Stellen Hochschultypen differenzielle Entwicklungsmilieus dar?

The German higher education system has three different types of universities. This study aims to investigate the effect of choosing one particular university type on central personality traits (vocational interests, vocational motives and the Big Five). Existing results clearly show that the individual types of universities recruit students with different backgrounds. Whether differential development during and after studies is determined by university type (i.e. socialization effects in a broader sense) or whether it denotes a consequence of pre-existing differences among students of the university types will, for the first time, be examined using propensity-score matching. To do this, data of a large longitudinal study in Baden-Wuerttemberg were used in order to compare 1568 students at traditional universities (Universit?ten), universities of applied sciences (Fachhochschulen), and universities of cooperative education (Berufsakademien) in their second, fourth and sixth year after university entrance examination. Socialisation effects were tested in propensity-score based parallelised sub-samples (N?=?622). Results show that differences between university types can mainly be explained with selection effects and that the effects of attendance at the university types itself were hardly differential.     

DIFFERENT TYPES OF ACTION RESEARCH TO PROMOTE CHEMISTRY TEACHERS’ PROFESSIONAL DEVELOPMENT—A JOINED THEORETICAL REFLECTION ON TWO CASES FROM ISRAEL AND GERMANY

Action research is defined as using research activities to develop concrete societal practices. Action research understands the change of practice as being already a central aim of the research process itself, and it also seeks to contribute to the professional development of all participants in the particular field of study. Even though (or maybe even because) action research has a long history in the literature, there is a wide variety of interpretations of it. These range all the way from research supportive, via interactive, to emancipatory approaches. There is also a broad range of objectives covering both improving professional environments and generating results of general interest. This paper explores the spectrum of justifications given for action research with a specific focus on science education. Two completely different examples of action research selected from Israel and Germany help illustrate the diversity of the topic. The Israeli case focuses primarily on the professional development of a group of teachers; the German example hones in on the development of suitable curricula and lesson plans for wide dissemination. Comparison of these two projects is embedded in a theoretical framework which categorizes the different action research modes and contemplates teachers?? professional development. The aim of this paper is to reflect upon the common potential inherent in differing forms of action research on science education, including the aspect of professional development among teachers.     

Correction to: A Multi-Perspective Reflection on How Indigenous Knowledge and Related Ideas Can Improve Science Education for Sustainability

Science & Education - A Correction to this paper has been published: https://doi.org/10.1007/s11191-021-00194-2     

Using natural language processing to support peer-feedback in the age of artificial intelligence: A cross-disciplinary framework and a research agenda

Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross-disciplinary framework that aims to facilitate the development of NLP-based adaptive measures for supporting peer-feedback processes in digital learning environments. To conceptualize this process, we introduce a peer-feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer-feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer-feedback process model to exemplify a range of NLP-based adaptive support measures. We also discuss the current challenges and suggest directions for future cross-disciplinary research on the effectiveness and other dimensions of NLP-based adaptive support for peer-feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer-feedback in digital learning environments.

Practitioner notes

What is already known about this topic

• There is considerable research in educational science on peer-feedback processes.
• Natural language processing facilitates the analysis of students' textual data.
• There is a lack of systematic orientation regarding which NLP techniques can be applied to which data to effectively support the peer-feedback process.

What this paper adds

• A comprehensive overview model that describes the relevant activities and products in the peer-feedback process.
• A terminological and procedural scheme for designing NLP-based adaptive support measures.
• An application of this scheme to the peer-feedback process results in exemplifying the use cases of how NLP may be employed to support each learner activity during peer-feedback.

Implications for practice and/or policy

• To boost the effectiveness of their peer-feedback scenarios, instructors and instructional designers should identify relevant leverage points, corresponding support measures, adaptation targets and automation goals based on theory and empirical findings.
• Management and IT departments of higher education institutions should strive to provide digital tools based on modern NLP models and integrate them into the respective learning management systems; those tools should help in translating the automation goals requested by their instructors into prediction targets, take relevant data as input and allow for evaluating the predictions.