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Record W1549620355 · doi:10.19173/irrodl.v7i2.295

A Multi-Island Situation Without the Ocean: Tutors' perceptions about working in isolation from colleagues

2006· article· en· W1549620355 on OpenAlexvenueno aff
Ilse Fouché

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingIsolation (microbiology)Reading (process)Distance educationPerceptionPsychologyWork (physics)Qualitative researchPedagogySocial isolationProfessional developmentMedical educationSociologySocial psychologyPolitical scienceMedicineEngineeringSocial science

Abstract

fetched live from OpenAlex

Distance education is generally seen as a very isolating experience for students, but one often forgets that it can be an equally isolating experience for teaching staff, who sometimes must work in isolation from colleagues. This study examines the experiences of nine tutors at one of the 10 biggest universities in the world, Universtiy of South Africa's (Unisa) Reading and Writing Centres. The tutors all work at different Regional Offices across South Africa. This study examines both quantitative data (closed-ended questions) and qualitative data (open-ended questions) obtained from questionnaires. This study seeks to determine to what extent administrative support, professional development support, and colleague support influence tutors' feelings of isolation. This paper takes the position that if feelings of isolation are curbed, staff retention will be improved, which in turn means that the university retains valuable experience. Findings show that contact with and collaboration between colleagues significantly decrease feelings of isolation. Other important methods of curbing isolation are regular training and continuous administrative support.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.072
GPT teacher head0.445
Teacher spread0.373 · 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 designObservational
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

Citations26
Published2006
Admission routes1
Has abstractyes

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