The Development of Connectivism Mobile Learning Expert System for Competency Advice on Job Positioning in Cooperative Education
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Abstract
Background and Objectives: A significant issue facing the Thai construction industry is the fact that civil engineers' actual performance falls below the industry standards. To address this issue, this research developed the mobile connectivism learning expert system that assists with dispensing requisite competency advice for users. It aims to help fulfill industry demands by offering competency advice that civil engineering graduates need to maximize their potential.
Methodology: The research samples were divided into two groups: (1) 410 civil engineering experts who supervised civil engineering students in a cooperative education program, and (2) 127 civil engineering students in a cooperative education program. A questionnaire was used to collect information. Statistical data analysis included the use of percentage, mean, standard deviation, and confirmatory factor analysis.
Main results: Firstly, the competencies that are significant for the self-assessment of cooperative education students in civil engineering consist of three main groups, including professional knowledge and skills, practical knowledge and skills, and attributes. Secondly, the expert system that was developed functions as a responsive web application, accommodating devices with varying screen sizes. It can be used on smartphones, tablets, mobile computers, and personal computers. It uses PHP language, developed mainly for use in smartphones, and users can choose from the following three types of use: (1) The evaluation of cooperative education positions, (2) The recommendations based on cooperative education job positions, and (3) competency-based recommendations. Thirdly, the connectivism mobile learning expert system offers a high level of connectivism in terms of autonomy, diversity, interaction, and openness. Fourthly, learning outcomes from the connectivism mobile learning environment in the expert system are rated at a high level. Regarding the usability testing of the expert system, the students agreed with the positive points and disagreed with or were not sure about the negative points.
Discussion: The connectivism mobile learning in this expert system allows students to develop competencies that are suitable for cooperative education positions without the limitations of place and time. The diversity in learning encourages learners to participate in a variety of readings. There are a variety of environments and diverse discussions. Learning independence encourages students to create a learning plan and track their own learning. Interaction is promoted between students and learners, and open participation removes restrictions that might hinder involvement. However, stimulation and assignment may be added during the use of the mobile associative learning specialist system to further support learning. Additionally, this connectivism mobile learning expert system aligns with the benefits of self-learning by encouraging students to create learning programs and closely evaluate their own progress. On the other hand, students participating in cooperative education must be passionate about creating their own lesson plans and reviewing what they have learned. It will be used effectively and efficiently, according to the connectivism mobile learning expert system.
Conclusion: This connectivism mobile learning expert system can be applied so that students can evaluate job positions that are suitable for their own competencies and receive advice on developing competencies to be suitable for cooperative education positions effectively.
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