.png)
Jason: Just recently, I have become the father of a child who has chosen to leave school at this late stage in Year 11. Our son has anxiety, and while he tried his best, it is clear that school is not for him at this stage. At home, he has been teaching himself countries using an interactive mapping system.
He might pick Europe and start choosing countries, and at the end, there is feedback on his % of correctness and what time limit this was achieved. What struck me about this is how intuitively he has approached this program like a Neural Network.
He starts with say 40 – 50% accuracy then he would keep repeating until he can get every country now with 95 - 100% accuracy. There is no emotion about getting the countries wrong, each time he plays he builds an extra few countries to his knowledge base, maybe set up some mnemonics until there comes a point where he gets 95 - 100% accuracy in a region - exactly like Machine Learning. Then he works on the time improvement.
My first thought was 'Why can't he apply himself like this to school subjects?' and the answer seems to be that for whatever reason, he has a fear of getting incorrect answers and low grades and only has one chance to pass exams.
Machine Learning Approach
Could there be an approach where the Machine Learning format is followed? It doesn't matter what the first attempt is, but it is graded and fed back to show where the student is at. Then they have as many attempts as needed to reach a milestone and continue until they have achieved the outcome.
The key here is that the students have to learn not to feel emotionally deflated by a low score; they know they need to repeat and make adjustments to achieve a result. At present (assignments aside), you get one attempt at an exam for ten weeks worth of content.
Weekly Assessment
Could we review the 'read, memorise, repeat' exam model to reflect the current times? Maybe an alternative to exams is that every week there is an online test for competency to understand the week's topics and these all have a 10% weighting each week adding up to 100% weighting for the term. No more frantic revision week and exam blocks.
If you get below say 70% in any week you have to retry before you can even access the second week's assessment. You have as many attempts as needed to achieve a week's competency. This could also help early on in the piece to determine students who aren't cut out for that particular subject. Let's look at this idea in detail:
Cumulative Approach
Week 1: 10 questions online platform that requires a 70% pass rate - students can have more attempts if they want to record a higher mark, overall mark is diluted by the amount of attempts.
Week 2: The 10 questions from week one are asked again as a 'password' access to week 2 material to revise. Then week 2 answers are recorded as a grade.
Week 3: 10 random questions between week 1 and 2 (bias towards week 2) are asked again as password access to week 3 questions. Week 3 answers are recorded as a grade.
Week 4: 10 random questions from week 1 to 3 (bias towards later weeks) are asked to access week 4 questions and so on.
Flaws
There are some flaws which would need to be ironed out. How do we determine 'A grade' students? Perhaps there is recognition for how many attempts there are for those that score 95% or higher on their first attempt or first few attempts.
Another problem could be the people that don't do any work and only do the weekly assessment until they pass. One solution could be that part of the grading is from the teachers observations of class effort.
Some students might screenshot the answers on attempt one - a solution could be that the answers to the incorrect answers are not shown - students will have to research to find the answers.
Constructive Discomfort
I (Karsten) recently heard the term 'constructive discomfort' and that describes that the learning environment should be constructive, but just enough challenging to give a bit of discomfort for the learner to keep going. In a neural network, the back-propagation learning process adds this discomfort. And then the ANN does another round or learning. It will stop when the discomfort has fallen below a pre-set threshold. In my experience, most self-directed learners are like this. They are immensely curious, but generally not satisfied. When children are young learners (pupils), their teachers often have to add the discomfort to get going. But as they become more self-directed learners (students), they can judge whether they need to keep going. As a mature learner, I have come to appreciate the lingering dissatisfaction in the back of my mind as an engine that keeps my learning going. The psychology of the discomfort should be encouraging, but not to lead the students to believe they have already mastered the topic. It is a fine line. But repetition is key to all of this because every repetition cycle extends one's understanding. Repetition is key to learning, but that's not the same as rote learning. However, I have to come to appreciate that my mind has automated many of the mundane tasks of timetables, definitions and equations, so I have them instantly available when I do things. This means I do not need to switch contexts (to do a Google search) when I work. By deeply engraining key knowledge into the deep layers of my brain, it can focus on higher-order thinking.
Conclusion
Machine learning is rapidly changing the world we live in, with computers being able to do some things faster and more efficiently than humans can. Could we also take from Machine Learning the approach itself, and apply it to where it could be demonstrated as an improvement over existing pedagogy?
Going back to the mapping game, if my (Jason's) son had only one chance to get 90% or higher, there would be fear of non-achievement, and he would not engage with the task. Could it be better to let people have as many repetitions of learning outcomes of school assessment as they need?
As with any rethinking of an existing paradigm, there are hurdles to overcome and some areas that are not as effective as the existing model. Our hope in contributing to this blog is to start some conversations and rethinking of how things could be done differently.
About the authors
Jason Vearing is a content creator for Digital Technologies Hub and is also working with Dr Jordan Nguyen on an eye controlled communication device for people affected by ALS.
Dr. Karsten Schulz is the chief nerd at the Digital technologies Institute and father of seven children. He is the creator of the MyComputerbrain AI and the B4 4-bit educational CPU.
As the review of the Australian Curriculum is underway, with Digital Technologies and Mathematics being the first subjects in this process, I wanted to take a moment to reflect on the opportunities that this review brings.
As an Engineer and Computer Scientist, I never had a particularly deep love for Biology, and this is probably not a surprise. When I did Biology at school, cell biology was superficial at best and focussed mainly on the parts that make the cell, and chemical reactions.
But this changed in recent times and it is now clear that our cells are not just chemical factories, but sophisticated information processing agents. I have outlined this in the blog 'We are walking supercomputers'.
The curriculum review is an opportunity to connect the different subjects better, specifically Digital Technologies and Biology. Information not just drives our computers, but every single biological cell. This is profound for students' understanding of life. Whilst the detailed functions surrounding the DNA are currently beyond the full grasp at the levels of primary and secondary education, the concepts underlying Digital Technologies can act as bridges. For example, code needs to be stored, read and processed in the cells. Computers do this. The different cell parts working together need to agree on data formats and protocols to reliably and securely exchange information. Computers do this too. When I designed my first CPU, the B4, I couldn't help but see the analogies with the inner workings of biological cells. And just like a 3D printer makes cool things, so do proteins in our cells build other proteins that are the building blocks of us.
And then there is artificial intelligence, specifically artificial neural networks, that are based on the research of their biological counterparts, which started over 30 years ago. I developed my AI to show my children what happens in their brains when they learn, and what the impact of learning strategies on their learning is. Imagine looking at a model of a brain and watch it learn. It is a profound experience!
I think that students can gain a deeper understanding of biological life and learning if we build them the bridge between the technologies and biology. Let's connect the learning area silos. After all, life itself exhibits aspects of what we teach in mathematics, physics, chemistry, biology, digital technologies (informatics), the arts, languages, and <insert your favourite subject here>.
To be clear, I do not argue for students to write more code. I think that current levels are fine. But I would like the curriculum review to consider the cross-curriculum links, specifically between Digital Technologies and Biology and connect the big ideas, such as 'What is life?' and 'How do we learn?'.
Just my $0.02.
Warm Regards, Dr. Karsten Schulz
We are very excited to announce the release of our latest Digital Technologies course on the hot topic of Artificial Intelligence and Robotics. We've made it for year 5-8 students.
The course is currently free under our COVID-19 policy. It is suitable for remote and self-directed learning.
In this course, students get to know artificial intelligence through hands-on experiments. They help an AI navigate a little robot through colourful landscapes. In the process, students get to know artificial neural networks and learn how they can use sensor data to control the power flow to motors. We can't be sure, but we believe we've made the world's first interactive AI.
The course is highly immersive and interactive. The AI and the robot respond to user input, and students can design their own colourful tracks to play capture the flag.
We've added a few subtle sounds, but no annoying music. We celebrate success with balloons, and at the end of the course, there's a certificate waiting for the students.
Without coding, the course teaches about data input, processing and output. A little sensor under the robot watches the track and provides the red-green-blue values to the AI, which decides which of the two motors (or both) it needs to activate. The little AI only needs five perceptrons to do this job.
The course intentionally avoids explicit coding, as this only distracts from the more profound understanding of the inner workings of the AI, which is data-centric.
In the Australian Curriculum: Digital Technologies, this course addresses aspects of the following content descriptors in the years 5+6 band, but are also suitable to students in years 7+8:
ACTDIK015
ACTDIP017
ACTDIP019
ACTDIP020
The learning outcomes from this course are valuable to understand the fundamentals of artificial neural networks and how they can be interface with the real world; in the case of this course, a little robot.
After students have mastered this course, they can dive even deeper with our AI Course, which looks further at the inner workings and application of AI, including home automation, science, and anti-bullying.
As a teacher, you can track student progress from the comfort of the course statistics page. That's especially useful if your students are remote.
Let us know what you think. We're very interested to hear your thoughts about future lessons in this course.
To access the course, click here.
Until next time,
Dr. Karsten Schulz
CEO and Founder
Digital Technologies Institute
Brisbane, Australia
