An efficient, reliable and valid assessment for affective states during online learning

Oi-Ling SIU, Kelvin F. H. LUI*, Yi HUANG, Ting Kin NG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review


The current study aims to develop an efficient, reliable and valid assessment, the affective states for online learning scale (ASOLS), for measuring learners’ affective states during online learning using a sample of 173 young learners. The assessment consists of 15 items which assess five affective states, including concentration, motivation, perseverance, engagement, and self-initiative. To improve efficiency, five items (one for each affective state) are randomly selected and presented every 30 min during online learning. In addition, 14 among the participants were further invited to perform on-site online learning, and their affective states were validated by observations conducted by two psychologists. The ASOLS was found to be reliable and valid, with high internal consistency reliabilities and good construct, convergent and criterion validity. Confirmatory factor analyses showed that the hypothesized five-factor structure demonstrated a satisfactory fit to the data. Moreover, engagement was found to be positively associated with learning performance. Our findings suggest that the ASOLS provides a useful tool for teachers to identify students in upper primary and junior secondary schools with deficits in affective states and offer appropriate remedy or support. It can also be used to evaluate the effectiveness of interventions aimed at enhancing students’ affective states during online learning.
Original languageEnglish
Article number15768
Number of pages11
JournalScientific Reports
Issue number1
Publication statusE-pub ahead of print - 9 Jul 2024


  • Affective states
  • Online learning
  • Assessment tool
  • Learning performance
  • Validity


Dive into the research topics of 'An efficient, reliable and valid assessment for affective states during online learning'. Together they form a unique fingerprint.

Cite this