Multi-agent systems in e-learning

Nadrljanski, Mila; Vukić, Dominik; Nadrljanski, Đorđe

doi:10.23919/mipro.2018.8400181

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…Ontologies as knowledge bases for metadata in e-learning systems have collaborated well with other AI techniques, including software agents. The development and evaluation of ontology-based recommender systems were examined in [4,13]. Several contexts of the usage of agents in ontology-based recommender systems were discussed in this research.…”

Section: Semantic Models and Intelligent Technologies In E-learningmentioning

confidence: 99%

A multi-agent architecture for learning paths-based personalized e-learning systems

Ivanova

2023

IJITS JOURNAL

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The application of artificial intelligence and semantic models for increasing learning and tutoring quality by personalization is an emerging research area. Мulti-agent frameworks facilitate the communication between the different components and ontological models can be used as knowledge sources for intelligent agents. In this research we analyse knowledge models and software architectures, outline trends in the personalized learning area and propose an agent-based architecture for e-learning systems that can conduct learning by generating and recommending personalized learning paths. Initial evaluation of prototype system is proposed and learning path generation scenarios are discussed.

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Section: Semantic Models and Intelligent Technologies In E-learningmentioning

confidence: 99%

A multi-agent architecture for learning paths-based personalized e-learning systems

Ivanova

2023

IJITS JOURNAL

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“…Online learning platforms and intelligent terminals provide flexible and pertinent learning modes for college students [1][2][3][4][5]. Online teaching and learning fully reflect the concept of student-oriented teaching, and effectively improve the initiative of students, exerting a huge impact on the teaching and evaluation ideas of traditional offline classroom [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Formative Evaluation of College Students’ Online English Learning Based on Learning Behavior Analysis

Zhang¹,

Ming-li²,

Song³

et al. 2022

Int. J. Emerg. Technol. Learn.

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To measure the achievements and progress of college students in online learning, online learning platforms and teachers must pay attention to the formative evaluation of the learning process. The relevant data should be fully utilized to analyze the online English learning behavior of college students, such that online learning platforms and teachers can make formative evaluation of the students’ online learning. However, the existing studies on formative evaluation are mostly theoretical. To solve the problem, this paper explores the formative evaluation of college students’ online English learning based on learning behavior analysis. Firstly, the density-based spatial clustering of applications with noise (DBSCAN) was adopted to analyze the data samples of college students’ online English learning behavior, the evaluation indices were selected for the formative evaluation of the said behavior, and the index weighting method was explained in details. Next, the school precaution function of online English learning was realized through the graph structure data prediction of students’ online learning behavior. Based on the proposed graph neural network, the clustering Euclidean distance weight was introduced to measure the similarity between two nodes. In addition, the weight update process was illustrated for the distance weight-based attention mechanism. The proposed formative evaluation approach was proved effective through experiments.

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“…For instance, the use of agent technology seems to be the key method for addressing this challenge. The use of intelligent agents makes it possible to achieve a robust system that caters to the needs and the preferences of the students which provides adaptability and intelligence to the e-learning system through the use of the utilization of agents (Nadrljanski et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Untitled

2021

OJIT

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Most traditional e-learning system fails to provide the intelligence that a learner may require during their learning process. Different learners have different learning styles but the current elearning systems are not able to provide personalized learning. In this paper, we discuss how intelligent agents can aid learners in their learning process. Three agents have been developed namely, learner agent, information agent, and tutor agents that will be integrated into a learning management system (Moodle). Learners are provided with a personalized recommendation based on the learning styles.

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Multi-agent systems in e-learning

Cited by 11 publications

References 13 publications

A multi-agent architecture for learning paths-based personalized e-learning systems

A multi-agent architecture for learning paths-based personalized e-learning systems

Formative Evaluation of College Students’ Online English Learning Based on Learning Behavior Analysis

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