21st Century Skills: Education & Values, Academy, Community and Research Development and Implementation of the EACH Program
Bibliographic record
Abstract
The social, economic and technological changes of the 21st century have increased the awareness of teachers and educational researchers of the importance of a relevant and interesting learning environment, collaborative learning, personalization and values in education.  However, most of the currently available programs focus on only one of these changes, making it difficult for the education system to implement all the changes together. In addition, the programs focus mainly on the pedagogical aspects and not on the constraints of the system. As a result, many of today’s programs are pedagogically correct, but are very difficult to implement in educational systems. The EACH model is unique in that it focuses on the combination of the learning environment, collaborative learning, personalization and values in education, and is designed in a way that takes into account the constraints of the educational system. The assimilation of the model in Herzliya brought with it a reinforcement of pedagogical processes with an emphasis on the learning skills required of a scholar in the 21st century. Accordingly, the EACH model is an implementable program for every municipal education system. The model uses a city’s resources to provide learners with a meaningful learning experience and provides them with tools and learning and thinking skills that are adapted to the complex reality of the 21st century. The EACH model is based on four principles: (1) education & values, (2) academy, (3) community, and (4) research. The model has been successfully applied in the city of Herzliya, Israel, and is recommended for other cities around the world. 
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".