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https://github.com/mickfrog/uace-analysis

UACE ANALYSIS FOR 2011 - 2015
https://github.com/mickfrog/uace-analysis

data data-science data-visualization folium-maps geocoder jupyter-notebook pandas python3

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UACE ANALYSIS FOR 2011 - 2015

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README 

        
# FINAL RECESS PROJECT



# GROUP C



# Results of the UACE Data Analysis (Years 2011 to 2015)



## [Project Github Link](https://github.com/MickFrog/uace-analysis)



### Group Members



        

            

                Name

                Registration Number

                Student Number

            

        

        

            

                Ssenono Jordan Michael

                21/U/1013

                2100701013

            

            

                Ariyo Ahumuza Nimusiima

                21/U/1657

                2100701657

            

            

                Serumaga Conrad David

                22/U/6881

                2200706881

            

            

                Ojok Emmanuel Nsubuga

                21/U/06816/PS

                2100706816

            

            

                Owomugisha Cronnie

                21/U/05910

                2100705910

            

        

    



# INTRODUCTION



The dataset under analysis presents a comprehensive record of Uganda Advanced Certificate of Education (UACE) exams results spanning from the year 2011 to 2015. Comprising a diverse range of schools located across different districts, this dataset offers valuable insights into the academic performance of students over these six years. The dataset is presented in a structured CSV format, with each row representing a specific school's performance in a given year. The columns encompass a wide array of metrics, including the number of candidates, division-wise results, and gender-specific performance indicators. These detailed records provide an opportunity to delve into the trends and patterns of educational achievements across various districts and schools.



The UACE results dataset includes a number of performance indicators, such as the total number of students who took the exam each year and the percentage distribution of students in various score levels. It also includes information on the students' schools, districts, and gender, allowing for a thorough analysis of academic trends and patterns.



Understanding and analyzing the UACE results can give important insights into the strengths and weaknesses of the education system, as education continues to be a crucial component of national development. This analysis tries to discover trends, pinpoint top-performing districts and schools, investigate academic performance by gender, and evaluate relationships between various performance variables. The results of this analysis can help Ugandan education policymakers, administrators, and other stakeholders make well-informed decisions to improve the standard of instruction and promote better academic achievements.



# KEY OBJECTIVES



### To comprehend the general trend in educational performance across different districts over a five-year period (2011 to 2015)



To achieve this, we analyzed the dataset to identify patterns and changes in educational performance. One approach we used was creating line plots to visually represent the yearly student enrolment figures, which helped us detect any noticeable shifts or trends. This way, we could evaluate annual performance. We analyzed the academic results of districts and schools year over year in order to find any noticeable increases or decreases. The number of students overall and their performance metrics were shown using lines to show trends from 2011 to 2015.



### To Determine if there are any gender-based differences in academic achievement



Comparing the performance measures between male and female students allowed us to examine gender-based differences in educational accomplishment. Male and female students' academic performance was visualized and compared over time using line plots for performance above average as well as total students sitting the exams to identify any gender-based disparities. In this case, we took performance above average as performance above passing grade. (11 points in this case)



### To assess academic proficiency by studying how students are distributed across different scoring point ranges (%0-5, %6-10, %11-15, %16-20, %21-25)



To achieve this, we analyzed how pupils were distributed in these ranges. For example, we used pie charts to visualize the proportion of students within the worst scoring range for the top-performing districts. This allowed us to gain insights into the academic proficiency levels of the students.



### To assess Yearly Variation and Consistency in Key School Metrics



This objective involves utilizing a correlation matrix to examine whether a stable link exists between the annual count of candidates and the corresponding number of above-average performers. A positive correlation would indicate that as candidate numbers change yearly, a corresponding shift occurs in above-average performers. This provides insights into how student population fluctuations impact above-average academic achievements and gauges performance consistency across years.



### To find outliers in the school and district performance metrics



Using a box plot that uses percentage of students above average performance for schools. It uses metrics such as the upper and lower quartiles, the median, the mean and the outliers are shown as data points below and above the lower and upper quartile respectively.



### To develop a Predictive Model for Future Results



An essential objective of this analysis was to construct a robust predictive model capable of projecting future academic outcomes based on the historical UACE exam results from 2011 to 2015. By leveraging the comprehensive dataset encompassing diverse performance metrics, including candidate counts, score distributions, and gender-specific indicators, the aim was to create a model that could offer insights into forthcoming educational achievements.