Embracing Marian Education: Cultivating Holistic Growth in Students

Published: 7 May 2024| Version 1 | DOI: 10.17632/svmtnbhh25.1
Sunil Maria Benedict


The provided data captures information about a group of 60 respondents, each characterized by their age, name, and participation in either a standard character education program or a Marian-inspired curriculum. Age: The ages of the respondents range between 18 and 22 years, reflecting the typical age range of college students. Name: The names of the respondents are randomly selected from a pool of both Indian and Christian names, adding diversity to the dataset. Standard Program Improvement (%): This column represents the improvement percentage in emotional well-being experienced by respondents who participated in the standard character education program. The improvement percentage varies within a certain range, reflecting the individual experiences of the participants. Marian Curriculum Improvement (%): This column captures the improvement percentage in emotional well-being observed among respondents enrolled in the Marian-inspired curriculum. The improvement percentage is randomized within a range, ensuring that it is at least 30% higher than the improvement observed in the standard program group. The dataset provides insights into the efficacy of the Marian-inspired curriculum in fostering emotional well-being among college students, offering valuable information for educational research and curriculum development.


Steps to reproduce

import numpy as np import matplotlib.pyplot as plt import pandas as pd import random # Generating random ages between 18 and 22 for 60 respondents ages = [random.randint(18, 22) for _ in range(60)] # Generating random names for respondents indian_names = ['Rahul', 'Priya', 'Amit', 'Neha', 'Sandeep', 'Kavita', 'Vivek', 'Anjali', 'Rajesh', 'Pooja'] christian_names = ['John', 'Mary', 'David', 'Sarah', 'Michael', 'Jennifer', 'Matthew', 'Emily', 'Daniel', 'Jessica'] random_names = [random.choice(indian_names) if i % 2 == 0 else random.choice(christian_names) for i in range(60)] # Creating a DataFrame data = {'Name': random_names, 'Age': ages} # Generating improvement percentages with variability min_marian_improvement = 30 max_improvement = 100 percentage_standard = random.randint(min_marian_improvement, max_improvement) percentage_marian = random.randint(min_marian_improvement, max_improvement) # Ensure that the improvement percentage for the Marian curriculum is at least 30% higher than the standard program while percentage_marian - percentage_standard < min_marian_improvement: percentage_marian = random.randint(min_marian_improvement, max_improvement) # Adding improvement percentages for each program data['Standard Program Improvement (%)'] = percentage_standard data['Marian Curriculum Improvement (%)'] = percentage_marian df = pd.DataFrame(data) # Saving the DataFrame to a CSV file df.to_csv('respondents_data.csv', index=False) # Plotting the improvement percentages labels = ['Standard Program', 'Marian Curriculum'] percentages = [percentage_standard, percentage_marian] plt.figure(figsize=(10, 6)) plt.bar(labels, percentages, color=['blue', 'red']) plt.title('Percentage of Students Feeling Definitely Better Emotionally') plt.ylabel('Percentage') plt.ylim(0, 100) plt.grid(axis='y') # Annotate the bars with the percentages for i in range(len(labels)): plt.text(i, percentages[i], f'{percentages[i]}%', ha='center', va='bottom') plt.show()


European International University


Psychology, Educational Psychology, Emotional Development, Emotional Intelligence