THE EFFECT OF CONCEPT MAPPING TEACHING METHOD ON THE LEARNING OF HEALTH ASSESSMENT LESSON IN NURSING STUDENTS OF URMIA NURSING AND MIDWIFERY FACULTY 2013

New educational strategies are needed in learning the basic issues in nursing education. Developed concept mapping is a strategy to facilitate the thinking, teaching, learning and evaluation. The aim of this study was to compare the effects of education in the form of concept mapping method on learning of health assessment lesson in nursing students.

In an experimental study with two groups, pre-test post-test design, 40 nursing students of the third semester were selected through convenient sampling method, and they were randomly divided into two groups of experiment and control. The pretest students in the experimental group were trained by using concept maps for six sessions of two hours, and the control group received conventional method training for five sessions of two hours. Then, they took the post-test. Data gathering tool was a researcher made test consisted of two parts: The first part included demographic characteristics and, and the second part consisted of forty multiple-choice questions to assess learning on health assessment. The results were analyzed by using binominal test, independent and paired t-tests by SPSS19 software.

The mean and standard deviation of the students’ scores before and after applying the conventional method were (8.4 ±1.57) and (9.05 ±1.71) respectively, whereas it was (8.55±2.31) and (12.15±3.74) in the concept map group, respectively. The study results revealed a significant difference between the pre and post training scores in concept map group (P<0001), and in conventional group (P=0/008). Post training scores in concept map group (12.15±3.74) revealed a significant difference comparing to the conventional group (P=0/001).

Due to the effectiveness of concept mapping teaching methods in learning and performance of the students, it is highly recommended to use this method in order to enhance students'' critical thinking and deep learning.