Journal of Information Systems Education (JISE)

Volume 30

Volume 30 Issue 1, Pages 57-66

Winter 2019


The Role of Flow in Learning Distributed Computing and MapReduce Concepts using Hands-On Analogy


Colin Conrad
Dalhousie University
Halifx, NS B3H 4R2, Canada

Michael Bliemel
University of Ontario Institute of Technology
Oshawa, ON L1H 7K4, Canada

Hossam Ali-Hassan
York University
Toronto, ON M4N 3M6, Canada

Abstract: The expansion of technical concepts into everyday business practices suggests a need for effectively teaching difficult subjects to non-technical users. This paper describes hands-on analogy, an innovative method for teaching technically difficult concepts using interactive, experiential learning activities and a gamified exercise. We demonstrate our technique by investigating Hadoop Hands On, an exercise designed to teach MapReduce. Students experienced how MapReduce functions work conceptually by envisioning students as compute and tracking nodes in a Hadoop system and playing cards as data processed to complete two tasks of varying complexity. A study of 56 students was conducted to validate the exercise and demonstrated the impact of triggered flow on perceived understanding. The main contributions of this work are 1) an alternative learning approach that communicates a technically difficult concept through analogy and 2) the demonstration of the role of flow in facilitating learning using this approach. We recommend using this approach to teach technically difficult concepts to non-technical students who can more easily comprehend the benefits of distributed computing methods interactively in a way that complements the traditional lecture approach.

Keywords: Active learning, Analogy learning, Game-based learning, Big data

Download this article: JISE - Volume 30 Issue 1, Page 57.pdf


Recommended Citation: Conrad, C., Bliemel, M., & Ali-Hassan, H. (2019). The Role of Flow in Learning Distributed Computing and MapReduce Concepts using Hands-On Analogy. Journal of Information Systems Education, 30(1), 57-66.