An empirical investigation of student online learning continuance intention in the post-COVID-19 pandemic era
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate students’ viewpoint regarding continuation of using online learning in the post-COVID-19 pandemic world. While during the pandemic years predominantly all formal learning was forced to move online, in the postpandemic environment traditional postsecondary education institutions generally resumed in-person (or face-to-face) learning. Nonetheless, it is possible that some students would like to continue using online learning after using such a system during COVID-19 restrictions. Therefore, it is important for postsecondary institutions to understand students’ views on continuing with online learning so that these institutions can better adapt their offerings to learners’ preferences. Design/methodology/approach This study uses a cross-sectional online survey-based approach grounded on an innovative theoretical framework blending the unified theory of acceptance and use of technology 2 into the expectation-confirmation model of information systems continuance. Data were collected from 247 students in Canada in Fall 2022 and were analyzed with partial least squares structural equation modeling techniques. Findings Perceptions of usefulness and of monetary benefits relative to costs together with developing positive habits regarding online learning are the most significant beliefs motivating students to want to continue with online learning. Furthermore, positive disconfirmation of initial expectations and satisfaction relying on previous use together with a favorable attitude with respect to online learning strongly influence the intention to continue with online education. Originality/value The study opens the door for similar research in other cultural contexts (e.g. with a different individualistic-collectivistic pattern) and for other domains that moved totally online during the COVID-19 pandemic (e.g. primary health care) to maximize people satisfaction while minimizing societal costs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".