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Record W7054454019

Accommodating the plurality of voices:A qualitative study exploring teachers’ challenges and needs in the institutionalization of public engagement in higher education

2023· article· en· W7054454019 on OpenAlexaff

Bibliographic record

VenueDigital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) · 2023
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsAthena Sustainable Materials Institute
Fundersnot available
KeywordsInstitutionalisationPublic engagementHigher educationPopularityCurriculumCommunity engagementQualitative researchCivic engagementBest practice
DOInot available

Abstract

fetched live from OpenAlex

In recent years, community service learning (CSL) has gained popularity as a form of public engagement in universities and higher education institutions (HEIs) worldwide (1,2). The positive impacts of CSL are being recognized and embraced, so the current challenge is no longer to only improve and expand these efforts, but also to institutionalize CSL in a meaningful way for all stakeholders involved (1,3–5). Achieving this requires both top-down and bottom-up strategies to embed public engagement in the institution’s mission and policies and to reflect it in daily activities to become part of a university’s culture (3,6–8). Teachers, as critical components of the educational system in HEIs, play a vital role in shaping students’ educational experiences and can provide key insights into effective ways of integrating public engagement into the curriculum (8–11). Encouraging teachers in their engaged educational efforts can help foster a culture of public engagement within the institution, as they can act as advocates for its implementation and promote it among colleagues. However, the challenges that teachers face in integrating public engagement into their teaching practice and institutional structures are diverse and not well understood, and strategies to further support and promote these efforts in this phase of the institutionalization process remain unclear. To address these gaps, this qualitative study aimed to gain a more in-depth understanding of teachers’ challenges and needs in their engaged educational practices and identify the strategies and actions needed to further thrive the institutionalization process. This can help HEIs to better understand how to institutionalize public engagement efforts in a way that is sustainable and responsive to the needs of teachers. To contribute to this, we performed 25 qualitative semi-structured interviews with teachers at the Vrije Universiteit (VU) Amsterdam who implemented CSL in their courses. Purposive sampling was used to recruit a diverse group of participants, including teachers from different faculties, using various formats of CSL, and with varying degrees of experience in implementing CSL in their courses. From our analysis, we identified three major needs of teachers. Firstly, they desire an internal community or network to exchange experiences and get inspired. Secondly, teachers indicated the need for resources, such as standardized tools (e.g., rubrics and assessment forms), training and support (e.g., training in the code of conduct wen working with societal partners), but also matching moments where they can meet societal partners. Finally, teachers expressed a need for enhancing both internal and external promotion of public engagement efforts. Additionally, teachers stressed that more time and budget for the design, implementation, monitoring, and evaluation of courses are needed. Including public engagement efforts in the academic reward system, alongside academic publications, was also mentioned as crucial for the continuation of their practices in the long run. In conclusion, this study highlights the challenges and needs of teachers in integrating public engagement into their teaching practices and institutional structures. By implementing policies and strategies that are responsive to teachers’ needs, HEIs can foster a culture of public engagement that aligns with their mission.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.144
GPT teacher head0.288
Teacher spread0.144 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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