Title: Using GA-optimal learning teams and extensive reading to improve medical university students' English language skills and their appreciation of humane values

Authors: Ya-huei Wang; Hung-Chang Liao

Addresses: Department of Applied Foreign Languages, Chung Shan Medical University, Taichung City, Taiwan; Department of Medical Education, Chung Shan Medical University Hospital, Taichung City, Taiwan ' Department of Health Services Administration, Chung Shan Medical University, Taichung City, Taiwan; Department of Medical Education, Chung Shan Medical University Hospital, Taichung City, Taiwan

Abstract: This study is an investigation of the use of an order-based Genetic Algorithm (GA) to divide students into GA-optimal learning teams based on their English listening, speaking and reading competencies, and of whether taking part in such teams improves students' English language competencies, which in turn improves their understanding of medical humanities and the meaning in life scale. A quasi-experimental design was used to verify the feasibility of the proposed GA-optimal learning teams: two learning groups (an experimental group of 33 students in a GA-optimal team learning environment and a control group of 30 students in a conventional team learning environment) underwent a 16-week extensive literature reading. The research results showed that those students in the GA-optimal team learning environment for extensive reading achieved higher scores on English language competencies and a better understanding of medical humanities and the meaning in life scale.

Keywords: order-based genetic algorithms; team learning; extensive reading; medical humanities; meaning in life; medical students; higher education; English language skills; humane values; language learning; language competencies; Taiwan; learning teams; team selection.

DOI: 10.1504/IJTPM.2014.062951

International Journal of Technology, Policy and Management, 2014 Vol.14 No.3, pp.271 - 286

Received: 20 Jun 2013
Accepted: 08 Jan 2014
Published online: 28 Oct 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article