World Wide Web

Social psychology

Developmental psychology

Economics

Computer science

Structural equation modeling

Coronavirus disease 2019 (COVID-19)

Confirmatory factor analysis

Psychology

Task (project management)

Management

Infectious disease (medical specialty)

Mood

Medicine

Procrastination

Disease

Online learning

Machine learning

Pathology

Personality and Individual Differences

During the lockdown due to SARS-CoV-2 (coronavirus lockdown), there has been a tremendous increase in the number of students taking online courses. Few studies, however, have examined the individual dispositions that influence self-regulated online learning during the coronavirus lockdown. To address this gap, the present study explored the ineffectiveness of online learning and examined how it can be predicted by self-regulated online learning and participants' procrastination disposition. Data of 433 participants were collected and subjected to confirmatory factor analysis with structural equation modeling. The results indicated that procrastination is negatively related to 6 sub-constructs of self-regulated online learning: task strategy, mood adjustment, self-evaluation, environmental structure, time management, and help-seeking. These sub-constructs were negatively related to the learners' perceived ineffectiveness of online learning. However, the relationship between perceived learning ineffectiveness and environmental structure or help-seeking was weaker than that with task strategy or mood adjustment, indicating that the latter two subtypes of self-regulated online learning should be considered before students engage in online learning.

Procrastination predicts online self-regulated learning and online learning ineffectiveness during the coronavirus lockdown