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Title: EXPERIMENTAL LEARNING: USING E-NUTRITION TO COLLECT DATA ON EATING PATTERN ON THE CAMPUS OF THE UNIVERSITY OF ZIMBABWE

Author
item Harris, Ellen
item NKUNGULA, ALICE - UNIV OF ZIMBABWE

Submitted to: African Journal of Food, Agriculture, Nutrition and Development
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/10/2004
Publication Date: 2/8/2005
Citation: Harris, E.W., Nkungula, A. 2004. Experimental learning: using e-nutrition to collect data on eating pattern on the campus of the University of Zimbabwe. 2005. African Journal of Food, Agriculture, Nutrition and Development. 4:2

Interpretive Summary: Experiential learning was used as a technique to teach nutrition assessment at the University of Zimbabwe. Class projects were designed to assess the University's food environment and its ability to allow people on campus to meet Food Guide Pyramid recommendations. The students collected various food and nutrition data. They analyzed the data using dietary analysis software and available food composition data on African foods. This project allowed the students to see for the first time nutrient breakdowns of their most common foods. Findings from this study will be useful to nutrition educators, researchers, and professionals in international development.

Technical Abstract: Experiential learning was used as a technique to teach nutrition assessment at the University of Zimbabwe. Students were divided into four groups to assess the University's food environment and its ability to allow people on campus to meet Food Guide Pyramid recommendations. The campus food environment was defined as (a) sources of food for staff and faculty, (b) sources of food for students, (c) staff and faculty food intake, and (d) student food intake. Students designed their class projects based on lectures, which systematically introduced them to basic concepts of survey research methodology, questionnaire development, interviewing techniques, data analysis, and presentation. Demographic, food practices, nutrition knowledge, food intake, and food frequency data were collected by the students. Diet Analysis+, Version 4 was used and modified using the Food Composition Table for Use in Africa, the Composition of Foods Commonly Eaten in East Africa, Nutritive Value of Foods of Zimbabwe, and Indigenous and Traditional Foods in Zimbabwe. Actual findings from the data collected by the students and the challenging aspects of using computer hardware, nutrient analysis software and modifying it to include limited local African food composition data are presented in the manuscript.