Impact of education digitalization and TIC in promoting social inclusion in universities
Bibliographic record
Abstract
This research aims to examine how using digital tools and Technologies, Information, and communication (TIC) in higher education can help advance the goal of social inclusion. This study uses a qualitative and quantitative methodology to help in providing adequate results. The study also conducted descriptive analysis and cluster group effect to determine the relationship between the respondents in this study. This study surveyed 179 undergraduates at Peru's University of Lima and some of the data collected includes age, education level, and gender. As shown in the result, cluster 1 (chi-sqr = 29.78; p .001,max = 5.1), Cluster 2 (chi-sqr.= 99.6; p .001), and Cluster 3 (chi-sqr.= 13.1; p =.001). From the results, incorporating TIC into the classroom can have far-reaching effects on fostering social inclusion in higher education. However, several factors affect the extent to which digitization promotes social inclusion, such as the quality of the digital infrastructure, the digital competence of students and teachers, and the educational method is taken.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".