approaches in nursing students of Kerman University of Medical Sciences Iran in 2010 Methods: This is a descriptive analytic study A questionnaire consisting of three sections including personal characteristics assessment methods and the revised twofactor study process questionnaire (RSPQ2F) was completed by 198 nursing students Data were analyzed using SPSS and by means standard deviation frequency Students ttest ANOVA Pearson correlation and Rho Results: The most used assessment methods were multiple choice question (MCQ) and Direct Observation Procedural Skills (DOPS) The mean score of deep learning approach was 3427 ± 5006 and of surface learning approach was 3121 ± 552 Regarding the relationship between learning approaches and assessment methods results showed a significant difference (P < 005) MCQ and DOPS alone resulted in the surface approach but a combination of tests and projects in written and DOPS tests lead to deep learning approach in clinical tests (P < 005) Conclusion: Using MCQ and DOPS without concentration on reflection and problem solving lead to surface approach As common assessment methods in this study led to surface approach using projects in written and clinical assessment for deep approach lifelong learning and student empowerment for their future responsibilities is suggested

کلیدواژه های The Assessment Methods and its Relationship to Learning Approaches of Nursing Students in Kerman University of Medical Sciences Iran:

Learning approaches ، Student evaluation ، Assessment methods ، Nursing students ، Medical surgical nursing courses