Conceptions of Effective Teaching and Perceived Use of Computer Technologies in Active Learning Classrooms
Bibliographic record
Abstract
This paper examined professors’ conceptions of effective teaching in the context of a course they were teaching in active learning classrooms and how the conceptions related to the perceived role and use of computers in their teaching. We interviewed 13 professors who were teaching in active learning classrooms in winter 2011 in a large research university in Canada. The interviews captured what professors consider effective teaching, expected learning outcomes for students, instructional strategies and the role participants saw for computers in their teaching. Analysis of interview transcripts using a holistic inductive and constant comparison approach resulted in three conceptions of effective teaching: transmitting knowledge, engaging students, and developing learning independence. Professors’ perception about the role and use of computers was found to be in line with their conceptions of effective teaching. Professors whose conception of effective teaching focused on developing learning independence used computers as tools for students’ learning; those with a transmitting knowledge conception considered computers as a means of accessing or presenting information. Data collected from students about their use and their professors’ use of computers in the course supports this conclusion. Results have implications for design of active learning environments and faculty development initiatives.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.003 |
| 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".