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Record W4385233633 · doi:10.5430/wjel.v13n7p222

Exploring the Efficacy of Social Media Narrative-Based Activities in Improving Digital Communication Skills and Digital Literacy Among Tertiary-Level ESL Learners

2023· article· en· W4385233633 on OpenAlexvenueno aff
S Vaishnavi, I Ajit

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDigital literacyCasualNarrativeSocial mediaPsychologyLiteracyDigital mediaMedia literacyMathematics educationMedical educationPedagogyComputer scienceWorld Wide WebPolitical scienceMedicine

Abstract

fetched live from OpenAlex

This research article aims to explore the efficacy of social media narrative-based activities in improving digital communication skills and digital literacy among tertiary-level English language learners in the Chennai district. Using a mixed-methods approach with pre- and post-surveys and semi-structured interviews; this study examined the impact of the intervention. The sample consisted of 102 participants who participated in a four-week social media narrative-based activity program. The initial outcomes indicated a significant improvement in digital literacy skills among the participants. The initial outcomes indicated a significant improvement in digital literacy skills among the participants. The students exhibited a better understanding of digital tools and were more confident in using them for academic and personal purposes. In addition to this, the qualitative findings revealed that the social media narrative-based activities were engaging and effective in enhancing the learners' motivation and kindling their interest in learning digital literacy skills. The study elucidates that narrative-based social media activities can be a practical pedagogical approach to enhancing digital communication skills and digital literacy among ESL learners. The study could aid in exploring new possibilities for merging the casual usage of social media with technology-integrated language learning.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
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 head0.028
GPT teacher head0.306
Teacher spread0.278 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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