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Record W2524635874 · doi:10.19173/irrodl.v17i5.2384

From On-Campus to Online: A Trajectory of Innovation, Internationalization and Inclusion

2016· article· en· W2524635874 on OpenAlexvenueno aff
Darlinda Moreira

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)Context (archaeology)InternationalizationPromotion (chess)General partnershipSociologyProcess (computing)Internationalization of Higher EducationHigher educationSocial connectednessPublic relationsPedagogyPolitical scienceComputer scienceBusinessPsychologySocial science

Abstract

fetched live from OpenAlex

<p class="1">This paper presents a study focused on a trajectory for developing an online operating mode on a campus-based university in the area of Massachusetts, USA. It addresses the innovation process and the changes and challenges faced by faculty and administrators. Methodologically-speaking, a mainly ethnographic approach was used for a systematic process of collecting data in context, in order to understand organizational strategies put in place to launch and improve online course provision. Leaders of the process and teachers of online courses were also interviewed. What emerged was: a) the online operating mode was prepared much in advance and linked to scenarios of internationalization and inclusion in higher education; b) there was an underlying discourse of inter-connectedness among different places and groups of people; and c) the partnership and collaboration between administration and faculty was essential. One of the main conclusions demonstrates that, despite careful formulation of the online component, it still does not enjoy the same status as the face-to-face element of courses, and, as a result, is largely ignored in terms of promotion in the teaching profession.</p>

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)0.005
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.077
GPT teacher head0.472
Teacher spread0.395 · 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.

Study designNot applicable
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

Citations24
Published2016
Admission routes1
Has abstractyes

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