Building ESL Learners’ Digital Literacy Skills Using Internet Memes
Bibliographic record
Abstract
Digital literacy skills are crucial twenty-first-century skills that are required for the successful use of technology and active engagement in today's world. However, there is a lack of effective resources and guidelines for developing English as a second language (ESL) learners’ digital literacy skills. Due to their increasingly important and influential role in contemporary society, Internet memes hold great potential for language teaching and learning, particularly given their infectious nature. Internet memes are digital compositions that convey concepts and ideas via a variety of media types, including image, text, audio, and video. In this article, we describe the characteristics and value of memes as a digital resource and explore the ways in which memes can be used to enhance ESL learners’ digital skills. Pegrum et al.’s digital literacies framework provides the lens to both examine memes and inform the development of a detailed guide that teachers can use when selecting appropriate memes for use in the ESL classroom. We also provide an exemplar task for using memes to build intermediate ESL learners’ language and digital literacy skills based on the guide and include links to some useful websites for meme resources and meme-related activities. Les compétences en littératie numérique constituent des compétences cruciales du 21e siècle qui sont essentielles pour utiliser la technologie avec succès et s’engager activement dans le monde d’aujourd’hui. Cependant, il existe un manque de ressources efficaces et de lignes directrices pour développer les compétences en littératie numérique des apprenants de l’anglais langue seconde (ALS). En raison de leur rôle important et croissant dans la société contemporaine, les mèmes de l’Internet représentent un grand potentiel pour l’apprentissage et l’enseignement des langues, particulièrement à cause de leur nature contagieuse. Les mèmes sont des œuvres numériques qui transmettent des concepts et des idées à travers des types variés de médias, y compris l’image, le texte, l’audio et la vidéo. Dans cet article, nous décrivons les caractéristiques et la valeur des mèmes en tant que ressources numériques et nous explorons leurs utilisations potentielles pour promouvoir les compétences numériques des apprenants. Nous utilisons le cadre des compétences numériques de Pegrum et al pour examiner les mèmes et soutenir l’élaboration d’un guide détaillé sur lequel les enseignants pourraient s’appuyer pour sélectionner les mèmes appropriés pour une classe d’ALS. Nous présentons également une tâche modèle, basée sur le guide, pour l’utilisation des mèmes dans la promotion des compétences langagières et de littératie numérique d’apprenants intermédiaires d’ALS. De plus, nous suggérons des liens de sites web utiles contenant des ressources sur les mèmes et des activités liées aux mèmes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".