scholarly journals Customisation of Learning Paths and Network Optimisation in E-learning Systems

10.5772/7292 ◽  
2009 ◽  
Author(s):  
Cristina De ◽  
Paolo Topp
Keyword(s):  
Learning Systems ◽  
E Learning ◽  
Network Optimisation ◽  
Learning Paths

scholarly journals A New Learning Path Model for E-Learning Systems

2021 ◽  
Vol 19 (2) ◽  
pp. 20-40
Author(s):  
David Brito Ramos ◽  
Ilmara Monteverde Martins Ramos ◽  
Isabela Gasparini ◽  
Elaine Harada Teixeira de Oliveira
Keyword(s):  
Student Behavior ◽  
Group Formation ◽  
Learning Systems ◽  
Learning System ◽  
Path Model ◽  
New Approach ◽  
Learning Path ◽  
E Learning ◽  
Learning Paths ◽  
Collaborative Activities

This work presents a new approach to the learning path model in e-learning systems. The model uses data from the database records from an e-learning system and uses graphs as representation. In this work, the authors show how the model can be used to represent visually the learning paths, behavior analysis, help to suggest group formation for collaborative activities, and thus assist the teacher in making decisions. To validate the practical utility of the model, the authors created two tools, one to visualize the learning paths and another to suggest groups of students for collaborative activities. Both tools were tested in a real environment, presenting useful results. The authors carried experiments with students from three programs: physics, electrical engineering, and computer science. Experiments show that it is possible to use the proposed learning path to analyze student behavior patterns and recommend group formation with positive results.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

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