Zhao, Yanling. An Online learning effectiveness guideline based on analytic hierarchy process. Master's Degree(Knowledge and Innovation Management). Chiang Mai University. Library. : Chiang Mai University, 2023.
An Online learning effectiveness guideline based on analytic hierarchy process
Abstract:
The global outbreak of COVID-19 has significantly impacted the lifestyles of individuals globally, with students being severely influenced due to a critical shift from traditional offline to the new norm of online learning. This relatively uncharted territory in learning-teaching methodology has resulted in challenges concerning adaptation both from a students perspective as well as the teachers. The primary objective of this research endeavour is to elucidate the factors influencing the efficacy of online learning for students and propose methodologies to enhance this efficacy. Pertinent data concerning the contemporary status of student learning was collected through comprehensive questionnaire surveys. Subsequently, the Delphi method was employed to identify factors contributing to the effectiveness of student learning online. These factors underwent evaluation by leveraging the Analytic Hierarchy Process (AHP) method. A strategic approach via CommonKADS was used to formulate practical guidelines, aiming at the augmentation of online learning effectiveness. This studys outcomes delineate two distinct levels of influencing factors, which include three primary (level 1) factors, along with ten secondary (level 2) factors. Both levels are accompanied by implications concerning their levels of importance. The research outputs, in turn, contribute to the compilation of recommendations and directives for stakeholders involved in the online learning process, concerning the identified factors. The insights derived from this study serve a dual purpose. They promote the effectiveness of online learning for students and also shed light on improvised teaching methods for teachers, institutions, and societal engagements during analogous occurrences in the future.