Enhancing Development of Competencies by Means of Continuous Improvement Processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it