Design and evaluation of a revised ARCS motivational model for online classes in higher education

高等教育在线课程中修订版ARCS激励模型的设计与评估

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Abstract

In recent years, online MOOCs (Massive Open Online Courses) have been quite popular among universities which helps learners to enhance their competency skills apart from learning the regular college/university curriculum. Although distance education and online learning have been adopted gradually recently, it has become the 'New Normal.' In this situation of uncertainty, facilitators must keep themselves updated with the various teaching/learning strategies and encourage learners to get accustomed to the online classroom environment. Furthermore, assisting the learners with active engagement in the classes is essential. Hence, to create an instigated environment for assessing the competency level and addressing the motivational behaviour of the learners in the online courses, a modified version of the "ARCS" (Attention, Relevance, Confidence, and Satisfaction) model is used in this research work. The core objective of this model is to apply a modified motivational model, namely "ARCS-PC," where PC represents Professional Competency. Professional competency includes Critical Thinking skills, Digital literacy, Creative Thinking, Problem-solving, and Time Management. The incorporation of digital quizzes, assignments, and interactive activities using Information and Communication Technology (ICT) tools was done in the ARCS-PC Model. The online classroom lectures and activities were conducted using the Microsoft Teams (MS Teams) educational platform. Linear regression is performed to analyze the modified ARCS-PC model. These technology-enabled online classes and ICT tools have helped teach lifelong learning, collaborative learning, a student-centric approach, and better competency skills to effectively engage students in online courses. In our proposed method, an improvement of 11.21 % was observed in the student's performance compared to a maximum of 8.8 % in the existing traditional models. Detailed analysis and quantification of the proposed method are given in the paper.

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