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Development of an adaptive educational route to prepare students for
computer science Olympiads
Zhambulov Sultanbek, 7M01503-“Informatics”
Abstract
Introduction
This research looks at how to use machine
learning to help students prepare for computer
science competitions. These competitions require
strong problem-solving skills, which traditional
teaching methods may not fully support.
Machine learning can help by creating
customized learning paths that adjust to each
student's strengths and weaknesses. This
approach makes training more effective and
engaging. By analyzing data on student
performance, we aim to develop a flexible
learning system that improves students’ skills
and helps them succeed in competitive
programming.
In recent years, education has changed a lot, especially with
new technology and the need for computer skills. One big
trend is competitive programming, where students solve
complex problems in events like the International Olympiad
in Informatics (IOI) and the International Collegiate
Programming Contest (ICPC). These contests help students
learn problem-solving and teamwork. However, traditional
teaching often doesn’t meet all students' needs. By using
machine learning, we can create personalized learning paths
that adapt to each student’s abilities. This study aims to
explore how machine learning can help students prepare
better for programming competitions and succeed in
computer science.
Methods
The methods in this research involve collecting data, developing machine learning
models, and testing their effectiveness. First, data from programming competitions
will be collected, including student scores and the types of problems they solved.
This data will be cleaned to remove mistakes. Then, using machine learning, we will
group students by skill level and predict their progress. With this information, an
adaptive learning plan will be created, adjusting as students learn. Finally, the
effectiveness of this plan will be tested by comparing students who use it to those
who follow a regular study path.
Conclusion
Results
In the future, the adaptive learning path is expected to
keep helping students prepare for computer science
Olympiads. The system will likely match tasks to each
student’s skill level with around 90% accuracy, giving
them the right challenges at the right time. Students on
this path should complete about 25% more problems
and spend 35% more time learning compared to those
on fixed paths, staying more engaged. They are also
expected to use extra resources 30% more, which helps
them learn faster. Overall, students should improve
their problem-solving accuracy by 10% and finish tasks
15% faster.
In summary, the evolving landscape of education, particularly in the
realm of competitive programming and computer science Olympiads,
underscores the need for innovative training methods. This study aims to
leverage machine learning technologies to create personalized
educational pathways that adapt to each student’s unique learning needs.
By addressing the limitations of traditional teaching methods, the
research seeks to enhance student engagement and improve performance
outcomes in programming competitions. Ultimately, this work aspires to
transform computer science education, equipping students with the skills
and strategies necessary for success in a rapidly changing technological
world.