Optimizing Dynamic Multi-Agent Performance in E-Learning Environment
24
TL;DR: A dynamic multi-agent system using particle swarm optimization for the e-learning systems is proposed and demonstrates the effectiveness of the proposed system in providing near-optimal solutions in considerably less computational time.
read more
Abstract: The main objective of e-learning systems is to improve the student learning performance and satisfaction. This can be achieved by providing a personalized learning experience that identifies and satisfies the individual learner’s requirements and abilities. The performance of the e-learning systems can be significantly improved by exploiting dynamic self-learning capabilities that rapidly adapts to prior user interactions within the system and the continuous changes in the environment. In this paper, a dynamic multi-agent system using particle swarm optimization for the e-learning systems is proposed. The system incorporates five agents that take into consideration the variations in the capabilities among the different users. First, the project clustering agent is used to cluster a set of learning resources/projects into similar groups. Second, the student clustering agent (SCA) groups students according to their preferences and abilities. Third, the student-project matching agent is used to map each learner’s group to a suitable project or particular learning resources according to specific design criteria. Fourth, the student-student matching agent is designed to perform the efficient mapping between different students. Finally, the dynamic SCA (DSCA) is employed to continuously track and analyze the student’s behavior within the system such as changes in knowledge and skill levels. Consequently, the DSCA adapts the e-learning environments to accommodate these variations. Experimental results demonstrate the effectiveness of the proposed system in providing near-optimal solutions in considerably less computational time.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Novel Teaching Strategy Through Adaptive Learning Activities for Computer Programming
TL;DR: The results showed that the presented approach outperforms others which lack adaptivity in domain knowledge and learning theories, improving significantly the students’ learning outcomes.
50
AISAR: Artificial Intelligence-Based Student Assessment and Recommendation System for E-Learning in Big Data
TL;DR: This artificial intelligence-based student assessment and recommendation (AISAR) system consists of score estimation, clustering, performance prediction, and recommendation, and the importance of student authentication is recognised in situations in which students must authenticate themselves prior to using the e-learning system using their identity, password, and personal identification number.
44
Combination of fuzzy and cognitive theories for adaptive e-assessment
TL;DR: A novel solution for adaptive e-assessment that has been used in two tutoring systems and has been fully evaluated and shows great accuracy in the selection of test items for each individual student.
30
Smart Cities after COVID-19: Building a conceptual framework through a multidisciplinary perspective
TL;DR: In this paper , the authors provide theoretical grounds for planning smart cities using multidisciplinary approaches, offering insightful suggestions to researchers and policy-and decision-makers, and contribute to the debate on the new connotations of the smart city paradigm in the context of the COVID-19 pandemic.
24
A Multilayer Prediction Approach for the Student Cognitive Skills Measurement
TL;DR: A multilayer CS measurement method that uses SRC for student’s skills prediction and simulates the nonlinear relationship between CS intervals and SRC layers using Gauss–Newton method is presented.
21
References
Document clustering using particle swarm optimization
Xiaohui Cui,Thomas E. Potok,Paul J. Palathingal +2 more
- 08 Jun 2005
TL;DR: This paper presents a particle swarm optimization (PSO) document clustering algorithm, which performs a globalized search in the entire solution space and shows that the hybrid PSO algorithm can generate more compact clustering results than the K-means algorithm.
390
Grouping Multidimensional Data
Jacob Kogan,Charles Nicholas,Marc Teboulle +2 more
- 01 Jan 2006
TL;DR: Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection as discussed by the authors.
314
Redefining the learning companion: the past, present, and future of educational agents
TL;DR: This study addresses issues that arise from different perspectives on the development of intelligent tutoring systems and redefined the learning companion for application to a wide spectrum of educational agent research.
Introduction to Multi-agent Systems
01 Jan 2022
TL;DR: In this article , the authors discuss the use of human-computer interaction (HCI) in multi-agent systems, where decisions are made by the agents on a particular action to solve the task and complete its objective using various inputs like the past actions and interactions with it.
289
A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms
Khalid Colchester,Hani Hagras,Daniyal M. Alghazzawi,Ghadah Aldabbagh +3 more
- 01 Jan 2017
TL;DR: A survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e- learning environments.