Subject Learning

When the first ‘Program for International Student Assessment’ (PISA) was to be launched in 2000, representatives of OECD member countries were discussing about the basic competencies of secondary school students for future. It was clear that not simply obsolete knowledge, but key skills to participate in social, economic and political life should be measured. The three core areas of expertise considered relevant to all students in all schools worldwide were described as mathematical literacy, reading literacy and scientific literacy. Mathematical literacy is necessary to understand quantitative relationships in reality and to use them in economic, social or political context (Lange, 2001). Reading literacy is important to comprehend, use and reflect on written texts to develop one’s knowledge and reach one’s own goals. Scientific literacy serves to understand scientific phenomena and encompasses the scientific knowledge that can be used to take part in social discourses (Jude & Klieme, 2010).

Large-scale studies like PISA are often accused of focusing primarily on the collection and comparison of basic competencies. Thereby, contributing little to the development of educational and psychological knowledge in the core areas of student learning (for an exception: Le Donné, Fraser, & Bousquet, 2016). Therefore, it is more necessary that other research groups concentrate on learning processes and structures in the fields of mathematics, reading and science. In this special issue on subject learning, four outstanding contributions present the findings that these research efforts have led to.

Krawitz, Schukajlow, and van Dooren (2018) illustrate with their research on unrealistic answers to realistic problems that learners sometimes lack realism in matters of mathematics. If a 12 m long rope has to be tied together from 2 m long pieces, then six pieces of rope are clearly not sufficient. In this case, students have to think beyond the context of the task and make their own assumptions about connecting the pieces of rope. As an instructive support, it is possible to point to the ambiguity of the solution, but students do not always succeed in thinking out of the box.

Madamurk, Kikas, and Palu (2018) address the question of what it actually depends on that learners develop good arithmetic and word problem solving skills. They show that prior knowledge to a large extent, reasoning and text comprehension to a medium extent, and subject-specific interest to a small extent determine the mathematics performance of secondary school students. Subsequent latent profile analysis reveals that the group of the best mathematics students not only has a higher level of prior knowledge but also significantly higher abilities in reasoning and text comprehension and a greater mathematics interest than other groups.

Ravand and Robitzsch (2018) use data of the reading comprehension section of a national university entrance examination to test various cognitive diagnostic models against each other. Cognitive diagnostic models can provide clues as to why students perform well in a test. They break test tasks into strategies, processes and knowledge, which are necessary to succeed in the tasks under investigation. The probability of success in a task is predicted by latent categorical variables calculated from measured data. This relatively new diagnostic approach draws attention to the actual capabilities underlying the successful completion of a language test.

McPherson, Banchefsky, and Park (2018) investigate gender differences in the selection of science-related majors from a socio-psychological perspective. They decided for this point of view as the large differences between men and women in pursuing a science-oriented career can hardly be explained by prior achievement or grades. Their research approach is not limited to a few factors, but draws on a whole set of explanatory influences. The most important factors in explaining gender differences are gender determinism, feelings of belonging, common goals and the existence of a role model in high school.

The four excellent contributions in this issue show that apart from large-scale assessment such as PISA exist more far-reaching studies on subject learning. All contributions have in common that they are not looking at the educational outcomes per se, but at the processes and explanations behind the outcomes. They show in the most excellent manner what constitutes the very core of research in educational psychology. I wish you, dear readers, lots of pleasure with the detailed study of the informative and well-founded articles.