基于遗传算法的个性化学习路径构建
Constructing a personalized learning path using genetic algorithms approach
摘要：
传统学习的一个实质性缺点是，所有学生都遵循相同的学习顺序，但并非所有学生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，如教科书，在大多数情况下，追求学生在学习过程中遵循固定的顺序，从而损害了他们的表现。学习顺序是学习过程中一个重要的研究课题，因为没有固定的学习路径适合所有的学习者。因此，许多研究论文都集中在开发机制，提供个性化的学习路径，考虑学习者的需求，兴趣，行为和能力。在大多数情况下，这些研究者完全集中在学生的喜好，忽略了难度的水平和之间的联系程度，在一门课程中的各种概念。本文提出了针对课程组成概念的难度和关联度，利用基于遗传算法的模型构建个性化学习路径的可能性。实验结果表明，遗传算法能够以学习对象的难度、持续时间、评分和学习对象之间的关联度作为学习路径序列的基本组成部分，生成最优的学习路径，并与传统的学习路径质量进行了比较。我们观察到，即使是由我们的GA方法生成的最弱学习路径的质量与传统学习路径的质量相比也是有利的，差异为3.59%，而最后生成的最高解与传统的学习路径相比，我们的建议得到了8.34%的支持.
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
