Perbandingan Sikap Menggunakan Komputer antara Dosen dan Anggota e-Learning Community
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".