Development, Implementation and Evaluation of E-Learning Materials for FFL with Adobe Captivate Software
Bibliographic record
Abstract
Developing technology has significantly affected the education and training process as it is in all areas of life. In today’s society, smart phones, tablets and computers which almost every individual owns have facilitated access to information, and besides classical teaching methods, technology-supported or technology-based education activities have also become a part of the process. E-learning tools are important tools by which technology is included in the education and training process. Especially during the COVID-19 pandemic which has been experienced around the world since the beginning of 2020 has revealed the importance of e-learning materials more. The aim of this study, carried out in the light of the information given above, is to develop, apply and evaluate Adobe Captivate, one of the most used e-learning software worldwide, for use in the French teaching process. The study was carried out in an experimental model. The universe of the study consisted of 53 French preparatory class students. The achievement test developed by the researcher was used as the data collection tool of the research. The data obtained as a result of the pre-test and post-test applications showed that the use of Adobe Captivate positively affected the general language competency of the students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".