International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 10, Issue 7 (July 2023), Pages: 138-144

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 Original Research Paper

Identifying low-performing regions in Moroccan education: A deep learning approach using the PISA dataset

 Author(s): 

 Ilyas Tammouch 1, *, Abdelamine Elouafi 1, Souad Eddarouich 2, Raja Touahni 1

 Affiliation(s):

 1Telecommunications Systems and Decision Engineering Laboratory, Faculty of Science, Ibn Tofail University, Kenitra, Morocco
 2Regional Educational Center, Rabat, Morocco

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-2752-4413

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.07.015

 Abstract:

This study highlights the ongoing nature of the school reform movement, emphasizing the need for continuous attention and action. Despite this effort, academic performance has exhibited relative stability in recent years, while significant regional performance disparities persist. Addressing these inequalities requires novel approaches to enhance educational quality. Past research has explored clustering algorithms in developed countries, providing insights into personalized teaching strategies based on students' learning style preferences. In response, our research aims to identify underperforming regions in Morocco, necessitating attention and intervention. We employ an unsupervised deep learning method called "deep embedding clustering" to group Moroccan students based on their performance. The results are subsequently visualized on a choropleth map, revealing intricate patterns and trends in educational performance that might not be immediately apparent. The analysis employs the comprehensive program for international student assessment (PISA) dataset, encompassing individual students' responses and plausible values reflecting cognitive abilities. The findings indicate that the "Guelmim-Oued Noun" region exhibits the highest performance level among all regions, while "Dakhla-Oued Eddahab," "Béni Mellal-Khénifra," and "Oriental" regions display lower performance levels. As a result, this study urges policymakers to incorporate tailored measures into regional policies to improve students' educational outcomes.

 © 2023 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: School reform movement, Academic performance, Regional performance inequalities, Clustering algorithms, Deep embedding clustering

 Article History: Received 22 January 2023, Received in revised form 17 May 2023, Accepted 22 May 2023

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Tammouch I, Elouafi A, Eddarouich S, and Touahni R (2023). Identifying low-performing regions in Moroccan education: A deep learning approach using the PISA dataset. International Journal of Advanced and Applied Sciences, 10(7): 138-144

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 

 Tables

 Table 1 Table 2 Table 3 

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