The Role of Asynchronous in Improving Student Achievement
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The Covid-19 pandemic has had a huge impact on the world of education. The learning process is usually carried out face-to-face, but during the Covid-19 pandemic, schools were required to follow government policies regulated by the Ministry of Education and Culture. Where the learning process is carried out in asynchronous learning, so that a teacher is highly demanded to be able to use interesting learning media, create a creative and innovative learning atmosphere so that students can understand learning material easily. Asynchronous is a learning system that is carried out delayed or indirectly, so that the teacher can only provide knowledge not in educating the student's character. However, students can easily access learning materials anytime and anywhere. This research was conducted aiming to describe the role of asynchronous in improving student achievement. This research uses quantitative methods using survey models, google forms and in-depth interviews. The results of this study indicate that student achievement will increase if these students are able to make good use of the asynchronous system in the learning process. The limitation of this study is that researchers have difficulty knowing student learning progress by using an asynchronous system in improving student achievement. Therefore, it is hoped that future researchers will be more thorough in reviewing student achievement using an asynchronous system.
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020). Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology, 296(2), E32–E40. https://doi.org/10.1148/radiol.2020200642
Aljarah, I., Mafarja, M., Heidari, A. A., Faris, H., Zhang, Y., & Mirjalili, S. (2018). Asynchronous accelerating multi-leader salp chains for feature selection. Applied Soft Computing, 71, 964–979. https://doi.org/10.1016/j.asoc.2018.07.040
Al-Maskari, A., Al-Riyami, T., & Kunjumuhammed, S. K. (2022). Students academic and social concerns during COVID-19 pandemic. Education and Information Technologies, 27(1), 1–21. https://doi.org/10.1007/s10639-021-10592-2
Alsoufi, A., Alsuyihili, A., Msherghi, A., Elhadi, A., Atiyah, H., Ashini, A., Ashwieb, A., Ghula, M., Ben Hasan, H., Abudabuos, S., Alameen, H., Abokhdhir, T., Anaiba, M., Nagib, T., Shuwayyah, A., Benothman, R., Arrefae, G., Alkhwayildi, A., Alhadi, A., … Elhadi, M. (2020). Impact of the COVID-19 pandemic on medical education: Medical students’ knowledge, attitudes, and practices regarding electronic learning. PLOS ONE, 15(11), e0242905. https://doi.org/10.1371/journal.pone.0242905
Aoudia, F. A., Gautier, M., Magno, M., Gentil, M. L., Berder, O., & Benini, L. (2018). Long-short range communication network leveraging LoRaTM and wake-up receiver. Microprocessors and Microsystems, 56, 184–192. https://doi.org/10.1016/j.micpro.2017.12.004
Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020). Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Computers in Biology and Medicine, 121, 103795. https://doi.org/10.1016/j.compbiomed.2020.103795
Asgari, S., Trajkovic, J., Rahmani, M., Zhang, W., Lo, R. C., & Sciortino, A. (2021). An observational study of engineering online education during the COVID-19 pandemic. PLOS ONE, 16(4), e0250041. https://doi.org/10.1371/journal.pone.0250041
Baas, P., Scherpereel, A., Nowak, A. K., Fujimoto, N., Peters, S., Tsao, A. S., Mansfield, A. S., Popat, S., Jahan, T., Antonia, S., Oulkhouir, Y., Bautista, Y., Cornelissen, R., Greillier, L., Grossi, F., Kowalski, D., Rodríguez-Cid, J., Aanur, P., Oukessou, A., … Zalcman, G. (2021). First-line nivolumab plus ipilimumab in unresectable malignant pleural mesothelioma (CheckMate 743): A multicentre, randomised, open-label, phase 3 trial. The Lancet, 397(10272), 375–386. https://doi.org/10.1016/S0140-6736(20)32714-8
Barroso, C., Ganley, C. M., McGraw, A. L., Geer, E. A., Hart, S. A., & Daucourt, M. C. (2021). A meta-analysis of the relation between math anxiety and math achievement. Psychological Bulletin, 147(2), 134–168. https://doi.org/10.1037/bul0000307
Baticulon, R. E., Sy, J. J., Alberto, N. R. I., Baron, M. B. C., Mabulay, R. E. C., Rizada, L. G. T., Tiu, C. J. S., Clarion, C. A., & Reyes, J. C. B. (2021). Barriers to Online Learning in the Time of COVID-19: A National Survey of Medical Students in the Philippines. Medical Science Educator, 31(2), 615–626. https://doi.org/10.1007/s40670-021-01231-z
Berjan, S., Vaško, Ž., Ben Hassen, T., El Bilali, H., Allahyari, M. S., Tomi?, V., & Radosavac, A. (2022). Assessment of household food waste management during the COVID-19 pandemic in Serbia: A cross-sectional online survey. Environmental Science and Pollution Research, 29(8), 11130–11141. https://doi.org/10.1007/s11356-021-16485-8
Biktasheva, I. V. (2020). Role of a habitat’s air humidity in Covid-19 mortality. Science of The Total Environment, 736, 138763. https://doi.org/10.1016/j.scitotenv.2020.138763
Borders, W. A., Pervaiz, A. Z., Fukami, S., Camsari, K. Y., Ohno, H., & Datta, S. (2019). Integer factorization using stochastic magnetic tunnel junctions. Nature, 573(7774), 390–393. https://doi.org/10.1038/s41586-019-1557-9
Bryois, J., Skene, N. G., Hansen, T. F., Kogelman, L. J. A., Watson, H. J., Liu, Z., Eating Disorders Working Group of the Psychiatric Genomics Consortium, Adan, R., Alfredsson, L., Ando, T., Andreassen, O., Baker, J., Bergen, A., Berrettini, W., Birgegård, A., Boden, J., Boehm, I., Boni, C., Boraska Perica, V., … Sullivan, P. F. (2020). Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease. Nature Genetics, 52(5), 482–493. https://doi.org/10.1038/s41588-020-0610-9
Cabani, A., Hammoudi, K., Benhabiles, H., & Melkemi, M. (2021). MaskedFace-Net – A dataset of correctly/incorrectly masked face images in the context of COVID-19. Smart Health, 19, 100144. https://doi.org/10.1016/j.smhl.2020.100144
Cao, D., Hu, W., Xu, X., Dragi?evi?, T., Huang, Q., Liu, Z., Chen, Z., & Blaabjerg, F. (2020). Bidding strategy for trading wind energy and purchasing reserve of wind power producer – A DRL based approach. International Journal of Electrical Power & Energy Systems, 117, 105648. https://doi.org/10.1016/j.ijepes.2019.105648
Chakaya, J., Khan, M., Ntoumi, F., Aklillu, E., Fatima, R., Mwaba, P., Kapata, N., Mfinanga, S., Hasnain, S. E., Katoto, P. D. M. C., Bulabula, A. N. H., Sam-Agudu, N. A., Nachega, J. B., Tiberi, S., McHugh, T. D., Abubakar, I., & Zumla, A. (2021). Global Tuberculosis Report 2020 – Reflections on the Global TB burden, treatment and prevention efforts. International Journal of Infectious Diseases, 113, S7–S12. https://doi.org/10.1016/j.ijid.2021.02.107
Cicerone, S., Di Stefano, G., & Navarra, A. (2019). Asynchronous Arbitrary Pattern Formation: The effects of a rigorous approach. Distributed Computing, 32(2), 91–132. https://doi.org/10.1007/s00446-018-0325-7
Clooney, A. G., Eckenberger, J., Laserna-Mendieta, E., Sexton, K. A., Bernstein, M. T., Vagianos, K., Sargent, M., Ryan, F. J., Moran, C., Sheehan, D., Sleator, R. D., Targownik, L. E., Bernstein, C. N., Shanahan, F., & Claesson, M. J. (2021). Ranking microbiome variance in inflammatory bowel disease: A large longitudinal intercontinental study. Gut, 70(3), 499–510. https://doi.org/10.1136/gutjnl-2020-321106
Coman, C., ?îru, L. G., Mese?an-Schmitz, L., Stanciu, C., & Bularca, M. C. (2020). Online Teaching and Learning in Higher Education during the Coronavirus Pandemic: Students’ Perspective. Sustainability, 12(24), 10367. https://doi.org/10.3390/su122410367
Cui, Y., Xu, Y., Yao, H., Bi, P., Hong, L., Zhang, J., Zu, Y., Zhang, T., Qin, J., Ren, J., Chen, Z., He, C., Hao, X., Wei, Z., & Hou, J. (2021). Single?Junction Organic Photovoltaic Cell with 19% Efficiency. Advanced Materials, 33(41), 2102420. https://doi.org/10.1002/adma.202102420
Dai, Y., Zhang, K., Maharjan, S., & Zhang, Y. (2021). Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks. IEEE Transactions on Industrial Informatics, 17(7), 4968–4977. https://doi.org/10.1109/TII.2020.3016320
Daniel, S. J. (2020). Education and the COVID-19 pandemic. PROSPECTS, 49(1–2), 91–96. https://doi.org/10.1007/s11125-020-09464-3
Davis, H. E., Assaf, G. S., McCorkell, L., Wei, H., Low, R. J., Re’em, Y., Redfield, S., Austin, J. P., & Akrami, A. (2021). Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine, 38, 101019. https://doi.org/10.1016/j.eclinm.2021.101019
Deeds, S. A., Dowdell, K. J., Chew, L. D., & Ackerman, S. L. (2019). Implementing an Opt-in eConsult Program at Seven Academic Medical Centers: A Qualitative Analysis of Primary Care Provider Experiences. Journal of General Internal Medicine, 34(8), 1427–1433. https://doi.org/10.1007/s11606-019-05067-7
Derakhshan, A., Kruk, M., Mehdizadeh, M., & Pawlak, M. (2021). Boredom in online classes in the Iranian EFL context: Sources and solutions. System, 101, 102556. https://doi.org/10.1016/j.system.2021.102556
Dong, S., Wu, Z.-G., Pan, Y.-J., Su, H., & Liu, Y. (2019). Hidden-Markov-Model-Based Asynchronous Filter Design of Nonlinear Markov Jump Systems in Continuous-Time Domain. IEEE Transactions on Cybernetics, 49(6), 2294–2304. https://doi.org/10.1109/TCYB.2018.2824799
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Ejsmont, K., Gladysz, B., & Kluczek, A. (2020). Impact of Industry 4.0 on Sustainability—Bibliometric Literature Review. Sustainability, 12(14), 5650. https://doi.org/10.3390/su12145650
Elahi, E., Khalid, Z., Weijun, C., & Zhang, H. (2020). The public policy of agricultural land allotment to agrarians and its impact on crop productivity in Punjab province of Pakistan. Land Use Policy, 90, 104324. https://doi.org/10.1016/j.landusepol.2019.104324
El-Shitany, N. A., Harakeh, S., Badr-Eldin, S. M., Bagher, A. M., Eid, B. G., Almukadi, H. S., Alghamdi, B. S., Alahmadi, A. A., Hassan, N. A., Sindi, N., Alghamdi, S. A., Almohaimeed, H. M., Mohammedsaleh, Z. M., Al-Shaikh, T. M., Almuhayawi, M. S., Ali, S. S., & El-Hamamsy, M. (2021). Minor to Moderate Side Effects of Pfizer-BioNTech COVID-19 Vaccine Among Saudi Residents: A Retrospective Cross-Sectional Study. International Journal of General Medicine, Volume 14, 1389–1401. https://doi.org/10.2147/IJGM.S310497
Engzell, P., Frey, A., & Verhagen, M. D. (2021). Learning loss due to school closures during the COVID-19 pandemic. Proceedings of the National Academy of Sciences, 118(17), e2022376118. https://doi.org/10.1073/pnas.2022376118
Fu, H., Niu, J., Wu, Z., Xue, P., Sun, M., Zhu, H., & Cheng, B. (2022). Influencing Factors of Stereotypes on Wastewater Treatment Plants- Case Study of 9 Wastewater Treatment Plants in Xi’an, China. Environmental Management, 70(3), 526–535. https://doi.org/10.1007/s00267-022-01663-2
Genders, W., & Razavi, S. (2018). Evaluating reinforcement learning state representations for adaptive traffic signal control. Procedia Computer Science, 130, 26–33. https://doi.org/10.1016/j.procs.2018.04.008
Glikson, M., Nielsen, J. C., Kronborg, M. B., Michowitz, Y., Auricchio, A., Barbash, I. M., Barrabés, J. A., Boriani, G., Braunschweig, F., Brignole, M., Burri, H., Coats, A. J. S., Deharo, J.-C., Delgado, V., Diller, G.-P., Israel, C. W., Keren, A., Knops, R. E., Kotecha, D., … Tolosana, J. M. (2021). 2021 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy. European Heart Journal, 42(35), 3427–3520. https://doi.org/10.1093/eurheartj/ehab364
He, Y., Wang, Y., Qiu, C., Lin, Q., Li, J., & Ming, Z. (2021). Blockchain-Based Edge Computing Resource Allocation in IoT: A Deep Reinforcement Learning Approach. IEEE Internet of Things Journal, 8(4), 2226–2237. https://doi.org/10.1109/JIOT.2020.3035437
Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020). Machine Learning in IoT Security: Current Solutions and Future Challenges. IEEE Communications Surveys & Tutorials, 22(3), 1686–1721. https://doi.org/10.1109/COMST.2020.2986444
Iglesias-Pradas, S., Hernández-García, Á., Chaparro-Peláez, J., & Prieto, J. L. (2021). Emergency remote teaching and students’ academic performance in higher education during the COVID-19 pandemic: A case study. Computers in Human Behavior, 119, 106713. https://doi.org/10.1016/j.chb.2021.106713
Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, M. S., Ali, K., John, C. N., Hussain, M. I., & Nabeel, M. (2020). AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Informatics in Medicine Unlocked, 20, 100378. https://doi.org/10.1016/j.imu.2020.100378
Ismailov, M., & Ono, Y. (2021). Assignment Design and its Effects on Japanese College Freshmen’s Motivation in L2 Emergency Online Courses: A Qualitative Study. The Asia-Pacific Education Researcher, 30(3), 263–278. https://doi.org/10.1007/s40299-021-00569-7
Iwaniec, J., & Wang, W. (2022). Motivations to enrol in EMI programmes in China: An exploratory study. Applied Linguistics Review, 0(0). https://doi.org/10.1515/applirev-2021-0180
Jaloudi, S. (2019). Communication Protocols of an Industrial Internet of Things Environment: A Comparative Study. Future Internet, 11(3), 66. https://doi.org/10.3390/fi11030066
Javaid, M., & Khan, I. H. (2021). Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic. Journal of Oral Biology and Craniofacial Research, 11(2), 209–214. https://doi.org/10.1016/j.jobcr.2021.01.015
Ji, D., Zhang, D., Xu, J., Chen, Z., Yang, T., Zhao, P., Chen, G., Cheng, G., Wang, Y., Bi, J., Tan, L., Lau, G., & Qin, E. (2020). Prediction for Progression Risk in Patients With COVID-19 Pneumonia: The CALL Score. Clinical Infectious Diseases, 71(6), 1393–1399. https://doi.org/10.1093/cid/ciaa414
Kaden, U. (2020). COVID-19 School Closure-Related Changes to the Professional Life of a K–12 Teacher. Education Sciences, 10(6), 165. https://doi.org/10.3390/educsci10060165
Khoirunurrofik, K., Abdurrachman, F., & Putri, L. A. M. (2022). Half-hearted policies on mobility restrictions during COVID-19 in Indonesia: A portrait of large informal economy country. Transportation Research Interdisciplinary Perspectives, 13, 100517. https://doi.org/10.1016/j.trip.2021.100517
Ki, D., & Lee, S. (2021). Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning. Landscape and Urban Planning, 205, 103920. https://doi.org/10.1016/j.landurbplan.2020.103920
Klein, P., Ivanjek, L., Dahlkemper, M. N., Jeli?i?, K., Geyer, M.-A., Küchemann, S., & Susac, A. (2021). Studying physics during the COVID-19 pandemic: Student assessments of learning achievement, perceived effectiveness of online recitations, and online laboratories. Physical Review Physics Education Research, 17(1), 010117. https://doi.org/10.1103/PhysRevPhysEducRes.17.010117
Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56(1–2), 508–517. https://doi.org/10.1080/00207543.2017.1351644
Lapitan, L. DS., Tiangco, C. E., Sumalinog, D. A. G., Sabarillo, N. S., & Diaz, J. M. (2021). An effective blended online teaching and learning strategy during the COVID-19 pandemic. Education for Chemical Engineers, 35, 116–131. https://doi.org/10.1016/j.ece.2021.01.012
Li, Y., Zeng, J., Shan, S., & Chen, X. (2019). Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism. IEEE Transactions on Image Processing, 28(5), 2439–2450. https://doi.org/10.1109/TIP.2018.2886767
Li, Z.-M., Chang, X.-H., & Park, J. H. (2021). Quantized Static Output Feedback Fuzzy Tracking Control for Discrete-Time Nonlinear Networked Systems With Asynchronous Event-Triggered Constraints. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(6), 3820–3831. https://doi.org/10.1109/TSMC.2019.2931530
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
Liu, L., Feng, J., Pei, Q., Chen, C., Ming, Y., Shang, B., & Dong, M. (2021). Blockchain-Enabled Secure Data Sharing Scheme in Mobile-Edge Computing: An Asynchronous Advantage Actor–Critic Learning Approach. IEEE Internet of Things Journal, 8(4), 2342–2353. https://doi.org/10.1109/JIOT.2020.3048345
Lukacik, E.-R., Bourdage, J. S., & Roulin, N. (2022). Into the void: A conceptual model and research agenda for the design and use of asynchronous video interviews. Human Resource Management Review, 32(1), 100789. https://doi.org/10.1016/j.hrmr.2020.100789
Machida, M., Nakamura, I., Kojima, T., Saito, R., Nakaya, T., Hanibuchi, T., Takamiya, T., Odagiri, Y., Fukushima, N., Kikuchi, H., Amagasa, S., Watanabe, H., & Inoue, S. (2021). Acceptance of a COVID-19 Vaccine in Japan during the COVID-19 Pandemic. Vaccines, 9(3), 210. https://doi.org/10.3390/vaccines9030210
Mahmood, S. (2021). Instructional Strategies for Online Teaching in COVID ?19 Pandemic. Human Behavior and Emerging Technologies, 3(1), 199–203. https://doi.org/10.1002/hbe2.218
Mastos, T. D., Nizamis, A., Vafeiadis, T., Alexopoulos, N., Ntinas, C., Gkortzis, D., Papadopoulos, A., Ioannidis, D., & Tzovaras, D. (2020). Industry 4.0 sustainable supply chains: An application of an IoT enabled scrap metal management solution. Journal of Cleaner Production, 269, 122377. https://doi.org/10.1016/j.jclepro.2020.122377
Mathieu, E., Ritchie, H., Ortiz-Ospina, E., Roser, M., Hasell, J., Appel, C., Giattino, C., & Rodés-Guirao, L. (2021). A global database of COVID-19 vaccinations. Nature Human Behaviour, 5(7), 947–953. https://doi.org/10.1038/s41562-021-01122-8
Michelson, R., DeWitt, A., Nagar, R., Hiniker, A., Yip, J., Munson, S. A., & Kientz, J. A. (2021). Parenting in a Pandemic: Juggling Multiple Roles and Managing Technology Use in Family Life During COVID-19 in the United States. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1–39. https://doi.org/10.1145/3479546
Murray, C. J., Ikuta, K. S., Sharara, F., Swetschinski, L., Robles Aguilar, G., Gray, A., Han, C., Bisignano, C., Rao, P., Wool, E., Johnson, S. C., Browne, A. J., Chipeta, M. G., Fell, F., Hackett, S., Haines-Woodhouse, G., Kashef Hamadani, B. H., Kumaran, E. A. P., McManigal, B., … Naghavi, M. (2022). Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. The Lancet, 399(10325), 629–655. https://doi.org/10.1016/S0140-6736(21)02724-0
Nagi Alsubari, S., N. Deshmukh, S., Abdullah Alqarni, A., Alsharif, N., H. H. Aldhyani, T., Waselallah Alsaade, F., & I. Khalaf, O. (2022). Data Analytics for the Identification of Fake Reviews Using Supervised Learning. Computers, Materials & Continua, 70(2), 3189–3204. https://doi.org/10.32604/cmc.2022.019625
Nakabi, T. A., & Toivanen, P. (2021). Deep reinforcement learning for energy management in a microgrid with flexible demand. Sustainable Energy, Grids and Networks, 25, 100413. https://doi.org/10.1016/j.segan.2020.100413
Namoun, A., & Alshanqiti, A. (2020). Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review. Applied Sciences, 11(1), 237. https://doi.org/10.3390/app11010237
Newman, D. B., Schug, J., Yuki, M., Yamada, J., & Nezlek, J. B. (2018). The negative consequences of maximizing in friendship selection. Journal of Personality and Social Psychology, 114(5), 804–824. https://doi.org/10.1037/pspp0000141
Nickbakhsh, S., Mair, C., Matthews, L., Reeve, R., Johnson, P. C. D., Thorburn, F., von Wissmann, B., Reynolds, A., McMenamin, J., Gunson, R. N., & Murcia, P. R. (2019). Virus–virus interactions impact the population dynamics of influenza and the common cold. Proceedings of the National Academy of Sciences, 116(52), 27142–27150. https://doi.org/10.1073/pnas.1911083116
Olver, M. E., Stockdale, K. C., Neumann, C. S., Hare, R. D., Mokros, A., Baskin-Sommers, A., Brand, E., Folino, J., Gacono, C., Gray, N. S., Kiehl, K., Knight, R., Leon-Mayer, E., Logan, M., Meloy, J. R., Roy, S., Salekin, R. T., Snowden, R., Thomson, N., … Yoon, D. (2020). Reliability and validity of the Psychopathy Checklist-Revised in the assessment of risk for institutional violence: A cautionary note on DeMatteo et al. (2020). Psychology, Public Policy, and Law, 26(4), 490–510. https://doi.org/10.1037/law0000256
on behalf of the COVID-19 Commission of Accademia Nazionale dei Lincei, Rome, Forni, G., & Mantovani, A. (2021). COVID-19 vaccines: Where we stand and challenges ahead. Cell Death & Differentiation, 28(2), 626–639. https://doi.org/10.1038/s41418-020-00720-9
Onan, A. (2021). Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach. Computer Applications in Engineering Education, 29(3), 572–589. https://doi.org/10.1002/cae.22253
Overmyer, K. A., Shishkova, E., Miller, I. J., Balnis, J., Bernstein, M. N., Peters-Clarke, T. M., Meyer, J. G., Quan, Q., Muehlbauer, L. K., Trujillo, E. A., He, Y., Chopra, A., Chieng, H. C., Tiwari, A., Judson, M. A., Paulson, B., Brademan, D. R., Zhu, Y., Serrano, L. R., … Jaitovich, A. (2021). Large-Scale Multi-omic Analysis of COVID-19 Severity. Cell Systems, 12(1), 23-40.e7. https://doi.org/10.1016/j.cels.2020.10.003
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Systematic Reviews, 10(1), 89. https://doi.org/10.1186/s13643-021-01626-4
Papapanou, M., Routsi, E., Tsamakis, K., Fotis, L., Marinos, G., Lidoriki, I., Karamanou, M., Papaioannou, T. G., Tsiptsios, D., Smyrnis, N., Rizos, E., & Schizas, D. (2022). Medical education challenges and innovations during COVID-19 pandemic. Postgraduate Medical Journal, 98(1159), 321–327. https://doi.org/10.1136/postgradmedj-2021-140032
Pérez López, E., Vázquez Atochero, A., & Cambero Rivero, S. (2020). Educación a distancia en tiempos de COVID-19: Análisis desde la perspectiva de los estudiantes universitarios. RIED. Revista Iberoamericana de Educación a Distancia, 24(1), 331. https://doi.org/10.5944/ried.24.1.27855
Pfeiffer, M., & Pfeil, T. (2018). Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience, 12, 774. https://doi.org/10.3389/fnins.2018.00774
Potra, S., Pugna, A., Pop, M.-D., Negrea, R., & Dungan, L. (2021). Facing COVID-19 Challenges: 1st-Year Students’ Experience with the Romanian Hybrid Higher Educational System. International Journal of Environmental Research and Public Health, 18(6), 3058. https://doi.org/10.3390/ijerph18063058
Purnama, S. G., & Susanna, D. (2020). Attitude to COVID-19 Prevention With Large-Scale Social Restrictions (PSBB) in Indonesia: Partial Least Squares Structural Equation Modeling. Frontiers in Public Health, 8, 570394. https://doi.org/10.3389/fpubh.2020.570394
Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education, 147, 103778. https://doi.org/10.1016/j.compedu.2019.103778
Ratanjee-Vanmali, H., Swanepoel, D. W., & Laplante-Lévesque, A. (2020). Patient Uptake, Experience, and Satisfaction Using Web-Based and Face-to-Face Hearing Health Services: Process Evaluation Study. Journal of Medical Internet Research, 22(3), e15875. https://doi.org/10.2196/15875
Rebecq, H., Ranftl, R., Koltun, V., & Scaramuzza, D. (2021). High Speed and High Dynamic Range Video with an Event Camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(6), 1964–1980. https://doi.org/10.1109/TPAMI.2019.2963386
Ren, C., He, S., Luan, X., Liu, F., & Karimi, H. R. (2021). Finite-Time L 2 -Gain Asynchronous Control for Continuous-Time Positive Hidden Markov Jump Systems via T–S Fuzzy Model Approach. IEEE Transactions on Cybernetics, 51(1), 77–87. https://doi.org/10.1109/TCYB.2020.2996743
Riedel, M., Eisenkolb, G., Amann, N., Karge, A., Meyer, B., Tensil, M., Recker, F., Dobberkau, A. M., Riedel, F., Kuschel, B., & Klein, E. (2022). Experiences with alternative online lectures in medical education in obstetrics and gynecology during the COVID-19 pandemic—Possible efficient and student-orientated models for the future? Archives of Gynecology and Obstetrics, 305(4), 1041–1053. https://doi.org/10.1007/s00404-021-06356-5
Sagi, M., Aurobind, G., Chand, P., Ashfak, A., Karthick, C., Kubenthiran, N., Murthy, P., Komaromy, M., & Arora, S. (2018). Innovative telementoring for addiction management for remote primary care physicians: A feasibility study. Indian Journal of Psychiatry, 60(4), 461. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_211_18
Shea, P., Richardson, J., & Swan, K. (2022). Building bridges to advance the Community of Inquiry framework for online learning. Educational Psychologist, 57(3), 148–161. https://doi.org/10.1080/00461520.2022.2089989
Shi, K., Wang, J., Tang, Y., & Zhong, S. (2020). Reliable asynchronous sampled-data filtering of T–S fuzzy uncertain delayed neural networks with stochastic switched topologies. Fuzzy Sets and Systems, 381, 1–25. https://doi.org/10.1016/j.fss.2018.11.017
Shields, B. J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J. I. M., Janey, J. M., Adams, R. P., & Doyle, A. G. (2021). Bayesian reaction optimization as a tool for chemical synthesis. Nature, 590(7844), 89–96. https://doi.org/10.1038/s41586-021-03213-y
Singal, A., Bansal, A., Chaudhary, P., Singh, H., & Patra, A. (2021). Anatomy education of medical and dental students during COVID-19 pandemic: A reality check. Surgical and Radiologic Anatomy, 43(4), 515–521. https://doi.org/10.1007/s00276-020-02615-3
Sitharthan, R., Karthikeyan, M., Sundar, D. S., & Rajasekaran, S. (2020). Adaptive hybrid intelligent MPPT controller to approximate effectual wind speed and optimal rotor speed of variable speed wind turbine. ISA Transactions, 96, 479–489. https://doi.org/10.1016/j.isatra.2019.05.029
Son, C., Hegde, S., Smith, A., Wang, X., & Sasangohar, F. (2020). Effects of COVID-19 on College Students’ Mental Health in the United States: Interview Survey Study. Journal of Medical Internet Research, 22(9), e21279. https://doi.org/10.2196/21279
Song, B., Zhao, Y., & Zhu, J. (2020). COVID-19-related Traumatic Effects and Psychological Reactions among International Students: Journal of Epidemiology and Global Health, 11(1), 117. https://doi.org/10.2991/jegh.k.201016.001
Szklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., Doncheva, N. T., Legeay, M., Fang, T., Bork, P., Jensen, L. J., & von Mering, C. (2021). The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Research, 49(D1), D605–D612. https://doi.org/10.1093/nar/gkaa1074
Tao, J., Wu, Z.-G., Su, H., Wu, Y., & Zhang, D. (2019). Asynchronous and Resilient Filtering for Markovian Jump Neural Networks Subject to Extended Dissipativity. IEEE Transactions on Cybernetics, 49(7), 2504–2513. https://doi.org/10.1109/TCYB.2018.2824853
Tasso, A. F., Hisli Sahin, N., & San Roman, G. J. (2021). COVID-19 disruption on college students: Academic and socioemotional implications. Psychological Trauma: Theory, Research, Practice, and Policy, 13(1), 9–15. https://doi.org/10.1037/tra0000996
the International Natural Product Sciences Taskforce, Atanasov, A. G., Zotchev, S. B., Dirsch, V. M., & Supuran, C. T. (2021). Natural products in drug discovery: Advances and opportunities. Nature Reviews Drug Discovery, 20(3), 200–216. https://doi.org/10.1038/s41573-020-00114-z
Totura, A. L., & Bavari, S. (2019). Broad-spectrum coronavirus antiviral drug discovery. Expert Opinion on Drug Discovery, 14(4), 397–412. https://doi.org/10.1080/17460441.2019.1581171
Trabelsi, K., Ammar, A., Masmoudi, L., Boukhris, O., Chtourou, H., Bouaziz, B., Brach, M., Bentlage, E., How, D., Ahmed, M., Mueller, P., Mueller, N., Hsouna, H., Romdhani, M., Hammouda, O., Paineiras-Domingos, L., Braakman-jansen, A., Wrede, C., Bastoni, S., … Hoekelmann, A. (2021). Globally altered sleep patterns and physical activity levels by confinement in 5056 individuals: ECLB COVID-19 international online survey. Biology of Sport, 38(4), 495–506. https://doi.org/10.5114/biolsport.2021.101605
Turnbull, D., Chugh, R., & Luck, J. (2021). Transitioning to E-Learning during the COVID-19 pandemic: How have Higher Education Institutions responded to the challenge? Education and Information Technologies, 26(5), 6401–6419. https://doi.org/10.1007/s10639-021-10633-w
Wang, C. J., Ng, C. Y., & Brook, R. H. (2020). Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing. JAMA, 323(14), 1341. https://doi.org/10.1001/jama.2020.3151
Wang, Z.-Y., Zhang, L.-J., Liu, Y.-H., Jiang, W.-X., Tang, S.-L., & Liu, X.-Y. (2021). Process evaluation of E-learning in continuing medical education: Evidence from the China-Gates Foundation Tuberculosis Control Program. Infectious Diseases of Poverty, 10(1), 23. https://doi.org/10.1186/s40249-021-00810-x
Whittington, R., Donat, K., Weber, M. F., Kelton, D., Nielsen, S. S., Eisenberg, S., Arrigoni, N., Juste, R., Sáez, J. L., Dhand, N., Santi, A., Michel, A., Barkema, H., Kralik, P., Kostoulas, P., Citer, L., Griffin, F., Barwell, R., Moreira, M. A. S., … de Waard, J. H. (2019). Control of paratuberculosis: Who, why and how. A review of 48 countries. BMC Veterinary Research, 15(1), 198. https://doi.org/10.1186/s12917-019-1943-4
Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., Karu, N., Sayeeda, Z., Lo, E., Assempour, N., Berjanskii, M., Singhal, S., Arndt, D., Liang, Y., Badran, H., Grant, J., … Scalbert, A. (2018). HMDB 4.0: The human metabolome database for 2018. Nucleic Acids Research, 46(D1), D608–D617. https://doi.org/10.1093/nar/gkx1089
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018). CBAM: Convolutional Block Attention Module. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision – ECCV 2018 (Vol. 11211, pp. 3–19). Springer International Publishing. https://doi.org/10.1007/978-3-030-01234-2_1
Wu, C., Wang, A. C., Ding, W., Guo, H., & Wang, Z. L. (2019). Triboelectric Nanogenerator: A Foundation of the Energy for the New Era. Advanced Energy Materials, 9(1), 1802906. https://doi.org/10.1002/aenm.201802906
Xia, J., Chen, G., Park, J. H., Shen, H., & Zhuang, G. (2021). Dissipativity-Based Sampled-Data Control for Fuzzy Switched Markovian Jump Systems. IEEE Transactions on Fuzzy Systems, 29(6), 1325–1339. https://doi.org/10.1109/TFUZZ.2020.2970856
Xie, F., & Derakhshan, A. (2021). A Conceptual Review of Positive Teacher Interpersonal Communication Behaviors in the Instructional Context. Frontiers in Psychology, 12, 708490. https://doi.org/10.3389/fpsyg.2021.708490
Xu, H., Yu, W., Griffith, D., & Golmie, N. (2018). A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective. IEEE Access, 6, 78238–78259. https://doi.org/10.1109/ACCESS.2018.2884906
Yang, J. D., Hainaut, P., Gores, G. J., Amadou, A., Plymoth, A., & Roberts, L. R. (2019). A global view of hepatocellular carcinoma: Trends, risk, prevention and management. Nature Reviews Gastroenterology & Hepatology, 16(10), 589–604. https://doi.org/10.1038/s41575-019-0186-y
Ye, Z., Zhang, D., Wu, Z.-G., & Yan, H. (2022). A3C-Based Intelligent Event-Triggering Control of Networked Nonlinear Unmanned Marine Vehicles Subject to Hybrid Attacks. IEEE Transactions on Intelligent Transportation Systems, 23(8), 12921–12934. https://doi.org/10.1109/TITS.2021.3118648
Yoo, J. J., Seo, G., Chua, M. R., Park, T. G., Lu, Y., Rotermund, F., Kim, Y.-K., Moon, C. S., Jeon, N. J., Correa-Baena, J.-P., Bulovi?, V., Shin, S. S., Bawendi, M. G., & Seo, J. (2021). Efficient perovskite solar cells via improved carrier management. Nature, 590(7847), 587–593. https://doi.org/10.1038/s41586-021-03285-w
Zerbino, D. R., Achuthan, P., Akanni, W., Amode, M. R., Barrell, D., Bhai, J., Billis, K., Cummins, C., Gall, A., Girón, C. G., Gil, L., Gordon, L., Haggerty, L., Haskell, E., Hourlier, T., Izuogu, O. G., Janacek, S. H., Juettemann, T., To, J. K., … Flicek, P. (2018). Ensembl 2018. Nucleic Acids Research, 46(D1), D754–D761. https://doi.org/10.1093/nar/gkx1098
Zhang, D., Zhou, S., & Tang, Y. (2018). A Low Duty Cycle Efficient MAC Protocol Based on Self-Adaption and Predictive Strategy. Mobile Networks and Applications, 23(4), 828–839. https://doi.org/10.1007/s11036-017-0878-x
Zhao, Y., Guo, Y., Xiao, Y., Zhu, R., Sun, W., Huang, W., Liang, D., Tang, L., Zhang, F., Zhu, D., & Wu, J.-L. (2020). The Effects of Online Homeschooling on Children, Parents, and Teachers of Grades 1–9 During the COVID-19 Pandemic. Medical Science Monitor, 26. https://doi.org/10.12659/MSM.925591
Zhao, Y., Shang, Y., Song, W., Li, Q., Xie, H., Xu, Q., Jia, J., Li, L., Mao, H., Zhou, X., Luo, H., Gao, Y., & Xu, A. (2020). Follow-up study of the pulmonary function and related physiological characteristics of COVID-19 survivors three months after recovery. EClinicalMedicine, 25, 100463. https://doi.org/10.1016/j.eclinm.2020.100463
Zhou, H., Lu, S., Chen, J., Wei, N., Wang, D., Lyu, H., Shi, C., & Hu, S. (2020). The landscape of cognitive function in recovered COVID-19 patients. Journal of Psychiatric Research, 129, 98–102. https://doi.org/10.1016/j.jpsychires.2020.06.022
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