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Student mobility, attendance and student achievement (or disruptions and student achievement) Looking at six years of Queensland state schooling data Research.

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Presentation on theme: "Student mobility, attendance and student achievement (or disruptions and student achievement) Looking at six years of Queensland state schooling data Research."— Presentation transcript:

1 Student mobility, attendance and student achievement (or disruptions and student achievement) Looking at six years of Queensland state schooling data Research Forum – 29 February 2008

2 Queensland Department of Education Training and the Arts Performance Monitoring and Reporting Branch Margo Bampton Susan Daniel Alistair Dempster Roland Simons with thanks to Andrea Findlay and Nicholas White

3 Section A – OUTLINE Section B – STUDENT TRACKING ACCURACY Section C – SOCIO ECONOMIC DATA Section D – STUDENT DISRUPTION DATA Section E – RESEARCH AIMS Section F – SIMPLE RELATIONSHIPS Section G – DETAILED ANALYSIS

4 Section A – OUTLINE Section B – STUDENT TRACKING ACCURACY Section C – SOCIO ECONOMIC DATA Section D – STUDENT DISRUPTION DATA Section E – RESEARCH AIMS Section F – SIMPLE RELATIONSHIPS Section G – DETAILED ANALYSIS

5 Section A. Outline The story so far Enduring relationship Socio-Economic Position (SEP)  achievement (i.e., Phillip Holmes Smith, Barry McGaw, Ken Rowe, Sue Thompson, Lisa De Bortoli). SEP is multi-dimensional, difficult to decompose into specific components Our data is amongst the most advanced nationally and internationally. We have EQID (unique student identifier) which allows for: Over 6 years of longitudinal tracking Approximately 40,000 students tracked longitudinally in this study.

6 Section A. Outline Main Aim Estimating: Impact of disruption (attendance and mobility) in Queensland Primary schooling.

7 Section A. Outline Structure of our analysis Year 7 Test Results August 2006 Year 7 Attendance Rate Sem Mobility Year 2 to Year Socio Economic Position Jan 2006

8 Section A. Outline Structure of our analysis Year 7 Test Performance August 2006 Year 7 Attendance Rate Sem Mobility Year 2 to Year 7 Socio Economic Position

9 Section A – OUTLINE Section B – STUDENT TRACKING ACCURACY Section C – SOCIO ECONOMIC DATA Section D – STUDENT DISRUPTION DATA Section E – RESEARCH AIMS Section F – SIMPLE RELATIONSHIPS Section G – DETAILED ANALYSIS

10 EQID is key to many of our analyses involving tracking students (attendance and mobility) Questions: –How reliable is EQID? (i.e. does the same EQID represent the same student over time?) Section B. Student Tracking Accuracy The Unique Student Identifier (EQID)

11 Section B. Student Tracking Accuracy The nature of student enrolments There are three enrolment collections per year – Feb, Aug, Nov. Using the EQID to track students from collection to collection, the degree of ‘churn’ was investigated. Students who were in one collection and not in the previous collection have been termed ‘New’ students. Students who were in one collection and not in the next collection have been termed ‘Left’ students. Students who were in one collection and in the next collection have been termed ‘Kept’ students.

12 Not at the Previous Collection (New) and Not at the Next Collection (Left) 2006 Section B. Student Tracking Accuracy ‘New’ and ‘Left’ students by collection 'Left' Students 'New' Students

13 Percentage of ‘New’ & ‘Left’ Students 2001/02 – 2006/07 Year level of Students who have ‘Left’ or are ‘New’ between the February and November Collection Dates Section B. Student Tracking Accuracy ‘New’ and ‘Left’ students Feb-Nov

14 Section B. Student Tracking Accuracy Matching across the Year 7-8 transition In this time of greatest “churn”, how reliable is the EQID? Q: Are the ‘new’ students really new or are they ‘left’ students with a different EQID?

15 Section B. Student Tracking Accuracy Matching across the Year 7-8 transition The 4 (non-EQID) Matching Criteria - 1.surname 2.first name 3.birth date 4.gender

16 Section B. Student Tracking Accuracy Non-EQID Matching across the Year 7-8 transition Year 7 to 8 Transition No of Non-EQID Matches(*)No of Year 7s % non-EQID matches of all year 7s Nov-01 – Feb % Nov-02 – Feb % Nov-03 – Feb % Nov-04 – Feb % Nov-05 – Feb % Nov-06 – Feb % * Matched on last name, first name, gender and birth date and not on EQID In all 6 years studied, less than 1% of students could be matched across the year 7 to 8 transition by non-EQID criteria. Only a very small proportion of ‘left’ students might possibly be still in EQ with a different EQID

17 Section B. Student Tracking Accuracy Matching across the Year 7-8 transition As the ‘kept’ students are the ones we use for our analyses, how reliable is the EQID within this group. Q: Are students with the same EQID really the same student?

18 Year % of ‘kept’ students with fewer than 4 of the matching criteria fulfilled % of students with all 4 criteria fulfilled Total ‘kept’ students %93.6% %92.1% %91.6% %91.8% %91.4% %91.2%33304 (2919 students) Section B. Student Tracking Accuracy Matching across the Year 7-8 transition

19 A visual inspection of the data for these 8.8% (n=2919) of students revealed the following: Visual inspection of students whose data fulfilled less than 4 of the matching criteria OKSurnameSuspiciousUnlikelyGrand Total %7.2%1.4%0.5%100.0% OKAppear to be Correct – mis-spelling, reversal of first and last name, abbreviation eg Sue for Susan NameOnly the last name is different – appears to be legitimate name change SuspSuspicious – Robert Smith and John Smith with all other details the same UnlikelyUnlikely – Robert Smith and Susan Smith Section B. Student Tracking Accuracy Matching across the Year 7-8 transition

20 Fewer than 4 criteria filled. (EQID matched) All 4 criteria filled as well as an EQID match Total Year 7 students Appears to be same student Family Name Change? Possible Different Student Unlikely to be same student %0.6%0.1%0.0%91.2%100.0% More than 99% of students who match on EQID appear to be the same student. 99.2% A visual inspection of the data for these 8.8% (n=2919) of students revealed the following: Section B. Student Tracking Accuracy Matching across the Year 7-8 transition

21 Section A – OUTLINE Section B – STUDENT TRACKING ACCURACY Section C – SOCIO ECONOMIC DATA Section D – STUDENT DISRUPTION DATA Section E – RESEARCH AIMS Section F – SIMPLE RELATIONSHIPS Section G – DETAILED ANALYSIS

22 Section C. Socio Economic Data Socio Economic Position (SEP) Parental Occupation and Parental Education information is collected but incomplete. Postcode is also used for IRSED calculations at the student and school levels Results indicate a small but clear and enduring relationship with achievement

23 Example gradient Section C. Socio Economic Data Student Achievement by Student IRSED

24 Example gradient Section C. Socio Economic Data Student Achievement by Parental Education

25 Please note: Highlighted categories were removed from the analyses Section C. Socio Economic Data Student Achievement by Parental Education

26 Example gradient Section C. Socio Economic Data Student Achievement by Parental Occupation

27 Given the similarity of the relationships we decided to use IRSED calculations (based on both student and school postcode) at both student and school levels Section C. Socio Economic Data

28 Section A – OUTLINE Section B – STUDENT TRACKING ACCURACY Section C – SOCIO ECONOMIC DATA Section D – STUDENT DISRUPTION DATA Section E – RESEARCH AIMS Section F – SIMPLE RELATIONSHIPS Section G – DETAILED ANALYSIS

29 School enrolment >> mobility [Since 2000 in primary schools] Student absence >> attendance [Sem in primary schools] Both student and school level data available We have the capacity to link this data to student test achievement and Socio-Economic Position (SEP) using EQID Section D. Student Disruption Data

30 Student enrolment data includes: Each year Feb, Aug, & Nov enrolment data collections We can track a student’s school location and infer stability / mobility Student movements can be tracked within Queensland state schooling only. Section D. Student Disruption Data Mobility

31 Transitions - number of times the student had a change of school code (may be under-reported - only three collections a year) - total number of schools the student has been enrolled in (may also be under-reported) Stability - how many years has student been at current school? Section D. Student Disruption Data Mobility

32 Timing - number of transitions between Years 2 and 5 - number of transitions between Years 5 and 7 - did the student move within 2006? - number of transitions at ‘natural breaks’ between years i.e. between November and February - number of ‘disruptive’ moves i.e. within a year Section D. Student Disruption Data Mobility

33 Distance - was the most recent move under 10km? - was the most recent move greater than 100km? - distance of most recent move in km Other - did the student have a break from the Queensland state system? - did the student leave the school and return? Section D. Student Disruption Data Mobility

34 School Mobility - percentage of students enrolled recently (Year 6 or Year 7) - percentage of students enrolled early (Year 1 or Year 2) - percentage of students with four or more transitions Section D. Student Disruption Data Mobility

35 Proportion of students in Year , by number of enrolment changes since Year 2 (N=40181)

36 Section D. Student Disruption Data Mobility

37 Less than 50% of these students were still at their original school Feb 2001 Section D. Student Disruption Data Mobility

38 Over 20% of these students had a new school in Year 6 or 7 Nov 2004

39 Section D. Student Disruption Data Mobility

40 Based only on students who started and stayed in state schooling, and who changed schools Section D. Student Disruption Data Mobility

41 Student absence data includes: Recordings of full and half day absences for each child We used full day absences This information is compared to total number of possible days attended so we can calculate attendance rate Section D. Student Disruption Data Attendance

42 Student Attendance - total days absent - attendance rate (days attended/days enrolled) - number of episodes of absence - maximum episode length - average episode length Section D. Student Disruption Data Attendance

43 School Attendance - centre level attendance rate (total student days attended/total student days enrolled) - total episodes for all students - average number of episodes per student - average episode length for all students Section D. Student Disruption Data Attendance

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48 Section A – OUTLINE Section B – STUDENT TRACKING ACCURACY Section C – SOCIO ECONOMIC DATA Section D – STUDENT DISRUPTION DATA Section E – RESEARCH AIMS Section F – SIMPLE RELATIONSHIPS Section G – DETAILED ANALYSIS

49 Logically: Disruptions are a constant challenge for education systems as they interfere with the delivery of curriculum. Research: Both mobility and absenteeism have been linked with achievement declines in research around the world. Context: Is this relevant in Qld? A state where children can travel very large distances when moving schools. Section E. Research Aims

50 Student attendance rates will be reported from the 2007 National Report on Schooling in Australia (ANR), MCEETYA, to be released in Measure: The number of actual fulltime equivalent ‘student days’ attended as a percentage of the total number of possible student days attended over the period Section E. Research Aims Context - Attendance

51 “Between 1997 and 2000, 4 out of every 10 adults in Queensland had moved at least once (Australian Bureau of Statistics (ABS), 2000). Of these,72% had relocated within 20 kilometres and 49% within five kilometres of their previous home. Employment opportunities and changes in housing were the main reasons cited for the moves. Seventy percent of the moves were made by unemployed persons. Thirty eight percent of couples with children and 43% of single parents with children moved during this period.” Source: Student mobility - reasons, consequences and interventions Dr. Reesa Sorin & Rosemary Iloste JCU and EQ Section E. Research Aims Context - Mobility

52 literacy and numeracy in Year 7 in 2006 (41,261 students) utilised the unique student identifier (USI) tracked over 38,000 primary school students across a six year period, from year 2 to year 7. compared a series of mobility measures compared a series of attendance measures Section E. Research Aims The Study

53 Analysis in three phases Phase 1 – student mobility Phase 2 – student attendance Phase 3 – combined Section E. Research Aims The Study

54 Phase 1: Mobility - removed part timers, repeaters  40,181 students Phase 2: Attendance - semester 1 data collected in November  39,467 students Phase 3: Attendance and Mobility - required attendance and mobility data  38,381 students Section E. Research Aims Data Sample

55 Socio-economic position is a generic (multi-factorial) indicator of advantage or disadvantage and as such reveals very little about the ways in which some students are differentially affected by advantage/disadvantage. Section E. Research Aims The Problem with SEP

56 Section A – OUTLINE Section B – STUDENT TRACKING ACCURACY Section C – SOCIO ECONOMIC DATA Section D – STUDENT DISRUPTION DATA Section E – RESEARCH AIMS Section F – SIMPLE RELATIONSHIPS Section G – DETAILED ANALYSIS

57 Many authors in the field have found… Direct relationships between attendance/mobility and achievement show a consistent negative correlation We can show this is true for Qld as well and also true using attendance data Section F. Simple Relationships

58 Section F. Simple Relationships Mobility

59 Section F. Simple Relationships Attendance

60 The literature suggests that the relationship is contingent…. in that it disappears when socio-economic characteristics have been controlled for We didn’t find this….. Section F. Simple Relationships

61 Attendance Rate Number of school moves Reading.29 SEP (IRSED) Section F. Simple Relationships Correlations (Student Level)

62 Attendance Rate Number of school moves Reading.29 SEP (IRSED) -.17 Section F. Simple Relationships Correlations (Student Level)

63 Attendance Rate Number of school moves Reading SEP (IRSED) -.17 Section F. Simple Relationships Correlations (Student Level)

64 Attendance Rate Number of school moves Reading SEP (IRSED) -.17 If you square this =.028 or 2.8% If you square this =.084 or 8.4% If you square this =.018 or 1.8% Section F. Simple Relationships Correlations (Student Level)

65 SEP (IRSED) Controlling for SEP Partial Correlations Attendance Rate Number of school moves Reading If you square this =.017 or 1.7% If you square this =.012 or 1.2% Section F. Simple Relationships Correlations (Student Level)

66 Section A – OUTLINE Section B – STUDENT TRACKING ACCURACY Section C – SOCIO ECONOMIC DATA Section D – STUDENT DISRUPTION DATA Section E – RESEARCH AIMS Section F – SIMPLE RELATIONSHIPS Section G – DETAILED ANALYSIS

67 Tells us the amount of variance in one variable (e.g., student reading results) that can be explained by one or more other variables (e.g., student attendance and mobility) Section G. Detailed Analysis What is a Regression?

68 A series of linear regressions using achievement on August 2006 Year 7 Tests as the dependent variable were conducted. In the first series of regressions, the number of school enrolment transitions was the only explanatory variable included. The results found that this measure of student mobility has limited association with student achievement: Section G. Detailed Analysis Regressions

69 # school transitions Step1 2.3% Student READING Additional Variance Explained Section G. Detailed Analysis Regression entry order

70 # school transitions Step1 2.4% Student WRITING Additional Variance Explained

71 # school transitions Step1 2.9% Student NUMERACY Additional Variance Explained Section G. Detailed Analysis Regression entry order

72 N ow we controlled for Socio Economic data and find results are still significant Section G. Detailed Analysis

73 Socio-Economic Step1 # school transitions Step2 8.3% 1.8% Student READING Additional Variance Explained Section G. Detailed Analysis Regression entry order

74 Socio-Economic Step1 # school transitions Step2 7.0% 1.3% Student WRITING Additional Variance Explained Section G. Detailed Analysis Regression entry order

75 Socio-Economic Step1 # school transitions Step2 7.6% 1.4% Student NUMERACY Additional Variance Explained Section G. Detailed Analysis Regression entry order

76 Then we let computer choose the order of entry (Stepwise entry) from a list of possible variables Using Reading scores as a case in point Section G. Detailed Analysis

77 Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 8.3% 2.0% 1.5% Student READING Total 12.1% Additional Variance Explained Section G. Detailed Analysis Best Case model for Attendance

78 Socio-Economic Step1 Indigenous Step2 Remote Step3 Gender Step4 8.3% 2.3% 0.1% Student READING Rural Step5 Step6 0.1% Total 10.6% Avg episode length Not significant Additional Variance Explained Section G. Detailed Analysis Worst Case model for Attendance

79 Socio-Economic Step1 Indigenous Step2 # school transitions Step3 5.7% 2.9% 1.3% Student NUMERACY Total 9.9% Additional Variance Explained Section G. Detailed Analysis Best Case model for Mobility

80 Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 Remote Step4 8.3% 2.3% 1.5% 0.1% Student READING Gender Step5 Rural Step6 0.1% Total 12.4% Previous move more than 100km Step7 0.0% Additional Variance Explained Section G. Detailed Analysis Worst Case model for Mobility

81 Using the best predictors of disruption we get the following models of prediction of student test scores Section G. Detailed Analysis

82 Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 # school transitions Step4 6.0% 2.8% 1.6% 0.7% Student READING Total 11.2% Additional Variance Explained Section G. Detailed Analysis Typical Model

83 Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 # school transitions Step4 5.5% 2.6% 1.7% 0.8% Student WRITING Total 10.6% Additional Variance Explained Section G. Detailed Analysis Typical Model

84 Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 # school transitions Step4 5.7% 3.3% 2.3% 1.0% Student NUMERACY Total 12.4% Additional Variance Explained Section G. Detailed Analysis Typical Model

85 This is what we found at a school level Section G. Detailed Analysis

86 Socio-Economic Step1 Indigenous Step2 Avg episodes of absence Step3 percentage since year 1/2 Step4 35.0% 3.1% 0.9% 0.3% School READING Total 39.2% Additional Variance Explained Section G. Detailed Analysis Typical Model

87 Socio-Economic Step1 Indigenous Step2 percentage since year 1/2 Step3 # episodes of absence Step4 32.5% 2.6% 1.5% 0.3% School WRITING Total 36.9% Additional Variance Explained Section G. Detailed Analysis Typical Model

88 Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 percentage since year 1/2 Step4 28.1% 3.6% 1.9% 0.8% School NUMERACY Total 34.2% Additional Variance Explained Section G. Detailed Analysis Typical Model

89 Finally, in forcing a different order we could glean some interpretation of the socio-economic variance Section G. Detailed Analysis

90 Attendance Rate Number of school moves Reading SEP (IRSED) -.17 We know from the correlations that there is some overlap in SEP measures and disruption Section G. Detailed Analysis Correlations (Student Level)

91 So if we force a different entry order in the regression we can estimate this overlap in explanation Section G. Detailed Analysis

92 Socio-Economic Step1 Avg episodes of absence Step2 35.0% 1.9% School READING Total 36.8% Original Stepwise Additional Variance Explained Section G. Detailed Analysis Typical Model

93 Avg episodes of absence Step1 Socio-Economic Step2 10.4% 26.5% School READING Total 36.8% Forced Order Additional Variance Explained Section G. Detailed Analysis Typical Model

94 So we can say that at a school level Of the 35% explanation provided by SEP That 8.5% (35.0% – 26.5%) could be explained by average number of episodes of absence. Or that 24.3% (8.5%/35%) of the SEP – reading achievement relationship (in Year ) can be explained by a measure of attendance at a school level Section G. Detailed Analysis

95 Socio-Economic Step1 Avg episodes of absence Step2 28.1% 3.3% School NUMERACY Total 31.4% Original Stepwise Additional Variance Explained Section G. Detailed Analysis Typical Model

96 Avg episodes of absence Step1 Socio-Economic Step2 11.8% 19.6% School NUMERACY Additional Variance Explained Total 31.4% Forced Order Section G. Detailed Analysis Typical Model

97 So we can say that at a school level Of the 28.1% explanation provided by SEP That 8.5% (28.1% – 19.6%) could be explained by average number of episodes of absence. Or that 30.2% (8.5%/28.1%) of the SEP – numeracy achievement relationship (in Year ) can be explained by a measure of attendance at a school level Section G. Detailed Analysis

98 We can present this in a more visually appealing way Section G. Detailed Analysis

99 School Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

100 Attendance (24.3% Total) School Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

101 Mobility (10.0% Total) Overlap Attendance-Mobility (4.2%) Attendance (24.3% Total) School Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

102 Student Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

103 Attendance (16.9% Total) Student Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

104 Attendance (16.9% Total) Mobility (9.6% Total) Overlap Attendance-Mobility (5.8%) Student Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

105 Attendance (16.9% Total) Mobility (9.6% Total) Indigeneity (27.7% Total) Overlap Indigeneity-Attendance (5.8%) Overlap Indigeneity- Mobility (4.5%) Overlap Attendance-Mobility (5.8%) Student Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

106 The effects appear to be linear Results demonstrate that attendance/mobility can explain significant proportions of the SEP-achievement relationship (but not everything) Indigeneity is a significant predictor Section G. Detailed Analysis Summary of findings

107 There do appear to be small cohort effects for school disruptions Section G. Detailed Analysis

108 Approximately 50% of the SEP – achievement relationship can be explained by Indigeneity, Attendance, and Mobility. Section G. Detailed Analysis Of the SEP/Achievement Relationship …

109 Value of USI for data integration Sophistication and integration of data emerging in Australia (exemplified by Qld) There is some sense in decomposing the influence of SEP on student achievement Prioritising effects for intervention - School disruptions (Attendance before Mobility) Can build risk profiles for students Section G. Detailed Analysis Implications

110 Other research

111 Contact us

112 The End


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