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Record W3191900283 · doi:10.11575/prism/38993

Leadership Factors Influencing Transition and Implementation of a Learning Management System in a Rural Community College Context

2021· dissertation· en· W3191900283 on OpenAlexaboutno aff
Michelle Tammy Mitchell

Bibliographic record

VenueOpen MIND · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Transition (genetics)Community collegePolitical scienceEducational leadershipTransition management (governance)PedagogyPsychologySociologyMathematics educationMedical educationManagementGeographyEconomicsMedicineCorporate governanceChemistry

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the leadership practices involved in the transition to a new Learning Management System (LMS) at a rural community college in western Canada. This study was important to inform future studies on technology transition, particularly in the rural community college context. The main research question explored in the study was how do leaders in a rural community college in western Canada understand practices of leadership when implementing LMSs? A qualitative case study was used that included data from questionnaires and a review of institutional documents. Thematic analysis was used to analyze the data. The data supported the concept that the leaders involved in a technology implementation decision making committee need to have understanding of leadership and technology adoption practices. The results highlighted the importance of professional learning for leaders who are involved in making decisions regarding educational technology. The data helped to inform a conceptual framework that supports leaders’ understanding of the practices of leadership when implementing a LMS in a rural community college in western Canada.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.963

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.102
GPT teacher head0.420
Teacher spread0.318 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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