A Probe into Student Responsiveness towards Synchronous and Asynchronous Communication in the Contemporary COVID 19 Pandemic Pedagogy
Effective educational communication strategies during this pandemic phase of COVID-19 keeps students bound to the learning goals, and Qatar schools have achieved successful continuous learning, adapting to the protocols set by the Ministry of Education, Qatar. Accordingly, educational communication strategies and protocols in schools play a great role, contributing towards a more sustainable and positive student responsiveness, generating quality attendance and quality performance. Therefore, the HSREP researches on students’ responsiveness and the educational communication and recommends sustainable education communication protocols for the future high school education.
Research Project #
SEED2020_16
Innovative AI driven Higher Education towards Effective Learning
Contemporary academia learning, despite its disruptive educational technology, as observed gets the learners through every other style of learning in terms of learning re-enforcement exercises on the learning management system, resulting in long hauled study lifestyle. This is identified as a constraint. Using Artificial Intelligence, this study intends to build a learner style prediction model for the students, identifying their learning style that varies from clusters to clusters, analyzing all possible variables that impact their learning style perception and academic results. Methodology is quantitative, analyzing the response data from the survey and reinforcement exercise observation data collected from students.
Research Project #
UREP28-069-5-006
Innovative AI driven Higher Education towards Effective Learning
Contemporary academia witnesses learners as currently exposed to work on all the activities, and accents of the disruptive technology; however, other available interactive modes of delivery don’t get higher adaptability. Proposed Research aims at an innovative and effective learning process, filling in the gap of student learning through Artificial Intelligence profiling their learning styles, thereby catering an effective learning, post-lectures, lessening the long consultation hours of repeated discussions / recordings of the lecture. Proposed methodology includes quantitative- surveying and testing through assessments. This SEED GRANT-17 builds the required lines of argument, insights, and data for strengthening towards the UREP proposal.
