Enhancing Development of Competencies by Means of Continuous Improvement Processes
Bibliographic record
Abstract
Pressure is mounting for post-secondary institutions to demonstrate that their students have the skills needed for the workforce, particularly in professional programs. Common expectations among professional and regulatory bodies in information and communication technologies (ICT) include professional knowledge, understanding of systematic impacts and needs, problem solving, teamwork, technology resources, modelling and analysis, programming, and self-management. This article focuses on intentional development of ICT competencies in post-secondary education. The authors present a continuous improvement model for integrated curriculum design, with activities and assessments specifically selected to develop professional skills in the context of the discipline. The approach involves building common understandings, providing examples of effective teaching practices, using data for evaluation, and implementing high-impact strategies for attainment of competencies. Activities at Queen’s University that have been supported by the authors are presented as a case study. Criterion-based assessment of authentic disciplinary tasks is shown to be effective in detecting incremental improvement in competencies, and learning gains on standardized tests were significant and meaningful for problem solving, critical thinking, and communication. The use of standardized assessment has limitations, but feedback to departments enabled learning needs to be addressed. These examples are highlighted as part of a continuous improvement model to develop competencies by tailoring curriculum and learning strategies, assessing student achievement, and using the data to inform high-impact strategies for attainment of competencies.
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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 | 0.035 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".