Relationships between Digital Literacy and Print Literacy: Predictors of Successful On-line Search
Notice bibliographique
Résumé
This study examines the influence of digital literacy vs. print literacy skills on the implementation of a web-based information search. One hundred and nine education students were tested on three measures of digital literacy (general exposure, recreational experience and educational experience) and three reading subskills (reading rate, vocabulary and comprehension). Two variables emerged as unique predictors of success during the web search: General Computer Exposure and Reading Comprehension. Webquests are becoming a popular teaching tool. “A WebQuest is a self-contained, inquiryoriented activity constructed in the form of a Web page” (Descy, 2003, p. 363). One objective of this study was to determine if students with different literacy skill profiles may be disadvantaged in completing such assignments. A second objective was to begin to develop a simple and short assessment tool of digital literacy for educators to use in the classroom. Definitions of Digital Literacy The terms digital literacy, digital competence, e-literacy, information literacy and computer literacy have all been used to describe different aspects of fluency with digital material and tools (Beetham, 2010). Although definitions of digital literacy share some common elements, at present, there is no overall consensus on what skill sets constitute digital literacy and how these skills should be measured. For example, Ranieri, Calvani and Fini (2010) would define digital competence as “the capability to explore and face new technological situations in a flexible way, to analyze, select and critically evaluate data and information, to exploit technological potentials in order to represent and solve problems and build shared and collaborative knowledge” (pg.542). Martin (2009) defines e-literacy as “awareness, skills, understanding and reflective evaluative approaches to operate in an information rich and IT supported environment” (p. 97). What is clear is that these definitions represent a set of complex, interconnected skills that need to be measured across several dimensions to fully understand a student’s level of digital literacy. This would likely require measurement on a number of subscales, across numerous items to capture the full repertoire of a student’s skills and experiences. But what about the educator who just needs to quickly understand where their students are at in order to implement a technology-based assignment? This study explores three simple and short assessment tools for gauging students’ current digital literacy status: 1) The Software Recognition Test to measure general exposure to digital tools and materials, 2) the Recreational Experience Scale to measure frequency of recreational use and 3) the Educational Activities Checklist to measure experience with specific educational digitally-based activities. To date, the measure of general exposure to computers (the Software Recognition Test) was found to predict incidental learning from a website better than other specific computer experience (e.g., educational vs. recreational computer activities) (Boechler, Leenaars & Levner, 2008). The relationship of traditional print literacy to digital literacy is being explored by many educators, researchers, agencies and institutions (see McLoughlin, 2010). Some would advocate that this distinction is no longer relevant (UNESCO, 2004) as, from a functional perspective one’s literacy skills must entail both traditional print literacies and digital literacies and that we would be better off referring to multimodal literacies or the plurality of literacy. Even with an all-encompassing definition of literacy, it is
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,004 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».