Achieving Academic Satisfaction through the use of Education-Based Technologies: Strengthening Students Personal Well-being While Facing Online Learning Mandates and Digital Disparities

In early 2020, 1.6 billion students were forced to engage in remote learning for the purposes of  education continuation. As a result of online learning mandates, digitized and mobile  technologies have influenced various dimensions of students’ personal well-being. Many  students are subjected to the digital inclusion of having the needed technologies to effectively engage in electronic-based learning platforms and infrastructures. Contrary to such, various  student bodies face pandemic and pre-pandemic digital disparities and social exclusions, thus  denying them the opportunity to participate in electronic-based educational practices required for  academic achievement and success. This study explores how educational-based technologies are  used to increase students' personal well-being and academic satisfaction while also facing online  learning mandates and being subjected to digital disparities. Moreover, this study evaluates how  multifactorial and innovative technologies for educational practices impede or proliferate well being, life and academic satisfaction, and happiness. Seligman’s (2011) PERMA Theory of  Well-being is adopted as a grounding method. The PERMA Theory of Well-being comprises  five distinct pillars: positive emotions, engagements, relationships, meaning, and achievement.  These distinguished dimensions are significant to commissioning educational-based types of  machinery and platforms for the purposes of knowledge acquisition and intellectual  sustainability. In a broader sense, the digital gap persists between communities, generations, and  geographically. Being provided or being able to afford the necessary physical materials and  resources is vital when propagating the idea of taking an online/remote/blended learning  approach. Implications and future suggestions are addressed and discussed.

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.