Exploring UK University Data: Understanding Student Satisfaction and Factors Affecting it

Today, I’m excited to share with you a project I recently worked on that explores university data to understand student satisfaction and the factors that influence it. Whether you’re a data enthusiast or just curious about what makes students happy at universities, I hope you’ll find this post informative and engaging.

As a recent immigrant to the United Kingdom, I’ve been keen on understanding various aspects of life here, including education and healthcare. During my exploration on the internet, I stumbled upon an intriguing article titled “London has excellent universities — but unhappy students” in The Economist. This article piqued my interest and prompted me to delve deeper into the reasons behind the dissatisfaction among students in London.

According to the article, London’s top universities seem to lag behind in terms of student satisfaction compared to their counterparts in other parts of Britain. The International Student Barometer, which gauges the perspectives of students studying abroad, indicates that international students in London are notably less likely to recommend their university compared to those in other British cities.

While the bustling nature and high costs associated with big cities like London may contribute to this dissatisfaction, the situation in New York’s top universities paints a different picture. Columbia University, for instance, stands out for both its academic excellence and high student ratings. So, what could be the underlying issues affecting student satisfaction in London?

The article suggests several potential factors:

1. Focus on Research Over Teaching: London’s universities may prioritize research activities at the expense of teaching quality.
2. Lack of Community Feel: Some of London’s universities struggle to foster a sense of community, possibly due to the absence of a traditional campus setting.
3. High Proportion of Foreign Students: The substantial presence of international students in London’s universities could contribute to the challenges of creating a cohesive student community.

In this post, I aim to explore and analyze these factors to uncover the reasons behind the dissatisfaction among students in London. By examining data and conducting thorough research, I hope to shed light on this important issue and contribute to the ongoing discussion on enhancing the university experience for students in London. Join me as we delve into the world of higher education and seek to understand what drives student satisfaction in one of the world’s most vibrant cities.

Data Collection and Preparation

To conduct my analysis, I used a comprehensive dataset sourced from Kaggle, containing information about universities across the UK. This dataset included details such as university rankings, student enrollment numbers, average fees, student satisfaction ratings, and more. You can download the dataset from here.

After downloading the dataset in CSV format, I installed the Seaborn and Scikit-learn libraries and imported other necessary packages and libraries required for tasks such as data analysis, manipulation, and visualization. Following this, I used the Pandas library to read the dataset to uncover the hidden insights.

pip install seaborn
pip install scikit-learn        

# Importing packages and libraries for data analysis, manipulation and visualization
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as impl

from sklearn.preprocessing 

import StandardScalerimport os        

# Reading the csv file
df = pd.read_csv('C:\\Users\\hello\\OneDrive\\BI\\uk_universities.csv')        

Data cleaning is an essential process in data analysis that involves detecting and rectifying errors within a dataset. This meticulous process is crucial as it ensures data accuracy and consistency, and eliminates bias, thereby facilitating the generation of valuable insights for informed decision-making.

Displaying the first five rows of the dataset allows for a rapid overview, providing insight into the dataset’s structure, column types, and potential areas requiring cleaning and transformation. By examining the data types of each column, which may include objects, integers, and floats, one can assess the variety of information present. Additionally, determining the number of rows and columns, and examining dataframe information lays the groundwork for subsequent data manipulation tasks.

# Displaying the top 5 data
df.head(5)

# Displaying the data types
df.dtypes

# Checking the number of rows and columns
df.shape

# Dataframe information
df.info()        

After creating a function to assess the distribution of missing values, the output revealed four columns with missing values. Subsequently, I replaced the missing values in the ‘Academic_calendar’ and ‘Campus_setting’ columns with the value ‘undisclosed’. Following this data cleansing step, a statistical summary was generated using the describe() function to gain insights into the central tendency and spread of the dataset.

# Checking for missing values
for u in df.columns:
    if df[u].isna().sum() != 0:
        print('null values in', u, 'column :', df[u].isna().sum())

# Replacing missing values
df['Academic_Calender'] = df['Academic_Calender'].fillna('undisclosed')
df['Campus_setting'] = df['Campus_setting'].fillna('undisclosed')

# describe method to compute statistics summary
df.describe()        

I converted certain columns from integers to objects, and then proceeded to eliminate unnecessary columns from the dataset.

# Change of data type from int64 to object for column 'World_rank'
df['World_rank'] = df['World_rank'].astype('object')
df.dtypes

# Converting data type from object to float
df['Student_satisfaction'] = df['Student_satisfaction'].str.rstrip('%').astype(float)
print(df.dtypes)

# Dropping unnecessary columns
columns_to_drop = ['Motto', 'Website']
df.drop(columns=columns_to_drop, inplace=True)        

I established a function called “Student_enrollment” to categorize student enrollment ranges into numerical groups for enhanced analysis and processing. Afterwards, I applied this function to create a new column ‘student_enrollment2’, and subsequently removed the original ‘student_enrollment’ column from the dataset.

# Creating a function to group student enrollment
def Student_enrollment(ex):
    if ex=='1,000-1,999':
        return 1
    elif ex=='2,000-2,999':
        return 2
    elif ex=='3,000-3,999':
        return 3
    elif ex=='4,000-4,999':
        return 4
    elif ex=='5,000-5,999':
        return 5
    elif ex=='6,000-6,999':
        return 6
    elif ex=='7,000-7,999':
        return 7
    elif ex=='8,000-8,999':
        return 8
    elif ex=='9,000-9,999':
        return 9
    elif ex=='10,000-14,999':
        return 10
    elif ex=='15,000-19,999':
        return 15
    elif ex=='20,000-24,999':
        return 20
    elif ex=='25,000-29,999':
        return 25
    elif ex=='30,000-34,999':
        return 30
    elif ex=='35,000-39,999':
        return 35
    else:
        return 40

# Applying the Student_enrollment function to create a new column
df['Student_enrollment2'] = df['Student_enrollment'].apply(Student_enrollment)

# Dropping the original 'Student_enrollment' column
df = df.drop('Student_enrollment', axis=1)        

Exploratory Data?Analysis

Delving into the analysis, I conducted exploratory data analysis (EDA) to familiarize myself with the dataset and identify any patterns or trends. This involved visualizing the data using charts and graphs to uncover insights into student satisfaction levels, the geographical distribution of universities, trends over time, and more.

I began by sorting universities based on world rank. The University of Oxford emerged as the second-best university by world ranking, followed by the University of Cambridge and Imperial College London, ranked fourth and eighth respectively. It is evident that the University of Oxford holds the top position among universities in the United Kingdom, while Imperial College London stands out as the leading school in the London region based on world rank.

highest_rank_universities = df.sort_values(['World_rank', 'University_name'], ascending=[True, False]).head(5)
highest_rank_universities.reset_index(drop=True, inplace=True)
highest_rank_universities.head()        

The script below facilitates the plotting of a bar chart showcasing the top five universities in the United Kingdom based on world ranking:

# Creating a color palette with a unique color for each university
colors= '#4A4679'

# Creating a bar plot
plt.figure(figsize=(17, 6))
bars = plt.bar(highest_rank_universities['University_name'], highest_rank_universities['World_rank'], color=colors)

# Adding data labels above each bar
for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width() / 2, height + 0.2, f'{height}', ha='center', va='bottom')

# Adding title and labels
plt.title('Top 5 Highest-Ranked Universities')
plt.xlabel('University')
plt.ylabel('World Rank')

# Rotating x-axis labels for better readability and enable text wrapping
plt.xticks(rotation=0, ha='center', wrap=True)

# Showing chart
plt.tight_layout()
plt.show()        

Focusing on the count of universities in each region, it’s evident that the London region has the highest number of universities. Following London, South East England and Scotland rank second and third, respectively, with the most universities. On the other hand, the regions with the least number of universities are East Midlands, North East England, and Northern Ireland, appearing at the bottom three positions in terms of university count.

# Count of number of universities in each region
region_counts = df['Region'].value_counts()

# Color definition for each region
colors= '#4A4679'

# Plotting a column chart
plt.figure(figsize=(17, 6))
region_counts.plot(kind='bar', color=colors)
plt.title('Number of Universities by Region')
plt.xlabel('Region')
plt.ylabel('Number of Universities')
plt.xticks(rotation=45, ha='right')

# Add data labels
for i, v in enumerate(region_counts):
    plt.text(i, v + 0.2, str(v), ha='center', va='bottom')

plt.tight_layout()
plt.show()        

Identifying the top five most expensive universities based on the estimated cost of living per year (in pounds) using the nlargest() function provides a clear visual representation. London Metropolitan University tops the list, requiring students to spend an estimated cost of ￡15,000 per year, followed by the University of Sussex with a cost of living of ￡12,500 per year for a student. Delving deeper into the top ten most expensive schools by estimated cost of living and region, it's noteworthy that five of these schools are located in the London region.

# Function to add data labels
def add_labels(ax):
    for p in ax.patches:
        ax.annotate(f'{p.get_height():.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),
                    ha='center', va='center', fontsize=10, color='black', xytext=(0, 5),
                    textcoords='offset points')

# Top 5 most expensive universities
top_expensive = df.nlargest(5, 'Estimated_cost_of_living_per_year_(in_pounds)')

# Create the bar chart
plt.figure(figsize=(17, 6))
bars = plt.bar(top_expensive['University_name'], top_expensive['Estimated_cost_of_living_per_year_(in_pounds)'], color='darkred')

# Add data labels
add_labels(plt.gca())

# Set the title and labels
plt.title('Top 5 Most Expensive Universities')
plt.xlabel('University Name')
plt.ylabel('Estimated Cost of Living per Year (in pounds)')
plt.xticks(rotation=0, ha='center', wrap=True)

# Show the plot
plt.tight_layout()
plt.show()        

The least expensive school is the University of Bolton, located in the North West England region. Interestingly, the top five least expensive schools are primarily concentrated within the North West England and West Midlands regions. This information was obtained using the nsmallest() function as part of the previous script.

Further analysis was conducted on the total cost of attending postgraduate universities by calculating the sum of postgraduate course fees and estimated living costs. This analysis revealed the top five most expensive universities. Imperial College London emerged as the most expensive university in the UK for postgraduate students, with a total cost of ￡40,600, followed by the London School of Economics and Political Science with a total cost of ￡36,500.

Similarly, the same analysis was performed for undergraduate students. Imperial College London was also identified as the most expensive university in the United Kingdom for undergraduates, with a total cost of ￡34,200, followed by the University of Cambridge with a total cost of ￡33,750 for undergraduates.

# Calculating the total cost (PG course fees + Living cost)
df['Total_Cost'] = df['PG_average_fees_(in_pounds)'] + df['Estimated_cost_of_living_per_year_(in_pounds)']

# Top 5 most expensive universities
top_expensive = df.nlargest(5, 'Total_Cost')

# Color definition
colors= '#4A4679'

# Creating the bar chart
plt.figure(figsize=(17, 6))
bars = plt.bar(top_expensive['University_name'], top_expensive['Total_Cost'], color=colors)

# Adding data labels
for bar in bars:
    plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), round(bar.get_height(), 2),
             ha='center', va='bottom')

# Setting the title and labels
plt.title('Top 5 Most Expensive Universities (PG Course Fees + Living Cost)')
plt.xlabel('University Name')
plt.ylabel('Total Cost (in pounds)')
plt.xticks(rotation=0, ha='center', wrap=True)

# Showing the plot
plt.tight_layout()
plt.show()        

Analyzing universities with the highest student satisfaction rates, the University of St Andrews leads the chart with a satisfaction rate of 87.90%, followed by the University of Oxford and Aberystwyth University with rates of 86.50% and 86.10%, respectively. Further analysis of student satisfaction rates by region reveals that the top three regions with the highest rates are Wales, West Midlands, and East Midlands, while universities in the London region have the lowest satisfaction rates.

# Sorting the DataFrame by 'Student_satisfaction' column in descending order and get the top 10 rows
top_10_satisfaction = df.nlargest(10, 'Student_satisfaction')

# Plotting chart
plt.figure(figsize=(15, 6))
sns.barplot(x='Student_satisfaction', y='University_name', data=top_10_satisfaction, palette='viridis')

# Adding data labels
for i in range(len(top_10_satisfaction)):
    plt.text(top_10_satisfaction['Student_satisfaction'].iloc[i], i, 
             f"{top_10_satisfaction['Student_satisfaction'].iloc[i]:.2f}%", ha='left', va='center')

plt.title('Top 10 Universities with Highest Student Satisfaction')
plt.xlabel('Student Satisfaction (%)')
plt.ylabel('University Name')
plt.show()        

# Grouping the DataFrame by 'Region' and calculating the average student satisfaction rate for each region
region_satisfaction_rate = df.groupby('Region')['Student_satisfaction'].mean().reset_index()

# Sorting the DataFrame by average student satisfaction rate in descending order
region_satisfaction_rate = region_satisfaction_rate.sort_values(by='Student_satisfaction', ascending=False)

# Plotting
plt.figure(figsize=(15, 6))
sns.barplot(x='Student_satisfaction', y='Region', data=region_satisfaction_rate, palette='viridis')

# Adding data labels
for i in range(len(region_satisfaction_rate)):
    plt.text(region_satisfaction_rate['Student_satisfaction'].iloc[i], i, 
             f"{region_satisfaction_rate['Student_satisfaction'].iloc[i]:.2f}%", ha='left', va='center')

plt.title('Student Satisfaction Rate by Region')
plt.xlabel('Average Student Satisfaction (%)')
plt.ylabel('Region')
plt.show()        

London, Scotland, and South East England emerge as the top three regions with the highest proportion of international students, each exceeding 15%. Upon closer examination, the University of Buckingham stands out with the highest percentage of international students at 50.50%, followed by the London School of Economics and Political Science at 46.80%, and the University of the Arts London at 42.7%.

Utilizing a scatter plot to explore the correlation between university rank and student satisfaction rate, I observed a concentration of universities outside London, particularly in the rest of Britain, exhibiting higher student satisfaction rates compared to those within London.

# Separating data for universities in London region and universities outside London region
London_df = df[df['Region'] == 'London']
Rest_of_Britain_df = df[df['Region'] != 'London']

# Color definition for London and outside London universities
London_color = '#31688e'
Rest_of_Britain_color = '#e5e419'

# Creating scatter plot for universities in London region
plt.figure(figsize=(16, 6))
plt.scatter(London_df['UK_rank'], London_df['Student_satisfaction'], s=100, c=London_color, label='London', alpha=0.7)

# Creating scatter plot for schools outside London region
plt.scatter(Rest_of_Britain_df['UK_rank'], Rest_of_Britain_df['Student_satisfaction'], s=100, c=Rest_of_Britain_color, label='Rest_of_Britain', alpha=0.7)

# Add university names as annotations
#for x, y, name in zip(df['UK_rank'], df['Student_satisfaction'], df['University_name']):
    #plt.text(x, y, name, fontsize=8)

# Setting labels and title
plt.xlabel('UK Rank')
plt.ylabel('Student Satisfaction')
plt.title('University Rank vs Student Satisfaction')

# Adding legend
plt.legend()

# Show the plot
plt.show()        

Upon exploring the correlation between university-founded year and student satisfaction rate, it appears that there is no discernible correlation between the year of university establishment and the student satisfaction rate.

Conducting a comparative analysis between universities in London and the rest of Britain reveals several key differences. London universities generally have a longer history, higher tuition fees for both undergraduates and postgraduates and a larger percentage of international students compared to universities in the rest of Britain.

Additionally, universities in the rest of Britain tend to have higher UK rankings compared to those in London. Universities in London often require students to have higher IELTS scores than their counterparts in the rest of Britain. However, the rest of Britain boasts a higher student satisfaction score compared to London. Furthermore, London tends to have a higher estimated cost of living per year compared to the rest of Britain, while the rest of Britain has slightly higher student enrollment compared to London.

# Filtering data for London and rest of Britain
df_London = df[df['Region'] == 'London'].select_dtypes(include='number').mean()
df_Rest_of_Britain = df[df['Region'] != 'London'].select_dtypes(include='number').mean()

# Creating separate DataFrames for London and rest of Britain
df_London = pd.DataFrame(df_London).T.round(2)
df_Rest_of_Britain = pd.DataFrame(df_Rest_of_Britain).T.round(2)

# Concatenate both DataFrames along the rows axis
df_mean = pd.concat([df_London, df_Rest_of_Britain], axis=0)

# Renaming the index for clarity
df_mean.index = ['London', 'Rest of Britain']

# Displaying the resulting DataFrame
df_mean        

The correlation analysis between student satisfaction and various factors for universities in London indicates a negative relationship between student satisfaction and the percentage of international students, as well as undergraduate and postgraduate average fees and estimated cost of living per year. Conversely, the correlation analysis for universities in the rest of Britain demonstrates a positive association between student satisfaction and these same factors. However, it’s important to note that the strength of correlation varies across regions.

column_list= ['International_students', 'UG_average_fees_(in_pounds)', 'PG_average_fees_(in_pounds)', 
'Estimated_cost_of_living_per_year_(in_pounds)']

df_London = df[df['Region'] == 'London']
df_Rest_of_Britain = df[df['Region'] != 'London']

#Plotting correlation analysis of universities in London region
fig = plt.figure(figsize=(16, 12))

for i in range(len(column_list)):
    plt.subplot(2, 2, i + 1)
    plt.title(column_list[i])
    sns.regplot(data=df_London, x=column_list[i], y='Student_satisfaction')

plt.tight_layout()
plt.show()

#Plotting correlation analysis of universities in the rest of Britain
fig = plt.figure(figsize=(16,12))

for i in range(len(column_list)):
    plt.subplot(2,2,i+1)
    plt.title(column_list[i])
    sns.regplot(data=df_Rest_of_Britain,x=df_Rest_of_Britain[column_list[i]],y=df_Rest_of_Britain['Student_satisfaction'])

plt.tight_layout()
plt.show()        

The distribution analysis of average fees (in pounds) for undergraduate and postgraduate programs unveils diverse ranges across universities. Upon further examination through correlation, a perfect positive linear relationship emerges between student satisfaction and both undergraduate and postgraduate average fees (in pounds). This implies that as the average fees for undergraduate and postgraduate programs increase, student satisfaction also tends to increase, and vice versa.

Additionally, linear regression analysis demonstrates a significant relationship between undergraduate average fees and student satisfaction. The regression analysis underscores a potential influence of undergraduate fees on student satisfaction levels.

from scipy import stats

# Distribution Analysis of average fees (in pounds)
plt.figure(figsize=(16, 6))
plt.subplot(1, 2, 1)
sns.histplot(df['UG_average_fees_(in_pounds)'], kde=True, bins=20, color='skyblue')
plt.title('Distribution of UG Average Fees')
plt.xlabel('UG Average Fees (in pounds)')

plt.subplot(1, 2, 2)
sns.histplot(df['PG_average_fees_(in_pounds)'], kde=True, bins=20, color='salmon')
plt.title('Distribution of PG Average Fees')
plt.xlabel('PG Average Fees (in pounds)')

plt.tight_layout()
plt.show()

# Calculating correlation matrix
correlation_matrix = df[['UG_average_fees_(in_pounds)', 'PG_average_fees_(in_pounds)', 'Student_satisfaction']].corr()

# Visualizing correlation matrix as a heatmap
plt.figure(figsize=(16, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)
plt.title('Correlation Matrix')
plt.xticks(rotation=0, ha='center', wrap=True)
plt.show()

# Performing a linear regression analysis
slope, intercept, r_value, p_value, std_err = stats.linregress(df['UG_average_fees_(in_pounds)'], df['Student_satisfaction'])

# Visualizing regression line
plt.figure(figsize=(16, 6))
sns.regplot(x='UG_average_fees_(in_pounds)', y='Student_satisfaction', data=df, line_kws={'color': 'red'})
plt.title('Linear Regression: UG Average Fees vs Student Satisfaction')
plt.xlabel('UG Average Fees (in pounds)')
plt.ylabel('Student Satisfaction')
plt.show()        

The boxplot visually portrays the distribution of student satisfaction scores across various fee categories: Low, Medium, and High. The Fee_Category column categorizes universities based on their undergraduate and postgraduate average fees. Within each fee category, the boxplot showcases the median student satisfaction score, offering insights into how student satisfaction varies across different fee categories. This allows for comparisons and analysis, indicating that institutions charging higher fees may have a different impact on student satisfaction.

# Define the conditions for fee categories (you may need to adjust the thresholds)
conditions = [
    (df['UG_average_fees_(in_pounds)'] < 10000) & (df['PG_average_fees_(in_pounds)'] < 10000),
    (df['UG_average_fees_(in_pounds)'] >= 10000) & (df['UG_average_fees_(in_pounds)'] < 20000) & (df['PG_average_fees_(in_pounds)'] >= 10000) & (df['PG_average_fees_(in_pounds)'] < 20000),
    (df['UG_average_fees_(in_pounds)'] >= 20000) & (df['PG_average_fees_(in_pounds)'] >= 20000)
]

# Define the corresponding fee categories
categories = ['Low', 'Medium', 'High']

# Create the 'Fee_Category' column based on the conditions and categories
df['Fee_Category'] = np.select(conditions, categories, default='Unknown')

# Plot the boxplot
plt.figure(figsize=(16, 6))
ax = sns.boxplot(x='Fee_Category', y='Student_satisfaction', data=df)
plt.title('Student Satisfaction vs Fee Category')
plt.xlabel('Fee Category')
plt.ylabel('Student Satisfaction')

# Add data labels (median values)
medians = df.groupby('Fee_Category')['Student_satisfaction'].median()
vertical_offset = df['Student_satisfaction'].median() * 0.05  # adjust this value for your preference
for xtick in ax.get_xticks():
    ax.text(xtick, medians[xtick] + vertical_offset, f'{medians[xtick]:.2f}', 
            horizontalalignment='center', size='small', color='w', weight='semibold')

plt.show()        

Utilizing the Folium library, I created interactive maps to display universities in London and the rest of Britain. The map is centered around the mean latitude and longitude coordinates of all universities in the dataset. Marker clusters are employed to group nearby universities for improved visualization and performance. Each university marker is color-coded based on its student satisfaction level: dark green for satisfaction over 80%, dark yellow for satisfaction between 71% and 80%, and dark red for satisfaction below 71%. Clicking on a marker reveals the university’s name as a popup, providing an interactive and informative experience for users exploring the geographical distribution of universities and their corresponding student satisfaction levels.

import folium
from folium.plugins import MarkerCluster

df_London = df[df['Region'] == 'London']
df_Rest_of_Britain = df[df['Region'] != 'London']

# Creating a map centered around London
map_rest_of_Britain = folium.Map(location=[df['Latitude'].mean(),df['Longitude'].mean()], zoom_start=10)

# Creating a MarkerCluster layer
marker_cluster = MarkerCluster().add_to(map_rest_of_Britain)

# Creating a function to assign color based on satisfaction level
def assign_color(satisfaction):
    if satisfaction > 80:
        return 'darkgreen' 
    elif satisfaction >= 71:
        return 'darkyellow' 
    else:
        return 'darkred'

# Adding markers for universities in London regions
for index, row in df_London.iterrows():
    folium.Marker(location=[row['Latitude'], row['Longitude']], 
                  popup=row['University_name'],
                  icon=folium.Icon(color=assign_color(row['Student_satisfaction']))).add_to(marker_cluster)

# Display the map
map_rest_of_Britain        

Key Findings

1. London Universities and Student Satisfaction: Despite being home to renowned universities, London’s universities exhibit lower levels of student satisfaction compared to universities in other regions of the UK. This trend is particularly noteworthy among international students studying in London.
2. Factors Influencing Student Satisfaction: The analysis revealed several factors that may influence student satisfaction levels, including the focus on research over teaching, the lack of a campus environment, and the high proportion of international students in London universities.
3. Geographical Distribution and Student Satisfaction: The geographical distribution of universities across regions in the UK also plays a role in student satisfaction. Regions like Wales, West Midlands, and East Midlands tend to have higher student satisfaction rates compared to London.
4. Financial Considerations: Financial factors, such as average fees for undergraduate and postgraduate programs and the estimated cost of living per year, impact student satisfaction. Higher fees and living costs in London contribute to lower satisfaction levels among students.

Conclusion

In conclusion, the analysis sheds light on the complex relationship between universities and student satisfaction in the UK. Despite London’s prestigious universities, challenges such as high costs, a lack of community feel, and a focus on research may affect student experiences and satisfaction levels. The findings underscore the importance of addressing these challenges to enhance the overall student experience and satisfaction in higher education institutions.

Next Steps

Moving forward, there are several potential next steps and future directions for this project:

• Qualitative Research: Conducting qualitative research, such as surveys or interviews with students, faculty, and staff, to gain deeper insights into the factors influencing student satisfaction.
• Comparative Analysis: Comparing student satisfaction levels across different types of universities (e.g., public vs. private, research-focused vs. teaching-focused) to identify best practices and areas for improvement.
• Longitudinal Analysis: Analyzing trends in student satisfaction over time to identify any improvements or deteriorations in the university experience and address potential areas for intervention.
• Policy Implications: Examining the policy implications of the findings and advocating for reforms or initiatives aimed at enhancing student satisfaction and well-being in universities.

Overall, this project serves as a foundation for further exploration and action aimed at improving the quality of higher education and student experiences in the UK.