Classroom Crisis in Higher Institutions of Learning under the New Normal: Administrative Coping Strategies

https://doi.org/10.55529/jaimlnn.23.9.16

Authors

  • Abubakar Musa Department of Science Education, Faculty of Education, Federal University, Wukari, Nigeria
  • Bala Bakwai Kwashabawa 2Department of Educational Foundation, Faculty of education and extension services, Usmanu Danfodiyo University, Sokoto, Nigeria

Keywords:

Challenges, Higher Education, New Normal, COVID-19, Administrative Strategies, Nigeria

Abstract

Educational institutions globally have suffered setback as a result of the COVID 19 pandemic which led to closure of schools for almost a year. This paper examined classroom crisis in higher institutions of learning under the new normal: administrative coping strategies. Secondary data were used to support the points raised in the paper. The data were sourced from print materials and online publications by recognized institutions and individual authors. The paper identified difficulty in the coverage of syllabus, poor knowledge of information and communication technology (ICT), cancelations of conferences, poor network service, inadequate ICT facilities, expensive data coupled with economic hardship, difficulty in social distance observation, pressure from school management, high rate of drop out and increased in workload as challenges facing higher educational institutions under the new normal. It was also identified that online academic activities, blended learning approach, arrangement of extra classes, running of parallel session and adjustment of academic calendar are some of the administrative strategies employed by schools. It was suggested that schools should provide ICT training for both staff and students, provide adequate ICT facilities, ensure stable electricity supply, amongst others.

Published

2022-04-27

How to Cite

Abubakar Musa, & Bala Bakwai Kwashabawa. (2022). Classroom Crisis in Higher Institutions of Learning under the New Normal: Administrative Coping Strategies. Journal of Artificial Intelligence,Machine Learning and Neural Network , 2(03), 9–16. https://doi.org/10.55529/jaimlnn.23.9.16

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