MACHINE LEARNING FOR INTELLIGENT ITEM BANK TO ENHANCE THE PROFESSIONAL COMPETENCIES OF SOFTWARE DEVELOPER

Abstract

                The objectives of the paper are to synthesize the components of the item – banking system, and present the framework of the intelligent item - banking by using machine learning techniques to enhance the professional competencies of software developer. Machine learning techniques is applied to simulate human learning and promote the item - banking system, which is a set of tests created with the same standard, using statistical data form the test as a significant tool to evaluate the professional competencies.  The synthesis of the components of the item - banking system was based on the synthetic tables of previous research from 2012 to 2017, the 13 research results from synthesis the item - banking system consisted of 5 main components as follows: user management, question management, examination management, evaluation management, and scoring management.

                The framework of the intelligent item - banking system based on machine learning approaches can be divided into 2 parts which are the relevant users including teachers, students, administrators and the framework of the intelligent item - banking system which is consisting of user management but without machine learning, question management with supervised machine learning to automatically group test items, examination management with supervised machine learning to choose suitable test items for a competency result, examination management with supervised machine learning to predict the test results and give the suggestion, and scoring management without machine learning.