A Markov Decision Processes Modeling for Curricular Analytics

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

doi:10.1109/ICMLA52953.2021.00071

A Markov Decision Processes Modeling for Curricular Analytics

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

Provost, Office of the

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

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 language	English (US)
Title of host publication	Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Editors	M. Arif Wani, Ishwar K. Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	415-421
Number of pages	7
ISBN (Electronic)	9781665443371
DOIs	https://doi.org/10.1109/ICMLA52953.2021.00071
State	Published - 2021
Event	20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 - Virtual, Online, United States Duration: Dec 13 2021 → Dec 16 2021

Publication series

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

Conference

Conference	20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Country/Territory	United States
City	Virtual, Online
Period	12/13/21 → 12/16/21

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

Access to Document

10.1109/ICMLA52953.2021.00071

Cite this

Slim, A., Yusuf, H. A., Abbas, N., Abdallah, C. T., Heileman, G. L., & Slim, A. (2021). A Markov Decision Processes Modeling for Curricular Analytics. In M. A. Wani, I. K. Sethi, W. Shi, G. Qu, D. S. Raicu, & R. Jin (Eds.), Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 (pp. 415-421). (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA52953.2021.00071

A Markov Decision Processes Modeling for Curricular Analytics. / Slim, Ahmad; Yusuf, Husain Al; Abbas, Nadine et al.
Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. ed. / M. Arif Wani; Ishwar K. Sethi; Weisong Shi; Guangzhi Qu; Daniela Stan Raicu; Ruoming Jin. Institute of Electrical and Electronics Engineers Inc., 2021. p. 415-421 (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Slim, A, Yusuf, HA, Abbas, N, Abdallah, CT, Heileman, GL & Slim, A 2021, A Markov Decision Processes Modeling for Curricular Analytics. in MA Wani, IK Sethi, W Shi, G Qu, DS Raicu & R Jin (eds), Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Institute of Electrical and Electronics Engineers Inc., pp. 415-421, 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Virtual, Online, United States, 12/13/21. https://doi.org/10.1109/ICMLA52953.2021.00071

Slim A, Yusuf HA, Abbas N, Abdallah CT, Heileman GL, Slim A. A Markov Decision Processes Modeling for Curricular Analytics. In Wani MA, Sethi IK, Shi W, Qu G, Raicu DS, Jin R, editors, Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 415-421. (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021). doi: 10.1109/ICMLA52953.2021.00071

Slim, Ahmad ; Yusuf, Husain Al ; Abbas, Nadine et al. / A Markov Decision Processes Modeling for Curricular Analytics. Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. editor / M. Arif Wani ; Ishwar K. Sethi ; Weisong Shi ; Guangzhi Qu ; Daniela Stan Raicu ; Ruoming Jin. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 415-421 (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021).

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AB - 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.

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A Markov Decision Processes Modeling for Curricular Analytics

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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