Factors Affecting ICT Adoption among Distance Education Students based on the Technology Acceptance Model—A Case Study at a Distance Education University in Iran
Bibliographic record
Abstract
<p class="apa">The incorporation of Information and Communication Technologies (ICT) into education systems is an active program and movement in education that illustrates modern education and enables an all-encompassing presence in the third millennium; however, prior to applying ICT, the factors affecting the adoption and use of these technologies should be carefully investigated. The present study was conducted to examine the factors affecting ICT adoption among distance education students based on the Technology Acceptance Model. The present descriptive survey was conducted in a statistical population consisting of all the distance education students residing in Isfahan, Iran, in 2013, 281 of who were selected as the sample population through simple random sampling. The data collection tool used was a researcher-made questionnaire designed based on field studies and using the questionnaires used in studies conducted by Nair (2012), Alanazy (2006) and Wikins (2008). The items in each section were designed based on the constructs and factors forming the Technology Acceptance Model examined in this study. Descriptive and inferential statistics were used to analyze the data in SPSS-21 and LISREL. The results of the analysis showed significant relationships between perceived usefulness and ease of ICT use and the attitude toward the use of these technologies, between the attitude toward ICT use and the decision to use ICT, and also between the decision to use ICT and its actual use.<strong></strong></p>
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".