Attitude Prediction of In-service Teachers Towards Blended Learning Using Machine Learning During COVID-19 Pandemic

Blended learning is an application of Information and Communication Technology which processes in such a way that it can support and complement face-to-face delivery models effectively. It has the potential to ensure educational equity for all learners with complete transparency of rendering education to the community of learners. Today, blended learning has become the need of the hour looking into the aspects of global pandemic of COVID-19 and implementation of Education 4.0. This study consists of 313 in-service teachers from India belonging to various types of Educational Institutions. Simple random technique of sampling was used to collect data. The interaction effect of gender and teachers who have attended/conducted webinars/workshops/conferences/FDPs online or not on their attitude towards blended learning and its six dimensions viz. learning flexibility, online learning, study management, technology, classroom learning and online interaction was studied. Also, the interaction between the effects of highest educational qualification of teachers and teachers who have attended/conducted webinars/workshops/conferences/FDPs online or not on their attitude towards Blended Learning and its six dimensions was considered. Analysis for the testing research hypothesis was done using ANOVA. The relations were evaluated and on the selected relations machine learning techniques for attitude prediction were applied. Out of the three ensemble machine learning techniques and three artificial neural network techniques applied, one gave very promising results.