Thinking Preference Awareness, Leadership Skills and Learning Behaviour
Bibliographic record
Abstract
Prior to Covid-19, studies identified a gap between the skills employers needed and the skills graduates had (IBM, 2012, 2016, WEF2016b). Still impacted by the pandemic, graduating classes of the future may not be any different. This paper shares findings from an Applied Research in Innovation and Education (ARIE) pilot project conducted between 2016-2017 at Mohawk College, Ontario Canada with 117 students enrolled in a 14week, mandatory Communications course. Specifically, the pilot project explored perceptions of leadership, employability skills and attributes in post-secondary students and how they perceived and assessed their leadership skills and developmental needs for future employment. The study also examined how learner awareness of their own and others thinking preferences when problem-solving impacted their academic behaviours and development goals. The aim was to determine the impact thinking preference awareness in learners might have on their communication, collaboration, and development of personal and professional learning targets. Using a mixed-methods, transformative-emancipatory critical approach, the research highlights the classroom as a living laboratory rich in emergent research questions and revealed additional areas of research regarding thinking preference theory and language, self-differentiated learning and inclusive, learner-focused curriculum design.
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.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".