few_shots: 
Example:
Problem/activity:
Task
 Arrange the fractions in order from least to greatest. Explain your answer with a picture.
 $\frac{1}{5}, \frac{1}{7}, \frac{1}{3} $
 $\frac{2}{5}, \frac{2}{7}, \frac{2}{3} $
 $\frac{5}{6}, \frac{3}{6}, \frac{1}{6} $
 $\frac{5}{12}, \frac{8}{12}, \frac{4}{12} $

Description: 
Explain equivalence of fractions in special cases, and compare fractions by reasoning about their size.
 Grade 3 expectations in this domain are limited to fractions with denominators 2, 3, 4, 6, and 8.

Answer: 
Yes. By arranging the fractions in this problem, students learn to compare fractions by reasoning about their size.

Now, assess whether the following problem or activity aligns with the provided description.

shot_info: im_task_000157_pos_0

3
standard_description: Understand solving an equation or inequality as a process of answering a question: which values from a specified set, if any, make the equation or inequality true? Use substitution to determine whether a given number in a specified set makes an equation or inequality true.
problem_activity: Task
Below is a table showing the approximate boiling point of water at different elevations:
{"table": [{"_attributes": {"class": ["regular"]}, "tbody": [{"tr": [{"th": [{"_value": "Elevation (meters above sea level)"}, {"_value": "Boiling Point (degrees Celsius)"}]}, {"td": [{"_value": "0"}, {"_value": "100"}]}, {"td": [{"_value": "500"}, {"_value": "98.2"}]}, {"td": [{"_value": "1000"}, {"_value": "96.5"}]}, {"td": [{"_value": "1500"}, {"_value": "94.7"}]}, {"td": [{"_value": "2000"}, {"_value": "93.1"}]}, {"td": [{"_value": "2500"}, {"_value": "91.3"}]}]}]}]}
Based on the table,
if we were to model the relationship between the elevation and the boiling point of water, would a linear function be appropriate? Explain why or why not.
Below are some additional values for the boiling point of water at higher elevations:
{"table": [{"_attributes": {"class": ["regular"]}, "tbody": [{"tr": [{"th": [{"_value": "Elevation (meters above sea level)"}, {"_value": "Boiling Point (degrees Celsius)"}]}, {"td": [{"_value": "5,000"}, {"_value": "83.2"}]}, {"td": [{"_value": "6,000"}, {"_value": "80.3"}]}, {"td": [{"_value": "7,000"}, {"_value": "77.2"}]}, {"td": [{"_value": "8,000"}, {"_value": "74.3"}]}, {"td": [{"_value": "9,000"}, {"_value": "71.5"}]}]}]}]}
Based on the table,
if we were to model the relationship between the elevation and the boiling point of water, would a linear function be appropriate? Explain why or why not.
When the information from both tables is combined, would a linear function be appropriate to model this data?  What kind of function would you use to model the data?  Why?


aligns: no