Students’ experiences and challenges during the COVID-19 pandemic: a multi-method exploration

Faiz Iqbal Hayat*, Safwan Shatnawi, Ella Haig

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Likert-type items or scales, as well as open-ended questions, are frequently used in the collection of student feedback. While the analysis of numeric feedback includes many established statistical techniques, systematic analyses of answers to open-ended questions are quite rare due to the challenges of dealing with textual data. In this paper, we leverage the use of pre-trained large language models (LLMs) to extract topics from students’ textual feedback about their experience during the COVID-19 pandemic. In particular, the open-ended questions focused on mental health and remote learning. There were 696 textual responses from 340 participants. Our analysis using a BERT-based pre-trained model resulted in the identification of 13 topics. To further understand
these, we also present results from the Likert-type items related to stress, worry and remote learning in relation to demographic characteristics including age, gender and year of study.
Original languageEnglish
Title of host publicationThe Nineteenth European Conference on Technology Enhanced Learning
PublisherSpringer Nature
Publication statusAccepted for publication - 31 May 2024
EventNineteenth European Conference on Technology Enhanced Learning: ECTEL 2024 - Krems, Austria
Duration: 16 Sept 202420 Sept 2024

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Nature
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceNineteenth European Conference on Technology Enhanced Learning


  • Topic Modeling
  • BERT
  • Online Learning
  • Remote learning
  • COVID-19
  • Education

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