Computer science courses in schools are often taught in an entirely different way from those in the real world.
That is, in the course of the lessons students learn, there are typically no “real world” problems to solve, and instead the students work in an attempt to solve problems from a certain point of view.
That’s not always a good thing, but as the course progresses, this “idealism” becomes apparent, as the students are asked to think more abstractly about what the real problem is.
In the process, students learn a lot about how computers work, and how to design them in a way that does not rely on a computer’s memory.
But a new approach to teaching computer science courses aims to change this, by introducing students to real-life problems, and asking them to think about how to solve them in real-time.
The project is called the New Scientist Computer Course, and is the result of a collaboration between a team of scientists at the Max Planck Institute for Mathematical and Computational Research and a number of engineering and computer science institutions.
The researchers used artificial intelligence and deep learning techniques to build a virtual environment in which students could explore the real-space problems they might encounter in a computer science course, with real-data data and simulated examples to help them do so.
This allowed the team to create a simulation that allows students to learn more about the problems and concepts, as well as to simulate the problem solving process in real time.
“We have a large amount of data from real computer science students, so we wanted to use that data to learn about real problems,” says Alexander Povirk of the Max-Planck Institute.
The aim is to make the computer science curriculum more accessible to students, and therefore to improve the quality of their work.
The goal is to use a new set of tools to help students understand the real problems students might encounter.
This new approach allows the students to think abstractly, and to use real-science data and techniques to solve the problem in a realistic way.
In this way, the simulation makes the student more comfortable with the problems, as they can be easily identified and solved in real life.
The simulation can be used to train computer scientists in the theory of computer science, to learn algorithms, and more.
In addition, the team aims to use this model to make it easier to design computers that can solve real-use problems, such as designing robots that can help in disaster relief.
This is a huge step towards making the teaching of computer-science courses more accessible, because many students are unable to get to the level of real-students in real situations.
“A computer science student will often not have the experience needed to develop a real- world solution, which means that they will have to rely on simulations,” says Povrikers.
For example, a student might work on solving a real problem such as how to make a radio system, while the real challenge is to figure out how to program a real radio transmitter.
“This is a problem that is difficult for students to solve,” says Ivan Shavloff, a researcher at the University of Waterloo.
The research is being presented at the International Conference on Artificial Intelligence and Systems in Vienna, as part of a larger effort by the Maxplanck Institute to develop an AI framework for teaching computer-sciences.
“It will be interesting to see if the model we use will help students learn how to think like real scientists,” says Shavlovs.
This research will be published in a forthcoming paper.
For more information about the research, contact Alexander Pevirk at Alexander.
[email protected] or +44 (0)1-2-7-2.
Follow the progress of the New Science Computer Course project at the MPI website.
The video below is the introduction to the project.