Síntesis de los elementos para la realización de un ensayo de aleatorización

III Congreso Internacional Virtual de Educación Estadística (CIVEEST), 21-24 febrero de 2019. [www.ugr.es/local/fqm126/civeest.html]Randomization tests offer an access to inference statistics, which is regarded as particularly simple in the didactic literature. Above all, the logic of the inferential reasoning should become particularly clear. Nevertheless, in order to carry out a randomization test, some elements are needed that must be understood in order to successfully draw statistical conclusions. In this article, various elements from the literature are collected and compiled in order to create a scheme for the hand of learners to carry out a randomization test.Las pruebas de aleatorización ofrecen un acceso a las estadísticas de inferencia, lo que se considera particularmente sencillo en la literatura didáctica. Sobre todo, la lógica del razonamiento inferencial debería ser particularmente clara. Sin embargo, para llevar a cabo una prueba de aleatorización, se necesitan algunos elementos que deben ser comprendidos para poder sacar conclusiones estadísticas con éxito. En este artículo, se recogen y compilan varios elementos de la literatura, con el fin de crear un esquema para que ayude a los alumnos a llevar a cabo una prueba de aleatorización

Minisymposium 05: Data Science

Im Schatten der Covid-19 Pandemie und im Zeitalter von Alternative Facts ist der kompetente Umgang mit Daten wichtiger denn je. Daten zu verstehen und geeignet darzustellen, bedeutet auch Macht, und so werden “Daten als Öl des 21. Jahrhunderts” angesehen (Spitz, 2016). Um sich die riesigen und komplexen Datenmengen zugänglich und zu Nutze zu machen, hat sich an der Schnittstelle von Mathematik, bzw. Statistik und Informatik immer zusammen mit einer Anwendungsdomäne seit einigen Jahren eine neue Disziplin unter dem Namen „Data Science“ etabliert. Publikationen, die sich mit der neuen Rolle von Daten in Bildung, Gesellschaft und Wirtschaft auseinandersetzen sind zum Beispiel von Weigend (2017) und O’Neil (2016) und gleichzeitig werden Forderungen nach entsprechenden Konsequenzen und Implikationen für die Bildung gestellt (Biehler et al., 2018). Auch die 2021 veröffentlichte Data Literacy Charta (https://hochschulforumdigitalisierung.de/sites/default/files/dateien/data-literacy-charta.pdf) betont die Wichtigkeit von Datenkompetenz. Ein wesentliches Konstrukt, um die riesigen Datenmengen effizient auszuwerten, sind Algorithmen. Der Begriff des Algorithmus kann somit auch im Mathematikunterricht wieder stärkere Betonung erfahren – mit Blick auf Nachbardisziplinen sollten Grundideen der Programmierung, Konzepte wie „Algorithm Literacy” sowie gesellschaftliche Bedingungen Lehr- und Lerngegenstand sein. Schließlich zeigen aktuelle Themenhefte wie MacGillivray et al. (2021) und Biehler et al. (2022) die aufsteigende Relevanz von Data Science im Schulunterricht und in der Hochschullehre

Activities for promoting civic statistical knowledge of preservice teachers

Critical statistical thinking and a sustainable knowledge in civic statistics is inevitably to become a concerned citizen. This kind of thinking and knowledge can already be enhanced in secondary school. For the implementation of civic statistics in mathematics classrooms in secondary school, teachers themselves have to be well educated in the field of civic statistics. For this purpose, we have designed and realized a university course about civic statistics in the winter term 2016/2017 at the University of Paderborn, where preservice teachers worked on projects and activities in regard to civic statistical contexts. For instance, they have analyzed official open data of the German Statistical Office on the German gender pay gap with digital tools, explored the distribution of net assets in German households and investigated the unemployment situation in different countries in the European Union via Google Public data files. In this paper, we will present some activities and derive first implications for re-designing these activities

Civic Engagement in Higher Education: A university course in civic statistics for mathematics preservice teachers

Civic statistics are statistics that concern us, such as labour statistics, health statistics, economic statistics, and so on. Thus, (civic) statistical thinking is necessary in order to be a responsible citizen, to be an active part of the modern society and to enhance civic engagement like in service learning. In particular, the school should educate children to be “mündig” – this can be done in mathematics lessons and especially in the field of statistics. In order to achieve this, teachers must be educated accordingly. We have designed a university course “Statistical literacy in mathematics classroom” with the aim of promoting civic statistical thinking. In this workshop report, we present activities from our course, share our experiences with the course 16/17 and describe how we have redesigned the (currently on-going) course for winter term 17/18

The impact of menopause on bone, zoledronic acid, and implications for breast cancer growth and metastasis

Recent data from the AZURE, ABCSG-12, and ZO-FAST clinical trials have challenged our understanding of the potential anticancer activity of zoledronic acid (ZOL). Although the results of these studies may appear to be conflicting on the surface, a deeper look into commonalities among the patient populations suggest that some host factors (i.e. patient age and endocrine status) may contribute to the anticancer activity of ZOL. Indeed, data from these large clinical trials suggest that the potential anticancer activity of ZOL may be most robust in a low-estrogen environment. However, this may be only part of the story and many questions remain to be answered to fully explain the phenomenon. Does estrogen override the anticancer activity of ZOL seen in postmenopausal women? Are hormones other than estrogen involved that contribute to this effect? Does the role of bone turnover in breast cancer (BC) growth and progression differ in the presence of various estrogen levels? Here, we present a review of the multitude of factors affected by different endocrine environments in women with BC that may influence the potential anticancer activity of ZOL

Data Science and Big Data in Upper Secondary Schools: A Module to Build up First Components of Statistical Thinking in a Data Science Curriculum

Within the framework of a design-based research project, computer science educators and statistics educators at Paderborn University designed a pilot course on the subject of data science and big data. It addresses upper secondary students and was realized by weekly sessions (three hours) over seven months. The whole course that is intended to introduce upper secondary school students to the field of data science consists of four modules. In module 1, the learners are introduced into the basics of statistics and big data and it aims at developing their data competence and data awareness. In the sec- ond module, learners are introduced to machine learning and programming based, among others, on examples from module 1. In the third and fourth module, learners can apply their knowledge gained in modules 1 and 2 and will work in small groups on real and meaningful data science projects. In this paper, we want to concentrate on the statistics components, especially of module 1, and we will present how we develop the data competence and data awareness of upper secondary school students to prepare them to work on data science projects in modules 3 and 4

Data Science and Big Data in Upper Secondary Schools: What Should Be Discussed From a Perspective of Computer Science Education?

The domain of data science is a large field, combining statistics, computer science and sociocultural issues. It is an open question which topics and which contents can and should be implemented in school, e.g. from the perspective of computer science education. Within the frame of a design-based research project a pilot course is designed by computer science and statistics educators at the Paderborn University, addressing upper secondary students. In this paper, we concentrate on the second of four modules, in which machine learning and neural networks are adressed. Some individual phases of the module are presented, followed by a metaperspective of the curriculum development that contributes to our project, and further research questions

Long Term Cyclic Pamidronate Reduces Bone Growth by Inhibiting Osteoclast Mediated Cartilage-to-Bone Turnover in the Mouse

Bisphosphonates, used to treat diseases exhibiting increased osteoclast activity, reduce longitudinal bone growth through an as yet undefined mechanism. Pamidronate, an aminobisphosphonate, was given weekly to mice at 0, 1.25, or 2.50 mg/kg/wk beginning at 4 weeks of age. At 12 weeks of age, humeral length, growth plate area, regional chondrocyte cell numbers, chondrocyte apoptosis, TRAP stained osteoclast number, and osteoclast function assessed by cathepsin K immunohistochemistry were quantified. Humeral length was decreased in pamidronate treated mice compared to vehicle control mice, and correlated with greater growth plate areas reflecting greater proliferative and hypertrophic chondrocyte cell numbers with fewer hypertrophic cells undergoing apoptosis. Pamidronate treatment increased TRAP stained osteoclast numbers yet decreased cathepsin K indicating that pamidronate repressed osteoclast maturation and function. The data suggest that long term cyclic pamidronate treatment impairs bone growth by inhibition of osteoclast maturation thereby reducing cartilage-to-bone turnover within the growth plate