Python 3 Code created by me during my master thesis research. Not very polished or explained, but fully functional.
In the ANN directory you'll find an implementation of a MADALINE artificial neural network in python 3. It uses the Rule 1 learning algorithm (MR1) with a OR output gate.
In the Data Set directory you'll find scripts that generate data sets with all possible student types in the Felder & Silverman Learning Style Model (FSLSM) acording to the Index of Learning Styles (ILS) classification of Strong, Moderate and Balanced studend axes. The script also uses the metrics of device stype (mobile or not), connection speed (based on the official reports of the brazilian communications agency we can group the connection speeed types into 5 great groups) and if the connection is by cable or mobile (2G,3G,4G and so on). The details are given in the script through a commentary.
You'll also find a script capable of creating all possible types of relevant learning objects in acordance to the method of mapping the IEEE-LOM to the FSLSM created by Anitha and Deisy (2015). The script also generates random values for some attributes that might be userful, such as video and audio bit rate and file size.
If you wish to use any of this, feel free.
OBS:
For completude sake, here's the Anitha and Deisy work I'm refering to:
ANITHA, D; DEISY, C. Proposing a novel approach for classification and sequencing of Learning Objects in E-learning systems based on learning style. Journal of Intelligent & Fuzzy Systems, IOS press, v. 29, n. 2, p. 539–552, 2015
And here is the paper that I used to create the network:
WIDROW, Bernard; LEHR, Michael A. 30 years of Adaptive Neural networks:Perceptron, Madaline, and Backpropagation.Madaline and BackpropagationProceedings of the IEEE, v. 78, n. 9, p. 1415–1441, Sept. 1990
My Master Thesis:
Oficial Repository of the Federal Univesity of Juiz de Fora - Minas Gerais, Brazil