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Record W2893137563 · doi:10.19173/irrodl.v19i4.3237

“Doing the courses without stopping my life”: Time in a professional Master’s program

2018· article· en· W2893137563 on OpenAlexaffvenueabout
Tami Oliphant, Jennifer Branch-Mueller

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCourseworkPerceptionFlexibility (engineering)Descriptive statisticsPsychologyCoding (social sciences)Medical educationQuality (philosophy)Mathematics educationPedagogyMedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

This study investigates how time intersects with student learning in Canada’s first, and only, Master of Library and Information Studies (MLIS) in an online teaching and learning stream. Thirty-two students responded to a survey that asked about their experiences, perceptions, and challenges after their first year of the program. Descriptive statistics and NVIVO 10 were used to analyze survey responses and to develop themes through open coding. The findings indicate that time shapes students’ decisions to pursue the MLIS online, their perception of what the degree might mean for their future, their experience in the program, the quality of their relationships, and their learning. The perceived flexibility of the MLIS program was incredibly important to students. However, the majority of students described themselves as “time poor” and many students underestimated the time commitment necessary to complete the program, to manage coursework, and to build and maintain relationships with others.

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)0.005
metaresearch head score (Gemma)0.017
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 score0.017
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0080.004
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.100
GPT teacher head0.506
Teacher spread0.406 · 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

Citations3
Published2018
Admission routes3
Has abstractyes

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