Clustering Applied to the Education: A K-means and Hierarchical Application
Bibliographic record
Abstract
Currently, most schools in the world use ICT, which is why students must make use of computers and mobile devices in and out of schools. Thanks to the use of technology, students are more interested and motivated to learn, considering that motivation is one of the main engines of learning, since it encourages activity and thought. On the other hand, motivation makes students spend more time working and therefore they are more likely to learn more. The aim of this paper was to present a clustering of European countries according to the number of desktop computers available to students in primary schools (ISCED 1), lower secondary schools (ISCED 2) and upper secondary schools (ISCED 3). Was used the database developed by the ES Open Data Portal for the year 2019 on "ICT in Education". For the classification were used the hierarchical clustering and K-means techniques and the statistical software Rcran 3.6.3. These techniques were used as they have the ability to group a large number of elements into clusters, based on the similarity learned. This paper concludes that the countries with the highest GDP are not the ones that have the most desktop computers in their schools. Bulgaria is the country with the major number of desktop computers in their schools.
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 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 | 0.000 | 0.000 |
| 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".