introduction to alis
DESCRIPTION
Introduction to Alis. Dr Robert Clark ALIS Project Manager. Ensuring Fairness. Principles of Fair Analysis : Compare ‘Like’ with ‘Like’ Appropriate Baseline Reflect Statistical Uncertainty. The Analysis. Linear Least Squares Regression. Subject X. A / B. C. 02468. - PowerPoint PPT PresentationTRANSCRIPT
Introduction to Alis
Dr Robert Clark
ALIS Project Manager
Ensuring Fairness
Principles of Fair Analysis :
1. Compare ‘Like’ with ‘Like’
2. Appropriate Baseline
3. Reflect Statistical Uncertainty
The Analysis
.
0 2 4 6 8
Subject X
Linear Least Squares Regression
A / B
C
.
Subject X
0
2
4
6
8
10
4 5 6 7 8
Baseline
Out
com
e
-ve VA+ve VA
Regression Line (…Trend Line, Line of Best Fit)
Outcome = gradient x baseline + intercept
Correlation Coefficient (~ 0.7)
Residuals
Subject X
Linear Least Squares Regression
Measuring Value-Added – An Example
Low Ability Average Ability High Ability
Baseline Score
A
U
B
C
D
E
Res
ult
Alf Bob
Chris+ve
-ve
National Trend
‘Average’ Student
The position of the national trend line is of critical importance
Subject A
Subject B
Some Subjects are More Equal than Others….
E
D
C
B
A
C
Gra
de
B A A*
Average GCSE
Physics
Maths
Psychology
Sociology
Latin
Photography
English Lit
>1 grade
Principle of Fair Analysis No1 : Compare ‘Like’ with ‘Like’
Some Subjects are More Equal than Others …
Performance varies between subjects, thus analysing and predicting each subject individually is essential.
e.g. Student with Average GCSE = 6.0
Subject Choices Predicted Grades
Maths, Physics, Chemistry, Economics
C, C, C/D, C/D
Sociology, Communication Studies,
Drama, Media
B, B/C, B/C, B/C
• (Raw) Residuals can be used to examine an individual’s performance
• Standardised Residuals are used to compare performance of groups
• Standardised Residuals are independent of year or qualification type
• For a class, subject, department or whole institution the Average Standardised Residual is the ‘Value-Added Score’
• Standardised Residual = Residual / Standard Deviation (National Sample)
• When using Standardised Residuals then for an individual subject
Standardisation of Residuals
• 95% Confidence Limit = 2.0 x Standard Error
• 99% Confidence Limit = 2.6 x Standard Error
• 99.7% Confidence Limit = 3.0 x Standard Error
N
1ErrorStandard where N = number of results in the
group
(for combinations of subjects consult the relevant project)
Subjects Covered…
•A / AS Levels
•Applied A / AS levels (including dual award)
•International Baccalaureate
•BTec Nationals (Diploma, Certificate, Award)
•CACHE DCE
•OCR Nationals
•ifs Diploma / Certificate in Financial Studies
•Limited pool of level 2 (BTec First)
How to Administer the Project
1. Submit a registration form (Y11 May onwards….)• We need this before we can process any data• We need this even if you are registering as part of a consortium• Choose Basic / Full (Basic + Attitudinal surveys) and whether you wish to do baseline test
2. Submit student details – ‘Registration Spreadsheet’ (Y12 Mid Sept onwards….)• This gives us student name details, GCSE scores and the subjects they are studying• We always need this even if the students are sitting a baseline test• Send spreadsheet once students are confirmed on courses (i.e. not first day of term….)
3. Organise baseline testing – ‘Adaptive Test’ (End Y11 June 15th onwards….)• This can happen before, at the same time as or after sending us the registration
spreadsheet (2 above)• Student details appear in ‘Check List’ on web site• Early prediction are available for students with Adaptive Test scores as soon as they
appear in the Check List. This function is removed one Alis has generated offical predictions (pdf reports).
• Don’t forget to click ‘Testing Complete’ once you have finished testing your students.
4. Prediction Reports Generated• Prediction reports, Intake Profiles, Adaptive Test data (IPR)• Reports created after receipt of Registration Spreadsheet
Guaranteed turnaround 4 weeks Normal deliverable turnaround 2 weeks
• When adaptive test data is ready (‘Testing Complete’ clicked), repots are updated.
5. Maintain Data• Keep reports up to date by using the Subject Editor on the Alis+ secure website to add
and remove students from subject registrations and request updated feedback
6. Submit Entries Data (Y12 & Y13 March / April)• For institutions offering A / AS options, submit EDI entries files to Alis
7. Entries data Matched and Check lists issued (Y12 & Y13 May - July)• These need to be completed to ensure complete matching of candidate numbers to
names held by Alis to ensure all EDI exam results are successfully processed in August
8. Results Collection (Y12 & Y13 August)• Submit A / AS results to Alis via EDI Results Files• Submit Other quals (IB, BTEc etc) to Alis using results spreadsheet (can opt to submit A /
AS data in spreadsheet as well instead of EDI files)• Submit results as soon after results day as possible
9. Preliminary VA Feedback (Beginning of September)• Preliminary feedback generated by 1st Monday in September. Prompt return of results in
August leads to early feedback
• Trend data not fixed, values may be subject to change
10. Definitive VA Feedback (End of September)• Trend data locked and feedback generated. Letter & CD sent to schools / colleges.
11. Maintain Data• Update results data (missing grades, withdrawals, remarks, appeals etc) using the
Results Editor on the Alis+ secure website and request updated feedback.
Entries Collection & Matching
Sept Nov Jan March May JulyOct Dec Feb April June AugY12
Sept Nov Jan March May JulyOct Dec Feb April June AugY13
Typical Timeline
Registration Form CABT
Early Preds
15th
Y11 Sept Nov Jan March May JulyOct Dec Feb April June Aug
Registration Form
Registration SSheet
CABT (+ Early Preds)
Entries Collection & MatchingMatching
Checklists
Matching Checklists
Prediction Reports (+Y13)
R
Results Collectio
n
Sept Nov Jan March May JulyOct Dec Feb April June AugY14
R
Results Collectio
n
Value Added Feedback
Value Added Feedback
Baseline Assessment
Choice of Baseline
• Average GCSE Score
• CABT (Computer Adaptive Baseline Test)
Why 2 Baselines ?
Why 2 Baselines ?
Average GCSE correlates very well to A-level / IB etc, but by itself is not sufficient….
• What is a GCSE ?
• Students without GCSE ?
• Years out between GCSE & A-level ?
• Reliability of GCSE ?
• Prior Value-Added ?
Principle of Fair Analysis No2 : Appropriate Baseline
The Effect of Prior Value Added
Beyond Expectation
+ve Value-Added
In line with Expectation
0 Value-Added
Below Expectation
-ve Value-Added
Average GCSE = 6 Average GCSE = 6 Average GCSE = 6
Do these 3 students all have the same ability ?
• Do students with the same GCSE score from feeder schools with differing value-added have the same ability ?
• How can you tell if a student has underachieved at GCSE and thus can you maximise their potential ?
• Has a student got v.good GCSE scores through the school effort rather than their ability alone ?
• How will this affect expectation of attainment in the Sixth Form ?
• Can you add value at every Key Stage ?
Baseline testing provides a measure of ability that (to a large extent) is independent of the effect of prior treatment.
Appropriate Baseline
Computer Adaptive Baseline Test (CABT)
• Test performed online – results automatically transmitted to CEM.
• Minimal installation / setup required - if any.
• Adaptive – difficulty of questions changes in relation to ability of student.
• Efficient – no time wasted answering questions that are far too easy or difficult.
• Wider range of ability
• Less stressful on students – more enjoyable experience than paper test.
• Less demanding invigilation.
• Test designed to be completed in 1 hour or less.
• No materials to courierIn 2010 / 2011 over 68,000 students sat this test in Alis
To try it out… www.intuproject.org/demos
Understanding Your Students:
Baseline & Predictive Feedback
Intake Profiles
Intake Profiles (Historical)
IPR...
Full Alis 2009 Demo School (999)
Banana, Brian
Banana, Brian
?
Studying :MathsPhysicsChemistryBiology
Prediction Reports
Probability of achieving
each grade
Expected Grade
Which predicted grades are the most appropriate for this student ?
Predictions Based on GCSE
(7.0)
B
B
C
B
B
Predictions Based on Test
(106)
C
B
D
B
C
What is this Student’s ability ?
What Grades should we expect her to get ?
If she gets C’s instead of B’s, is this a problem ?
Why is the predicted grade not always equal to the highest bar ?
Most likely grade
Predicted (‘expected’) grade
Subject Report
Prediction Reports
A2 vs AS predictions and the impact of the A* Grade
2009 Regression Equations
0
10
20
30
40
50
60
70
4 4.5 5 5.5 6 6.5 7 7.5 8
Average GCSE Score
AS
UC
AS
Po
ints
0
20
40
60
80
100
120
140
A2 U
CA
S P
oin
ts
AS Physics
A2 Physics
2010 Regression Equations
0
10
20
30
40
50
60
70
4 4.5 5 5.5 6 6.5 7 7.5 8
Average GCSE Score
AS
UC
AS
Po
ints
0
20
40
60
80
100
120
140
A2 U
CA
S P
oin
ts
AS Physics
A2 Physics
2009 Regression Equations
0
10
20
30
40
50
60
70
4 4.5 5 5.5 6 6.5 7 7.5 8
Average GCSE Score
AS
UC
AS
Po
ints
0
20
40
60
80
100
120
140
A2 U
CA
S P
oin
ts
AS Psychology
A2 Psychology
2010 Regression Equations
0
10
20
30
40
50
60
70
4 4.5 5 5.5 6 6.5 7 7.5 8
Average GCSE Score
AS
UC
AS
Po
ints
0
20
40
60
80
100
120
140
A2 U
CA
S P
oin
ts
AS Psychology
A2 Psychology
Worked Examples:
Baseline Data & Predictions
Refer to the Intake Data on the next 2 slides
• For each school what deductions might you make ?
• What implications are there (if any) for teaching & learning ?
School A
School B
Refer to the Y12 data on the next 2 slides.
• What impact might there be on the pupil’s learning ?
• What subjects would you be worried about them studying ?
Note : Non Verbal section includes Perceptual Speed and Accuracy, Pattern Matching, logical reasoning and dice folding
Y12 - Pupil D
Y12 – Pupil E
Refer to the data on the next 3 slides.
• Does the data show any ‘warnings’ about future potential achievement?
• Based only on the information provided, what would be realistic subject targets for the students, and why?
Student 1
Student 2
Student 3
Worked Examples:
Target Setting
Basing Targets on Prior VA – One Methodology from an Alis School
• Discuss previous value added data with each HoD
• Start with an agreed REALISTIC representative figure based, if available on previous (3 years ideally) of value added data
• add to each pupil prediction, and convert to grade (i.e. in-built value added)
• Discuss with students, using professional judgment and the chances graphs, adjust target grade
• calculate the department’s target grades from the addition of individual pupil’s targets
DEPARTMENT: ATarget Setting
yearno. of pupils av. GCSE av. TDA raw resid.
Std. Resid
3yr. Av. Std resid
2002 2 6.8 49.0 24.5 1.2 0.72003 7 7.1 49 13.3 0.6 0.82004 6 6.6 51 18.2 0.7 0.82005 12 6.17±0.22 44.50±3.84 12.82±4.05 0.60±0.29 0.65
From and including 2002, a raw residual of 20.0 is equivalent to one grade
SUGGESTED TARGETS FOR 2007, based on ALIS pred and dept's value added historyThe target grade has an in-built value added of 15 points (one grade is 20 points)
target grade
dept adj target
the total target grades are as follows: A 1 0B 2 3C 6 5D 1 1E 0 0
Surname Forename AveGCSE TDA Prediction TARGETtarget grade
Teacher adj target RESULT
4.7 28 49.3 64.3 D D D5.8 30 73.2 88.2 C C C6.9 48 96.4 111.4 A B B6.2 61 80.8 95.8 B C C5.1 39 57.8 72.8 C B B5.5 30 66.3 81.3 C C D5.4 54 63.4 78.4 C C B5.2 33 59.9 74.9 C C C6.1 53 79.1 94.1 B B B
AVERAGE 5.7 41.8 69.6 84.6 C
DEPARTMENT: B
yearno. of pupils av. GCSE av. TDA raw resid.
av. Std. Resid
3yr. Av. Std resid
2005 6 5.41±0.20 45.33±3.34 -15.42±14.15 -0.60±0.41SUGGESTED TARGETS FOR 2007, based on ALIS predictionThe target grade has an in-built value added of 0 points (one grade is 20 points)
target grade
dept adj target
the total target grades are as follows: A 0 2B 1 1C 6 4D 1 1E 0 0
Surname Forename AveGCSE TDA Prediction TARGETtarget grade
dept adj grade RESULT
4.9 50 50.7 50.7 D D D6.3 38 83.4 83.4 C C C6.5 53 88.2 88.2 C B A5.8 34 71.7 71.7 C C B7.4 53 108.4 108.4 B A A6.3 42 82.7 82.7 C A A6.1 46 78.7 78.7 C C B6.2 59 81.1 81.1 C C D
AVERAGE 6.2 46.9 80.6 80.6
Discussion
• Assess the merits and concerns you may have with this value-added model of setting targets
Alis
Value Added Feedback
Burning Question :
What is my Value-Added Score ?
Better Question :
Is it Important ?
Principle of Fair Analysis No3 : Reflect Statistical Uncertainty
Value Added Feedback…
SPC Chart
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Year
Subject Summary - 3 Year Average
Subject Summary - Current Year
-0.60
-0.48
-0.36
-0.24
-0.12
0.00
0.12
0.24
0.36
0.48
0.60
2002 2003 2004
Ave
rage
Sta
ndar
dise
d Res
idua
l
Year
-0.60
-0.48
-0.36
-0.24
-0.12
0.00
0.12
0.24
0.36
0.48
0.60
A2-English Literature
Statistical Process Control (SPC) Chart
2008 2009 2010
Year
Student Level Residuals (SLR) Report
Scatter Plot
A2 – English Literature
General Underachievement ?
Student Level Residuals (SLR) Report
Scatter Plot
A2 – English Literature
Too many U’s ?
Other things to look for…
Why did these students do so badly ?
Why did this student do so well ?
How did they do in their other subjects ?
Summary of Process
• Examine Subject Summary
• Determine ‘interesting’ (i.e. statistically significant) subjects
• Look at 3 year average as well as single year
• Look at trends in ‘Interesting Subjects’
• Examine student data – SLR Report, scatter graphs
• Identify students over / under achieving (student list in SLR or Paris)
• Any known issues ?
• Don’t forget to look at over achieving subjects as well as under achieving
• Phone / E-Mail ALIS when you need help understanding / interpreting the data / statistics !
Attitudinal Surveys
There is more to school / college than exams….
• Student attitudes• Student Welfare & Safety• Non-academic activities• Support• Social and personal development
Full ALIS
Self Evaluation (Every Child Matters)
Attitude to Institution
• I like school / college this year
• I like the classes
• I like the teachers / lecturers
• I would advise others to do their studies here
• In this school / college, you are treated like an adult
• The general atmosphere is good for students
Response Score
Not true at all 1
Not True 2
Not sure 3
Fairly true 4
Very true 5
Attitude to Subject
• I find it hard to get down to work in this subject
• I find the work challenging
• I like exams and tests in this subject
• I look forward to lessons in this subject
• I regret taking this subject
• I think about this subject a lot, even in my spare time
• I would advise others to take this subject here
Response Score
Not true of me at all 1
Not really true of me 2
Occaisionally true of me 3
This is fairly true of me 4
This is very true of me 5
Use of Private Tutors
% used at least once a term
Extended Attitudes – Attitude to Institution
Extended Attitudes – Resources
Extended Attitudes
Aspirations
Extended Attitudes
Pastoral Care
Extended Attitudes
Extra Curricula
Teaching and Learning Processes
(In Class)
Teaching and Learning Processes
(Out of Class)
SEF Survey
•Extent of Bullying
•Extent of Racism
•Extent of Sectarianism
•Healthy Lifestyles
•How Well do Learner's Make a Positive Contribution to the Community
•How Well do Learner's Prepare for Their Future Economic Well Being
•Other Health and Safety Issues
•School's/College's Action on Bullying
•School's/College's Action on Racism
•School's/College's Action on Sectarianism
•School's/College's Action on Sexual Harassment
•Spiritual, Moral, Emotional and Cultural Development
To try it out… www.intuproject.org/demos
School’s / College’s Action on Sexual Harassment
Summary
Alis History : • Alis began in 2003 (started life called COMBSE)
• Developed in partnership with schools by professional educational researchers
• After spreading locally in the North East, Alis grew rapidly nationwide, largely through decisions by individual schools and colleges to subscribe
• Alis is part of CEM which is affiliated to the School of Education at Durham University.
• Research by CEM acknowledged by Durham in contributing significantly to the international research reputation of the School of Education.
• Alis – developed in an educational context, by educational professionals for use by educational professionals.
Alis Coverage : • Approx 1700 school / colleges anually
• > 50% UK A-levels anually
• A/AS; IB; BTec National; OCR National; Cache DCE
• Developing BTec First; GCSE Resit
Alis Provides… Baseline Tests : • GCSE not always an appropriate or reliable measure of
ability
• GCSE Scores depend on KS4 value-added performance
• Alternative baseline test available
• Provides predictions and value-added analysis independent of performance at prior key stage
Alis Provides… Predictions :
• Predictions targeted at the individual subject (on average, students with similar GCSE scores get different grades in different A-level subjects)
• Predictions from GCSE and Alis baseline test (how reliable is GCSE as a measure of ability ? Does the student have GCSE’s ?)
• Predictions at 50th and 75th percentile
• Chances Data (what is the probability of achieving grades different to those predicted?)
• Standardised Scores from the baseline tests including section breakdown (IPR Report) – what are the student’s strengths & weaknesses ?
Alis Provides… Value Added :
• All VA scores are specific to the student and each individual subject
• Reports at school, subject and student level.
• Current and historical trend data
• Three sets of reports available:-• Subject Level (Whole cohort)• Syllabus Level (Whole Cohort)• Subject Level (Specific to your school type)
• VA available from GCSE and from the Alis baseline test
• All data shown against appropriate confidence limits
• Analysis available from beginning of September
• Consortia / area / LA analysis available
Alis Provides… Attitudes :
• In depth subject related attitudinal survey
• In depth student welfare survey covering:• Extent of Bullying• Extent of Racism• Extent of Sectarianism• Healthy Lifestyles• How well do learners make a positive contribution to the community• How well do learners prepare for their future economic well being• School / College action on bullying• School / College action on racism• School / College action on sectarianism• School / College action on sexual harassment• Spiritual, moral, emotional and cultural development
• Can provide evidence to use in Self Evaluation
To them out… www.intuproject.org/demos
Alis data can be used:
• To support teaching and learning School Band Profile Graphs IPR Data ‘Predictive’ data for target setting and monitoring Paris software for data analysis and on course monitoring
• To aid target setting and monitoring Use reliable predictive data (e.g. Alis data) Use professional judgment, including knowledge of the student Consider school/department expectations and ethos Give consideration to previous value added data where it is available (e.g.
Alis data)
• For Value Added analysis
• For Self Evaluation
Inset provision is available on any aspect of Alis to support any of the above issues.
Dr Robert ClarkAlis Project Manager
[email protected] 33 44 193