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Enregistrement W1505635857 · doi:10.18438/b82s4t

Learners with Low Self-Efficacy for Information Literacy Rely on Library Resources Less Often But Are More Willing to Learn How to Use Them

2014· article· en· W1505635857 sur OpenAlexvenueno aff
Dominique Daniel

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2014
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueLibrary Science and Information Literacy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInformation literacySelf-efficacyLikert scalePsychologyTest (biology)Medical educationLiteracyDescriptive statisticsMathematics educationScale (ratio)Information seekingComputer scienceSocial psychologyPedagogyMedicineLibrary scienceDevelopmental psychologyStatistics

Résumé

récupéré en direct d'OpenAlex

A Review of:
 Tang, Y., & Tseng, H. W. (2013). Distance learners’ self-efficacy and information literacy skills. The Journal of Academic Librarianship 39(6): 517-521. doi:10.1016/j.acalib.2013.08.008
 
 Abstract
 
 Objectives – To determine whether there is a relationship between self-efficacy (i.e., confidence) regarding information literacy skills and self-efficacy for distance learning; and to compare the use of electronic resources by high and low information literacy self-efficacy distance learners and their interest in learning more about searching.
 
 Design – Online survey.
 
 Setting – A small public university in the United States of America.
 
 Subjects – Undergraduate and graduate students enrolled in one or more online courses. Most respondents were in their twenties, 76% were female, 59% were undergraduates, and 69% were full time students.
 
 Methods – Students were asked six demographic questions, eight questions measuring their self-efficacy for information literacy, and four questions measuring their self-efficacy for online learning. All self-efficacy questions were adapted from previous studies and used a one to five Likert scale. The response rate was 6.2%. Correlational analysis was conducted to test the first two hypotheses (students who have higher self-efficacy for information seeking are more likely to have higher self-efficacy for online learning and for information manipulation). Descriptive analysis was used for the remaining hypotheses, to test whether students who have higher information literacy self-efficacy are more likely to have high library skills (hypothesis three) and are more interested in learning about how to use library resources (hypothesis four). Among respondents high information literacy self-efficacy and low self-efficacy groups were distinguished, using the mean score of information literacy self-efficacy.
 
 Main Results – There was a significant correlation between self-efficacy for information seeking and self-efficacy for online learning (r = .27), as well as self-efficacy for information manipulation (r = .79). Students with high information seeking self-efficacy were more likely to use library databases (28.72%), while low self-efficacy respondents more often chose commercial search engines (30.98%). However those respondents were more likely to be interested in learning how to use library resources.
 
 Conclusion – Distance students with higher self-efficacy for information seeking and use also had higher self-efficacy for online learning. It is important to encourage such self-efficacy since studies have shown that it relates to better information literacy skills and a higher ability to be self-regulated learners. Confident learners process information, make effective decisions, and improve their learning more easily. Furthermore many respondents in this survey had little or false knowledge of how to use appropriate resources for their learning needs. This points to the need for effective library instruction. This study also shows that low self-efficacy students would like to have library instruction, especially to help them plan specific research assignments.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante
Catégories consensuellesCommunication savante
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,891
Score d'incertitude au seuil0,994

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0070,736
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,021
Tête enseignante GPT0,268
Écart entre enseignants0,246 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2014
Routes d'admission1
Résumé présentoui

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