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Analyzing Middle School Students' Distance Education Experiences in COVID-19 via Sentiment Analysis and Topic Modeling

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Title: Analyzing Middle School Students' Distance Education Experiences in COVID-19 via Sentiment Analysis and Topic Modeling
Language: English
Authors: Ekrem Bahçekapılı, Bülent Kandemir, Elif Baykal Kablan
Source: International Review of Research in Open and Distributed Learning. 2026 27(1):107-129.
Availability: Athabasca University Press. 1200, 10011-109 Street, Edmonton, AB T5J 3S8, Canada. Tel: 780-497-3412; Fax: 780-421-3298; e-mail: irrodl@athabascau.ca; Web site: http://www.irrodl.org
Peer Reviewed: Y
Page Count: 23
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Junior High Schools
Middle Schools
Secondary Education
Descriptors: Foreign Countries, Middle School Students, Student Experience, Distance Education, COVID-19, Pandemics, Technology Uses in Education, Student Attitudes, School Closing, Affordances, Barriers
Geographic Terms: Turkey
ISSN: 1492-3831
Abstract: This study investigated middle school students' experiences with emergency remote education during the COVID-19 pandemic using natural language processing (NLP), sentiment analysis, and topic modeling techniques. A total of 2,739 valid responses from Turkish students (ages 9-15) were collected through open-ended survey questions regarding the perceived advantages and disadvantages of distance learning. Sentiment classification was performed using a semi-supervised machine learning approach, combining TF-IDF, Word2Vec, and FastText vectorization with five classification algorithms. The TF-IDF + support vector machines (SVM) combination yielded the highest performance (F1 = 0.85). Results show a total of 1,867 positive and 2,542 negative opinions, indicating that students generally adopted a more critical view of distance education. To explore the thematic structure of opinions, topic modeling was applied with six topics. Positive sentiments clustered around themes such as educational continuity, health protection, time savings, flexible scheduling, self-regulated learning, and digital literacy. Negative sentiments were dominated by themes including limited interaction, screen fatigue, perceived low quality, technical barriers, and structural inequalities. Findings suggest that while students appreciated the safety and flexibility of remote learning, they also faced significant pedagogical, physical, and technological challenges. The study contributes methodologically by demonstrating the effectiveness of AI-based text analysis and offers practical implications for designing more equitable and student-centered digital education models. These results underscore the importance of integrating NLP and machine learning tools into educational research to uncover deeper insights from student-generated content at scale.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1501189
Database: ERIC
FullText Links:
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  Data: Analyzing Middle School Students' Distance Education Experiences in COVID-19 via Sentiment Analysis and Topic Modeling
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  Data: <searchLink fieldCode="AR" term="%22Ekrem+Bahçekapılı%22">Ekrem Bahçekapılı</searchLink><br /><searchLink fieldCode="AR" term="%22Bülent+Kandemir%22">Bülent Kandemir</searchLink><br /><searchLink fieldCode="AR" term="%22Elif+Baykal+Kablan%22">Elif Baykal Kablan</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Review+of+Research+in+Open+and+Distributed+Learning%22"><i>International Review of Research in Open and Distributed Learning</i></searchLink>. 2026 27(1):107-129.
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  Data: Athabasca University Press. 1200, 10011-109 Street, Edmonton, AB T5J 3S8, Canada. Tel: 780-497-3412; Fax: 780-421-3298; e-mail: irrodl@athabascau.ca; Web site: http://www.irrodl.org
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Junior+High+Schools%22">Junior High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Middle+Schools%22">Middle Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Middle+School+Students%22">Middle School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Experience%22">Student Experience</searchLink><br /><searchLink fieldCode="DE" term="%22Distance+Education%22">Distance Education</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22Pandemics%22">Pandemics</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22School+Closing%22">School Closing</searchLink><br /><searchLink fieldCode="DE" term="%22Affordances%22">Affordances</searchLink><br /><searchLink fieldCode="DE" term="%22Barriers%22">Barriers</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Turkey%22">Turkey</searchLink>
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  Label: ISSN
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  Data: 1492-3831
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study investigated middle school students' experiences with emergency remote education during the COVID-19 pandemic using natural language processing (NLP), sentiment analysis, and topic modeling techniques. A total of 2,739 valid responses from Turkish students (ages 9-15) were collected through open-ended survey questions regarding the perceived advantages and disadvantages of distance learning. Sentiment classification was performed using a semi-supervised machine learning approach, combining TF-IDF, Word2Vec, and FastText vectorization with five classification algorithms. The TF-IDF + support vector machines (SVM) combination yielded the highest performance (F1 = 0.85). Results show a total of 1,867 positive and 2,542 negative opinions, indicating that students generally adopted a more critical view of distance education. To explore the thematic structure of opinions, topic modeling was applied with six topics. Positive sentiments clustered around themes such as educational continuity, health protection, time savings, flexible scheduling, self-regulated learning, and digital literacy. Negative sentiments were dominated by themes including limited interaction, screen fatigue, perceived low quality, technical barriers, and structural inequalities. Findings suggest that while students appreciated the safety and flexibility of remote learning, they also faced significant pedagogical, physical, and technological challenges. The study contributes methodologically by demonstrating the effectiveness of AI-based text analysis and offers practical implications for designing more equitable and student-centered digital education models. These results underscore the importance of integrating NLP and machine learning tools into educational research to uncover deeper insights from student-generated content at scale.
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 107
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Middle School Students
        Type: general
      – SubjectFull: Student Experience
        Type: general
      – SubjectFull: Distance Education
        Type: general
      – SubjectFull: COVID-19
        Type: general
      – SubjectFull: Pandemics
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Student Attitudes
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      – SubjectFull: School Closing
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      – SubjectFull: Affordances
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      – SubjectFull: Barriers
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      – SubjectFull: Turkey
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