New Oppia server and app releases

Last week we released updated versions of both the Oppia server and app.

The latest version of the server is v0.12.0 and it’s quite a big change since now we have moved to using Python 3 and Django 2 (primarily because support for Python 2.x will be dropped at the end of this year).

Since there are a lot of changes, the upgrade process is more complex than our previous releases have been, but we have some (hopefully) complete instructions here:

On the app side (now at v6.9.0), the changes are relatively minor, some small refactoring and bug fixes.

The code on the master branches of both the server and app are up to date with server v0.12.0 and app v6.9.0, and the full release notes on the issues fixed can be found at: and

Any feedback welcome, and if you find any issues then please let us know via our community site (

OppiaMobile Community site is now open…

We recently set up an OppiaMobile Community site (, for discussions on all the different aspects of OppiaMobile, getting help and support, ideas for new features and integrations, UI and UX designs, along with the core development and questions about how to implement OppiaMobile.

So if you have any questions about Oppia, or want to see the latest discussions, then please sign up to the community site (it’s free!).

For any queries/feedback about the OppiaMobile Community site, then please post them in the Community Feedback category (

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Literacy + technology = better health? Panel Discussion

Alex Little – co-founder and director of Digital Campus will be participating in a panel discussion event organised by Feed the Minds around the question of ‘Does technology help or hinder low literacy communities engage in health projects?

For full details of the other panelists and to register to attend the event please visit:


Mobile Learning for Health Workers – Budget Considerations

We’ve recently released a document describing the budget and cost considerations that should be taken into account when designing a program for training health workers using mobile learning. You can download the full document here: Mobile-Learning Budget Considerations.

The information is mainly based on our experiences implementing OppiaMobile in our UK Aid Direct project in Ethiopia, but includes experiences from other implementations, and can also be a basis when using other mobile learning tools and platforms too.

The key areas and factors discussed are:

  • Technical
  • Content development and adaptation
  • Training and Support
  • Monitoring and Evaluation
  • Factors for scaling-up/replication

It is released under a Creative Commons license, and we would welcome any feedback or experiences that could help update and inform future revisions of this.

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Quality of routine health data collected by health workers using smartphones at primary health care in Ethiopia

We have just published in the International Journal of Medical Informatics a new research paper entitled, “Quality of routine health data collected by health workers using smartphones at primary health care in Ethiopia.”. This article gives a comprehensive account of Health Extension Workers’s experiences using mobile health data collection tools. Here is the abstract:

Mobile phone based applications are considered by many as potentially useful for addressing challenges and improving the quality of data collection in developing countries. Yet very little evidence is available supporting or refuting the potential and widely perceived benefits on the use of electronic forms on smartphones for routine patient data collection by health workers at primary health care facilities.

A facility based cross sectional study using a structured paper checklist was prepared to assess the completeness and accuracy of 408 electronic records completed and submitted to a central database server using electronic forms on smartphones by 25 health workers. The 408 electronic records were selected randomly out of a total of 1772 maternal health records submitted by the health workers to the central database over a period of six months. Descriptive frequencies and percentages of data completeness and error rates were calculated.

When compared to paper records, the use of electronic forms significantly improved data completeness by 209 (8%) entries. Of a total 2622 entries checked for completeness, 2602 (99.2%) electronic record entries were complete, while 2393 (91.3%) paper record entries were complete. A very small percentage of error rates, which was easily identifiable, occurred in both electronic and paper forms although the error rate in the electronic records was more than double that of paper records (2.8% vs. 1.1%). More than half of entry errors in the electronic records related to entering a text value.

With minimal training, supervision, and no incentives, health care workers were able to use electronic forms for patient assessment and routine data collection appropriately and accurately with a very small error rate. Minimising the number of questions requiring text responses in electronic forms would be helpful in minimizing data errors.

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