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MicroSim Search and and Similarity Course Description

Course Title: MicroSim Search and and Similarity Target Audience: Eductors or software developers building and customizing MicroSim search tools Prerequisites: Basic knowledge of how to use web browsers and search tools

Overview

This course teaches readers how to create high-quality search and retrieval applications that work with MicroSim metadata. These MicroSim search applications and services can be used by both humans and intelligent agents. We will show how to use these services to create better AI MicroSim generators. Finding the right MicroSim is critical for building high-reuse systems where instructors can quickly leverage the work of others and extend the features of a MicroSim for their classroom. We show that great search and similarity measures can help AI MicroSim generators by using the ideal set of MicroSims to reference when they are building new MicroSims that have a consistent working user interface.

Target Audience

The ideal audience for this course is someone that is already familiar with MicroSim or is curious about how teachers or instructional designers can reuse other similar components. We don't expect our audience to have a strong background in search and we cover all the basics of search and metadata in our introductory chapters.

Prerequisites

All students should have access to the web and be able to use a modern browser. Users should be familiar with how web search engines work and how search results are ranked according to relevancy. There are additional optional exercises and labs at the end of the course that are targeting software developers that want to customize these search tools. However, these advanced exercises and labs are optional.

Topics Covered

  1. MicroSims
  2. Interactivity
  3. Simplicity
  4. AI Generation
  5. Web Embedding
  6. Iframe
  7. Search
  8. Findability
  9. Reuse
  10. MicroSim Background
  11. MicroSim Structure
  12. MicroSim Metadata
  13. JSON Files
  14. JSON Metadata
  15. Schemas
  16. JSON Schemas
  17. Required Fields
  18. Optional Fields
  19. Validation
  20. Data Quality Score
  21. Dublin Core
  22. Dublin Core Elements
  23. Title
  24. Creator
  25. Subject
  26. Description
  27. Publisher
  28. Contributor
  29. Date
  30. Type
  31. Format
  32. Identifier
  33. Source
  34. Language
  35. Relation
  36. Coverage
  37. Rights
  38. Grade Levels
  39. Taxonomies
  40. Learning Objectives
  41. Bloom Taxonomy
  42. Remember
  43. Understand
  44. Apply
  45. Analyze
  46. Evaluate
  47. Create
  48. Subject Normalization
  49. Precision
  50. Recall
  51. Keyword Search
  52. Boolean Search
  53. Faceted Search
  54. Lightweight Search
  55. Search Engines
  56. Semantic Search
  57. Embeddings
  58. Similar MicroSims
  59. Gathering Data
  60. MicroSim Repositories
  61. MicroSim Standards
  62. Tags
  63. Folksonomies
  64. Keywords
  65. Visualization Type
  66. Interaction Level
  67. User Controls
  68. Complexity
  69. Education
  70. Grade Level
  71. Topic
  72. Prerequisites
  73. Difficulty
  74. Curriculum Standards
  75. Cognitive Load
  76. Guided Discovery
  77. Worked Examples
  78. Infographics
  79. Workflows
  80. Flowcharts
  81. Hover
  82. Drilldown
  83. Hints
  84. Feedback
  85. Progressive Disclosure
  86. Modeling
  87. Coaching
  88. Technical Metadata
  89. JavaScript Library
  90. p5.js
  91. mermaid.js
  92. vis-network.js
  93. chart.js
  94. vis-timeline.js
  95. plotly.js
  96. leaflet.js
  97. Learning Theory
  98. Misconceptions
  99. Transfer Skills
  100. Assessment Rubric
  101. Scaffolding Adaptability
  102. Responsive Design
  103. Width Responsive
  104. Iframe Fixed Height
  105. Canvas Dimensions
  106. Drawing Region
  107. Control Region
  108. Browser Compatibility
  109. Performance
  110. Device Requirements
  111. State Management
  112. Data Flow
  113. Accessibility
  114. P5 Describe
  115. Layout Type
  116. Fixed Layout
  117. Two Panel Layout
  118. Three Panel Layout
  119. Color Scheme
  120. Control Types
  121. Button
  122. Start-Pause
  123. Slider
  124. Multi Select
  125. Visual Elements
  126. Simulation Types
  127. Model Types
  128. Equations
  129. Algorithms
  130. Assumptions
  131. Limitations
  132. Variables
  133. Scenarios
  134. Analytics
  135. Learning Indicators
  136. Engagement Metrics
  137. Progress Tracking
  138. Adaptive Elements
  139. xAPI Verbs
  140. Privacy
  141. Compliance
  142. FERPA
  143. GDPR
  144. Instructional Strategies
  145. Assessments
  146. Extensions
  147. index.md
  148. main.html
  149. style.css
  150. data.json
  151. metadata.json
  152. MicroSim Completeness Score
  153. MicroSim Quality Score
  154. Social Network
  155. MicroSim Likes
  156. Popularity Ranking
  157. Download Count
  158. Comments
  159. Peer Review
  160. A/B Testing
  161. Hosted Search
  162. Deployment
  163. RAG
  164. Context Window
  165. AI MicroSim Generation
  166. Search UI
  167. Search Result Item
  168. Adding Images

Learning Objectives Sorted by Bloom Taxonomy

Remember

  1. List the 15 Dublin Core metadata elements
  2. Name the six levels of Bloom's Taxonomy
  3. Identify the JavaScript libraries used in MicroSims (p5.js, mermaid.js, vis-network.js, chart.js, vis-timeline.js, plotly.js, leaflet.js)
  4. Recall the standard file structure of a MicroSim (index.md, main.html, style.css, data.json, metadata.json)
  5. Define key search terms: precision, recall, faceted search, semantic search

Understand

  1. Explain the purpose of metadata in MicroSim discovery and reuse
  2. Describe how Dublin Core elements support resource description
  3. Summarize the difference between keyword search and semantic search
  4. Interpret MicroSim quality and completeness scores
  5. Explain how embeddings enable similar MicroSim recommendations

Apply

  1. Create valid metadata.json files that conform to the MicroSim schema
  2. Use faceted search to find MicroSims by subject area, grade level, and difficulty
  3. Implement responsive design patterns for MicroSim layouts
  4. Apply JSON Schema validation to verify metadata completeness
  5. Use the search interface to locate MicroSims for specific learning objectives

Analyze

  1. Compare different visualization types and their appropriate use cases
  2. Analyze metadata quality across MicroSim repositories
  3. Distinguish between required and optional metadata fields
  4. Examine the relationship between learning objectives and Bloom's Taxonomy levels
  5. Evaluate the effectiveness of different search strategies for finding relevant MicroSims

Evaluate

  1. Assess the quality and completeness of MicroSim metadata
  2. Judge the appropriateness of a MicroSim for a specific grade level and learning objective
  3. Critique search results based on precision and recall metrics
  4. Evaluate the educational effectiveness of different scaffolding approaches
  5. Recommend improvements to metadata based on schema requirements

Create

  1. Design new MicroSim metadata schemas for specialized domains
  2. Develop custom search interfaces using ItemsJS
  3. Generate embeddings for semantic similarity calculations
  4. Build data pipelines to crawl and aggregate MicroSim metadata
  5. Create instructional strategies that leverage MicroSim search for curriculum development

Topics Not Covered

  1. Deep Neural Networks
  2. Machine Learning
  3. Stemming
  4. TF-IDF
  5. Tokenization
  6. AI Skills
  7. AI Agents
  8. mkdocs
  9. mkdocs-material
  10. github
  11. version control