My name is Amy and I want to be your new personal maths tutor. I work really hard so I am always available and I want to help you when you are at school and at home. If you make a mistake I will tell you what you did wrong and how to do it right you can learn as you go and never get stuck again. I am super smart so I will notice if you have forgotten something so I can remind you straight away. I have only just started teaching so I am currently only teaching year 11 maths but I will be adding new topics soon. Please like my facebook page and tell me what you want to learn next.
I make it fun and easy to learn maths.
I give you feedback as you solve problems so you learn faster and never get stuck.
I know when you have forgotten something so I can remind you before you even notice.
Amy is a friendly personal maths tutor, but she is better than human tutors because she makes learning easy and she is ready to help you 24/7. You will notice that Amy keeps getting smarter when you use her because she is actually an AI. This also means we can provide private tuition at a fraction of the cost of a human tutor. Amy can be used at home or alongside teachers at school. For students, she provides interactive 1:1 tuition. she helps them select exercises and uses real time feedback to guide them through the process of solving them. She also gives them feedback on their progress to keep them motivated. What makes her really powerful is her detailed understanding of the student which allows her to give them highly specific feedback. This also enables her to do dynamic teaching which allows her to fill gaps in the students’ knowledge whenever they need it.
Amy understands you and knows exactly what you know and uses the latest pedagogical research to make her teaching effective.
Amy gives you really specific feedback as you solve a problem.
Easy to Use
Dynamic teaching automatically fills your knowledge gaps when needed.
We are creating a special version of Amy just for teachers. This makes it easy to manage your class and see how everyone is progressing. This also makes it easy to set and automatically mark assignments. These assignments are individualised so students can’t copy each other. You can also allow Amy to automatically adjust the assignments you give your students so they actually learn what you get them to do. Our real time feedback allows you to teach something and quickly check if everyone understood and you can also use it to see which of your students need help the most.
Automatic creation and marking of individualised assignments which prevent plagiarism.
Real Time Feedback
Easy individualisation of your classes and assignments so every student is learning.
Reporting and Assignment
Real time feedback on the progress of each student so you know who needs your help and real time feedback on your classes so you know how everyone is learning.
How Amy Is Different
We have modelled Amy on a human tutor because this is the most effective way to learn. She teaches in an interactive way and individualises her classes to the student.
Amy is the only online system that understands the student on a skill level.This means that she can give them really specific feedback as they learn. This also allows her to automatically fill any gaps they have in their knowledge as they learn so students never need to get stuck again
Most importantly, we use cutting edge pedagogical research to guide the design and implementation of our system so it gives students the best possible learning outcomes as well as a fun experience.
We are currently creating a system which teaches maths and will localise it for every curriculum we work in. Our Beta version teaches material from Year 9-11 from the New Zealand curriculum and we are adding new topics on a regular basis
Based On Proven Pedagogy
Humans have many special qualities which make them good,
inspiring teachers but unfortunately not all of them are inspiring or good, moreover
many teachers were trained long ago and find it hard to adapt to new teaching
practices or keep up-to-date with pedagogical research. One of the big advantages of
AI teachers is that they are easy to re-programme, so they can instantly start using
new research. They can also draw on their experience from teaching millions of
students to continuously improve what they do, never forgetting a single
interaction, and retrospectively drawing on all of their unbiased experience to
optimise how they teach.
Amy draws on the research of many of the world leaders in education pedagogy and over time will go on to learn new insights we have not been able to uncover. Some of the key researchers we drew on during her development are Carol Dweck who is best known for her research around fixed and growth mindsets, Jo Bowler who further developed Carols work advocating for de-streaming schools and encouraging students to explore solutions instead of finding a correct answer. John Hatties long term meta-analysis has also given a lot of insight into the relative efficacy of a number of interventions which allows us to optimise and validate what we are doing. A short summary of some of the key finding from some of the pedagogical leaders we drew on can be seen below.
Carol Dweck is a professor at Stanford who is a pioneering researcher in the field of motivation. Her research found that people have one of two mind-sets. A growth mind-set or a fixed mind-set. These are developed as a result of how we are praised and educated. Her work showed that people with growth mind-sets tend to enjoy taking on challenges and will get much better as a result while people with fixed mind-sets will avoid challenges and do things they know they can do. These mind-sets get developed as a result of our education and upbringing, specifically by how we are praised. Her work shows that praising someone for their effort and attitude will make them develop a growth mind-set while praising them for their intellect or talent will make them get a fixed mind-set. Amy uses this research to guide how she complements and guides students to help them get a growth mind-set. This is nicely summarised in this short video and a more comprehensive overview can be found in Carol’s TED Talk
Jo is also from Stanford and builds on Carols work. She looks at some of the implications that teaching practices have on the development of the mindsets Carol discovered and strongly advocates against streamed schools. She argues that the outcomes of everyone are much better when students of all abilities are grouped together. Our system makes this possible because our dynamic teaching will allow better students to get extended whilst automatically filling the gaps of weaker students without anyone being told they are bad at maths. She also says it is really important to challenge students and get them to make mistakes so long as they have the resources available to solve them in the end. Amy is perfectly suited to this as she can challenge students but always gives them everything they need to get through the problem in the end. This shifts them towards a growth mindset which is focussed on exploring and learning instead of answer getting. Jo’s TEDx talk can be seen here
John undertook a long term meta-analysis of educational research to find the most important factors in terms of educational outcome. We are able to implement a number of the key factors he identified with our system such as good feedback, acceleration and formative assessment. One of the factors we are particularly interested in is his identification of spaced rather than mass practice as better method of learning. Our revision system will establish the ultimate spacing and will remind students to revise at the best times to optimise their learning and retention. He has given a number of good talks on his research including this Ted Talk
Dylan has done some really important research into the efficacy of different assessment styles. He found that there was very little value in giving students assessment which contains only a grade and even formative assessment with feedback and grades had almost no impact. He found that the only valuable assessment,both in terms of learning and interest, was assessment where the student only received feedback. We are exploring the best way to balance teachers needs to comply with school requirements and balance this with student outcomes in the way we assess a students progress. More information can be found here and an interesting talk by him on education is found here .
Derek, the founder of Veritasium, completed his PhD in physics education and found that students learn almost nothing from most educational videos. He found that students only start learning if they are told about their incorrect assumptions first. We have built this into our feedback system and are undertaking our own research to understand if this applies to feedback and maths education . More about his research can be found in his Ted Talk .
The Team and Story
Raphael met Antonia at Startup Weekend Wellington where they developed another education related idea. They worked really well together and afterwards she told him about the algorithm she had developed, Raphael immediately saw its potential and she asked if he could help her and after a few skype sessions she asked him to cofound what was to become Osnova. Raphael met Jürgen at a number of events in Christchurch including Startup weekend and they became good friends. They are brought together by their passion to make education better and the cool tech they get to work with to make this possible. They are currently in the Flux accelerator in Auckland and are working to release a public beta in the second half of 2017.
Raphael Nolden - CEO
Raphael studied history and medical physics and codeveloped a patent in medical technology before becoming a lecturer in mathematical modelling. He is passionate about developing the startup ecosystem and mentors at Startup Weekends around New Zealand and sits on the board of Canterbury Angels.
Jürgen Brandstetter- CTO
Jürgen has a background in Computer Science and Human-Robot-Interaction. His entrepreneurial spirit led him to found his first web company during his high school years. Jurgen has completed a PhD with the HITlab at Canterbury University on the persuasiveness of robots and completed his masters in computer science researching positive impact games at TUV.
Antonia Modkova - Content
Antonia studied Computer Science and Law at the University of Auckland and went on to become a patent attorney. Her years of experience as a private tutor inspired her to explore scaleable alternatives to human tutors, which ultimately led to the technology behind our system.