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DA: Denne artikel afsøger den seneste internationale engelsk­sprogede forskningslitteratur om anvendelse af digitale lærings­platforme. Reviewet behandler forskning fra i alt 21 studier og sammenfatter resultaterne i tre hovedkategorier, der henholds­vis omfatter implementering af læringsplatforme, kompetence­udviklingsbehov i forbindelse med platforme og relationen mellem elevers brug af platforme og deres faglige udbytte. Reviewet afsluttes med en diskussion af, hvordan resultaterne af litteraturgennemgangen kan have relevans for en dansk uddan­nelseskontekst i forhold til særlige opmærksomhedspunkter i den pædagogiske praksis, forskning og beslutningstagning.ENG: The article is a review of current international research literature on the use of digital learning platforms in educational settings. The review includes a total of 21 studies and summarizes the results in three main categories: 1. Support of students’ learning and performance, 2. implementation of digital learning platforms, and 3. competence development of instructors. The relevance of the results in a Danish educational context is discussed in relation to educational practice, research and decision making. The article is a review of current international research literature on the use of digital learning platforms in educational settings. The review includes a total of 21 studies and summarizes the results in three main categories: 1. Support of students’ learning and performance, 2. implementation of digital learning platforms, and 3. competence development of instructors. The relevance of the results in a Danish educational context is discussed in relation to educational practice, research and decision making.

This study shows how liking politicians' public Facebook posts can be used as an accurate measure for predicting present-day voter intention in a multiparty system. We highlight that a few, but selective digital traces produce prediction accuracies that are on par or even greater than most current approaches based upon bigger and broader datasets. Combining the online and offline, we connect a subsample of surveyed respondents to their public Facebook activity and apply machine learning classifiers to explore the link between their political liking behaviour and actual voting intention. Through this work, we show that even a single selective Facebook like can reveal as much about political voter intention as hundreds of heterogeneous likes. Further, by including the entire political like history of the respondents, our model reaches prediction accuracies above previous multiparty studies (60-70%). The main contribution of this paper is to show how public like-activity on Facebook allows political profiling of individual users in a multiparty system with accuracies above previous studies. Beside increased accuracies, the paper shows how such parsimonious measures allows us to generalize our findings to the entire population of a country and even across national borders, to other political multiparty systems. The approach in this study relies on data that are publicly available, and the simple setup we propose can with some limitations, be generalized to millions of users in other multiparty systems.

Learning Tech 2 har temaoverskriften “Læreplaner og it” og indeholder fem peer reviewed artikler, der undersøger og forholder sig til, hvilken rolle it har spillet i læreplaner gennem tiden, og hvordan it spiller en stadig større rolle i skolernes hverdag og generelt i måden at drive uddannelse på.