Proposed Student Learning Model in a Hybrid Learning Environment

Abstract

Hybrid learning has become a new norm in higher education institutions, particularly since the COVID-19 pandemic. The hybrid learning environment is faced with many challenges and issues. The motivation for this study is to further substantiate this potential new normal by exploring strategies to address the challenges faced by students in the new learning environment. A learning model was proposed as a strategy for tailoring learning activities according to individual preferences to achieve optimal productivity. Although there are many existing learning strategies and styles, there is a need to propose a model that accommodates student engagement online and, in the classroom, simultaneously. The proposed learning model is formulated to harness the advantages and address the challenges faced by students in hybrid learning environments. The proposed model is called the AIMS (agile, intermittent, meteoric and self-pace) learning model and consists of six characteristic features (study habit/skills, study medium, class participation, learning environment, teaching method format, and assessment preference). The characteristic features were identified from the literature and from several existing learning style models to form the basis for the AIMS learning model. Proposing this learning model is necessary to address the challenges students face in hybrid learning environments, and addressing these challenges can lead to the development of tailored learning and teaching strategies that cater to diverse learning styles, thereby improving the overall learning experience of students. These improved practices positively affect academic performance and can help in the development of tools and platforms that better cater to the diverse learning styles of students. Universities can help distribute resources more effectively, and academics/educators can benefit from these research findings by gaining insights into effective pedagogical approaches to ensuring an optimized learning environment.