HELP BUILD

Helping maximise social mobility through educational achievement insights

Created by Panella Logique.

I'm a Developer in Richmond, United Kingdom. I work at Panlogic. I earned a B.A. from Leicester University.

Project Description

Our aim is to help reveal high educational performers irrespective of their social demographic. For example:
- Average achievers in a high achieving school who, due to their individual circumstances, are doing particularly well despite adversity
- High achievers in a moderately achieving school
- Average/high achievers in a poorly achieving school
In identifying pupils with the potential for high achievement, we aim to give educational providers the ability to support these pupils to maximise positive educational outcomes for them and hence (over time) help improve social mobility.
A tool for identifying pupils with the potential for high achievement so that they are given maximum opportunity. We seek to do this by overlaying various datasets on top of each other:
1. Some of these will come from the key organisations involved in this project (e.g. the DfE, ODI & Haringey).
2. Others we are able to bring to the party either directly (e.g. we have existing databases that we have created that overlay Free School Meal (FSM) data and Indices of Multiple Deprivation (IMD) data) or indirectly (e.g. bespoke datasets from our educational partners like Achievement for All (www.afa3as.org.uk/) the Nuffield Foundation (www.nuffieldfoundation.org/) and Higher Apprenticeships (a new initiative from Lord Lucas http://lordlucas.blogspot.co.uk/).
3. Other datasets from either the public or commercial sectors.
Essentially, we would be overlaying per pupil attainment data with social demographic markers (such as FSM, IMD, IDACI etc.) to help identify individuals who are doing particularly well despite adverse circumstances. Each set of data will add to the whole – that is each dataset would add something a little bit different to bring more clarity and refinement to the pupil identification process.
To maximise educational outcomes for pupils from disadvantaged, under-performing or non-traditional backgrounds to help enable them to reach beyond their social demographic based on attainment and hence to improve social mobility
To differentiate between:
- Average achievers in a high achieving school who, due to their individual circumstances, are doing particularly well despite adversity
- High achievers in a moderately achieving school
- Average/high achievers in a poorly achieving school
To help raise aspirations
To identify students systematically who have the profile to reach high achievement levels
Ideally, if it could be achieved in a way that was not too reductive, we would seek to create a ‘score’ for an individual pupil. Taking a more longitudinal approach, this score could then be compared with historical data for other, previous pupils’ educational outcomes who had a similar score at a similar age (e.g. looking at students who were successful at university and analysing their attainment record from KS1 onwards either in raw/un-redacted form or through a computed comparative measure) to see the potential for the initial pupil to follow a similar educational path and to help improve their likely educational outcomes, and hence wider societal mobility. Essentially, we seek to generate insights and then apply those insights to current pupils and make predictions about their educational trajectory/likely attainment levels.
The outputs of our tool could also be commercialised/monetised (either in the form of a sustainable, commercial business or otherwise):
- Reduce costs and maximise investment decisions both within individual schools and the educational system itself
- Commercial players in the educational space in terms of sponsorship of schools or individual pupils
- Universities in helping them reach their admissions targets with regard to disadvantage
- Cost benefits to government in terms of eventual improvements to social mobility
- Identifying best datasets to be working with
- Educationalist to help us understand the data
- Ideally access to an Educational Scientist to work with us to identify a comparative measure based on a “hash” of the student attainment record
- Appropriate partners in the commercial side of the educational space who might have use of this data

Project Goals

  • Establish the core data and/or comparative measures to enable potential high achieving pupils to be identified

  • Encapsulate this in a tool and database Use what we have developed against the Haringey admissions data as a test case

  • If not too reductive, establish a ‘score’ to enable pupils to be compared longitudinally

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Naouress Akrouti

I am new to this site. I liked your project. I would like to know how collaborators can contribute to it.

Kindest Regards.

03/15/2014
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