scholarly journals Assembling Learning Objects for Personalized Learning: An AI Planning Perspective

2013 ◽  
Vol 28 (2) ◽  
pp. 64-73 ◽  
Author(s):  
Antonio Garrido ◽  
Eva Onaindia
Keyword(s):  
Personalized Learning ◽  
Learning Objects ◽  
Ai Planning

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 Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 6637-6644
Author(s):  
H. El Fazazi ◽  
M. Elgarej ◽  
M. Qbadou ◽  
K. Mansouri
Keyword(s):  
Reinforcement Learning ◽  
Learning Style ◽  
Learning Process ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
E Learning ◽  
Multi Agent

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.

Personalized Mobile Learning and Course Recommendation System

2021 ◽  
Vol 13 (1) ◽  
pp. 38-48
Author(s):  
Madhubala Radhakrishnan
Keyword(s):  
Mobile Learning ◽  
Recommendation System ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning System ◽  
Mobile Learning System ◽  
Media Formats ◽  
Educational Contents ◽  
Different Levels

Mobile-based learning provides new experience to the learners to learn anything from anywhere and anytime by using their portable or mobile device. Vast educational contents and also different media formats can be supported by the mobile devices. Access speed of those materials has also improved a lot. With this advancement, providing required content or materials in the desired format to the learner is essential to the learning management system. Also, it is very important to guide the learner based on their interest in learning. With this outset, the proposed mobile learning system helps the learners to access different courses under different levels and different specializations. The course contents are in different formats called learning objects (LO). In order to provide personalized learning experience to the learner, the system finds the learner's preferences and selects the desired learning objects. It also recommends some specializations with level to the learners to achieve higher grades.

A Scalable Heuristic Based on Hypercube Graph for Automated Planner

2011 ◽  
Vol 474-476 ◽  
pp. 1830-1835
Author(s):  
Hong Qian ◽  
Ming Xiang Sui ◽  
Yun Fei Jiang ◽  
Dong Hui Zhang ◽  
Heng Guo
Keyword(s):  
Domain Knowledge ◽  
Learning Objects ◽  
Ai Planning ◽  
Training Material ◽  
Graph Topology ◽  
Topology Construction ◽  
The Past ◽  
Modern Computer ◽  
Learning Rules ◽  
Domain Independent

There are much research into Artificial Intelligence (AI) and Semantic Web over the past few years and intelligent behaviour such as learning, analysing, problem solving, planning and abstracting is displayed by modern computer systems. Automatically acquiring domain-knowledge for planning, as it is the case for Machine Learning in general, strongly depends on the training material. In planning, there is a novel ways to search knowledge when solving problems. This Paper presents a new heuristic for carrying out searches of training material, where metadata and the knowledge build into them are captured and fully scalable. These insfrastructure use AI Planning and Ontology technologies, allowing to construct learning rules dynamically based on the general Domain independent Planner even from disjoint learning objects, and meeting the learner’s profile, preferences needs and abilitity. We provide an efficient topology construction and maintenance algorithm, and show how our scheme can be made even more efficient by using a globally known ontology to determine the organization of nodes in the graph topology, allowing for efficient concept-based search.

scholarly journals Panel Discussion: Learning Object Definitions, Metadata, Instructional Design, and Repositories

10.28945/2904 ◽  
2005 ◽  
Author(s):  
Jacques du Plessis ◽  
Alex Koohang ◽  
Jared Schaalje ◽  
Xiangming Mu ◽  
Johannes Britz
Keyword(s):  
Higher Education ◽  
Learning Community ◽  
Panel Discussion ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning Object ◽  
Quality Instruction ◽  
Learning Instruction ◽  
E Learning ◽  
The Many

The many promises of learning objects (readily available quality instruction, reducing cost of production, personalized learning, interoperability, reusability, discoverability/accessibility, scalability, durability, content customization, and many more) have been the talk of the e-learning community in recent years. Higher education institutions have begun to capitalize on these promises by adopting, developing, and deploying learning objects in e-learning instruction.

Corporeità, movimento e benessere: il potenziale educativo di itinerari motorio-sportivi inclusivi giovanili

2019 ◽  
pp. 3121-334
Author(s):  
Carmen Palumbo ◽  
Antinea Ambretti ◽  
Giovanna Ferraioli
Keyword(s):  
Young People ◽  
Learning Process ◽  
Personalized Learning ◽  
Barriers To Learning ◽  
Educational Value ◽  
School Activities ◽  
The Past ◽  
Full Participation ◽  
Inclusive Approach ◽  
Teaching Learning

Over the past few decades, the adoption of an inclusive approach to education has stimulated a reflection on the educational value of body and movement within teaching-learning process in order to break down all barriers to learning and promote the full participation of young people to school activities. Indeed,body and movement represent an important didactic "medium" for developing individualized and personalized learning paths that take into account the specific needs and characteristics of students thus contributing to their global and harmonious development.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

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