Looking out and looking in: Exploring a case of faculty perceptions during e-learning staff development
Bibliographic record
Abstract
This explorative study captured the perceptions of faculty members new to technology enhanced learning and the longitudinal observations of the e-learning manager during dedicated professional development in order to compile a socially transformative emergent learning technology integration framework for open and distance learning at the School of Continuing Teacher Education at North-West University, South Africa. A pragmatic approach guided the bounded case study. The study followed a fully mixed sequential equal status design of mixing sequential qualitative and quantitative findings. Data collection strategies concern a custom-made questionnaire, interviews with faculty members, and longitudinal observations by the e-learning manager. The first phase uncovered 34 qualitative codes. After quantitating of the data, a t-test indicated significant differences for 17 variables between faculty perceptions and observations of the e-learning manager. Ward’s method of Euclidean distances grouped the variables into five clusters according to the researchers’ paradigm of looking in and looking out from the development context. The clusters formed the basis of a model for faculty development towards socially transformative learning technology integration for open distance learning. The five aspects of the model comprise (i) the environment in which faculty members should gain support from the institution; (ii) the environment in which faculty have to address the realities of adopting TEL; (iii) human factors relating to the adoption of TEL; (iv) concerns and reservations about the use of TEL; and (v) continuing professional development needs, expectations, and motivators. The sustainable integration of ICT into higher education institutions remains a major challenge for the adoption of TEL.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".