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Record W4399879666 · doi:10.55016/ojs/ajer.v50i1.55039

"I Don't Like Ambiguity": An Exploration of Students' Experiences During a Qualitative Methods Course

2004· article· en· W4399879666 on OpenAlexvenueno aff
Serge F. Hein

Bibliographic record

VenueAlberta Journal of Educational Research · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCourse (navigation)Mathematics educationPsychologyAmbiguityQualitative researchPedagogySociologySocial sciencePhilosophyLinguisticsEngineering

Abstract

fetched live from OpenAlex

Although some of the literature on teaching qualitative research methods courses has included students' experiences during courses, these experiences have not been made a primary focus of study and examined systematically. To gain a fuller understanding of students' experiences during a graduate-level qualitative methods course, 13 reflective journals were analyzed. Eight major categories emerged from the analysis: (a) struggling with a new paradigm, (b) changes in perspective on quantitative research, (c) struggling with phenomenological and other qualitative concepts and practices, (d) becoming more aware of one's role in the research process, (e) challenges faced during the research process, (f) gaining new insights into the research process, (g) gaining new insights into phenomenology and the qualitative paradigm, and (h) valuing phenomenological and other forms of qualitative research. Implications of the findings for designing and teaching qualitative methods courses are also discussed.

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)0.041
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.041
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.083
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0190.013
Scholarly communication0.0110.006
Open science0.0050.011
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0030.001

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 head0.538
GPT teacher head0.721
Teacher spread0.183 · 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.

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

Citations10
Published2004
Admission routes1
Has abstractyes

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