Assessment Study For E-Learning Using Bayesian Network
Abstract
E-Learning for educational institutions has created a challenging situation due to the COVID-19 pandemic. The universities and institutions can impart knowledge. However, evaluation of the learner’s learning and outcomes remained a challenge for them. The article aims to add its dimension towards the evaluation of outcomes especially for the learners of E-learning platforms. E-Learning delivers the training using the online mode of knowledge dissemination. E-learning has a wide range of resources that may improve the learning assessment. Stil,l assessment in education remains a challenge. E-Learning for learner’s knowledge is extremely significant because the beginner does not realize learning assessment properly. To improve the learner performance of an evaluation system for classified learners, the Bayesian Network (BN) for a random process is used. A BN is a graphical representation of the probabilistic relationships of a complex system. BN is the most challenging task in the e-learning framework. The goal of this work is achieved by constructing a BN to make causal analysis and then provide personalized interventions for different learners to improve learning. This network was effectively implemented with the accuracy of 45% of a slow learner, 15% of the average learner, and 40% of excellent learners from the required quantitative data set. Further the results of the experiments are promising.
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Bayesian mastery models predict a student's mastery by analyzing the expected number of correct responses.
“These models predict the student's mastery by analyzing the expected number of correct responses, thereby enabling the system to make some inferences about the ability of a student in general.”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [101]
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Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328 · see citing works
- Authors
- Kaliwal, R. B., & Deshpande, S. L.
- Affiliations
- Visvesvaraya Technological University (IN)
- Venue
- 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)
- Publisher
- IEEE
- Published
- 2021-03-25
- Pages
- 79-84
- OpenAlex
- W3153276445
- Cited by
- 5
- References
- 15
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2022: 2
- FWCI
- 0.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Artificial Intelligence and Smart Energy (ICAIS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Keywords
- online learning, learning assessment, Bayesian networks, personalized learning interventions, causal analysis, COVID-19 pandemic impact on education, online learning platforms
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