Bibliographic record
Abstract
Purpose This paper aims to outline a proof of concept for a framework to support students in reflecting on and in articulating their disciplinary, contextual and professional learning in the workplace. The purpose of the framework was to help students to recognize and articulate their transferable employability skills in preparation for the workplace or further studies upon graduation. Design/methodology/approach Researchers developed a Learning Evaluation and Reflection Narrative (LEARN) activity to facilitate real-world articulation of workplace learning. A group of work placement students completed pre- and post-work surveys, prompting reflection on their learning goals and behaviors. The Transferable Learning Orientation Survey comprised five constructs: goal orientation, learning belief, self-efficacy, transfer (deep learning) and organization. Subsequently, they completed a written reflection and a mock interview scenario, where they verbally articulated their abilities and the applicability of their skills. Results of thematic analysis are presented. Findings Survey results demonstrated changes in students’ orientation toward learning. Additionally, students were able to deliver sophisticated responses through engagement in the LEARN framework, articulating recognition and self-awareness of their personal and professional learning, as well as relevance of their learning within and beyond their workplace setting. Research limitations/implications The sample is small, and the authors therefore recommend further work to evaluate the effectiveness and practicality of the LEARN framework in larger cohorts and in alternate work environments. Social implications The responses suggest the LEARN framework are worthy of further investigation as a tool for students to articulate lifelong learning skills and behaviors, as it offers an opportunity for students to engage in reflective, deep learning. Originality/value This research builds on existing studies on the evaluation of lifelong learning, adapting a framework and testing its implementation in the workplace setting.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".