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Cited by 744Education during COVID-19 pandemicEducational Outcomes and InfluencesE-Learning and COVID-19

The DOI matches its Crossref record; 44 of the 45 references it cites were found in a registry or an index.

Influence of COVID-19 confinement on students’ performance in higher education

Open access
Gonzalez, T., de la Rubia, M. A., Hincz, K. P., Comas-Lopez, M., Subirats, Laia, Fort, Santi, Sacha, G. M.·2020·PLOS ONE
DOI 10.1371/journal.pone.0239490PMC PMC7546684
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Abstract

This study analyzes the effects of COVID-19 confinement on the autonomous learning performance of students in higher education. Using a field experiment with 458 students from three different subjects at Universidad Autónoma de Madrid (Spain), we study the differences in assessments by dividing students into two groups. The first group (control) corresponds to academic years 2017/2018 and 2018/2019. The second group (experimental) corresponds to students from 2019/2020, which is the group of students that had their face-to-face activities interrupted because of the confinement. The results show that there is a significant positive effect of the COVID-19 confinement on students' performance. This effect is also significant in activities that did not change their format when performed after the confinement. We find that this effect is significant both in subjects that increased the number of assessment activities and subjects that did not change the student workload. Additionally, an analysis of students' learning strategies before confinement shows that students did not study on a continuous basis. Based on these results, we conclude that COVID-19 confinement changed students' learning strategies to a more continuous habit, improving their efficiency. For these reasons, better scores in students' assessment are expected due to COVID-19 confinement that can be explained by an improvement in their learning performance.

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Crossref · 10.1371/journal.pone.0239490

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Figures

Fig 1 Representation of the CAT model. Green arrows indicate the path students take when they answer a question correctly. Students follow red arrows when an incorrect answer is given. Grades are represented at the bottom of the figure, improving with each step to the right.
Fig 1 Representation of the CAT model. Green arrows indicate the path students take when they answer a question correctly. Students follow red arrows when an incorrect answer is given. Grades are represented at the bottom of the figure, improving with each step to the right.CC BY · PubMed Central
Fig 2 Scheme of the strategy followed to accurately measure the autonomous work of students and the influence of rewards.
Fig 2 Scheme of the strategy followed to accurately measure the autonomous work of students and the influence of rewards.CC BY · PubMed Central
Fig 3 Description of the experiment.
Fig 3 Description of the experiment.CC BY · PubMed Central
Fig 4 Distribution of self-evaluation activities in “Applied Computing”. (a) Individual scores of all the students in the academic year 2017/2018 (ordered chronologically). Each dot corresponds to a single attempt of a student. (b) Number of attempts in the academic year 2017/2018 distributed in time periods. Inset shows the distribution in the days between May 16 and May 21.
Fig 4 Distribution of self-evaluation activities in “Applied Computing”. (a) Individual scores of all the students in the academic year 2017/2018 (ordered chronologically). Each dot corresponds to a single attempt of a student. (b) Number of attempts in the academic year 2017/2018 distributed in time periods. Inset shows the distribution in the days between May 16 and May 21.CC BY · PubMed Central
Fig 5 Distribution of attempts in the self-evaluation/learning phase in “Design of Water Treatment Facilities”. The horizontal axis corresponds to the dates and the vertical axis to the grades obtained in every individual attempt. The attempts made by all students in the 3 stages are presented.
Fig 5 Distribution of attempts in the self-evaluation/learning phase in “Design of Water Treatment Facilities”. The horizontal axis corresponds to the dates and the vertical axis to the grades obtained in every individual attempt. The attempts made by all students in the 3 stages are presented.CC BY · PubMed Central
Fig 6 Attempts vs. scores ordered chronologically for the three academic years under study in “Applied Computing”.
Fig 6 Attempts vs. scores ordered chronologically for the three academic years under study in “Applied Computing”.CC BY · PubMed Central
Fig 7 Students’ scores in the face-to-face period (before confinement) and in the distance teaching period (during confinement). Data is presented for the 3 academic courses (2017/2018 and 2018/2019 being the control group and 2019/2020 the experimental group) as mean±SD. Symbols indicate statistically significant differences (p<0.05) between course 2019/2020 and 2017/2018 (*), 2019/2020 and 2018/2019 (#) or between the period before and after the confinement during the same course (¶).
Fig 7 Students’ scores in the face-to-face period (before confinement) and in the distance teaching period (during confinement). Data is presented for the 3 academic courses (2017/2018 and 2018/2019 being the control group and 2019/2020 the experimental group) as mean±SD. Symbols indicate statistically significant differences (p<0.05) between course 2019/2020 and 2017/2018 (*), 2019/2020 and 2018/2019 (#) or between the period before and after the confinement during the same course (¶).CC BY · PubMed Central
Fig 8 Results of students enrolled in Metabolism during the last 3 academic years. (a) The score obtained by students in the different activities of the continuous assessment is represented. (b) Proportion of students that passed the continuous assessment activities (score≥5). (c) Proportion of students that did not carry out the continuous assessment activities. From the 11 activities, only 2 are in-person activities under normal conditions (number 5 and 9, highlighted with a box), the other activities were always on-line. The end of in-person teaching and beginning of confinement is indicated with a red arrow. Data is presented as mean±SD. Symbols indicate statistically significant differences (p<0.05) between courses 2017/2018 and 2018/2019 (§), 2017/2018 and 2019/2020 (*) and 2018/2019 and 2019/2020 (#).
Fig 8 Results of students enrolled in Metabolism during the last 3 academic years. (a) The score obtained by students in the different activities of the continuous assessment is represented. (b) Proportion of students that passed the continuous assessment activities (score≥5). (c) Proportion of students that did not carry out the continuous assessment activities. From the 11 activities, only 2 are in-person activities under normal conditions (number 5 and 9, highlighted with a box), the other activities were always on-line. The end of in-person teaching and beginning of confinement is indicated with a red arrow. Data is presented as mean±SD. Symbols indicate statistically significant differences (p<0.05) between courses 2017/2018 and 2018/2019 (§), 2017/2018 and 2019/2020 (*) and 2018/2019 and 2019/2020 (#).CC BY · PubMed Central

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Research works printed but not linked: 2

  1. FJ García-Peñalvo, 2020, Online Assessment in Higher Education in the Time of COVID-19, Education in the Knowledge Society, 21, 12
  2. FM Lord, 1980, Applications of item response theory to practical problems

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Metadata

Authors
Gonzalez, T., de la Rubia, M. A., Hincz, K. P., Comas-Lopez, M., Subirats, L., Fort, S., & Sacha, G. M.
Affiliations
  • Universidad Autónoma de Madrid (ES)
  • Universitat Oberta de Catalunya (ES)
  • EURECAT Centre Tecnològic de Catalunya (ES)
Corresponding author
Sacha, G. M.
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2020-10-09
Volume
15
Issue
10
Pages
e0239490
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0239490
PMC
PMC7546684
OpenAlex
W3091995338
Cited by
743
References
45
Citations by year
2026: 23 · 2025: 52 · 2024: 71 · 2023: 142 · 2022: 203 · 2021: 230 · 2020: 22
FWCI
226.20
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Hispana (submittedVersion)
  • Biblos-e Archivo (Universidad Autónoma de Madrid) (submittedVersion, public-domain)
Funders
Erasmus+ Program (2017-1-ES01-KA203-038266)
Topics
  • Social Sciences › Education › Education during COVID-19 pandemic
  • Social Sciences › Education › Educational Outcomes and Influences
  • Social Sciences › Education › E-Learning and COVID-19
Keywords
COVID-19 lockdown, university students, student performance, self-directed learning, learning strategies, assessment performance, student workload, Universidad Autónoma de Madrid
MeSH terms
Betacoronavirus, COVID-19, SARS-CoV-2, Adult, Educational Measurement, Female, Humans, Male, Models, Theoretical, Pneumonia, Viral, Spain, Students, Medical, Coronavirus Infections, Education, Distance, Young Adult, Pandemics

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