A Markov Decision Processes Modeling for Curricular Analytics

Ahmad Slim, Husain Al Yusuf, Nadine Abbas, Chaouki T. Abdallah, Gregory L. Heileman, Ameer Slim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The curricular structure and the complexity of the prerequisite dependencies in a curriculum are essential factors that impact student progression, and ultimately graduation rates. However, we are not aware of any closed-form methods for quantifying the relationship between the complexity of a curriculum and the graduation rate of those attempting to complete the curriculum. This paper introduces a new method that quantifies this relationship using Markov Decision Processes (MDP). The non-deterministic nature of student progress along with their evolving states at each semester make MDP a suitable framework for this work. We propose a novel model that is useful due to the fact that it provides a closed-form solution approach that can be utilized to perform 'what-if' analyses around student progress through a curriculum. The results confirm the inverse relationship between the complexity of a curriculum and the graduation rate of those students attempting to complete it. This is validated using a Monte Carlo simulation method. The results also provide useful insights that may guide future work in this area.

Original languageEnglish (US)
Title of host publicationProceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
EditorsM. Arif Wani, Ishwar K. Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages415-421
Number of pages7
ISBN (Electronic)9781665443371
DOIs
StatePublished - 2021
Externally publishedYes
Event20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 - Virtual, Online, United States
Duration: Dec 13 2021Dec 16 2021

Publication series

NameProceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021

Conference

Conference20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/13/2112/16/21

Keywords

  • Curricula complexity
  • Curricular analytic
  • Graduation rate
  • Markov Decision Processes
  • Student success

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Health Informatics
  • Artificial Intelligence
  • Computer Science Applications

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