# Analysis of the Lingering Effects of Covid-19 on Distance Education

Emirhan Azgun, Elif Yıldırım, Fatma Patlar Akbulut, Büşra Kocaçınar, Nasibullah Qarizada, Cihan Dikkaya

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Emirhan Azgun, Elif Yıldırım, Fatma Patlar Akbulut, Büşra Kocaçınar, Nasibullah Qarizada, et al.. Analysis of the Lingering Effects of Covid-19 on Distance Education. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.189-200, 10.1007/978-3-031-34111-3_17. hal-05449797

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# Analysis of the Lingering Effects of Covid-19 on Distance Education

Büşra Kocaçınar<fnref n="1" />(✉) ID, Nasibullah Qarizada<fnref n="1" />, Cihan Dikkaya<fnref n="1" />, Emirhan Azgun<fnref n="1" />, Elif Yıldırım<fnref n="1" /> ID, and Fatma Patlar Akbulut<fnref n="2" /> ID

**Abstract.** Education has been severely impacted by the spread of the COVID-19 virus. In order to prevent the spread of the COVID-19 virus and maintain education in the current climate, governments have compelled the public to adopt online platforms. Consequently, this decision has affected numerous lives in various ways. To investigate the impact of COVID-19 on students' education, we amassed a dataset consisting of 10,000 tweets. The motivations of the study are; (i) to analyze the positive, negative, and neutral effects of COVID-19 on education; (ii) to analyze the opinions of stakeholders in their tweets about the transition from formal education to e-learning; (iii) to analyze people's feelings and reactions to these changes; and (iv) to analyze the effects of different training methods on different groups. We constructed emotion recognition models utilizing shallow and deep techniques, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long-short Term Memory (LSTM), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Logical Regression (LR). RF algorithms with a bag-of-words model outperformed with over 80% accuracy in recognizing emotions.

**Keywords:** Deep learning · social media · sentiment analysis · word embedding · distance education

## 1 Introduction

Covid-19, also known as SARS-CoV-2, has deeply shaken and altered all aspects of human life, including working, studying, and participating in social activities. Education was undoubtedly one of the most affected fields. In so that governments and institutes decided to close schools, courses, universities, and education centers which was going to be the main reason for changing their formal face-to-face education to online platforms under the title of online learning or e-learning in different ways. This decision would also push the socio-economically

This research is supported by Istanbul Kultur University under ULEP-2022-2023.

© IFIP International Federation for Information Processing 2023

Published by Springer Nature Switzerland AG 2023

I. Maglogiannis et al. (Eds.): AIAI 2023, IFIP AICT 675, pp. 189–200, 2023.

https://doi.org/10.1007/978-3-031-34111-3_17

<footnote n="1">¹ Department of Computer Engineering, Istanbul Kültür University, Istanbul, Turkey {b.kocacinar,elif.yildirim}@iku.edu.tr</footnote>

<footnote n="2">² Department of Software Engineering, Istanbul Kültür University, Istanbul, Turkey f.patlar@iku.edu.tr</footnote>
disadvantaged groups into obscurity, where serious consequences awaited. Subsequently, this caused educators, families, and academics to be concerned about online learning inequalities. In less than two months, virtually every educational institution has adopted remote operation and e-learning. Recent research indicates that 1.52 billion students [11] are impacted by the closure of educational facilities, putting them at risk for socioeconomic concerns such as academic integrity, homelessness, internet, housing, and student indebtedness.

During the pandemic, students not only lost educational experiences but also social and emotional development. Some experienced the displacement of family members, others lost their employment and levels of revenue, and the vast majority were alienated. In order to alleviate anxiety, it is crucial to comprehend how these changes influence pupils [2,18] and to establish whether they have beneficial or bad consequences on the learning process. These impacts can also vary from person to person, and various learning abilities may react differently. The study of learning is, in a nutshell, the examination of the connections between students and their ecosystems. If considered logical, these insights can have very unique influences on educational practitioners.

Sentiment analysis is a field with the potential to extract and analyze the opinions of individuals regarding a particular case, personality, or concept. Today, machine learning and deep learning are used extensively and effectively in natural language processing and text analysis, the preferred methodologies in this field of study. In this study, we analyzed the perspectives of students, instructors, and families regarding the effects of Covid-19 on their lives, particularly their academic lives. Our aim is to study this topic from multiple perspectives and dimensions and to do sentiment analysis utilizing tweets shared by different users. The main contributions of this study are three-fold as;

- To analyze effects of Covid-19 on education within emotions and reactions of people as positive, negative, and neutral forms.

- Analyzing thoughts of individuals' tweets regarding changes from formal education to e-learning.

- To analyze the effects of different educational methods on different groups.

## 2 Related Work

In recent years, a vast amount of research has been conducted to analyze people's perspectives on particular issues [17]. Unquestionably, one of the most explored issues is how COVID-19 affects our everyday lives. The physical closure of educational institutions and the enforcement of online learning may have an adverse impact on student performance through several touchpoints: clinical signs of stress, fewer hours spent having to learn, and a lack of student commitment. Initial research [1] indicates that only 73% of students have access to the internet, and only 71.4% are fully equipped to access online classes via a mobile device. Due to the interruption of their academic habits, some students reported experiencing poor mental health [7]. In addition, students who are quarantined at
home with their parents [6] as a result of COVID-19 may experience increased anxiety and depression. Aside from the discussion of the efficacy of in versus online courses, research shows that fewer hours spent studying can result in having learned decrease. Another issue is the peer effects which are a well-known factor influencing academic performance in the school setting. Participating in a classroom and, thus, having the chance to engage with peers may yield significant positive outcomes [13]. Social impacts may manifest through several routes, such as instructing one another and improving collectively, and individuals may be inspired to work harder by the accomplishments of their peers.

One of the ways to understand the effects of all these factors on people is to examine social media data closely. During a given time period, individuals who were confined to their houses began to make their opinions known using online social media platforms. Numerous individuals have both favorable and negative perceptions of online vs. traditional education [12]. Particularly, tweets are frequently utilized as a source to analyze people's emotions, particularly in terms of online learning during the COVID-19 pandemic [5, 19]. Despite the fact that they contain so many diverse words, abbreviations, informal language, and symbols, it is difficult to determine which words or groups of words produce positive or negative statements. Sentiment analysis solves these problems by organizing unstructured data [10, 14, 15]. Examining the thoughts and emotions that people have concerning COVID-19, based on their perspectives can be accomplished by first transforming the unstructured text-based sequential data that is associated with distance education into structured data. In order to gather structured data, one must first complete a number of pre-processing procedures, including text cleaning, dimension reduction, and transformation, amongst others. In order to investigate the sentiments of posts that are related to education, the quality of the assessment serves as the major yardstick for judging whether or not this organized data is reliable. As a consequence of this, the analysis of these data with up-to-date natural language processing (NLP) technologies is an essential component in the process of determining the impact that the field of education and in providing useful feedback so that educational institutions can improve the quality of education.

Text-based sentiment analysis is an NLP classification operation that generates actionable insights from documented linguistic concepts. This is one of the first NLP topics addressed by many academics fresh to machine learning. This is because of its logical simplicity and applicability, as well as the availability of conventional and deep learning approaches. During COVID-19, several studies [2, 3, 16–18] analyzed the positive, negative, and neutral feelings of tweets pertaining to education with state-of-the-art approaches. Even though people couldn't agree on the results, it was clear that their socioeconomic status affected the results and their points of view.

# 3 Methodology

To study the impacts of pandemics on education, we gathered a dataset from specified social media tweets and then applied pre-processing techniques such as
Textblob [9], NLTK [4], and Vader [8]. Following that, we conducted an analysis using deep learning approaches. As shown in Fig. 1, the entirety of the analytic technique is suitable for reflecting the findings of the design, which consists of data retrieval, preprocessing, feature engineering, training, and testing.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Procedure of data fetching, preprocessing, feature extraction, and classification of the proposed method</figcaption></figure>

## 3.1 Fetching Dataset

In order to investigate the effects, we collected 10,000 tweets using Twint<fnref n="1" />, an advanced scraping tool for Twitter posts. As the primary objective of the research, the majority of tweets were collected from the days when covid-19 spread peaked ("May of 2020 to June of 2021") in order to conduct a robust analysis. We selected certain keywords to identify individuals who offered their opinion and perspective on the effects of COVID-19 on schooling. COVID-19 education, COVID-19, Education, COVID-19 Schools, distant learning, corona education, online learning, and e-learning are some of the keywords utilized. Another potential issue while generating a dataset is the language of tweets. It can be elusive during the creation of a dataset, but we eliminated it before the primary preparation phase.

## 3.2 Text Pre-processing

In the raw format of the dataset, it is evident that most tweets contain punctuation marks, images, emoticons, and links. Using datasets in their default format has the potential to mislead the analysis technique and model's performance. Consequently, data must be cleansed and organized prior to further analysis. We

<footnote n="1">¹ available at https://github.com/twintproject/twint.</footnote>
## 3.4 Tweet Sentiment Analysis

In order to gain a complete understanding of the actual scenario and level of satisfaction with online education during the epidemic, 10,000 tweets are evaluated for the sentiment. After purifying the data and making it computer-readable, one of the most important aspects of the research that contributes to the final outcomes is sentiment analysis. There are several pretrained libraries that are compatible with various methodologies. In this study, positive, negative, and neutral tweets were analyzed using the TextBlob and Vader libraries. Textblob, a lexicon-based Python module that is commonly used in sentiment analysis, was the first tool we employed. TextBlob gives a polarity between -1, 0, and 1, which is typically reconstructed as negative, neutral, and positive in sentiment analysis. For instance, in the tweets we analyzed with Textblob, the tweet with a polarity result of 0.6 is labeled as positive, while the tweet with a polarity result of -0.09 is classified as negative. Table 2 shows illustrative sentiment analysis values and their corresponding outcomes from our dataset.

<table><caption>**Table 2.** Sentiment analysis samples by TextBlob</caption><thead><tr><td>Tweet</td><td>Polarity</td><td>Subjectivity</td><td>Sentiment</td></tr></thead><tbody><tr><td>Maybe people would take Covid more seriously if it was as physically evident as chickenpox ormeasles. Both are contagious. You can't just walk around with chickenpox infecting others at work or school. Anyway, long Covid will be a thing</td><td>0.1</td><td>0.32</td><td>Positive</td></tr><tr><td>Believe it's airborne I went nowhere" people got grocery got gas kids went school live apartment/townhouse live home flu sit surface minute surely Covid</td><td>0.26</td><td>0.63</td><td>Positive</td></tr></tbody></table>

TextBlob is utilized to annotate sentiment data that returns a tuple of sentiment values (polarity and subjectivity). Polarity can take on values between -1 and 1 [9]. Accordingly, tweets with a polarity score below 0 are labeled as “negative,” whereas tweets with a polarity score above 0 are labeled as “positive.” If it is equal to 0, the tweet is marked as “neutral.” As a second option, we favored Vader, a Lexicon and Rule-based library that is functionally equivalent to Lexicon. TextBlob employs a collection of positive, negative, and neutral terms. Vader has the ability to interpret emoticons such as “:);(”, slang, and even short sentences like “LOL.” In a different technique, we did sentiment analysis using the Vader library. Initially, the Sentiment Intensity Analyzer was used to generate the polarity score that consists of negative, positive, neutral, and combined findings. Considering the compound results, a labeling procedure was done which has taken into account a particular time frame. Positive tweets have a compound value above 0.05, whereas negative tweets have a value below -0.05. Tweets whose total value
falls between these two thresholds are neutral. The positive, negative, neutral, and null distributions of these two techniques are depicted in Table 3. According to the findings of our analysis, TextBlob performed better without providing null data. In the interim, Vader returned 2040 null values.

<table><caption>**Table 3.** TextBlob vs VADER for Sentiment Analysis</caption><thead><tr><th></th><th>Positive</th><th>Negative</th><th>Neutral</th><th>Null</th></tr></thead><tbody><tr><th>TextBlob</th><td>4439</td><td>3480</td><td>2086</td><td>0</td></tr><tr><th>Vader</th><td>4153</td><td>2293</td><td>1519</td><td>2040</td></tr></tbody></table>

The relationship between data analysis outcomes and the methods and procedures employed by the analyst in generating research findings is comprehensive. Simply said, the more diverse methods we employ to achieve an objective, the more resilient the result. Then, we put frequently used words in Fig. 2 to find out what people are going through these days.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Most frequently used words from the negative corpus and their distributions</figcaption></figure>

## 3.5 Word Embedding

Word embeddings bridge human and computer language understanding. They learn to represent text in n-dimensional space such that words with the same meaning seem alike. Comparable words are represented as vectors near together in a vector space. This study included three distinct approaches for word embedding: a bag of words (BoW), Term Frequency Inverse Document Frequency (TF-IDF), and doc2vec. The “bag of words” model is used to extract characteristics
from texts and is a textual representation of the occurrence of words in a document. BoW reorganizes its text data by tokenizing k-grams. TF-IDF computes the relevance of a term inside a string or corpus to a text. Doc2Vec vectorizes word association connections.

## 3.6 Proposed Sentiment Models

To understand the patterns in a long set of data, we need networks that can look at patterns over time. Typically, Recurrent Networks (LSTM, RNN) are utilized for such data learning. They can comprehend long-term and short-term inter-dependencies and temporal disparities. In addition, 1D CNNs are excellent for processing 1D signals because of their compact architecture and minimal computational requirements. In the proposed study, therefore, three distinct deep models consisting of an LSTM, an RNN, and a CNN were created. In addition, four shallow models were constructed utilizing the naive Bayes, logistic regression, random forest, and support vector machine methods. Textblob and Vader were used in two distinct investigations to identify sentiment labels.

**LSTM Model.** As a preliminary step, we transformed our data by tokenizing language based on a set of rules. We built our model with five layers. The first layer is the embedding layer, with parameters such as “vocabulary size,” “embedding vector length,” and “input length = 200.” SpatialDropout1D is used as a second layer to prevent overfitting. The LSTM layer is set as the third layer with 50 units, a dropout value of 0.5, and a recurrent dropout value of 0.5. Follow-up layer consisting of dropout with probability p = 0.2 to reduce overfitting. The final layer is the output layer, which has a sigmoid activation function, and the Adam optimizer is used to update the weights of the model.

**RNN Model.** The RNN model employs seven layers, some of which are utilized in pairs. The embedding layer is initially utilized for the input layer. Then there are two RNN layers with 512 hidden units and “ReLu” for the activation function. Then, 1D global average pooling is utilized as the transition layer before feeding the data to the output layer, which is composed of a dense layer. Dense layers are utilized in two distinct ways. Initially, with 64 hidden units and an activation function that contains “ReLu”. Before moving on to the next dense layer, the dropout layer is utilized to prevent overfitting. The second dense layer was then applied, but this time the activation function was “softmax” to scale and normalize output according to probabilities.

**CNN Model.** The proposed 1D-CNN model employs ten layers to model textual information. The embedding layer is initially utilized for the input layer. Then there is the 1D convolution layer that contains a kernel with size 5 × 5, 512 hidden units, and “ReLu” for an activation function. The following 1D convolution layer consists of a kernel with a size of 3 × 3. Then, we utilized max-pooling to achieve downsampling by dividing the input into 1-D pooling regions.
The subsequent layers are dropout with probability p = 0.4, a 1D global average pooling layer, a dense layer with 64 hidden units and a ReLu activation function, and dropout again with probability p = 0.4. The final dense layer has an input size of 3 and an activation function of softmax.

**Naïve Bayes, Logistic Regression, Random Forrest, and Support Vector Machine models.** After performing feature engineering approaches such as a BoW, TF-IDF, and Word2Vec, we have preferred widely used classifiers such as support vector machine (SVM), Random Forest (RF), Nave Bayes (NB), and Logistic Regression (LR) to validate the findings and compare them to the proposed deep model. The kernel type for the SVM model is determined as “linear”. The regularization parameter of the error term is set to 1 for C. The “probability” parameter is set to True to enable probability estimates. For the RF classifier, the “n;estimators” parameter, which represents the number of trees in the forest, is set to 400.

## 4 Results

In this section, we investigated and compared the performance of different classifiers for detecting sentiment in the dataset. Table 4 shows the validation accuracy of our LSTM, CNN, and RNN models with Vader and Textblob. A binary classification study was conducted on the LSTM model to determine the positive and negative classes. Textblob achieved a precision of 0.51, whereas Vader achieved 0.48. Using RNN and CNN models, a 3-class study was done to identify negative, positive, and neutral feelings. Comparing the accuracy results for Textblob (Fig. 3), it was found that the CNN model achieved 0.81 accuracies, whereas the RNN model performed 0.70 accuracies overall. When evaluating the RNN and CNN findings for Vader, the validation accuracy was 0.72 and 0.59, respectively. It has been observed that TextBlob produces superior results. We have discovered that our models can be enhanced in numerous ways.

<table><caption>**Table 4.** Validation accuracy results of CNN, LSTM, and RNN models</caption><thead><tr><td></td><td>Textblob</td><td>Vader</td></tr></thead><tbody><tr><td>Convolutional Neural Network</td><td>0.81</td><td>0.72</td></tr><tr><td>Recurrent Neural Network</td><td>0.70</td><td>0.59</td></tr><tr><td>Long Short-Term Memory</td><td>0.51</td><td>0.48</td></tr></tbody></table>

When examining the accuracy results of shallow classification models, we aimed to obtain as many distinct outcomes as feasible. Therefore, we began testing four distinct models: Random Forest, Logistic Regression, Naïve Bayes, and Support Vector Machine. In addition, employing alternative approaches as a
<figure><img src="image_5.png" /><figcaption>Fig. 3. Training and validation accuracy of RNN and CNN models over four epoch</figcaption></figure>

<table><caption>**Table 5.** Validation accuracy and F1 Score results of shallow classifiers with three different word embedding methods</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">TextBlob</th><th colspan="2">Vader</th></tr><tr><th>Validation Accuracy</th><th>F1 Score</th><th>Validation Accuracy</th><th>F1 Score</th></tr></thead><tbody><tr><td rowspan="3">LR</td><td>BoW</td><td>0.39</td><td>0.35</td><td>0.34</td><td>0.36</td></tr><tr><td>TF-IDF</td><td>0.42</td><td>0.33</td><td>0.37</td><td>0.34</td></tr><tr><td>Word2Vec</td><td>0.45</td><td>0.31</td><td>0.36</td><td>0.32</td></tr><tr><td rowspan="3">NB</td><td>BoW</td><td>0.39</td><td>0.34</td><td>0.35</td><td>0.34</td></tr><tr><td>TF-IDF</td><td>0.39</td><td>0.34</td><td>0.35</td><td>0.34</td></tr><tr><td>Word2Vec</td><td>0.37</td><td>0.29</td><td>0.34</td><td>0.29</td></tr><tr><td rowspan="3">SVM</td><td>BoW</td><td>0.39</td><td>0.35</td><td>0.36</td><td>0.36</td></tr><tr><td>TF-IDF</td><td>0.39</td><td>0.36</td><td>0.36</td><td>0.36</td></tr><tr><td>Word2Vec</td><td>0.39</td><td>0.31</td><td>0.35</td><td>0.31</td></tr><tr><td rowspan="3">RF</td><td>BoW</td><td>0.82</td><td>0.82</td><td>0.75</td><td>0.81</td></tr><tr><td>TF-IDF</td><td>0.81</td><td>0.82</td><td>0.75</td><td>0.82</td></tr><tr><td>Word2Vec</td><td>0.65</td><td>0.64</td><td>0.66</td><td>0.65</td></tr></tbody></table>

feature engineering technique was yet another way we achieved distinct and more effective outcomes. The results of the comparison are shown briefly in Table 5. Considering the results of limited data, model performance can be increased by expanding the data collection phase.

## 5 Conclusion

COVID-19 has taken the globe to a state in which all aspects of conventional life have been altered. Education was among the things most affected. Online
learning is the new face of education and has been preferred by nearly all governments and institutions to prevent the spread of COVID-19. Among its advantages, online learning had some serious and effective disadvantages. Financial problems, social limitations, a lack of access to technology, and the effectiveness of education were the problems that raised the worries of stakeholders. In order to analyze the reactions of people to find the problems and focus on ways to solve or reduce them. Therefore, to analyze the effects of the pandemic, we focused on the tweets and used several techniques to recognize the sentiments of individuals. After applying feature engineering techniques such as BoW, TF-IDF, and Word2Vec, we categorize inputs with TextBlob and Vader methods and recognize emotions with LSTM, RNN, and CNN. According to the ratio of tweets classified with Textblob, roughly 0.45 tweets are positive, while the ratio of tweets classified by Vader is 0.52 tweets that are positive. We can see that a greater number of people see distance learning under pandemic conditions favorably. We observed that LSTM performed with a value of 0.51 for TextBlob and 0.48 for Vader, while CNN accuracy for TextBlob is 0.81 and RNN is 0.70. In the case of comparing our models with the shallow models, we preferred to use LR, NB, SVM, and RF. RF was the best-performing model by TextBlob, with an accuracy of 0.82 (F1 score = 0.82).

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