Resistance to Change Concerning Use of Educational Online Technologies in Blended Tertiary Environments
Bibliographic record
Abstract
The rapid emergence, adoption and demand for educational online technologies (EOTs) has engendered significant advances across the higher education sector. Traditional learning spaces have evolved into dynamic blended tertiary environments (BTEs), providing a modern means through which tertiary education institutes (TEIs) can augment delivery to meet stakeholder needs. Despite the significant growth and demand for web-enabled learning, considerable obstacles face key stakeholders concerning the adoption and use of EOTs. These obstacles challenge the continued success and sustainability of blended implementations in higher education. Resistance to change was identified as one of these challenges during interviews with 13 blended learning experts from New Zealand, Australia and Canada. This paper discusses this issue as it relates to EOT usage, how it is demonstrated, and the extent to which it impacts on key stakeholders. As technology advances and usage accelerates, it is important for TEIs to understand and address this issue, and provide support for the effective use of EOTs. As TEIs keep pace with digital advancements, the outcomes of this study will enable them to design relevant approaches to tackle the issue of resistance to change as it relates to EOT usage, and deliver meaningful support to key stakeholders in BTEs.
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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 | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".