Principles of effective teaching in the online classroom
Bibliographic record
Abstract
1. A Theoretical Framework for the Online Classroom: A Defense and Delineation of a Student-Centered Pedagogy (Dave S. Knowlton). 2. Designing Instruction for Learning in Electronic Classrooms (Gary R. Morrison & Peter F. Guenther). 3. Components of the Online Classroom (Zane L. Berge). 4. Making Decisions: The Use of Electronic Technology in Online Classrooms (Michael Simonson). 5. Students as Seekers in Online Courses (Mark Canada). 6. Accommodating Students with Special Needs in the Online Classroom (Thomas J. Buggey). 7. Humanizing the Online Classroom (Renee E. Weiss). 8. Promoting Deep and Durable Learning in the Online Classroom (Douglas J. Hacker & Dale S. Niederhauser). 9. Evaluating Students' Written Performance in the Online Classroom (John F. Bauer & Rebecca S. Anderson). 10. The Academy, Online Classes, and the Breach in Ethics (Bruce W. Speck). 11. Epilogue: A Cautionary Note About Online Classrooms (R. W. Carstens & Victor L. Worsfold).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".