Introduction to Alis

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


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


R

Results Collectio

n

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


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


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


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


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

Documents

Introduction to Alis