Approaches Using Social Media Platforms for Teaching English Literature Online
Bibliographic record
Abstract
In the modern era of globalization, language and literature learning and comparative literary competencies are inextricably intertwined. Online learning and teaching, and telecollaboration are a great benefit to literature students who do not have face-to-face intercultural opportunities with members of diverse languages, literature, and cultures. Even, online teaching and learning make academics borderless and remove walls. The present study explores principles and new strategies for teaching English literature online. This article addresses how to adopt literary lesson plans for different types of learners, set clear expectations with students, and build rapport and community with students in teaching literature online. This research discusses the right EdTech tools and curricula support and investigates the way to use social media platforms e.g. YouTube as supplementary sources in e-teaching and e-learning for literary content. The results show that online pedagogies can develop Literature educators and students' skills and promote their literary knowledge along with converting a Web-primarily based totally environment into a social network with social media platforms crossing teach, learn and lands, inaccessible areas, and those who have limited instructive supports and facilities for creating equal opportunities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".