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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;

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

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.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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