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Record W3096904380 · doi:10.5430/ijhe.v10n1p147

Student Voice in an Extended Curriculum Programme in the Era of Social Media: A systematic Review of Academic Literature

2020· review· en· W3096904380 on OpenAlexvenueno aff
Joshua Ebere Chukwuere

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typereview
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumSocial mediaHigher educationPedagogyMathematics educationSociologyPolitical sciencePsychology

Abstract

fetched live from OpenAlex

Student voice in this digital age and across higher learning institutions is increasing exponentially with the function of social media. Student voice provides a vibrant communication pathway to extended curriculum programme students in higher education institutions. Social media ensures active participation of extended curriculum programme students in generating views and ideas that define the higher learning environment and experience towards better learning conditions and outcomes. A systematic literature review was used in gathering scientific papers through trusted academic databases. The systematic literature review was conducted between the period of 1 April 2019 and 28 September 2019, by looking into the contents of articles covering the current research objectives. The study’s findings show that social media provides an effective and instant spread of the extended curriculum programme students’ voice across higher education learning institutions. It also allows the students in the extended curriculum programme to engage with each other and the institutional management promptly. Social media promotes extended curriculum programme students’ voice in reaching the right audience at the right time. The results of this study are key for extended curriculum programme students, lecturers, and university management in understanding and applying social media effectively and in bringing transformation to South African higher education institutions and beyond.

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)0.003
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.593
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.061
GPT teacher head0.475
Teacher spread0.414 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations3
Published2020
Admission routes1
Has abstractyes

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