CEAB's Graduate Attribute 3.1.3: Investigations – Is it really possible for engineering graduates to possess this attribute?
Bibliographic record
Abstract
Starting 2014, engineering programs in Canada will be evaluated by CEAB based on twelve Graduate Attributes and institutions must demonstrate that their graduates possess these attributes at the time of graduation. One of these attributes is “Investigations” which is defined by CEAB as “an ability to conduct investigations of complex problems by methods that include appropriate experiments, analysis and interpretation of data, and synthesis of information in order to reach valid conclusions.” This is similar to one of ABET's student outcomes which states that students attain "an ability to design and conduct experiments, as well as to analyze and interpret data". In this paper, it will be argued that with the current curriculum of most, if not all, engineering schools in Canada, it is almost impossible for graduates to possess this attribute unless a compulsory course is introduced to specifically teach the subject. Proper design, conduct, and analysis of experiments of complex problems cannot be learned by osmosis or by doing standard labs where the procedure and analysis methods are given. Engineering educators and graduates thinking that somehow that the skill to design, conduct and analyze experiments will be learned in an engineering program do not fully appreciate the myriad of issues that are involved with experimentation to study a complex problem. Examples of these issues include: a large number of variables, multiple responses with conflicting objectives, linear or nonlinear responses, interaction among variables, etc. In this paper, these issues and many others will be discussed. How they can be addressed will be discussed and a course that will help graduates achieve competence in “Investigations” is also proposed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".