Intelligent Achievement of Participation in E-Governance Using Machine Learning: A Socio-Technical Approach to Programming Language Education
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This paper explores how e-governance principles and machine learning (ML) technologies can be integrated to support participatory design in educational systems, and, in particular, learning the programming language curriculum. Based on the socio-technical systems theory of Mumford and the ETHICS methodology, we are able to propose a new framework, which will utilize three supervised ML classifiers, including Support Vector machine (SVM), Random Forest (RF) and Decision Tree (DT), to systematically analyze the data of teacher questionnaires collected via e-governance channels. The data set included the answers of 94 teachers in Baghdad, Iraq, and was used to categorize the needs of instructional improvement into six categories: Hardware Development (DH), Demonstration Tools Development (DDT), Teacher Experience Development (DTE), Redistribution of Teacher Sessions (RDTS), Syllabus Development (DS) and Student Ability Development (DSA). The results of the experiment show that SVM attains the maximum classification accuracy of 99.90% in training and 98.00% in testing. The analysis of the results of questionnaires has shown that the most pressing areas of improvement are Syllabus Development (85%), and Student Ability Development (75%). This paper highlights that an active participatory, socio-technical approach to e-governance, one that actively engages educators in decision making, can go a long way in improving the quality and relevance of the educational policy outcomes
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References
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