Connecting inside out: Development of the social brain in infants and toddlers with a focus on myelination as a marker of brain maturation

Early childhood is a sensitive period for learning and social skill development. The maturation of cerebral regions underlying social processing lays the foundation for later social-emotional competence. This study explored myelin changes in social brain regions and their association with changes in parent-rated social-emotional development in a cohort of 129 children (64 females, 0–36 months, 77 White). Results reveal a steep increase in myelination throughout the social brain in the first 3 years of life that is significantly associated with social-emotional development scores. These findings add knowledge to the emerging picture of social brain development by describing neural underpinnings of human social behavior. They can contribute to identifying age-/stage-appropriate early life factors in this developmental domain.     

What motivates enrolment in programming MOOCs?

Learners who enrol in massive open online courses (MOOCs) have different backgrounds and tend to have different motivations than learners in traditional courses. Based on value-expectancy theory, an instrument was developed to measure motivation for enrolling in a programming MOOC. A study with 1229 adult participants in Estonian-language programming course “About Programming” was conducted to validate the instrument. Results of confirmatory factor analysis validated the 7-factor scale named factors influencing enrolment in MOOC (FIEM). FIEM comprises three factors of expectancies, three factors of values and one factor of social influence. The highest and lowest rated motivational factors influencing enrolment in programming MOOC are discussed in the paper. Interest in and expectations for the course, personal suitability of distance learning and suitability for family and work are the highest-rated motivational factors for those who enrol in MOOC. Usefulness related to own children, social influence and usefulness to related to certification were the lowest rated. The results of this study can be useful for designers of programming MOOCs and the developed scale might be used in future studies.     

Digital Module 05: Diagnostic Measurement—The G‐DINA Framework https://ncme.elevate.commpartners.com

In this ITEMS module, we introduce the generalized deterministic inputs, noisy “and” gate (G‐DINA) model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models. The module contains a nontechnical introduction to diagnostic measurement, an introductory overview of the G‐DINA model, as well as common special cases, and a review of model‐data fit evaluation practices within this framework. We use the flexible GDINA R package, which is available for free within the R environment and provides a user‐friendly graphical interface in addition to the code‐driven layer. The digital module also contains videos of worked examples, solutions to data activity questions, curated resources, a glossary, and quizzes with diagnostic feedback.