Intelligent Personal Assistant for Educational Material Recommendation Based on CBR

Méndez, Néstor Darío Duque; Marín, Paula Andrea Rodríguez; Carranza, Demetrio Arturo Ovalle

doi:10.1007/978-3-319-62530-0_7

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…For instance, Duque Méndez et al (2018) designed a personal intelligent assistant to help users select educational materials from repositories of learning objects. They implemented a recommendation system based on the artificial intelligence technique known as CBR, which leverages past outcomes of similar learners to enhance the relevance of materials for each individual [6]. Similarly, Mamcenk et al (2019) examined the use of CBR to provide relevant recommendations in online learning contexts.…”

Section: Background Theory and Methodologymentioning

confidence: 99%

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Anoir,

Chelliq,

Khaldi

et al. 2024

IJCDS

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The impact of Artificial Intelligence (AI) has significantly remodelled the educational environment, with tutoring systems emerging as essential tools for adapting personalized learning tracks. This article explores the significant benefits achieved through the smooth integration of Intelligent Tutoring Systems (ITS) and Multi-Agent Systems (MAS) with Case-Based Reasoning (CBR). Intelligent tutoring systems, which operate as an interactive platform, exploit the strength of educational data mining to construct meticulously personalized learner profiles. In tandem, multi-agent systems facilitate dynamic collaboration between a whole range of agents, including profile agents, recommendation agents, assessment agents and adaptation agents. This collaborative effort aims to orchestrate personalized learning activities that are finely adjusted to respond to the specific needs of each learner. The introduction of case-based reasoning elevates the sophistication of personalized learning by exploiting the depth of prior knowledge and experience. By systematically exploring a specific knowledge base of similar cases, the system provides recommendations and proven solutions. This ensures a learning experience that not only works with each learner's unique profile but also guarantees relevance and effectiveness. This article embarks on a comprehensive exploration of personalized learning activities by integrating ITS, MAS and CBR transparently. The main objective is to optimize learning engagement and effectiveness by proactively adapting educational content to the individual needs of each learner. This exploration is part of the continued focus on improving the educational experience through the advancement of AI and educational technologies.

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Section: Background Theory and Methodologymentioning

confidence: 99%

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Anoir,

Chelliq,

Khaldi

et al. 2024

IJCDS

View full text Add to dashboard Cite

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“…Pueden ser orientadores a usuarios, recolectores de información, motivadores, asistentes personales, etc. [9].…”

Section: Agentes Compañeros (Virtual Peers Vp)unclassified

Agente compañero como apoyo a actividades colaborativas en cursos virtuales

Méndez¹,

Morales²

2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

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In many virtual learning environments, interaction and accompaniment activities are not attended to with the importance they deserve. The literature shows that virtual peers are a valid alternative to the impossibility of the permanent presence of human officials to support users. This article presents a proposal of virtual peer supported in intelligent agents (campaign agents) as support to students in virtual education platforms. It is possible to give these agents differentiated behavior to act depending on some characteristics and conditions of the apprentices and the environment, which is reflected in positive responses in human companions. The architecture and behavior of the partner agent is presented, within a system of evaluation on a specific topic.

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A comparative analysis of different recommender systems for university major and career domain guidance

et al. 2022

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Academic advising is inhibited at most of the high schools to help students identify appropriate academic pathways. The choice of a career domain is significantly influenced by the complexity of life and the volatility of the labor market. Thus, high school students feel confused during the shift period from high school to university, especially with the enormous amounts of data available on the Web. In this paper, an extensive comparative study is conducted to investigate five approaches of recommender systems for university study field and career domain guidance. A novel ontology is constructed to include all the needed information for this purpose. The developed approaches considered user-based and item-based collaborative filtering, demographic-based recommendation, knowledge base supported by case-based reasoning, ontology, as well as different hybridizations of them. A case study on Lebanese high school students is analyzed to evaluate the effectiveness and efficiency of the implemented approaches. The experimental results indicate that the knowledge-based hybrid recommender system, combined with the user-based collaborative filtering and braced with case-based reasoning as well as ontology, generated 98% of similar cases, 95% of them are personalized based on the interests of the high school students. The average usefulness feedback and satisfaction level of the students concerning this proposed hybrid approach reached 95% and 92.5% respectively, which could be a solution to similar problems, regardless of the application domain. Besides, the constructed ontology could be reused in other systems in the educational domain.

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Intelligent Personal Assistant for Educational Material Recommendation Based on CBR

Cited by 6 publications

References 14 publications

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Agente compañero como apoyo a actividades colaborativas en cursos virtuales

A comparative analysis of different recommender systems for university major and career domain guidance

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