Impact of Design thinking - Creativity, Problem Solving, Collaboration and Critical thinking
Description
The data illustrates the contributions of eight design thinking principles—User-Centered Design, Empathy, Clarity, Consistency, Feedback, Flexibility, Aesthetics, and Accessibility—to four key student outputs: Critical Thinking, Creativity, Collaboration, and Problem Solving. Each principle exhibits varying degrees of impact across these outputs, with some principles showing strong influence, such as Empathy contributing significantly to Creativity (30%) and User-Centered Design enhancing Problem Solving (25%). Conversely, principles like Aesthetics and Accessibility play more modest roles, contributing minimally to outputs like Critical Thinking and Creativity. This distribution highlights the multifaceted nature of design thinking in fostering diverse skills, demonstrating how different principles uniquely enhance specific aspects of student performance. The visualization further emphasizes these insights through color-coded bar charts, providing a clear and comparative view of each principle's impact.
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Steps to reproduce
import matplotlib.pyplot as plt import numpy as np # Define the design thinking principles and their impacts on student outputs principles = [ "User-Centered Design", "Empathy", "Clarity", "Consistency", "Feedback", "Flexibility", "Aesthetics", "Accessibility" ] # Define the outputs we want to analyze outputs = ["Critical Thinking", "Creativity", "Collaboration", "Problem Solving"] # Define the contribution of each principle to each output (in percentage) data = { "User-Centered Design": [20, 15, 10, 25], "Empathy": [25, 30, 20, 15], "Clarity": [15, 10, 25, 20], "Consistency": [10, 15, 15, 10], "Feedback": [20, 20, 20, 15], "Flexibility": [5, 10, 10, 5], "Aesthetics": [0, 0, 5, 5], "Accessibility": [5, 0, 5, 5] } # Create a bar chart for each output fig, axs = plt.subplots(2, 2, figsize=(14, 10)) axs = axs.flatten() for i, output in enumerate(outputs): # Extract contribution data for the current output contributions = [data[principle][i] for principle in principles] # Set bar positions and width x_pos = np.arange(len(principles)) # Create the bar chart axs[i].bar(x_pos, contributions, color=['#66b3ff', '#ff9999', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6', '#c2f0c2', '#b3b3cc']) # Add titles and labels axs[i].set_title(f'Impact of Design Thinking Principles on {output}', fontsize=14) axs[i].set_xticks(x_pos) axs[i].set_xticklabels(principles) # Rotate x-axis labels vertically for better readability axs[i].tick_params(axis='x', rotation=90) axs[i].set_ylabel('Contribution (%)', fontsize=12) # Add data labels on top of bars for j in range(len(contributions)): axs[i].text(j, contributions[j] + 1, f"{contributions[j]}%", ha='center') # Adjust layout for better spacing plt.tight_layout() plt.show()