{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T18:19:48Z","timestamp":1780424388876,"version":"3.54.1"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,11]],"date-time":"2021-04-11T00:00:00Z","timestamp":1618099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The risk of COVID-19 in higher education has affected all its degrees and forms of training. To assess the impact of the pandemic on the learning of university students, a new reference framework for educational data processing was proposed. The framework unifies the steps of analysis of COVID-19 effects on the higher education institutions in different countries and periods of the pandemic. It comprises both classical statistical methods and modern intelligent methods: machine learning, multi-criteria decision making and big data with symmetric and asymmetric information. The new framework has been tested to analyse a dataset collected from a university students\u2019 survey, which was conducted during the second wave of COVID-19 at the end of 2020. The main tasks of this research are as follows: (1) evaluate the attitude and the readiness of students in regard to distance learning during the lockdown; (2) clarify the difficulties, the possible changes and the future expectations from distance learning in the next few months; (3) propose recommendations and measures for improving the higher education environment. After data analysis, the conclusions are drawn and recommendations are made for enhancement of the quality of distance learning of university students.<\/jats:p>","DOI":"10.3390\/info12040163","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T03:04:06Z","timestamp":1618196646000},"page":"163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Effects of COVID-19 Pandemic on University Students\u2019 Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7504-8576","authenticated-orcid":false,"given":"Galina","family":"Ilieva","sequence":"first","affiliation":[{"name":"Department of Management and Quantitative Methods in Economics, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria"},{"name":"Intelligent Systems Department, Institute of Information and Communication Technologies, 1113 Sofia, Bulgaria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5877-3098","authenticated-orcid":false,"given":"Tania","family":"Yankova","sequence":"additional","affiliation":[{"name":"Department of Management and Quantitative Methods in Economics, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1703-412X","authenticated-orcid":false,"given":"Stanislava","family":"Klisarova-Belcheva","sequence":"additional","affiliation":[{"name":"Department of Management and Quantitative Methods in Economics, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetlana","family":"Ivanova","sequence":"additional","affiliation":[{"name":"Department of Management and Quantitative Methods in Economics, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1038\/s41563-020-0678-8","article-title":"Coronavirus pushes education online","volume":"19","author":"Sun","year":"2020","journal-title":"Nat. Mater."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Coman, C., \u021a\u00eeru, L.G., Mese\u0219an-Schmitz, L., Stanciu, C., and Bularca, M.C. (2020). Online Teaching and Learning in Higher Education during the Coronavirus Pandemic: Students\u2019 Perspective. Sustainability, 12.","DOI":"10.3390\/su122410367"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Popa, D., Repanovici, A., Lupu, D., Norel, M., and Coman, C. (2020). Using Mixed Methods to Understand Teaching and Learning in COVID 19 Times. Sustainability, 12.","DOI":"10.3390\/su12208726"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105194","DOI":"10.1016\/j.childyouth.2020.105194","article-title":"Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India","volume":"116","author":"Kapasia","year":"2020","journal-title":"Child. Youth Serv. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1080\/07294360.2020.1823945","article-title":"Toward a \u2018new normal\u2019 with e-learning in Vietnamese higher education during the post COVID-19 pandemic","volume":"39","author":"Pham","year":"2020","journal-title":"High. Educ. Res. Dev."},{"key":"ref_6","first-page":"536","article-title":"Remote education development trends during pandemic in Russia","volume":"11","author":"Kabanova","year":"2020","journal-title":"Rev. Univ. Zulia"},{"key":"ref_7","first-page":"1","article-title":"The COVID-19 Distance Learning: Insight from Ukrainian students","volume":"5","author":"Nenko","year":"2020","journal-title":"Rev. Bras. Educ. Ccedil Campo"},{"key":"ref_8","first-page":"15","article-title":"Students\u2019 Perceptions of the Twists and Turns of E-learning in the Midst of the Covid 19 Outbreak","volume":"12","author":"Minghat","year":"2020","journal-title":"Rev. Rom. Pentru Educ. Multidimens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lassoued, Z., Alhendawi, M., and Bashitialshaaer, R. (2020). An Exploratory Study of the Obstacles for Achieving Quality in Distance Learning during the COVID-19 Pandemic. Educ. Sci., 10.","DOI":"10.3390\/educsci10090232"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106176","DOI":"10.1016\/j.dib.2020.106176","article-title":"Dataset on the Acceptance of e-learning System among Universities Students\u2019 under the COVID-19 Pandemic Conditions","volume":"32","author":"Alqudah","year":"2020","journal-title":"Data Brief"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.compedu.2017.05.007","article-title":"Analyzing undergraduate students\u2019 performance using educational data mining","volume":"113","author":"Asif","year":"2017","journal-title":"Comput. Educ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.tele.2019.01.007","article-title":"Educational data mining and learning analytics for 21st century higher educa-tion: A review and synthesis","volume":"37","author":"Aldowah","year":"2019","journal-title":"Telemat. Inform."},{"key":"ref_13","first-page":"161","article-title":"University Dropout Prediction through Educational Data Mining Techniques: A Systematic Review","volume":"15","author":"Agrusti","year":"2019","journal-title":"J. eLearn. Knowl. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"42","DOI":"10.21714\/2238-104X2020v10i2-48085","article-title":"Educational Data Mining to Improve E-learning Management: A Systematic Literature Review","volume":"10","author":"Marques","year":"2020","journal-title":"Teor. Pr\u00e1t. Adm. TPA"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"92","DOI":"10.3991\/ijet.v14i14.10310","article-title":"Student academic performance prediction using supervised learning techniques","volume":"14","author":"Imran","year":"2019","journal-title":"Int. J. Emerg. Technol. Learn."},{"key":"ref_16","first-page":"743","article-title":"Early Prediction of University Dropouts\u2014A Random Forest Approach","volume":"240","author":"Behr","year":"2020","journal-title":"Jahrb\u00fccher Natl. Stat."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Vidhya, R., and Vadivu, G. (2020). Towards developing an ensemble based two-level student classification model (ESCM) using advanced learning patterns and analytics. J. Ambient. Intell. Humaniz. Comput., 1\u201311.","DOI":"10.1007\/s12652-020-02375-3"},{"key":"ref_18","first-page":"711","article-title":"Deep Learning with Data Transformation and Factor Analysis for Student Performance Prediction","volume":"11","author":"Dien","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1007\/s10639-019-10068-4","article-title":"Predictive analytics in education: A comparison of deep learning frameworks","volume":"25","author":"Doleck","year":"2020","journal-title":"Educ. Inf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chang, W., Ji, X., Liu, Y., Xiao, Y., Chen, B., Liu, H., and Zhou, S. (2020). Analysis of University Students\u2019 Behavior Based on a Fusion K-Means Clustering Algorithm. Appl. Sci., 10.","DOI":"10.3390\/app10186566"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/08923647.2020.1696140","article-title":"Student Engagement Level in an e-Learning Environment: Clustering Using K-Means","volume":"34","author":"Moubayed","year":"2020","journal-title":"Am. J. Distance Educ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100650","DOI":"10.1016\/j.tsc.2020.100650","article-title":"Discussion-record-based prediction model for creativity education using clustering methods","volume":"36","author":"Chien","year":"2020","journal-title":"Think. Ski. Creat."},{"key":"ref_23","first-page":"12","article-title":"Enhanced Analytical Hierarchy Process for U-Learning with Near Field Communication (NFC) Technology","volume":"9","author":"Osman","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_24","unstructured":"Yakup, \u00c7., and T\u00fcyl\u00fc, A.N. (2019). Prioritizing the components of e-Learning systems by using fuzzy DEMATEL and ANP. Interact. Learn. Environ., 1\u201322."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Naveed, Q.N., Qureshi, M.R.N., Tairan, N., Mohammad, A., Shaikh, A., Alsayed, A.O., Shah, A., and Alotaibi, F.M. (2020). Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0231465"},{"key":"ref_26","first-page":"344","article-title":"Early Multi-criteria Detection of Students at Risk of Failure","volume":"9","author":"Ilieva","year":"2020","journal-title":"TEM J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"130","DOI":"10.3102\/0091732X20903304","article-title":"Mining Big Data in Education: Affordances and Challenges","volume":"44","author":"Fischer","year":"2020","journal-title":"Rev. Res. Educ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Domingos, P., and Hulten, G. (2000, January 20\u201323). Mining high-speed data streams. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining\u2014KDD\u201900, Boston, MS, USA.","DOI":"10.1145\/347090.347107"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Adams, N.M., Robardet, C., Siebes, A., and Boulicaut, J.F. (2009). Adaptive Learning from Evolving Data Streams. Advances in Intelligent Data Analysis VIII. IDA 2009. Lecture Notes in Computer Science, 5772, Springer.","DOI":"10.1007\/978-3-642-03915-7"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., Yu, P.S., Han, J., and Wang, J. (2003, January 9\u201312). A Framework for Clustering Evolving Data Streams. Proceedings of the 2003 VLDB Conference, Berlin, Germany.","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ackermann, M.R., Raupach, C., Lammersen, C., Sohler, C., M\u00e4rtens, M., and Swierkot, K. (2010, January 16). StreamKM++: A Clustering Algorithm for Data Streams. Proceedings of the Twelfth Workshop on Algorithm Engineering and Experiments (ALENEX 2010), Austin, TX, USA.","DOI":"10.1137\/1.9781611972900.16"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Udupi, P.K., Sharma, N., and Jha, S.K. (2016, January 7\u20139). Educational data mining and big data framework for e-Learning environment. Proceedings of the 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India.","DOI":"10.1109\/ICRITO.2016.7784961"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s10639-018-9838-8","article-title":"A framework for smart academic guidance using educational data mining","volume":"24","author":"Mimis","year":"2018","journal-title":"Educ. Inf. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Villegas-Ch, W., Palacios-Pacheco, X., and Luj\u00e1n-Mora, S. (2020). A Business Intelligence Framework for Analyzing Educational Data. Sustainability, 12.","DOI":"10.3390\/su12145745"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"298","DOI":"10.15547\/tjs.2017.s.01.053","article-title":"Business intelligence and analytics\u2014Contemporary system model","volume":"15","author":"Ilieva","year":"2017","journal-title":"Trakia J. Sci."},{"key":"ref_36","unstructured":"Ilieva, G., Yankova, T., Klisarova-Belcheva, S., and Ivanova, S. (2021). Impact of lockdown on university students\u2019 learning process during the COVID-19 pandemic in Southern Central Bulgaria. Mendeley Data 2021, Elsevier."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5162","DOI":"10.1016\/j.eswa.2010.10.046","article-title":"Determination of weights for ultimate cross efficiency using Shannon entropy","volume":"38","author":"Wu","year":"2011","journal-title":"Expert Syst. Appl."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/4\/163\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:28:00Z","timestamp":1760365680000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/4\/163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,11]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["info12040163"],"URL":"https:\/\/doi.org\/10.3390\/info12040163","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,11]]}}}