Demystifying Neural Networks: a stage-based learning approach for novice students

Joseph El Gemayel; Marc Kydd

doi:10.1145/3754508.3754528

Demystifying Neural Networks: a stage-based learning approach for novice students

Joseph El Gemayel, Marc Kydd

Department of Cybersecurity and Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Understanding Neural Networks can be overwhelming for novices due to the abstract and jargon-heavy nature of the subject. This poster presents a stage-based, interactive learning environment (ILE) designed to simplify key Neural Network concepts through hands-on experimentation and visual feedback. The ILE guides learners from foundational concepts such as neurons and activation functions, through to how these elements interact with more developed concepts such as multi-layer networks and propagation. By adjusting parameters in real time, students can directly observe how changes affect network behaviour and performance. The ILE aims to enhance conceptual understanding, increase engagement, and lower barriers to entry in AI education, offering a practical solution to common challenges in computing education.

Original language	English
Title of host publication	UKICER '25
Subtitle of host publication	proceedings of the 2025 Conference on UK and Ireland Computing Education Research
Editors	Fiona McNeill, Cristina Alexandru, Sue Sentance, Quintin Cutts
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Number of pages	1
ISBN (Print)	9798400720789
DOIs	https://doi.org/10.1145/3754508.3754528
Publication status	Published - 4 Sept 2025
Event	United Kingdom and Ireland Computing Education Research - University of Edinburgh, Edinburgh, United Kingdom Duration: 4 Sept 2025 → 5 Sept 2025 https://www.ukicer.com/

Conference

Conference	United Kingdom and Ireland Computing Education Research
Abbreviated title	UKICER '25
Country/Territory	United Kingdom
City	Edinburgh
Period	4/09/25 → 5/09/25
Internet address	https://www.ukicer.com/

Keywords

Neural networks
Interactive learning
Educational tools
Computing education
Visual pedagogy

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10.1145/3754508.3754528Licence: CC BY-NC-ND

Kydd_DemystifyingNeuralNetworks_Published_2025Final published version, 350 KBLicence: CC BY-NC-ND

Cite this

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title = "Demystifying Neural Networks: a stage-based learning approach for novice students",

abstract = "Understanding Neural Networks can be overwhelming for novices due to the abstract and jargon-heavy nature of the subject. This poster presents a stage-based, interactive learning environment (ILE) designed to simplify key Neural Network concepts through hands-on experimentation and visual feedback. The ILE guides learners from foundational concepts such as neurons and activation functions, through to how these elements interact with more developed concepts such as multi-layer networks and propagation. By adjusting parameters in real time, students can directly observe how changes affect network behaviour and performance. The ILE aims to enhance conceptual understanding, increase engagement, and lower barriers to entry in AI education, offering a practical solution to common challenges in computing education.",

author = "\{El Gemayel\}, Joseph and Marc Kydd",

note = "{\textcopyright} 2025 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Data availability statement: Not present.; United Kingdom and Ireland Computing Education Research, UKICER '25 ; Conference date: 04-09-2025 Through 05-09-2025",

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day = "4",

doi = "10.1145/3754508.3754528",

language = "English",

isbn = "9798400720789",

editor = "Fiona McNeill and Cristina Alexandru and Sue Sentance and Quintin Cutts",

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El Gemayel, J & Kydd, M 2025, Demystifying Neural Networks: a stage-based learning approach for novice students. in F McNeill, C Alexandru, S Sentance & Q Cutts (eds), UKICER '25: proceedings of the 2025 Conference on UK and Ireland Computing Education Research., 21, Association for Computing Machinery (ACM), New York, United Kingdom and Ireland Computing Education Research, Edinburgh, United Kingdom, 4/09/25. https://doi.org/10.1145/3754508.3754528

Demystifying Neural Networks: a stage-based learning approach for novice students. / El Gemayel, Joseph; Kydd, Marc.
UKICER '25: proceedings of the 2025 Conference on UK and Ireland Computing Education Research. ed. / Fiona McNeill; Cristina Alexandru; Sue Sentance; Quintin Cutts. New York: Association for Computing Machinery (ACM), 2025. 21.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Demystifying Neural Networks

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AU - El Gemayel, Joseph

AU - Kydd, Marc

N1 - © 2025 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Data availability statement: Not present.

PY - 2025/9/4

Y1 - 2025/9/4

N2 - Understanding Neural Networks can be overwhelming for novices due to the abstract and jargon-heavy nature of the subject. This poster presents a stage-based, interactive learning environment (ILE) designed to simplify key Neural Network concepts through hands-on experimentation and visual feedback. The ILE guides learners from foundational concepts such as neurons and activation functions, through to how these elements interact with more developed concepts such as multi-layer networks and propagation. By adjusting parameters in real time, students can directly observe how changes affect network behaviour and performance. The ILE aims to enhance conceptual understanding, increase engagement, and lower barriers to entry in AI education, offering a practical solution to common challenges in computing education.

AB - Understanding Neural Networks can be overwhelming for novices due to the abstract and jargon-heavy nature of the subject. This poster presents a stage-based, interactive learning environment (ILE) designed to simplify key Neural Network concepts through hands-on experimentation and visual feedback. The ILE guides learners from foundational concepts such as neurons and activation functions, through to how these elements interact with more developed concepts such as multi-layer networks and propagation. By adjusting parameters in real time, students can directly observe how changes affect network behaviour and performance. The ILE aims to enhance conceptual understanding, increase engagement, and lower barriers to entry in AI education, offering a practical solution to common challenges in computing education.

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DO - 10.1145/3754508.3754528

M3 - Conference contribution

SN - 9798400720789

BT - UKICER '25

A2 - McNeill, Fiona

A2 - Alexandru, Cristina

A2 - Sentance, Sue

A2 - Cutts, Quintin

PB - Association for Computing Machinery (ACM)

CY - New York

Y2 - 4 September 2025 through 5 September 2025

ER -

Demystifying Neural Networks: a stage-based learning approach for novice students

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Keywords

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