scholarly journals Interactive monitoring and assessment of knowledge based on an adaptive learning system

2018 ◽  
Vol 19 (12) ◽  
pp. 1051-1054
Author(s):  
Lyudmyla Dzhuguryan
Keyword(s):  
Learning Process ◽  
Adaptive Learning ◽  
Learning Systems ◽  
Learning System ◽  
Technical Aspects ◽  
Software Products ◽  
Knowledge Based ◽  
Adaptive Learning System ◽  
Adaptive Learning Systems

The article deals with the problems, disadvantages and advantages of using adaptive learning systems in interactive monitoring and assessment of knowledge. Methodical and technical aspects of interactive monitoring and assessment of knowledge based on the adaptive learning system are defined. The schemes of the algorithms based on which the learning process with simultaneous interactive monitoring and assessment of knowledge is realized is offered. Recommendations on the use of software products for the implementation of interactive monitoring and assessment of knowledge based on an adaptive learning system are proposed.

scholarly journals Enhancing Students’ Ability in Learning Process of Programming Language using Adaptive Learning Systems: A Literature Review

2020 ◽  
Vol 10 (1) ◽  
pp. 820-829
Author(s):  
Natasha Alyaa Anindyaputri ◽  
Rosihan Ari Yuana ◽  
Puspanda Hatta
Keyword(s):  
Literature Review ◽  
Learning Process ◽  
Adaptive Learning ◽  
System Development ◽  
Learning Systems ◽  
Learning System ◽  
Advantages And Disadvantages ◽  
Adaptive Learning System ◽  
Adaptive Learning Systems ◽  
Learning Programming

AbstractThere have been some hindrances in the process of programming learning. An adaptive learning system, such as ELaC, Java Guide, and Java Grader provides an adaptable learning content that can accommodate the learning styles as well as preferences of each learning individual. Moreover, an adaptive learning system can help students of different capabilities in learning programming. This study examined the outcomes of the implementation of an adaptive learning system in programming learning, as well as some finding results that were conducted according to the Systematic Literature Review framework. The research questions of this research were: problems faced during learning of programming as a background of system development, advantages and disadvantages of the system characteristics, technology, features, and effectiveness of the developed adaptive learning system. This research produced concepts that are summed up upon the related resources. The results of this study summarized whether the use of adaptive learning systems in learning programming could overcome the problems encountered during the learning process.

scholarly journals Adaptive Learning Systems and Interference in Causal Inference

2021 ◽  
Author(s):  
Alexander Olof Savi ◽  
Nick ten Broeke ◽  
Abe Dirk Hofman
Keyword(s):  
Causal Inference ◽  
Adaptive Learning ◽  
External Validity ◽  
Learning Systems ◽  
Learning System ◽  
Adaptive Learning System ◽  
Adaptive Learning Systems

Adaptive learning systems can be susceptible to between-subject cross-condition interference by design. This interference has important implications for the implementation and evaluation of A/B tests in such systems, as it obstructs causal inference and hurts external validity. We illustrate the problem in an Elo based adaptive learning system, discuss sources and degrees of interference, and provide solutions, using an example in the study of dropout.

HE METHOD OF COMBINED ONLINE ASSESSMENT ENGAGEMENT IN ADAPTIVE LEARNING SYSTEMS

2021 ◽  
pp. 27-36
Author(s):  
A. A. Voronina ◽  
O. A. Shabalina ◽  
A. V. Kataev
Keyword(s):  
Learning Process ◽  
Adaptive Learning ◽  
Web Application ◽  
Physiological State ◽  
Learning Systems ◽  
Learning System ◽  
Linguistic Variable ◽  
Joint Analysis ◽  
User Engagement ◽  
Adaptive Learning Systems

Modern trends in the development of educational software are associated with adaptive learning systems that can personalize the learning process. One of the key quality indicators of any learning system is user engagement in learning process. The known methods for assessing engagement of users of computer systems are based on collecting of various data on the user’s behavior, his emotional and neurophysiological state, etc., and interpreting it in the context of involvement. Confidence in the results of assessments based on interpretations may be insufficient to make decisions based on these results, which is of fundamental importance in case of using in adaptive learning systems. And besides not all the methods can be used online, which is a prerequisite for application in adaptive learning systems. A method for online assessing engagement has been developed, based on a combination of oculography, emotion analysis and web analytics methods, which is applicable for adapting the learning process in adaptive learning systems. The joint analysis of the physiological state and behavior (actions) of the user allows to take into account various aspects of the possible manifestation of the user engagement, and increase the confidence in the results of the assessment of engagement. Quantitative engagement indicators based on metrics applicable to online engagement assessment are suggested. Due to ambiguity of possible interpretations of quantitative indicators of involvement, a generalized assessment of engagement is determined using a fuzzy inference mechanism. To assess engagement a linguistic variable is used and a method for assessing user engagement is based on fuzzy rules. The proposed method is implemented in a web application that can be used to assess involvement online in adaptive learning systems. To set the linguistic variable for assessing engagement, an experiment will be conducted with the participation of users of learning systems and external experts. The values of the linguistic variable will be defined in such a way that the result of assessing involvement, obtained using the developed method, does not contradict the results of expert assessments.

scholarly journals INCA: A cognitive multi-agents architecture for designing intelligent & adaptive learning systems

2005 ◽  
Vol 2 (2) ◽  
pp. 99-114 ◽  
Author(s):  
Thierry Nabeth ◽  
Liana Razmerita ◽  
Albert Angehrn ◽  
Claudia Roda
Keyword(s):  
Knowledge Sharing ◽  
Adaptive Learning ◽  
Management Practices ◽  
Learning Systems ◽  
Learning System ◽  
Cognitive Agents ◽  
E Learning ◽  
Personal Agent ◽  
Adaptive Learning Systems ◽  
Multi Agents

This paper presents a cognitive multi-agents architecture called Intelligent Cognitive Agents (InCA) that was elaborated for the design of Intelligent Adaptive Learning Systems. The InCA architecture relies on a personal agent that is aware of the user's characteristics, and that coordinates the intervention of a set of expert cognitive agents (such as story telling agents, assessment agents, stimulation agents or help agents). This InCA architecture has been applied for the design of K"InCA, an e-learning system aimed at helping people to learn and adopt knowledge-sharing management practices.

Development of Adaptive Kanji Learning System for Mobile Phone

2012 ◽  
pp. 181-192
Author(s):  
Mengmeng Li ◽  
Hiroaki Ogata ◽  
Bin Hou ◽  
Satoshi Hashimoto ◽  
Yuqin Liu ◽  
...  
Keyword(s):  
Language Learning ◽  
Mobile Phone ◽  
Learning Style ◽  
Adaptive Learning ◽  
Learning Systems ◽  
Learning System ◽  
Difficulty Level ◽  
Proficiency Level ◽  
Main Emphasis ◽  
Adaptive Learning System

This paper describes an adaptive learning system based on mobile phone email to support the study of Japanese Kanji. In this study, the main emphasis is on using the adaptive learning to resolve one common problem of the mobile-based email or SMS language learning systems. To achieve this goal, the authors main efforts focus on three aspects: sending the contents to a learner following his or her interests, adjusting the difficulty level of the tests to suit the learner’s proficiency level, and adapting the system to his or her learning style. Additionally, this system has already been evaluated by the learners and the results show that most of them benefited from the system and would like to continue using it.

scholarly journals A Cognitive Diagnosis Method in Adaptive Learning System Based on Preconceptions

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Jue Wang ◽  
Kaihua Liang
Keyword(s):  
Junior High School ◽  
Adaptive Learning ◽  
Cognitive Status ◽  
Learning System ◽  
Application Process ◽  
Precise Diagnosis ◽  
Learning Pathways ◽  
Diagnosis Method ◽  
Adaptive Learning System ◽  
Adaptive Learning Systems

One advantage of an adaptive learning system is the ability to personalize learning to the needs of individual users. Realizing this personalization requires first a precise diagnosis of individual users’ relevant attributes and characteristics and the provision of adaptability-enabling resources and pathways for feedback. In this paper, a preconcept system is constructed to diagnose users' cognitive status of specific learning content, including learning progress, specific preconcept viewpoint, preconcept source, and learning disability. The “Force and Movement” topic from junior high school physics is used as a case study to describe the method for constructing a preconception system. Based on the preconception system, a method and application process for diagnosing user cognition is introduced. This diagnosis method is used in three ways: firstly, as a diagnostic dimension for an adaptive learning system, improving the ability of highly-adaptive learning systems to support learning activities, such as through visualization of the cognition states of students; secondly, for an attribution analysis of preconceptions to provide a basis for adaptive learning organizations; and finally, for predicting the obstacles users may face in the learning process, in order to provide a basis for adaptive learning pathways.

Development of Adaptive Kanji Learning System for Mobile Phone

2010 ◽  
Vol 8 (4) ◽  
pp. 29-41 ◽  
Author(s):  
Mengmeng Li ◽  
Hiroaki Ogata ◽  
Bin Hou ◽  
Satoshi Hashimoto ◽  
Yuqin Liu ◽  
...  
Keyword(s):  
Language Learning ◽  
Mobile Phone ◽  
Learning Style ◽  
Adaptive Learning ◽  
Learning Systems ◽  
Learning System ◽  
Difficulty Level ◽  
Proficiency Level ◽  
Main Emphasis ◽  
Adaptive Learning System

This paper describes an adaptive learning system based on mobile phone email to support the study of Japanese Kanji. In this study, the main emphasis is on using the adaptive learning to resolve one common problem of the mobile-based email or SMS language learning systems. To achieve this goal, the authors main efforts focus on three aspects: sending the contents to a learner following his or her interests, adjusting the difficulty level of the tests to suit the learner’s proficiency level, and adapting the system to his or her learning style. Additionally, this system has already been evaluated by the learners and the results show that most of them benefited from the system and would like to continue using it.

scholarly journals From LMS to adaptive training systems

2019 ◽  
pp. 58-64 ◽  
Author(s):  
Y. B. Popova
Keyword(s):  
Learning Process ◽  
Adaptive Learning ◽  
Learning Management Systems ◽  
Individual Characteristics ◽  
Educational Process ◽  
Learning Systems ◽  
Learning System ◽  
Management Systems ◽  
Artificial Neural ◽  
Learning Management

The use of information technology and, in particular, learning management systems, increases the ability of both the teacher and the learner to achieve their goals in the educational process. Such systems provide educational content, help organize and monitor training, collect progress statistics, and can also take into account the individual characteristics of each user of the system. The purpose of this study is to determine the direction of development of modern learning systems and technologies for their implementation. The evolution of learning management systems, the transition to intelligent learning systems, the main stages of such systems were reviewed, the types of learning sequences were analyzed, the transformationinto adaptive learning systems was identified, and the scheme of the system and its mathematical model were presented. Expertise systems, the theory of fuzzy sets and fuzzy logic, cluster analysis, as well as genetic algorithms and artificial neural networks are defined as the mechanisms for implementing the learning systems. An artificial neural network in an adaptive learning system will allow you to create a unique training program that will build on existing knowledge and the level of perception of educational material by students. By formalizing the intellectual processes that both the teacher and the student carry out, it is possible to automate a certain part of the teacher’s functions, reduce the cost of manual labor, which will make it easier to monitor the learning process and also make the learning process more efficient.

scholarly journals Personalization of learning through adaptive technologies in the context of sustainable development of teachers’ education

2020 ◽  
Vol 166 ◽  
pp. 10015 ◽  
Author(s):  
Maiia Marienko ◽  
Yulia Nosenko ◽  
Alisa Sukhikh ◽  
Viktor Tataurov ◽  
Mariya Shyshkina
Keyword(s):  
Sustainable Development ◽  
Adaptive Learning ◽  
Adaptive Systems ◽  
Systems Development ◽  
Learning Systems ◽  
Learning System ◽  
System Structure ◽  
Educational Systems ◽  
Teachers Training ◽  
Adaptive Learning Systems

The article highlights the issues of personalized learning as the global trend of the modern ICTbased educational systems development. The notion, the main stages of evolution, the main features and principles of adaptive learning systems application for teachers’ training are outlined. It is emphasized that the use and elaboration of the adaptive cloud-based learning systems are essential to provide sustainable development of teachers’ education. The current trends and peculiarities of the cloud-based adaptive learning systems development and approach of their implementation for teachers’ training are considered. The general model of the adaptive cloud-based learning system structure is proposed. The main components of the model are described; the issues of tools and services selection are outlined. The methods of the cloudbased learning components introduction within the adaptive systems of teacher training are considered. The current research developments of modeling and implementation of the adaptive cloud-based systems are outlined.

scholarly journals Designing and modeling of a multi-agent adaptive learning system (MAALS) using incremental hybrid case-based reasoning (IHCBR)

2020 ◽  
Vol 10 (2) ◽  
pp. 1980
Author(s):  
El Ghouch Nihad ◽  
Kouissi Mohamed ◽  
En-Naimi El Mokhtar
Keyword(s):  
Learning Style ◽  
Adaptive Learning ◽  
Active Agent ◽  
Learning Systems ◽  
Learning System ◽  
Case Based Reasoning ◽  
Dynamic Learning ◽  
Multi Agent ◽  
Adaptive Learning System ◽  
Case Based

Several researches in the field of adaptive learning systems has developed systems and techniques to guide the learner and reduce cognitive overload, making learning adaptation essential to better understand preferences, the constraints and learning habits of the learner. Thus, it is particularly advisable to propose online learning systems that are able to collect and detect information describing the learning process in an automatic and deductive way, and to rely on this information to follow the learner in real time and offer him training according to his dynamic learning pace. This article proposes a multi-agent adaptive learning system to make a real decision based on a current learning situation. This decision will be made by performing a hypride cycle of the Case-Based Reasonning approach in order to follow the learner and provide him with an individualized learning path according to Felder Silverman learning style model and his learning traces to predict his future learning status. To ensure this decision, we assign at each stage of the Incremental Hybrid Case-Based Reasoning at least one active agent performing a particular task and a broker agent that collaborates between the different agents in the system.

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