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Good practice Imported

Can't Wait to Learn — Machine-Learning-Diagnosed Adaptive Learning for Refugee and Vulnerable Children in Jordan

Jordan · Amman · See the Jordan profile · See the Amman profile

Evidence: Observational / pre–post Top 17% 67/100 · Ask Evidence Copilot about this practice

War Child Holland's tablet-based Can't Wait to Learn uses a Stanford-co-developed ML layer to detect when a child is academically "stuck" and adapt content; it expanded from refugee-settlement tablets into 28 Jordanian public schools reaching 1,113+ children.

28
Public schools reached in Jordan
1,113
Children reached in the Jordan public-school expansion
up to 3,000
Target reach across Jordan
95%
Children completing pre/post assessments who showed significant academic progress (Jordan, self-reported)
97%
Improvement in math (Jordan, self-reported)
92%
Improvement in Arabic (Jordan, self-reported)
~50%
Children experiencing 'wheel-spinning' at least once (Uganda/Lebanon validation study)
Can't Wait to Learn — Machine-Learning-Diagnosed Adaptive Learning for Refugee and Vulnerable Children in Jordan

Details

Maturity
Scaling
Promoter
War Child Holland, with Jordan's Ministry of Education (Queen Rania Al Abdullah Centre for Education and Information Technology)
Period
2015–present (Jordan public-school expansion 2023–ongoing)
Keywords
adaptive learning, refugee education, literacy, numeracy, machine-learning diagnostics

Context

Can't Wait to Learn (CWTL) is a self-paced, curriculum-aligned tablet and web game teaching literacy and numeracy, originally built by War Child Holland to reach Syrian refugee children in Jordan's informal tented settlements who had no access to formal schooling. Content, characters and storylines were co-created with Jordanian children to align with the national curriculum.

Objectives

Extend a proven refugee-education tool from informal settlements into Jordan's mainstream public-school system, reaching both refugee and vulnerable host-community children.

Activities

In partnership with Jordan's Ministry of Education (Queen Rania Al Abdullah Centre for Education and Information Technology) and Al-Oun Charitable Society, the programme has expanded into 28 public schools reaching 1,113 children, with a stated ambition to reach up to 3,000 children across northern, central and southern Jordan. The platform's distinguishing feature is a machine-learning diagnostic layer, developed jointly by War Child Holland and Stanford University researchers, that analyses usage-log data to detect 'wheel-spinning' — a child repeatedly failing a skill without making progress — and classify what kind of intervention is needed.

Results

Published research on the ML classifier found it matched expert human judgement on what a stuck child needed, and that roughly half of children experience wheel-spinning at least once during their use of the app; that validation study documents CWTL's Uganda and Lebanon deployments rather than Jordan directly, though the same underlying platform and algorithm run in all country deployments including Jordan's. War Child's own programme reporting for Jordan states that 95% of children who completed both pre- and post-assessments showed significant academic progress, with a 97% improvement in math and 92% in Arabic.

Conclusions

These Jordan figures are self-reported by the implementing organisation rather than drawn from an independent randomised evaluation of the Jordan site specifically — the closest rigorous third-party evaluation, a quasi-experimental study published in the Journal of Development Effectiveness, covers CWTL's Sudan deployment, not Jordan. No gender-disaggregated outcome data for Jordan was found, and CWTL is not a girls-only programme — it serves all vulnerable refugee and host-community children.

Implementation

Indicative cost
Medium (€50k–€500k)
Time to results
Long (> 3 years)
Staffing & skills
Self-paced, student-facing design with no dedicated teacher-training component documented for the Jordan deployment, Delivered via partnership with Jordan's Ministry of Education (Queen Rania Al Abdullah Centre for Education and Information Technology) and Al-Oun Charitable Society

Conditions for success

  • Curriculum-aligned content co-created with local children to fit the national curriculum
  • Machine-learning diagnostic layer to identify and address 'wheel-spinning' without requiring a live tutor
  • Formal government/NGO partnership to move from refugee-settlement pilot into the mainstream public-school system

Commonly funded by

AMIF — Asylum, Migration and Integration Fund Philanthropic / foundation funding Erasmus+

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Data sources

Where this practice's information was retrieved from, and when.

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