The Views of Teacher Candidates on Using Cloud Technologies in Education
Bibliographic record
Abstract
This study aims to describe the views of student IT teachers’ and the factors which affect their priorities to use cloud services progressively. The study is conducted by qualitative research approach. The data obtained from the department of Computer Education and Instructional Technology students are collected by structured form for the use of cloud technologies. The data is analyzed by content analysis method. According to the findings, student teachers use cloud systems for file sharing and they do not use cloud systems because they do not need them or they do not know them mostly; cloud systems’ main advantage is the flexibility of use independence from time and space and their main disadvantage are the security issues and the fact that they rely on the internet connection; cloud systems’ main benefits for educational purposes are the flexibility and cost of use, and the fact that they support connection and collaboration between different users; participants think cloud services may have benefits on personal development of individuals’ information literacy knowledge and can improve teaching skills and on connection and collaborative work with various people and finally student teachers are in the opinion that cloud services are more appropriate for application-project courses and both group and individuals’ instruction. Due to cloud services’ benefits on education, adopting them into education system is important to reach the developed education level. Therefore, teachers should be trained among those innovative technologies by IT teachers who are the core element for this process.
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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 | 0.002 | 0.008 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".