Like most curricula in the humanities and social sciences, the curriculum of pre-service teacher training in educational sciences often includes time-consuming reading and writing tasks, which ...require high quality support and feedback in a timely manner. A well-known way to provide this support to students is one-to-one mentoring. However, limited time and resources in the German university context require to effectively scale the benefits of individual feedback. The use of scalable technologies to support learning processes seems to be promising, but its development usually requires a deep technical understanding. With an interdisciplinary approach, this contribution investigates how personal mentoring can be made available to as many students as possible, taking into account the didactic, organizational and technical frameworks at universities. We describe the development and implementation process of two chatbots that both aim to support students of educational sciences in their self-study of the seminar topics and literature. The chatbots were used by over 700 students during the course of 1 year and our evaluations show promising results that bear the potential to improve the availability of digital mentoring support for all students.
To accelerate the growth of Industry 4.0 technologies, the digitalization of mechanical testing laboratories as one of the main data-driven units of materials processing industries is introduced in ...this paper. The digital lab infrastructure consists of highly detailed and standard-compliant materials testing knowledge graphs for a wide range of mechanical testing processes, as well as some tools that enable the efficient ontology development and conversion of heterogeneous materials’ mechanical testing data to the machine-readable data of uniform and standardized structures. As a basis for designing such a digital lab, the mechanical testing ontology (MTO) was developed based on the ISO 23718 and ISO/IEC 21838-2 standards for the semantic representation of the mechanical testing experiments, quantities, artifacts, and report data. The trial digitalization of materials mechanical testing lab was successfully performed by utilizing the developed tools and knowledge graph of processes for converting the various experimental test data of heterogeneous structures, languages, and formats to standardized Resource Description Framework (RDF) data formats. The concepts of data storage and data sharing in data spaces were also introduced and SPARQL queries were utilized to evaluate how the introduced approach can result in the data retrieval and response to the competency questions. The proposed digital materials mechanical testing lab approach allows the industries to access lots of trustworthy and traceable mechanical testing data of other academic and industrial organizations, and subsequently organize various data-driven research for their faster and cheaper product development leading to a higher performance of products in engineering and ecological aspects.
•Digital materials mechanical testing lab generates FAIR data for data-driven research.•Infrastructure: knowledge graphs, mapping and conversion tools, data spaces.•Converting test data to machine-readable/queryable data of industrial applications.•Standard-oriented mechanical testing ontology (MTO) and process graphs.•Open-access approaches map the material data in their process graphs.
EAs.LiT is an e-assessment management and analysis software for which contextual requirements and usage scenarios changed over time. Based on these factors and further development activities, the ...decision was made to adopt a microservice architecture for EAs.LiT version 2 in order to increase its flexibility to adapt to new and changed circumstances. This architectural style and a few adopted technologies, like RDF as a data format, enabled an eased implementation of various use cases. Thus we consider the microservice architecture productive and recommend it for usage in other educational projects. The specific architecture of EAs.LiT version 2 is presented within this article, targeting to enable other educational projects to adopt it by using our work as a foundation or template.
In this article, we present a concept of how micro- and e-assessments can be
used for the mathematical domain to automatically determine acquired and
missing individual skills and, based on these ...information, guide individuals to
acquire missing or additional skills in a software-supported process. The
models required for this concept are a digitally prepared and annotated
e-assessment item pool, a digital modeling of the domain that includes topics,
necessary competencies, as well as introductory and continuative material, as
well as a digital individual model, which can reliably record competencies and
integrates aspects about the loss of such.
Knowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as ...well as scientific disciplines. KGs surpass any other form of representation in terms of effectiveness. However, Knowledge Graph Engineering (KGE) requires in-depth experiences of graph structures, web technologies, existing models and vocabularies, rule sets, logic, as well as best practices. It also demands a significant amount of work. Considering the advancements in large language models (LLMs) and their interfaces and applications in recent years, we have conducted comprehensive experiments with ChatGPT to explore its potential in supporting KGE. In this paper, we present a selection of these experiments and their results to demonstrate how ChatGPT can assist us in the development and management of KGs.
In this article, we present a concept of how micro- and e-assessments can be used for the mathematical domain to automatically determine acquired and missing individual skills and, based on these ...information, guide individuals to acquire missing or additional skills in a software-supported process. The models required for this concept are a digitally prepared and annotated e-assessment item pool, a digital modeling of the domain that includes topics, necessary competencies, as well as introductory and continuative material, as well as a digital individual model, which can reliably record competencies and integrates aspects about the loss of such.
In addition to formal learning at universities, like in lecture halls and seminar rooms, students are regularly confronted with self-study activities. Instead of being left to their own devices, ...students might benefit from a proper design of such activities, including pedagogical interventions. Such designs can increase the degree of activity and the contribution of self-study activities to the achievement of learning outcomes. Especially in times of a global pandemic, self-study activities are increasingly executed at home, where students already use technology-enhanced materials, processes, and digital platforms. Thus we pick up these building blocks and introduce TecCoBot within this paper. TecCoBot is not only a chatbot, supporting students in reading texts by offering writing assignments and providing automated feedback on these, but also implements a design for self-study activities, typically only offered to a few students as face-to-face mentoring.
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