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Abstract-This paper aims to determine the distribution of problem spaces in learning activities, when geovisual analytics is introduced into social science education. We know that various dimensions of complexity emerge in learning activities including this kind of technology. This paper clarifies the features of the problem spaces in such activities. The study was conducted in three middle schools in Sweden, in four social science classes with students aged 10 to 13 years. The specific geovisual analytics platform used was Statistics eXplorer. The learning activities were followed for two to four weeks at each school using video observations. Drawing on actor-network theory, we conducted material discursive analyses of the learning activities. The geovisual analytics generally support student understandings, but the didactic design of the classroom was not completely supportive. Six central aspects were found in the distribution of problem spaces within the learning activities. Novel approaches to pedagogy and teaching employing geovisual analytics could benefit students' knowledge building as they work with visualized data.

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Machine learning and other artificial intelligence (AI) technologies are predicted to play a transformative role in primary education, where these technologies for automation and personalization are now being introduced to classroom instruction. This article explores the rationales and practices by which machine learning and AI are emerging in schools. We report on ethnographic fieldwork in Sweden, where a machine learning teaching aid in mathematics, the AI Engine, was tried out by 22 teachers and more than 250 primary education students. By adopting an Actor-Network Theory approach, the analysis focuses on the interactions within the network of heterogeneous actors bound by the AI Engine as an obligatory passage point.The findings show how the actions and accounts emerging within the complex ecosystem of human actors compensate for the unexpected and undesirable algorithmic decisions of the AI Engine. We discuss expectations about AI in education, contradictions in how the AI Engine worked and uncertainties about how machine learning algorithms 'learn' and predict. These factors contribute to our understanding of the potential of automation and personalisation-a process that requires continued re-negotiations. | 585 SPERLING et al. 1 | INTRODUC TI ON VLADIMIR: A-. What are you insinuating? That we have come to the wrong place? ESTRAGON: He should be here. VLADIMIR: He did not say for sure he'd come. ESTRAGON: And if he does not come? VLADIMIR: We'll come back tomorrow. ESTRAGON: And then the day after tomorrow.

This study chrono-logically investigates teachers’ professional knowledge in relation to the digitally ‘boosted’ educational landscape caused by the COVID-19-pandemic. The aim is to describe how teachers employ their competences under extreme digitized circumstances compared to ordinary, to a greater extent analogically organized schooling. The study is inspired by action research where five secondary and upper secondary teachers document their work between March 19 and April 2020. A contrastive analysis highlights qualitative aspects of teachers’ (digital) competence when teaching is affected by digital “interferences” in its corporeal and material framing, a dissolved spatiality and “truncated” senses/sensuousness. Employing different dimensions of knowledge in terms of intellectus and ratio, the study argues that subjective, emotional and relational processes of teachers’ digital competence need to be prioritized in contrast to the easily measurable aspects that tend to overrun the discussions about educational digitalization and its knowledge in society.