Definition of a Feature Vector to Characterise Learners in Adaptive Learning Systems

2019 ◽  
pp. 75-89 ◽  
Author(s):  
Alberto Real-Fernández ◽  
Rafael Molina-Carmona ◽  
María L. Pertegal-Felices ◽  
Faraón Llorens-Largo
Keyword(s):  
Adaptive Learning ◽  
Feature Vector ◽  
Learning Systems ◽  
Definition Of ◽  
Adaptive Learning Systems

Does Extra Knowledge Necessarily Improve Generalization?

1996 ◽  
Vol 8 (1) ◽  
pp. 202-214 ◽  
Author(s):  
David Barber ◽  
David Saad
Keyword(s):  
Adaptive Learning ◽  
Performance Measure ◽  
Learning Systems ◽  
Simple Approximation ◽  
Generalization Error ◽  
Generalization Performance ◽  
Standard Definition ◽  
Intuitive Idea ◽  
Definition Of ◽  
Adaptive Learning Systems

The generalization error is a widely used performance measure employed in the analysis of adaptive learning systems. This measure is generally critically dependent on the knowledge that the system is given about the problem it is trying to learn. In this paper we examine to what extent it is necessarily the case that an increase in the knowledge that the system has about the problem will reduce the generalization error. Using the standard definition of the generalization error, we present simple cases for which the intuitive idea of “reducivity”—that more knowledge will improve generalization—does not hold. Under a simple approximation, however, we find conditions to satisfy “reducivity.” Finally, we calculate the effect of a specific constraint on the generalization error of the linear perceptron, in which the signs of the weight components are fixed. This particular restriction results in a significant improvement in generalization performance.

Emotion-Based Adaptive Learning Systems

2020 ◽  
pp. 273-286
Author(s):  
Sai Prithvisingh Taurah ◽  
Jeshta Bhoyedhur ◽  
Roopesh Kevin Sungkur
Keyword(s):  
Adaptive Learning ◽  
Learning Systems ◽  
Adaptive Learning Systems

scholarly journals TECHNOLOGY OF PERSONALLY ORIENTED ADAPTIVE LEARNING SYSTEMS

2021 ◽  
Vol 85 (4-5) ◽  
Author(s):  
Maksadkhon Yakubov ◽  
Nasibakhon Rasulova
Keyword(s):  
Adaptive Learning ◽  
Learning Systems ◽  
Adaptive Learning Systems

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.

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.

Evaluation of Clustering Methods for Adaptive Learning Systems

2015 ◽  
pp. 237-260 ◽  
Author(s):  
Wilhelmiina Hämäläinen ◽  
Ville Kumpulainen ◽  
Maxim Mozgovoy
Keyword(s):  
Data Mining ◽  
Adaptive Learning ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Optimal Choice ◽  
Learning Systems ◽  
Learning Tools ◽  
Clustering Methods ◽  
Central Task ◽  
Adaptive Learning Systems

Clustering student data is a central task in the educational data mining and design of intelligent learning tools. The problem is that there are thousands of clustering algorithms but no general guidelines about which method to choose. The optimal choice is of course problem- and data-dependent and can seldom be found without trying several methods. Still, the purposes of clustering students and the typical features of educational data make certain clustering methods more suitable or attractive. In this chapter, the authors evaluate the main clustering methods from this perspective. Based on the analysis, the authors suggest the most promising clustering methods for different situations.

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