Approaches To Learning And Learning Environments In Pbl And Lecture Environments
Bibliographic record
Abstract
One desired outcome of our educational goals is that our student's approach to studying by searching for meaning rather than superficially memorizing and regurgitating knowledge.To some extent, students have their own preferred approaches to studying.However, research by Ramsden and Entwistle suggests that the learning environment we use in our classrooms also affects the student's approaches to studying.Two published inventories to measure these effects are the Lancaster Approaches to Studying Questionnaire, LASQ, and the Course Perceptions questionnaire, CPQ.Data from the short version of these questionnaires were analyzed for a group of students concurrently registered in two programs.Students were registered in a cross-section of disciplines in humanities, social science, science and engineering where the method of instruction was primarily the conventional lecture.Those same students were concurrently registered in the "Theme School" program, an interdisciplinary program of 33 credits where the method of instruction was small group, selfdirected problem-based learning.These sophomore students who selected the Theme School program scored high on the LASQ on both the strategic and "deep" learning scales and relatively low on the "surface" learning orientation.They scored high on the Perry inventory.On the CPQ they rated their home departments as 21.6 with a standard deviation of 10.32.They rated the theme school as 40.09 with a standard deviation of 7.57.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".