Simulation-Based Learning via Cisco Packet Tracer to Enhance the Teaching of Computer Networks

Allison, Jordan ORCID: 0000-0001-8513-4646 (2022) Simulation-Based Learning via Cisco Packet Tracer to Enhance the Teaching of Computer Networks. In: ITiCSE 2022, 11-13 July 2022, Dublin, Ireland. ISBN 9781450392013

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11214 Allison, J. (2018) Simulation_Based_Learning_via_Cisco_Packet_Tracer_to_Enhance_the_Teaching_of_Computer_Networks.pdf - Accepted Version
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Abstract

Teaching and learning computer networks is a crucial part of a computing undergraduates’ education. However, it is an abstract topic with theory that can be challenging for students to comprehend. Simulation-based learning can be used as a teaching tool to enhance networking-based lecture topics and help students more easily understand and visualise how it really works in a safe, user-friendly environment. One such simulation tool, Cisco Packet Tracer, can be used to good effect, and this paper reports on the practical implementation of the tool as identified in existing literature. Additionally, this paper discusses how Packet Tracer was utilised as part of a UK based first year undergraduate module ’Computers and Security’, and details the content of nine practical sessions, whilst also identifying the main benefits and challenges of their implementation from a practitioner’s perspective. It was found that while Packet Tracer can be an additional benefit to augment the teaching of networking concepts, there are limitations and challenges which educators must be aware of if implementing the tool. The paper therefore concludes with providing recommendations which should help educators and curriculum designers in creating and delivering more effective and interactive networking sessions.

Item Type: Conference or Workshop Item (Paper)
Article Type: Article
Uncontrolled Keywords: Simulation-Based Learning; Packet Tracer; Networking Education; Computing Education; Computer Networks
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas: Applied Business & Technology
Depositing User: Kate Greenaway
Date Deposited: 28 Jul 2022 12:17
Last Modified: 31 Oct 2023 11:32
URI: https://eprints.glos.ac.uk/id/eprint/11214

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