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Overheads Use of data in Schools

Overheads Use of data in Schools. Dr Daniel Muijs Dept. of Education University of Newcastle. Main uses of Data. Tracking Pupils Identifying school/departmental strengths/weaknesses. 1. Tracking Pupils.

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Overheads Use of data in Schools

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  1. OverheadsUse of data in Schools Dr Daniel Muijs Dept. of Education University of Newcastle

  2. Main uses of Data • Tracking Pupils • Identifying school/departmental strengths/weaknesses

  3. 1. Tracking Pupils • Data for each pupil are collected each year, e.g. cognitive ability tests (give an indication of pupil’s potential), reading tests (give an indication of pupil’s reading ability), attendance etc. • Important: all these kept on one file/database, so data on pupils are available at the touch of a button

  4. One can now also track a pupil’s evolution over time, e.g. by comparing her/his reading test scores from year to year • So far, we have looked at data on individual variables. We can, however, also look at the relationship between variables at the pupil level, e.g. to identify discrepancies between cognitive ability and reading ability, or cog. ab and GCSE performance

  5. Targetting pupils at the C-D GCSE borderline, using YELLIS and NFER predictors

  6. 2. Identifying school/dept. strengths/weaknesses • Use of YELLIS/ALIS residuals by department • Departmental/school level value added GCSE performance (multilevel analysis) • Dept./school performance on other outcome measures, e.g. affective data

  7. Probing deeper: school/dept performance for different pupil subgroups, e.g. exacerbating or diminishing gender differences • Differences between year groups, i.e. in intake, but also in relationships between variables such as CAT and reading test scores

  8. Caveats • These kind of data cannot answer WHY-type questions. One can indicate e.g. differences between departments or pupils, but not why these differences exist. You can pinpoint problems, but not the causes • Do not set too much store by small differences or relationships. They are not necessarily statistically significant

  9. Crucial: get all data onto one electronic database

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