Publikationen

In Rebo at Work, chatbot Rebo helps apprentices to reflect on a work experience and associate it with their training’s learning objectives. Rebo poses questions that motivate the apprentice to look at a work experience from different angles, pondering how it went, the problems they encountered, what they learned from it, and what they take away for the future. We present preliminary results of a 9-month field study (analysis of 90 interactions of the first 6 months) with 51 apprentices in the fields of metal technology, mechatronics, and electrical engineering. During reflection with Rebo at Work, 98% of apprentices were able to identify their work experience as a learning opportunity and reflect on that, and 83% successfully connected it with a learning objective. This shows that self-monitoring of learning objectives and reflection on work tasks can be guided by a conversational agent and motivates further research in this area.

Artificial intelligence (AI) is by now used in many different work settings, including construction industry. As new technologies change business and work processes, one important aspect is to understand how potentially affected workers perceive and understand the existing and upcoming AI in their work environment. In this work, we present the results of an exploratory case study with 20 construction workers in a small Austrian company about their knowledge of and attitudes toward AI. Our results show that construction workers’ understanding of AI as a concept is rather superficial, diffuse, and vague, often linked to physical and tangible entities such as robots, and often based on inappropriate sources of information which can lead to misconceptions about AI and AI anxiety. Learning opportunities for promoting (future) construction workers’ AI literacy should be accessible and understandable for learners at various educational levels and encompass aspects such as i) conveying the basics of digitalization, automation, and AI to enable a clear distinction of these concepts, ii) building on the learners’ actual experience realm, i.e., taking into account their focus on physical, tangible, and visible entities, and iii) reducing AI anxiety by elaborating on the limits of AI.

The COVID-19 pandemic initiated a fundamental change in learning and teaching in (higher-) education [HE]. On short notice, traditional teaching in HE suddenly had to be transformed into online teaching. This shift into the digital world posed a great challenge to in-service teachers at schools and universities, and pre-service teachers, as the acquisition of digital competences was no longer an option but a real necessity. The previously rather hidden or even neglected importance of teachers’ digital competences for successful teaching and learning became manifest and clearly visible. In this work, we investigate necessary digital competences to ensure high quality teaching and learning in and beyond the current COVID-19 pandemic. Based upon the European DigComp 2.1 (Carretero et al., 2017), DigCompEdu (Redecker, 2017) frameworks, the Austrian Digi.kompP framework (Virtuelle PH, 2021), and the recommendations given by German Education authorities (KMK 2017; KMK 2021; HRK 2022), we developed a curriculum consisting of 5 modules: 2 for individual digital media competence, and 3 for media didactic competence. For each module, competence-oriented learning goals and corresponding micro-learning contents were defined to meet the needs of teachers while considering their time constraints.Based on three online workshops, the curriculum and the corresponding learning goals were discussed with university teachers, pre-service teachers, and policymakers. The content of the curriculum was perceived as highly relevant for these target groups; however, some adaptations were required. From the university teachers’ perspective, we got feedback that they were overwhelmed with the situation and urgently needed digital competences. Policymakers suggested that further education regarding digital competences needs to offer a systematic exchange of experiences with peers. From the perspective of in-service teachers, it was stated that teacher education should focus more on digital competences and tools.In this paper, we will present the result of the workshop series that informed the design process of the DIGIVID curriculum for teaching professionals.

Clear formulation and communication of learning goals is an acknowledged best practice in instruction at all levels. Typically, in curricula and course management systems, dedicated places for specifying learning goals at course-level exist. However, even in higher education, learning goals are typically formulated in a very heterogeneous manner. They are often not concrete enough to serve as guidance for students to master a lecture or to foster self-regulated learning. In this paper, we present a systematics for formulating learning goals for university courses, and a web-based widget that visualises these learning goals within a university's learning management system. The systematics is based on the revised version of Bloom's taxonomy of educational objectives by Anderson and Krathwohl. We evaluated both the learning goal systematics and the web-based widget in three lectures at our university.The participating lecturers perceived the systematics as easy-to-use and as helpful to structure their course and the learning content. Students' perceived benets lay in getting a quick overview of the lecture and its content as well as clear information regarding the requirements for passing the exam. By analysing the widget's activity log data, we could show that the widget helps students to track their learning progress and supports them in planning and conducting their learning in a self-regulated way. This work highlights how theory-based best practice in teaching can be transferred into a digital learning environment; at the same time it highlights that good non-technical systematics for formulating learning goals positively impacts on teaching and learning.

This paper describes the analysis of temporal behavior of 11--15 year old students in a heavily instructionally designed adaptive e-learning environment. The e-learning system is designed to support student's acquisition of health literacy. The system adapts text difficulty depending on students' reading competence, grouping students into four competence levels. Content for the four levels of reading competence was created by clinical psychologists, pedagogues and medicine students. The e-learning system consists of an initial reading competence assessment, texts about health issues, and learning tasks related to these texts. The research question we investigate in this work is whether temporal behavior is a differentiator between students despite the system's adaptation to students' reading competence, and despite students having comparatively little freedom of action within the system. Further, we also investigated the correlation of temporal behaviour with performance. Unsupervised clustering clearly separates students into slow and fast students with respect to the time they take to complete tasks. Furthermore, topic completion time is linearly correlated with performance in the tasks. This means that we interpret working slowly in this case as diligence, which leads to more correct answers, even though the level of text difficulty matches student's reading competence. This result also points to the design opportunity to integrate advice on overarching learning strategies, such as working diligently instead of rushing through, into the student's overall learning activity. This can be done either by teachers, or via additional adaptive learning guidance within the system.