What should Graduating Geological Engineers Know and Be Able to Do? Redesigning Curricula on the Basis of Graduate Attributes
Bibliographic record
Abstract
Curriculum revision is often a process of evaluating existing courses and working out slight modifications in order to satisfy external agencies and internal teaching rosters.In 2008, the Geological Engineering Curriculum Committee (GECC) at Queen's University, a team of eight faculty members, began by concept mapping what skills, knowledge and attitudes are required for geological engineering students to begin their careers.The motivation of curricular redesign was the desire to reduce the program from four options to one flexible program.Additional pressures included the loss of one faculty member and anticipated financial constraints within the university.From the original concept mapping several key diagrams of graduate attributes were created to guide the revision process.The resulting program, with revised courses, has several advantages: it is the result of a shared vision, it intentionally develops the technical knowledge, and design and professional skills from second year to fourth through a common core, it allows students flexibility in stream and technical electives, and satisfies the current CEAB Accreditation Units.In addition, by starting with the knowledge, skills and attitudes that we view as essential to practicing geological engineer we are well positioned to transition to the planned CEAB graduate attribute assessment.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".