Learning Paths Analytics

Learning Paths Analytics provides deep insights into how learners navigate through your courses, helping you identify optimal learning sequences, discover problem areas, and optimize the learning experience.

What is Learning Paths Analytics?

Learning Paths Analytics tracks and analyzes the routes learners take through your course content. Instead of just knowing if learners completed a course, you can see exactly how they moved through it—which slides they visited, in what order, how much time they spent, and where they got stuck.

Why Learning Paths Matter

Understanding learning paths helps you:

• Identify Optimal Routes: Discover which navigation patterns lead to the best learning outcomes
• Find Problem Areas: Pinpoint slides or sections where learners struggle
• Improve Course Design: Make data-driven decisions about content sequencing
• Personalize Learning: Use path data to recommend customized learning sequences
• Measure Engagement: Understand how learners actually interact with your content

Accessing Learning Paths Analytics

1. Navigate to Analytics in your main menu
2. Click on Learning Paths
3. Select the course you want to analyze from the dropdown
4. Explore the four analysis tabs:
   • Navigation Patterns
   • Optimal Paths
   • Stuck Points
   • Time on Slides

The Four Analytics Views

1. Navigation Patterns

What it shows: Overall statistics about how learners navigate through your course.

Key Metrics

Total Sessions

• Total number of learning sessions analyzed
• Each session represents one learner's journey through the course
• Higher numbers provide more reliable insights

Linear Rate

• Percentage of sessions that followed a straight-forward path (1 → 2 → 3 → 4)
• High linear rate (70%+): Learners follow the intended sequence
• Low linear rate (<50%): Learners are jumping around or backtracking frequently

Backtrack Rate

• Percentage of sessions where learners went backwards to previous slides
• Some backtracking is normal and healthy (reviewing concepts)
• Very high backtracking (>40%) may indicate confusion or unclear content

Skip Rate

• Percentage of sessions where learners skipped slides in the sequence
• Moderate skipping: Learners may already know material or are previewing
• High skipping (>30%): Content may not be engaging or too easy

Most Common Learning Paths

Shows the actual navigation sequences learners followed, ranked by frequency.

Example:

1-2-3-5-8-9-10          (45 sessions)
1-2-3-4-5-8-9-10        (38 sessions)
1-2-5-8-10              (22 sessions)

How to interpret:

• First path shows 45 learners skipped slide 4 and slides 6-7
• Second path is the "intended" linear sequence with 38 sessions
• Third path shows learners who jumped ahead significantly

What to do:

• If many learners skip certain slides, consider if that content is necessary
• If a non-linear path is common, it might reveal a better content sequence
• Look for patterns that differ significantly from your intended design

Average Path Length

Shows the average number of slides visited per session.

Example: If your course has 25 slides but average path length is 18, learners are skipping about 7 slides on average.

What to consider:

• Are skipped slides redundant or off-topic?
• Do learners have enough time to complete everything?
• Is navigation too complex or confusing?

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2. Optimal Paths

What it shows: Navigation sequences that correlate with the best learning outcomes (high quiz scores and pass rates).

How Optimal Paths Are Calculated

The system analyzes all learning paths where quiz completion events exist, then:

1. Groups similar navigation patterns
2. Calculates average quiz scores for each pattern
3. Determines pass rates for each pattern
4. Ranks patterns by performance

Requirements:

• At least 3 sessions with quiz data
• Quiz completion events with scores
• Similar navigation patterns for comparison

Understanding the Results

Each optimal path shows:

Path Sequence

1-2-3-4-5-8-9-10-12

The exact order of slides visited

Sessions How many learners followed this path (higher = more reliable data)

Average Score Mean quiz score for learners who took this path

Pass Rate Percentage of learners who passed (met passing criteria)

Interpreting Optimal Paths

Example Scenario:

Path A: 1-2-3-4-5-6-7-8 (25 sessions, 92% avg score, 96% pass rate)
Path B: 1-2-5-8         (18 sessions, 68% avg score, 61% pass rate)

Analysis:

• Path A (linear, comprehensive) leads to much better outcomes
• Path B (skipping content) results in lower performance
• This suggests the skipped slides (3, 4, 6, 7) contain important information

Actions you might take:

• Emphasize importance of slides 3, 4, 6, 7 in course introduction
• Consider making those slides mandatory/required
• Investigate why learners skip them—maybe they're mislabeled or seem unimportant?

When No Optimal Paths Are Shown

If you see "Not enough data to identify optimal paths yet," it means:

• Your course doesn't have quiz completion events with scores
• Fewer than 3 sessions have followed similar navigation patterns
• The system needs more learner data to identify patterns

Solutions:

• Add quizzes or assessments to your course
• Ensure quiz events are being tracked
• Wait for more learners to complete the course

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3. Stuck Points

What it shows: Slides where learners spend significantly more time than expected, indicating difficulty or confusion.

How Stuck Points Are Detected

The system:

1. Calculates median time spent on each slide across all learners
2. Identifies slides where average time is significantly higher than median
3. Flags these as "stuck points" where learners struggle

Threshold: Stuck points are typically slides where average time exceeds the median by 2x or more.

Understanding the Results

Each stuck point shows:

Slide Name/Number The specific slide learners struggle with

Sessions Affected How many learners experienced extended time on this slide

Average Time Mean time spent on this slide

Median Time Typical time most learners spend (the "normal" amount)

Excess Time How much longer than normal learners are spending

Interpreting Stuck Points

Example:

Slide 12: "Advanced Functions"
Sessions: 47
Avg Time: 6m 45s
Median Time: 2m 30s
Excess Time: +4m 15s

Analysis:

• Nearly half of learners (47 sessions) spent extra time here
• They're spending almost 3x the normal time
• This slide is clearly challenging or confusing

Possible causes:

• Content is too complex
• Instructions are unclear
• Interactive element is confusing
• Technical issues (video loading slowly)
• Missing prerequisite knowledge

Actions to consider:

• Review the slide content for clarity
• Add explanatory text or examples
• Break complex content into multiple slides
• Add a practice activity before this concept
• Check for technical issues

When No Stuck Points Are Shown

"No stuck points detected!" means:

• Learners are moving smoothly through the course
• No slides have unusually long dwell times
• Good content pacing and clarity

This is excellent! But still review your Time on Slides data to ensure learners are engaging adequately with all content.

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4. Time on Slides

What it shows: Detailed timing statistics for every slide in your course.

Data Provided

For each slide, you'll see:

Slide Name/Number Identifier for the slide

Avg Time Mean time learners spend on this slide

Median Time The "middle" time—half spend more, half spend less

Min Time Shortest time any learner spent on this slide

Max Time Longest time any learner spent on this slide

Sessions How many learners visited this slide

Why Both Average and Median Matter

Average can be skewed by outliers (one learner who left their browser open for an hour)

Median better represents typical learner behavior

Rule of thumb: If average is much higher than median, you have some learners spending excessive time (could indicate issues or high engagement).

Interpreting Time on Slides

Example Data:

Slide	Avg Time	Median	Min	Max	Sessions
Slide 1: Introduction	45s	38s	12s	2m 15s	127
Slide 5: Complex Diagram	3m 22s	2m 45s	45s	12m 30s	124
Slide 8: Video Content	5m 10s	5m 05s	4m 50s	6m 20s	122
Slide 12: Quick Tip	22s	18s	8s	1m 45s	115

Analysis:

Slide 1 (Introduction):

• Very short engagement (38s median)
• Wide range (12s to 2m 15s)
• Interpretation: Some learners rush through, others read carefully
• Consider: Is this an appropriate amount of time for your intro content?

Slide 5 (Complex Diagram):

• Good engagement time (2m 45s median)
• Some learners take much longer (12m 30s max)
• Interpretation: Challenging content that requires study
• Consider: This is working well—learners are taking time to understand

Slide 8 (Video Content):

• Very consistent times (narrow range)
• Median almost equals average
• Interpretation: Most learners watch the full video (likely ~5 minutes long)
• Consider: Video is engaging and appropriate length

Slide 12 (Quick Tip):

• Very short engagement (18s median)
• Interpretation: Quick reference slide, as intended
• Consider: Appropriate if this is meant to be a brief tip

Red Flags to Watch For

Very Short Times (<10 seconds):

• Learners may be clicking through without reading
• Content might be redundant or uninteresting
• Action: Review content value and presentation

Very Long Times (>10 minutes):

• Technical issues (page loading problems)
• Content is too complex
• Interactive element is confusing
• Action: Check for technical problems and content clarity

Wide Variance (min/max very different):

• Content may be optional or some learners already know it
• Some learners are struggling while others breeze through
• Action: Consider providing "skip if familiar" option or adaptive content

Few Sessions Compared to Other Slides:

• Learners are not reaching this slide
• Navigation issue or drop-off point
• Action: Investigate why learners aren't seeing this content

Practical Applications

Use Case 1: Improving Pass Rates

Scenario: Your course has a 70% pass rate, and you want to improve it.

Steps:

1. Go to Optimal Paths tab
2. Identify the path with the highest pass rate
3. Compare it to paths with lower pass rates
4. Note which slides are skipped in low-performing paths
5. Emphasize importance of those slides in course design

Result: By guiding more learners toward the optimal path, you can increase overall pass rates.

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Use Case 2: Reducing Course Completion Time

Scenario: Learners report the course takes too long.

Steps:

1. Go to Time on Slides tab
2. Sort by average time to find longest slides
3. For each long slide, ask:
   • Is this appropriate given the content?
   • Is this slide a "stuck point"?
   • Can content be broken into smaller chunks?
4. Review Navigation Patterns to see if learners are backtracking excessively

Result: Identify opportunities to streamline content without sacrificing learning outcomes.

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Use Case 3: Identifying Content Gaps

Scenario: Many learners fail the final quiz despite completing all slides.

Steps:

1. Review Optimal Paths to see if high performers follow a specific sequence
2. Check Stuck Points to find where learners struggle
3. Review Time on Slides to see if critical content gets sufficient attention
4. Compare high-performing paths vs. low-performing paths

Result: Discover that high performers spend more time on slides 5-7, revealing those slides contain critical content that needs more emphasis.

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Use Case 4: Validating Course Redesign

Scenario: You've redesigned your course and want to measure the impact.

Steps:

1. Take screenshots of all four analytics tabs BEFORE the redesign
2. Implement your changes
3. Wait for sufficient new data (at least 50 sessions)
4. Compare new analytics to previous data
5. Look for improvements in:
   • Higher linear rate (clearer navigation)
   • Fewer stuck points (better content clarity)
   • Higher scores in optimal paths (better learning outcomes)
   • More consistent time on slides (better pacing)

Result: Data-driven validation of your redesign's effectiveness.

Best Practices

Data Collection

Ensure Adequate Sample Size

• Wait for at least 30-50 sessions before drawing conclusions
• More data = more reliable insights
• Consider seasonal variations (new hires vs. experienced staff)

Regular Monitoring

• Check analytics monthly or after significant course updates
• Set up a schedule to review learning paths data
• Track changes over time

Analysis Approach

Start with Navigation Patterns

• Get the big picture before diving into details
• Understand general learner behavior
• Identify major trends

Investigate Anomalies

• If one slide has very different metrics, investigate why
• Don't ignore outliers—they might reveal important issues
• Compare similar courses to find patterns

Combine Multiple Views

• Don't rely on just one tab
• Cross-reference stuck points with time on slides
• Connect optimal paths with navigation patterns

Acting on Insights

Prioritize High-Impact Changes

• Focus on stuck points that affect many learners
• Optimize paths that lead to best outcomes
• Address technical issues before content issues

Test Changes Incrementally

• Make one change at a time
• Measure the impact before moving on
• Keep what works, revert what doesn't

Document Your Findings

• Keep notes on insights and actions taken
• Track which changes improved metrics
• Build institutional knowledge over time

Limitations and Considerations

What This Analytics CAN'T Tell You

Why learners made specific choices:

• You can see they skipped a slide, but not why
• Consider surveys or interviews for qualitative insights

Individual learner context:

• Personal learning styles
• Prior knowledge levels
• External interruptions or technical issues

Content quality or accuracy:

• Analytics show engagement, not correctness
• You still need subject matter experts to validate content

Data Privacy

• All analytics data is aggregated and anonymized
• Individual learner paths can be viewed in Learner Management section
• Comply with your organization's privacy policies
• Consider informing learners that navigation data is collected

Technical Considerations

Data Requirements:

• Course must have CoursePipelines tracking installed
• Tracking code must capture slide navigation events
• Quiz events should include score data for optimal paths analysis

Browser Support:

• Analytics work best with modern browsers
• Some tracking may be limited in older browsers or with ad blockers

Troubleshooting

No Data Showing

Problem: Analytics tabs are empty or show "No data available."

Solutions:

• Verify tracking code is properly installed in your course
• Ensure learners have actually accessed the course
• Check that slide navigation events are being captured
• Review server logs for any errors

Unexpected Patterns

Problem: Analytics show unusual navigation patterns.

Possible Causes:

• Learners testing or exploring the course (admins, instructors)
• Technical issues causing navigation problems
• Course menu allows non-linear access
• Learners revisiting course for reference (not learning)

Solutions:

• Filter out test sessions if possible
• Distinguish between first-time learners and returning users
• Consider context when interpreting data

Optimal Paths Not Calculating

Problem: "Not enough data to identify optimal paths yet."

Solutions:

• Ensure quizzes are configured to send completion events with scores
• Wait for more learners to complete the course
• Verify quiz events are being properly tracked
• Check that passing criteria are defined

Future Enhancements

Planned improvements to Learning Paths Analytics:

• Path Prediction: Suggest optimal paths to new learners based on their profile
• A/B Testing: Compare different course sequences to find best performers
• Cohort Analysis: Compare learning paths across different learner groups
• Real-time Alerts: Notify instructors when learners get stuck
• Adaptive Navigation: Automatically guide learners based on performance
• Export and Reporting: Download detailed path data for further analysis

Getting Help

If you have questions about Learning Paths Analytics:

• Review this guide and the general Analytics documentation
• Check the FAQ section in the help center
• Contact support with specific questions about your data
• Schedule a training session for your team

Related Documentation

• Analytics Dashboard - Overview of all analytics features
• Variables & Analytics - Understanding variable tracking
• Questions & Assessments - Setting up quizzes for optimal path tracking
• Learner Management - Viewing individual learner paths