Embracing Marian Education: Cultivating Holistic Growth in Students
Description
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.
Files
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()