Predictive Analytics for Sustainable E-Learning: Tracking Student Behaviors Article Swipe
Naif Al Mudawi
,
Mahwish Pervaiz
,
Bayan Alabduallah
,
Abdulwahab Alazeb
,
Abdullah Alshahrani
,
Saud S. Alotaibi
,
Ahmad Jalal
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su152014780
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su152014780
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su152014780
- https://www.mdpi.com/2071-1050/15/20/14780/pdf?version=1697096217
- OA Status
- gold
- Cited By
- 44
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387573316
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4387573316Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su152014780Digital Object Identifier
- Title
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Predictive Analytics for Sustainable E-Learning: Tracking Student BehaviorsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-10-12Full publication date if available
- Authors
-
Naif Al Mudawi, Mahwish Pervaiz, Bayan Alabduallah, Abdulwahab Alazeb, Abdullah Alshahrani, Saud S. Alotaibi, Ahmad JalalList of authors in order
- Landing page
-
https://doi.org/10.3390/su152014780Publisher landing page
- PDF URL
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https://www.mdpi.com/2071-1050/15/20/14780/pdf?version=1697096217Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2071-1050/15/20/14780/pdf?version=1697096217Direct OA link when available
- Concepts
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Silhouette, Computer science, Categorization, Artificial intelligence, Learning analytics, Object (grammar), Curriculum, Psychological intervention, Learning object, Eye tracking, Machine learning, Human–computer interaction, Mathematics education, Psychology, Pedagogy, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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44Total citation count in OpenAlex
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2025: 10, 2024: 33, 2023: 1Per-year citation counts (last 5 years)
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67Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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