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Record W1489266961

Support Services That Matter: An Exploration of the Experiences and Needs of Graduate Students in a Distance Learning Environment

2007· article· en· W1489266961 on OpenAlexvenueno aff
Darrell L. Cain, Chip Marrara, Paul E. Pitre, Sabrina Armour

Bibliographic record

VenueInternational journal of e-learning & distance education · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsGraduate studentsSociologyHumanitiesLibrary sciencePsychologyPedagogyArtComputer science
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this case study was to investigate the support needs, experiences, and expectations of a group of graduate distance learners. The method of inquiry involved the qualitative research techniques of interviews and document analysis. The results revealed three important findings. First, most students were not likely to take advantage of student support services. Second, students perceived their peers as important sources of academic and social support. Last, students expected their instructor to be a support resource and to be knowledgeable about the on-campus academic and administrative services. L’objectif de cette etude de cas etait d’examiner les besoins de support, les expe¬riences et les attentes d’un groupe d’etudiants de deuxieme cycle en formation a distance. La methode de recherche incluait les techniques qualitative de l’entrevue et de l’analyse de document. Les resultats ont revele trois conclusions importantes. Premierement, la plupart des etudiants avaient peu de chances de profiter des services de support aux etudiants. Deuxiemement, les etudiants percoivent leurs pairs comme etant des sources importantes de support pedagogique et social. Finalement, les etudiants s’attendaient a ce que leur professeur soit une source de support et qu’il soit au courant des services pedagogiques et administratifs offerts sur le campus.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.357
Teacher spread0.329 · 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

Citations53
Published2007
Admission routes1
Has abstractyes

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