How to Use Learning Data to Improve Instructional Design Decisions

In the evolving landscape of eLearning, instructional design can no longer rely on assumptions or outdated metrics like course completion rates. Modern learning ecosystems—powered by xAPI (Experience API) and Learning Record Stores (LRS)—provide deep insights into learner behaviors, preferences, and performance. But how can instructional designers and eLearning developers use this data to make informed design decisions?

This article will walk you through the step-by-step process of leveraging learning data to enhance your instructional design, ensuring your courses are not only engaging but also effective.

Why Learning Data Matters in Instructional Design

Instructional design is about crafting learning experiences that drive desired outcomes. However, without real-time feedback loops, it's difficult to understand what's working and what’s not.

Learning data helps you:

• Identify content gaps and friction points.
• Understand learner engagement at a granular level.
• Personalize learning paths based on actual behaviors.
• Validate (or refute) instructional hypotheses with evidence.

With tools like xAPI, you're no longer limited to tracking page views or quiz scores—you can track any learning experience across platforms.

Key Types of Learning Data to Analyze

Before diving into data-driven design decisions, it’s important to understand which types of learning data are valuable:

• Engagement Data
  • Time spent on activities
  • Video interactions (pause, rewind, skip)
  • Clickstream and navigation paths
• Performance Data
  • Quiz attempts and scores
  • Assessment patterns (first-time pass, retries, etc.)
  • Mastery of learning objectives
• Behavioral Data
  • Sequence of actions taken by learners
  • Device and environment usage (mobile, desktop, offline)
• Feedback Data
  • In-course surveys and reactions
  • Open-text learner feedback

Step-by-Step Process: Using Learning Data to Inform Design Decisions

1. Define Your Learning Goals Clearly

Start with crystal-clear learning objectives. What competencies should the learner achieve? This clarity will guide what data points you need to collect.

2. Design xAPI Data Collection Strategy

Ensure your LMS or LRS is set up to collect relevant xAPI statements from:

• Authoring tools (Articulate, Captivate, iSpring)
• Videos and simulations
• Quizzes and assessments
• Offline or real-world activities (if applicable)

Design statements that provide context, not just events. Example:

{
  "actor": "John Doe",
  "verb": "attempted",
  "object": "Module 2 Quiz",
  "result": { "success": false }
}

3. Analyze Engagement Patterns

Use your LRS dashboard to visualize how learners interact with your content. Look for:

• Drop-off points in courses or videos
• Activities with unusually long or short interaction times
• Repeated replays or retries

These patterns often highlight confusing content or engagement bottlenecks.

4. Assess Performance Trends

Review quiz data and assessments:

• Are certain questions consistently answered incorrectly?
• Which modules have low first-attempt pass rates?
• Is the difficulty curve aligned with learning progression?

5. Gather Learner Feedback & Correlate with Behaviors

Compare subjective feedback with objective data:

• If learners say "Module 3 is too fast-paced", does data show higher rewind rates there?
• Are learners who skip optional resources performing worse?

6. Iterate Design Based on Data Insights

Now, redesign based on insights:

• Simplify complex modules or add supporting materials.
• Break long videos into micro-learning chunks.
• Adjust quiz difficulty or add more practice scenarios.

Data-driven iteration ensures that changes are targeted and justified.

7. Set Up Continuous Improvement Loops

Instructional design is not a “one-and-done” process. Implement automated data reporting to:

• Monitor improvements after redesign.
• Catch emerging patterns with each learner cohort.
• Feed data back into future instructional strategies.

Real-World Example: Data-Driven Course Improvement

An eLearning developer noticed a high dropout rate in Module 4 of a compliance training course. By analyzing xAPI video interaction data, they discovered that most learners paused or rewound a complex animation segment multiple times.

Solution:

• The instructional designer added an interactive explainer activity before the animation.
• A post-animation micro-quiz reinforced key points.

Result:

• Dropout rate reduced by 35%.
• Learner feedback highlighted increased clarity and engagement.

Tools That Help Instructional Designers Leverage Data

• Learning Record Stores (LRS): GrassBlade, Learning Locker, Watershed
• Analytics Dashboards in xAPI-enabled LMS platforms
• Authoring Tools with xAPI support: Articulate Storyline, Adobe Captivate, iSpring Suite
• Visualization Tools: Power BI, Google Data Studio connected with LRS data exports

Conclusion: Data-Driven Instructional Design is the Future

Using learning data isn’t just a technical exercise—it’s a strategic necessity for instructional designers aiming to create impactful learning experiences. By tapping into xAPI data and leveraging LRS analytics, you gain a powerful feedback mechanism that informs every design decision with precision.

Start small, iterate, and let data guide your instructional creativity.