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.

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[44] “Complexity Explorables.” https://www.complexity-explorables.org/, last visited 22021. Random Walk Generation and Classification Within an Online Learning Platform 543 Afrah Mousa is a recent college graduate with a Bachelor's Degree in Software Engineering from Bethlehem University – Palestine. She works as a Machine Learning Engineer at Jereer Company- Palestine. Her main interest in the field of artificial intelligence and machine learning, and this is what prompted her to get a job related to it. Afrah enjoys using my skills to contribute to the exciting technological advances that happen at Jereer. Thorsten Auth received his Ph.D. in Theoretical Physics from University of Cologne in 2004. He is currently a staff scientist at Forschungszentrum Jülich, where he works on theoretical physics of living matter. In particular, he focuses on computer simulations to study dynamics and self-organisation in systems containing self-propelled particles and on nanostructure-membrane interactions. Anas Samara is an experienced Software Engineer with a demonstrated history of working in the software industry. Competent researcher in a wide range of computer science topics with a Doctor of Philosophy (PhD) from Ulster University. Proficient in data mining and development of supervised and ensemble predictive models. Main research interests are focused on Affective Computing and Human-Computer Interaction disciplines. Has many publications in affective state detection within a human–computer interaction contexts using different input channels and modalities to improve the quality of interaction through generating a more engaging user experience and intelligent and adaptive interfaces. Suhail Odeh Born in Bethlehem - Palestine, earned PhD degree (2006) in Computer Engineering from the Department of Computer Architecture and Technology, University of Granada -Spain. He joins Bethlehem University community as an assistant professor in the Computer and Information System department in the faculty of science at (2006). Dr. Odeh has been a Postdoctoral researcher in the Department of Information Engineering and Computer science at L’Aquila University, Italy. Also, he has been a visiting professor The Department of Computer Science at the University of Cyprus, University of Granada, and University of Salamanca. He is an active researcher in the field of Artificial intelligence, Pattern recognition, Intelligent Systems, Brain Computer Interface, Multiagent.