An ANN model was designed on the MATLAB toolkit to predict the academic performance of students on the basis of various touch-points.

1. 1 Introduction
Fig 1. Biological Neuron
Fig 2: Artificial Neuron
The key features of ANNs are:
•Learning from Experience.
•Generalizing from Examples.
•Fault Tolerance.
•Input-Output Mapping.
•Non-Linearity.
Input Layer Output Layer
Project Aim
The main aim of the project is to build an expert classifier that can distinguish between students
at risk and those not at risk during their academic study.
2
Fig 3: Feed-Forward 3-2-3 Neural Network
3 Questionnaire
Category Title
1 Academic Expectations
Net1
2 Subject Interest
3 Understanding Ability
4 Exam Nervousness
5 Mathematical Ability
6 Visual Learning Dimension
Net2
7 Verbal Learning Dimension
8 Kinaesthetic Learning Dimension
9 Global / Sequential Dimension
10 Academic Risk Net3
11 Personal Risk Net4
Hidden Layer
4 Experiments - MATLAB®
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EXP1 EXP1_1 EXP1_2 EXP1_3 EXP1_4 EXP1_5
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Experiment1a
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Experiment2
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EXP2_3
EXP2_4
EXP22
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Experiment2a
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Experiment3
EXP3
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EXP3_6
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Experiment3a
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Experiment1
EXP1
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EXP1_2
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EXP1_5
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Experiment4
Exp 4
EXP4_1
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Experiment4a
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Experiment6
EXP6
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EXP6_5
EXP6_6
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EXP6 EXP6_1 EXP6_2 EXP6_3 EXP6_4 EXP6_5 EXP6_6
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Experiment6a
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5 Results 6 Conclusion
Fig 4.1: net 1 Experiments Fig 4.2: net 2 Experiments Fig 4.5: net 6 ExperimentsFig 4.3: net 3 Experiments Fig 4.4: net 4 Experiments
Fig 4.1a: net 1 Experiments Fig 4.2a: net 2 Experiments Fig 4.3a: net 3 Experiments Fig 4.4a: net 4 Experiments Fig 4.5a: net 6 Experiments
28 28
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Potential Academic Risk Indicator
Started A Course
Before
First Time Away
From Home
Not First Choice
Applied Through
Clearing
First In Family At
Uni.
34%
32%
34%
Learning Style Categories
VISUA_ LEARN
AUDITORY_LEARN
KINAESTHETIC_LEA
RN
Fig 5:Potential Academic Risk Indicator.
Fig 6: Principle Learning Preference
Table 1: Questionnaire Categories
Fig 5.1: Analysis of Student not at risk. Fig 5.2: Analysis of Student at risk.
The questionnaire consists of 11 different categories
using 44 questions to determine the potential risk factor
and the learning style preference for the individual
student. Figure 5 and Figure 6 indicates the principal
learning preferences and Potential Academic Risk
Indicator for 134 students whose data sets were
provided for the project experiments.
The goal to build an expert classifier that can distinguish
between students at risk and not at risk during their
studies was successfully completed. The valuable
technical and optimization skills gained in the project can
be used in further implementation of complex neural
networks for the required application.
a=f(wp+b)
Σ fp w
b
n a
Neural Network
including connections
(called weights)
Input between neurons
Comp
are
Input
Target
Output
Adjust Weights
Fig 4: Basic Neural Network Mechanism