Consensus-based course design and implementation of constructive alignment theory in a power system analysis course
Bibliographic record
Abstract
This article presents the implementation of the constructive alignment theory (CAT) in a power system analysis course through a consensus-based course design process. The consensus-based design process involves both the instructor and graduate-level students and it aims to develop the CAT framework in a holistic manner with the goal of including different perceptions. The considerations required to implement this approach are described in detail. To examine the effect of this approach, three different course evaluations were conducted by querying the students during different stages of the course. These evaluations show that most of the students find a benefit for their learning in the implementation of CAT within the new course design. These observations are supported by a comparison of the students’ performance in the new course and the previous one. Finally, the revised two-factor study process questionnaire (R-SPQ-2F) is utilised to identify the students’ learning approach towards the course. The aim is to correlate the students’ approach with their final grade to assess if students adopting a deep learning approach are rewarded with higher marks and vice versa, that is, to check if the CAT implementation was successful. Meanwhile, some of the R-SPQ-2F limitations, which affect the quality of the results, are identified and discussed. Additionally, to facilitate the practical usage of R-SPQ-2F, an algorithm was developed by the authors to rank the students’ approach towards the course. The results of the new ranking algorithm demonstrate positive correlation with the students’ final grade, which is an indication of the effective CAT implementation.
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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 | 0.033 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".