Integration Of Learning Resources Research Articles

Student perceptions of knowledge management and institutional readiness for online classes amid Covid-19 pandemic

Higher education has undergone substantial digital change due to the impacts of the COVID-19 pandemic, which altered the status quo in education. Universities across the globe have initiated and adapted various course delivery modes to mitigate this unprecedented situation. Among these responses, online classes continue to be the most common. Therefore, this study examines institutional knowledge management and readiness to sustain online course delivery. Adopting a qualitative research design, this study used two methods, semi-structured interviews (n15) and online open questionnaires (n42), to gather data. Reflexive thematic analysis was then used to analyse the data. The study findings underline that the adaptability, flexibility, and approachability of the virtual learning experience are critical to determining an institution’s readiness for online classes. Based on student perceptions, the faculties of digital skills, integration of innovative pedagogies, student readiness, skills and experience, and integration of learning resources are the main aspects that determine the readiness of universities for online learning.

Closing the Loop in Feedback Driven Learning Environments Using Trust Decision Making and Utility Theory

Contemporary learning systems are an integration of learning resources with human interactions. To close the loop in feedback driven learning environments, the utility of learning objectives needs to be measured. To this end, a comprehensive trust evaluation model for decision making is required to utilize feedback ratings along with other key parameters such as previous course result percentage, active participation and reputation of learners. This paper proposes a novel utility theory based trust evaluation model, wherein the utility of a learning objective is computed in terms of trust applicable to big data-sets. The utility is computed by allowing users to weigh the course related attributes according to their preferences. The utility value facilitates learners to select trustworthy learning objectives and enables instructors to improve different aspects of learning objectives. In addition, a satisfaction index is proposed for the assessment of the usefulness of the computed utility value. The performance of the model is evaluated on a big data-set, which is collected from learners enrolled in different courses of a postgraduate degree program for the purposes of decision making. The results indicate that the proposed unique intelligent model is effective for dynamic and user-specified trust evaluations of learning objectives for the purposes of decision making.