Dear Colleagues,

This Special Issue seeks contributions which focus on the learning and the specific and/or joint influences of intelligence and competencies. Intelligence is used as an umbrella term for a variety of cognitive abilities at different hierarchical levels. Competence refers to ideas such as those outlined in Blömeke, S., Gustafsson, J. E., and Shavelson, R. J. (2015). Beyond dichotomies: Competence viewed as a continuum. Zeitschrift für Psychologie, 223, 3-13. doi.org/10.1027/2151-2604/a000194

Thus, papers should ideally combine all three phenomena (learning, intelligence, and competence), but at the very least, we are looking for papers that combine two of the following:

1. a focus on some aspect of human intelligence/cognitive ability either as an antecedent, a consequence, or a correlate of learning;
2. a focus on competencies that are often (but not always) domain-bound. Competencies that have been focused on international large-scale assessments such as mathematics, reading, or problem solving can be the focus, but contributions on other competencies are also welcome;
3. a focus on some forms of learning or learning outcomes in an educational context. With regard to learning we are interested in research focusing the micro and / or macro level. In other words, research looking at specific learning processes is just as welcome as research focusing on longitudinal development or learning results. We expect most contributions from the field of secondary education, but contributions that consider primary or tertiary education, lifelong learning, or learning in informal contexts are equally welcome.

In general, we are looking for papers which are built on an empirical basis. However, papers proposing theoretical developments are also welcome. Contributions may apply a host of methods as long as these methods serve the purpose of adequately addressing the underlying research questions. This includes methods such as structural equation modeling and IRT analyses. Given that we also want to look at learning processes, other methods might encompass longitudinal methods, such as time series analyses or latent growth analyses. In addition to this, we are particularly interested in contributions that employ big data methods such as (educational) data mining, machine learning, or artificial intelligence that offer new opportunities to describe and trace the complex interplay between intelligence, competencies, and learning.

Prof. Dr. Matthias Ziegler
Prof. Dr. Samuel Greiff
Guest Editors

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