This brief article introduces the special issue 'Learning through Networks".

Existing research has identified feelings of responsibility as having major motivational implications for a person’s actions. A person identifying as being responsible for a certain task will perceive themselves as self-determined and thus invest considerable effort in the task. Despite being coneptualised as an individual’s sense of internal obligation, responsibility in everyday contexts is often attributed by and to other people. Different perspectives on responsibility may, however, not always overlap, especially in the school context where tasks and liabilities often remain ill-defined. This paper thus presents a framework of responsibility in the school context which assumes teachers, students and parents to share a certain number of microsystems which may (indirectly) influence one another. In order to test the usefulness of the proposed framework, a series of studies were conducted collecting data on teachers’, students’ and parents’ views of their own and one another’s responsibility in the school context. 4339 statements were assigned to categories representing different parts of the framework and reveal its usefulness for describing the complexity of responsibility attributions and its influences in the school context. Findings show the framework will be helpful to embrace existing research and develop questions for further research that address central educational issues such as student and teacher motivation, teacher burnout as well as prerequisites for students’ high or low achievement.

This commentary to the recent article by Musso et al. (2013) discusses issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling. We start by questioning the extremely high classification accuracy with an empirical data from a complex domain. There is a risk that we model perfect nonsense perfectly. Our second concern is related to the relevance of comparing multilayer perceptron neural networks and linear discriminant analysis classification accuracy indices. We find this problematic, as it is like comparing apples and oranges. It would have been easier to interpret the model and the variable (group) importance’s if the authors would have compared MLP to some discriminative classifier, such as group lasso logistic regression. Finally, we conclude our commentary with a discussion about the predictive properties of the adopted data modeling approach.

In this article we summarise research that discusses „teacher teams‟. The central questions guiding this study are „How is the term „teacher team‟ used and defined in previous research?‟ and „What types of teacher teams has previous research identified or explored?‟. We attempted to answer these questions by searching literature on teacher teams and comparing what these articles present as being teacher teams. We attempted to further grasp the concept of teacher teams by creating a typology for defining different types of teacher teams. Overall, the literature pertaining to teacher teams appeared to be characterised by a considerable amount of haziness and teacher „teams‟ mostly do not seem to be proper „teams‟ when keeping the criteria of a team as defined by Cohen and Bailey (1997) in mind. The proposed typology, characterising the groups of teachers by their task, whether they are organised disciplinary or interdisciplinary, whether they are situated within or cross grades, their temporal duration and degree of team entitativity or „teamness‟, appears to be a useful framework to further clarify different sorts of teacher „teams‟.