Improving code while preserving its functionality is a common task in the hi-tech industry. Yet students have difficulties improving an algorithm’s run-time complexity by an order of magnitude. The paper focuses on assessing students’ abilities in this area. We designed a Structure of the Observed Learning Outcome (SOLO) taxonomy, using software quality factors, to assess students’ cognitive ability while improving complexity. The research was conducted with college students studying for their bachelor’s degree in engineering. We established a classification based on their solutions for a given task. Later, we used the same task to validate our classification with another group of engineering students. We then compared the previous average grades of the second group of students with their SOLO levels. The results show that the higher the students’ previous average grades, the greater the probability that their solutions would be classified at higher taxonomic levels. These results indicate that our SOLO classification is indeed accurate. The paper presents our novel SOLO taxonomic levels for tasks improving run-time complexity and offers several suggestions to assist students and enhance the teaching process.

在保留代码功能的同时改进代码是高科技行业的一项常见任务。但是，学生很难将算法的运行时复杂度提高一个数量级。本文着重评估学生在这方面的能力。我们使用软件质量因子设计了可观察的学习成果（SOLO）分类法，以评估学生的认知能力，同时提高其复杂性。这项研究是针对正在攻读工程学学士学位的大学生进行的。我们根据给定任务的解决方案建立了分类。后来，我们使用相同的任务与另一组工程专业的学生来验证我们的分类。然后，我们将第二组学生之前的平均成绩与他们的SOLO水平进行了比较。结果表明，学生以前的平均成绩越高，他们的解决方案被分类到更高分类级别的可能性就越大。这些结果表明我们的SOLO分类确实是准确的。本文介绍了我们用于提高运行时复杂性的任务的新颖SOLO分类标准，并提供了一些建议来帮助学生并改善教学过程。