Centre of Excellence in Learning Dynamics and Intervention Research (InterLearn)

Main funder

Funder's project number: 364197

Funds granted by main funder (€)

Funding program

Project timetable

Project start date: 01/01/2024

Project end date: 31/12/2026

Summary

Learning problems (LPs) are among the most frequently identified childhood conditions, with prevalence rates of approximately 10-20%. These disorders have a high impact on individual’s quality of life, and the economic burden they place on education, health, social care and criminal justice systems are high. Importantly, these disorders do not develop in isolation; environmental factors play a crucial role in why some children manage and some don’t with the increasing demands of fastly changing and digitalized society at all levels, including home, social, school and service environments. Traditional research views LPs as distinct categories, such as dyslexia (reading problems), developmental language disorder (DLD), dyscalculia (math learning problems) or attention deficit hyperactivity disorder (ADHD). Research on LPs has also often overlooked learning related emotional problems, motivational factors and overlap of both underlying risk factors and learning problems themselves in the same children. Research on heterogeneity of LPs and individual profiles has also been scarce leading to neglecting the fact that LPs are not invariant, discrete disorders but rather dimensional in nature with multiple risk factors. Further, purely cognitively based interventions on LPs have shown modest effects. This challenge is met in a multidisciplinary and integrative large scale setting in this Center of Excellence. Our CoE including experts with diverse research traditions uses integrative and multilevel person oriented approach to identify the principal neurobiological, cognitive, behavioural, socio-emotional mechanisms and environmental factors underpinning development and LPs. We employ latest research designs and methods including psychometrics, modeling and machine learning applied to large, longitudinal datasets. The CoE will create a multi-dimensional model of learning problems and inter-related emotional problems that takes developmental changes into account. We use these discoveries to detect, predict and affect individual developmental learning outcomes. CoE results will be translated to knowledge promoting students’ self-efficacy and self-awareness while helping them to support their full potential in learning in increasingly complex environments.

Principal Investigator

Other persons related to this project (JYU)

Primary responsible unit

Follow-up groups

Profiling area: Multidisciplinary research on learning and teaching (University of Jyväskylä JYU) MultiLeTe