# Championing Young People’s Learning YPLA Strategic Analysis & Research team LAT Value Added Knowledge Share Part 2 Championing Young People’s Learning.

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Championing Young People’s Learning YPLA Strategic Analysis & Research team LAT Value Added Knowledge Share Part 2 Championing Young People’s Learning

The Methodology Championing Young People’s Learning

Value Added Specification It’s long and it’s got some complex equations Producing a version with a bit more plain English in it Methodology independently reviewed by NfER Underlying concepts are complex… …but individual steps are not so bad

Championing Young People’s Learning Multi Level Modelling: More detail MLM applied at qualification & subject level (e.g. A level History) Attempting to fit a polynomial equation Complexity of equation based on number of records & insts Where there are very few records things are grouped together at SSA level

Championing Young People’s Learning Multi Level Modelling: Polynomials Quadratic (order 2) A + Bx + Cx 2 Cubic (order 3) A + Bx + Cx 2 + Dx 3 Quartic (order 4) A + Bx + Cx 2 + Dx 3 + Ex 4 80 to 500 cases500 to 5000 cases5000+ cases

Championing Young People’s Learning Multi Level Modelling: Polynomials Quadratic (order 2) A + Bx + Cx 2 Cubic (order 3) A + Bx + Cx 2 + Dx 3 Quartic (order 4) A + Bx + Cx 2 + Dx 3 + Ex 4 80 to 500 cases500 to 5000 cases5000+ cases A, B, C, D and E are known as ‘gamma’ coefficients

Championing Young People’s Learning Multi Level Modelling: Provider Lines

Championing Young People’s Learning Multi Level Modelling: Provider Lines National Line= 10 – 5x + 0.5x 2 + 0.01x 3 Provider A Line= 12 + 10x + 0.5x 2 + 0.01x 3 Provider B Line=1000 – 3x + 0.5x 2 + 0.01x 3 Provider C Line= -400 + 10x + 0.5x 2 + 0.01x 3

Championing Young People’s Learning Multi Level Modelling: Equations Error National Line Provider Line Variation Distribution Of Results (Variance)

Championing Young People’s Learning Multi Level Modelling (S+ and R) Cannot be done with SQL or version of SPSS we have R & S+ are programmes that support MLM calculation Data Service use S+ but has licence issues YPLA getting open source programme R onto estate Both use the same programming language Code used works in a similar way to SPSS

Championing Young People’s Learning Multi Level Modelling Basic R Syntax base<-read.table('C:\\DriveD\\LAT VA\\Q_111_S12330.dat', header = TRUE, fill = TRUE) library (lme4) a<-lmer(POINTS~PRIORC+PRIOR2C+PRIOR3C+PRIOR4C +(PRIORC+PRIOR2C|LAESTAB),data=base) fixef(a)

Championing Young People’s Learning The Calculation Process Championing Young People’s Learning

Step 1: Data from FFT Received in SPSS format FFT can advise on issues with the data Includes data fields on: Provider code (UPIN & LAESTAB) Qualification type (A09 and LAT VA qual codes) Prior attainment and outcome attainment Learner Names

Championing Young People’s Learning Step 2: Check and sort data Data service do a range of checks on the data Drop any qualifications that are too small to include Check all providers we expect to be included are in the data Produce “centred variables” They then split into lots of small files (one per qual & subject)

Championing Young People’s Learning Centred Variables Many equations include quartic term (i.e. x to the power 4) For a prior attainment score of 58 this is a big number 58 x 58 x 58 x 58 = 11,316,496 To make the MLM calculation run quicker the variables are centred This means smaller numbers are used Involves some fiddly calculations but just basic maths If you hear reference to “Prior C”, “catalyst file” or “beta variables” these are interim steps used in this centring process

Championing Young People’s Learning Step 3: Apply MLM Data is fed into S+ programme one file after another This will fit the national line and give details on distribution of results Gives an output as text file These text files are then grouped together using a compiler routine

Championing Young People’s Learning Step 4: Check solutions The individual solutions are fed into a spreadsheet to check whether they look reasonable This spreadsheet is known as the “Batch LAT” Original FfE version was very complex (Over 6000 lines of code) For 2009/10 we will be using a simplified version (1000 lines of code) General checking by eye Some mathematical checks too (positive definite matrix) Produces “decentered coefficients”

Championing Young People’s Learning Step 5: Re-apply MLM If the solution does work then apply a lower order equation …or group data up to SSA level Then re-check solution End point of this is national lines for all of the qualifications

Championing Young People’s Learning Step 6: Upload data to Online LAT The national lines and individual provider data are uploaded into the Online LAT This undertakes the same calculations as the Batch LAT It also generates reports for all providers These are viewable through the provider gateway

Championing Young People’s Learning Step 7: Extract result files Bulk data files can be exported from the Online LAT The “new style” reports will be generated using these files Results files sent to OfSted to be used in their reports Results files uploaded to SQL for use by YPLA Results for 2007/08 and 2008/09 are on MISVS001 in LSC_MI_DB_PUB(the filenames start LATVA_)

Championing Young People’s Learning The Ready Reckoner Championing Young People’s Learning

Ready Reckoner Providers were keen to get an early view of LAT VA scores Excel spreadsheet that allows them to model their own data Due for release in early October Uses data from LAT VA 08/09 amended data release First line support by Data Service’s Service Desk Second line support by YPLA

Input Data

Output Data

Championing Young People’s Learning Work scheduled for the next 6 months Championing Young People’s Learning

Timescales Unamended run mid November Updated LAT Handbook and communications at same time Amended run planned for mid January

Conclusion Championing Young People’s Learning

Average GCSE grade ABCDE A level result A B C D E National average achievement Remember the basics

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