lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Investigating MOOCs with the use of sentiment analysis of learners' feedback. What makes great MOOCs across different domains?
Linnaeus University, Faculty of Technology, Department of Informatics.
2022 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Recently, distance education has become popular and has gotten much attention. Information and Communication Technology advances fostered distance learning creation and enabled individuals to participate in the education process via various web-based platforms and study entirely online. Thus, the notion of e-learning and distance learning emerged. Massive Open Online Courses (MOOCs) appeared as part of e-learning in 2008 and attracted great interest, especially during the COVID-19 pandemic. It was anticipated that this kind of study also could be integrated into higher education and revolutionize the learning approach. However, several issues related to MOOCs limit their full potential. One of the most significant problems is substantial rate of learners’ attrition. It was discovered that only 5-10 percent of MOOC learners complete a course. This thesis aims to examine what influences individuals’ decision to leave MOOCs and how learners perceive various course components to get ideas regarding how MOOCs could be enhanced. To do this, the mixed-method study was undertaken where quantitative data analysis of learners’ reviews from discussion forums and qualitative interviews were adopted. It allowed to get two perspectives and broaden the thesis out- come. For the current research, data was collected from six courses in three different subjects-«Health», «Art and Humanity/Design» and «Computer/Data Science». In the first part of the work, sentiment analysis and topic modeling using Python packages were carried out, and then the results were used to construct an interview questionnaire. Lexicon-based sentiment analysis technique and LDA topic modeling algorithm were utilized and proved to be robust methods to extract texts’ polarity and peoples’ opinions. In the qualitative part, 19 topics of discussion were identified, which were consolidated into eight topics with higher abstraction – materials, instructor, content, time, assignment, feedback, program(course), and algorithms. Then during the qualitative part, participants expressed their opinions regarding these topics, and analysis codes were predefined, and new topics did not emerge. The results showed learners’ perceptions related to presented topics and how these aspects influence experience with MOOCs. The outcome also showed a slight disparity between different subject learners, in both qualitative and quantitative studies identified topics of discussion were not exactly the same, showing that learners from different educational domains tend to discuss different themes.

Place, publisher, year, edition, pages
2022. , p. 68
Keywords [en]
MOOC, Massive Open Online Courses, Sentiment Analysis, Distance Learning, Topic modeling
National Category
Information Systems
Identifiers
URN: urn:nbn:se:lnu:diva-115508OAI: oai:DiVA.org:lnu-115508DiVA, id: diva2:1683599
Subject / course
Informatics
Educational program
Master Programme in Information Systems, 120 credits
Presentation
2022-06-02, 09:00 (English)
Supervisors
Examiners
Available from: 2022-08-03 Created: 2022-07-17 Last updated: 2022-08-03Bibliographically approved

Open Access in DiVA

Degree project(1905 kB)157 downloads
File information
File name FULLTEXT01.pdfFile size 1905 kBChecksum SHA-512
a93b97ea0cba110d83ec4af6d7e6ae9140bd436698168ecc4b35417c1617af679e679dc3c2080f0819b9824838b62e1269c03daf66399e6decf702f0d1c7c757
Type fulltextMimetype application/pdf

By organisation
Department of Informatics
Information Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 157 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 412 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf