IDS UCLA is a niche app, with 2.6 thousand installs. It doesn't have enough ratings yet to judge what users think.
IDS UCLA is made and published by
UCLA.
AppBrain is an independent app data site and isn't affiliated with UCLA.
The app name, icon, screenshots and description belong to UCLA and are shown as
published on Google Play.
About IDS UCLA
“IDS UCLA is a data collection tool to strengthen STEM instruction”
IDS UCLA was an education app developed by UCLA. It was removed from Google Play Aug 26, 2024.
Install Statistics
IDS UCLA had been installed 2.6 thousand times before it became unavailable.
User Ratings
IDS UCLA was rated 3.18 out of 5 stars, based on 12 ratings.
App Information
IDS UCLA was free. The app size was 2.88 MB. The last available version was 4.0.0. The last update was on September 17, 2020.
Technical Requirements
The app had a content rating of Everyone. The app had been available on Google Play since August 2018.
Description by UCLA(from Google Play)
IDS UCLA is a data collection tool. It provides mobile-based data capture through inquiry-based surveys and temporally triggered reminders. All captured data are automatically timestamped, geocoded and uploaded to a centralized server for analysis and visualization. IDS UCLA is fully functional without network connectivity and can run on multiple mobile phone platforms.
Through IDS UCLA, students use mobile phones and web services to systematically collect and interpret data about issues important to them and their communities. The goal of IDS UCLA is to strengthen STEM instruction throughout our educational system and to develop innovative methods for educating and engaging students in computational thinking and data analysis.
The core lessons in the IDS UCLA units will be framed around principles of Computational Thinking. Specifically, through Participatory Sensing campaigns, students will identify phenomena to study, consider "designs" for how data are to be collected, and rally the computational resources to execute their plans. Along the way they will grapple with questions related to the nature of data (its representation, formats and protocols for sharing) and of algorithms (rules governing data collection, strategies for analysis). Students will learn about classical themes in Computer Science, from databases to networking, as well as lessons in Statistics, from visualization to basic inference.
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