Bibliographic record
Abstract
Although educational online technologies (EOTs) present an extraordinary range of higher education opportunities, significant gaps in knowledge about their purpose and functionality may impede levels of adoption. As the demand for online learning grows, it is critical that tertiary education institutes (TEIs) address gaps in knowledge by developing their understandings of EOT applications. This paper aimed to identify, and describe the application of a range of EOTs popularly used in blended tertiary environments (BTEs). Through qualitatively designed semi-structured interviews with 13 blended learning experts from New Zealand, Australia and Canada, and a 5-step analyses of data, it verified the use of 35 different EOTs in BTEs, including Adobe Connect, Blackboard, Facebook, Instagram, and YouTube. Their key characteristics were summarised using a multi-dimensional taxonomy, called the Pentexonomy, which synergised a range of perspectives into a robust, contextualised, and multi-dimensional framework for categorising EOTs. An outline of recommendations for the effective use of some of these EOTs was also provided. As EOTs advance and usage accelerates, the outcomes of this research will assist TEIs in their efforts to keep abreast of EOT developments, make informed choices about EOT use, and contribute to the delivery of relevant, meaningful EOT support.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".