Using Alis Predictive Data Dr Robert Clark Alis Project Manager.

Slides:



Advertisements
Similar presentations
Using CEM Data in Practice: Feedback from CEM Secondary Assessments
Advertisements

Predicting Future Attainment: Is GCSE Always the Answer ? Dr Robert Clark ALIS Project Manager.
MIDYIS – A BRIEF INTRODUCTION
Introduction to CEM and the Baseline Tests
Peter Finlayson Quality improvement Officer February 2013.
NEW USERS OF ALIS WORKSHOP JUNE 2011 London Conference Geoff Davies.
An Introduction to CEM Secondary Monitoring Systems Assessment for Excellence S1/S2 Baseline Assessment (MidYIS) & S2 Curriculum-based.
Yr 12 Parents Forum: ALIS data West Island School November 2012.
Use of Data At start of each academic year, HODs are provided with the following data GCE and GCSE Broadsheets and summaries Residual data for courses,
Introduction to Value-Added Data Dr Robert Clark.
GL Assessment is part of the GL Education Group. In case of enquiries please contact GL Assessment by ing Summary presentation.
Secondary Information Systems
FFT Data Analysis Project – Supporting Self Evaluation  Fischer Family Trust / Fischer Education Project Extracts may be reproduced for non commercial.
Introduction to Value-Added Data Robert Clark Neil Defty Nicola Forster.
Secondary Information Systems
M.Greenaway. Analysing Data.
Neil Defty, Secondary Systems Programme Manager New Developments from CEM London, January 2011.
Data for Monitoring Target Setting and Reporting
Introduction to CEM and Computer Adaptive Assessments
A Working Paper: Art Assessment Dan China FFT and Targets.
Year 7 Settling – in Evening. Assessment Process and Ability Grouping.
Introduction to Alis Dr Robert Clark ALIS Project Manager.
STATISTICS IN SCHOOLS Vinay Bhardwaj Kim Jackson Catherine Rich Amy Zaffarese.
Introduction to Value-Added Data Dr Robert Clark.
Value Added Project Practitioners Conference 3 rd February 2006 Biology group.
Interpreting Feedback from Baseline Tests – Predictive Data Course: CEM Information Systems for Beginners and New Users Day 1 Session 3 Wednesday 17 th.
CEM Data and Self-Evaluation Dr Robert Clark ALIS Project Manager.
Yr 12 Parents Forum: ALIS data West Island School October 2013 Mike Williams (Data Development Leader)
The Learner Achievement Tracker (LAT)
Mike Treadaway Director of Research Fischer Family Trust Using FFT Live Secondary Schools.
Baseline testing in Reporting and Assessment Patrick Moore – Head of Assessment and Reporting.
Introduction to CEM Secondary Pre-16 Information Systems Neil Defty Secondary Systems Programme Manager.
Working with Colleagues, Parents and Students Course: Using CEM Data in Practice Day 2 Session 3 Thursday 28 th February 2013 Rob Smith: CEM Inset Provider.
Introduction to CEM Secondary Pre-16 Information Systems Nicola Forster & Neil Defty Secondary Systems Programme Managers London, June 2011.
TARGET SETTING AT KEY STAGE 4. TARGET SETTING Achieve your potential. Effective when used properly. Motivate. Rewards & Intervention.
CEM (NZ) Centre for Evaluation & Monitoring College of Education Dr John Boereboom Director Centre for Evaluation & Monitoring (CEM) University of Canterbury.
Using CEM Data for Self-Evaluation and Improvement Running Your School on Data 7 th June 2011
Monitoring Achievement and Progress in Independent Schools Running Your School on Data January 2011 Peter Hendry: CEM Consultant
Assessment without Levels 2015 Meadow Primary School Parents as Partners.
Data for Target Setting and Monitoring Course: Using CEM data in Practice Day 2 Session 3 Wed 30 th May 2012 Peter Hendry: CEM Consultant
What is CVA? ….Contextual Value Added. Attainment Eg. 62% 5+ A*- C…the headline figure Attainment – where we are now (eg 62%) Achievement – the progress.
Interpreting Feedback from Baseline Tests - Whole School & Individual Student Data Course: CEM Information Systems for Beginners and New Users Day 1 Session.
CEM (NZ) Centre for Evaluation & Monitoring College of Education Dr John Boereboom Director Centre for Evaluation & Monitoring (CEM) University of Canterbury.
Rothersthorpe CE Primary School The New National Curriculum & Assessment Without Levels September 2015.
Making the most of Assessment Data in the Secondary Years Dr Robert Clark.
Data update Autumn Overview About the new targets progress attainment Raise On Line (ROL) data reports and analyses historic results future estimates.
Setting Consistent Appraisal Targets. Starter: Think about targets that you have been set How did you feel? DepressedScaredStimulatedWorriedChallenged.
Good Morning and welcome. Thank you for attending this meeting to discuss assessment of learning, pupil progress and end of year school reports.
Monitoring Attainment and Progress from September 2016 John Crowley Senior Achievement Adviser.
FFT Data Analysis Project Who wants to be in the top 1 percent?
CEM (NZ) Centre for Evaluation & Monitoring College of Education Dr John Boereboom Director Centre for Evaluation & Monitoring (CEM) University of Canterbury.
Feedback from CEM Assessments: Individual Pupil Records & Predictions Belfast, March 6 th 2013 Neil Defty Business & Development Manager CEM
HWS PSA Meeting Thursday 29th September 2016.
Objectives To explore the data analyses that are available in RAISEonline and how they can be used to identify differences in progression rates To consider.
Year 8 Curriculum Evening
Types of School Value-Added Reports
CEM (NZ) Centre for Evaluation & Monitoring College of Education
Target Setting at KS3 and KS4
Who wants to be in the top 1 percent?
Governors’ Update RaiseOnline & Fischer Family Trust
Y7 DATA.
Introduction to Alis Dr Robert Clark ALIS Project Manager.
Calculating Value Added
Experienced Users of Alis
Introduction to CEM Secondary Pre-16 Information Systems
The MidYIS Test.
Understanding Progress 8
Course: CEM Information Systems for Beginners and New Users
Using CEM data for T and L
3rd-5th Year Grade Card Changes
Presentation transcript:

Using Alis Predictive Data Dr Robert Clark Alis Project Manager

Predictions vs Targets

What is an Alis ‘Prediction’ ?  NOT a forecast of the grade the student will get An indication of the grade (points score) achieved on average by students of similar ability in the previous year Targets ? Minimum Targets – Round Alis prediction down ? Realistic Targets – Use nearest Alis grade ? Challenging Targets –75 th percentile ? Prior Value Added ? Arbitrary grade fraction ?

75 th Percentile Predictions Excel spreadsheet - ‘Predictions – Spreadsheet (75 th Percentile)’ If all students attain 75 th percentile predictions, School VA will be at top 25% Approx 1/5 grade per student per subject Can also be generated in PARIS software Prior Value Added Only where prior VA is positive ? 1 year or 3 ? Reasonable to use raw residual figures as this is an approximate measure and raw residuals give grade fractions Can be calculated using PARIS software Data used to inform, not replace, professional judgement

Understanding Your Students: Baseline & Predictive Data

Intake Profiles

Intake Profiles (Historical)

IPR... Full Alis 2009 Demo School (999) Banana, Brian ? Studying : Maths Physics Chemistry Biology

Prediction Reports Probability of achieving each grade Expected Grade

Which predicted grades are the most appropriate for ths student ?

Predictions Based on GCSE (7.0) B C 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 pedicted 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

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 judgement and the chances graphs, adjust target grade calculate the department’s target grades from the addition of individual pupil’s targets

Discussion Assess the merits and concerns you may have with this value- added model of setting targets

Dr Robert Clark Alis Project Manager