2023, Jäger, Janine, Eisemann, Christoph, Pimmer, Christoph, Gómez Chova, Luis, González Martínez, Chelo, Lees, Joanna

Fake news has become a major societal concern, particularly in the online and social media sphere. Higher education institutions have an important role to play in fostering critical thinking and promoting media and news literacy. Therefore, educators need to explore effective and innovative ways to teach news literacy. Online games have emerged as a potentially promising tool. Against this background, the paper presents a framework that conceptualises competences relevant for news literacy and 'fake' news literacy. It then uses the framework to conduct an evaluation of 17 popular and freely available online games about misinformation and disinformation and their suitability for developing competences relevant to (fake) news literacy in a higher education context. The findings indicate that the games were generally limited in scope and covered only specific news literacy competence areas. The paper discusses the results of the analysis and develops some suggestions for the integration of games in the higher education landscape.

2021, Sabbioni, Giordano, Zhong, Jia, Jäger, Janine, Schmiedel, Theresa, Ahram, Tareq, Taiar, Redha

The present study demonstrates the ongoing development of a service robot application in a campus library through the application of Design Science Research (DSR) combined with user-centered design (UCD). In particular, we involved librarians and co-created an application solution addressing two use cases: giving directions to a desired subject area/book and providing recommendations for an event/book. Additionally, we involved potential end users to evaluate the robot artifact through an onsite usability test (N = 14) already within the design cycle. This process resulted in a successful application of the robot artifact for the identified use cases and shows feasibility of combining DSR with UCD.

2023, Jäger, Janine, Korkut, Safak, Inglese, Terry, Schmiedel, Theresa

The paper presents a project of the School of Business of the University of Applied Sciences and Arts Northwestern Switzerland in which the project team is developing case study podcasts together with Swiss-based technology startups to apply them in case-based teaching in Business and Information Systems study programs. The goal of the project is to facilitate students' access to case study contexts by allowing them to listen to podcasts for self-study and develop solutions for practice-oriented business and technology challenges in the classroom and guided group work. This provides an engaging blended learning approach for the students through increased motivation to consume the learning material as well as a deeper connection to the study material, compared to the more commonly applied text-based case studies. This can enable much more productive classroom discussions and group work and could therefore provide improved learning outcomes, such as increased reflection, critical thinking, as well as analytical and problem-solving skills. The paper enriches the blended learning debate with details about the case study podcast production from a content-related, technological and didactical perspective as well as provides insights into the planned evaluation of the application of case study podcasts with regard to learning outcomes.

2022, Zhong, Jia, Mürset, Nicolas, Jäger, Janine, Schmiedel, Theresa

Like in human-human interaction, people tend to interact in human-robot settings with those they like. Therefore, it is important to understand what variables affect robot likeability. The present study aims at providing insights into how robots’ anthropomorphism, voice, gestures, approaching behaviors as well as perceived warmth and competence play a role in robot likeability. We conducted an online survey (N=191) studying two humanoid robots with different characteristics. Our exploratory study empirically indicates that the investigated variables are significantly correlated with robot likeability for both robots but with differing strengths. Further, the likeability of the two robots is predicted by differing variables, with robot voice being the only common predictor for both robots.