Annemarie Boschloo

Academic motivation mediates the influence of temporal discounting on academic achievement during adolescence

Lee, N. C., Krabbendam, L., Dekker, S. J., Boschloo, A. M., De Groot, R. H. M., & Jolles, J. (2012). Academic motivation mediates the influence of temporal discounting on academic achievement during adolescence. Trends in Neuroscience and Education, 1(1), 43-48.

Measures of school performance for use in Educational neuropsychology

The Abelson helper integration site 1 (AHI1) gene locus on chromosome 6q23 is among a group of candidate loci for schizophrenia susceptibility that were initially identified by linkage followed by linkage disequilibrium mapping, and subsequent replication of the association in an independent sample. Here, we present results of a replication study of AHI1 locus markers, previously implicated in schizophrenia, in a large European sample (in total 3907 affected and 7429 controls). Furthermore, we perform a meta-analysis of the implicated markers in 4496 affected and 18,920 controls. Both the replication study of new samples and the meta-analysis show evidence for significant overrepresentation of all tested alleles in patients compared with controls (meta-analysis; P = 8.2 x 10(-5)-1.7 x 10(-3), common OR = 1.09-1.11). The region contains two genes, AHI1 and C6orf217, and both genes-as well as the neighbouring phosphodiesterase 7B (PDE7B)-may be considered candidates for involvement in the genetic aetiology of schizophrenia.

Subjective sleepiness and sleep quality in adolescents are related to objective and subjective measures of school performance.

This study investigated the relation between sleep and school performance in a large sample of 561 adolescents aged 11–18 years. Three subjective measures of sleep were used: sleepiness, sleep quality, and sleep duration. They were compared to three measures of school performance: objective school grades, self-reported school performance, and parent-reported school performance. Sleepiness – “I feel sleepy during the first hours at school” – appeared to predict both school grades and self-reported school performance. Sleep quality on the other hand – as a measure of (un)interrupted sleep and/or problems falling asleep or waking up – predicted parent-reported school performance. Self- and parent-reported school performance correlated only moderately with school grades. So it turns out that the measures used to measure either sleep or school performance impacts whether or not a relation is found. Further research on sleep and school performance should take this into account. The findings do underscore the notion that sleep in adolescence can be important for learning. They are compatible with the hypothesis that a reduced sleep quality can give rise to sleepiness in the first hours at school which results in lower school performance. This notion could have applied value in counseling adolescents and their parents in changing adolescents’ sleep behavior.

Age and educational track influence adolescent discounting of delayed rewards

This study examined age-related changes in a specific aspect of adolescent decision-making, namely the preference for future versus immediate outcomes. A sample of 622 Dutch adolescents aged 12–17 years completed a temporal discounting task. Participants were asked to choose between a delayed reward of €50 or an immediate reward of lower value. The delay interval was varied in three blocks (1 week, 1 month, 6 months). Results showed that preferences for large delayed rewards over smaller immediate rewards increased with age: late adolescents made more long-term decisions than early adolescents. This change was related to educational track. In the lower educational track, an age-related decrease in discounting was found for all three delay intervals. In the higher educational track this decrease only occurred for the 6 month delay interval. However, across all delay intervals enrolment in a higher level educational track was associated with an increased preference for long-term rewards. These results suggest that late adolescents are less susceptible than early adolescents to the competing presence of an immediate reward when making long-term decisions, a skill which becomes increasingly important as they transition into adulthood.

Sorting Test, Tower Test, and BRIEF-SR do not predict school performance of healthy adolescents in preuniversity education.

Executive functions (EF) such as self-monitoring, planning, and organizing are known to develop through childhood and adolescence. They are of potential importance for learning and school performance. Earlier research into the relation between EF and school performance did not provide clear results possibly because confounding factors such as educational track, boy-girl differences, and parental education were not taken into account. The present study therefore investigated the relation between executive function tests and school performance in a highly controlled sample of 173 healthy adolescents aged 12–18. Only students in the pre-university educational track were used and the performance of boys was compared to that of girls. Results showed that there was no relation between the report marks obtained and the performance on executive function tests, notably the Sorting Test and the Tower Test of the Delis-Kaplan Executive Functions System (D-KEFS). Likewise, no relation was found between the report marks and the scores on the Behavior Rating Inventory of Executive Function—Self-Report Version (BRIEF-SR) after these were controlled for grade, sex, and level of parental education. The findings indicate that executive functioning as measured with widely used instruments such as the BRIEF-SR does not predict school performance of adolescents in preuniversity education any better than a students grade, sex, and level of parental education.

Affective Processes in Learning

In the last decade the role of emotions in education seems to have been rediscovered (Maehr, 2001). Affective processes are now recognised as playing an important role in learning. Students’ emotions, such as, enjoyment, boredom, pride, and anxiety are seen to affect achievement by influencing the student’s involvement and attitude towards learning and learning environments, which also affects how (intensively) students process and/or interpret information (for a discussion see e.g., Boekaerts, 2003; Boekaerts & Simons, 1995; Pekrun, 2005; Pekrun, Goetz, Titz, & Perry, 2002). This (renewed) attention for affect is the result of several developments. A first development is the change from teacher-directed to learner-centred approaches in education, which often involve giving more responsibility for and control over the learning process to the learners.

Issues from Neuroscience

The present volume takes educational issues as a starting point and looks at possible contributions which could be given from the point of view of cognitive neuroscience. Issues that directly arise from the neurosciences which might be of relevance for education are only described in relation to educational issues. The interested reader is referred to recent reviews and ‘opinion’ articles which take a more cognitive neuroscience stance (e.g., Ansari & Coch, 2006; Casey, Getz, & Galvan, 2008; Jolles, 2007a,b; OECD, 2002, 2007; Steinberg, 2008) and other papers, mentioned in the Introduction. Yet, two issues deserve a short elaboration, because they are pertinent to the scientific findings and directions described in earlier sections. This concerns the issues of ‘plasticity’ and ‘maturation’.

Second) Language Learning and Literacy

Literacy is incredibly complex, and a full report on the links between neuroscience and language instruction would be an undertaking all on its own. In this chapter we therefore focus on a number of focal questions: What is the neurological basis of development of literacy? Can cognitive neuroscience help to distinguish between competing models discussed in educational research? What is the role of age of acquisition in second language (L2) learning? Does early L2 learning have a negative impact on acquisition of literacy in the native language? Can late L2 learners process an L2 in a native-like way? First, we give a brief overview of educational research developments in these areas, followed by an overview of neurocognitive contributions.

Numeracy and Mathematics Learning

Because numeracy, like literacy, results from the interplay of biology and experience it is the natural domain of both cognitive neuroscience and educational science. Although there is no single agreed upon definition, numeracy implies an understanding of the concept of number and the ability to reason quantitatively. As such, it is considered the basis of both simple and complex mathematics.