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Record W2902011818 · doi:10.19173/irrodl.v19i5.3765

The Inherent Tensions of “Instant Education”: A Critical Review of Mobile Instant Messaging

2018· review· en· W2902011818 on OpenAlexvenueno aff
Christoph Pimmer, Patient Rambe

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2018
Typereview
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsImmediacyAffordanceDialecticNegotiationComputer scienceDimension (graph theory)InterdependenceInstantInstant messagingEducational technologyKnowledge managementPsychologySociologyHuman–computer interactionEpistemologyPedagogyWorld Wide Web

Abstract

fetched live from OpenAlex

This paper critically reviews literature on the role of Mobile Instant Messaging (MIM) applications, such as WhatsApp, in supporting learning and teaching practice. Using formal qualitative synthesis as its methodology, and dialectical theory as an analytical framework, our main objective was to identify tensions, affordances, constraints, and resolution strategies in educational uses of MIM. In contrast to prior work, the analysis offers a nuanced and complex picture of the use of MIM in learning and teaching settings. Instead of facilitating the creation of educational outcomes in a straightforward manner, the realities of MIM use are socially constructed and the subject of conflictual negotiations. The educational use of MIM requires users to navigate the interdependent dialectical tensions of immediacy versus delays (temporal dimension), intimacy versus detachment (relationship dimension) and task versus ludic orientation (intellectual dimension). The findings also reveal a number of behavioural and technical resolution strategies that users deploy to manage these tensions.

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)0.018
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.758
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.182
GPT teacher head0.562
Teacher spread0.380 · 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 designOther design
Domainnot available
GenreReview

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

Citations37
Published2018
Admission routes1
Has abstractyes

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Same venueThe International Review of Research in Open and Distributed LearningSame topicImpact of Technology on AdolescentsFrench-language works237,207