Learning Mathematics in the 21st Century: Adding Technology to the Equation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The early twenty-first century has witnessed an explosion of technological changes that have revolutionized the way we travel, shop, interact and play. Technology can also transform education by boosting motivation, personalizing instruction, facilitating teamwork, enabling feedback, and allowing real-time monitoring. However, a gap exists between the potential impact of technology and the actual results of public initiatives. This book brings together leading regional and international experts in the field to shed light on how governments can take better advantage of the potential of technology to improve student learning. Specifically, the book focuses on mathematics, a critical learning area in which most students in the region do not attain even basic levels of proficiency. The first part of the book presents a thorough diagnosis of the main challenges to mathematics learning in the region. The second part of the book describes a range of technological models and assesses their capacity to tackle these challenges and produce improvements in learning. By combining theoretical and empirical approaches, reviewing innovative initiatives, and drawing lessons from psychology, education, and economics, the book aims to become a reference for policymakers who want to make the promise of technology in education a reality for all students in the region.
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.
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 | 0.001 | 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 | 0.002 | 0.001 |
| 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 it