Recently, computational thinking has attracted much research attention, especially within primary and secondary education settings. However, incorporating computational thinking (CT) in mathematics or other disciplines is not a straightforward process and introduces many challenges concerning the way disciplines are organised and taught in school. The aim of this paper is to identify what characterises CT in mathematics education and which CT aspects can be addressed within mathematics education. First, we present a systematic literature review that identifies characteristics of computational thinking that have been explored in mathematics education research. Second, we present the results of a Delphi study conducted to capture the collective opinion of 25 experts in both the fields of mathematics education and computer science regarding the opportunities for addressing computational thinking in mathematics education. The results of the Delphi study, which corroborate the findings of the literature review, highlight three important aspects of computational thinking to be addressed in mathematics education: problem solving, cognitive processes, and transposition.

Computer modeling has been widely promoted as a means to attain higher order learning outcomes. Substantiating these benefits, however, has been problematic due to a lack of proper assessment tools. In this study, we compared computer modeling with expository instruction, using a tailored assessment designed to reveal the benefits of either mode of instruction. The assessment addresses proficiency in declarative knowledge, application, construction, and evaluation. The subscales differentiate between simple and complex structure. The learning task concerns the dynamics of global warming. We found that, for complex tasks, the modeling group outperformed the expository group on declarative knowledge and on evaluating complex models and data. No differences were found with regard to the application of knowledge or the creation of models. These results confirmed that modeling and direct instruction lead to qualitatively different learning outcomes, and that these two modes of instruction cannot be compared on a single ''effectiveness measure''.

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