scholarly journals Determinant Factors and Adaptation Features of Mobile Personalized Learning System

2021 ◽  
Vol 1858 (1) ◽  
pp. 012008
Author(s):  
Almed Hamzah
Keyword(s):  
Personalized Learning ◽  
Learning System ◽  
Determinant Factors

Design of English reading and learning management system in college education based on artificial intelligence

2021 ◽  
pp. 1-10
Author(s):  
Fen Zhang ◽  
Min She
Keyword(s):  
Artificial Intelligence ◽  
Model Performance ◽  
College Education ◽  
Personalized Learning ◽  
Learning System ◽  
Model Function ◽  
English Reading ◽  
Function Modules ◽  
Learning Platforms ◽  
Learning Management

English reading learning in college education is an efficient means of English learning. However, most of the current English reading learning platforms in colleges and universities only put different English books on the platform in electronic form for students to read, which leads to blindness of reading. Based on artificial intelligence algorithms, this paper builds model function modules according to the needs of English reading and learning management in college education and implements system functions based on artificial intelligence algorithms. Moreover, according to the above design principles of personalized learning model and the characteristics of personalized network learning, this paper designs a personalized learning system based on meaningful learning theory. In addition, this article verifies and analyzes the model performance. The research results show that the model proposed in this paper has a certain effect.

scholarly journals The system of personalized instruction in school education: problems of implementation and adaptation

2020 ◽  
pp. 142-152
Author(s):  
Polina O. Kraynova ◽  
Alexey S. Obukhov
Keyword(s):  
General Education ◽  
Personal Characteristics ◽  
Personalized Learning ◽  
Educational Process ◽  
Learning System ◽  
High Quality Research ◽  
Global Trends ◽  
Learning Platform ◽  
The Usa ◽  
The Individual

In the context of global trends in the humanization of education, issues of differentiation, individualization and personalization of education are actively discussed. At the same time, the key question remains – how to preserve the individual capabilities, interests and needs of each student while maintaining collective learning formats? How to take into account the personal characteristics and capabilities of each when passing and mastering general education programs? One such solution was the PCBL personalized learning platform developed in the USA. Currently, the Khoroshevskaya school is introducing and adapting this platform to the Russian conditions of education. The article examines the specific case of implementing a system of personalized competency-based education in a separate school – what problems, barriers and difficulties are encountered in its implementation. The study is built in the logic of qualitative research on the basis of high-quality research interviews with the main participants in the educational process in the context of introducing a personalized learning system.

scholarly journals A design of a multi-agent recommendation system using ontologies and rule-based reasoning: pandemic context

2022 ◽  
Vol 12 (1) ◽  
pp. 515
Author(s):  
Amina Ouatiq ◽  
Kamal ElGuemmat ◽  
Khalifa Mansouri ◽  
Mohammed Qbadou
Keyword(s):  
Mental State ◽  
Adaptive Systems ◽  
Recommendation System ◽  
Unified Modeling Language ◽  
Social Situation ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning System ◽  
Holistic View ◽  
Rule Based

Learners attend their courses in remote or hybrid systems find it difficult to follow one size fits all courses. These difficulties have increased with the pandemic, lockdown, and the stress they cause. Hence, the role of adaptive systems to recommend personalized learning resources according to the learner's profile. The purpose of this paper is to design a system for recommending learning objects according learner's condition, including his mental state, his COVID-19 history, as well as his social situation and ability to connect to the e-learning system on a regular basis. In this article, we present an architecture of a recommendation system for personalized learning objects based on ontologies and on rule-based reasoning, and we will also describe the inference rules required for the adaptation of the educational content to the needs of the learners, taking into account the learner’s health and mental state, as well as his social situation. The system designed, and validated using the unified modeling language (UML). It additionally allows teachers to have a holistic view of learners’ progress and situations.

scholarly journals Methodological aspects of the web course of chemical disciplines for non-chemical specialties in universities

2020 ◽  
Vol 1 (1) ◽  
pp. 37-42
Author(s):  
Naila Guliyeva ◽  
Keyword(s):  
Inorganic Chemistry ◽  
Online Training ◽  
Personalized Learning ◽  
Learning System ◽  
Learning Platform ◽  
Web Course ◽  
Effective Use ◽  
Methodological Aspects ◽  
Theoretical Foundations ◽  
The Web

The article analyzes the possibilities of effective use of interactive learning elements, which is a learning platform designed to provide teachers, administrators and students with a reliable, safe and comprehensive learning system to create a personalized learning environment. It is acknowledged that the utilization of online training tools has shown to be effective for studying the “Theoretical Foundations of Chemistry” and “Inorganic Chemistry” disciplines.

Personalized Information Retrieval in a Semantic-based Learning Environment

2011 ◽  
pp. 270-288 ◽  
Author(s):  
Antonella Carbonaro ◽  
Rodolfo Ferrini
Keyword(s):  
Active Learning ◽  
Learning Environment ◽  
Information Sources ◽  
Common Knowledge ◽  
Personalized Learning ◽  
Learning System ◽  
Intelligent Support ◽  
Useful Knowledge ◽  
The Common ◽  
The Web

Active learning is the ability of learners to carry out learning activities in such a way that they will be able to effectively and efficiently construct knowledge from information sources. Personalized and customizable access on digital materials collected from the Web according to one’s own personal requirements and interests is an example of active learning. Moreover, it is also necessary to provide techniques to locate suitable materials. In this paper, we introduce a personalized learning environment providing intelligent support to achieve the expectations of active learning. The system exploits collaborative and semantic approaches to extract concepts from documents and maintaining user and resources profiles based on domain ontologies. In such a way, the retrieval phase takes advantage from the common knowledge base used to extract useful knowledge and produces personalized views of the learning system.

Personalized Information Retrieval in a Semantic-Based Learning Environment

2011 ◽  
pp. 370-389
Author(s):  
Antonella Carbonaro ◽  
Rodolfo Ferrini
Keyword(s):  
Active Learning ◽  
Learning Environment ◽  
Information Sources ◽  
Common Knowledge ◽  
Personalized Learning ◽  
Learning System ◽  
Intelligent Support ◽  
Useful Knowledge ◽  
The Common ◽  
The Web

Active learning is the ability of learners to carry out learning activities in such a way that they will be able to effectively and efficiently construct knowledge from information sources. Personalized and customizable access on digital materials collected from the Web according to one’s own personal requirements and interests is an example of active learning. Moreover, it is also necessary to provide techniques to locate suitable materials. In this chapter, we introduce a personalized learning environment providing intelligent support to achieve the expectations of active learning. The system exploits collaborative and semantic approaches to extract concepts from documents, and maintaining user and resources profiles based on domain ontologies. In such a way, the retrieval phase takes advantage of the common knowledge base used to extract useful knowledge and produces personalized views of the learning system.

A Personalized Learning System for Parallel Intelligent Education

2020 ◽  
Vol 7 (2) ◽  
pp. 352-361 ◽  
Author(s):  
Ying Tang ◽  
Joleen Liang ◽  
Ryan Hare ◽  
Fei-Yue Wang
Keyword(s):  
Personalized Learning ◽  
Learning System
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