Perbandingan Sikap Menggunakan Komputer antara Dosen dan Anggota e-Learning Community
Notice bibliographique
Résumé
Background: E-learning community (eLC) of the School of Medicine Atma Jaya Catholic University of Indonesia consisted of twelve students. eLC trained lecturers about e-learning in personal or small group format. This study aimed to compare the differences between the development of computer-related attitude between lecturers and e-learning community members upon the service from e-learning community for lecturers. Method: This research was an experimental quantitative and qualitative study. Subjects were 12 students of eLC and 32 lecturers who received eLC’s services. The quantitative data was collected through questionnaires of the Computer Anxiety Rating Scale (CARS) and Computer Self-Efficacy (CSE). The qualitative data was collected through focus group discussion and in-depth interviews. CARS and CSE data were collected four times: (1) prior to the eLC trainings, (2) right after the eLC first training, (3) after the second training of eLC, and (4) right after one month of the last training from eLC. Data analysis was conducted using Friedman test, Mann-Whitney test. Qualitative data analysis were performed using content analysis.Results: There was a significant decrease from the score of CARS 1 to the score of CARS 4 for the eLC members (p=0,045). Results of CSE for eLC members showed no significant differences across the data collection. For faculty members, the significant differences were found between CARS 3 and CARS 4 (p=0,014). CSE scores of faculty members showed no significant differences. Comparison of CARS and CSE between faculty members and eLC members showed no significant differences. The qualitative data analysis showed some important aspects found in both of the groups. There are communication, interaction, the importances of eLC trainings, as well as suggestions to both of the groups about e-learning. Subjects’ opinions were divided into two groups: one who experienced positive changes in their computer-related attitude and one who did not experience any changes. Conclusion: Faculty members found that eLC were important in relation to e-learning training for lecturers. Students strongly agreed that being the member of eLC made him/her had a great opportunity to closely communicate to their lecturers. The faculty members’ anxiety level of computer using was low; on the other hand, their awareness of computer technology was good enough. The institution should employ this opportunity to apply e-learning more seriously and extensively.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,003 |
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 ».