A Case Study on How Distance Education May Inform Post-Pandemic University Teaching
Bibliographic record
Abstract
Higher education recently found itself in the unprecedented situation of being forced to rapidly switch to online education as a demand of the COVID-19 pandemic. The aim of this article is to compare and contrast the experiences of university tutors who teach in two distance education universities with those who teach in a traditional university concerning their online lessons during lockdown. Forty university tutors participated in a survey to capture their teaching experiences. The survey was based on the transactional distance theory. Both qualitative and quantitative data were collected from both groups. Analysis of the quantitative data indicates no significant differences between the two groups in scores regarding course structure flexibility and the degree of student autonomy; however, significant difference with a high effect size was found regarding instructional dialogue, in favor of the distance tutors’ group. Thematically analyzing the qualitative data allowed the researchers to group the data into three main themes focused on how the instructional dialogue was manifested in the classes of both groups: (a) the learning design approach adopted, (b) the tutor-led interaction for student support, and (b) learner-to-learner communication and the sense of an online community. Ensuing recommendations involve adopting social-constructivist approaches that can sustain high-quality instructional dialogue in online learning settings and creating distance education faculty development programs in traditional universities that will help tutors support dialogical forms of online pedagogy.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 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".