Metaphors we’re colonised by? The case of data-driven educational technologies in Brazil

ABSTRACT

This article discusses issues concerning the spread of data-driven educational technologies in Brazil. Here, as elsewhere, educational technology continues to be promoted optimistically as the bearer of a panacea for historically-rooted social problems. Whilst some of these technologies have indeed contributed to important widening-participation programmes in the last two decades, widespread advocacy of technological ‘solutionism’, reflected in gradually stronger policy demands for efficiencies to be improved through ‘innovation’, has supported a relentless marketisation of the country’s educational systems. As transnational corporations position themselves to take control of key areas of these systems, threatening to restructure the whole sector, data-driven educational technologies provide the latest example in a series of ‘new’ ideas offered in an ever-expanding market. Based on the notion of ‘conceptual metaphors’, which encapsulate specific ways of perceiving, thinking and relating with the world, this article examines key metaphors underpinning discourses surrounding data-driven educational technologies in Brazil. In particular, the article analyses ways in which these specific metaphors may be promoting perspectives that ignore difference and obscure broader questions concerning education, thus contributing to the reproduction of previously existing problems and supporting new forms of colonisation.     

Waist Circumference,Pedometer Placement,and Step-Counting Accuracy in Youth

This study examined whether differences in waist circumference (WC) and pedometer placement (anterior vs. midaxillary vs. posterior) affect the agreement between pedometer and observed steps during treadmill and self-paced walking. Participants included 19 pairs of youth (9–15 years old) who were matched for sex, race, and height and stratified by WC (high WC: HWC; low WC: LWC). Participants performed 3-min treadmill-walking trials at speeds of 59, 72, and 86 m·min-1 and a 400-m self-paced walking trial on level ground. Bland-Altman plots were used to assess the agreement between pedometer and observed steps of spring-levered pedometers by WC, pedometer placement, and walking speed. In the HWC group, the posterior pedometer placement consistently agreed most closely with observed steps at all treadmill speeds and during self-paced walking. In the LWC group, no single pedometer placement consistently agreed most closely with observed steps at all treadmill speeds and during self-paced walking. We conclude that a posterior pedometer placement improves step-count accuracy in most youth with an HWC at a range of walking speeds on level ground.     

Correlation analysis of performance measures for multi-label classification

In many important application domains, such as text categorization, scene classification, biomolecular analysis and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research in multi-label classification. In order to evaluate and compare multi-label classifiers, researchers have adapted evaluation measures from the single-label paradigm, like Precision and Recall; and also have developed many different measures specifically for the multi-label paradigm, like Hamming Loss and Subset Accuracy. However, these evaluation measures have been used arbitrarily in multi-label classification experiments, without an objective analysis of correlation or bias. This can lead to misleading conclusions, as the experimental results may appear to favor a specific behavior depending on the subset of measures chosen. Also, as different papers in the area currently employ distinct subsets of measures, it is difficult to compare results across papers. In this work, we provide a thorough analysis of multi-label evaluation measures, and we give concrete suggestions for researchers to make an informed decision when choosing evaluation measures for multi-label classification.     

Grundlagen des Burrows-Wheeler-Kompressionsalgorithmus

Der Burrows-Wheeler-Kompressionsalgorithmus hat als ein universelles Kompressionsverfahren in den letzten Jahren aufgrund seiner hervorragenden Kompressionsraten und hohen Verarbeitungsgeschwindigkeiten eine bemerkenswerte Beachtung erfahren. Das Herzstück des Algorithmus stellt die sogenannte Burrows-Wheeler-Transformation dar. Es handelt sich bei dieser Transformation um eine Permutation der Eingabedaten, welche Zeichen mit ähnlichem Kontext nahe beieinander anordnet. Die Transformation sowie die zugehörige Rücktransformation werden zusammen mit den weiteren Stufen des Algorithmus in einer gegenüber der Standardversion verbesserten Variante vorgestellt und die Ergebnisse mit denen anderer Datenkompressionsalgorithmen verglichen.