An explanation for internet use obstacles concerning e-learning in Iran
Bibliographic record
Abstract
E-learning is advancing in Iran right now. The Iranian higher education system is applying electronic learning in order to conquer the limitations of the existing education system. These limitations include the growing number of applicants for entering universities, lack of classrooms for education, and universities’ tensions in replying to these needs. Also, ease of access to e-learning and a lack of financial resources are reasons for applying e-learning in Iran. In addition, the Iranian higher education system wants to progress with global changes in the information era and they see it as necessary to acquire information and knowledge. Meanwhile, web technology enjoys a special and significant role. This paper investigated barriers to using internet technology for e-learning in the Iranian context. The methodology employed both qualitative and quantitative techniques. In the qualitative stage, exploratory observations of eight virtual institutes for higher education and interviews with 20 experts in these institutes were used. The analysis of the data showed that socio-cultural, structural, educational, economic, and legal factors were the most prominent obstacles to web technology use; each factor comprised a number of components. So as to check the primacy of the factors and the extracted components at large, the researchers developed a Likert-type questionnaire; the questionnaire, which comprised the five types of obstacles and their related components, enjoyed a high degree of validity and reliability. Twenty students in each of the eight institutes were asked to fill out the questionnaire. The analysis of the data showed that socio-cultural factors are the most influential barriers to use of the Internet in e-learning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".