Random Walk Generation and Classification Within an Online Learning Platform

Abstract

Advancements in technology have introduced new approaches in teaching and learning processes. Machine learning algorithms analyse and recognize patterns of data and subsequently become able to make reasonable decisions. In playing complex games, such as chess and go, machine learning algorithms have even already outperformed humans. This paper presents a software platform ‘DiscimusRW’ that introduces a novel approach for teaching, learning, and researching random walk theory and getting hands-on experience in machine learning. Random walk theory represents the foundations of many fundamental processes, including the diffusion of substances in solvents, epidemics’ spread, and financial markets’ development. ‘DiscimusRW’ is composed of three main features: 1. Random walk generation using mathematical Equations, 2. Random walk classification using supervised learning algorithms, and 3. Random walk visualization. A few users who explored ‘DiscimusRW’ showed an interest and positive feedback that assured the experiential learning experience achieved using this software, which will therefore reinforce random walk teaching and learning.