Nudging Lifelong Learning and Reflective Thinking in Engineering Students Utilizing LinkedIn Learning
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
Abstract—Most engineering and technology-focused program curricula are firmly fixated on the required technical skills to meet the profession’s needs. Yet, in today’s rapidly changing, globalized world, engineers and technologists need more than technical competencies to meet the requirements of their professional work. This work illustrates how the LinkedIn Learning (LiL) platform was used as a “learning partner” to complement undergraduate engineering management courses to enrich metacognition and nudge lifelong learning tendencies. The rationale for integrating LiL into the course framework is examined, including study design and survey results. Summary research indicates students appreciated the LiL coursework assignments. Most respondents perceived that the LiL courses increased their knowledge and skills in the subject matter presented. The study illustrated movement towards self-determined learning behaviour and improved reflective capabilities.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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