The Vaal University of Technology (VUT) has, in its faculty of Engineering and Technology a flagship community engagement project referred to as the French South African Schneider Electric Education Center (FSASEC) where young underprivileged students are recruited and trained to become occupational electricians. There is a significant skills gap in South Africa for artisans in the field of Electrical Engineering and this gap can be closed if state of the art training facilities exits where the students are prepared for landing in the workplace useful and productive. It is of primary value to be able to identify students that perform well in order to maximize the efficiency of the training program in Electrical Engineering. This paper presents some tools that can be used in identifying potentially excellent performers, at risk and underperforming students. This process ranges from when the student is admitted to the time he exits the programmed.

1. International Journal of Modern Research in Engineering & Management (IJMREM)
||Volume|| 1||Issue|| 4 ||Pages|| 35-40 ||April 2018|| ISSN: 2581-4540
www.ijmrem.com IJMREM Page 35
Identifying Top Perfomers in the Electrician Training
Programme at FSASEC - VUT using Regression Analysis: Who
are the stars
1,
Langa Hendrick Musawenkosi , 2,
Twala Bhekisipho
1,
Department of Electrical Engineering Vaal University of Technology
Vanderbijlpark, South Africa
2,
Department of Electrical & Mining Engineering University of South Africa Florida, Johannesburg, South
Africa
--------------------------------------------------ABSTRACT--------------------------------------------------------
The Vaal University of Technology (VUT) has, in its faculty of Engineering and Technology a flagship
community engagement project referred to as the French South African Schneider Electric Education Center
(FSASEC) where young underprivileged students are recruited and trained to become occupational electricians.
There is a significant skills gap in South Africa for artisans in the field of Electrical Engineering and this gap
can be closed if state of the art training facilities exits where the students are prepared for landing in the
workplace useful and productive. It is of primary value to be able to identify students that perform well in order
to maximize the efficiency of the training program in Electrical Engineering. This paper presents some tools
that can be used in identifying potentially excellent performers, at risk and underperforming students. This
process ranges from when the student is admitted to the time he exits the programmed.
KEYWORDS: Regression; FSASEC; Machine Learning; Linear Regression; Multiple Regression Model;
SVM; KNN; Noted Courses; APS Score.
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Date of Submission: Date, 17 April 2017 Date of Accepted: 23 April 2018
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I. INTRODUCTION
The French South African Schneider Electric Education Center (FSASEC) is a partnership between the Vaal
University of Technology, the French Ministry of Higher Education and Research and Schneider Electric. The
primary goal of this partnership is to train and develop underprivileged students into successful electricians,
artisans, and entrepreneurs in South Africa. These are students could not have been admitted in a university due to
financial constraints or not meeting university requirements.
These students are identified and recruited to enroll for this training in the state of the art equipment. Their
Mathematics and Physical Science are not necessarily exceptional but they should have the passion and
motivation to become electrical artisans. Because we are training artisans, more emphasis is placed on practical
work and so they spend 20 hours per week on practical work and 20 hours per week on theoretical training. The
following shows the laboratory setup at FSASEC.
Fig. 1. FSASEC students receive training in Home Wiring

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The South African white paper sets out strategies to improve the capacity of the post school education and
training system to meet the country’s needs. It outlines some of the policy objectives as follows:
• A single coordinated post-school education and training system
• A stronger and more cooperative relationship between education and training institutions and the workplace
• A post-school education and training system that is responsive to the needs of the individual citizen,
employers in both public and private sector, as well as broader societal and developmental objectives.
The South African government and funders can save a lot of resources when funding these institutions. Therefore,
the application of rigorous methods of machine learning can improve the efficiency in the academic sector. For
the most part in South Africa, the largest contributor of funding in public education is government. The ministry
of education takes no account of income that is raised from student fees and other private sources, these public
institutions have to account by submitting annual financial statements which reflect all income and all
expenditure from all public and private sources, (Ministry of Education, February 2004).
II. BACKGROUND OF SOME MACHINE LEARNING ALGORITHMS
Simple Linear Regression :Regression modeling represents a powerful and elegant method for estimating the
value of a continuous target variable (Larose, et al, 2015). Linear regression is calculated using the least squares
criterion to fit a line. It involves finding the “best” line to fit two attributes (or variables) so that the attribute can
be used to predict the other (Han, et al, 2012).
Decision Trees, DTs : They are simple yet successful techniques for supervised classification learning. This
classification method consists of decision nodes, connected by branches, extending from the root node until the
terminating leaf nodes, (Larose and Larose, 2015). Starting at the root node attributes are tested at the decision
node, with each possible outcome resulting in a branch.
Multiple Regression : Multiple regression is an extension of linear regression, where more than two attributes
are involved and the data are fit to a multidimensional surface (Han, et al, 2012). In multiple regression, we look
at how a single response is related to a number of predictors.
K-Nearest Neighbor, KNN : The K – Nearest Neighbor is an example of instance based learning, in which the
data set is stored, so that the classification for a new unclassified record may be found by simply comparing it to
the most similar records in the training set (Larose and Larose, 2015).
The Support Vector Machine, SVM : Support Vector Machines (SVM) is an algorithm that uses nonlinear
mapping to transform the original data into a higher dimension, (Han, Kamber and Pei, 2012:408). SVM’s are
pattern classifiers that can be expressed in the form of hyperplanes to discriminate between positive instances
and negative instances pioneered by Vapkin (Twala, 2012).
III. ADMISSION POLICIES AND CRITERIA
Underprivelelged Young students : FSASEC aims to reach young underprivileged children who do not have
an opportunities as the rest of the students to further their education due to financial constraints. It makes
business sense to target youth living in an accessible radius to afford transport. If they are able to walk to the
campus, that is an added advantage to them because the burden of transport is eliminated. It had been generally
assumed that went to community college s out of high school for two main reasons: lower academic
achievement and lower tuition cost (Anderson-Rowland, 2000).
Academic Requirements : The registration department in universities usually regulates a set of rules in order to
accept new students. These rules are set to select applicants who have the abilities and skills to pursue and
succeed in their academic career in a particular field of studies (Alenezi, et al, 2009). The admission body of
college firstly makes sure that the qualifications and other particulars of the individual student fulfill the
requirements of college admission according to its rules and regulations. Secondly, they arrange the acceptance
tests that result in accepting or rejecting applicants. However, it has been noticed throughout the years that that
the decisions based solely on the result of these acceptance tests, including an additional personal interviews, are
not sufficient (Alenezi, et al, 2009).

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Passion and Motivation : It is a paramount criteria at FSASEC to be sure that the students that are absorbed
into the system are highly passionate and motivated about the program and the career path they are about to
follow. Selection interviews are therefore regarded as an important component in the screening processes for
admission. The success of students can be measured through retention, graduation and job placement rates.
Specifically, first year first time students who continue in their second year (Pokrajac, et al, 2016). Kypuros,
Pierce and Monforti reported that the University of Texas-Pan American faces some unique regional challenges
that hinder retention, success, persistence and progression (Kypuros, et al, 2016).
IV. LINEAR REGRESSION ANALYSIS
The following algorithm is used to determine whether the student is in fact a star or not. The definition of a star
meets the following criteria,
Criterion 1
Mathematics >= 60 %
Engineering Science >= 60 %
Industrial Electronics >= 60%
Electrical Trade Theory >= 60%
Criterion 2
The average for all the marks is greater than 70%.
The APS score is used as a guide since the student may wish articulate to the main stream university curriculum.
The APS score is used to calculate the students’ eligibility for entry in a particular field of study. These minimum
percentages are only guides which can be adjusted to meet the requirement. For example, the criteria can be
increased if the intention is to find scholarships for them. In FSASEC, the APS score is not a major focus as
would be at the main stream courses because some of the students have done Mathematical Literacy and not
Mathematics. Others have not done Physical Sciences as that would a requirement in the main stream courses.
Algorithm 1.1 High Performance Star Student Algorithm
Start
{
int n = 0;
Boolean Star = False;
Boolean Found = False
Enter Student Number;
Read Student Number;
for (n = 1, to number of entries, n++)
{
Scan the list of students for student number
If (Found) then
{
Found = True
Scan the list of marks
{
If {all greater than 60 %)
{
If (Average marks >= 70 %)
{
Star = True;
} End if
} End if
} End if
} End For
Print “Student is a star
}
End Start

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Regression is a form of supervised learning and it is used to make predictions. A simple linear regression enables
us to develop a model to explain the relationship between a dependent variable and one dependent variable. We
often get better prediction if we use more than one variable and lands us in a concept of multiple regression. It is
an extension of a simple linear regression.
 ++= xy 10 (2)
 +++++= kk xxxy ...22110 (3)
Where:
k ...,, 000 are the parameters
 is the random error.
Linear regression tries to formulate the relationship between a target feature and a predictor feature using a line.
The straight-line regression uses the least squares to predict the outcome (Halde, et al, 2016).
There is only one y-intercept for the simple regression and more than one for the multiple regression models. The
least method is used to develop the estimate regression equation. In the graph below is shown a simple linear
regression to identify the best performing students.
Fig. 2. Corelation between Mathematics and Performance
It is clear from this graph that there is a strong correlation between how strong the student is in Mathematics and
the overall performance of the student. It is, therefore, important to consider the Mathematics aptitude of the
student when making a selection for students who wish to enroll for the artisan training course.
Table 1: Correlation between each subject and Overall Performance
Subject Correlation
Mathematics N1 0.913695474
Engineering Science N1 0.930111824
Industrial Electronics N1 0.873010527
Electrical Trade Theory N1 0.794074669
A particularly strong correlation between Mathematics and overall performance as well as Engineering Science
and overall performance although all other correlation coefficients are relatively high. Engineering Science is in
fact equivalent to Physics in the school system. So, when a selection of students is made it is, therefore, critical
to use Mathematics and Physics criteria for new student intake. Nated-courses or N-courses are used for
articulating into the main stream university degree studies in engineering for instance.

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V. MULTIPLE REGRESSION ANALYSIS
In multiple regression, the following approximate formula for predicting whether the student is a star or not was
obtained:
Y = -1.18975756 + 0.016735756X1 + 0.013419993X2 (4)
Where:
X1 = Mathematics
X2 = Industrial Electronics
The following table show prediction results where Mathematics and Industrial electronics were used.
Table 2: Multiple Regression Prediction Results
Observation Prediction Round off Actual
1 0.928247235 1 1
2 0.150672342 0 0
3 1.283170819 1 1
4 0.904879952 1 1
5 0.877726087 1 1
6 -0.438551829 0 0
7 1.118815141 1 1
8 0.773995786 1 1
9 0.780784252 1 1
10 0.539224379 1 1
The predictive values for Mathematics and Industrial Electronics are very strong while the predictive values for
Engineering Science and Electrical Trade Theory have less impact, therefore they were excluded in the regression
model. If the students is a star then he or she receives a value close to a 1 else he or she receives a value close to
0, hence, it becomes appropriate to round off the result to 0 If its closest 0 or 1 if its closest to 1. It can be seen in
this particular table that prediction is relatively accurate and thence classification can be made by rounding off to
the nearest digit. All students who scored a 1 in the rating are the stars and those who scored a 0 are not stars.
Some of our students have articulated to the main stream program in Engineering at the university which is
plausible outcome. They have, in fact been found to excel in the field of Electrical Engineering. This addresses
some of the objectives of the South African white paper on post school education.
VI. CONCLUSION
This paper has presented the application of regression in predicting whether the students qualify stars in
preparation for their examination. Linear regression was applied to determine whether there is a correlation
between the subject performance and the overall performance of the students. It was discovered that there was a
correlation which was stronger in some subjects compared to others. Multiple regression was used to predict
whether the student is a star or not and the results indicate 100% prediction accuracy for this determination. An
algorithm to confirm star students was also developed.
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