Self-regulated learning has been regarded as essential for effective learning. Research suggests that self-regulated learning is associated with academic performance, but different self-regulated learning strategies are not equally effective. Addressing the gap that occurred for Chinese pupils because few studies conducted in Asia were included in a previous meta-analysis, a meta-analysis published in Frontiers in Psychology has investigated what the most effective strategies for Chinese pupils were.
Using Chinese academic databases, Junyi Li and colleagues analysed 264 independent samples that involved 23,497 participants from 59 studies. In order to be included in this meta-analysis, studies had to be conducted in real teaching situations; studies based on online learning environments were excluded. Furthermore, participants had to be primary, middle, or secondary school pupils in China. The effect sizes of self-regulated learning strategies on academic achievement were analysed. The results showed that:
- Among the self-regulated learning strategies, self-efficacy (ES = +0.70), self-evaluation (ES = +0.72), and task strategies (ES = +0.60) had relatively large effect sizes on academic achievement.
- On the other hand, the effect sizes of goal orientation (ES = +0.09) and attributions (ES = +0.27) were relatively small.
- The effect sizes of self-regulated learning on science (ES = +0.45) were larger than those on language (ES = +0.29).
authors suggest that task strategies supported learning by reducing a task to
its key parts, that self-evaluation helped learners compare results with their
goals, and that self-efficacy helped learners to use their resources.
Source: What are the effects of self-regulation phases and strategies for Chinese students? A meta-analysis of two decades research of the association between self-regulation and academic performance (December 2018), Frontiers in Psychology
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
An intervention that trained teachers to improve and monitor the quality of classroom talk had a positive impact on primary pupils’ test scores in English, maths and science, a report published by the Education Endowment Foundation (EEF) reveals.
Seventy-six primary schools with higher-than-average proportions of disadvantaged pupils took part in a randomised control trial of the Dialogic Teaching intervention, which is designed to improve the quality of classroom talk as a means of increasing pupils’ engagement, learning and achievement. Year 5 teachers in 38 schools (2,493 pupils), and a teacher mentor from each school, received resources and training from the delivery team and then implemented the intervention over the course of the autumn and spring terms in the 2015/16 school year. A control group of 38 schools (2,466 pupils) continued with business as usual. Following the intervention, pupils were tested in English, maths and science.
The results showed that pupils in the intervention schools did better in the main outcome measures of English (effect size = +0.16), science (+0.12), and maths (+0.09) when compared with pupils in the control schools who didn’t receive the intervention. For pupils who received free school meals, the intervention had a higher impact on maths (+0.16), but around the same for English (+0.12) and science (+0.11). Teachers reported positive effects on pupil engagement and confidence, and on the whole the intervention was highly regarded by participating schools. However, some teachers felt that it would take longer than two terms to fully embed a Dialogic Teaching approach in their classrooms.
The Dialogic Teaching intervention was developed by the Cambridge Primary Review Trust and the University of York. This University of York news story has more.
Source: Dialogic teaching: evaluation report and executive summary (July 2017), Education Endowment Foundation
Regional Educational Laboratory (REL) Appalachia has conducted a systematic review of research on the effects of increased learning time on student achievement in US schools (Grades 2 to 10, equivalent to Key Stages 2 to 4). Increased learning time programmes offer students additional instruction beyond the regular school day in English, maths, and other subjects.
REL screened 7,000 studies and found 30 that met their inclusion criteria. Results were mixed and showed that achievement depended on types of students targeted, the setting, and the features of the programme implemented. Overall patterns noted were:
- Increased learning time programmes improved academic motivation.
- Gains were dependent upon type of instruction and instructor qualifications.
- Increased learning time had a large positive effect on struggling students.
Source: What Does the Research Say About Increased Learning Time and Student Outcomes? U.S. Department of Education. Retrieved from http://ies.ed.gov/ncee/edlabs
This report from the University of Minnesota presents findings from a three-year study on high school (age 14-18) start times. It examined whether or not a delay in start times had an impact on students’ overall health and academic performance.
The study consisted of three parts:
- Collecting survey data from over 9,000 students across eight high schools in five school districts. Students were individually surveyed about their daily activities, substance use, and sleep habits.
- Collecting data on students’ academic performance, such as grades earned, attendance, timekeeping, and performance on state and national tests. The researchers also examined car crash data for the communities involved in the project.
- An examination of the processes by which local school districts made the decision to change to a later start time.
Key findings included:
- High schools that start at 8:30am or later allow for more than 60% of students to obtain at least eight hours of sleep per school night;
- Teens getting less than eight hours of sleep reported significantly higher depression symptoms, greater use of caffeine, and are at greater risk for making poor choices for substance use;
- Academic performance outcomes, including grades earned in core subject areas of mathematics, English, science, and social studies, plus performance on state and national achievement tests, attendance rates, and reduced tardiness, show significantly positive improvement with the start times of 8:35am or later; and
- The number of car crashes for teen drivers from 16 to 18 years of age was significantly reduced (by 70%) when a school shifted start times from 7:35am to 8:55am.
Source: Examining the Impact of Later High School Start Times on the Health and Academic Performance of High School Students: A Multi-Site Study (2014), University of Minnesota.
A new review from MDRC analyses the evidence on how families’ involvement in children’s learning and development affects literacy, mathematics, and social-emotional skills at ages 3 to 8. A total of 95 studies, primarily from the last ten years, were included. Four categories were considered: learning activities at home, family involvement at school, school outreach to engage families, and supportive parenting activities.
The review found that overall family involvement had small to moderate effects on children’s outcomes. Numerous studies confirmed a link between family involvement and children’s literacy skills. A number of studies also demonstrated positive associations with children’s mathematics skills, and a few with children’s social-emotional skills. The weakest association was between family involvement at school and children’s outcomes.
The review concludes that family involvement is potentially important in terms of efforts to improve children’s early learning and development, particularly as all parents, when given direction, can increase their involvement with their children’s learning. The authors dismiss the idea that certain groups of parents do not care or will not become involved in their children’s education.
A future edition of Better: Evidence-based Education will be looking at the issue of parents and schools.
Source: The Impact of Family Involvement on the Education of Children Ages 3 to 8 (2013), MDRC.