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Record W7018038833

Considerations for The Instruction Of Research Methodologies In Graduate-Level Distance Education Degree Programs

2004· article· en· W7018038833 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2004
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationGraduate educationSocial distanceEducational researchDegree programHigher educationGraduate students
DOInot available

Abstract

fetched live from OpenAlex

Considerations for The Instruction Of Research Methodologies In Graduate-Level Distance Education Degree Programs Tom JONES, Ph.D. Associate Professor Centre for Distance Education Athabasca University, CANADA M. Cleveland-INNERS, Ph.D. Assistant Professor Centre for Distance Education Athabasca University, CANADA ABSTRACT The growth of basic and applied research activity in distance education requires redirection on several fronts, including the instruction of research methods in the education of graduate students. The majority of graduate students in distance education are practitioners whose goals range from carrying out original research to acquiring the concepts and skills necessary to become a practitioner. We argue that the best foundation for achieving both of those goals in distance education is developed by means of an understanding and internalization of sound research design methodologies, primarily acquired by formal instruction, and that an emphasis on research in graduate programs in distance education will encourage theory development. This paper presents the rationale for a general curricular model that attempts to address the sets of research competencies for graduate students in graduate-level distance education programs while at the same time moving students toward an appreciation and understanding of the epistemological foundations for social science research.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablemedium
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.143
metaresearch head score (Gemma)0.323
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score0.756

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1430.323
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0060.005
Scholarly communication0.0140.008
Open science0.0050.010
Research integrity0.0120.015
Insufficient payload (model declined to judge)0.0090.004

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 head0.952
GPT teacher head0.741
Teacher spread0.211 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Theoretical or conceptual
Domainnot available
GenreOther · Commentary

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
Published2004
Admission routes1
Has abstractyes

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