Teacher and Student Behaviors in Face-to-Face and Online Courses: Dealing with Complex Concepts
Bibliographic record
Abstract
The objective of this research was to compare the quality and quantity of teacher and student interaction in an on-line versus face-to-face learning environment. A Master’s level course on nursing theories was taught by the same professor by both methods. Transcripts of the face-to-face class and on-line postings were analyzed to identify professor behaviors and also to rate the levels of student responses using the Gunawardena, Lowe and Anderson (1997) Analysis Model for Social Construction of Knowledge. Categories of teacher behaviors were identified and frequencies calculated in each course. While numbers of interventions were different, the professor showed similar facilitation behaviors in both environments. Student participations were counted and rated using the five major phases of the model. While most student interactions reflected the lower levels of the model, some students in each delivery context demonstrated higher levels of knowledge construction. Students experiencing each delivery method were successful in the course and mastered complex, abstract concepts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".