Testing the validity of value‐added measures of educational progress with genetic data

Value‐added measures of educational progress have been used by education researchers and policy‐makers to assess the performance of teachers and schools, contributing to performance‐related pay and position in school league tables. They are designed to control for all underlying differences between pupils and should therefore provide unbiased measures of school and teacher influence on pupil progress, however, their effectiveness has been questioned. We exploit genetic data from a UK birth cohort to investigate how successfully value‐added measures control for genetic differences between pupils. We use raw value‐added, contextual value‐added (which additionally controls for background characteristics) and teacher‐reported value‐added measures built from data at ages 11, 14 and 16. Sample sizes for analyses range from 4,600 to 6,518. Our findings demonstrate that genetic differences between pupils explain little variation in raw value‐added measures but explain up to 20% of the variation in contextual value‐added measures (95% CI = 6.06% to 35.71%). Value‐added measures built from teacher‐rated ability have a greater proportion of variance explained by genetic differences between pupils, with 36.3% of their cross‐sectional variation being statistically accounted for by genetics (95% CI = 22.8% to 49.8%). By contrast, a far greater proportion of variance is explained by genetic differences for raw test scores at each age of at least 47.3% (95% CI: 35.9 to 58.7). These findings provide evidence that value‐added measures of educational progress can be influenced by genetic differences between pupils, and therefore may provide a biased measure of school and teacher performance. We include a glossary of genetic terms for educational researchers interested in the use of genetic data in educational research.     

EVALUATION IN ADULT EDUCATION BROADCASTING

The paper suggests that a useful step towards linking research with decision‐making is to attempt some working definitions. “Evaluation” simply means setting a value to something: in this case a learning process, provided or supported by educational broadcasting. “Research” is a more slippery word, meaning different things at different times. Here it is taken to mean any systematic collection of evidence, designed to test a hypothesis or to answer specific questions, which evidence can be used in the making of future decisions. So evaluation is a purpose and research is a method. It is hoped that the method serves the purpose.

This largely depends on its success in answering three basic questions: Are the objectives clear and likely to be useful? Are the methods effective and manageable? Are the applications practicable? The paper considers these questions in relation to the two main styles of evaluation in adult education: formative and summative.     

The dissertation examination: identifying critical factors in the success of Indigenous Australian doctoral students

Indigenous Australians represent 2.2% of the working age population, yet account for only 1.4% of all university enrolments. In relation to higher degree research students, Indigenous Australians account for 1.1% of enrolments, but only 0.8% of all higher degree research completions. This paper reports on findings that emerged from an Australian Research Council-funded study which aimed to establish a model of best practice for the supervision of Indigenous doctoral students. The project identified the dissertation examination as one of the critical factors underpinning the success of doctoral candidates. Whilst research into the examination process for doctoral students is limited, the research that specifically explores the examination of dissertations submitted by Indigenous students is entirely inadequate. Our research identified key epistemological concerns that impact approaches to the examination process, to demonstrate how the dominance of Western methods of research impact the examination process for Indigenous doctoral students. This paper explores the experiences of 50 successful Indigenous Australian doctors with a specific focus on their examiner preference and disciplinary requirements. It highlights the limitations that some Indigenous students and their supervisors experience in finding an appropriate examiner.     

Overlap Probabilities and Delay Detonators

A problem in mining engineering concerning detonators for rock blasting is considered. Simple probability calculations give very useful information about the detonation sequence.     

Game Over? No Main or Subgroup Effects of the Good Behavior Game in a Randomized Trial in English Primary Schools

Abstract

This study aimed to examine the impact of a universal, school-based intervention, the Good Behavior Game (GBG), on children’s behavior, and to explore any subgroup moderator effects among children at varying levels of cumulative risk (CR) exposure. A 2-year cluster-randomized controlled trial was conducted comprising 77 primary schools in England. Teachers in intervention schools delivered the GBG, whereas their counterparts in control schools continued their usual provision. Behavior (specifically disruptive behavior, concentration problems, and pro-social behavior) was assessed via the checklist version of the Teacher Observation of Classroom Adaptation. A CR index was calculated by summing the number of risk factors to which each child was exposed. Multilevel models indicated that no main or subgroup effects were evident. These findings were largely insensitive to the modeling of CR although a small intervention effect on disruptive behavior was found when the curvilinear trend was used. Further sensitivity analyses revealed no apparent influence of the level of program differentiation. In sum, our findings indicate that the GBG does not improve behavior when implemented in this sample of English schools.