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Record W2936172188

Creating a reflective voice- patchwork writings

2018· article· en· W2936172188 on OpenAlexaboutno aff
Annie Noble

Bibliographic record

VenueBristol Research (University of Bristol) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsCommunicationLinguisticsComputer sciencePsychologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Background & Purpose TLHP is a medical education course at Bristol University. As with many such courses we have predominantly written assignments where students are required to demonstrate reflecting learning and critical engagement with literature. Mindful that our students are required to join an unfamiliar discourse (in education) we have moved to an innovative formative assessment strategy using patchwork texts, as proposed by the HEA and educational literature. These short pieces of writing are generated by students before and during Certificate units encouraging reflective writing and self & peer assessment. Furthermore, they are mapped to the Professional Standards Framework of the HEA, thereby demonstrating achievement through the course of the HEA fellowship. Methodology The evaluation of this initiative is ongoing. All 69 students entering the Certificate in 2017/18 have written at least two patchworks, with a further 31 writing 2 patchworks for their second unit. Ongoing analysis is looking at the development of a depth of reflective analysis as measured against Koole et al’s operational indicators of the reflection process, which is now embedded in our marking criteria. Furthermore, student and teacher evaluations of the scheme will be collected at the end of the academic year and thematically analysed to show impact on teaching and learning. Results Early analysis of the patches show students reflecting on their own learning and teaching, and in particular identifying areas that they want to improve. One patchwork asks for a definition of learning and although the ‘acquisition’ metaphor of learning is prevalent, there is also discussion of what it means to ‘know’ something. In addition, teachers report they are able to use the patches (which are produced before teaching sessions) to drive early discussions on teaching and learning. In the first teaching assessment a small number of students have quoted from their patchwork texts as part of the reflective assignment (6%), which we are now encouraging in later units. A fuller description of the patchwork texts strategy will be shared at the conference. In addition, further content analysis of the patches and the student and staff evaluations will be presented. Discussion & Conclusions Students are already demonstrating progression in reflective writing. The final unit of the Certificate will also incorporate peer assessment and feedback into the process. We expect the use of patchwork texts as a formative assessment strategy to strengthen our students’ confident use of educational discourse and to find their own ‘educational voice’ more quickly and confidently than in previous years. References Winter, 2010, Race, 1995. Brown, 1994. Prosser, 1999. Mabbett, 2011. Brockbank and McGill, 1998 Koole et al (2011) Factors confounding the assessment of reflection: a critical review. BMC Medical Education 2011, 11:104 Wegner, E. & Nückles, M. (2015) Knowledge acquisition or participation in communities of practice? Academics’ metaphors of teaching and learning at the university. Studies In Higher Education 40: 4

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.114
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.004
Scholarly communication0.0070.007
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0170.009

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.

Opus teacher head0.098
GPT teacher head0.447
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
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