WGU C284 Task 3 |Latest Update with Complete
Solution
Kelsi Offerman
Mathematics Learning and Teaching – OPT 2
April 4, 2025
Task 3: Teaching through Problem-Solving Lesson Plan
Secondary/Middle Grades Problem-Based Lesson Plan Template
General Information
Lesson Title: Connecting the Dots
Subject(s): Integrated Math I
Grade Level: 9th
Prerequisite Skills/Prior Knowledge:
This lesson will build on students' knowledge of and experience with functions. Students will also use their
knowledge of scatterplots and the line of best fit.
Instructional Setting: (e.g., group size, learning context, location [classroom, field trip to zoo, etc.], seating
arrangement, bulletin board displays)
This will take place in a classroom. The students will be sitting in rows, but close enough to talk to each other.
Standards and Objectives
State/National Academic Standard(s): These are the California state standards.
Focus Standards: HSS-ID.C.8 - Compute (using technology) and interpret the correlation coefficient of a linear fit.
HSS-ID.C.9 - Distinguish between correlation and causation.
Supporting Standards: 8.SP.A.1, 8.SP.A.2, HSS-ID.B.6
Learning Objective(s):
Students will understand bivariate data and when it is used to find relationships between variables. These
relationships will sometimes be linear.
Students will understand that the strength of a linear relationship is indicated by the correlation coefficient.
Students will understand that a statistical relationship between variables is not necessarily a cause-and-effect
relationship.
Materials Technology
• Cards for a card sorting activity We will use Chromebooks to access the Desmos graphing
• Desmos graphing website calculator to aid in understanding the scatterplots and the
correlation coefficient.
, Language Demands
Specific ways that academic language (vocabulary, functions, discourse, syntax) is used by students to participate in learning tasks through
reading, writing, listening, and/or speaking to demonstrate their understanding.
Language Function:
The content and language focus of the learning task is represented by the active verbs within the learning outcomes.
Students will use academic language to describe, interpret, and analyze relationships between two variables using
bivariate data, focusing on correlation and the idea that correlation does not imply causation.
Vocabulary:
Includes words and phrases that are used within disciplines including: (1) words and phrases with subject-specific meanings that differ from
meanings used in everyday life (e.g., table); (2) general academic vocabulary used across disciplines (e.g., compare, analyze, evaluate); and (3)
subject-specific words defined for use in the discipline.
Bivariate data, Causation, Correlation, Correlation coefficient, observed value, Scatter plot, Variable (statistics)
Discourse and/or Syntax:
Discourse includes the structures of written and oral language, as well as how members of the discipline talk, write, and participate in
knowledge construction. Syntax refers to the set of conventions for organizing symbols, words, and phrases together into structures (e.g.,
sentences, graphs, tables).
• Describe patterns in data using visual aids like scatter plots.
• Explain relationships between variables using evidence (e.g., correlation coefficient).
• Justify reasoning for why correlation does not imply causation.
• Compare and contrast different types of relationships (e.g., strong vs. weak, linear vs. nonlinear).
• Use data-specific language to participate in academic discussions and write explanations.
Planned Language Supports:
The scaffolds, representations, and pedagogical strategies teachers intentionally provide to help learners understand and use the concepts of
language they need to learn within disciplines.
Sentence frames:
• “The scatter plot shows a relationship between and . This relationship is (strong/weak,
positive/negative, linear/nonlinear).”
• “The correlation coefficient is , which means the relationship is (strong/weak, positive/negative).”
• “Even though there is a correlation between and , it does not mean that one causes the other.”
• “In this example, the observed value of is . We compare it to the predicted value to find the
residual.”
• “Bivariate data includes two variables: and . We use this to look for a possible relationship.”
Instructional Plan
Solution
Kelsi Offerman
Mathematics Learning and Teaching – OPT 2
April 4, 2025
Task 3: Teaching through Problem-Solving Lesson Plan
Secondary/Middle Grades Problem-Based Lesson Plan Template
General Information
Lesson Title: Connecting the Dots
Subject(s): Integrated Math I
Grade Level: 9th
Prerequisite Skills/Prior Knowledge:
This lesson will build on students' knowledge of and experience with functions. Students will also use their
knowledge of scatterplots and the line of best fit.
Instructional Setting: (e.g., group size, learning context, location [classroom, field trip to zoo, etc.], seating
arrangement, bulletin board displays)
This will take place in a classroom. The students will be sitting in rows, but close enough to talk to each other.
Standards and Objectives
State/National Academic Standard(s): These are the California state standards.
Focus Standards: HSS-ID.C.8 - Compute (using technology) and interpret the correlation coefficient of a linear fit.
HSS-ID.C.9 - Distinguish between correlation and causation.
Supporting Standards: 8.SP.A.1, 8.SP.A.2, HSS-ID.B.6
Learning Objective(s):
Students will understand bivariate data and when it is used to find relationships between variables. These
relationships will sometimes be linear.
Students will understand that the strength of a linear relationship is indicated by the correlation coefficient.
Students will understand that a statistical relationship between variables is not necessarily a cause-and-effect
relationship.
Materials Technology
• Cards for a card sorting activity We will use Chromebooks to access the Desmos graphing
• Desmos graphing website calculator to aid in understanding the scatterplots and the
correlation coefficient.
, Language Demands
Specific ways that academic language (vocabulary, functions, discourse, syntax) is used by students to participate in learning tasks through
reading, writing, listening, and/or speaking to demonstrate their understanding.
Language Function:
The content and language focus of the learning task is represented by the active verbs within the learning outcomes.
Students will use academic language to describe, interpret, and analyze relationships between two variables using
bivariate data, focusing on correlation and the idea that correlation does not imply causation.
Vocabulary:
Includes words and phrases that are used within disciplines including: (1) words and phrases with subject-specific meanings that differ from
meanings used in everyday life (e.g., table); (2) general academic vocabulary used across disciplines (e.g., compare, analyze, evaluate); and (3)
subject-specific words defined for use in the discipline.
Bivariate data, Causation, Correlation, Correlation coefficient, observed value, Scatter plot, Variable (statistics)
Discourse and/or Syntax:
Discourse includes the structures of written and oral language, as well as how members of the discipline talk, write, and participate in
knowledge construction. Syntax refers to the set of conventions for organizing symbols, words, and phrases together into structures (e.g.,
sentences, graphs, tables).
• Describe patterns in data using visual aids like scatter plots.
• Explain relationships between variables using evidence (e.g., correlation coefficient).
• Justify reasoning for why correlation does not imply causation.
• Compare and contrast different types of relationships (e.g., strong vs. weak, linear vs. nonlinear).
• Use data-specific language to participate in academic discussions and write explanations.
Planned Language Supports:
The scaffolds, representations, and pedagogical strategies teachers intentionally provide to help learners understand and use the concepts of
language they need to learn within disciplines.
Sentence frames:
• “The scatter plot shows a relationship between and . This relationship is (strong/weak,
positive/negative, linear/nonlinear).”
• “The correlation coefficient is , which means the relationship is (strong/weak, positive/negative).”
• “Even though there is a correlation between and , it does not mean that one causes the other.”
• “In this example, the observed value of is . We compare it to the predicted value to find the
residual.”
• “Bivariate data includes two variables: and . We use this to look for a possible relationship.”
Instructional Plan
