An evaluation of the integration of m-learning in Total Reading Approach for Children Plus (TRAC+): Enhancing literacy of early grade students in Cambodia.
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 Total Reading Approach for Children (TRAC) project was first implemented in Cambodia from 2013 to 2014 by World Education, Inc. (WEI) to improve early grade reading outcomes among Grade 1 and Grade 2 students. This was made possible through a grant from All Children Reading: A Grand Challenge for Development (ACR GCD). ACR GCD, which was launched in 2011 by the United States Agency for International Development (USAID), World Vision, and the Australian Government, is an ongoing series of competitions that leverages science and technology to source, test, and disseminate scalable solutions to improve the literacy skills of early grade learners in developing countries. End-of-project assessments of TRAC were encouraging: over 90% of performance indicators were successfully achieved. As a result, WEI was awarded follow-on funding by World Vision International – Cambodia to scale up TRAC. Called TRAC Plus (TRAC+), the scale up rolled out in 13 World Vision area development programs in five provinces in Cambodia in December 2014. In Year 1, TRAC+ ran in 170 schools, and continued to work in 138 of the 170 original target schools in Year 2. By the end of the project in September 2017, TRAC+ had directly reached about 20,000 students. This report presents the findings of an independent evaluation of TRAC+ conducted from February to September 2017 by Dr. Grace Oakley, Dr. Mark Pegrum, and Dr. Thida Kheang—all from the Graduate School of Education, The University of Western Australia—assisted by Cambodian researcher Mr. Krisna Seng. The primary focus of the evaluation was the m-learning component of TRAC+—the use of Aan Khmer, a game-based app developed with funding from ACR GCD to teach Khmer alphabetical principles, vocabulary, and fluency in low resource environments. The evaluation set out to answer the question, “How and to what extent does the integration of m-learning in TRAC+ enhance the literacy of early grade students?” The findings of this study contribute to the body of knowledge on the effectiveness, sustainability, and scalability of m-learning integrated into TRAC+ in the Cambodian primary school context. Equity and efficiency issues were also addressed. This evaluation was conducted under the Digital Learning for Development (DL4D) project of the Foundation for Information Technology Education and Development (FIT-ED) of the Philippines. As part of the Information Networks in Asia and Sub-Saharan Africa (INASSA) program of the International Development Research Centre (IDRC) of Canada and the Department for International Development (DFID) of the United Kingdom, DL4D aims to improve educational systems in developing countries in Asia through testing digital learning innovations and scaling proven ones. Funding for the evaluation was provided jointly by DL4D and ACR GCD.
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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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 it