Statistical Analysis to Explore the Factors of ICT that Effect and Promote Global Citizenship among Undergraudate Students: A Case Study of Karachi
Bibliographic record
Abstract
This study aimed to explore the underlying relation between ICT and the factors that promote global citizenship among university undergraduate of Karachi, Pakistan through quantitative method approach. Globalization has had numerous and multifaceted effects on education policies and practices at various levels. Therefore, this study attempted to highlight the significance of ICT uses as a tool for fostering global citizenship among undergrads of both sectors in Karachi. The impact of ICT on factors that promote global citizenship among university students are also examined. The Global Citizenship Survey questionnaire was used for quantitative data. A total of 400 students from private and public sector universities participated in this study. The reliability of the responses calculated through Cronbach’s alpha and found to be almost 0.82 for all constructs. This indicates that responses are highly consistent within each construct. An advanced Multivariate Statistical tool “ Exploratory Factor Analysis” (EFA) was also carried out to identify the hidden pattern of the data and identify the most important factors of ICT that promote global citizenship. For the adequacy of data that is the data are suitable for the factor analysis Kaiser Meyer Olkin (KMO) criteria was considered and its value is found to be 0.784. This value indicates that the Global citizenship survey data are adequate and good enough to carry out factor analysis. For the interpretation and discussion of the results we consider first 5 factors with eigen value greater than 1. The results of factor analysis indicate that factors with items of high positive loading are the communication skill and world perspective. In the view of research findings it may be concluded that ICT promotes global citizenship among undergraduate university students.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".