Completion of an Online Library Module Improves Engineering Student Performance on Information Literacy Skills Tests
Notice bibliographique
Résumé
A Review of: Zhang, Q., Goodman, M., & Xie, S. (2015). Integrating library instruction into the Course Management System for a first-year engineering class: An evidence-based study measuring the effectiveness of blended learning on students’ information literacy levels. College & Research Libraries, 76(7), 934-958. http://dx.doi.org/10.5860/crl.76.7.934 Objective – To assess the efficacy of an online library module and of blended learning methods on students’ information literacy skills. Design – Multi-modal, pre- and posttests, survey questionnaire, and focus groups. Setting – Public research university in London, Ontario, Canada. Subjects – First-year engineering students. Methods – Of 413 students enrolled in Engineering Science (ES) 1050, 252 volunteered to participate in the study. Participants were asked to complete the online module, a pretest, a posttest, an online follow-up survey, and to take part in a focus group. Researchers generated a pretest and a posttest, each comprised of 15 questions:; multiple choice, true or false, and matching questions which tested students’ general and engineering-specific information literacy skills. The pretest and posttest had different, but similarly challenging, questions to ensure that students involved in the study would not have an advantage over those who had opted out. While all components of the study were voluntary, the posttest was a graded course assignment. In-person tutorials were offered on 4 occasions, with only 15 students participating. Both tutorial and module content were designed to cover all questions and competencies tested in the pretest and the posttest, including Boolean operators, peer review, identifying plagiarism, engineering standards, engineering handbooks, search strategies, patents, article citations, identifying reliable sources, and how to read journal articles. The posttest survey was delivered in the CMS immediately after the posttest was completed. It measured self-reported student behaviours and preferences concerning the online modules. Two focus groups were convened after all posttest surveys were completed to gather qualitative data about student preferences. Main Results – Of the 252 volunteers, 239 students (57.9% of enrolled students) completed both the pretest and the posttest, 89 filled out the follow-up survey, and 7 students participated in a focus group. Students used the online module content differently; accordingly those numbers were not reported. Researchers compared pretest and posttest scores to find that the posttest scores were significantly higher than the pretest scores (p < 0.001). Of 239 pretest and posttest pairs evaluated, the mean pretest score was 10.456 and the mean posttest score was 13.843. A t-test survey and focus group data evaluated student perceptions of the module. Students reported a slight preference for online instruction. Conclusion – After completing an online library module, students’ performance on information literacy skills tests improved from the pretest to the posttest. Focus group and survey data indicate a slight student preference for online tutorials over in-person instruction. Although intended as a blended approach to library instruction, the voluntary in-person instruction was not well attended and has subsequently been changed to mandatory in-class instruction. The authors recommend further research to evaluate how the medium and format of instruction impacts student learning outcomes.
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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 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,833 |
| Science ouverte | 0,000 | 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 ».