Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature

Abstract

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.

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Notes

  1. 1.

    Artifacts outside of the traditional commercial or academic publishing, and distribution channels are known as grey literature. Usually, they are not exposed to a peer-review process. Examples of grey literature include: conference presentations; regulatory data; unpublished trial data; government publications; reports (such as white papers, working papers, internal documentation); dissertation/thesis; patents; and policies and procedures.

  2. 2.

    https://github.com/marcelomachado/acs-slrm/blob/master/potencial_primary_studies.csv.

  3. 3.

    https://github.com/marcelomachado/acs-slrm.

  4. 4.

    Generated using Circos (http://circos.ca/software/download/).

  5. 5.

    A high-resolution version is available at https://github.com/marcelomachado/acs-slrm/blob/master/Figures/Citation_Graph.png.

  6. 6.

    https://www.wjx.cn/jq/7090233.aspx.

  7. 7.

    http://edutechwiki.unige.ch/en/Learning_Object_Metadata_Standard.

  8. 8.

    www.tvtc.gov.sa.

  9. 9.

    https://analyse.kmi.open.ac.uk/open_dataset.

  10. 10.

    http://scholar.google.com/.

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Funding

The authors thank the financial support provided. This study was financed in part by the Coordination of Superior Level Staff Improvement—Brazil (CAPES)—Finance Code 001. Also, the authors thank the financial support provided by FAPEMIG (APQ-00337-18), CNPq (312682/2018-2), and UFJF.

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Correspondence to Marcelo de Oliveira Costa Machado.

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Appendix

See Table 12.

Table 12 Paper identification used in Fig. 4

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Machado, M.d.O.C., Bravo, N.F.S., Martins, A.F. et al. Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature. Artif Intell Rev 54, 711–754 (2021). https://doi.org/10.1007/s10462-020-09864-z

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Keywords

  • Adaptive learning
  • Evolutionary computing
  • Learning path
  • Student modeling
  • Knowledge domain modeling
  • Intelligent tutoring systems
  • Artificial intelligence in education