A Cognitive Diagnosis Method in Adaptive Learning System Based on Preconceptions

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.

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Adaptive Learning Systems and Interference in Causal Inference

10.31234/osf.io/4ax25 ◽

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.

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Analysis of Adaptive Learning Knowledge Model Based on Cloud Computing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.623.241 ◽

2014 ◽

Vol 623 ◽

pp. 241-244

Author(s):

Wei Ying Li

Keyword(s):

Cloud Computing ◽

Tacit Knowledge ◽

Adaptive Learning ◽

Learning System ◽

Knowledge Assessment ◽

Computing Environment ◽

Knowledge Model ◽

Cloud Computing Environment ◽

Adaptive Learning System ◽

Adaptive Learning Systems

There is a clear lack of respect about traditional network adaptive learning system teaching on individualized assessment knowledge and building tacit knowledge, and this paper presents a knowledge model that supports personalized knowledge assessment, knowledge stored in the cloud computing environment, and construct tacit knowledge for learning body, to provide a personalized learning services for learners to achieve user to adapt the system to adapt to the user's system and two-way adaptation, this paper has guiding significance for further studies of adaptive learning systems.

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

AUTOBUSY – Technika Eksploatacja Systemy Transportowe ◽

10.24136/atest.2018.548 ◽

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.

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

Open Engineering ◽

10.1515/eng-2020-0092 ◽

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.

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