Edtech Concepts Transforming The Lives Of Africa’s Next Generation

Technology in education has changed over the years. Classroom learning ways have made learning simple. According to a recent stat report, funds are being invested in Edtech sectors of Africa to bring a change in the educational ecosystem. Cape innovation technology initiative has launched an open Edtech innovation cluster in Africa.

Its main aim is to collect the best Edtech startups and companies that’ll help to work on educational problems in Africa. Sparkschools and UNICEF are also doing their bit to change the concepts of learning in Africa. Sparkschools has raised an amount of USD 9, 000,000 for high-quality education in Africa.

Transforming the Educational Setup:

Augmented Reality and Artificial Intelligence (AI) are two technologies that are making a way in educational setup. Its revolutionizing simple learning ways through mobiles and tablets in Africa.

Let us look at Edtech Concepts that are Changing the Educational Scene in Africa:

Xander Educational App:

Xander has been specifically developed to suit the educational needs of young African children. It addresses numeric and literacy skills for children who are not able to access mainstream educational apps in Africa. The app is easy to access and it is not so expensive so every young child can use the app for educational purposes. The app is small in size but its reach is great. It can be accessed from any region of Africa and cost wise also it’s cheap. It is designed keeping in mind the young minds. The interface is simple so children can access this educational app on their own.

Snapplify:

Snapplify is a content and media technology company that caters to mobile publishing and content distribution. Its app has a simple reader interface that lets you read large content without any strain on the eyes. It allows children to read, integrate and learn properly.

It also allows reading books on android Smartphones or tablets. The best part is anyone can read from the app at any place. There is no specific access required for reading. It is good to increase the vocabulary power of children. Reading is a good habit that should be inculcated from a young age in children.

Beblocky:

Beblocky uses the AR technology that powers the robot into the real world. Blockybot uses a specific bot type programming that enables the children to stack together all the programming components. The children can stack action, events and different components that’ll help them to develop and learn new skills. The robot combines different features of blocks and actions that make the learning process interactive and fun. Beblocky is a programmable friendly robot that can help children in their learning process. The best part is there is nothing technical, it’s easy to use and the interface is simple.

Wethinkcode:

It is a training program for people who are aged 17 to 35. It caters to training that helps to fill up the tech skill gap of unemployment in Africa. The program is delivered as a learning system that helps students to learn about special skills of employment. It helps them to develop new skills that are future proof. It helps them to get trained for new technologies and updated versions. It is the best route for training and skilled learning. The best part about Wethinkcode is it’s a free training program so students with a low-income group can also get trained for the future and get employment opportunities.

The startups that are Making a Difference:

– Student hub

Hertzy Kabeya started this student-based platform in 2015. This simple E-com platform offers smart technology to educational institutions, students and Government in Africa. It also publishes eBooks with added resources for students to make them learn better. The startup also helps students connect with authors and publishers of the book. It assists students if they are stuck with any subject matter.

– Obami

Obami Founded by Barbara Mallison, this startup is based in Cape Town. It is an online learning and communication platform that brings together teachers, individuals, and students. It provides a unified learning experience to students that help them to understand things in a simplified way. It lets students and teachers discuss new resources and lessons. It helps to connect students globally and that is an advantage of this startup. The interface is inspired by Facebook so it integrates social media style as well. Business Insider also named this startup as the top 20 companies in the world.

Technology Changed Concepts:

Technology has changed the educational prospects of Africa. Through educational apps and startups, students can connect.

This is bringing a promising change in educational technology and is helping students adopt new and innovative ways of learning. The future is great with many startups and educational apps making way for students of Africa.

Edtech concepts help students to grasp and learn easily. Its also making a difference in the lives of children who can’t afford costly studies. Edtech is helping them in each possible way. Edtech is accessible and provides a simple and comprehensive approach. With Artificial Intelligence and Augmented Reality integrating with Edtech, the future looks bright for students of Africa.

Modernization of technology creates a huge scope for the Education sector among the lives of Africa’s next generation. Parents and Teachers wanting their kids to learn and have fun at the same time – Educational App development and mobile apps are the answer to such requirements.

Let’s Nurture, a leading custom mobile app development company delivers a highly customized On-demand teaching platform with personalized accessible and real-time solutions for our clients in the education industry leveraging custom education app games development.

To get a free quote for your Educational Game App Development requirement, Feel Free to Contact Us now.

Ever Wondered How Music Streaming Apps Know You So Well?

That new artist you just favorited; that new genre you just discovered or that new song you are saving for a new day, are all products of music discovery and algorithms. Although we are perfectly capable of curating our own playlists, searching up new artists and bands, and finding new genres to enjoy, sometimes we just don’t have the time for it or don’t feel like thumbing through hundreds of thousands of songs. This is where your music streaming app becomes your best friend because it can turn a bad day into a bearable one and a boring transit ride into a silent concert, all with the push of a button. Welcome to automated music curation, a product of big data, machine learning, and artificial intelligence. Yes, this is how your music streaming app knows you so well; let’s explore how recommendation models work.

The Three Types of Music Recommendation Models:

Generally speaking, there are three types of music recommendation models that use three different types of analysis. The first is collaborating filtering models, which looks at a user’s behavior on the music streaming app as well as other people’s behavior. The second is natural language processing (NLP models), which analyze text, and the third are audio models which take a look at the raw audio tracks themselves. In most cases, music streaming applications will use a combination of all three types of analysis, as this provides a unique and powerful discovery engine.

1. What Is Collaborative Filtering and How Does It Work?

The most common example of collaborative filtering is star-based movie ratings that you get on Netflix. It provides you with a basic understanding of which movies you may like based on your previous ratings and it provides Netflix with the ability to recommend movies/television shows based on what similar users have enjoyed. For music applications, the collaborative filtering is based on implicit feedback data, meaning the live stream of your music is counted. This includes how many tracks are streamed and additional streaming data like whether a song is saved to a playlist or whether a user visits that particular artist’s page after listening to one of their songs. Although this is great, how does it actually work?

A user has a set of track preferences, such as P, R, Q, and T, whereas, another user may have a set of preferences denoted as R, F, P, and Q. The collaborative data shows that both users like Q, R, and P and so you probably are both very similar in what you like. This is furthered to say, you will probably enjoy what each other listen to and so you should check out the only track currently not mentioned in the preference list. So in this instance, for the first user it is F and for the second, it is track T. Now the question is, how does a music streaming app do this for millions of preferences? By using matrix math. Essentially, at the end of the mathematical equation, you get two types of vectors, where X is the user and Y is the song. When you compare these vectors, you find out which users have similar music tastes and which song’s are similar to the current song you are looking at.

2. Natural Language Processing and Text Data:

The second type of music recommendations come from natural language processing which is sourced from text data. This can include news articles, blogs, text through the internet, and even metadata. What occurs here is your music streaming applications crawl the web, constantly looking out for written text about music, such as blogs or song titles, and figures out what people are saying about these songs or artists. Now because natural language processing allows a computer to understand human speech, it is able to see which adjectives are being used frequently in reference to the songs and artists in question. This data then gets processed into cultural vectors and top terms and given an associated weight that has corresponding importance. Basically, the probability that someone is going to use that specific term to describe a song, band, genre, or artist. If the probability is high, that piece of music is likely to be categorized as similar.

3. Raw Audio Tracks and Pinpoint Accuracy:

The third type of music recommendation comes from analyzing raw audio tracks. Although it may not seem like you would need this in your music streaming app if you have the first two, what this particular model does is improves the accuracy of recommendations by taking both old and new songs into account. An example of this would be a new song coming onto the music application and only getting 50-100 listens but since there are so few filtering against it, this type of song could end up on a discovery playlist alongside popular songs.

This is because raw audio models do not discriminate against new and popular songs, especially if natural language processing hasn’t picked the track up through text online. So how does raw audio tracks get analyzed? Through convolutional neural networks that form spectrograms. This works by creating convolutional layers or “thick and thin” bars that showcase time-frequency representations of audio frames and inputs. After passing through each layer, you are able to see what computing statistics are learned across the time of the song, or in layman’s terms the features of the songs. This can include a time signature, mode, tempo, loudness, and key of the song. Your music streaming application can then understand the fundamental similarities of songs and recommend them based on their listening features.

When it comes down to it, your music streaming application knows you because of the massive amount of data that it stores and analyzes. In order to work correctly though, audio files, matrices, mathematics, and text must all be analyzed in real-time, applied, and updated through machine learning processes. Yes, a form of artificial intelligence powers that perfect recommended playlist you tap into on a daily basis.

Want to develop beautiful music streaming app like Spotify for Android OR iOS? Let’s start a new project together!