Digital Assistants for Self-Regulated Learning: Towards a State-Of-The-Art Overview

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

A State of the Art Overview on Biosignal-based User-Adaptive Video Conferencing Systems

Video conferencing systems are widely used in times of distributed teams since they support flexible work arrangements. However, they have negative impacts on users, such as lacking eye gaze or zoom fatigue. Adaptive interventions in video conferences based on user behavior provide interesting solutions to overcome these challenges, for example, by alerting users when looking tired. Specifically, biosignals measured by sensors like microphones or eye-trackers are a promising basis for adaptive interventions. To provide an overview of current biosignal-based user-adaptive video conferencing systems, we conducted a systematic literature review and identified 24 publications. We summarize existing knowledge in a morphological box and outline further research directions. Thereby, a focus on biooptical signals is visible. Current adaptations target audience feedback, expression understanding and eye gaze mostly by image and representation modifications. In future, we recommend including further biosignals and addressing more diverse problems by investigating adaptation capabilities of further software elements

Aligning Crowdworker Perspectives and Feedback Outcomes in Crowd-Feedback System Design

Leveraging crowdsourcing in software development has received growing attention in research and practice. Crowd feedback offers a scalable and flexible way to evaluate software design solutions and the potential of crowd-feedback systems has been demonstrated in different contexts by existing research studies. However, previous research lacks a deep understanding of the effects of individual design features of crowd-feedback systems on feedback quality and quantity. Additionally, existing studies primarily focused on understanding the requirements of feedback requesters but have not fully explored the qualitative perspectives of crowd-based feedback providers. In this paper, we address these research gaps with two research studies. In study 1, we conducted a feature analysis (N=10) and concluded that from a user perspective, a crowd-feedback system should have five core features (scenario, speech-to-text, markers, categories, and star rating). In the second study, we analyzed the effects of the design features on crowdworkers’ perceptions and feedback outcomes (N=210). We learned that offering feedback providers scenarios as the context of use is perceived as most important. Regarding the resulting feedback quality, we discovered that more features are not always better as overwhelming feedback providers might decrease feedback quality. Offering feedback providers categories as inspiration can increase the feedback quantity. With our work, we contribute to research on crowd-feedback systems by aligning crowdworker perspectives and feedback outcomes and thereby making the software evaluation not only more scalable but also more human-centered

The Relationship of Collaboration Technologies and Cognition and Affect

Collaboration technologies (CT) are integral for today’s workplaces and the use of CT impacts human brain and behavior. The consequences on cognition and affect of CT users have been empirically investigated since the 1970s. However, the research landscape is scattered and a comprehensive overview is missing. Consequently, we systematically analyze research about the relationship of CT and cognitive and affective user states and processes through an advanced systematic literature review based on the conceptual foundation of the time-space matrix, the stimulus-organism-response paradigm, and the workplace outcomes framework. Our results show an increase in remote CT, alongside a focus on individual analysis and affective constructs, while group level studies concentrate relatively stronger on collocated scenarios. We contribute with avenues for future research like the underrepresentation of group level analysis, a need for unified conceptualization and understanding of cognitive and affective constructs in theory and for deriving design knowledge to create advanced, cognition- and affect-sensitive CT features

Fatigued by yourself? Towards understanding the impact of self-view designs in virtual meeting software

Video meetings are essential for our life. Besides their relevance and advantages, users increasingly observe the negative phenomenon of virtual meeting fatigue (VMF). VMF is described as the feeling of exhaustion during virtual meetings when overusing virtual meeting software. Virtual meeting software presents additional technical stimuli that are not present in offline meetings and require additional cognitive resources. One of these stimuli is the self view feature, a mirrored image of oneself shown during the meeting which allows controlling the personal appearance. However, research is scarce about the trade off between the benefits of control and the disadvantages of the additional cognitive load of the self view. In our pilot study with 17 participants, we want to understand the impact of three self view designs (self view on, off, initial) on users’ cognitive load, self awareness, perceived control, and VMF. Therefore, we propose a research model and report initial results

The Impact of Anthropomorphic and Functional Chatbot Design Features in Enterprise Collaboration Systems on User Acceptance

Information technology is rapidly changing the way how people collaborate in enterprises. Chatbots integrated into enterprise collaboration systems can strengthen collaboration culture and help reduce work overload. In light of a growing usage of chatbots in enterprise collaboration systems, we examine the influence of anthropomorphic and functional chatbot design features on user acceptance. We conducted a survey with professionals familiar with interacting with chatbots in a work environment. The results show a significant effect of anthropomorphic design features on perceived usefulness, with a strength four times the size of the effect of functional chatbot features. We suggest that researchers and practitioners alike dedicate priorities to anthropomorphic design features with the same magnitude as common for functional design features in chatbot design and research

Emotion-Adaptive Learning Systems for Transferrable Skill Learning

In traditional school education, acquiring factual knowledge is still the major focus. The advancement in artificial intelligence (AI) and the digitalization of socio-economical processes, however, have fundamentally changed the way humans work and live. Today, humans have the ability to access facts fast through smartphones, computers, and the internet. They can access this knowledge flexibly whenever they want. Thus, obtaining factual knowledge by heart has become less essential. In a fast-paced world, individuals must quickly adapt to new environments. Transferrable skills have become essential as they support humans to adapt to dynamic environments. They are seen as a key catalyst for helping the workforce adapt (ILO, 2021)