Adaptation of a Research-based Teaching-Learning Format with Approaches of Online Learning in the STEM Field

Research-based learning (RBL) familiarizes students with the academic research process at an early stage and at the same time offers them the opportunity to actively shape their own learning process. Working on their own research problems allows students to go through the entire research cycle, which promotes not only subject and methodological competencies but also the students' self-competencies. The handling of subject-specific and didactic challenges of this form of teaching-learning has been discussed many times in the past. New challenges for research-based learning now arise from the shift of teaching and learning to virtual space associated with the COVID-19 pandemic. This paper focuses on the adaptation of a research-based teaching-learning format to the demands of e-learning in the course Interaction Science with Artificial Intelligence. We evaluated the adapted teaching-learning format with 18 students in a master's program in STEM. The students stated an increase in professional competence in the areas of programming, data preparation and data visualization. Our results suggest that peer group and direct interaction via synchronous communication channels are important structural frameworks for research-based learning in an online learning context. From our results, we can derive initial implications for online-based RBL in the field of computational education. © 2022 IEEE.

You can't drive my car -a method to fingerprint individual driving styles in a sim-racing setting

The rapidly growing market of eSports has become a lucrative industry. Sim racing is one of the traditional sports that gain increasing popularity in eSports. With raising profits, lucrative sponsoring contracts, and competitive price money, incentives for fraud are also on the upraise. This is further facilitated by the COVID-19 pandemic, which led to the substitution of live eSport events by virtual formats. Particularly, in sim racing it becomes increasingly challenging to verify the contestants identity in virtual events. Here, we propose and evaluate a generic workflow to identify personal driving style by transforming raw racing data (including measurements extracted from the simulation software and connected simulator hardware) into a comparable representation (fingerprint). As data base we used an extensive collection of telemetry data recorded from the racing simulation Assetto Corsa on 3 immersive motion simulators. The set contains over 2,000 laps of seven player recorded in weekly sessions. The experiments demonstrate the feasibility to distinguish players using the proposed method on different tracks, car models, and motion simulators. Due to the extensive experiments, we could achieve a driver separation score of up to 89%. The raw data and the raw results are made publicly available on Github. © 2022 IEEE.