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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".