# Investigating the Impact of COVID-19 on E-Learning: Country Development and COVID-19 Response

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## Abstract

**:**

## 1. Introduction

## 2. Background

#### 2.1. The Notion of E-Learning

#### 2.2. E-Learning during the COVID-19 Era

## 3. Methodology

#### 3.1. Data Sources

#### 3.2. Methodological Process

#### 3.2.1. Step 1: Analysis of E-Learning in 2017 and 2020 in Selected European Countries

#### 3.2.2. Step 2: Fuzzy C-Means Clustering Analysis of E-Learning Indicators in 2017 and 2020

#### 3.2.3. Step 3: The Economic and COVID-19 Indicators in E-Learning Clusters in 2017 and 2020

## 4. Results

#### 4.1. Step 1: Analysis of E-Learning in 2017 and 2020 in Selected European Countries

#### 4.2. Step 2: Fuzzy C-Means Clustering Analysis of E-Learning Indicators for 2017 and 2020

#### 4.2.1. Determining the Number of Clusters

#### 4.2.2. Cluster Validation

#### 4.2.3. Cluster Solution

#### 4.2.4. Cluster Members and Characteristics

#### 4.3. Step 3: The Impact of Economic and COVID-19 Indicators on E-Learning Clusters for 2017 and 2020

## 5. Discussion and Conclusions

#### 5.1. Summary of the Research

#### 5.2. Main Conclusions

#### 5.3. Practical Implications

#### 5.4. Research Limitations and Future Research Directions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Mean values of e-learning indicators in 2017 and 2020. Source: Authors’ work based on Eurostat data.

**Figure 3.**Box plots for e-learning indicators in (

**a**) 2017, and (

**b**) 2020. Note: 14—Iceland; 7—Denmark; 31—Switzerland; 9—Finland. Source: Authors’ work based on Eurostat data;

^{⋆}extreme outliner; ° mild outlier.

**Figure 4.**Elbow method plots for (

**a**) 2017, and (

**b**) 2020 to determine the number of clusters. Source: Authors’ work based on Eurostat data.

**Figure 5.**the -SNE cluster plots for the (

**a**) 2017, and (

**b**) 2020 cluster solutions. Source: Authors’ work based on Eurostat data.

**Figure 6.**Mean values of e-learning indicators according to clusters in (

**a**) 2017, and (

**b**) 2020. Source: Authors’ work based on Eurostat data.

**Figure 7.**European countries according to clusters in 2017 (

**upper**), and 2020 (

**below**). Source: Authors’ work based on Eurostat data using mapchart.net.

Variable Code | Measurement; Year | Description |
---|---|---|

E-learning indicators | ||

ONLINE_C_2017 | % of individuals; 2017 | Doing an online course (any subject) |

ONLINE_C_2020 | % of individuals; 2020 | |

ONLINE_M_2017 | % of individuals; 2017 | Using online learning materials |

ONLINE_M_2020 | % of individuals; 2020 | |

ONLINE_COM_2017 | % of individuals; 2017 | Communicating with instructors or students using educational websites/portals |

ONLINE_COM_2020 | % of individuals; 2020 | |

ONLINE_ANY_2017 | % of individuals; 2017 | Doing any of the e-learning activities |

ONLINE_ANY_2020 | % of individuals; 2020 | |

ONLINE_C_M_2017 | % of individuals; 2017 | Doing an online course (any subject) or using online learning materials |

ONLINE_C_M_2020 | % of individuals; 2020 | |

Economic indicators | ||

GDP_PC_2017 | Absolute value; 2017 | GDP per capita in EUR |

GDP_PC_2020 | Absolute value; 2020 | |

COVID-19 indicators | ||

Avg_string_index | Measure 0–100, with 100 being the strictest response; 2020 | The Average Stringency Index is a composite measure based on 9 response indicators (school closures, workplace closures, public event cancellations, meeting restrictions, public transportation closures, requests to stay home, restrictions on internal movement, international travel restrictions, and public information campaigns) |

SI_reopen | Measure 0–100 with 100 being completely open; 2020 | Stringency index at the reopening of a country; shows days of reopening. The values of the SI_reopening index range from 0 to 100 and measure the days when there were no reported COVID-19 cases. |

Step 1 | Step 2 | Step 3 | |
---|---|---|---|

Research Question | RQ1 | RQ2 | RQ3 |

Presumption | COVID-19 had a significant impact on the e-learning indicators | The European countries can be divided into homogeneous groups based on the e-learning indicators in the periods before and during the pandemic | COVID-19 influenced the digitization of countries that had lower economic development in the period before COVID-19 and thus had a significant impact on e-learning |

Observed variables | e-learning indicators in 2017 and 2020 | e-learning indicators in 2017 and 2020 | Cluster membership; economic and COVID-19 indicators in the period before and during COVID-19 across clusters |

Data sources | Eurostat | Eurostat | Results of Step 2; Oxford COVID-19 Government Response Tracker |

Statistical methods | Descriptive statistics; box plot; paired t-test | Fuzzy C-means clustering, ANOVA | Descriptive statistics (economic and COVID-19 indicators); ANOVA; post hoc LSD test |

Expected results of the analysis | Significant differences between e-learning in 2020 and 2017 | Significant differences between clusters in e-learning indicators in 2020 and 2017 | Significant differences between GDP per capita and COVID-19 indicators between clusters |

Expected conclusion | European countries have responded appropriately by increasing e-learning | The level of e-learning in European countries is not uniform, with different implementation levels of e-learning in the periods before and during the pandemic | Both in the period before COVID-19 and the period during COVID-19, the more developed countries used e-learning to a greater extent |

Valid | Mean | Std. Dev. | Min. | Max. | |
---|---|---|---|---|---|

ONLINE_C_2017 | 34 | 7.235 | 4.626 | 2.000 | 20.000 |

ONLINE_M_2017 | 34 | 16.441 | 11.789 | 3.000 | 71.000 |

ONLINE_COM_2017 | 34 | 9.529 | 5.316 | 2.000 | 24.000 |

ONLINE_ANY_2017 | 34 | 21.471 | 12.776 | 4.000 | 74.000 |

ONLINE_C_M_2017 | 34 | 18.853 | 12.263 | 3.000 | 73.000 |

ONLINE_C_2020 | 34 | 14.882 | 6.650 | 3.000 | 32.000 |

ONLINE_M_2020 | 34 | 21.824 | 11.761 | 6.000 | 72.000 |

ONLINE_COM_2020 | 34 | 16.088 | 6.995 | 3.000 | 30.000 |

ONLINE_ANY_2020 | 34 | 29.324 | 12.829 | 10.000 | 76.000 |

ONLINE_C_M_2020 | 34 | 26.059 | 12.170 | 8.000 | 75.000 |

2017 vs. 2020 | t | df | p | Mean Difference | SE Difference | Cohen’s d |
---|---|---|---|---|---|---|

ONLINE_C | 11.412 | 33 | <0.001 *** | 7.647 | 0.670 | 1.957 |

ONLINE_M | 5.035 | 33 | <0.001 *** | 5.382 | 1.069 | 0.864 |

ONLINE_COM | 8.410 | 33 | <0.001 *** | 6.559 | 0.780 | 1.442 |

ONLINE_ANY | 8.472 | 33 | <0.001 *** | 3.265 | 0.385 | 1.453 |

ONLINE_C_M | 6.969 | 33 | <0.001 *** | 7.206 | 1.034 | 1.195 |

**Table 5.**AIC, BIC, and Silhouette indicators of the fuzzy C-means clustering solution for 2017 and 2020.

Year | Clusters | N | R² | AIC | BIC | Silhouette |
---|---|---|---|---|---|---|

2017 | 3 | 34 | 0.570 | 97.510 | 120.410 | 0.260 |

2020 | 3 | 34 | 0.466 | 94.460 | 117.360 | 0.320 |

Metrics | 2017 | 2020 |
---|---|---|

Pearson’s γ | 0.393 | 0.388 |

Calinski–Harabasz index | 22.383 | 24.173 |

Dunn index | 0.030 | 0.051 |

Entropy | 1.003 | 1.058 |

Sum of Squares | df | Mean Square | F | Sig. | ||
---|---|---|---|---|---|---|

2017 | ||||||

ONLINE_C_2017 | Between Groups | 477.940 | 2 | 238.970 | 32.466 | <0.001 *** |

Within Groups | 228.178 | 31 | 7.361 | |||

Total | 706.118 | 33 | ||||

ONLINE_M_2017 | Between Groups | 2045.171 | 2 | 1022.586 | 12.474 | <0.001 *** |

Within Groups | 2541.211 | 31 | 81.975 | |||

Total | 4586.382 | 33 | ||||

ONLINE_COM_2017 | Between Groups | 681.626 | 2 | 340.813 | 42.119 | <0.001 *** |

Within Groups | 250.844 | 31 | 8092 | |||

Total | 932.470 | 33 | ||||

ONLINE_ANY_2017 | Between Groups | 3110.426 | 2 | 1555.213 | 21.182 | <0.001 *** |

Within Groups | 2276.044 | 31 | 73.421 | |||

Total | 5386.470 | 33 | ||||

ONLINE_C_M_2017 | Between Groups | 2595.320 | 2 | 1297.660 | 16.996 | <0.001 *** |

Within Groups | 2366.944 | 31 | 76.353 | |||

Total | 4962.264 | 33 | ||||

Sum of Squares | df | Mean Square | F | Sig. | ||

2020 | ||||||

ONLINE_C_2020 | Between Groups | 940.101 | 2 | 470.050 | 28.053 | <0.001 *** |

Within Groups | 519.429 | 31 | 16.756 | |||

Total | 1459.529 | 33 | ||||

ONLINE_M_2020 | Between Groups | 1890.062 | 2 | 945.031 | 10.952 | <0.001 *** |

Within Groups | 2674.879 | 31 | 86.286 | |||

Total | 4564.941 | 33 | ||||

ONLINE_COM_2020 | Between Groups | 1254.444 | 2 | 627.222 | 53.967 | <0.001 *** |

Within Groups | 360.291 | 31 | 11.622 | |||

Total | 1614.735 | 33 | ||||

ONLINE_ANY_2020 | Between Groups | 3597.507 | 2 | 1798.754 | 30.405 | <0.001 *** |

Within Groups | 1833.934 | 31 | 59.159 | |||

Total | 5431.441 | 33 | ||||

ONLINE_C_M_2020 | Between Groups | 2684.185 | 2 | 1342.092 | 18.880 | <0.001 *** |

Within Groups | 2203.698 | 31 | 71.087 | |||

Total | 4887.882 | 33 |

2017 | 2020 | |||||
---|---|---|---|---|---|---|

Cluster | Cluster 1 | Cluster 2 | Cluster 3 | Cluster 1 | Cluster 2 | Cluster 3 |

Size | 10 | 18 | 6 | 14 | 13 | 7 |

Explained proportion within-cluster heterogeneity | 0.783 | 0.196 | 0.021 | 0.126 | 0.809 | 0.065 |

Within-sum of squares | 52.877 | 13.248 | 1.385 | 8.131 | 52.164 | 4.170 |

Cluster | Descriptives | ONLINE_C | ONLINE_M | ONLINE_COM | ONLINE_ANY | ONLINE_C_M |
---|---|---|---|---|---|---|

2017 | ||||||

Cluster 1 | Mean | 12.90 | 27.70 | 16.10 | 35.20 | 31.50 |

N = 10 | Std. Dev. | 4.254 | 15.791 | 3.446 | 14.382 | 15.255 |

Var. Coeff. | 0.33 | 0.57 | 0.21 | 0.41 | 0.48 | |

Cluster 2 | Mean | 5.44 | 13.61 | 7.77 | 18.22 | 15.72 |

N = 18 | Std. Dev. | 1.822 | 3.928 | 2.819 | 4.735 | 3.770 |

Var. Coeff. | 0.33 | 0.28 | 0.36 | 0.26 | 0.24 | |

Cluster 3 | Mean | 3.16 | 6.16 | 3.83 | 8.33 | 7.16 |

N = 6 | Std. Dev. | 1.329 | 2.639 | 1.329 | 2.582 | 2.483 |

Var. Coeff. | 0.42 | 0.43 | 0.35 | 0.31 | 0.35 | |

Total | Mean | 7.23 | 16.44 | 9.52 | 21.47 | 18.85 |

N = 34 | Std. Dev. | 4.626 | 11.789 | 5.316 | 12.776 | 12.263 |

Var. Coeff. | 0.64 | 0.71 | 0.55 | 0.59 | 0.65 | |

2020 | ||||||

Cluster 1 | Mean | 21.00 | 30.23 | 23.38 | 41.38 | 36.31 |

N = 13 | Std. Dev. | 5.339 | 14.388 | 3.906 | 11.362 | 12.822 |

Var. Coeff. | 0.25 | 0.47 | 0.17 | 0.27 | 0.35 | |

Cluster 2 | Mean | 13.00 | 19.71 | 13.36 | 25.43 | 23.07 |

N = 14 | Std. Dev. | 3.038 | 3.099 | 2.872 | 3.936 | 3.540 |

Var. Coeff. | 0.23 | 0.15 | 0.21 | 0.15 | 0.15 | |

Cluster 3 | Mean | 7.29 | 10.43 | 8.00 | 14.71 | 13.00 |

N = 7 | Std. Dev. | 3.094 | 3.309 | 3.416 | 3.729 | 3.367 |

Var. Coeff. | 0.42 | 0.32 | 0.43 | 0.25 | 0.26 | |

Total | Mean | 14.88 | 21.82 | 16.09 | 29.32 | 26.06 |

N = 34 | Std. Dev. | 6.650 | 11.761 | 6.995 | 12.829 | 12.170 |

Var. Coeff. | 0.45 | 0.54 | 0.44 | 0.44 | 0.47 |

2017 | 2020 | ||
---|---|---|---|

Cluster | Countries | Cluster | Countries |

Cluster 1(n = 10) | Estonia, Finland, Iceland, Luxembourg, Malta, Netherlands, Norway, Spain, Sweden, the United Kingdom | Cluster 1(n = 14) | Belgium, Denmark, Estonia, Finland, Iceland, Luxembourg, Malta, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland |

Cluster 2(n = 18) | Austria, Belgium, Cyprus, Czechia, Denmark, France, Germany, Ireland, Italy, Latvia, Lithuania, Montenegro, Portugal, Romania, Serbia, Slovakia, Slovenia, Switzerland | Cluster 2(n = 13) | Austria, Croatia, Cyprus, France, Germany, Hungary, Ireland, Italy, Latvia, Lithuania, Montenegro, Slovakia, Slovenia, the United Kingdom |

Cluster 3(n = 6) | Bulgaria, Croatia, Greece, Hungary, Poland, Turkey | Cluster 3(n = 7) | Bulgaria, Czechia, Greece, Poland, Romania, Serbia, Turkey |

**Table 11.**Descriptive statistics for the economic indicator (Gross Domestic Product per capita in EUR) in 2017 and 2020 according to clusters.

GDP_PC_2017 | GDP_PC_2020 | |||||
---|---|---|---|---|---|---|

Cluster 1 | Cluster 2 | Cluster 3 | Cluster 1 | Cluster 2 | Cluster 3 | |

Valid | 18 | 10 | 6 | 14 | 13 | 7 |

Mean | 33,360.000 | 24,964.000 | 15,793.333 | 29,102.214 | 30,586.154 | 19,812.857 |

Std. Dev. | 12,506.964 | 14,989.428 | 10,669.414 | 13,037.195 | 18,187.007 | 11,314.240 |

Shapiro-Wilk | 0.952 ** | 0.933 ** | 0.781 ** | 0.877 ** | 0.928 ** | 0.863 ** |

Sum of Squares | df | Mean Square | F | Sig. | ||
---|---|---|---|---|---|---|

GDP_PC_2017 | Between Groups | 1,501,831,014.902 | 2 | 750,915,507.451 | 4.434 | 0.020 ** |

Within Groups | 5,250,538,973.333 | 31 | 169,372,224.946 | |||

Total | 6,752,369,988.235 | 33 | ||||

GDP_PC_2020 | Between Groups | 571,153,709.829 | 2 | 285,576,854.914 | 1.274 | 0.294 |

Within Groups | 6,946,868,602.907 | 31 | 224,092,535.578 | |||

Total | 7,518,022,312.735 | 33 |

**Table 13.**Descriptive statistics for average stringency index and average stringency index in days of reopening in 2020 according to clusters.

Avg_Stringency_Index_2020 | SI_Days_Reopen | |||||
---|---|---|---|---|---|---|

Cluster 1 | Cluster 2 | Cluster 3 | Cluster 1 | Cluster 2 | Cluster 3 | |

Valid | 13 | 13 | 7 | 13 | 12 | 7 |

Missing | 0 | 1 * | 0 | 1 * | 1 * | 0 |

Mean | 51.869 | 58.194 | 57.139 | 85.470 | 73.072 | 83.996 |

Std. Dev. | 7.273 | 6.492 | 5.236 | 8.623 | 12.965 | 8.413 |

Shapiro-Wilk | 0.976 | 0.923 | 0.956 | 0.923 | 0.870 | 0.923 |

Sum of Squares | df | Mean Square | F | Sig. | ||
---|---|---|---|---|---|---|

GDP_PC_2017 | Between Groups | 1,501,831,014.902 | 2 | 750,915,507.451 | 4.434 | 0.020 ** |

Within Groups | 5,250,538,973.333 | 31 | 169,372,224.946 | |||

Total | 6,752,369,988.235 | 33 | ||||

GDP_PC_2020 | Between Groups | 571,153,709.829 | 2 | 285,576,854.914 | 1.274 | 0.294 |

Within Groups | 6,946,868,602.907 | 31 | 224,092,535.578 | |||

Total | 7,518,022,312.735 | 33 | ||||

Avg_stringency_indeks_2020 | Between Groups | 284.521 | 2 | 142.261 | 3.271 | 0.052 * |

Within Groups | 1304.882 | 30 | 43.496 | |||

Total | 1589.403 | 32 | ||||

SI_reopen | Between Groups | 1068.813 | 2 | 534.407 | 4.895 | 0.015 ** |

Within Groups | 3166.126 | 29 | 109.177 | |||

Total | 4234.939 | 31 |

**Table 15.**Post hoc LSD test for differences between clusters according to the economic indicator and stringency indices.

Dependent Variable | Cluster Group | Mean Difference (I-J) | Std. Error | Sig. | |
---|---|---|---|---|---|

GDP_PC_2017 | Cluster 1 | Cluster 2 | −8396.000 | 5132.912 | 0.112 |

Cluster 3 | 9170.667 | 6720.560 | 0.182 | ||

Cluster 2 | Cluster 1 | 8396.000 | 5132.912 | 0.112 | |

Cluster 3 | 17,566.667 | 6135.004 | 0.007 *** | ||

Cluster 3 | Cluster 1 | −9170.667 | 6720.560 | 0.182 | |

Cluster 2 | −17,566.667 | 6135.004 | 0.007 *** | ||

Avg_stringency_indeks_2020 | Cluster 1 | Cluster 2 | 6.324 | 2.586 | 0.021 ** |

Cluster 3 | 1.055 | 3.091 | 0.735 | ||

Cluster 2 | Cluster 1 | −6.324 | 2.586 | 0.021 ** | |

Cluster 3 | −5.269 | 3.091 | 0.099 | ||

Cluster 3 | Cluster 1 | −1.055 | 3.091 | 0.735 | |

Cluster 2 | 5.269 | 3.091 | 0.099 | ||

SI_reopen | Cluster 1 | Cluster 2 | 12.398 | 4.182 | 0.006 *** |

Cluster 3 | 1.474 | 4.898 | 0.766 | ||

Cluster 2 | Cluster 1 | −12.398 | 4.182 | 0.006 *** | |

Cluster 3 | −10.924 | 4.969 | 0.036 ** | ||

Cluster 3 | Cluster 1 | −1.474 | 4.898 | 0.766 | |

Cluster 2 | 10.924 | 4.969 | 0.036 ** |

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**MDPI and ACS Style**

Pejić Bach, M.; Jaković, B.; Jajić, I.; Meško, M.
Investigating the Impact of COVID-19 on E-Learning: Country Development and COVID-19 Response. *Mathematics* **2023**, *11*, 1520.
https://doi.org/10.3390/math11061520

**AMA Style**

Pejić Bach M, Jaković B, Jajić I, Meško M.
Investigating the Impact of COVID-19 on E-Learning: Country Development and COVID-19 Response. *Mathematics*. 2023; 11(6):1520.
https://doi.org/10.3390/math11061520

**Chicago/Turabian Style**

Pejić Bach, Mirjana, Božidar Jaković, Ivan Jajić, and Maja Meško.
2023. "Investigating the Impact of COVID-19 on E-Learning: Country Development and COVID-19 Response" *Mathematics* 11, no. 6: 1520.
https://doi.org/10.3390/math11061520