University Student GPA Results: Factors and Discussion Report

This report analyzes student GPA results, focusing on factors influencing academic performance within a statistics unit. The analysis begins with data cleansing to address errors such as missing or misplaced values, and then selects relevant features like age, gender, admission type, and nationality. Descriptive techniques are used to analyze the data, as inferential statistics were limited. The analysis includes the calculation of descriptive statistics, such as mean, standard deviation, kurtosis, and skewness, and the creation of comparative tables and charts to discern connections between variables and termGPA. The results, presented through a visual dashboard, reveal insights into student performance based on factors like age, gender, location, and degree type. The report also acknowledges ethical considerations, ensuring the analysis aims to improve student performance while maintaining confidentiality. Frequency charts demonstrate the relationship between different variables, with references to relevant literature.

Discussion of GPA Results 1
DISCUSSION OF GPA RESULTS FOR STUDENTS
By (Name)
The Name of the Class (Course)
Professor (Tutor)
The Name of the School (University)
The City and State where it is located
The Date

Discussion of GPA Results 2
Discussion of GPA Results for Students
Problem Statement and Data Cleansing
The main focus in this assessment is to discern the effect that different academic performance
factors have on GPA scores of Students. Therefore, we will assess the effect of different types of
student degrees, gender, language, and nationality have on GPA results of students in a given
statistics unit. There were several data errors caused by missing, misplaced, and incorrect data.
For example, the inclusion of “NA” and “Unknown” into then dataset is meant to symbolize
missing data: however, it does transform continuous variables into character variables.
Therefore, for the purpose of formulating accurate and correct pivot tables and charts it was
necessary to remove all characters found inside continuous variables. In Microsoft Excel we are
able to eliminate “NA”, “Unknown”, and “NA%” by replacing them with blank values that will
be interpreted by excel as missing values. The misplace values for age that are found under the
CarerHours and HoursWorked variables were also replaced with blank characters. An
assumption was made that some values were intentionally shortened for spacing issues e.g.
“Intertiol” in place of International. The pictures below show the data before and after the data
cleansing process.

Discussion of GPA Results 3

Discussion of GPA Results 4
Variable Selection and Ethical Consideration
The features that were chosen for this assessment are age, gender, admission, citizenship
(nationality), language, location, and termGPA. The main focus is given to the effect that the
aforementioned variables have on term GPA of statistics students. Admission is considered to be
the filter variable that is used to control the dashboard. All the variables selected were considered
to be correlated to student performance in statistics. Therefore, by assessing these variables one
is able to understand the reason for low or high performance of students in a particular area of
study. There are two main ethical considerations that are associated with this study. The first is
that any of the analysis and assessment performed on the data should be done with the intention
of gathering insight that will aid in the betterment of student performance. The second and final
ethical consideration is that all results from the analysis will be shared with the relevant parties
only to avoid issues with breach of student confidentiality and right to privacy.
Modeling and Analysis
The data was analyzed using descriptive techniques, due to a limitation in continuous variable
inferential statistics were not performed; for example, hypothesis analysis to assess whether
there is a significant difference between the term GPA scores of males and females taking a
statistics unit (Kemp, et. al. 2018). The descriptive results for the variable termGPA are
presented below. The mean GPA score can be considered to slightly below average. The fact
that standard deviation is smaller than the mean indicates that the term GPA scores of most
students are clustered close to the figure of 3.42. The kurtosis and skewness figures imply that
the termGPA variable is normally distribution; this is further exemplified by the histogram
charted below. Comparative tables and charts we used to discern the connection between each of
the aforementioned variables and termGPA. For example, the GPA scores of males and females

Discussion of GPA Results 5
are compared to see whether or not gender has an impact on student performance in statistics
(Kemp, et. al. 2018).
TermGPA
Mean 3.41577465
Standard Error 0.23109943
Median 3.75
Mode 0
Standard
Deviation
1.94727844
Sample Variance 3.79189332
Kurtosis -0.98995102
aSkewness -0.31295589
Range 6.5
Minimum 0
Maximum 6.5
Sum 242.52
Count 71
0 1 2 3 4 5 6 7
0
5
10
15
20
25
Histogram for Term GPA
Term GPA
Axis Title
frequncy
Present Results
The overall visual dashboard has several charts and a single slicer that controls all transformation
based on the admission filter. The chart for Age indicates that the highest scores in termGPA are

Discussion of GPA Results 6
recorded amongst individuals aged between 35 and 44 years. There seems to be a slightly better
GPA performance in females compared to males. However, the difference cannot be proved or
disproved as being statistically significant because no hypothesis analysis was performed(Pillai,
2015). There is a large difference between the GPA scores of domestic students compared to
international students. Looking at the doughnut chart for termGPA based on location we are able
to deduce that external studies students performed relatively better that Casuari campus students.
It is also correct to deduce that degree type is a factor when it comes to the performance of
student in a statistics unit. Another factors observed are with regard to the nationality and
language spoken by the student.
17 - 24 25 - 34 35 - 44 45 - 54 55+
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Term GPA By Age

Discussion of GPA Results 7
3.50666666666667; 51%3.39142857142857; 49%
Term GPA by Gender
F M
Domestic Intertiol
0
0.5
1
1.5
2
2.5
3
3.5
4
3.59372549019608
2.962
Term GPA by Nationality

Discussion of GPA Results 8
0
1
2
3
4
5
6
7
Term GPA by Student Degree
No
Yes
2.9 3 3.1 3.2 3.3 3.4 3.5 3.6
Term GPA by Language
2.94
4.14
3.25
Term GPA by Location
CASUARI CAMPUS EXTERL STUDIES (blank)

Discussion of GPA Results 9
The student system data contains categorical variables; As such, frequency charts were generated
that demonstrate the relationship between different variables. For example, from the bar chart for
Grade by Correspondence Cat we can see that those individual with a “W” GPA we very few an
were only observed under the correspondence cat of “HE-MAIN”.

Discussion of GPA Results 10
References
Kemp, S. E., Hort, J., & Hollowood, T. 2018. Descriptive Analysis in Sensory Evaluation. Hoboken, NJ: John
Wiley & Sons.
Pillai, N. V. 2015. Data Analysis and Interpretation. Conference: Presented to the participants of an
Induction Training Programme organized by the Institute of Management in Government in
collaboration with DoPT, Government of India (pp. 1-31). Thiruvananthapuram, India: Centre for
Development Studies.