Bibliographic record
Abstract
Your early summer reading is here!Or is it?!Clearly, for an international journal, this seasonal reference represents a Western/Northern-based sentiment.Perhaps that's just one small illustration of the scope and accompanying challenges of making sense of the diverse contributions that IRRODL attracts.In this issue of more than 15 articles, we once again present to you the range of research that you have come to expect from us -research that spans the world and many aspects of the ODL field.What *don't* we have in this issue?There is nothing on OER this time around; however, watch for an upcoming special issue that will feature current research in this quickly developing area.This issue features several pieces that highlight the business of successful teaching, faculty-learner and learner-learner interaction.From Serbia, Raspopovic, Jankulovic, Runic, and Lucic examine, in a case study, success factors in e-learning, from the perspective of a developing country in transition from traditional modes of learning to technology-enhanced modes of learning.Similarly, Mbatha gives us a case study that considers global transition in higher education as the University of South Africa transitions from a traditional model of learning to a new socially mediated model.And in another case study, Sadykova examines mediating knowledge through peer interaction in a multicultural online course offered in the US.Samuels-Peretz considers the nature of learners' interactions with others, also at an American institution, with a special emphasis on gendered knowing, using Belenky, Clinchy et al.'s 1986 seminal study that produced Women's Ways of Knowing.Chang, Shen, and Liu explore the faculty role in online instruction while Vu, Cao, Vu, and Cepero researched success factors for learners in an online professional development course.Clearly, we are still intrigued, internationally, with what makes online teaching and learning successful.How can we make it better?Borup, West, Thomas, and Graham suggest, in their piece on the impact on students of video feedback, that the resultant learner-instructor connection is enriched.Video use is also the topic of
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.075 | 0.055 |
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".