It is quite challenging to learn complex mathematical algorithms used in molecular simulations, stressing the importance of using the most advantageous teaching methods. Ideally, individuals should learn at their pace and deal with tasks fitting their levels. Web‐based exercises make it possible to tailor every small step of the learning process, but this requires continuous monitoring of the learner. Differentiation based on the scores after the first round of common tasks can be demotivating for all students, as they will experience the initial set of tasks as being either too easy or too hard. We designed two tests, a self‐monitoring test and a rapid test (RT) in which the students explained equations relating to the current topic. The first test was aimed to see if the students were able to evaluate their own level of knowledge, whereas the RT was aimed to find a fast way to determine the level of the students. We compared both tests with traditional measures of knowledge and used a relatively new method, which was originally designed for the analysis of molecular simulation data, to interpret the results. Based on this analysis, we concluded that self‐evaluation, rather than an RT, is a valuable tool to automatically steer individual students through a tree of web‐based exercises to match their skill levels and interests.

中文翻译：

---

教授复杂的分子模拟算法：使用自我评估在个人层面定制基于网络的练习

学习分子模拟中使用的复杂数学算法非常具有挑战性，强调使用最有利的教学方法的重要性。理想情况下，个人应该按照自己的节奏学习并处理适合自己水平的任务。基于网络的练习可以定制学习过程的每一个小步骤，但这需要对学习者进行持续监控。在第一轮普通任务之后根据分数进行区分可能会让所有学生都失去动力，因为他们会觉得初始任务集要么太容易要么太难。我们设计了两个测试，一个自我监控测试和一个快速测试 (RT)，学生在其中解释与当前主题相关的方程式。第一个测试的目的是看学生是否能够评估自己的知识水平，而RT旨在找到一种快速确定学生水平的方法。我们将这两种测试与传统的知识测量进行了比较，并使用了一种相对较新的方法来解释结果，该方法最初是为分析分子模拟数据而设计的。基于这一分析，我们得出结论，自我评估，而不是 RT，是一种有价值的工具，可以自动引导个别学生通过基于网络的练习树，以匹配他们的技能水平和兴趣。