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Record W2108946898 · doi:10.5430/ijhe.v3n4p72

Living up to Our Students’ Expectations – Using Student Voice to Influence the Way Academics Think about Their Undergraduates Learning and Their Own Teaching

2014· article· en· W2108946898 on OpenAlexvenueno aff
Lorna Goodwin, Andrea Cameron

Bibliographic record

VenueInternational Journal of Higher Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsTUTORPsychologyHigher educationPedagogyFocus groupMathematics educationLearning stylesSociologyPolitical science

Abstract

fetched live from OpenAlex

Understanding the student learning experience is essential if Higher Education Institutions (HEI) are to provide an education for the 21 st century. This study investigated students’ perspectives on their learning experiences and offered undergraduates a chance to influence the way academics think about learning and teaching. Participants were drawn from two UK HEIs and a semi structured focus group approach was adopted. A total of nine focus groups consisting of 3-7 participants were drawn from across all Sport degree year groups in both institutions. Assessment, pedagogy and teacher characteristics emerged as primary concerns across both institutions. Assessment was appreciated by all students as key to their learning but was exposed as being overly traditional and rigid in its application. Students were unanimous in their support for small group pedagogies, rejecting traditional powerpoint dominated lecturing styles. The emphasis on the behaviour of, and delivery by, tutors was noteworthy. Students appraised the development of their academic skills and confidence, linking these to motivation, knowledge, self-awareness and critical reflection. In doing so they understood the impact of inconsistencies in tutors’ teaching practices. The onus is on every tutor to combine imaginative assessment with dynamic and relational experiences in order to provide a strong foundation for flexible, reflective and creative graduates.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.428
Teacher spread0.387 · 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 teacher head, 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".

Quick stats

Citations13
Published2014
Admission routes1
Has abstractyes

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