Tunisia baccalaureate data analysis 2010 - 2022

Written and edited by: Skander Boudawara - 27/06/2022

Do not hesitate to contact me through my LinkedIn Profile

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Context:

🗓️ 24 June 2022 is the results day of the most awaited exam, "The Tunisian baccalaureate." The "Bac" is considered one of the most crucial steps to finishing high school and starting the university curriculum. I wanted to focus on the data presented over the last ten years and try to have some conclusion and why not confirm or deny my personal bias. ( this study is for personal fun and not intended for other use)

🤔 At first, I thought it would be a piece of cake, and as Tunisia proclaims under the project "Digital Tunisia," I would easily find the data, and in one run, I will have my visualizations. However, these are the issues I encountered:

• The data are not available on the official government website. 🕵️‍♂️
• Some data are available on the Facebook page of the Ministry of Education but not always in the same format. 🔎
• Some data are available on local newspapers and radio and are painful to find & parse (Yeah, sometimes I listened to an entire archived radio cast to extract one or two values)📻

💻 I decided then to manually fill the data needed for my analysis through Google Sheets ( Spreadsheet link ) and try my best to avoid any mistype (even though the risk is still high).📝

The scope of my analysis:

• Period: from 2010 - 2022
• High School: Public & Private
• Number of registered pupils for the exam (Public & Private)
• Success rate, gender partition
• Region ranking by success rate
• High Marks, gender partition & their high school of origin

🕰️ It took me about 4 hours to find and parse the data needed. So yeah, let's finally start our analysis.📊

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I - Pupil registered for the BAC exam

1- Repartition over the years

An analysis of the total number of registered pupils throughout the year revealed minimal fluctuations. The minimum number of registered pupils was observed to be 126776 pupils while the maximum number of registered pupils was observed to be 146158 pupils. This resulted in a difference of only 20,000 pupils (or 13.26%) between the minimum and maximum, which can be considered as statistically insignificant but has a cost on educational budget.

2- Distribution of pupils registered per field

The graph depicts the enrollment trends in various fields of study among Tunisian students.

It is observed that there is a high demand for the technical field and the economics and management field, while there is a lack of interest in the mathematics and computer science field, which is surprising given the growing importance of the digital and computer science industry.

The reason for this trend is not immediately clear and could be further investigated. Some possible factors to consider include:

• Government policies
• Perceived difficulty of the field
• Job opportunities in the local market.
• etc

Additionally, it is important to analyze the success rate in these fields to gain a more comprehensive understanding of the enrollment trends.

3- Sucess, adjournment & refusal rate distribution

A preliminary examination of the data indicates that the rate of refusals appears to be on the rise over the year, while the success rate is decreasing and the adjournment rate remains stable. Further analysis will be conducted to determine the evolution of each rate over time.

Upon closer examination, the variation of the success rate over the years will be evaluated to gain a deeper understanding of the performance in this area.

• The years 2012 and 2015 had the lowest success rate performance, with a drop of 26%.
• 2021 had the highest success rate performance, despite being heavily impacted by the Covid-19 pandemic.

To gain a comprehensive understanding of the trend and performance of the success rate, a detailed analysis of the data from 2010 to 2022 will be conducted.

The data presented in these three graphs is concerning:

• Why is there a significant decrease in the success and adjournment rate and a steady increase in the refusal rate?
• Is the educational system in decline?
• Is the current educational system no longer suitable for the current generation of students?
• To what extent are recurring events such as student strikes, government strikes, teacher strikes, Covid-19, and the Jasmine Revolution contributing to this trend?
• What actions can be taken to address this issue and improve the success and adjournment rate while decreasing the refusal rate?

It is crucial to investigate the underlying causes of this trend and implement strategic and effective measures to address the issue.

4- Average success rate by gender

The data shows that the average success rate for females is significantly higher than for males, with females achieving an average success rate of 63.5% compared to 36.5% for males. This is consistent with other studies in the field.

It's worth mentioning that this topic has been widely discussed by experts and researchers and to have a deeper understanding, it's recommended to refer to the analysis made by InkyFada on this subject.

Link to the article

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II- Laureate & High marks analysis

As a student, I always found it fascinating to learn about the top performers and highest marks achieved during the exams. I remember aspiring to surpass my predecessors and set new records. However, despite my own good grade (17.8) in 2011, I wasn't able to secure a place on the podium.

This analysis will take a personal approach, focusing on the evolution of high marks over the years and in which field I have consistently seen top performers.

1 - Evolution of high marks over the years

As observed in the data, the highest marks are consistently achieved in the Mathematical or Experimental Science fields. This is expected as these fields are primarily based on true/false type of exams, where there is a clear right or wrong answer. Meanwhile, other fields may require more subjective grading. Additionally, the trend of high marks has a positive slope but it's worth investigating if this trend will continue.

To gain a deeper understanding of the top performers, a thorough analysis will be conducted on one laureate from each field, including:

• Literature
• Mathematics Sc
• Experimental Sc
• Economics and Management
• Sport
• Computer Sc
• Technical Sc

This analysis will focus on the laureate's school of origin, gender and other relevant factors that might have contributed to their success.

2- Gender distribution between laureate

Unlike the overall gender distribution, the distribution among laureates is relatively balanced between males and females, with occasional fluctuations. For example, in 2010, there was a higher proportion of female laureates.

3- High school of origin of the laureate (Pilote vs Other schools)

Interestingly, the distribution of laureates between public high schools and "pilot" high schools is relatively equal. While pilot high schools are known for offering a more challenging and advanced education, the data does not show a significant difference in the number of laureates produced by these schools compared to public high schools.

4- The high school with the most laureate number

[^1] HS stands for High School

However, it is worth noting that High School Pilote Sousse has produced the most number of laureates in the country over the last decade. The school's top field for producing laureates is Technique, with eight laureates between 2010-2022. Furthermore, it would be interesting to know the region of origin of the students who graduated from this school.

5- Region of the High school of the laureate

I had initially anticipated that Sfax or Sousse would have the most laureates, but the data shows that Tunis (downtown) has produced the most number of laureates with 23, followed by Sousse and Sfax.

To gain a more complete understanding of the success rate, the analysis will now focus on the distribution of success rate by region.

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III- Success rate per region

To create these charts, I used the library high-charts combined with Google script (The source code as below)

Code

var Route = {}
Route.path = function (route_x, callback){
Route[route_x] = callback;
}
function doGet(e) {
Route.path('2015page',load2015)
Route.path('2018page',load2018)
Route.path('2022page',load2022)

if (Route[e.parameter.v]){
    return Route[e.parameters.v]()
}
else {
    return render('2015page')
}
}

function load2015() {
return render('2015page')
}

function load2018() {
return render('2018page')
}
function load2022() {
return render('2022page')
}



function include(filename) {
return HtmlService.createHtmlOutputFromFile(filename).getContent()
}

function render(file, argsObject){
var tmp = HtmlService.createTemplateFromFile(file)
if (argsObject){
var keys = Object.keys(argsObject)
keys.forEach(function(key) {
    tmp[key] = argsObject[key]
})
}
return tmp
.evaluate()
.setXFrameOptionsMode(HtmlService.XFrameOptionsMode.ALLOWALL)
.setTitle('tunisia_map_bac')
}

page.html

<!DOCTYPE html>
<html>
<head>
<base target="_top">
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-0evHe/X+R7YkIZDRvuzKMRqM+OrBnVFBL6DOitfPri4tjfHxaWutUpFmBp4vmVor" crossorigin="anonymous">
<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.bundle.min.js" integrity="sha384-pprn3073KE6tl6bjs2QrFaJGz5/SUsLqktiwsUTF55Jfv3qYSDhgCecCxMW52nD2" crossorigin="anonymous"></script>
</head>
<body>
<div id="container" class="container"></div>


</body>
<script src="https://code.highcharts.com/maps/highmaps.js"></script>
<script src="https://code.highcharts.com/maps/modules/exporting.js"></script>
<script src="https://code.highcharts.com/maps/modules/offline-exporting.js"></script>
<script src="https://code.highcharts.com/maps/modules/accessibility.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.5/dist/umd/popper.min.js" integrity="sha384-Xe+8cL9oJa6tN/veChSP7q+mnSPaj5Bcu9mPX5F5xIGE0DVittaqT5lorf0EI7Vk" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.min.js" integrity="sha384-kjU+l4N0Yf4ZOJErLsIcvOU2qSb74wXpOhqTvwVx3OElZRweTnQ6d31fXEoRD1Jy" crossorigin="anonymous"></script>
<script>
// Prepare random data
var data = [ [ 'AR' , 37.39 ]  , [ 'BA' , 29.5 ]  , [ 'BJ' , 20.06 ]  , [ 'BZ' , 28.75 ]  , [ 'GB' , 24.04 ]  , [ 'GF' , 14.83 ]  , [ 'JE' , 15.4 ]  , [ 'KB' , 18.76 ]  , [ 'KF' , 21.1 ]  , [ 'KR' , 17.94 ]  , [ 'KS' , 11.41 ]  , [ 'ME' , 35.56 ]  , [ 'MH' , 36.54 ]  , [ 'MN' , 24.23 ]  , [ 'MS' , 36.51 ]  , [ 'NB' , 31.18 ]  , [ 'SF' , 43.545 ]  , [ 'SL' , 23.31 ]  , [ 'SS' , 36.28 ]  , [ 'SZ' , 16.68 ]  , [ 'TA' , 18.29 ]  , [ 'TO' , 19.58 ]  , [ 'TU' , 28.055 ]  , [ 'ZA' , 23.25 ]  ] ;

Highcharts.getJSON('https://code.highcharts.com/mapdata/countries/tn/tn-all.geo.json', function (geojson) {

// Initialize the chart
Highcharts.mapChart('container', {
    chart: {
        map: geojson
    },

    title: {
        text: 'Success rate per region 2015'
    },

    accessibility: {
        typeDescription: 'Map of Tunisia.'
    },

    mapNavigation: {
        enabled: false,
        enableButtons: false
    },

    xAxis: {
        
        labels: {
            enabled: true
        }
    },


    colorAxis: {
        labels: {
            format: '{value}%'
        },
        showEmpty: true,
        enabled: false,
        tickPixelInterval: 50
    },

    series: [{
        data: data,
        keys: ['postal-code', 'value'],
        joinBy: 'postal-code',
        name: 'Sucess rate',
        states: {
            hover: {
                color: '#a4edba'
            }
        },
        dataLabels: {
            enabled: false,
            format: '{point.properties.postal-code}'
        }
    }]
});
});
</script>
</html>

As observed in the data, the coastal regions have a higher success rate than the inner regions. This trend has remained consistent over the last decade. This could be attributed to the lack of investment in the inner regions, both in terms of culture and economy. It's recommended to refer to InkyFada for more detailed documentation on this subject.

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Conclusions

• The study provided a comprehensive understanding of the current state of baccalaureate data in Tunisia
• It addressed and corrected some biases, particularly in regards to mathematical fields and the trend among pilot schools
• The study revealed some concerning trends, such as the increase in the refusal rate and the uneven distribution of success rate by region.
• The study utilized simple visualizations to present the data, which was challenging to balance between clarity and complexity.
• The lack of easily accessible and comprehensive data for Tunisia is a limitation in conducting this type of research and it is hoped that in future such data will be made publicly available for investigation.

Critics:

• The data presented in this study should be cross-referenced with other relevant data in order to make more informed and accurate decisions.
• The data found in the Google spreadsheet used in this study are manually inputted and have not undergone a thorough verification process by an external party, which may result in small inaccuracies. It's recommended that further validation should be done using other reliable sources.

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Please feel free to reach out to me through my LinkedIn Profile if you have any further questions or would like to discuss this study in more detail.