Research Results

Research Results

In the project, the field of data management in particular is examined from an informatics didactic perspective. For this purpose, various perspectives proposed by the model of didactic reconstruction for computer science education were examined and the requirements of students, teachers, society and, of course, the scientific perspective, are taken into account equally.

Key Concepts of Data Management

A main focus of the work was on the technical grounding. For this, the field data management was examined with regard to its key concepts and a model of these concepts was developed by adapting the Great Principles of Computing by Denning. The result is a model that takes into account various core technologies of data management as well as practices that are relevant in this context, and design principles and mechanisms for data management systems.

The model of key concepts of data management [1]

The core technologies of data management are a selection of currently relevant topics from this field, which are not only important in research but also in data management practice and thus represent the central point of contact to this field.

The practices describe central activities in the area of data management and at the same time provide an insight into the data life cycle (see below). They give an insight into the handling of data and thus emphasize a comprehensive view of the processes that are central in this field, but also open up important intersections to other fields and, for example, to societal issues – especially in the practices that will be established later in the life cycle.

The design principles of data management describe decisions that are central for developing, but also for selecting and using data management systems: They represent characteristics influencing these systems, give insight into their central characteristics and by means of which different data management systems can be distinguished.

The mechanics represent the operating principles of the systems, which are therefore most strongly based at the technical level. They describe how different parts of a data management system interact to achieve a particular goal.

The key concepts of data management do not only present central aspects of the subject area in a concise and comprehensible manner. Instead, the practices and principles presented also contribute to a deeper understanding of various topics in computer science. For, example, …

  • Metadata is central for the handling of data in various facets, e.g. it enables the retrieval of data, structures them and explicates relationships to other data or reveals further information about data (e.g. the location of a photo). Metadata is therefore relevant in all data management practices and is linked to various principles: Since metadata is never introduced for its own sake, but always with a specific goal, knowledge about the mechanism of structuring, for example, clearly contributes to understanding which task structuring metadata take on and how they must be developed, while, for example, only an understanding of representation exposes the necessity or sense of administrative metadata such as file type.
  • Databases are a central technology for data storage that exists in many different forms. In order to understand what distinguishes databases from a normal file system, for example, knowledge about integrity, consistency and isolation, but also about concurrency, is indispensable. If these four aspects are not addressed in the context of the databases, only a vague picture of the databases is drawn, which may not be sufficient to recognize the meaning and purpose of such systems and to distinguish them from other data storage systems.
  • Distributed data storage, of which cloud data storage, in particular, has become an all-embracing technology, is another central technology of data management. When such data storage devices are used, a variety of phenomena occur, for example due to the synchronisation of data between the data source and the data storage device, which are not hidden from the user. In order to understand these phenomena, but also the difference from other data storage devices and the benefits of these technologies, and to be able to use them sensibly, it is essential to understand how synchronization and replication work, which problems can occur during the transport of data, or what distinguishes partition tolerance in a distributed system and what the consequences are if this cannot be ensured.

These three examples show that the principles of data management can in particular help to understand and contextualize informatics concepts (such as metadata), to develop a technically correct understanding of informatics technologies (such as databases), and to understand phenomena that occur when dealing with informatics technologies (such as distributed or cloud data stores).

But data management practices can also provide an important insight into the field: By being understood as a lifecycle model (see below), these practices provide an overview of the entire process from data collection, storage, processing, analysis and exchange to archiving and deletion. This process not only gives pupils but also teachers an important orientation, but can also be used to identify interesting questions: An orientation of data management teaching towards this life cycle model suggests, for example, the questions of where the data comes from, which data should/can be obtained at all, how it can be cleaned up and structured, how its quality is ensured, how it can be processed and analysed, how it can be made understandable to people (through visualisation), etc.

The data lifecycle model

Development of a Data Literacy Competency Model

In addition, this project has also created a basis for research in another important area of data handling: While research is increasingly showing the emergence of a new data-oriented research paradigm and university teaching is also increasingly considering competencies in data analysis and, to some extent, machine learning as relevant outside of computer science, this data literacy has not yet been examined from the perspective of general school teaching, although basic skills and competences in handling data are becoming increasingly central in all areas of life today and can contribute to self-determined participation in social life. For this reason, as a basis for further research in this area, a competence model of data literacy with a focus on lower secondary education has been developed based on empirical work, which, however, is potentially adaptable to other educational levels.

The data literacy competency model [3]

Further Information / Sources

The development of the model of key concepts of data management is described in detail in the contributions [1, 2]. The article [3] (a detailed article is currently in progress) and the poster [4] provide a brief insight into the development of the competence model.

[1] Grillenberger, A. & Romeike, R.: Key Concepts of Data Management: An Empirical Approach, In Proceedings of the 17th Koli Calling International Conference on Computing Education Research, ACM, 2017.

[2] Grillenberger, A. & Romeike, R.: Empirische Ermittlung der Schl├╝sselkonzepte des Fachgebiets Datenmanagement, In Informatische Bildung zum Verstehen und Gestalten der digitalen Welt, Proceedings der 17. GI-Fachtagung Informatik und Schule, Oldenburg, 2017.

[3] Grillenberger, A. & Romeike, R.: Data Literacy und das Modell der Schl├╝sselkonzepte des Datenmanagements, In Data Literacy und Data Science Education: Digitale Kompetenzen in der Hochschulausbildung, 2018.

[4] German Poster at Data Literacy Symposium des Stifterverbands / Hoschulforum DigitalisierungDownload (PDF)