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Record W1950026227 · doi:10.19173/irrodl.v13i1.1057

Rapport in distance education

2012· article· en· W1950026227 on OpenAlexafffundvenueabout
Elizabeth Murphy, María A. Rodríguez‐Manzanares

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAsynchronous communicationDistance educationPsychologyConversationPedagogyFace (sociological concept)Mathematics educationSociologyComputer scienceCommunication

Abstract

fetched live from OpenAlex

<p>Rapport has been recognized as important in learning in general but little is known about its importance in distance education (DE). The study we report on in this paper provides insights into the importance of rapport in DE as well as challenges to and indicators of rapport-building in DE. The study relied on interviews with 42 Canadian high-school DE teachers. Findings revealed that rapport is necessary in DE because of the absence of face-to-face communication. Challenges to building rapport relate to the geographic dispersion of students, the asynchronous nature of DE, teacher workload, limits of the software, teachers and students not seeing the need for rapport, and DE traditions. We identified six categories of rapport-building in DE as follows: <em>Recognizing the person/individual; Supporting and monitoring; Availability, accessibility, and responsiveness; Non text-based interactions; Tone of interactions; Non-academic conversation/interactions</em>. We break the categories into subcategories and provide indicators for each one. The indicators might also be used in contexts of DE teacher professional development as a springboard for discussion, or, more prescriptively, as guides to DE teacher behaviour. A follow-up study using a more fine-grained focus on specific indicators might provide insights into specific rapport-related behaviours.</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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
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 science0.0010.000
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 head0.113
GPT teacher head0.504
Teacher spread0.391 · 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 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

Citations79
Published2012
Admission routes4
Has abstractyes

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