The pandemic semesters: Examining public opinion regarding online learning amidst <scp>COVID</scp>‐19

Asare, Andy Ohemeng; Yap, Robin; Truong, Ngoc Le Van; Sarpong, Eric Ohemeng

doi:10.1111/jcal.12574

Cited by 41 publications

(20 citation statements)

References 43 publications

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“…The impact of COVID-19 on education has created an urgent need to evaluate the opinion of distance learning ever than before especially due to the wide mixed perception towards remote learning. Public opinion on the use of Emergency Remote Learning (ERL) was analyzed using the data collected from Twitter (Asare et al 2021 ). The study was able to detect positive and negative sentiments and revealed topics for each of the clusters thus serving as a recommendation to improve the online educational systems amidst the pandemic.…”

Section: Related Workmentioning

confidence: 99%

Does technology assist to continue learning during pandemic? A sentiment analysis and topic modeling on online learning in south asian region

Shafana

Safnas

2022

Soc. Netw. Anal. Min.

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Online mode of education has been identified as the subtle solution to continue learning during the pandemic. However, the accessibility to online platforms, suitable devices, and connections are not equal across the globe thus raising the question of whether the opinion of the public in the South Asian region where the technology is not comparatively higher as in the western world would be the same as that to the global perspective. This study involves the sentiment analysis of natural language processing on recently tweeted data and concludes that the sentiment of the South Asian public remains positive as online education is the most suitable approach to overcome the learning difficulties during a pandemic. The study performs a ternary classification based on the polarity scores obtained from two robust lexicon-based sentiment analyzer tools namely VADER and TextBlob and observes that 63.2% of the tweets were positive, 30.5% of the tweets were neutral and around 6.3% of them were negative. Finally, topic modeling was also performed using the Latent Dirichlet Allocation method to gain insight into each of the classes.

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Section: Related Workmentioning

confidence: 99%

Does technology assist to continue learning during pandemic? A sentiment analysis and topic modeling on online learning in south asian region

Shafana

Safnas

2022

Soc. Netw. Anal. Min.

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“…The following problems have been studied in selected PRISMA research papers: Higher education quality assurance ( Alakbarov 2021 ; Allayarova 2019 ; Asare et al 2021 ); Knowledge-based recommender system ( Barabash et al 2021 ; Barón et al 2015 ; Bin-Noor et al 2021 ; Brunello and Wruuck 2021 ); E-learning and distance learning ( Bukralia et al 2015 ; Burman et al 2021 ; Casselman 2021 ; Chahal et al 2020 ); Inter-professional education ( Cheng 2017 ); Network course recommendation system ( Cinquin et al 2019 ; Dhar and Jodder 2020 ; Dolgikh 2021 ; Ehimwenma and Krishnamoorthy 2021 ); Modern information communication technologies in the higher education sector ( El Gourari et al 2021 ; Elahi et al 2022 ; Ellyatt 2021 ; Elumalai et al 2019 ); Transformation of education during the COVID-19 pandemic ( Erridge 2006 ; Ezz and Elshenawy 2020 ; Fedushko and Ustyianovych 2020 ); Digitalization in higher education ( Gao et al 2021 ; Garay-Jiménez et al 2021 ; Gil et al 2019 ); Personalized recommendation system for learning resources ( Habib et al 2021 ; Ibrahim et al 2019 ; Karan and Asgari 2021 ; Khan and Ramzan 2018 ; Khosravi et al 2021 ); Artificial intelligence techniques for education tasks solving ( Kim Rosemary et al 2014 ; ...…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Academic Specialties Selection in Higher Education for Ukrainian Entrants: A Recommendation System

Федушко

Ustyianovych²,

Syerov³

2022

J. Intell.

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In this article, we provide an approach to solve the problem of academic specialty selection in higher educational institutions with Ukrainian entrants as our target audience. This concern affects operations at universities or other academic institutions, the labor market, and the availability of in-demand professionals. We propose a decision-making architecture for a recommendation system to assist entrants with specialty selection as a solution. The modeled database is an integral part of the system to provide an in-depth university specialties description. We consider developing an API to consume the data and return predictions to users in our future studies. The exploratory data analysis of the 2021 university admission campaign in Ukraine confirmed our assumptions and revealed valuable insights into the specifics of specialty selection among entrants. We developed a comprehension that most entrants apply for popular but not necessarily in-demand specialties at universities. Our findings on association rules mining show that entrants are able to select alternative specialties adequately. However, it does not lead to successful admission to a desired tuition-free education form in all cases. So, we find it appropriate to deliver better decision-making on specialty selection, thus increasing the likelihood of university admission and professional development based on intelligent algorithms, user behavior analytics, and consultations with academic and career orientation experts. The results will be built into an intelligent virtual entrant’s assistant as a service.

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“…Against the background of the COVID-19 pandemic, online learning has become a vital alternative learning mode worldwide [1,2]. As learners and instructors have been separated physically, the convenience and flexibility of online environments have guaranteed basic educational needs [3,4]. However, the socially isolated features of online learning settings have also caused challenges for instructors in monitoring their learners' progress and in identifying learners who are at risk [5].…”

Section: Introductionmentioning

confidence: 99%

Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning

Wang

Zhang

2022

Sustainability

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Online learning has become a vital option for ensuring daily instruction in response to the emergence of the COVID-19 epidemic. However, different from conventional massive online learning, inadequate available data bring challenges for instructors to identify underachieving students in school-based online learning, which may obstruct timely guidance and impede learning performance. Exploring small-sample-supported learning performance prediction and personalized feedback methods is an urgent need to mitigate these shortcomings. Consequently, considering the problem of insufficient data, this study proposes a machine learning model for learning performance prediction with additional pre-training and fine-tuning phases, and constructs a personalized feedback generation method to improve the online learning effect. With a quasi-experiment involving 62 participants (33 in experimental group and 29 in control group), the validity of the prediction model and personalized feedback generation, and the impact of the personalized feedback on learning performance and cognitive load, were evaluated. The results revealed that the proposed model reached a relatively high level of accuracy compared to the baseline models. Additionally, the students who learned with personalized feedback performed significantly better in terms of learning performance and showed a lower cognitive load.

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The pandemic semesters: Examining public opinion regarding online learning amidst COVID‐19

Cited by 41 publications

References 43 publications

Does technology assist to continue learning during pandemic? A sentiment analysis and topic modeling on online learning in south asian region

Does technology assist to continue learning during pandemic? A sentiment analysis and topic modeling on online learning in south asian region

Intelligent Academic Specialties Selection in Higher Education for Ukrainian Entrants: A Recommendation System

Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning

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