An Adaptive Learning System Based on Learner’s Knowledge Level
Abstract
personalizing learning contents to learners become an important field in recent years. This paper proposes an adaptive e-learning system based on students learning style and knowledge level. The system also measures the learner’s knowledge level using the C4.5 decision tree algorithm on their quiz results. The findings show the students who used the system has increased their knowledge level after performing knowledge tests prior to course personalization and after finishing the course. students who are considered in the beginner level has reduced from 58.3% to 25.3%, whereas the intermediate level has increased greatly from 28.1% to 41.6%. finally, the advanced level has increased from 13.3% to 33.3%.
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In [28], the C4.5 decision tree algorithm was used to measure and categorize a learner's knowledge level based on quiz results to personalize the e-learning system.
“The C4.5 decision tree algorithm is used to measure and categorize a learner's knowledge level (e.g., beginner, intermediate, advanced) based on their quiz results.The results then inform the personalization of the e-learning system to cater to the individual needs of the students, which subsequently enhances their knowledge level.”
From Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review (2023) · cites this paper as [28]
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Self-citationA Learner Model for Adaptable e-Learning
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Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023) · Education Sciences · doi:10.3390/educsci13121216 · Cited by 980 · see citing works
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- Authors
- AL-Chalabi, H. K. M., Hussein, A. M. A., & Apoki, U. C.
- Affiliations
- University of Craiova (RO)
- Alexandru Ioan Cuza University (RO)
- Venue
- 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)
- Publisher
- IEEE
- Published
- 2021-07-01
- Pages
- 1-4
- OpenAlex
- W3193482554
- Cited by
- 19
- References
- 14
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 5 · 2023: 3 · 2022: 1 · 2021: 1
- FWCI
- 1.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- adaptive e-learning, personalized learning, learning styles, C4.5 decision tree, knowledge assessment
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