# data[].attributes.headline
Only considered one parabola so the pattern observed is not general. More equations and parabola instead of X^2/X^2+1 can be considered 

# data[].attributes.subject
Only considered one parabola so the pattern observed is not general. More equations and parabola instead of X^2/X^2+1 can be considered 

# data[].attributes.headline
The language used to explain is not clear and doesn't express the knowledge that the student is trying to express

# data[].attributes.subject
The language used to explain is not clear and doesn't express the knowledge that the student is trying to express

# data[].attributes.headline
Overall, the essay is very clear and well organized. However, some of the datas are not present neatly, such as graph labor, table and so on. The discovering process could be more detailed.

# data[].attributes.subject
Overall, the essay is very clear and well organized. However, some of the datas are not present neatly, such as graph labor, table and so on. The discovering process could be more detailed.

# data[].attributes.body
order by number before each procedure to make it more clear
Subtopics

# data[].attributes.headline
The diagrams are not clearly labeled when its trying to represent the movement of the tower and therefore it is difficult to understand that is going on. The process of discovering a pattern is not clearly presented in a table and not explained and therefore hard to understand. 

# data[].attributes.subject
The diagrams are not clearly labeled when its trying to represent the movement of the tower and therefore it is difficult to understand that is going on. The process of discovering a pattern is not clearly presented in a table and not explained and therefore hard to understand. 

# data[].attributes.headline
give each graph, table and diagram a title, knowing what are they indicating. 

# data[].attributes.subject
give each graph, table and diagram a title, knowing what are they indicating. 

# data[].attributes.headline
1-2, there are quite limited personal engagement 

# data[].attributes.subject
1-2, there are quite limited personal engagement 

# data[].attributes.headline
2-3. the ia shows the related knowledge and mathematic equation. but the logic is not that clear. 

# data[].attributes.subject
2-3. the ia shows the related knowledge and mathematic equation. but the logic is not that clear. 

# data[].attributes.headline
2, the response is organized into separate parts with subtitles for each. However, there are gaps in explanation so is lack in coherence.

# data[].attributes.subject
2, the response is organized into separate parts with subtitles for each. However, there are gaps in explanation so is lack in coherence.

# data[].attributes.headline
2-3, the exploration is coherent, but the aim is partially identified, and different parts are clearly identified, but there are unnecessary gaps that may lead to confusion in reading. No irrelevant information is included.

# data[].attributes.subject
2-3, the exploration is coherent, but the aim is partially identified, and different parts are clearly identified, but there are unnecessary gaps that may lead to confusion in reading. No irrelevant information is included.

# data[].attributes.headline
3-4, The mathematics explored is partially correct. Some knowledge and understanding are demonstrated. There are incorrect mathematical concepts/notations used. 

# data[].attributes.subject
3-4, The mathematics explored is partially correct. Some knowledge and understanding are demonstrated. There are incorrect mathematical concepts/notations used. 

# data[].attributes.headline
2, there are relevant mathematical notations but many are poor expressed

# data[].attributes.subject
2, there are relevant mathematical notations but many are poor expressed

# data[].attributes.headline
2-3, there are titles and subtitles used to guild and it also follows the working out process. The organisation is a little messy. 

# data[].attributes.subject
2-3, there are titles and subtitles used to guild and it also follows the working out process. The organisation is a little messy. 

# data[].attributes.body
20 does not belong

# data[].attributes.headline
Part 2 Choices. One of the items in the list does not belong to the order. Identify this item by number. Arrange the rest. 

# data[].attributes.subject
Part 2 Choices. One of the items in the list does not belong to the order. Identify this item by number. Arrange the rest. 

# data[].attributes.body
10. Generate a general statement in words and algebraically based on one varying variable.
11. Generate enough pattern or data until a pattern can be observed.
12. Vary another variable from smallest to the largest.
13 Comment on the implication of a general statement. Comment on its weaknesses and how you expect the general statement to change if another variable is taken into consideration.
14. Identify of this general statement can be improved. Propose step to improve or collect more data.
15. Again we tabulate, graph, write down all observations.
16. Have a conclusion that summarizes all previous results in the document.
17. Comment on the reasonableness of the result from the final general statement.
18. Produce a more general statement with two variables or parameters in words and algebraically.
19. Have a conclusion that answers the research question.
20. Use the final general statement to solve other problems.
21. Comment on the implications the final general statement.

# data[].attributes.body
315248769

# data[].attributes.body
315284769

# data[].attributes.body
1. Explain to the reader why the research question was personally significant.
2. Identify all parameters and variables explicitly in the document with symbols and definition in the context of the task.
3. Write down a clear research question.
4. Generate pattern by starting with the smallest variable or parameter possible.
5. Introduce some relevant background mathematics that will be used in the exploration. Keep this brief.
6. Write down all observations from your graph or table.
7. Graph or drawn out the results from table.
8. Tabulate the results by varying only one variable at a time.
9. Link the observations with symbols and terminology.