Linking Theory and Practice: A Structured Approach to Developing Subject Expertise and Professional Skills in Higher Education
Bibliographic record
Abstract
This session presents the FES Subject Passports initiative, originally developed for teacher training but adaptable to other profession-facing programmes, such as nursing, social work, and engineering. The initiative bridges theoretical knowledge with practical application, ensuring students not only gain subject expertise but also develop the professional skills necessary for success in their fields. This approach aligns with debates in higher education on curriculum integration, employability, and the development of transferable skills (Schleicher, 2018; Tight, 2023). By offering subject-specific frameworks, the initiative supports the development of both subject mastery and pedagogical competence, ensuring students are well-prepared for professional practice. The FES Subject Passports were developed in response to the need for higher education programmes to prepare graduates for the workforce, as outlined in the UK’s Industrial Strategy (Department for Business, Energy & Industrial Strategy, 2017). This initiative draws on Eraut’s (2004) work, which highlights the importance of integrating theory with practice in professional education. Research indicates that when academic learning is applied in real-world scenarios, students are better equipped for professional roles (Brockbank & McGill, 2007). The success of the FES Subject Passports pilot, particularly in the development of the English Language Passport, demonstrates the effectiveness of structured, subject-specific resources in enhancing student learning and fostering professional confidence (Ofsted, 2024). The Passport initiative initially addresses the challenges of delivering education in the diverse FES sector, which spans subjects ranging from English and Mathematics to vocational courses like automotive engineering and equine studies. The flexibility of the FES Subject Passports allows mentors to contextualise theoretical knowledge, making it relevant to specific subject areas and professional practice, a feature supported by studies on practice-based education (Eraut, 2007). This makes the Passport model adaptable to other professional disciplines, such as nursing, social work, and engineering, ensuring a broader applicability across higher education. This session will show participants how the FES Subject Passports model and approaches to deliberate-practice (Ericsson, 2008; Christodoulou, 2017; Ericsson, 2019) can be adapted for use in various profession-facing degree programmes, supporting the achievement of Sustainable Development Goal (SDG) 4 (Quality Education) by improving the quality of education and SDG 10 (Reduced Inequalities) by ensuring equitable support for diverse learners. By linking curriculum design with professional practice and employability, the initiative ensures that all learners have access to the tools necessary to succeed. Participants will leave the session with an actionable plan for implementing subject-specific frameworks within their own disciplines. Through an interactive component, attendees will begin developing their own version of a subject passport, enabling them to apply the principles of subject mastery and professional skills development in their specific educational contexts.
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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 | 0.039 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".