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.

The lack of interest among pre-university students to choose STEM subjects for their higher education is a heavily debated issue in many western-world countries. To boost Danish students' interest in biology, a study event on artificial reefs was introduced when teaching marine biology in lower secondary school and upper secondary school (student age 13-20 years). The purpose was to investigate if the focus on artificial reefs could generate an increased interest in natural science and marine biology among the students. The students' interest in science was evaluated using electronic questionnaires before and after they had completed the teaching program. The students were significantly more interested in natural sciences and marine biology after than before the teaching program. The development in situational interest in science and in the oceans was different for males and females with females being most interested. Thus, it is possible to trigger a situational interest for science and marine biology by teaching about artificial reefs, but the way interest is triggered differs between different age groups and sexes.

Formålet med artiklen er at analysere og diskutere, hvordan læsevejledere i danske folkeskoler arbejder med testning iagttaget som en organisatorisk rutine, og hvilken betydning det har for den organisatoriske læring i skolen. Artiklen tager udgangspunkt i et casestudie, hvor fire læsevejledere er blevet skygget og interviewet om deres professionelle praksis. Det empiriske materiale viser, hvordan læsevejledere arbejder rutinemæssigt med at gennemføre og følge op på testning af elever. Vi diskuterer på baggrund af disse fund sammenhængen mellem rutiner og forandringer i skolen med særligt fokus på testning i læsevejledningen og peger på muligheder for fremtidig forskning på området.

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.

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