Within the field of health psychology, there has been an enormous increase in behaviour change interventions that use digital technology. Answering questions and providing tailored feedback based on the answers provided by participants is the key working mechanism when using computer-tailoring in behaviour change interventions. This behaviour change method has proven to be (cost-)effective and results in participants being exposed to material that is tailored to their social-cognitive profile. At the same time, answering questions to assess this profile increases participant burden and might contribute to low levels of engagement - key challenges in digital health. This article provides insight into whether and how routinely collected data and novel self-assessment methods can be used in computer-tailoring to measure psychological constructs and address these key challenges. The examples presented suggest that the development of novel proxy measures for measuring psychological constructs relevant to computer-tailoring is indeed possible. However, the extent to which measures are valid and actually do reduce participant burden, increase engagement and have other potential benefits is speculative and needs further investigation. The recommendations provided for future research and practice are hoped to serve as a stimulance for driving further momentum in this area.

To cite this article: Andis Klegeris (2021) Mixed-mode instruction using active learning in small teams improves generic problem-solving skills of university students,

Despite a plethora of research, associations between individual differences in personality and electroencephalogram (EEG) parameters remain poorly understood due to concerns of low replicability and insufficiently powered data analyses due to relatively small effect sizes. The present article describes how a multi-laboratory team of EEG-personality researchers aims to alleviate this unsatisfactory status quo. In particular, the present article outlines the design and methodology of the project, provides a detailed overview of the resulting large-scale dataset that is available for use by future collaborators, and forms the basis for consistency and depth to the methodology of all resulting empirical articles. Through this article, we aim to inform researchers in the field of Personality Neuroscience of the freely available dataset. Furthermore, we assume that researchers will generally benefit from this detailed example of the implementation of cooperative forking paths analysis.

A large and varied evidence base supporting the efficacy of social therapies to improve the social behaviors of children with autism spectrum disorders (ASD) does not permit a clear understanding of which specific types of social behavior are improved by specific social therapies. Social maintenance behaviors function to form and sustain relationships, which has been associated with a reduction in negative social experiences in children with ASD. The present systematic review investigates the effectiveness of interactive social therapy in increasing these specific behaviors in this population. A systematic search of PsycArticles, Medline, Education Resources Information Centre, Child Development and Adolescent Studies, and Scopus databases identified 18 articles as relevant for inclusion. The extant evidence suggests that interactive social therapies are effective in increasing social maintenance behaviors in children with ASD. Explicit targeting of these behaviors and inclusion of reinforcement are highlighted as potentially active components in this regard.