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Objective Decomposition Tree

Run the Objective Decomposition Tree Fullscreen

About This MicroSim

This interactive tree visualization demonstrates how complex, compound learning objectives can be systematically broken down into teachable atomic concepts. The hierarchical structure reveals prerequisite relationships and helps instructional designers plan effective learning sequences.

How to Use

  1. Click "Next" to step through the guided exploration
  2. Click any node to see its details, Bloom's level, and description
  3. Click "Reset" to start the exploration over
  4. Notice the solid lines (decomposition) vs dashed lines (prerequisites)

Node Types

  • Compound Objectives (red boxes): Complex goals requiring multiple skills
  • Skill Clusters (orange boxes): Groups of related atomic concepts
  • Atomic Concepts (green circles): Single, teachable units of knowledge

Embedding This MicroSim

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<iframe src="https://dmccreary.github.io/automating-instructional-design/sims/objective-decomposition/main.html" height="500px" width="100%" scrolling="no"></iframe>

Lesson Plan

Learning Objectives

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

  1. Differentiate between compound objectives and atomic concepts
  2. Decompose a compound learning objective into its constituent parts
  3. Identify prerequisite relationships between atomic concepts
  4. Create a decomposition tree for a given learning goal

Suggested Activities

  1. Exploration (5 min): Step through all guided exploration steps
  2. Analysis (10 min): Click each node and discuss why it's classified as compound, cluster, or atomic
  3. Practice (15 min): Given a compound objective, create a decomposition tree on paper
  4. Application (10 min): Decompose a learning objective from your own subject area

Assessment

  • Quiz on identifying atomic vs compound objectives
  • Practical: Create a decomposition tree for a complex learning goal

References

  • Anderson, L.W., & Krathwohl, D.R. (2001). A Taxonomy for Learning, Teaching, and Assessing
  • Clark, R.C. & Mayer, R.E. (2016). e-Learning and the Science of Instruction