Online microlearning and student engagement in computer games higher education
Clicks: 124
ID: 277722
2022
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.3
/100
124 views
30 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #8 of 10 articles by views in research in learning technology
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Microlearning, in which lecture recordings are segmented into parts, saw renewed focus as a means of maintaining student engagement amid the challenging conditions of the COVID-19 pandemic. While many institutions shifted to remote provision with segmented lecture recordings, there is a lack of consensus about the length that these segments should be in order to best maintain engagement. Using a self-reported system of Likert-based diagnostics, 135 videos in use at Solent University’s computer games area were analysed. Ninety-four students were asked to agree or disagree with statements in the format ‘I understand X’, each tailored to the subject material of the video in question. Repeated questions before and after the video allowed for a change in confidence to be measured, as an indicator of engagement. The resulting 4198 responses showed an optimum range of 5–8 min overall. However, the year of study emerged as a significant factor in this regard – with an optimum range for first years at 6–12 min, and for second and third years at under 8 min. There is a need for institutional-level change in this area, as many institutions currently recommend use of lecture video segments far longer than either figure.
| Reference Key |
mckee2022onlineresearch
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | McKee, Connor;Ntokos, Konstantinos; |
| Journal | research in learning technology |
| Year | 2022 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.