EXTREME LEARNING MACHINES BASED ANALYSIS OF THE IMPACT OF ACTION LEARNING ON DECISION-MAKING STYLES OF SCHOOL ADMINISTRATORS

Year 2018, Volume: 3 Issue: 2, 15 - 23, 15.09.2018

Dönüş Şengür

Abstract

School administrators need to be trained by using practice-based training approaches to make right decisions. Action learning (AL) is one of the approaches to serve this aim. But, there is a
need for empirical evidences to show the impact of learning action on school administrators’ decision-making styles. In this paper, a novel framework is proposed for determination of the school administrators who trained through an AL course where they improved their decision-making skills for various conditions and environments. To this end, a popular single layered feed forward neural network structure namely extreme learning machine (ELM) is used to distinguish the trained and nontrained school administrators based on their Melbourne Decision Making Questionnaire (MDMQ) output. The MDMQ output is a data set where it was constructed based on a pre-test and post-tests. The pre and post-tests were applied to a number of school administrators and school administrator candidates in Elazig providence in Turkey. MDMQ was used to collect data before and after the AL course. A series of computer simulations were carried out on MATLAB environment. 5-fold cross validation technique is used in evaluation of the proposed method. The achievements were measured by accuracy, sensitivity and specificity criteria. The computer simulations show that ELM produced reasonable results in distinguishing trained and non-trained school administrators. We further compare the ELM results with several support vector machines (SVM) classifiers. In comparisons, it is seen that both ELM and SVM methods performed better in three different simulations. Results showed that AL based training course has a measurable impact on school managers' decision-making styles. 

Keywords

Decision-making, action learning, school administrators, MDMQ, ELM and SVM

EXTREME LEARNING MACHINES BASED ANALYSIS OF THE IMPACT OF ACTION LEARNING ON DECISION-MAKING STYLES OF SCHOOL ADMINISTRATORS

Abstract

School
administrators generally encounter various problems while managing and
providing services where some urgent decisions need to be taken. Thus, the
right decision-making makes the school administrators stronger while managing
the chaotic environments. School administrators need to be trained by using
practice-based training approaches to make right decisions. Action learning
(AL) is one of the approaches to serve this aim. But, there is a need for
empirical evidences to show the impact of learning action on school
administrators’ decision-making styles. In this paper, a novel framework is
proposed for determination of the school administrators who trained through an AL
course where they improved their decision-making skills for various conditions
and environments. To this end, a popular single layered feed forward neural
network structure namely extreme learning machine (ELM) is used to distinguish
the trained and non-trained school administrators based on their MDMQ output.
The MDMQ output is a dataset where it was constructed based on a pre-test and
post-tests. The pre and post-tests were applied to a number of school
administrators and school administrator candidates in Elazig providence in
Turkey. Melbourne Decision-Making Questionnaire (MDMQ) was used to collect data
on school administrators' decision-making styles before and after the AL
course. A series of computer simulations (experimental works) were carried out on
MATLAB environment. 5-fold cross validation technique is used in evaluation of
the proposed method. The achievements were measured by accuracy, sensitivity
and specificity criteria. The computer simulations were conducted based on the
two scenarios. All MDMQ outputs were used in the first scenario to distinguish
the trained and non-trained school administrators. In the second scenario, each
factor of the MDMQ output is used independently to distinguish the trained and
non-trained school administrators. The computer simulations show that ELM
produced reasonable results in distinguishing trained and non-trained school
administrators. We further compare the ELM results with several support vector
machines (SVM) classifiers. In comparisons, it is seen that both ELM and SVM
methods performed better in three different simulations. Results showed that AL
based training course has a measurable impact on school managers'
decision-making styles.

Keywords

Decision-making, action learning, school administrators, MDMQ, ELM

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