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.

Evaluation of Clustering Methods for Adaptive Learning Systems

2016 ◽  
pp. 519-542
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.

scholarly journals Informing and Performing: A Study Comparing Adaptive Learning to Traditional Learning

10.28945/2140 ◽  
2015 ◽  
Author(s):  
Meg Coffin Murray ◽  
Jorge Pérez
Keyword(s):  
Learning Outcomes ◽  
Learning Style ◽  
Adaptive Learning ◽  
Digital Literacy ◽  
Learning Systems ◽  
Potential Game ◽  
Learning Tools ◽  
Literacy Course ◽  
Delivery Options ◽  
Adaptive Learning Systems

Technology has transformed education, perhaps most evidently in course delivery options. However, compelling questions remain about how technology impacts learning. Adaptive learning tools are technology-based artifacts that interact with learners and vary presentation based upon that interaction. This paper compares adaptive learning with a conventional teaching approach implemented in a digital literacy course. Current research explores the hypothesis that adapting instruction to an individual’s learning style results in better learning outcomes. Computer technology has long been seen as an answer to the scalability and cost of individualized instruction. Adaptive learning is touted as a potential game-changer in higher education, a panacea with which institutions may solve the riddle of the iron triangle: quality, cost and access. Though the research is scant, this study and a few others like it indicate that today’s adaptive learning systems have negligible impact on learning outcomes, one aspect of quality. Clearly, more research like this study, some of it from the perspective of adaptive learning systems as informing systems, is needed before the far-reaching promise of advanced learning systems can be realized. A revised version of the paper was published in Informing Science: the International Journal of an Emerging Transdiscipline, Volume 18, 2015

scholarly journals Informing and Performing: A Study Comparing Adaptive Learning to Traditional Learning

10.28945/2165 ◽  
2015 ◽  
Vol 18 ◽  
pp. 111-125 ◽  
Author(s):  
Meg Coffin Murray ◽  
Jorge Pérez
Keyword(s):  
Learning Outcomes ◽  
Learning Style ◽  
Adaptive Learning ◽  
Digital Literacy ◽  
Learning Systems ◽  
Potential Game ◽  
Learning Tools ◽  
Literacy Course ◽  
Delivery Options ◽  
Adaptive Learning Systems

Technology has transformed education, perhaps most evidently in course delivery options. However, compelling questions remain about how technology impacts learning. Adaptive learning tools are technology-based artifacts that interact with learners and vary presentation based upon that interaction. This paper compares adaptive learning with a conventional teaching approach implemented in a digital literacy course. Current research explores the hypothesis that adapting instruction to an individual’s learning style results in better learning outcomes. Computer technology has long been seen as an answer to the scalability and cost of individualized instruction. Adaptive learning is touted as a potential game-changer in higher education, a panacea with which institutions may solve the riddle of the iron triangle: quality, cost and access. Though the research is scant, this study and a few others like it indicate that today’s adaptive learning systems have negligible impact on learning outcomes, one aspect of quality. Clearly, more research like this study, some of it from the perspective of adaptive learning systems as informing systems, is needed before the far-reaching promise of advanced learning systems can be realized.

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

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

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.

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