Evaluation of factors associated with the adoption of ICT in education using machine learning
Bibliographic record
Abstract
Information and Communication Technologies (ICT) affect all aspects of our daily lives. Using them is considered a symbol of modernization and social advancement. The global expansion and interconnection of ICT offers a significant opportunity to promote the advancement of humanity, bridge the digital gap and promote the growth of societies built on knowledge. In this study, we analyzed and identified the most influential factors in the adoption of ICT in education from the data set called “Final Survey-Digital Inclusion Teachers” of the Plurinational State of Bolivia, which consists of 871 instances and 189 columns. We performed feature selection by carefully combining the results of three feature selection methods: filter (chi-square, ANO-VA and mutual information), wrapper (RFE) and intrinsic (Classification And Regression Trees, Random Forest, Gradient Boosting and XGBoost). The results demonstrated that a teacher's motivation for curricular planning that includes ICT, teaching experience and the institutional environment are key factors in the adoption of these technologies in education. Furthermore, we identified that the Random Forest algorithm is the most appropriate for analyzing and predicting the adoption of ICT in education, we affirmed this after this algorithm obtained the highest values in four of the six metrics evaluated: a sensitivity of 77.7%, an F1 Score of 77.9%, a Cohen's Kappa coefficient of 60.8% and a Jaccard Score of 64.3%. These results suggest that Random Forest is the most effective algorithm to analyze the factors related to the adoption of ICT in educational environments.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".