Assessing the media literacy of adolescents in information search and multimedia creation
Bibliographic record
Abstract
In this presentation, we report on the first results of an international study (2018-2022) on the assessment of the media literacy of teenagers related to online information search and multimedia creation, in the French-speaking community of Belgium (Université catholique de Louvain, our team), Québec (Université du Québec à Montréal), France (Université de Rouen) and Switzerland (Université de Genève). We consider media literacy as a set of competencies. Competencies involve “a personal ability to adapt oneself to novel situations in new and non-stereotypical ways” (Rey, Carette, Defrance, & Kahn, 2012, p. 13) that exceeds the mere acquisition and mastery of knowledge and skills, and includes the contextual selection and combination of the most relevant among them in complex situations. Our theoretical framework draws on prior work to distinguish between competencies pertaining to the semiotic, technical and social dimensions (Fastrez, 2010; Fastrez & De Smedt, 2012) of information search and multimedia creation. Additionally, we examine the relationship between the level of media literacy of individuals and their dispositions towards self-learning (Carré, 2005; Grasset, 2016). Our work combines different types of instruments that have traditionally been used separately. On the one hand, cognitive approaches to media and digital literacy tend to measure knowledge and skills through standardized tests (Gui & Argentin, 2011; Mittermeyer, 2004; Thirion & Pochet, 2008; van Deursen & van Dijk, 2010) and self-assessments (Literat, 2014; van Deursen, Helsper, & Eynon, 2015), which do not account for how individuals mobilize their knowledge and skills in the context of novel situations. On the other hand, critical-cultural approaches have focused on the qualitative description of situated media practices (e.g. (Aillerie, 2011; Coutant & Domenget, 2014)), which lack generalizability, and seldom make the leap from practice description to competence assessment (Litt, 2013; Martens, 2010). Our project is structured in three cumulative phases on three different samples. Phase 1 (2018-2019) includes a survey of information search and multimedia creation practices, the assorted self-assessed competencies, and dispositions towards self-learning. Phase 2 (2019-2020) will combine the survey with a test including simple tasks requiring predetermined bits of knowledge or skills, and a complex task (producing an explanatory website summarizing a web search) drawing on their knowledge and skills. Phase 3 (2020-2021) will add qualitative interviews with a subsample of respondents to the survey and test. Data collection for Phase 1 (self-reported competencies and practices, dispositions towards self-learning) is underway with a large sample (N=2000 in each country) of 13 to 15-year-old adolescents, and will be completed in May 2019. Our presentation will focus on the first results concerning the relationship between self-assessed competencies and declared practices, as well as on the relationship between self-assessed competencies and declarations toward self-learning items. A correlation between perceived media literacy and perceived self-learning would cast a light on media education and education towards learning to learn in autonomy as being potentially mutually beneficial. More generally, the project will produce a multifaceted model of media literacy articulating its elementary levels (knowledge and skills) with its advanced levels (competencies), and a multilevel measurement method that could be adapted to other age groups and media activity domains. The results will also inform media education public policies, by identifying how to reduce media competencies gaps.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".