Sangle Santosh
1*,
Joshi Ajinkya
2,
Pawar Padmakar
3,
Nandurkar Keshav
4
- Assistant Professor, Production Engineering, K. K. Wagh Institute of Engineering Education and Research, Nashik, India
- Associate Professor and I/C Registrar, K. K. Wagh Institute of Engineering Education and Research, Nashik, India
- Professor and Head, Production Engineering, K. K. Wagh Institute of Engineering Education and Research, Nashik, India
- Principal, K. K. Wagh Institute of Engineering Education and Research, Nashik, India
Abstract
The comparison of pre and post Covid-19 performance is now being made in many areas. The invisible enemy in the form of Covid-19 virus has put a sturdy impact on many sectors and has forced these sectors to change their mode of operation. Education sector is the most affected sector due to this pandemic. Teachers have adapted to the online teaching methodology through various e-platforms. Feedback collected from the teachers suggested that most of the teachers are facing the problem of engaging the students effectively during the online lectures. Also it is challenging to ensure that students have acquired all knowledge levels as per Bloom's Taxonomy during online learning.A study was undertaken wherein 200 students were randomly divided into four equal groups. Online lectures on the same topic by the same instructor were arranged for every group. A different methodology of content delivery namely a) explanation by display of textual information, b) use of pictures, c) use of video content and d) use of numerical examples based on different learning styles reported in the literature. At the end of the lectures, students were assigned six questions corresponding to each level of the Bloom�s Taxonomy.The student responses were mapped and analyzed. The ranking of groups has been calculated using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). It shows that group number four where text, picture at low level and video, numerical at high level has first rank with score of 0.350. The Pearson correlation between the groups was also determined. The correlation indicates that there is a positive correlation between group two and group four that means a combination of pictures, video and numerical at high level may leads to better results.This result can be useful for teachers to finding the most effective proportion of content type to map various levels of Bloom�s Taxonomy and thereby improve the learning performance of students during online teaching.
References
- Alsaaty, F. M., Carter, E., Abrahams, D. and mAlshameri, F. (2016), Traditional Versus Online Learning in Institutions of Higher Education: Minority Business Students� Perceptions, Business and Management Research, 5(2), pp. 31-41.
- Alshahrani, S., Ahmed, E. and Ward, R. (2017), The influence of online resources on student-lecturer relationship in higher education: a comparison study, Journal of Computers in Education, 4(2), pp. 87-106.
- Brindha, V. E. (2020), Outcome Based Education and Revised Bloom's Taxonomy as a Catalyst for Redesigning Teaching and Learning in Engineering Education, Journal of Engineering Education Transformations, 34(1), pp. 109-114.
- Chug, K. L. and Madhuravani, B. (2016), On-Line Engineering Education with Emphasis on Application of Bloom�s Taxonomy, Journal of Engineering Education Transformations, Special Issue 2016, DOI: 10.16920/jeet/2016/v0i0/85709
- Elfaki, N. K., Abdulraheem, I. and Abdulrahim, R. (2019), Impact of E-Learning vs Traditional Learning on Student�s Performance and Attitude, International Journal of Medical Research & Health Sciences, 8(10), pp. 76-82.
- Felder, R. M. and Silverman, L. K. (1988), Learning and Teaching styles in Engineering Education, Engineering. Education, 78(7), pp. 674-681.
- Gillett-Swan, J. (2017), The Challenges of Online Learning Supporting and Engaging the Isolated Learner, Journal of Learning Design, 10(1), pp. 20-30.
- Hurlbut, A. R. (2018), Online vs. traditional learning in teacher education: a comparison of student progress, American Journal of Distance Education, 32(4), pp. 248 266.
- Hwang, C. L. and Yoon, K. (1981), Multiple Attribute Decision Making: Methods and Applications: a state-ofthe art survey, Springer-Verlag, New York.
- Hwang, C. L., Lai, Y. J. and Liu, T. Y. (1993), A New Approach for Multiple Objective Decision Making, Computers and Operational Research, 20(8), pp. 889-899.
- Johan, R. and Harlan, J. (2014), Education Nowadays, International Journal of Educational Science and Research, 4(5), pp. 51-56.
- Joshi, A. S., Deshpande, V. S. and Pawar, P. J. (2017), An application of TOPSIS for selection of appropriate e Governance practices to improve customer satisfaction, Journal of Project Management, 2(3), pp. 93-106.
- Mirakian, E. A. and Hale, L. S. (2007), A Comparison of Online Instruction vs. Traditional Classroom Instruction in an Undergraduate Pharmacology Course, Proceedings of the 3rd Annual GRASP Symposium, Wichita State University, pp. 95-96.
- Paul, J. and Jefferson, F. (2019), A Comparative
- Analysis of Student Performance in an Online vs. Face-to Face Environment Science Course From 2009 to 2016, Frontiers in Computer Science, 1:7, DOI: 10.3389/fcomp.2019.00007
- Rao, R. V. (2007), Decision making in the manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods, Springer Verlag, London.
- Sadiku, M. N. O., Adebo, P. O. and Musa, S. M. (2018), Online Teaching and Learning, International Journals of Advanced Research in Computer Science and Software Engineering, 8(2), 73-75.
- Sangle, S. B., Nandurkar, K. N. and Pawar, P. J. (2020), Incorporating E-assessment tools in teaching for effective and authentic assessment, Journal of Engineering Education Transformations, 33, Special Issue, ICTIEE 2020, pp. 130-136. DOI:10.16920/jeet/2020/v33i0/150081
- Shahabadkar, P., Joshi, A. and Nandurkar, K. (2019), Developing IT Enabled Mechanism for SWOC Analysis: A Case Study, Proceedings of the 2nd International Conference on Manufacturing Excellence, pp. 158-164. ISBN: 978-93-88441-69-8.
- Singh, G. (2016), Challenges for Teachers in the Era of E-learning in India, Scholedge International Journal of Multidisciplinary & Allied Studies, 3(2), pp. 14-18.
- Vijaykumar, N., Gaitonde, V. N., Madhusudana, H. K., Lakkundi, A. R. and Praveenkumar (2016), Attainment of Higher Level of Blooms Taxonomy through Open Ended Activity in Metrology & Quality Engineering lab, Journal of Engineering Education Transformations, Special Issue 2016, DOI: 10.16920/jeet/2016/v0i0/85516
- Yoon, K. (1987) �A Reconciliation Among Discrete Compromise Situations�, Journal of Operational Research Society, 38(3), pp. 277-286.
