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Record W1567297997 · doi:10.19173/irrodl.v15i2.1604

An elaborated model of student support to allow for gender considerations in Asian distance education

2014· article· en· W1567297997 on OpenAlexfundvenueno aff
Insung Jung, Seongyoun Hong

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsDistance educationChinaPsychologyQuality (philosophy)Perspective (graphical)PerceptionHigher educationMathematics educationPedagogyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

<p align="left">Research indicates that distance education (DE) students regard learner support systems as the key element in quality provision. This study sought to identify the key concerns of Asian DE students regarding support provision in different types of DE and dual-mode providers and formulate a student support model which took account of gender issues. An online survey was conducted with 1,113 distance learners in Japan, Korea, Hong Kong SAR China, Malaysia, India, Pakistan, Philippines, Singapore, and Thailand which required them to respond to open-ended questions regarding their main concerns over the quality of distance education. Their responses were analyzed with Nvivo 2.0 based on the framework of the Atkins (2008) ARCS model of distance learner support. It was found that in assessing the quality of DE the students valued 13 types of student support across five domains: affective, reflective, cognitive, systemic, and gender-considerate. It was also confirmed that there were gender differences in the students’ perceptions of the need for student support. Building on these findings, an elaborated model for student support for Asian distance learners was developed from a systems perspective, and from this, a list of supporting strategies was proposed. <br /><strong></strong></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)0.007
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.138
GPT teacher head0.530
Teacher spread0.392 · 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 designTheoretical or conceptual
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

Citations18
Published2014
Admission routes2
Has abstractyes

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