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Record W2973289871 · doi:10.19173/irrodl.v20i3.3913

MOOCs Readiness

2019· article· en· W2973289871 on OpenAlexvenueno aff
T. Subramaniam, Nur Amalina Diyana Suhaimi, Latifah Abdol Latif, Zorah Abu Kassim, Mansor Fadzil

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2019
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaDescriptive statisticsPsychologyStructural equation modelingVariance (accounting)Pearson product-moment correlation coefficientSelf-efficacySample (material)Medical educationReliability (semiconductor)Applied psychologyMathematics educationPsychometricsSocial psychologyStatisticsDevelopmental psychologyMathematics

Abstract

fetched live from OpenAlex

This study seeks to investigate the readiness levels of adult students studying in Malaysian higher education institutions. The online questionnaire used in this study consists of 18 demographic variables and 43 items based on six constructs: technical competencies, communication competencies, social competencies, self-efficacy, self-directedness, and readiness. With a sample of 413 respondents, the constructs were evaluated using measures based on students’ self-identification with each item. Descriptive statistics depict competency, demographic profile of students, and level of readiness. The statistical analyses used for this study were Pearson correlation, multivariate analysis of variance, and structural equation modelling. All six constructs were reliable with Cronbach’s alpha (α) above 0.7. Findings indicate that self-efficacy was significant for massive open online course readiness, and additional factors that could influence this readiness are explored. The findings from this study provide important input towards designing effective massive open online courses.

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)0.004
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.668
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 science0.0020.001
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 head0.063
GPT teacher head0.444
Teacher spread0.382 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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