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Record W4220733491 · doi:10.1155/2022/9202921

Assessing the Impact of the MOOC Learning Platform on the Comprehensive Development of English Teachers at College Level under “Double First-Rate” by Utilization of the SWOT Analysis in Hunan Province, China

2022· article· en· W4220733491 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Post-publication record

NatureRetraction
ReasonConcerns/Issues about Data;Concerns/Issues about Results and/or Conclusions;Concerns/Issues about Referencing/Attributions;Concerns/Issues about Peer Review;Investigation by Journal/Publisher;Investigation by Third Party;Paper Mill;Computer-Aided Content or Computer-Generated Content;Unreliable Results and/or Conclusions;
Date8/9/2023 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueJournal of Advanced Transportation · 2022
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Technologies in Various Fields
Canadian institutionsnot available
Fundersnot available
KeywordsSWOT analysisComputer scienceMassive open online courseReliability (semiconductor)Online learningQuality (philosophy)MultimediaMathematics educationWorld Wide WebMathematics

Abstract

fetched live from OpenAlex

This research is to investigate the impact of massive open online course (MOOC) learning platforms on teacher development, promote the development and innovation of online learning models under the “Double First-Rate,” and especially expand the application of the MOOC platforms combining language learning with professional development paths. The MOOC online learning platform has a problem of a high abandonment rate. This paper first proposes a MOOC learning recommendation algorithm based on the learning sequence and similarity distance analysis as well as evaluates its accuracy. Then, the reliability test and the MOOC learning recommendation algorithm are used to evaluate the quality evaluation system of English teaching in the MOOC utilizing the structural equation model. Finally, the strengths, weaknesses, opportunities, and threats (SWOT) are determined to analyze the impact of the MOOC regarding the English teaching platform on teachers' comprehensive development. The results show that the MOOC platform-based learning recommendation algorithm has higher recommendation accuracy and efficiency, improving the learning effect with the utilization of the MOOC. Also, it can effectively reduce the abandonment rate and has a positive effect of resolving the interaction problem pertinent to characteristic differences and sequences in the learning recommendation. The quality evaluation system of online English teaching in the MOOC has higher reliability and convergent validity, which shows better stability and consistency in all dimensions. If teachers can actively learn from the resources of the MOOC platform, then they continuously update teaching concepts, improve online teaching, give full play to their language advantages, accurately locate student needs, and develop unique courses. Therefore, it will promote the overall development of their careers and improve innovation.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.298
Teacher spread0.262 · 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