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IntroductionCOVID-19 had a major impact on education, resulting in learning losses among students. The Dutch ministry set-up a subsidy for schools to implement catch-up programs in tackling learning losses. In this study, we examine (a) which students participated in the programs, and (b) effectiveness of these programs in remediating learning losses in secondary school students.MethodsSixteen program in eight secondary schools were analyzed using data of 16,675 students (9,784 individual students; 1,336 participating in a catch-up program). Schools implemented three program types: tutoring, homework support, and general skills. Per school, a difference-in-difference design was used, computing two effect sizes: comparing grades of participating and non-participating students; and grades in tutoring-specific subjects to non-tutored subject (specifically for tutoring programs). Effect sizes were combined using meta-analytic regressions in JASP.ResultsAt program onset, students selected for participation had significantly lower overall grades than non-participants, or – for subject-specific tutoring – lower grades specifically in the tutored subject. Tutoring programs significantly increased students’ grades: with higher grades for participants than non-participants, and – for students receiving subject-specific tutoring - higher grades in tutored subjects compared to those in non-tutored subjects. No significant effects were found for homework support and general study skill programs.ConclusionSchools selected students most in need for catch-up programs. Tutoring interventions seemed to remediate part of secondary school students’ learning losses, whereas general skills programs and homework support programs did not. Large between-school heterogeneity was found, implying that program implementation was at least as important as program type and content.

Experiencing school burnout symptoms can have negative consequences for learning. A growth mindset, the belief that human qualities such as intelligence are malleable, has previously been correlated with fewer school burnout symptoms in late adolescents. This might be because adolescents with a stronger growth mindset show more adaptive self-regulation strategies and thereby increasing resilience against academic setbacks. Here we confirmed in a sample of 426 Dutch young adolescents (11–14 years old; 48% female) that this relationship between growth mindset and school burnout symptoms holds after controlling for other potential predictors of school burnout symptoms such as academic achievement, school track, gender, and socio-economic status. Our second aim was to increase our understanding of the mechanism underlying the relation between mindset and school burnout, by measuring physiological resilience (vagal activity, a measure of parasympathetic activity, also known as heart rate variability or HRV) in a subsample (n = 50). We did not find any relation between vagal activity and growth mindset or school burnout symptoms, nor could we establish a mediating effect of vagal activity in their relation. In conclusion, we found evidence for a potential protective effect of a growth mindset on school burnout symptoms in young adolescents, but not for physiological resilience (vagal activity) as an underlying mechanism. The protective effect of growth mindset as confirmed in our younger sample can be leveraged in interventions to prevent increasing school burnout symptoms.

IntroductionMachine learning algorithms use data to identify at-risk students early on such that dropout can be prevented. Teachers, on the other hand, may have a perspective on a student’s chance, derived from their observations and previous experience. Are such subjective perspectives of teachers indeed predictive for identifying at-risk students, and can these perspectives help increase the prediction performance of machine learning algorithms? This study puts 9 teachers in an upper secondary vocational education program to the test.MethodsFor each of the 95 freshmen students enrolled in the program, these teachers were asked whether a student would drop out by the end of their freshman year. Teachers answered this question at the beginning of the program and again after the first 10 weeks of the program.ResultsTeachers predicted dropout better than the machine learning algorithms at the start of the program, in particular, because they were able to identify students with a very high likelihood of dropout that could not be identified by the algorithms. However, after the first period, even though prediction accuracy increased over time for both algorithms and teachers, algorithms outperformed the teachers. A ranking, combining the teachers composite and the random forest algorithm, had better sensitivity than each separately, though not better precision.

Over the past two decades, educational policymakers in many countries have favored evidence-based educational programs and interventions. However, evidence-based education (EBE) has met with growing resistance from educational researchers. This article analyzes the objections against EBE and its preference for randomized controlled trials (RCTs). We conclude that the objections call for adjustments but do not justify abandoning EBE. Three future directions could make education more evidence-based whilst taking the objections against EBE into account: (1) study local factors, mechanisms, and implementation fidelity in RCTs, (2) utilize and improve the available longitudinal performance data, and (3) use integrated interventions and outcome measures.

The COVID-19 pandemic caused lockdowns and sudden school closures around the world in spring 2020, significantly impacting the education of students. Here, we investigate how the switch to distance learning affected study activity and performance in an online retrieval practice tool used for language learning in Dutch secondary education. We report insights from a rich data set consisting of over 115 million retrieval practice trials completed by more than 133 thousand students over the course of two consecutive school years. Our findings show that usage of the tool increased substantially at the start of lockdown, with the bulk of study activity occurring on weekday mornings. In general, students’ progress through the material was largely unaffected by lockdown, although students from the highest educational track were somewhat more likely to be on or ahead of schedule than students from lower tracks, compared to the previous year. Performance on individual study trials was generally stable, but accuracy and response time on open answer questions went up, perhaps as a result of students being more focused at home. These encouraging findings contribute to a growing literature on the educational ramifications of distance learning during lockdown.

Ability grouping has been argued to improve study success because it allows teachers to better tailor their instruction and guidance to the educational needs of students. However, teachers must then be able to assess what to tailor their instruction to. One test, based on the Life-Cycle Theory of Leadership, assesses cognitive skills and personality traits to generate advice on the optimal guidance style per student. This test was used in an upper secondary vocational education school to group students with the same advised guidance style. We investigated whether students actually prefer the advised guidance style and whether teachers can adapt their guidance to the styles advised for the group. The results indicate that students are somewhat more likely than chance to prefer the advised guidance style. Similarly, teachers are somewhat more likely than chance to correctly identify this guidance style of a class, but they do not adapt their guidance to the advised style even though they were motivated to do so. This suggests that assessment can be used to diagnose guidance styles, but not with sufficient fidelity so that it can be acted upon.