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Learning Record Store

In this section we will explain the IEEE standard for the Learning Record Store (LRS) and how it can be used to track MicroSim effectiveness.

IEEE Experience API (xAPI) and Learning Record Store (LRS)

The IEEE Experience API (xAPI), formally known as IEEE 1484.12.3-2016, is a specification that enables the collection of data about a wide range of learning experiences. The Learning Record Store (LRS) is a core component of this architecture. The LRS is used to store the xAPI events for all users of an intelligent textbook and the MicroSims in a central location using a standard JSON format.

What is a Learning Record Store (LRS)?

A Learning Record Store is a system that serves as the repository for learning experience data formatted according to the xAPI specification. The LRS stores and retrieves "statements" that capture learning activities in a standardized format. You can think of it as a structured event log that stores JSON records for users of an intelligent textbook.

Each xAPI statement is a JSON file that follows a simple structure that stores who did what and when they did it:

  • Actor (who performed the action)
  • Verb (what action was taken)
  • Object (what the action was performed on)
  • Result (outcome of the action)
  • Context (additional information about the action)

Using an LRS to Track MicroSim Effectiveness

MicroSims, as interactive educational simulations, generate valuable learning data that can be captured through the xAPI standard. Here's how an LRS would be used to track their effectiveness:

1. Capturing Interaction Data

When students interact with a MicroSim, the following types of data could be captured and sent to the LRS:

  • Time spent on specific components of the simulation
  • Number of attempts to complete tasks
  • Specific interactions (button clicks, parameter adjustments)
  • Completion status of simulation objectives
  • Answers to embedded assessment questions

2. Analyzing Learning Outcomes

The LRS allows for detailed analysis of how students engage with MicroSims:

  • Identifying which concepts students struggle with (based on time spent or multiple attempts)
  • Tracking progression through related concepts
  • Comparing performance across different student populations
  • Measuring knowledge retention through pre/post assessments

3. Integration with Learning Graphs

The data stored in the LRS can be particularly powerful when mapped to learning graphs:

  • Track student progression through conceptual nodes
  • Identify where students struggle in concept mastery
  • Adapt learning paths based on demonstrated understanding
  • Measure the effectiveness of specific MicroSims at teaching target concepts

4. Implementation Example

For a MicroSim teaching complex physics concepts:

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// Example xAPI statement when student completes a physics simulation
const statement = {
  actor: {
    name: "Student123",
    mbox: "mailto:student123@example.edu"
  },
  verb: {
    id: "http://adlnet.gov/expapi/verbs/completed",
    display: { "en-US": "completed" }
  },
  object: {
    id: "http://example.edu/microsims/physics/momentum-conservation",
    definition: {
      name: { "en-US": "Conservation of Momentum Simulation" },
      type: "http://adlnet.gov/expapi/activities/simulation"
    }
  },
  result: {
    success: true,
    completion: true,
    score: {
      scaled: 0.85
    },
    extensions: {
      "http://example.edu/microsim/attempts": 3,
      "http://example.edu/microsim/time-spent": "PT4M32S"
    }
  },
  context: {
    contextActivities: {
      parent: [
        {
          id: "http://example.edu/course/physics101",
          definition: {
            name: { "en-US": "Introductory Physics" }
          }
        }
      ]
    }
  }
};

Benefits for Intelligent Textbooks

Implementing an LRS with MicroSims in intelligent textbooks provides several advantages:

  1. Hyper-personalization based on actual usage data
  2. Evidence-based refinement of learning materials
  3. Detailed analytics on which simulations effectively teach concepts
  4. Integration with concept and learning graphs for adaptive learning paths

The data collected can help identify which MicroSims are most effective at teaching specific complex concepts, allowing for continuous improvement of the learning experience. The LRS can have a front-end tool that allows complex A/B testing of different versions of MicroSims to see what versions are the most effective at helping students learn.

References