A substantial disadvantage of traditional learning is that all students follow the same learning sequence, but not all of them have the same background of knowledge, the same preferences, the same learning goals, and the same needs. Traditional teaching resources, such as textbooks, in most cases pursue students to follow fixed sequences during the learning process, thus impairing their performance. Learning sequencing is an important research issue as part of the learning process because no fixed learning paths will be appropriate for all learners. For this reason, many research papers are focused on the development of mechanisms to offer personalization on learning paths, considering the learner needs, interests, behaviors, and abilities. In most cases, these researchers are totally focused on the student's preferences, ignoring the level of difficulty and the relation degree that exists between various concepts in a course. This research paper presents the possibility of constructing personalized learning paths using genetic algorithm-based model, encountering the level of difficulty and relation degree of the constituent concepts of a course. The experimental results shows that the genetic algorithm is suitable to generate optimal learning paths based on learning object difficulty level, duration, rating, and relation degree between each learning object as elementary parts of the sequence of the learning path. From these results compared to the quality of the traditional learning path, we observed that even the quality of the weakest learning path generated by our GA approach is in a favor compared to quality of the traditional learning path, with a difference of 3.59\%, while the highest solution generated in the end resulted 8.34\% in favor of our proposal compared to the traditional learning paths.

中文翻译：

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使用遗传算法构建个性化的学习路径

传统学习的一个主要缺点是，所有学生都遵循相同的学习顺序，但并非所有人都具有相同的知识背景，相同的偏好，相同的学习目标和相同的需求。传统的教学资源（例如教科书）在大多数情况下会要求学生在学习过程中遵循固定的顺序，从而影响他们的学习表现。学习排序是学习过程中重要的研究问题，因为没有固定的学习路径适合所有学习者。因此，许多研究论文都集中在开发机制上，以考虑学习者的需求，兴趣，行为和能力来提供个性化的学习路径。在大多数情况下，这些研究人员完全专注于学生的偏好，忽略课程中各种概念之间存在的难度和相关程度。该研究论文提出了使用基于遗传算法的模型构建个性化学习路径的可能性，遇到课程构成概念的难度和相关程度。实验结果表明，该遗传算法适合作为学习路径序列的基本组成部分，基于学习对象的难度，持续时间，等级以及每个学习对象之间的关联度来生成最优学习路径。从这些结果与传统学习路径的质量进行比较，我们观察到，即使是我们的遗传算法所产生的最弱学习路径的质量，也比传统学习路径的质量更受青睐，