electronics Communication Analyzing the Features Affecting the Performance of Teachers during Covid-19: A Multilevel Feature Selection Alqahtani Saeed 1 , Raja Habib 2 , Maryam Zaffar 2, *, Khurrum Shehzad Quraishi 3 , Oriba Altaf 2 , Muhammad Irfan 4 , Adam Glowacz 5 , Ryszard Tadeusiewicz 6 , Mohammed Ayed Huneif 7 , Alqahtani Abdulwahab 7 , Sharifa Khalid Alduraibi 8 , Fahad Alshehri 8 , Alaa Khalid Alduraibi 8 and Ziyad Almushayti 8   Citation: Saeed, A.; Habib, R.; Zaffar, M.; Quraishi, K.S.; Altaf, O.; Irfan, M.; Glowacz, A.; Tadeusiewicz, R.; Huneif, M.A.; Abdulwahab, A.; et al. Analyzing the Features Affecting the Performance of Teachers during Covid-19: A Multilevel Feature Selection. Electronics 2021, 10, 1673. https://doi.org/10.3390/ electronics10141673 Academic Editors: Jing Jin, Ali Zemouche, Ian Daly, Peng Xu, Ren Xu, Feng Duan and Junfeng Sun Received: 30 May 2021 Accepted: 9 July 2021 Published: 13 July 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Surgery, Faculty of Medicine, Najran University, Najran 61441, Saudi Arabia; alhafezsaeed@gmail.com 2 Department of Computer Science and Information Technology, University of Lahore, Islamabad 44000, Pakistan; raja.habib@se.uol.edu.pk (R.H.); Aribakahan@gmail.com (O.A.) 3 Department of Process Engineering, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 44000, Pakistan; qureshiksq@pieas.edu.pk 4 Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia; miditta@nu.edu.sa 5 Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, aleja Adama Mickiewicza 30, 30-059 Kraków, Poland; adglow@agh.edu.pl 6 Department of Biocybernetics and Biomedical Engineering, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, aleja Adama Mickiewicza 30, 30-059 Kraków, Poland; rtad@agh.edu.pl 7 Department of Pediatrics, College of Medicine, Najran University, Najran 61441, Saudi Arabia; huneif@hotmail.com (M.A.H.); aaalsharih@nu.edu.sa (A.A.) 8 Department of Radiology, College of Medicine, Qassim University, Buraidah 52571, Saudi Arabia; dr.s.alduraibi@gmail.com (S.K.A.); f.alshehri@qu.edu.sa (F.A.); AI.alderaibi@qu.edu.sa (A.K.A.); ziyadalmushayti@qu.edu.sa (Z.A.) * Correspondence: maryam.zaffar82@gmail.com Abstract: COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM). Keywords: feature selection; teachers; COVID-19; educational data mining; machine learning Electronics 2021, 10, 1673. https://doi.org/10.3390/electronics10141673 https://www.mdpi.com/journal/electronics