Applications of the Elo rating system in adaptive educational systems

2016 ◽  
Vol 98 ◽  
pp. 169-179 ◽  
Author(s):  
Radek Pelánek
Keyword(s):  
Rating System ◽  
Educational Systems ◽  
Elo Rating System ◽  
Adaptive Educational Systems

scholarly journals Who is the ‘Journal Grand Master’? A new ranking based on the Elo rating system

2017 ◽  
Vol 11 (3) ◽  
pp. 800-809 ◽  
Author(s):  
Robert Lehmann ◽  
Klaus Wohlrabe
Keyword(s):  
Rating System ◽  
Elo Rating System

scholarly journals Evaluating and improving adaptive educational systems with learning curves

2010 ◽  
Vol 21 (3) ◽  
pp. 249-283 ◽  
Author(s):  
Brent Martin ◽  
Antonija Mitrovic ◽  
Kenneth R. Koedinger ◽  
Santosh Mathan
Keyword(s):  
Learning Curves ◽  
Educational Systems ◽  
Adaptive Educational Systems

scholarly journals A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System

2020 ◽  
Author(s):  
Vasiliki Demertzi ◽  
Konstantinos Demertzis
Keyword(s):  
Recommendation System ◽  
Learning System ◽  
Educational Systems ◽  
Learning Needs ◽  
Pedagogical Strategies ◽  
Substantive Content ◽  
Teaching Content ◽  
Adaptive Educational Systems ◽  
Educational Conditions ◽  
Content Based Filtering

The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the pedagogical strategies that adapt to the real individual skills of the students. An important innovation in this direction is the Adaptive Educational Systems (AES) that support automatic modeling study and adjust the teaching content on educational needs and students' skills. Effective utilization of these educational approaches can be enhanced with Artificial Intelligence (AI) technologies in order to the substantive content of the web acquires structure and the published information is perceived by the search engines. This study proposes a novel Adaptive Educational eLearning System (AEeLS) that has the capacity to gather and analyze data from learning repositories and to adapt these to the educational curriculum according to the student skills and experience. It is a novel hybrid machine learning system that combines a Semi-Supervised Classification method for ontology matching and a Recommendation Mechanism that uses a hybrid method from neighborhood-based collaborative and content-based filtering techniques, in order to provide a personalized educational environment for each student.

Forecasting sales of new virtual goods with the Elo rating system

2014 ◽  
Vol 13 (6) ◽  
pp. 457-469 ◽  
Author(s):  
Lin Yang ◽  
Stanko Dimitrov ◽  
Benny Mantin
Keyword(s):  
Rating System ◽  
Elo Rating System
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