Exploring Experiences of Accounting Student Teachers with Digitally Mediated Learning: A Phenomenological Perspective
Bibliographic record
Abstract
Conducted against the backdrop of forced online learning imposed by the COVID-19 pandemic, this study sought to explore the learning experiences of accounting student teachers with digitally mediated learning. Anchored in phenomenological research design, focus group interviews were used to generate qualitative data from purposefully selected accounting student teachers while member checking was used for validation. Content analysis of data revealed sufficient concurrence in the phenomenological voices of students that they experienced anxiety, stress, isolation, demotivation and lack of contact with their classmates. In mitigation of these experiences, the study recommends that lecturers need to develop learning material with which students can interact meaningfully, and create and maintain a live, interactive virtual learning environment in which student learning is monitored and evaluated continuously. The students appreciated the flexibility of digitally mediated learning and its provision for real opportunities for learning beyond the physical learning environment. The study found that digitally mediated learning creates a platform for a creative, innovative and non-contact learning environment in the new educational dispensation of the COVID-19 pandemic era. It therefore calls for a radical paradigm shift in the pedagogical assumptions and practices of lecturers towards a student-centred virtual learning environment which thrives on digital technology.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".