Suchergebnisse

The media environment has radically changed over the past few decades. Transition and transformation of media platforms has enabled algorithms and automation to take over media processes such as production, content generation, curation, delivery, recommendation, and filtering of information. It has also enabled tracking of users' actions, data mining, profiling, and the use of computational and machine learning techniques for purposes like behavior engineering, targeted advertisement, spread of mis- and disinformation, swaying political moods, and many others. In the field of media literacy education, the need to understand algorithm-driven media requires educators to re-think the connections between media literacy education and computing education. This article provides an overview of some computational mechanisms of new media, and it provides new perspectives for media literacy education. The article suggests ways of intertwining media literacy education with computing education in order to improve students' readiness to cope with modern media and to become critical and skilled actors to navigate in the new media landscape.

With growing concerns over children's data agencies, researchers have begun to draw attention to children's and young people's privacy practices in social media environments. However, little is known about the experiences of pre-service teachers who play a key role in educating future generations. This study aimed to address this gap by exploring Finnish pre-service teachers' conceptions and experiences of data agency in social media environments. Drawing from in-depth interviews of pre-service teachers ( N = 14), the analysis revealed that pre-service teachers construct their data agency in terms of social frames and shared social norms, and they also recognize the lack of understanding regarding wider socio-technical systems within which data agencies are situated. This research argues that without a sophisticated understanding of algorithmic governance and commercial use of data, it is unlikely that these future teachers would be prepared to facilitate children's and youth's agentive actions in a data-driven society.

Despite the growing concerns surrounding algorithmic biases in generative AI (artificial intelligence), there is a noticeable lack of research on how to facilitate children and young people's awareness and understanding of them. This study aimed to address this gap by conducting hands-on workshops with fourth- and seventh-grade students in Finland, and by focusing on students' ( N = 209) evolving explanations of the potential causes of algorithmic biases within text-to-image generative models. Statistically significant progress in children's data-driven explanations was observed on a written reasoning test, which was administered prior to and after the intervention, as well as in their responses to the worksheets they filled out during a lesson that focused on algorithmic biases. The article concludes with a discussion on the development and facilitation of children's understanding of algorithmic biases.