Identifying Key Skills for the Future of Work and the Assessments to Build Them
Bibliographic record
Abstract
Identifying skills for the future of work and how assessment design and implementation can help support the building of these skills are necessary to support learners in this era of technological disruption. The acceleration of various forms of disruptive technologies, from automation and the expansion of artificial intelligence, the increasing embeddedness of remote collaboration and communication technologies, and the expansion of the gig economy, are rapidly transforming the interconnected realities of work and learning. As such, the article sets to do the following: (1) identify key skills, mindsets and knowledge required to succeed in the future of work; (2) assess various technological, economic and other factors that are transforming the landscape of work and learning; (3) explore the role of curriculum and instructional design, particularly community-engaged learning, in creating opportunities for learners to develop and practice key skills, mindsets and knowledge; and (4) explain how post-secondary institutions, including polytechnics, are ideal sites for a more robust alignment of skill development and assessment design.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".