Researchers from the Medical Research Council (MRC) Cognition and Brain Sciences Unit at the University of Cambridge have used machine learning – a type of computer algorithm – to identify clusters of learning difficulties which did not match the previous diagnosis children had been given.
The study, published in Developmental Science, used a sample of 530 children (ages 5–18) who were referred to the Centre for Attention Learning and Memory (CALM) by health and education professionals because they were struggling in school. All the children in the sample completed a number of cognitive and learning assessments, they underwent a structural MRI scan, and their parents completed behaviour questionnaires.
Based on the data collected from these tests, the computer algorithm identified four groups that the children could be matched to: (1) broad cognitive difficulties and severe reading, spelling and maths problems; (2) age-typical cognitive abilities and learning profiles; (3) difficulties with working memory skills; and (4) difficulties with processing sounds in words.
While these groups aligned closely with other data on the children, such as the parents’ reports of their communication difficulties and educational data on reading and maths, there was no correspondence with their previous diagnoses. To check if these groupings corresponded to biological differences, the groups were checked against MRI brain scans from 184 of the children. The groupings mirrored patterns in connectivity within parts of the children’s brains, suggesting that the machine learning was identifying differences that partly reflect underlying biology.
The researchers conclude that these findings reinforce the need for children to receive detailed assessments of their cognitive skills to identify the best type of support.
Source: Remapping the cognitive and neural profiles of children who struggle at school (September 2018), Developmental Science
A study conducted by Neil Humphrey and colleagues, published in Public Health Research, reports on the findings of a randomised controlled trial of the social and emotional learning intervention, Promoting Alternative Thinking Strategies (PATHS).
PATHS aims to promote children’s social skills via a taught curriculum, which is delivered by the class teacher. A total of 5,218 children in Years 3–5 (ages 7–9) from 45 primary schools in Greater Manchester participated in the trial. Schools were randomly allocated to deliver PATHS for two years or to continue as normal.
The findings of the study suggest that the impact of PATHS was modest and limited. Immediately after the intervention, there was tentative evidence that PATHS made a small improvement on children’s social skills (effect size = +0.09) as assessed by the Social Skills Improvement System. A small improvement in children’s psychological well-being (effect size = +0.07) was also found immediately after the intervention. However, there were no differences between children from PATHS and control schools for any outcomes at the 12- or 24-month post-intervention follow-ups.
Source: The PATHS curriculum for promoting social and emotional well-being among children aged 7–9 years: a cluster RCT. Public Health Research 6 (10).
The Education Endowment Foundation has published an evaluation of two trials of programmes developed by the University College-London (UCL) Institute of Education investigating approaches to grouping pupils: Best Practice in Setting and Best Practice in Mixed Attainment Grouping.
The main trial, “Best Practice in Setting”, tested an intervention that aimed to get schools to improve their setting practice (grouping pupils in classes by their current achievement levels). A total of 127 schools took part in the trial, which ran over the course of two academic years. Teachers were randomly allocated to sets to prevent “lower” sets from being disproportionately assigned less-experienced teachers, while pupils in Years 7 and 8 were assigned to sets based on independent measures of achievement, rather than more subjective judgements such as behaviour and peer interactions. There were opportunities throughout the year to re-assign pupils to different sets based on their current level of achievement.
The evaluation found no evidence that the intervention improves outcomes in maths (effect size = -0.01) or English (effect size = -0.08). The process evaluation revealed mixed views from participants, and many interviewees thought that what they were being asked to do represented little change from what they already do.
The researchers noted that because school and teacher buy-in was low and attrition rates for follow-up testing were high, half of the schools in the math trial and more than half of the schools in the English trial stopped the intervention before follow-up, and this makes it difficult to conclude anything certain about the impact of Best Practice in Setting.
Source: Best practice in grouping students. Intervention A: Best practice in setting evaluation report and executive summary, (September 2018). Education Endowment Foundation
Best practice in grouping students. Intervention B: Mixed attainment grouping. Pilot report and executive summary, (September 2018). Education Endowment Foundation
The Education Endowment Foundation has published an evaluation of 1stClass@Number, a 10-week numeracy intervention, delivered by teaching assistants, that provides intensive support for pupils struggling with maths.
A randomised controlled trial was conducted in 133 schools in south and west Yorkshire. Schools each nominated four children in Year 2 to participate, and the schools were then randomly assigned to either receive the intervention or to continue with normal teaching. A team from the University of Oxford evaluated the programme, which was delivered three times a week for 10 weeks in addition to normal mathematics instruction. A process evaluation collected additional data through observations, questionnaires and phone interviews.
Results showed that the intervention had a positive effect on Quantitative Reasoning Tests (effect size = +0.18) compared to pupils in the control group. Among pupils eligible for free school meals, those in the intervention group did not make any additional progress in the Quantitative Reasoning Test compared to control group pupils.
1stClass@Number seemed to have no impact on performance in end of Key Stage 1 maths tests compared to pupils in the control group. However, there was some evidence that the intervention widened the gap in Key Stage 1 maths results between pupils eligible for free school meals and their peers.
Source: 1stClass@Number: Evaluation report and executive summary (July 2018), Education Endowment Foundation
An evaluation conducted for the Education Endowment Foundation looked at whether the Good Behaviour Game (GBG) improved pupils’ reading skills and behaviour.
The GBG intervention is a classroom management approach designed to improve pupil behaviour and build confidence and resilience. The game is played in groups and rewards pupils for good behaviour. More than 3,000 Year 3 pupils from 77 UK schools took part in a randomised controlled trial of GBG over two years. Around a quarter of the pupils in the schools were eligible for free school meals, around a fifth were pupils with special educational needs, and 23% had English as an additional language.
The analysis indicated that, on average, GBG had no significant impact on pupils’ reading skills (effect size = +0.03) or their behaviour (concentration, disruptive behaviour and pro-social behaviour) when compared to the control group pupils. However, there was some tentative evidence that boys at risk of developing conduct problems showed improvements in behaviour.
Source: Good Behaviour Game: Evaluation report and executive summary (July 2018), Education Endowment Foundation
In England there is currently a shortage of maths teachers; among the factors that might be influencing this shortage are that departments lose 40% of teachers during their first six years in the profession, and there are higher private sector wages for maths graduates. At the same time, demand for maths teachers has increased due to policy measures to increase participation in maths for 16 to 18 year olds. To examine what impact this has had, the Nuffield Foundation commissioned researchers from FFT Education Datalab to look at how secondary schools have responded to the shortage.
Rebecca Allen and Sam Sims used data from England’s School Workforce Census and found that schools are using their most experienced and well-qualified maths teachers for year groups taking high-stakes exams (GCSEs, A-levels, and GCSE retakes), and using inexperienced maths teachers and teachers who trained in other subjects to fill staffing gaps elsewhere.
In the most disadvantaged schools (those with more pupils eligible for free school meals), pupils across all year groups are more likely to be taught by an inexperienced teacher. At Key Stage 5 (age 16-18) pupils in the most disadvantaged schools are almost twice as likely to have an inexperienced teacher as in the least disadvantaged schools (9.5% versus 5.3%).
Source: How do shortages of maths teachers affect the within-school allocation of maths teachers to pupils? (June 2018), Nuffield Foundation