Effective Methods for Teaching Information Literacy Skills to Undergraduate Students: A Systematic Review and Meta-analysis
Notice bibliographique
Résumé
Objective - The objective of this review was to assess which library instruction methods are most effective for improving the information skills of students at an introductory, undergraduate level, using cognitive outcomes (measuring changes in knowledge). The study sought to address the following questions: 1) What is the overall state of research on this topic? 2) Which teaching methods are more effective? Methods - Systematic review methodology was used. Fifteen databases were searched for relevant articles retrieving 4356 potentially relevant citations. Titles and abstracts were reviewed for relevance. Of those, 257 full articles were considered in-depth using a predetermined inclusion/exclusion form. 122 unique studies met the inclusion criteria and underwent an extensive data extraction and critical appraisal process. 55 of these studies met author defined quality criteria to provide information on the effectiveness of different teaching methods. Of these, 16 studies provided sufficient information to enable meta-analyses using standardized mean difference to be undertaken. Results - The overwhelming majority of studies were conducted in the United States (88%). 79 studies (65%) used experimental or quasi-experimental research methods. Teaching methods used in the studies varied, with the majority focused on traditional methods of teaching, followed by computer assisted instruction, and self-directed independent learning. Studies measured outcomes that correlated with Bloom’s lower levels of learning (Remember, Understand, Apply). 16 studies compared traditional instruction with no instruction, and 12 found a positive outcome. Meta-analysis of the data from 4 of these studies agreed with the positive conclusions favouring traditional instruction. 14 studies compared computer assisted instruction with traditional instruction. 9 of these showed a neutral result, and meta-analysis of 8 of these studies agreed with this neutral result. 6 studies compared self-directed independent learning with no instruction, and meta-analysis of 5 of these agreed that the result was positive in favour of self-directed independent learning. Conclusions - Based on the results of this meta-analysis, there is sufficient evidence to suggest that computer assisted instruction is as effective as traditional instruction. Evidence also suggests that both traditional instruction and self-directed independent instruction are more effective than no instruction. Additional comparative research needs to be done across different teaching methods. Studies comparing active learning, computer assisted instruction, and self-directed independent learning would greatly enrich the research literature. Further studies utilizing appropriate methodologies and validated research tools would enrich our evidence base, and contribute to the growth of knowledge about effectiveness of particular teaching methods.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,020 | 0,029 |
| Bibliométrie | 0,016 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 source (Gemma direct ou Codex distillé), 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 ».