Add to ORKGThe specific problem in this project is to predict the large scale connectivity of a certain random network arising in the area of combinatorics. The motivation behind the model is to predict how individuals *learn* when presented with a large network of related *concepts*. We created generating functions to count the large connected *components*. These *components* are the related *concepts* that we referred to previously. The creation of these generating functions enabled us to analyze different graphs to connect the idea of how students understand related concepts to the Percolating Threshold. The model is a dynamical system coming from Percolation Theory. There are potential applications that fall into the areas of machine learning and...
Organizational learning can be understood as a spontaneous development of routines. Mathematically, ...
Percolation is an important topic in climate, physics, materials science, epidemiology, finance, and...
This rigorous introduction to network science presents random graphs as models for real-world networ...
AbstractIn recent years there has been a great deal of interest in ‘connectionism’. This name covers...
Knowledge can be seen as a complex system that does not reveal clearly how it works. However, some m...
In this study, learning pathways are modelled by networks constructed from the log data of student–L...
I aim to show that models, classification or generating functions, invariances and datasets are algo...
A network graph describes the web of connections between entities in a system. Network graphs are a ...
By modeling pedagogical scenarios as directed geometrical graphs and proposing an associated modelin...
This rigorous introduction to network science presents random graphs as models for real-world networ...
A new connectionist model (named RASHNL) accounts for many "irrational" phenomena found in nonmetric...
This paper explores the feasibility of a graph-based approach to model student knowledge in the doma...
Artificial Neural Networks (ANNs) aim at mimicking information processing in biological networks. In...
A number of neural network models of categorization have been proposed. The models differ notably in...
Organizational learning can be understood as a spontaneous development of routines. Mathematically, ...
Percolation is an important topic in climate, physics, materials science, epidemiology, finance, and...
This rigorous introduction to network science presents random graphs as models for real-world networ...
AbstractIn recent years there has been a great deal of interest in ‘connectionism’. This name covers...
Knowledge can be seen as a complex system that does not reveal clearly how it works. However, some m...
In this study, learning pathways are modelled by networks constructed from the log data of student–L...
I aim to show that models, classification or generating functions, invariances and datasets are algo...
A network graph describes the web of connections between entities in a system. Network graphs are a ...
By modeling pedagogical scenarios as directed geometrical graphs and proposing an associated modelin...
This rigorous introduction to network science presents random graphs as models for real-world networ...
A new connectionist model (named RASHNL) accounts for many "irrational" phenomena found in nonmetric...
This paper explores the feasibility of a graph-based approach to model student knowledge in the doma...
Artificial Neural Networks (ANNs) aim at mimicking information processing in biological networks. In...
A number of neural network models of categorization have been proposed. The models differ notably in...
Organizational learning can be understood as a spontaneous development of routines. Mathematically, ...
Percolation is an important topic in climate, physics, materials science, epidemiology, finance, and...
This rigorous introduction to network science presents random graphs as models for real-world networ...