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Generating A Course Description Based on Blooms Taxonomy of Learning

Prompt

Based on the Bloom Taxonomy updated in 2001, Please suggest improvements 
to this following course description:

# Graph Algorithms Course Description

**Course Title:** Learning Graph Algorithms with AI

## Course Description

This 10-week course is designed
for college students that desire to understand the
how graph algorithms are used to solve business
problems.  This course is unique in that assumes
that all students have full access to generative AI
tools to generate the code and the visualizations
of these algorithms in action.

## Prerequisites

All students should have basic familiarity with data structures
and databases.  Students should have access to a web browser
and a software development environment that can execute
Python code.  Familiarity with JavaScript and Python is
helpful but not required for this course.

## Course Objectives

After this course students will be able to:

* Describe graph data structures and their components such as nodes, edges and properties.
* Describe both the RDF and LPT data models and their strengths and weaknesses.
* Be familiar with different types of graphs such as directed and undirected graphs, dependency graphs, acyclic graphs, concept graphs, taxonomies and ontologies.
* Be familiar with basic graph algorithms and algorithm categories such as depth-first search, pathfinding, clustering, page rank, similarity and centrality.
* Be able to create generative AI prompts to create sample
graph data and graph algorithms.
* Understand how browser-based libraries such as D3.js and vis.js are used to view graphs within a web browser.
* Be able to match a problem with an appropriate representation and graph algorithm.
* Be able to write and modify graph algorithms with the assistance of generative AI tools

ChatGPT o1-preview Results

Here are some suggestions to improve the course description, aligning it with Bloom’s updated taxonomy, which emphasizes higher-order thinking and more precise learning objectives.

Graph Algorithms Course Description

Course Title: Learning Graph Algorithms with AI

Course Description

This 10-week course is designed for college students eager to learn how graph algorithms can be applied to solve real-world business problems. A distinctive feature of this course is its integration of generative AI tools, enabling students to generate code, create visualizations, and experiment with algorithms in real time.

Prerequisites

Students should have a foundational understanding of data structures and databases. Access to a web browser and a software development environment capable of running Python code is required. While familiarity with Python and JavaScript is beneficial, it is not mandatory.

Course Objectives

By the end of this course, students will be able to:

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2
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1.  **Remember:** Define key graph components (nodes, edges, properties) and basic graph types (e.g., directed, undirected, acyclic, concept graphs).
2.  **Understand:** Explain and compare the RDF and LPG data models, discussing their relative strengths and weaknesses.
3.  **Apply:** Use generative AI tools to generate graph data and algorithms for real-world business scenarios.
4.  **Analyze:** Distinguish between various types of graph algorithms (e.g., depth-first search, clustering, page rank, similarity, centrality), and identify their use cases.
5.  **Evaluate:** Assess the suitability of different graph algorithms for specific business problems and justify algorithm choices.
6.  **Create:** Develop and refine custom graph algorithms using browser-based libraries like D3.js and vis.js, incorporating generative AI for assistance.

Additional Skills:

  • Utilize generative AI prompts to accelerate learning and enhance algorithm creation.
  • Visualize complex graphs in web environments for better comprehension and presentation.

These adjustments focus on a clear progression through Bloom’s taxonomy: from basic knowledge and comprehension to higher-order skills like evaluation and creation. Each objective clearly states the desired outcome and the action verb to match the taxonomy level.