Do you remember how hard it was to read books at school?
The development of tech drastically reduced the time spent on reading.
Our solution is KreadIt, as kid and read it. KreadIt is a platform meant to be used at school to create engagement in reading through gamification.
Team membersAntonio Chiappetta, Neza Dukic, Michele Marabelli, Sarah Mouffok, Hien Nguyen
Members roles and background
Antonio: NLP expert & leader
Neza: UI Design & Graphics expert
Mihele: Recommender Systems & Creative
Sara: UX Design & Designer
Hien: UI Design & Game design
Kreaditis composed by three tools: a mobile app for the teacher, to create groups and assignments related to a book; a mobile app for the kids, where they play mini-games related to the contents of the book in a cooperative way, in order to solve the assignments; and a back-end system, that manages the adaptation of mini-games to the book and the recommendation of similar books
The kid takes control of the protagonist and has to go through a series of rebuses focusing on a particular section of the book, like a chapter or a paragraph, answering questions such as:
- Who is the antagonist?
- What is the main emotion you can feel?
- What are the main topics?
Kids will need collaboration in order to solve group tasks and get to the end of the story, and this will create further discussion and engagement about the book itself.
The teacher can propose a new book, but the platform itself also provides suggestions on similar books in terms of genre or emotional tone.
The use of gamification and recommendations becomes a key addition to the reading experience.
The game shares a common structure across all the books. It always has tasks in the form of questions about characters, topics, emotions. And its graphics style and background music. are going to be updated in the best way to join the theme and specific feelings (in the section) related to the book, obtained through NLP.
When we apply NLP, we extract features from the text, and from that information set up the questions, style, and music for a particular book automatically, so we can customize the experience consequently.
For the recommendations, we use again the information on the genre and the overall emotional tone. Then we use a recommender system that combines two main approaches to generate suggestions: collaborative filtering, predicting how users are similarly based on what books they read; content-based filtering, predicting how books are similarly based on the genre and emotions prevailing.
The solution is meant to be used in classrooms, where teachers assume the role of master of the games and can provide books to read to the students, that can then follow the game in the app to improve the reading experiecence, and while doing so, our goal is that they can get slowly passionate about reading, which as Pennac said, it's a thing that people enjoy only when they approach it with freedom and interest.
Solution target group
Our target group is kids ages 6 to 10 since they are in the most critical phase where they start reading and they start having the most important experiences that will determine their future view of this vital task.
Our desired impact is to make young children discover literature and enjoy it. The vision is creating a generation of highly literate people that could act for the best of society.
Solution tweet textKreadit is an adventure your kids will never forget, that will open the doors to a world of knowledge, entertainment and a future of success.
Differently from any other reading companion app already existing, we are automating the process to adapt it to any input book, characterizing the experience with related tones, graphics, and musical effects, to help the children immerse into the world created by the author.
With little effort, this technology could be applied to more complex themes, like university courses; while on a closer note it could also be applied to subjects like history and geography. In general, the idea is also applicable to re-wake the interest in reading in people of all ages.
In general, this technology could be opened more and more to create an automatically customized
During this hackathon, we focused on the definition of the system, and on validating our hypothesis on the exiting technology. All the technology we need is there and just needs to be put together. The composition of the team then helped us shape our action timeline: two of us have experience with NLP and recommender systems, other members have experience with UX and UI design: that’s why we think we could have an MVP, with a basic UI, in a couple of weeks. But since our product is meant to be used by children, we think we should dig deeper into a careful development of the UI and stick to an agile methodology to quickly build, deploy and validate our UI in an iterative manner, until it acquires full usability for our end users. We plan to end this design process and enter the PlayStore and the AppStore in a three months period.
Solution team work
The teamwork we experienced during this hackathon was both relaxed and very productive: we synchronized very well on dividing tasks, and we all did put lots of effort, keeping also a livable environment, that encouraged us to never stop, creating an idea that we all agree to be not only feasible, but that could be fun and useful to realize and test.
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