Understanding the Effect of the COVID-19 Pandemic on Management Accounting Students
Bibliographic record
Abstract
A switch to emergency remote teaching, learning, and assessment (TLA) has become necessary as a result of the social distancing brought about by the recent COVID-19 pandemic. Higher Education Institutions (HEIs) were forced to switch from face-to-face to online teaching and learning to ensure successful completion of the academic year as well as the safety of their staff and students from a global pandemic. This arrangement has created teaching problems in terms of familiarizing oneself with technology, losing face-to-face contact, and limiting access to essential facilities such as laboratories and libraries. The new normal is when remote learning is employed to fulfil TLA obligations. Therefore, students are expected to adjust from a traditional to a remote learning environment. This change in environment highlights the importance of exploring students’ perceptions as the recipients of this novel learning. Hence, the aim of this study was to explore the impact that the COVID-19 pandemic has had on student learning, underpinned by the activity theory. Quantitative research methods were applied to elicit students’ perceptions of remote learning through the use of an online questionnaire. The target population comprised undergraduate management accounting students. The paper provides interesting implications for government, policymakers, regulatory bodies, and other researchers because it offers a student perspective on the challenges experienced with remote learning.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".