Neural network-based data analysis for medical-surgical nursing learning

Author

Fernandez-Aleman, Jose Luis ; Jayne, Chrisina ; Sanchez, A.B. ; Carrillo-de-Gea, J.M. ; Toval, Ambrosio

Author_Institution

Res. Group of Software Eng., Univ. of Murcia, Murcia, Spain

fYear

2012

fDate

Aug. 28 2012-Sept. 1 2012

Firstpage

6036

Lastpage

6039

Abstract

This paper presents the results of a project on neural network-based data analysis for knowledge clustering in a second-year course on medical-surgical nursing. Data was collected from 208 nursing students which performed one Multiple Choice Question (MCQ) test at the end of the first term. A total of 23 pattern groups were created using snap-drift. Data obtained can be integrated with an on-line MCQ system for training purposes. Findings about how students are classified suggest that the level of knowledge of the individuals can be addressed by customized feedback to guide them towards a greater understanding of particular concepts.

Keywords

biomedical education; computer aided instruction; data analysis; educational courses; further education; medical computing; neural nets; patient care; pattern clustering; surgery; MCQ test; customized feedback; individual knowledge level; knowledge clustering; medical-surgical nursing learning; multiple choice question test; neural network-based data analysis; nursing students; online MCQ system; pattern group; second-year course; snap-drift; training purpose; Biological neural networks; Feature extraction; Knowledge engineering; Medical services; Reliability; Training; Cluster Analysis; Education, Nursing; Humans; Inservice Training; Learning; Neural Networks (Computer); Nursing;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE

Conference_Location

San Diego, CA

ISSN

1557-170X

Print_ISBN

978-1-4244-4119-8

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/EMBC.2012.6347370

Filename

6347370