Monthly Archives: June 2015

Update on FieldDB

Its been 3 years since the FieldDB project was launched at CAML in Patzun, Guatemala. Since then the project has graduated into its own GitHub organization with 50+ collaborators and 50+ universities that we know about have been using it. In March we made sure that all the clients and libraries had Google Analytics integration to better understand how users were working with the apps.

This week Veronica has been learning Google Analytics to see how the app has grown. One of the questions Hisako had was  been where in the world are the users and how much time do users spend in the app on average.

Average_User_TImeReturning_Visitor_Time_ComparisonNew_Visitor_TIme_Comparison

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Taking a look at iLanguageCloud user reviews

Its been a few years since Josh originally released the iLanguageCloud project. The iLanguageCloud project uses Jason Davies D3.js cloud library and some statistics to tokenize and identify stopwords so that it can support text in any unicode charset in any language.

Since the app was released a surprising number of users have found the app and have been using it. Users have been requesting features and providing feedback on the Play Store and Chrome Store.

Using @iLanguageLab word cloud to collect & display words to describe the moon. One S uses Word Central for help!

Some teachers have even tweeted about the app!

This summer Veronica will be looking over iLanguageCloud user reviews in order to document what needs to be done in the next releases. First she found that most of the reviews indicate that there are different user groups who have different goals when they open the iLanguageCloud project. Some users want to paste a full text and see a cloud, but most users want to see all the words they paste.

She started by identifying the user types with a CouchBD map reduce and learning how to do statistical analysis in LibreOffice. Once she had identified stats to categorize user types, she added tests for these user types in the codebase using Jasmine.

Users are often creating tag clouds, not full text clouds. We attribute this to users being used to having to pre-filter their words to only the words they want to show with random text sizes rather than text size which depends on their frequency or other factors.

 

While she is learning the tools (Angular.js, Travis) to make the modifications so that her user types tests pass, Veronica created a video tutorial showing how you can use the Chrome app so that users can have some instructions.

 

To help decide features get done first visit our GitHub feature list.