Mathematics and Statistics - annotated exemplar Level 3 AS91580

Investigate time series data (3.8)

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This annotated exemplar is intended for teacher use only. The student work shown does not always represent a complete sample of what is required. Selected extracts are used, focused on the grade boundaries, in order to assist assessors to make judgements at the national standard.

Low Excellence

Commentary
Student work extract

Student 1 (PDF, 381KB)

For Excellence, a student is required to investigate time series data, with statistical insight.

This involves integrating statistical and contextual knowledge throughout the statistical enquiry cycle, and may include reflecting about the process, considering other relevant variables, evaluating the adequacy of any models, or showing a deeper understanding of models.

This evidence is from a student’s response to the TKI task ‘Polar ice’.

This student has investigated time series data with an insightful evaluation of the adequacy of the model (1), and with consideration of other variables (2). Statistical and contextual knowledge, derived from appropriate research, is integrated throughout most of the response (3) (4) (5).

This extract is from student response which also provided evidence of using an existing data set and selecting a variable to investigate to an appropriate level for the award of Excellence.

For a more secure Excellence, the student could link the adequacy of the model and the forecast more closely to contextual knowledge. The student could also investigate the series for the Antarctic and/or combined ice by finding and discussing an appropriate model.

High Merit

Commentary
Student work extract

Student 2 (PDF, 209KB)

For Merit, a student is required to investigate time series data, with justification.

This involves linking components of the statistical enquiry cycle to the context, and referring to evidence such as statistics, data values, trends, or features of visual displays in support of statements made.

This evidence is from a student’s response to the TKI task ‘Polar ice’.

This student has investigated time series data, linking the findings to the context throughout the response, and using evidence to support statements in describing the trend (1), the feature of seasonal variation (2), the appropriateness of the model (3) and the reliability of the forecast (4).

To reach Excellence, the student would need to provide a more detailed summary in the conclusion, and more clearly integrate statistical and contextual knowledge derived from research. For example, the student could link their statements to supporting or contradicting evidence found in the research.

Low Merit

Commentary
Student work extract

Student 3 (PDF, 205KB)

For Merit, a student is required to investigate time series data, with justification.

This involves linking components of the statistical enquiry cycle to the context, and referring to evidence such as statistics, data values, trends, or features of visual displays in support of statements made.

This evidence is from a student’s response to the TKI task ‘Polar ice’.

This student has investigated time series data, linking the findings to the context, and using evidence from the displays to support the statements about the trend (1), and features such as the seasonal effects (2) and the forecasts (3).

For a more secure Merit, the student could strengthen the justification of statements, for example by developing the comments about the reliability of the forecasts.

High Achieved

Commentary
Student work extract

Student 4 (PDF, 207KB)

For Achieved, a student is required to show evidence of using each component of the statistical enquiry cycle to investigate time series data.

This involves: using existing data sets, selecting a variable to investigate, selecting and using appropriate display(s), identifying features in the data and relating this to the context, finding an appropriate model, using the model to make a forecast, and communicating findings in a conclusion.

There is evidence of some research related to the purpose of the investigation. The student has selected an appropriate variable to investigate (1), selected and used appropriate displays (2), identified features (trend and seasonal variation) in the data and related them to the context (3), found an appropriate model (4), used the model to make a forecast (5) and communicated findings in a conclusion (6).

To reach Merit, the forecasts need to be appropriately rounded, and a comment on the accuracy of the forecasts is needed. The description of the seasonal pattern would also need to be more clearly related to the context, showing understanding of the seasons.

Low Achieved

Commentary
Student work extract

Student 5 (PDF, 189KB)

For Achieved, a student is required to show evidence of using each component of the statistical enquiry cycle to investigate time series data.

This involves: using existing data sets, selecting a variable to investigate, selecting and using appropriate display(s), identifying features in the data and relating this to the context, finding an appropriate model, using the model to make a forecast, and communicating findings in a conclusion.

There is evidence of some research related to the purpose of the investigation. The student has selected an appropriate variable to investigate (1), selected and used appropriate displays (2), identified features (trend and seasonal variation) of the data and related them to the context (3), found an appropriate model (4), used the model to make a forecast (5) and communicated findings in a conclusion (6).

For a more secure Achieved, there needs to be a better overall understanding of the context. In the description of the trend, the student needs to refer to the decrease in the mean area of sea ice in the Arctic as an ‘average’ effect. It is also expected that there would be evidence of research into the context.

High Not Achieved

Commentary
Student work extract

Student 6 (PDF, 199KB)

For Achieved, a student is required to show evidence of using each component of the statistical enquiry cycle to investigate time series data.

This involves: using existing data sets, selecting a variable to investigate, selecting and using appropriate display(s), identifying features in the data and relating this to the context, finding an appropriate model, using the model to make a forecast, and communicating findings in a conclusion.

The student has selected a variable to investigate (1), selected and used appropriate displays (2), found an appropriate model (3), and used the model to make a forecast (4).

To reach Achieved, the student would need to provide a purpose for the investigation that is developed from research, accurately describe the trend in context, relate the seasonal variation to the context, and communicate findings in a conclusion.

 
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