Bibliographic record
Abstract
This timely topic will most definitely be of interest to our readers as educators, researchers, and practitioners in open and distributed learning, though an exploration of opportunities and challenges surrounding AI, from multiple perspectives, and from a variety of spaces including distance education, hybrid learning, and blended learning.We encourage you to share with your networks and consider submitting an article yourself!The CFP will close on January 31 st , 2024."Educational Technology Undergraduates' Performance in a Distance Learning Course Using Three Courseware Formats" provides results from a quasi-experimental design from Nigeria.The researchers, Falode and Mohammed, found that printed, video, and Moodle-based courseware formats were each needed to support student retention, achievement, and satisfaction.Cisel researched "On the Ethical Issues Posed by the Exploitation of Users' Data in MOOC Platforms: Capturing Learners' Perspectives."This article examines the ethical implications and potential risks of learning analytics and MOOC participants' viewpoints regarding use of learner data.Use of OER for English language learners in Iran was the focus of Dashtestani and Suhrawardi's study, "Discrepancies and Similarities Between Online and Face-to-Face Teachers' Use of Open Educational Resources (OER) for Teaching Purposes."Auger, Baker, Connors, and Martin examined Indigenous students' experiences in "Understanding Indigenous Learners' Experiences During the First and Second Wave of the COVID-19 Pandemic."This study provides insights about the importance of listening as part of Indigenous online learning experiences."Measuring the Impact of an Open Educational Resource and Library e-Resource Adoption Program Using the COUP Framework" is a study from California.Squibb, Salmon, and Yan applied the cost, outcomes, usage, and perceptions to evaluate a zero-cost course materials program that further confirms OER beneficial cost-savings contributing to student success.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.055 | 0.047 |
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".