Predictive Analytics for Sustainable E-Learning: Tracking Student Behaviors

Naif Al Mudawi, Mahwish Pervaiz, Bayan Ibrahimm Alabduallah (), Abdulwahab Alazeb, Abdullah Alshahrani, Saud S. Alotaibi and Ahmad Jalal ()
Additional contact information
Naif Al Mudawi: Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia
Mahwish Pervaiz: Department of Computer Science, Bahria University, Islamabad 44000, Pakistan
Bayan Ibrahimm Alabduallah: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Abdulwahab Alazeb: Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia
Abdullah Alshahrani: Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
Saud S. Alotaibi: Information Systems Department, Umm Al-Qura University, Makkah 24382, Saudi Arabia
Ahmad Jalal: Department of Computer Science, Air University, E-9, Islamabad 44000, Pakistan

Sustainability, 2023, vol. 15, issue 20, 1-18

Abstract: The COVID-19 pandemic has sped up the acceptance of online education as a substitute for conventional classroom instruction. E-Learning emerged as an instant solution to avoid academic loss for students. As a result, educators and academics are becoming more and more interested in comprehending how students behave in e-learning settings. Behavior analysis of students in an e-learning environment can provide vision and influential factors that can improve learning outcomes and guide the creation of efficient interventions. The main objective of this work is to provide a system that analyzes the behavior and actions of students during e-learning which can help instructors to identify and track student attention levels so that they can design their content accordingly. This study has presented a fresh method for examining student behavior. Viola–Jones was used to recognize the student using the object’s movement factor, and a region-shrinking technique was used to isolate occluded items. Each object has been checked by a human using a template-matching approach, and for each object that has been confirmed, features are computed at the skeleton and silhouette levels. A genetic algorithm was used to categorize the behavior. Using this system, instructors can spot kids who might be failing or uninterested in learning and offer them specific interventions to enhance their learning environment. The average attained accuracy for the MED and Edu-Net datasets are 90.5% and 85.7%, respectively. These results are more accurate when compared to other methods currently in use.

Keywords: crowd management; human verification; machine learning; big data analytics; GA classifier; Viola–Jones (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/20/14780/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/20/14780/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:20:p:14780-:d:1258030

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().