Tutorial experience during online learning: a topic modelling approach

Chioma Okoro, Peter Baur, Oliver Takawira

Research output: Contribution to journalArticlepeer-review

Abstract

The role of tutoring in teaching and learning cannot be overemphasised. However, limited studies exist on tutors’ strategies, tools, and techniques to assist in their role as teaching assistants. This study aimed to identify the strategies, tools, techniques, and challenges encountered during online tutoring during the lockdown periods necessitated by the COVID-19 pandemic. The study employed a quantitative approach to collect data among tutors within a faculty in a higher education institution. Short-text data were analysed to output themes using topic modelling in supervised machine learning. Findings indicated that technology and tutors were helpful and appreciated during the period under investigation. The challenges were primarily technical and social. Similarities between students’ and tutors’ perceptions were noted. The study’s findings are beneficial to higher education policymakers and authorities to better support tutors going forward. This is especially important as universities gradually reopen contact learning with blended/online approaches.

Original languageEnglish
Pages (from-to)458-488
Number of pages31
JournalInternational Journal of Innovation and Learning
Volume33
Issue number4
DOIs
Publication statusPublished - 2023
Externally publishedYes

Keywords

  • higher education
  • sentiment analysis
  • students’ performance
  • topic modelling
  • tutoring

ASJC Scopus subject areas

  • Education
  • Management of Technology and Innovation

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