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Enregistrement W2469362410 · doi:10.2196/mededu.5606

Health Sciences Students’ Self-Assessment of Information and Communication Technology Skills and Attitude Toward e-Learning

2016· article· en· W2469362410 sur OpenAlexvenueno aff
Ali Jasem Buabbas, Hamza Mohammad Hassan Al-Shawaf, Abdullah Almajran

Notice bibliographique

RevueJMIR Medical Education · 2016
Typearticle
Langueen
DomaineDentistry
ThématiqueDental Research and COVID-19
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInformation and Communications TechnologyMedical educationInformaticsComputer literacyHealth informaticsObservational studyPsychologyWord processingMathematics educationComputer scienceMedicineNursingEngineeringWorld Wide WebPublic health

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: In medical education, information and communication technology (ICT) knowledge and skills have become a necessity and an integral part of preparing tomorrow's doctors to be sufficiently competent to use informatics resources effectively and efficiently for the best practice of medicine. OBJECTIVE: This research aimed to study the literacy of the preprofessional students in ICT before and after taking the basic informatics course at the Health Sciences Center at Kuwait University, to understand their potential and their attitudes toward using ICT, including e-learning. METHODS: A validated questionnaire was used to collect data from 200 students in 2 stages: before and after the informatics course on the preprofessional program. In addition, the tutors' observational assessments of the students' achievements during the informatics course were obtained. RESULTS: The response rate of students before the course was 85.5% (171/200) and after was 77% (154/200). Of 200 students, 85% were female, and 15% were male. This disproportional representation of genders was due to the fact that 85% of registered students were female. Approximately 59% (101/171) of the students assessed themselves before the course as computer literate; afterward, this increased to 70.1% (108/154). Students who were still computer illiterate (29.2%; 45/154) mostly used the excuse of a lack of time (60%; 27/45). In generic ICT skills, the highest levels were for word processing, email, and Web browsing, whereas the lowest levels were for spreadsheets and database. In specific ICT skills, most respondents were reported low levels for statistical package use and Web page design. The results found that there was a significant improvement between students' general ICT skills before and after the course. The results showed that there were significant improvement between how frequently students were using Medline (P<.001), Google Scholar (P<.001), and Cochrane Library (P<.001) before and after the informatics course. Furthermore, most of the students who completed the course (72.8%; 110/151) chose the learning management system as the most useful e-learning tool. The results of the tutors' assessments confirmed the obvious improvement in most of the students' skills in using ICT. CONCLUSIONS: The ICT knowledge and skills of the students before the course seemed insufficient, and the magnitude of the improvements that were acquired throughout the informatics course was obvious in most of the students' performance. However, the findings reveal that more practice was required. The attitudes of most of the students toward the potential of e-learning were considered positive, although the potential of Web-based learning in medical training was not well known among the students.

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)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,286
Score d'incertitude au seuil0,247

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
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 Opus0,012
Tête enseignante GPT0,435
Écart entre enseignants0,423 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations19
Publié2016
Routes d'admission1
Résumé présentoui

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