Bibliographic record
Abstract
study as we present nine main articles, two book reviews, three technical reviews, and two archived audio-graphic CIDER Sessions from IRRODL's publisher, the Canadian Institute of Distance Education Research (http://cider.athabascau.ca). Main SectionThe issue leads off with an excellent paper and call to action for researchers and theorist relating to systems theory in distance education.Steven C. Shaffer in System Dynamics in Distance Education and a Call to Develop a Standard Model, makes a soundly reasoned call for a standard model of distance education (DE).Drawing from the discipline of systems dynamics, Shaffer tell us that, "Systems thought in an educational context is problematic; authors sometimes write about looking at an educational situation from "a systems perspective," but then do not apply the tools and techniques of systems theory or system dynamics."We are long overdue for standard models for DE research and practice.Though 'systems thinking' does not reveal all of potential interest to distance educators, the demands for quality, cost and learning effectiveness that permeate much of our thinking suggests that systems models do have much to offer to both researchers and practitioners.Clearly, it is hard work applying standard models to messy world of human systems; doing so takes tremendous insight into both the intricacies of the system (DE in our instance) and the model (System Dynamics).I believe Shaffer has done an admirable job and has pointed researchers in a workable direction.For this we applaud his effort and suggest that we heed his call.The next two papers come to us in a 'natural pair' focused on science education at a distance.The first is a case study by James Cheaney and Thomas Ingebritsen entitled: Problem-based Learning in an Online Course.Cheaney and Ingebritsen start off by reminding us that Problem Based Learning (PBL) uses 'real world' problems or situations as a context for learning.For this case study, the authors analyze an online biotechnology science course wherein students grappled with real life ethical, legal, social, and human issues surrounding pre-symptomatic DNA testing for Huntington's disease.Cheaney and Ingebritsen first provide evidence that suggests that PBL can stimulate higher-order learning in students.Unlike many studies of PBL use, however, this case study show that students' actual acquisition of knowledge was slightly lower for PBL students than for students who learned the same material via a traditional lecture format.The authors go on to explore the differences between the online PBL and lecture-based PBL, and suggests that further research on this topic is warranted.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.328 | 0.242 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".