Towards the Use of Slice-based Cohesion Metrics with Learning Analytics to Assess Programming Skills
This work addresses the need for appropriate and quick skill assessment in programming education to prevent boredom and support learning, but it is incremental as it applies existing metrics to a new educational context.
The paper tackles the problem of assessing programming skills in education by proposing the use of slice-based cohesion metrics to analyze program construction processes, aiming to identify programmers' trains of thought based on variable-level cohesion.
In programming education, it makes a difference whether you are dealing with beginners or advanced students. As our future students will become even more tech-savvy, it is necessary to assess programming skills appropriately and quickly to protect them from boredom and optimally support the learning process. In this work, we advocate for the use of slice-based cohesion metrics to assess the process of program construction in a learning analytics setting. We argue that semantically related parts during program construction are an essential part of programming skills. Therefore, we propose using cohesion metrics on the level of variables to identify programmers' trains of thought based on the cohesion of semantically related parts during program construction.