返回榜单
Virginia Montessori Academy

Virginia Montessori Academy

Seabird Apps

Education免费v20.0.0
App Store
评分

5.0

1 条评分

星级

★★★★★

最近更新

2025年11月18日

发布日期

2024年3月21日

更新内容

v20.0.0

We improved the performance and design of this app by upgrading our core technology. Learn more about the software powering this app at onespotapps.com

应用信息

开发者
Seabird Apps
分类
Education
价格
免费
版本
20.0.0
App ID
6479611457

简介

This mobile app is the best way to get everything related to Virginia Montessori Academy in Leesburg, Virginia. This beautiful, customizable design features a clear layout of everything you could possibly want to use as a VAMA parent, staff, visitor, or any other member of the VAMA community. You can use this app to... • Get notified about important school updates • Submit absence & attendance notifications • Contact important departments with the push of a button • Access important VAMA websites • See what upcoming events are happening • Browse the latest VAMA social media and news • Learn more about Virginia Montessori Academy • And much more! Your VAMA app is fully customizable by you: Rearrange your portals to easily access whichever features you use most. If you want to check school events frequently, you can put that portal front and center. If you never check school social media, you can turn that portal off. This app is built with cutting-edge technology and a modern, user-friendly design that's optimized based on millions of usage data points. As the technology continues to improve, you'll notice your app getting better and better over time. If you have ideas, suggestions, questions, or feedback about anything in the app, you can easily submit them through your app's suggestion box (in the "Profile" screen). This feedback will always be taken into account to continue improving the VAMA app experience for everyone. To contact the developers directly, email team@seabirdapps.com.

下载量预测

专业 · 预览

预估总下载量

7450143
保守估计乐观估计

2

低 / 月

3

预估 / 月

6

高 / 月

基于1 条评分
假设评分率1.4%
应用年龄26 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。