Andreas Grillenberger

Big Data – Challenges for Computer Science Education

Data processing is a central topic of computer science and hence also in secondary computer science education, which includes strategies for storing, managing and retrieving data. In the context of Big Data, this field changes tremendously: established ideas, such as avoiding redundancies and storing data in a persistent and consistent way, are dropped in order to speed up the access to distributed stored data as well as its availability. Furthermore, with the rapidly growing impact of data processing on everyone’s daily life, computer science education needs to address these aspects as well as their social and ethical implications, such as privacy issues.

Analyzing the Twitter Data Stream Using the Snap! Learning Environment

In the last few years, tremendous changes have occurred in the field data management, especially in the context of big data. Not only approaches for data analysis have changed, but also real–time data analyses gain in importance and support decision–making in various contexts. One of the most exciting approaches for processing and analyzing large amounts of data in nearly real–time are data stream systems.

Big data and data management: a topic for secondary computing education

The topics data management and data analysis are currently discussed in various contexts, e.g. in Computer Science but also in daily life and society. The recent developments in this field, which are often summarized under the term Big Data, did not only lead to the emergence of new database models, but also comprise new threats, e.g. for data privacy. These topics include many aspects that are important for everyone, but they only gain in relevance slowly in higher education and hardly in secondary education. Hence, I will evaluate data management as a topic for secondary education, with a view on the long-lasting concepts and aspects in this field.

Key concepts of data management: an empirical approach

When preparing new topics for teaching, it is important to identify their central aspects. Sets of fundamental ideas, great principles or big ideas have already been described for several parts of computer science. Yet, existing catalogs of ideas, principles and concepts of computer science only consider the field data management marginally. However, we assume that several concepts of data management are fundamental to CS and, despite the significant changes in this field in recent years, have long-term relevance. In order to provide a comprehensive overview of the key concepts of data management and to bring relevant parts of this field to school, we describe and use an empirical approach to determine such central aspects systematically. This results in a model of key concepts of data management. On the basis of examples, we show how the model can be interpreted and used in different contexts and settings.

Developing a theoretically founded data literacy competency model

Today, data is everywhere: Our digitalized world depends on enormous amounts of data that are captured by and about everyone and considered a valuable resource. Not only in everyday life, but also in science, the relevance of data has clearly increased in recent years: Nowadays, data-driven research is often considered a new research paradigm. Thus, there is general agreement that basic competencies regarding gathering, storing, processing and visualizing data, often summarized under the term data literacy, are necessary for every scientist today. Moreover, data literacy is generally important for everyone, as it is essential for understanding how the modern world works. Yet, at the moment data literacy is hardly considered in CS teaching at schools. To allow deeper insight into this field and to structure related competencies, in this work we develop a competency model of data literacy by theoretically deriving central content and process areas of data literacy from existing empirical work, keeping a school education perspective in mind. The resulting competency model is contrasted to other approaches describing data literacy competencies from different perspectives. The practical value of this work is emphasized by giving insight into an exemplary lesson sequence fostering data literacy competencies.

Real-Time Data Analyses in Secondary Schools Using a Block-Based Programming Language

Data management is central to many CS innovations: Smart home technologies and the Internet of Things, for example, are based on processing data with high velocity. One of the most interesting topics emphasizing several challenges in this field is real-time data analysis. In secondary CS education, it is only considered marginally. So far, there are no tools suitable for general-purpose real-time data analysis in school. In this paper, we discuss this topic from a secondary CS education perspective. Besides central concepts and differences to traditional data analysis using relational databases, we describe the development of a general-purpose \(\textsf {Snap}{\textit{!}}\) extension that allows accessing and processing data from various sources. Thereby, students are enabled to conduct data analyses using, for example, sensor data or web APIs. With the example of a weather station, we outline how this tool can be used in school for analyzing sensor data generated in the classroom.

What Teachers and Students Know About Data Management

Data management is a highly innovative field of CS, which evolved from the original field databases in the last years. With the ongoing developments, several topics from this field, such as cloud computing, large data collections or data analyses, pervade our daily lives. Although more and more students and teachers come in contact with data management topics and need to develop competencies in this field, current CS education typically does not sufficiently address them. Yet, both students and teachers already have experience with certain aspects of data management and may have built up knowledge and perceptions, which need to be considered in CS teaching. Hence, in a qualitative study, we investigated the attitudes and prior knowledge of teachers on several data management topics and explored students’ knowledge in this field.