Ein Überblick über unsere wissenschaftlichen Publikationen im Rahmen des AMSL-Projekts.
Immersive learning environments such as virtual classrooms in Virtual Reality (VR) offer learners unique learning experiences, yet providing effective learner support remains a challenge. While prior HCI research has explored in-lecture support for immersive learning, little research has been conducted to provide post-lecture support, despite being critical for sustained motivation, engagement, and learning outcomes. To address this, we present AttentiveLearn, a learning ecosystem that generates personalized quizzes on a mobile learning assistant based on learners' attention distribution inferred using eye-tracking in VR lectures. We evaluated the system in a four-week field study with 36 university students attending lectures on Bayesian data analysis. AttentiveLearn improved learners' reported motivation and engagement, without conclusive evidence of learning gains. Meanwhile, anecdotal evidence suggested improvements in attention for certain participants over time. Based on our findings of the field study, we provide empirical insights and design implications for personalized post-lecture support for immersive learning systems.
Self-regulated learning (SRL) is an important skill for academic performance. Still, most university students show deficiencies in one or all areas of SRL. This skill gap can negatively impact the learning process, leading to subpar outcomes or even students dropping out of tertiary education. The good news is that self-regulated learning can be taught. However, traditional learning formats such as coaching or online courses come with the limitations of either not scaling well or lacking effectiveness. Mobile digital assistants represent one promising approach to address these problems. This work presents the preliminary results of an ongoing design science research project to design the mobile digital assistant Amsl. We describe initial design knowledge in the form of meta requirements, design principles, and design decisions. In addition, we describe the first instantiation of Amsl, intending to help students become successful self-regulated learners.
We observe a continuous shift from face-to-face to hybrid or online education. Today, learners are exposed to a high level of autonomy and, at the same time, have less contact with peers and teachers. In this environment, the ability to self-regulate one’s learning is becoming more relevant to achieve positive learning results and academic success. However, the application of self-regulated learning is not trivial. A potential solution for this challenge comes in the form of digital assistants like chatbots or pedagogical agents that provide structure for the learners. Existing research on digital assistants for self-regulated learning is scattered across several fields. In this research-in-progress paper, we present preliminary results of a systematic literature review (SLR) study providing an overview of the state-of- the-art of digital assistants supporting SRL. Our results show that future research in this domain should focus on affect, behavioral, and context regulation and more longitudinal studies are required.