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Record W2554401887 · doi:10.5539/elt.v9n12p79

Graduate Students’ Needs and Preferences for Written Feedback on Academic Writing

2016· article· en· W2554401887 on OpenAlexvenueno aff
Manjet Kaur Mehar Singh

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyGraduate studentsPreferenceConstructiveMathematics educationAcademic writingEnglish for academic purposesMedical educationHigher educationPedagogyComputer scienceProcess (computing)Medicine

Abstract

fetched live from OpenAlex

<p>The aim of this research is to examine graduate students’ needs and preferences for written feedback on academic writing from their lecturers and thesis supervisors. Quantitative method via survey questionnaire was used to collect data from 21 respondents. The data collection involved Master and Doctorate students at a tertiary level institution in Malaysia. The data was analyzed and tabulated using descriptive analysis. Results indicate that graduate students regularly need written feedback on their academic writing and they preferred electronic method to obtain feedback from their supervisors. Findings also indicate that graduate students appreciated feedback which was straightforward, provided clear instructions, directed them to other related resources, and feedback which was detailed or specific. This research filled the gap in literature by providing awareness among thesis supervisors and lecturers on the students’ needs and preference for written feedback on their academic writing. Apart from that, greater understanding of students’ perceived needs for feedback and preferences of feedback is also vital for lectures and thesis supervisors to increase the effectiveness of providing constructive written feedback.</p>

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.514

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 integrity0.0000.000
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 head0.044
GPT teacher head0.368
Teacher spread0.324 · 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
Published2016
Admission routes1
Has abstractyes

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