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Overview

Relaciona provides teachers with powerful analytics tools to understand student learning preferences, track assessment participation, and identify engagement patterns. This guide explains how to access and interpret the analytics available in your teacher dashboard.
All analytics respect student privacy settings. Students control what information they share through their profile preferences.

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Accessing Class Analytics

There are two main levels of analytics in Relaciona:

Class-Level Statistics

View aggregated data about learning style distribution and assessment participation across your entire class

Individual Student Analytics

Dive deep into individual student profiles, assessment results, and engagement patterns

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Class-Level Statistics

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Accessing Group Statistics

1

Navigate to Your Class

From “Mis Grupos” (My Groups), click on the class you want to analyze.
2

Open Statistics View

Click on “Estadísticas” or navigate to /grupo/<group_id>/estadisticas/ to access the analytics dashboard.
3

Review the Data

The statistics page displays visual charts and metrics about your class’s learning style distribution.

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VARK Learning Style Distribution

The primary class-level analytic is the VARK learning style distribution chart: What It Shows:
  • Number of students in each learning style category
  • Visual (V): Students who learn best through images and spatial understanding
  • Auditivo (A): Students who prefer listening and verbal instruction
  • Lectura/Escritura (R): Students who excel with reading and writing activities
  • Kinestésico (K): Students who need hands-on, tactile learning experiences
How It’s Calculated: The system aggregates VARK assessment results across all students in your class: 
# Source: teachers/views.py:62-74
students = group.students.all()
results = UserResult.objects.filter(user__in=students, questionnaire__title="VARK")

counts = {'V': 0, 'A': 0, 'R': 0, 'K': 0}
for r in results:
    counts[r.dominant_category] += 1

labels = ['Visual', 'Auditivo', 'Lectura/Escritura', 'Kinestésico']
data = [counts['V'], counts['A'], counts['R'], counts['K']]
The chart only displays if at least one student has completed the VARK assessment. If has_data is False, you’ll see a prompt to encourage student participation.

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Interpreting the Distribution

Use the learning style distribution to:

Plan Lesson Variety

Ensure your teaching methods address all learning styles represented in your class

Group Students Strategically

Form diverse groups with mixed learning styles for collaborative projects

Identify Dominant Patterns

If one style dominates, consider whether your content naturally attracts that type of learner

Track Assessment Completion

See how many students still need to complete the VARK questionnaire

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Individual Student Analytics

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Accessing Student Detail Pages

1

Open Class Roster

Navigate to your class detail page to see the list of enrolled students.
2

Select a Student

Click on any student’s name to access their individual profile and analytics at /alumno/<student_id>/.
3

Review Student Data

The student detail page shows their profile information, assessment results, and participation history.

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Available Student Metrics

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Learning Assessment Results

For each student, you can view: VARK Learning Style
  • Whether they’ve completed the VARK assessment
  • Their dominant category (V, A, R, or K)
  • The complete result object with category scores
The result is retrieved from the database: 
# Source: teachers/views.py:118-121
vark_result = UserResult.objects.filter(
    user=student,
    questionnaire__title="VARK"
).first()
Chapman Emotional Reinforcement (if available)
  • Students may complete additional questionnaires
  • Results are stored in the same UserResult model
  • Filter by questionnaire title to access different assessments

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Profile Completeness

Monitor how complete each student’s profile is:

Personal Information

Full name, date of birth, gender, nickname, residence area

Interests & Preferences

Favorite music, movies, places, and Spotify links

Reflections

Gratitude responses, happy memories, motivations

Privacy Settings

Whether they’re sharing their profile with the class

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Game Participation Tracking

Relaciona tracks student participation in various learning activities:

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Understanding UserResult Model

The UserResult model (quizzes/models.py:41-50) stores assessment outcomes: Fields:
  • user: Links to the student’s UserProfile
  • questionnaire: Which assessment they completed (VARK, Chapman, etc.)
  • dominant_category: Their result category (1 character code)
Uniqueness:
  • Each student can have only one result per questionnaire
  • The unique_together constraint on (user, questionnaire) prevents duplicates

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Viewing Participation Status

1

Check Class Roster

On your class detail page, learning styles are displayed next to each student’s name. Missing styles indicate incomplete assessments.
2

Review Statistics Dashboard

The group statistics page shows how many students have completed each assessment through the chart data.
3

Visit Individual Profiles

Student detail pages show which specific assessments they’ve completed and when.

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Identifying Students Who Need Engagement

Use analytics to proactively support students who may need encouragement:

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Students Without Assessment Results

If a student’s learning style shows as “None” in your class roster, they haven’t completed the VARK assessment yet.
Why This Matters:
  • Students miss personalized content recommendations
  • You lack data to tailor your teaching approach
  • Class-level statistics become less representative
Action Steps:
  1. Identify students with missing VARK results from your class roster
  2. Send reminders about completing the assessment
  3. Explain the benefits of completing their learning profile
  4. Offer technical support if students have trouble accessing questionnaires

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Students With Incomplete Profiles

Profile completeness indicates engagement level:

Minimal Profile

Signs: Only required fields filled, no interests sharedMay indicate: New user, privacy concerns, or low platform engagement

Partial Profile

Signs: Basic info complete, some interests addedMay indicate: Active user still exploring features

Complete Profile

Signs: Most fields filled, assessments completedMay indicate: Highly engaged, comfortable with the platform

Private Profile

Signs: share_with_class = FalseMay indicate: Valid privacy preference - respect their choice

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Interpreting Game Scores and Engagement

While the current codebase focuses on learning assessment tracking, game participation features are built on the same foundation:

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Assessment Score Patterns

VARK Scoring System:
  • Each question option has a category (V, A, R, or K) and a value (default: 1)
  • User answers are stored in the UserAnswer model
  • Scores are tallied to determine the dominant category
  • The highest-scoring category becomes the student’s learning style
Implementation Reference: 
# Source: quizzes/models.py:22-29
class Option(models.Model):
    question = models.ForeignKey(Question, on_delete=models.CASCADE)
    text = models.CharField(max_length=255)
    category = models.CharField(max_length=1, choices=ALL_CATEGORIES)
    value = models.IntegerField(default=1)

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Engagement Indicators

Look for these patterns in your analytics:
1

Assessment Completion Rate

What percentage of your class has completed each questionnaire? Low rates may indicate:
  • Students need more time to complete assessments
  • Technical barriers preventing access
  • Unclear instructions about the assessment process
2

Profile Sharing Patterns

How many students have enabled share_with_class? This reflects:
  • Student comfort with the platform
  • Classroom community strength
  • Privacy awareness and preferences
3

Consistency Across Classes

If you teach multiple classes, compare participation rates. Significant differences might indicate:
  • Varying student age groups or tech comfort
  • Different class cultures around sharing
  • Need for targeted onboarding in specific classes

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Using Analytics to Improve Teaching

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Personalize Your Approach

Use learning style data to differentiate instruction: For Visual Learners (V):
  • Provide diagrams, charts, and infographics
  • Use color coding and spatial organization
  • Encourage mind mapping and visual note-taking
For Auditory Learners (A):
  • Facilitate discussions and verbal explanations
  • Use podcasts, recordings, and verbal instructions
  • Encourage students to explain concepts aloud
For Reading/Writing Learners (R):
  • Assign text-based resources and articles
  • Use written summaries and note-taking
  • Provide written instructions and documentation
For Kinesthetic Learners (K):
  • Incorporate hands-on activities and experiments
  • Allow movement and physical engagement
  • Use real-world examples and practical applications

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Plan Balanced Activities

Even if one learning style dominates your class, include activities for all modalities. Students benefit from developing multiple learning approaches.

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Monitor Progress Over Time

Regularly check your class statistics:
  • Weekly: Review assessment completion rates
  • Bi-weekly: Check for new student enrollments who need onboarding
  • Monthly: Analyze learning style distribution for curriculum planning
  • Per term: Evaluate overall engagement patterns and adjust strategies

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Privacy and Data Considerations

All analytics respect student privacy. Only view the class groups where you’re the assigned teacher.

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Access Control

The system ensures you only see your own students’ data: 
# Source: teachers/views.py:38-39
group = get_object_or_404(ClassGroup, id=group_id, teacher=request.user)
students = group.students.all()
Security Features:
  • Each view verifies teacher=request.user before showing data
  • Students must be enrolled in your class to appear in analytics
  • Profile sharing is controlled by student preferences

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Ethical Use of Data

Use Data to Support, Not Label

Learning styles are preferences, not limitations. Avoid pigeonholing students based on assessment results.

Respect Privacy Choices

If a student hasn’t shared their profile with the class, honor that decision. Don’t pressure students to share more than they’re comfortable with.

Keep Data Confidential

Don’t share individual student analytics publicly or with other students without permission.

Focus on Growth

Use analytics to identify opportunities for support and improvement, not to judge or compare students.

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Troubleshooting Analytics Issues

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No Data Showing in Statistics

Problem: The group statistics page says there’s no data to display. Cause: No students in the class have completed the VARK assessment yet. Solution:
  1. Verify students are enrolled in the class (check class roster)
  2. Ensure students have access to the VARK questionnaire
  3. Send reminders to complete the assessment
  4. Check if students need technical support accessing questionnaires

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Student Learning Style Not Appearing

Problem: A student’s learning style shows as “None” in the class roster. Cause: The student hasn’t completed the VARK assessment, or their result wasn’t saved properly. Solution:
  1. Ask the student to complete or retake the VARK questionnaire
  2. Verify the questionnaire is titled exactly “VARK” in the database
  3. Check the UserResult table for entries with questionnaire__title="VARK"

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Can’t Access Student Detail Page

Problem: Clicking on a student doesn’t show their detail page. Cause: The student must be in one of your class groups to be accessible. Solution:
  1. Verify the student is enrolled in at least one of your classes
  2. Check that the student’s role is set to ‘student’ (not ‘teacher’)
  3. Ensure you’re logged in with a teacher account

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Next Steps

Creating Classes

Learn how to set up new class groups with invite codes

Managing Students

Master adding, removing, and organizing students in your classes

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Technical Reference

Key Views:
  • Group statistics: teachers/views.py:62-81
  • Student detail: teachers/views.py:113-130
  • Group detail with learning styles: teachers/views.py:38-59
Key Models:
  • UserResult: quizzes/models.py:41-50
  • ClassGroup: teachers/models.py:10-43
  • UserProfile: accounts/models.py:5-74
Database Queries:
  • VARK results by class: UserResult.objects.filter(user__in=students, questionnaire__title="VARK")
  • Student’s VARK result: UserResult.objects.filter(user=student, questionnaire__title="VARK").first()

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