IMPLEMENTING CHANGE BASED ON THE AUSTRALIAN

HISTORY COMPETITION

CHARLES BOOTES

MARIST COLLEGE CANBERRA

INTRODUCTION

Aim:

“To use data from an external source to inform

pedagogy and therefore implement change.”

- The Australian History Competition

LITERATURE REVIEW

• Bedwell, L. E. (2004). “Data-driven instruction.” Phi Delta Kappa, 516, 3, 7-33.

• The purpose of classroom data should therefore be understood as a means of

diagnosing problems and analysing solutions rather than simply being about

the evaluation of students (p. 19).

LITERATURE REVIEW CONT.

Young, V. M. (2006). “Teachers’ Use of Data: Loose Coupling, Agenda Setting, and Team Norms.” American

Journal of Education, 112(4), 521-548.

1. Furnishing instructional resources linked to issues arising from data analyses: Aiding teachers in accessing

professional development, lesson plans, curricular materials, and colleagues’ expertise to act on data

analyses.

2. Facilitating meetings so that teachers answer “so what”: Purposefully moving teachers’ discussions

toward implications for instruction and concrete instructional plans that address problems revealed

in data analyses.

3. Following up with teachers on responses to data analyses: Translating plans into action by charting teachers’

progress on expected reforms, reassessing the effectiveness of supports and resources available to them,

and establishing professional accountability for instructional changes that address identified concerns.

• While appearing “common-sense”, these roles or practices within an organisation are able to help support

teachers with using classroom data for improved teaching and developing assessment literacy.

LITERATURE REVIEW CONT.

Kirkup, C., Sizmur, J., Sturman, L., & Lewis, K. (2005). Schools’ use of data in teaching and

learning. England: National Foundation for Educational Research.

• Kirkup, Sizmur, Sturman, and Lewis (2005) identify the following as examples of how the

analysis of data can be used to:

• inform accurate curricular targets for individual pupils;

• highlight weaknesses in specific topics for the class;

• highlight specific weaknesses for individual pupils; and

• provide evidence to support decisions as to where to focus resources and teaching.

• Based on their study, they add that there are a number of perceived negative

outcomes of the use of data: Data can easily translate into numerical targets that are,

in themselves, meaningless. Numerical data only becomes meaningful if it serves to

pose questions about the actual learning that is (or isn’t) taking place and how it can

be developed further.

RESEARCH METHODS

• Australian History Competition

Data

• Limitations

PROCESS 1- AVERAGES

0%

10%

20%

30%

40%

50%

60%

70%

80%

2014 - Year 8 2016 - Year 7 2016 - Year 8 2018 - Year 7 2018 - Year 8 2018 - Year 9 2018 - Year 10

Historical Averages

Marist Average National Average

PROCESS 1- AVERAGES

58%

62%

56%

57%

58%

59%

60%

61%

62%

63%

OVERALL HISTORICAL AVERAGE

Marist Historical Average National Historical Average

PROCESS 1- AVERAGES

57% 56%

63%

47%

60% 60%

64%61%

59%

67%

56%

61%63%

66%

0%

10%

20%

30%

40%

50%

60%

70%

80%

2014 - Year 8 2016 - Year 7 2016 - Year 8 2018 - Year 7 2018 - Year 8 2018 - Year 9 2018 - Year 10

Historical Averages

Marist Average National Average

PROCESS 2- CATEGORISATION

0

2

4

6

8

10

12

14

16

18

Skills Knowledge Understanding

Question Responses Below the National Average 2018

Year 7 Year 8 Year 9 Year 10

PROCESS 2- CATEGORISATION

-20

-15

-10

-5

0

5

Skills Knowledge Understanding

2018 Year 7 Cohort Comparison of Above and Below the National Mean

Below Average Above Average

PROCESS 2- CATEGORISATION

0

1

2

3

4

5

6

7

8

9

Skills Knowledge Understanding

Question Responses Above the National Average 2018

Year 7 Year 8 Year 9 Year 10

PROCESS 2- CATEGORISATION

0

2

4

6

8

10

12

14

Skills Knowledge Understanding

Comparison of Above and Below Averages in 2018 Cohort

Categories Below the National Mean 2018 Average

Categories Above the National Mean 2018 Average

PROCESS 2- CATEGORISATION

9.5

10

10.5

11

11.5

12

12.5

Skills Knowledge Understanding

Responses Below the National Mean 2018 Average

PROCESS 3 – SPECIFIC QUESTIONS

• Not -10%

• Instead outliers or where both year groups struggled

• YR 7+8

• Q47 -24% (YR7)

• Q49 -32% (YR 7) -17% (YR8)

• Q50 -32% (YR7)

• YR 9+10

• Q12 -16% (YR9) -13% (YR10)

PROCESS 3 – SPECIFIC QUESTIONS – Q47

• This questions comes

under the skills category

– “analysis of use of

resources, perspective,

interpretation,

chronology” – as defined

by the History

Competition.

• Students scored an

average of 44% which

was 24% below the

national mean.

• Not an issue for the Year

8 – 71%

PROCESS 3 – SPECIFIC QUESTIONS – Q49

• This questions comes under the

skills category – “analysis of

use of resources, perspective,

interpretation, chronology” –

as defined by the History

Competition.

• Yr. 7 received 35% which was

32% below the national mean

and the Yr. 8 scored 57%

which was 17% below the

national mean.

QUESTIONS?