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Record W2139298454 · doi:10.5430/ijhe.v2n1p1

Curricular Goals and Personal Goals in Master's Thesis Projects: Dutch Student-Supervisor Dyads

2012· article· en· W2139298454 on OpenAlexvenueno aff
Renske de Kleijn, Paulien C. Meijer, Mieke Brekelmans, Albert Pilot

Bibliographic record

VenueInternational Journal of Higher Education · 2012
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPerspective (graphical)ConstructiveSupervisorPedagogyMedical educationComputer scienceProcess (computing)Management

Abstract

fetched live from OpenAlex

One of the most important tasks for master’s thesis supervisors is to provide constructive feedback. This feedback should be goal-related to be effective, but is not perceived as such. In order to better understand goal-related feedback in Master’s thesis projects, the present study explores goals of students and supervisors and similarities and differences within and between supervision dyads. Twelve supervisors and students were interviewed, and their goals were categorized using a curricular perspective and personal goals perspective. Results indicated that most students and supervisors pursue both curricular and personal goals. Within dyads these goals vary greatly. Also, supervisors and students perceive each other’s goals in only a few cases. The findings suggest that goal-related feedback in Master’s thesis supervision appears to be complex, as the students and supervisors of this study (1) pursue different goals and (2) do not perceive each other’s goals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.163
GPT teacher head0.531
Teacher spread0.368 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainIncentives
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

Citations20
Published2012
Admission routes1
Has abstractyes

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