Lesson 3: Measuring Influence – Understanding Engagement Metrics

• How do platforms measure engagement?
• Is high engagement the same as high reach?
• How can we compare posts fairly across different audience sizes?
• What does engagement rate reveal that raw numbers do not?
• Can influence be measured objectively?

Engagement
Reach
Impressions
Interaction
Watch time
Engagement rate
Metric
Proportion
Signal
Influence
Audience size

HS ETS1-2
Design a solution to a complex real-world problem by breaking it into manageable components.

Science and Engineering Practices:
Using Mathematics and Computational Thinking
Analyzing and Interpreting Data
Constructing Explanations

Crosscutting Concepts:
Scale, Proportion, and Quantity
Patterns
Cause and Effect

• Interpreting proportional relationships
• Evaluating claims using quantitative reasoning
• Comparing data sets
• Translating verbal claims into numerical logic
• Writing structured explanations supported by calculation

Students practice interpreting proportions and defending conclusions using simple ratio reasoning, skills directly aligned to standardized reasoning demands.

Day 1 – Conceptualizing Metrics

Students revisit previously analyzed posts and identify measurable engagement elements:

Likes
Comments
Shares
Views
Watch time

Class discussion distinguishes between:

Reach – how many people saw the post
Engagement – how many interacted
Impressions – how often it appeared

Students analyze hypothetical examples where a post has:

High reach but low interaction
Low reach but high interaction

Purpose:
Clarify that visibility and engagement are related but distinct.

DOK: 2 – Interpret data relationships.

Day 2 – Introducing Informal Engagement Rate

Students are introduced to a simplified proportional model:

Engagement Rate = Total Interactions ÷ Total Reach

The formula is presented conceptually, not algebraically complex.

Students compare two example posts:

Post A:
10,000 views, 500 interactions

Post B:
1,000 views, 200 interactions

Students calculate and compare engagement rates.

They then explain:

Which post performed better relative to audience size?
Why is raw follower count misleading?

Purpose:
Shift from absolute numbers to proportional reasoning.

DOK: 2 for calculation, 3 for explanation.

Day 3 – Evaluating Influence Claims

Students analyze statements such as:

“This creator is more influential because they have more followers.”
“This post is more successful because it has more likes.”

Students must:

• Apply engagement rate reasoning
• Identify missing variables
• Write a short evidence-based response

Optional extension includes analyzing hypothetical brand sponsorship scenarios.

Purpose:
Prevent simplistic conclusions and reinforce analytical evaluation.

DOK: 3 – Evaluate and defend claims using quantitative reasoning.

Students learn that brands and advertisers use engagement rate, not follower count alone, to determine sponsorship value. They begin to see influence as measurable within systems rather than assumed through popularity.

Students also reflect on how engagement metrics can shape self-worth and online identity.

• More followers always equals more influence.
• More likes automatically means better performance.
• Engagement rate is fixed regardless of audience context.
• Metrics alone explain success without considering design or audience.
• Proportions are unnecessary if totals are large.

• Provide structured calculation templates.
• Offer sentence stems for explanation.
• Allow use of calculators as needed.
• Provide guided small-group support for proportional reasoning.
• Extension: Compare three posts and rank by engagement rate.

Formative Assessments:

• Engagement rate calculation comparison
• Written explanation defending which post performed better
• Claim evaluation response paragraph

Exit Ticket Prompt:

Explain why engagement rate is a better measure of influence than follower count alone. Provide a numerical example.

Evaluation Criteria:

Accuracy of proportional reasoning
Clarity of explanation
Appropriate use of evidence
Recognition of scale and context