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Research on optimisation of electronic information engineering course structure and modular design based on graph neural network

Xinli Ma

Xinli Ma

School of Information Science and Engineering, NingboTech UniversityNingbo, China
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Ma, Xinli
  
Mar 17, 2025
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Published Online: Mar 17, 2025
Received: Oct 02, 2024
Accepted: Jan 29, 2025
DOI: https://doi.org/10.2478/amns-2025-0162
Keywords
© 2025 Xinli Ma, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

GCN model
GCN model

Figure 2.

Triplet of curriculum knowledge graph
Triplet of curriculum knowledge graph

Figure 3.

KGCN frame diagram
KGCN frame diagram

Figure 4.

Model training and testing of loss-precision results
Model training and testing of loss-precision results

Figure 5.

Experimental results of ablation based on MOOCCube data set
Experimental results of ablation based on MOOCCube data set

Figure 6.

The recommended results compare the results of the experiment
The recommended results compare the results of the experiment

Figure 7.

Overall architecture of the learning path recommendation platform
Overall architecture of the learning path recommendation platform

Figure 8.

Platform function module diagram
Platform function module diagram

Figure 9.

Test results of the first two classes of knowledge
Test results of the first two classes of knowledge

Figure 10.

Test results of T class knowledge level after experiment
Test results of T class knowledge level after experiment

Figure 11.

Students’ evaluation of the teaching effect of the online learning platform
Students’ evaluation of the teaching effect of the online learning platform

Test level statistics before and after the experiment

Pre Post
CK class 68.40 72.27
T class 68.94 79.83
P value 0.542 0.012

Electronic information engineering curriculum modular teaching content

Module Module content Time
Module 1 Overview of information technology 4
Module 2 Signal and data Time domain and frequency domain 10
Analog and number words
Coding and modulation
Field and wave
Module 3 Electronic devices and circuits Circuit simulation and basic law 6
Transistor
Integrated circuit and operational advanced
Module 4 Logic and digital system Digital logic circuit 8
Sequence logic and priority machine
Computer and microprocessor
Embedded system and eda technology
Module 5 Interconnection and computation Communication and networkperception and calculation 4
Communication and networkperception and calculation
Module 6 Professional and course guidance 2
Module 7 Course summary 2

Effects of different parameters on recommended effects

Project Size or way Precision Recall F1
Learning rate 0.0001 0.35 0.26 0.33
0.0005 0.41 0.31 0.37
0.001 0.47 0.39 0.42
0.005 0.54 0.46 0.49
0.01 0.37 0.33 0.41
Entity embedded dimensions 32 0.31 0.28 0.34
64 0.46 0.41 0.47
128 0.34 0.36 0.33
Polymerization mode Concatenate 0.59 0.49 0.46
Sum 0.53 0.37 0.33
Bi-interaction 0.63 0.55 0.57
Language:
English
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1 times per year
Journal Subjects:
Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
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