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Record W2127578158 · doi:10.19173/irrodl.v11i3.842

Development of interactive and reflective learning among Malaysian online distant learners: An ESL instructor’s experience

2010· article· en· W2127578158 on OpenAlexvenueno aff
Puvaneswary Murugaiah, Siew Ming Thang

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)TUTORDistance educationProcess (computing)Educational technologyPlan (archaeology)Active learning (machine learning)PedagogyPsychologyComputer scienceMathematics educationEngineering

Abstract

fetched live from OpenAlex

Technology has brought tremendous advancements in online education, spurring transformations in online pedagogical practices. Online learning in the past was passive, using the traditional teacher-centred approach. However, with the tools available today, it can be active, collaborative, and meaningful. A well-developed task can impel learners to observe, to reflect, to strategize, and to plan their own learning. This paper describes an English as a Second Language (ESL) instructor’s attempt to foster interactive and reflective learning among distance learners at a public university in Malaysia, working within the framework proposed by Salmon (2004). The authors found that proper planning and close monitoring of a writing activity that incorporates interactive and reflective learning helped to raise the students’ awareness of their own learning process and consequently helped them to be more responsible for their learning. The students acquired significant cognitive benefits and also valuable practical learning skills through the online discussions. However, there were challenges in carrying out the writing task to promote this form of learning, including students’ professional and family commitments and cultural attitudes as well as communication barriers in the online environment. To overcome these challenges, the authors recommend the following: ensure tutor guidance, enforce compulsory participation, address technical problems quickly, commence strategic training prior to the beginning of a task, and implement team teaching with each instructor taking on certain roles.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.078
GPT teacher head0.489
Teacher spread0.411 · 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

Citations38
Published2010
Admission routes1
Has abstractyes

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