The presentation of learning materials in a sequence, which considers the association of students’ individual characteristics with those of the knowledge domain of interest, is an effective learning strategy in online learning systems, especially if related to traditional approaches. However, this sequencing, called Adaptive Curriculum Sequencing (ACS), represents a problem that falls in the NP-Hard class of problems given the diversity of sequences that could be chosen from ever-larger repositories of learning materials. Thus, metaheuristics are usually employed to tackle this problem. This study aims to present a systematic review and mapping of the literature to identify, analyze, and classify the published solutions related to the ACS problem addressed by metaheuristics. We considered 61 studies in the mapping and 58 studies in the review from 2005 to 2018. Even though the problem is longstanding, it is still discussed, especially considering new modeling and used metaheuristics. In this sense, we emphasize the use of Swarm Intelligence and Genetic Algorithm. Moreover, we have identified that various parameters were considered for students and knowledge domain modeling, however, few student’s intrinsic parameters have been explored in ACS literature.

中文翻译：

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基于元启发式的自适应课程排序方法：文献的系统回顾和映射

将学习材料按顺序呈现，考虑到学生的个人特征与感兴趣的知识领域的特征的关联，是在线学习系统中的一种有效学习策略，尤其是与传统方法相关时。然而，这种称为自适应课程排序 (ACS) 的排序代表了一个属于 NP-Hard 问题类别的问题，因为可以从越来越大的学习材料存储库中选择序列的多样性。因此，通常采用元启发式来解决这个问题。本研究旨在对文献进行系统回顾和映射，以识别、分析和分类与元启发式解决的 ACS 问题相关的已发布解决方案。从 2005 年到 2018 年，我们在映射中考虑了 61 项研究，在审查中考虑了 58 项研究。尽管这个问题由来已久，但仍在讨论中，尤其是考虑到新建模和使用的元启发式算法。在这个意义上，我们强调使用群智能和遗传算法。此外，我们已经确定为学生和知识域建模考虑了各种参数，但是，在 ACS 文献中很少探索学生的内在参数。