Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective

Poushy, Lamisha Haque; Bhuiyan, Salauddin Ahmed; Parvin, Masuma; Hossain, Refath Ara; Moon, Nazmun Nessa; Nooder, Jarin; Mahbuba, Ashrarfi

doi:10.11591/ijece.v12i5.pp5553-5561

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…Image data, in contrast to temporal information, has a topology that is just two-dimensional. This model may be used to time series prediction with great performance, as demonstrated by this state-of-the-art research [28]. Cell state ct and filtered input ot control the output layer, which selects the result.…”

Section: Related Workmentioning

confidence: 99%

A secure framework of blockchain technology using CNN long short-term memory hybrid deep learning model

Sekhar

Rajendran²

2022

IJEECS

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Generation Z is embracing blockchain technology, which is appropriate for the digital age. Internet of things (IoT) can benefit from blockchain technology IoT. The proliferation of IoT technology has led to breakthroughs in distributed system architecture. For the blockchain network to store, communicate, and exchange data, it needs a randomized data management system. This shows how difficult it may be to provide consistent and safe data replication in a distributed system, an issue blockchain technology may overcome. We need a solid prediction model that improves results. This article describes an innovative way to overcome the limitations of third-party transactions using Bitcoin. In this article, convolutional neural networks-long short term memory (CNN-LSTM) deep learning forecasting models are introduced. Convolutional layers help extract relevant data from instances. It has an long short-term memory (LSTM) layer, which lets it find long-and short term dependencies. The experiment's goal was to test the multivariate statistical model we suggested and compare its performance to well-established models. The addition of convolutional layers to a forecasting model may improve its accuracy, according to an experiment. The research shows that this strategy has a better chance of success and is more trustworthy than others.

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Section: Related Workmentioning

confidence: 99%

A secure framework of blockchain technology using CNN long short-term memory hybrid deep learning model

Sekhar

Rajendran²

2022

IJEECS

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“…The Decision Tree model has the lowest accuracy, according to a comparative study, at 82.29%. In Poushy et al [17] analysis, they obtained 93.75% (LR and SVM) accuracy in their dataset, which indicated that the majority of students are dissatisfied with the way that online classes are currently being run and the speed of the internet.…”

Section: Customer Satisfaction Predictionmentioning

confidence: 99%

“…Based on the test results, the Naïve Bayes Method can be used to predict how satisfied a lecturer will be with the performance of the institution. In Poushy et al [17] analysis, they obtained 93.75% (LR and SVM) accuracy in their dataset, which indicated that the majority of students are dissatisfied with the way that online classes are currently being run and the speed of the internet. In contrast, when compared to LR, KNN, and SVM, Naïve Bayes is the classifier with the lowest accuracy.…”

Section: Customer Satisfaction Predictionmentioning

confidence: 99%

Prediction of US airline passenger satisfaction using machine learning algorithms

Hong

Khaw

Chew

et al. 2023

DAAM

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Due to the COVID-19 pandemic, the U.S. financial system and economy have also been severely affected. The U.S. airline industry has been hit particularly hard by the COVID-19 pandemic. Additionally, the aviation industry is also full of competition. One of the ways to attract customers and compete with other airline companies is by improving their service quality. Therefore, this study aims to predict the satisfaction of airlines based on the machine learning model and discover which features are more correlated with the target variable. In this study, the dataset consists of 129,880 observations and 1 target, 22 features or attributes (not including the identification). In this study, the result showed that the features that slightly correlate more with customer satisfaction are 'Online boarding', 'Inflight entertainment', 'Seat comfort', 'On-board service', 'Leg room service', 'Cleanliness', 'Flight Distance' and 'Inflight wifi service'. Then, K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB) and AdaBoost were used to build the classification models. Data cleaning, exploratory data analysis, feature selection and One Hot Encoding were also performed before building the models. Finally, the models were evaluated based on their accuracy, precision, recall and F1-score. The results suggest that the champion model for this study is Random Forest, which achieved 89.20% accuracy, 93.04% precision and 88.80% F1-score. The results of this study can be used as a guide in applying machine learning to predict the satisfaction of airline passengers. This can also contribute to attracting passengers by improving the airline service quality.

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“…Institute of Technology PLN is a private university which during the pandemic used the Microsoft Teams as an online learning medium, and now Microsoft Teams is still used to support hybrid learning (online and offline). The research also to find out how student acceptance during online learning is towards the use of technology, especially Microsoft Teams [33].…”

Section: Introductionmentioning

confidence: 99%

Assessing actual usage and satisfaction factors of Microsoft Teams in online learning

Wulandari,

Soeprobowati,

Kusumo Nugraheni

2024

IJEECS

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This study examines the factors influencing students’ online learning using Microsoft Teams at the PLN Institute of Technology. Using primary data from 301 students, the study found that the use of Microsoft Teams improved student achievement and learning performance during the pandemic. The structural equation model (SEM) method tested the model with low validity, with all variables having an AVE value more than 0.5 and higher than the cross-loading factor valid, the outer model analysis demonstrated good convergent validity. Dependability and trustworthiness were shown by the composite reliability rating, which was over 0.70. Both perceived usefulness (PU) and perceived ease of use (PEOU) were shown to have a strong correlation, and the inner model analysis revealed positive path coefficient values without any weak variables. These results supported by hypothesis testing imply that lecturers can modify their curricula to enhance student performance in the event of a pandemic. There was shown to exist a strong relationship between the perceived ease of use and perceived usefulness.

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Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective

Cited by 4 publications

References 20 publications

A secure framework of blockchain technology using CNN long short-term memory hybrid deep learning model

A secure framework of blockchain technology using CNN long short-term memory hybrid deep learning model

Prediction of US airline passenger satisfaction using machine learning algorithms

Assessing actual usage and satisfaction factors of Microsoft Teams in online learning

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