Suchergebnisse

The shift from electromechanical computing to fully electronic, digital, Turing-complete computing was one of the most in?uential technological developments of the twentieth century. The social, economic, political, interdisciplinary, and cultural aspects behind that shift were signi?cant, but are often ignored. When the contingencies and controversies behind the birth of modern computing are forgotten, the history of computing is often misrepresented as one of uncomplicated linear progress. In this article some of the sociocultural aspects of the birth of modern computing are reviewed. The signi?cance of interdisciplinary work is discussed. The concept of the stored-program paradigm is introduced, and some sociocultural factors behind its birth are discussed. Finally, it is argued that some traits of research that are often considered to be negative, such as opportunism, eclecticism, and stubbornness, have played a positive role in the birth of modern computing technology.

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.

One-to-one computing has lately become one of the buzzwords in the educational technology initiatives in developing countries. Various versions of one-to-one computing have recently been implemented around the developing world. Governments and educational organizations have often felt a pressure to acquire new technology as an effort to leapfrog development, but this has in most cases been done without any deeper analysis of the complete framework that successful one-to-one computing initiatives require. A number of One Laptop Per Child (OLPC) projects have subscribed to the view that one-to-one computing should not delve into non-technical areas like teacher training, curriculum development, or content development. We, however, consider the non-technical issues to be of great importance for the success of computer-enhanced learning in primary schools. This paper presents and analyzes nine crucial and often neglected considerations for successfully implementing one-to-one computing initiatives: a pedagogical framework, teacher training, a support model, content in local languages, adaptation to local conditions, parental agreement and support, monitoring and evaluation, administrative support, and sustainability. We disagree with the view that one-to-one computing is only about leveraging the children themselves. By properly considering our nine propositions, one–to–one computing initiatives can increase their positive impacts in primary schools in developing regions.

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.

In many parts of Africa the application of information and communication technologies (ICTs) in higher education institutions offers a particularly interesting research problem. This is because of the far-reaching consequences that information and communication technology (ICT) services and education have on the university graduates during their careers in African societies. Our extensive and well-documented experience of utilizing ICTs in a Tanzanian private university has lead us to propose a four-level approach, called CATI model, that can be used as the basis for implementing new ICTs, for evaluating how ICT is used, and for planning ICT education in developing countries. We conceptually categorize the four levels that we use in our model – contextualize, apply, transfer and import – as an ethnocomputing approach. This four-level model enable educators to use ICT services and ICT education in African universities in a way that will prepare university graduates to make a positive contribution to their own societies. The success of this model is attributable to the emphasis that we placed on local needs and creativity and on applications of ICTs that were sensitive to local culture, conditions and understanding.

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.