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Record W3010736344 · doi:10.5430/ijhe.v9n3p109

The Research-Teaching Nexus and Its Influence on Student Learning

2020· article· en· W3010736344 on OpenAlexvenueno aff
Begoña Gros Salvat, Manel Viader, A. Cornet, M. Jesus Martinez, Jordi Palés, Marta Sancho

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
FundersUniversitat de Barcelona
KeywordsNexus (standard)Context (archaeology)Mathematics educationHigher educationTeaching and learning centerWork (physics)Knowledge managementPedagogyPsychologyTeaching methodComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

The relationship between teaching and research in universities has been widely studied in the higher education literature, but no clear relationship between the two has been identified. Nevertheless, in recent years, research has been linked to a form of teaching that is more focused on the development of competences and learning capacity through enquiry and the generation of new knowledge. In this context, it is important for teachers and students to work together on the design of shared spaces for research and learning. This work examines the case of the University of Barcelona to analyse whether there is enough connection between research and teaching to allow students to experience this link and to successfully develop research competences. Teaching plans of the academic year 2018-19 were screened to identify research-related competences, the modules they appear in, and the descriptions of the evaluation systems. This information was compared to the students’ perceptions of the actual training they had received on these research competences. Results showed that teaching plans establish numerous competences related to research and generating new knowledge. However, students consider that this knowledge is not developed until the final year project.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.676

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 communication0.0000.000
Open science0.0000.000
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 head0.107
GPT teacher head0.496
Teacher spread0.388 · 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 designNot applicable
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

Citations26
Published2020
Admission routes1
Has abstractyes

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