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Record W4414970950 · doi:10.5430/wjel.v16n2p105

Supervisory Styles and Graduate Expectations: Perceptions of Saudi Master’s Students

2025· article· en· W4414970950 on OpenAlexvenueno aff
Basim Alamri

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionStyle (visual arts)Graduate studentsNarrativeLearning styles

Abstract

fetched live from OpenAlex

The success and timely completion of postgraduate degrees depends heavily on the supervision provided to graduate researchers. The importance of supervision is well recognized yet there is limited research about how Saudi graduate students view their supervisors' styles and if these styles fulfil their needs. The current narrative inquiry study examined how Saudi graduate students view their supervisors' supervisory styles. Seven graduate students pursuing their M.A. TESOL degrees at a Saudi university participated in semi-structured interviews to share their supervisory experiences. The data analysis used Gatfield's (2005) four-styles framework to identify supervisory styles and emerging themes. The research participants described two main supervisory styles: the Contractual style which combines high structure with high support and the Laissez-faire style which features low support with low structure. The participants experienced two additional supervisory styles which included Pastoral (low structure, high support) and Directorial (high structure, low support). Most participants showed satisfaction with their thesis supervision, yet some students encountered obstacles and displayed minimal satisfaction. The study suggests several recommendations for program stakeholders. The supervisor-supervisee relationship requires improvement through better open communication and mutual understanding development. Both students and supervisors need clear explanations about their responsibilities and expectations during the initial stages of thesis work.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.327
Teacher spread0.296 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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