Teaching Reading the Relationships in Data in Grades 9-10 (Level 1): Oklahoma Standard L1.DA.IM.01

Teaching data relationships computational models in grades 9-10 (level 1) does not have to be complicated. Picture a data analyst modeling the relationship between advertising spend and sales figures. That kind of thinking is exactly what Oklahoma's grades 9-10 (level 1) computer science standard L1.DA.IM.01 asks students to practice — and it is very teachable with the right materials. This post walks through what the standard means, the misconceptions students bring to it, and discussion starters you can use tomorrow, whether you teach in a classroom or at your kitchen table.

What Does Standard L1.DA.IM.01 Actually Ask?

Illustrate and explain the relationships between collected data elements using computational models. — Oklahoma Academic Standards for Computer Science (February 2023)

In plain language: This standard asks Level 1 students (grades 9-10) to build a model — a chart, spreadsheet, or simulation — that shows how two or more pieces of collected data relate to each other, and to explain that relationship in their own words.

In student-friendly terms, the learning target is: "I can build a computational model that illustrates the relationship between collected data elements, and explain that relationship in words, including whether it shows correlation or causation."

What Students Should Be Able to Do

• I can build a model (chart, spreadsheet, or simulation) that accurately represents the relationship between two or more data elements.
• I can describe the pattern a model reveals: positive, negative, or no clear relationship.
• I can distinguish correlation from causation and explain why a correlation alone does not prove causation.
• I can identify a limitation in a dataset that could affect how much a model's relationship should be trusted.

Along the way, students pick up the working vocabulary of the topic: model, variable, dataset, correlation, causation, trend, outlier, pattern, simulation, inference, input, output, prediction.

Data Relationships Computational Models: Misconceptions to Watch For

These are the wrong turns students reliably take with this standard — knowing them ahead of time is half the lesson plan. Each correction strategy below comes straight from the unit's teacher guide (the paragraph and activity references point into the unit itself).

1. "A correlation between two data elements proves that one causes the other."

Return to paragraph 4. A correlation only shows that two data elements tend to change together; establishing real causation almost always requires controlled evidence, not just a matching trend.

2. "An outlier is always a mistake and should be removed from the data."

Point back to paragraph 2. An outlier can reveal a genuine exception or a real data-entry error — it deserves examination, not automatic deletion.

3. "A model with no visible pattern is a failed or useless model."

Revisit paragraph 3. A scattered model with no clear pattern is still a real, reportable finding — it tells you the two data elements likely do not relate the way you expected.

4. "A prediction from a model is trustworthy no matter what data the model was built from."

Emphasize paragraph 6. A prediction is only as trustworthy as the data and reasoning behind the model; data collected from one context may not transfer reliably to a different context.

Discussion Starters You Can Use Tomorrow

• Think of an app or website you use that shows you a chart or a recommendation. What data elements do you think its model is comparing?
• Why might a company prefer to describe a correlation as if it were causation in an advertisement? What is the risk of that choice?
• Describe a real-world decision where mistaking correlation for causation could lead to a costly or unfair mistake.

Bringing It Home

This topic is a natural one for families. One ten-minute activity to try: Together, pick two things your family could track for a week — screen time and bedtime, exercise and energy level, or weather and mood. Keep a simple daily tally, then look at it together at the end of the week and talk about whether the two things seem related, and whether your student thinks one causes the other or they just happen to move together. There are no wrong answers — the goal is hearing their reasoning.

Where This Leads

Students who can build a computational model that illustrates the relationship between collected data elements, and explain that relationship in words, including whether it shows correlation or causation are building skills used every day in data analysis, public health research, software engineering, and market research.

See the Unit in Action

Get the Complete L1.DA.IM.01 Unit

I built a complete, no-prep unit for this standard — Reading the Relationships in Data: Building Computational Models That Explain — covering 3-4 days of instruction across 43 pages:

• Teacher guide — day-by-day pacing, misconceptions to watch for, discussion questions, differentiation for support / ELL / extension, and a 4-point rubric
• Student learning target page — a kid-friendly "I can" statement with success criteria
• Full content lesson with 3 embedded "Check Your Understanding" checkpoints
• 12-question assessment (6 multiple choice, 4 true/false, 2 short answer) with a complete answer key, explanations, and exemplar responses
• Group activity — "Build a Model: The Relationship Investigation" (25-30 minutes)
• Individual activity — "My Data Model Log" (20 minutes)
• Crossword and word search built from all 13 vocabulary terms (with answer keys)
• Family connection letter — a plain-language page for parents, with dinner-table questions and a 10-minute home activity
• Certificate of achievement — ready to sign and send home
• Investigation Scenario Card Set: Build a Model (separate printable, 2 pages)
• Reference Sheet: Building and Reading a Data Model (separate printable, 2 pages)
• My Data Model Log (separate printable, 2 pages)

Get Reading the Relationships in Data on Teachers Pay Teachers →

Also aligned to CSTA 3A-DA-11: Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.

Every Sooner Standards resource is built directly from the official Oklahoma Academic Standards for Computer Science (February 2023) — standard text verified, never paraphrased from memory.