The relevance of open research data is already acknowledged in many disciplines. Demanded by publishers, funders, and research institutions, the number of published research data increases every day. In learning analytics though, it seems that data are not sufficiently published and re-used. This chapter discusses some of the progress that the learning analytics community has made in shifting towards open practices, and it addresses the barriers that researchers in this discipline have to face. As an introduction, the movement and the term open science is explained. The importance of its principles is demonstrated before the main focus is put on open data. The main emphasis though lies in the question, Why are the advantages of publishing research data not capitalized on in the field of learning analytics? What are the barriers? The authors evaluate them, investigate their causes, and consider some potential ways for development in the future in the form of a toolkit and guidelines.

Der Europäische Referenzrahmen DigCompEdu beschreibt in 22 Kompetenzen die digitale Kompetenz von Lehrkräften aller Fächer. Im Rahmen einer halbstrukturierten schriftlichen Expertenbefragung mittels eines Online-Fragebogens wurden 24 Expert*innen dazu befragt, ob und inwiefern eine MINT-spezifische Anpassung der DigCompEdu-Kompetenzen notwendig sei. Die Ergebnisse wurden dazu verwendet, einen ersten Vorschlag für sieben MINT-spezifische Items des DigCompEdu-Selbsteinschätzungsinstruments zu entwickeln. The European Framework DigCompEdu describes in 22 competences the digital competence of educators of all subjects. As part of a semi-structured written expert survey using an online questionnaire , 24 experts were asked whether and to what extent a STEM-specific adaptation of DigCompEdu competences might be necessary. The results were used to develop a first proposal for seven STEM-specific items of the DigCompEdu self-assessment tool.

Background: Recent developments in STEM and computer science education put a strong emphasis on twenty-first-century skills, such as solving authentic problems. These skills typically transcend single disciplines. Thus, problem-solving must be seen as a multidisciplinary challenge, and the corresponding practices and processes need to be described using an integrated framework. Purpose: We present a fine-grained, integrated, and interdisciplinary framework of problem-solving for education in STEM and computer science by cumulatively including ways of problem-solving from all of these domains. Thus, the framework serves as a tool box with a variety of options that are described by steps and processes for students to choose from. The framework can be used to develop competences in problem-solving. Sources of evidence: The framework was developed on the basis of a literature review. We included all prominent ways of domain-specific problem-solving in STEM and computer science, consisting mainly of empirically orientated approaches, such as inquiry in science, and solely theory-orientated approaches, such as proofs in mathematics. Main argument: Since there is an increasing demand for integrated STEM and computer science education when working on natural phenomena and authentic problems, a problem-solving framework exclusively covering the natural sciences or other single domains falls short. Conclusions: Our framework can support both practice and research by providing a common background that relates the ways, steps, processes, and activities of problem-solving in the different domains to one single common reference. In doing so, it can support teachers in explaining the multiple ways in which science problems can be solved and in constructing problems that reflect these numerous ways. STEM and computer science educational research can use the framework to develop competences of problem-solving at a fine-grained level, to construct corresponding assessment tools, and to investigate under what conditions learning progressions can be achieved.