Abstract:
This article aimed to propose the guideline for selecting academic personnel in computer
with K-Means combined with Association rules (K-MAs). Data were collected from the
questionnaire on computer instruction according to curricula approved by the Offi ce of the
Higher Education Commission (OHEC). In this study, key informants were academic
personnel of the Faculty of Science and Technology in Rajamangala University of Technology
Suvarnabhumi that encountered problems in the diff erences in instructional ability, causing
the diffi cult and complicated instructional management that will aff ect potential curriculum
improvement. For this reason, the researcher searched for the solutions by using K-MAs,
which dividing data set of academic personnel method adjustment of K-Means combined
with association rules. However, K-Mas could produce the results that consider the
importance of the instructors and know the group members. Besides, the rule of associations
indicate that members of the group were able to instruct the same course. Therefore, it could
be concluded that K-MAs technique could be used to support instructor management under
the balance between instructor competency and profi cient profession and to enhance
human relations with fellow scholars due to the realization of professional groups. The results
of this research showed that under the classifi cation of instructor groups based on suitable
professional group, there were fi ve professional groups. Up to 3 instructors could instruct
interchangeably using 18 association rules. The evaluative results of the web application for
selecting academic personnel indicated that overall evaluation result was at a good level.
An aspect with the lowest mean was clear and easy-to-read size of fonts (mean = 3.71). An aspect
with the highest mean was accurate instructor data (mean = 4.86). Therefore, it could be
concluded that the developed web application could be usefully applied to analyze
computer instructor management methods with K-Means combined with Association rules.