Bibliographic record
Abstract
We are pleased to present another general issue of IRRODL to our research community, distance educators, and general readers throughout the world.This delightful general issue has a variety of themes, including instructional design for distance education, support for distributed adjunct faculty, and mobile learning.The first research article by Simon Paul Atkinson presents a new and, I think, a very practical instructional design model for online education.His article, "Embodied and Embedded Theory in Practice: The Student-Owned Learning-Engagement (SOLE) Model," describes the rationale for, and a good description of, a toolkit that is designed to help instructors and designers create online courses that make the most of both the technical and pedagogical affordances of the Web.Our second research article challenges us to look beyond the hype and sales talk too often associated with online learning and to confront the challenges of high dropout and low prestige and lack of acceptance by mainstream academics.In "Head of Gold, Feet of Clay: The Online Learning Paradox," researchers Thomas Michael Power and Anthony Morven-Gould propose a way out of John Daniel's iron triangle of cost, accessibility, and quality by combining both synchronous and asynchronous models to create "blended" online learning design (BOLD).Many models of distance education achieve their economy of scale and reduce costs by employing part-time adjunct faculty.Thus, they are a critical and arguably the most important component of any distance education system.However providing adequate training and support to these distributed educators has long been a challenge to distance education systems.We are pleased to publish two articles that investigate ways to support adjunct faculty.The first by Julie Shattuck, Bobbi Dubins, and Diana Zilberman is titled "Maryland Online's Inter-Institutional Project to Train Higher Education Adjunct Faculty to Teach Online," and it evaluates a program designed to help adjunct faculty become highly effective online teachers.The lessons learned and the interventions developed and piloted in Maryland and described in this article will be useful in guiding professional development and support units across the world.The second by Vera Dolan
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.136 | 0.106 |
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".