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Institute
- Institut Mensch - Computer - Medien (72) (remove)
Sonstige beteiligte Institutionen
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In this article, we present approaches to interactive simulations of biohybrid systems. These simulations are comprised of two major computational components: (1) agent-based developmental models that retrace organismal growth and unfolding of technical scaffoldings and (2) interfaces to explore these models interactively. Simulations of biohybrid systems allow us to fast forward and experience their evolution over time based on our design decisions involving the choice, configuration and initial states of the deployed biological and robotic actors as well as their interplay with the environment. We briefly introduce the concept of swarm grammars, an agent-based extension of L-systems for retracing growth processes and structural artifacts. Next, we review an early augmented reality prototype for designing and projecting biohybrid system simulations into real space. In addition to models that retrace plant behaviors, we specify swarm grammar agents to braid structures in a self-organizing manner. Based on this model, both robotic and plant-driven braiding processes can be experienced and explored in virtual worlds. We present an according user interface for use in virtual reality. As we present interactive models concerning rather diverse description levels, we only ensured their principal capacity for interaction but did not consider efficiency analyzes beyond prototypic operation. We conclude this article with an outlook on future works on melding reality and virtuality to drive the design and deployment of biohybrid systems.
Few topics have been the subject of more controversy than those encapsulated by the terms "sex" and "gender". Social-cultural and biological-evolutionary argumentation patterns frequently clash and especially the public debate appears to be stuck in a stalemate between the two competing parties.
From a psychological perspective both topics appear deeply intertwined and are not easy to be separated. This study pursues an integrative approach to better understand the roots of differences best subsumed under the term sex/gender. It will become apparent that both nature and nurture variables interact and form the complex system of human behavior and experience.
The field of human-computer interaction (HCI) strives for innovative user interfaces. Innovative and novel user interfaces are a challenge for a growing population of older users and endanger older adults to be excluded from an increasingly digital world. This is because older adults often have lower cognitive abilities and little prior experiences with technology.
This thesis aims at resolving the tension between innovation and age-inclusiveness by developing user interfaces that can be used regardless of cognitive abilities and technology-dependent prior knowledge.
The method of image-schematic metaphors holds promises for innovative and age-inclusive interaction design. Image-schematic metaphors represent a form of technology-independent prior knowledge. They reveal basic mental models and can be gathered in language (e.g. bank account is container from "I put money into my bank account").
Based on a discussion of previous applications of image-schematic metaphors in HCI, the present work derives three empirical research questions regarding image-schematic metaphors for innovative and age-inclusive interaction design.
The first research question addresses the yet untested assumption that younger and older adults overlap in their technology-independent prior knowledge and, therefore, their usage of image-schematic metaphors. In study 1, a total of 41 participants described abstract concepts from the domains of online banking and everyday life. In study 2, ten contextual interviews were conducted. In both studies, younger and older adults showed a substantial overlap of 70% to 75%, indicating that also their mental models overlap substantially.
The second research question addresses the applicability and potential of image-schematic metaphors for innovative design from the perspective of designers. In study 3, 18 student design teams completed an ideation process with either an affinity diagram as the industry standard, image-schematic metaphors or both methods in combination and created paper prototypes. The image-schematic metaphor method alone, but not the combination of both methods, was readily adopted and applied just as a well as the more familiar standard method.
In study 4, professional interaction designers created prototypes either with or without image-schematic metaphors. In both studies, the method of image-schematic metaphors was perceived as applicable and creativity stimulating.
The third research question addresses whether designs that explicitly follow image-schematic metaphors are more innovative and age-inclusive regarding differences in cognitive abilities and prior technological knowledge. In two experimental studies (study 5 and 6) involving a total of 54 younger and 53 older adults, prototypes that were designed with image-schematic metaphors were perceived as more innovative compared to those who were designed without image-schematic metaphors. Moreover, the impact of prior technological knowledge on interaction was reduced for prototypes that had been designed with image-schematic metaphors. However, participants' cognitive abilities and age still influenced the interaction significantly.
The present work provides empirical as well as methodological findings that can help to promote the method of image-schematic metaphors in interaction design. As a result of these studies it can be concluded that the image-schematic metaphors are an applicable and effective method for innovative user interfaces that can be used regardless of prior technological knowledge.
Meal-concurrent media use has been linked to several problematic outcomes, including higher caloric intake and an increased risk for obesity. Nevertheless, the sociocultural and dispositional predictors of using media while eating are not yet well-understood, including potential cross-cultural differences. Inspired by the recent emergence of a new food-related media phenomenon called “mukbang”—digital eating broadcasts that have become immensely popular throughout East and Southeast Asia—we inquire 296 participants from two cultures (Germany and South Korea) about their meal-concurrent media use. Our results suggest that South Koreans tend to use media more frequently during meals than Germans, especially for social purposes. Meanwhile, younger age only predicts meal-concurrent media use in the German sample. Apart from that, however, many other examined predictors (e.g., gender, living situation, body-esteem, the Big Five) remain statistically insignificant, indicating notable universality for the behavior in question. In the second part of our study, we then put special focus on the emerging mukbang trend and conduct a theory-driven exploration of its gratifications. Doing so, we find that participants' parasocial and social experiences during eating broadcasts significantly predict their enjoyment of the genre.
When interacting with sophisticated digital technologies, people often fall back on the same interaction scripts they apply to the communication with other humans—especially if the technology in question provides strong anthropomorphic cues (e.g., a human-like embodiment). Accordingly, research indicates that observers tend to interpret the body language of social robots in the same way as they would with another human being. Backed by initial evidence, we assumed that a humanoid robot will be considered as more dominant and competent, but also as more eerie and threatening once it strikes a so-called power pose. Moreover, we pursued the research question whether these effects might be accentuated by the robot’s body size. To this end, the current study presented 204 participants with pictures of the robot NAO in different poses (expansive vs. constrictive), while also manipulating its height (child-sized vs. adult-sized). Our results show that NAO’s posture indeed exerted strong effects on perceptions of dominance and competence. Conversely, participants’ threat and eeriness ratings remained statistically independent of the robot’s depicted body language. Further, we found that the machine’s size did not affect any of the measured interpersonal perceptions in a notable way. The study findings are discussed considering limitations and future research directions.
According to research examining self‐regulated learning (SRL), we regard individual regulation as a specific sequence of regulatory activities. Ideally, students perform various learning activities, such as analyzing, monitoring, and evaluating cognitive and motivational aspects during learning. Metacognitive prompts can foster SRL by inducing regulatory activities, which, in turn, improve the learning outcome. However, the specific effects of metacognitive support on the dynamic characteristics of SRL are not understood. Therefore, the aim of our study was to analyze the effects of metacognitive prompts on learning processes and outcomes during a computer‐based learning task. Participants of the experimental group (EG, n=35) were supported by metacognitive prompts, whereas participants of the control group (CG, n=35) received no support. Data regarding learning processes were obtained by concurrent think‐aloud protocols. The EG exhibited significantly more metacognitive learning events than did the CG. Furthermore, these regulatory activities correspond positively with learning outcomes. Process mining techniques were used to analyze sequential patterns. Our findings indicate differences in the process models of the EG and CG and demonstrate the added value of taking the order of learning activities into account by discovering regulatory patterns.
In computer-supported learning environments, the deployment of self-regulatory skills represents an essential prerequisite for successful learning. Metacognitive prompts are a promising type of instructional support to activate students’ strategic learning activities. However, despite positive effects in previous studies, there are still a large number of students who do not benefit from provided support. Therefore, it may be necessary to consider explicitly the conditions under which a prompt is beneficial for a student, i.e., so-called adaptive scaffolding. The current study aims to (i) classify the effectiveness of prompts on regulatory behavior, (ii) investigate the correspondence of the classification with learning outcome, and (iii) discover the conditions under which prompts induce regulatory activities (i.e., the proper temporal positioning of prompts). The think-aloud data of an experiment in which metacognitive prompts supported the experimental group (n = 35) was used to distinguish between effective and non-effective prompts. Students’ activities preceding the prompt presentation were analyzed using data mining and process mining techniques. The results indicate that approximately half of the presented prompts induced metacognitive learning activities as expected. Moreover, the number of induced monitoring activities correlates positively with transfer performance. Finally, the occurrence of orientation and monitoring activities, which are not well-embedded in the course of learning, increases the effectiveness of a presented prompt. In general, our findings demonstrate the benefits of investigating metacognitive support using process data, which can provide implications for the design of effective instructional support.
The current dissertation addresses the analysis of technology-enhanced learning processes by using Process Mining techniques. For this purpose, students’ coded think-aloud data served as the measurement of the learning process, in order to assess the potential of this analysis method for evaluating the impact of instructional support.
The increasing use of digital media in higher education and further educational sectors enables new potentials. However, it also poses new challenges to students, especially regarding the self-regulation of their learning process. To help students with optimally making progress towards their learning goals, instructional support is provided during learning. Besides the use of questionnaires and tests for the assessment of learning, researchers make use increasingly of process data to evaluate the effects of provided support. The analysis of observed behavioral traces while learning (e.g., log files, eye movements, verbal reports) allows detailed insights into the student’s activities as well as the impact of interventions on the learning process. However, new analytical challenges emerge, especially when going beyond the analysis of pure frequencies of observed events. For example, the question how to deal with temporal dynamics and sequences of learning activities arises. Against this background, the current dissertation concentrates on the application of Process Mining techniques for the detailed analysis of learning processes. In particular, the focus is on the additional value of this approach in comparison to a frequency-based analysis, and therefore on the potential of Process Mining for the evaluation of instructional support.
An extensive laboratory study with 70 university students, which was conducted to investigate the impact of a support measure, served as the basis for pursuing the research agenda of this dissertation. Metacognitive prompts supported students in the experimental group (n = 35) during a 40-minute hypermedia learning session; whereas the control group (n = 35) received no support. Approximately three weeks later, all students participated in another learning session; however, this time all students learned without any help. The participants were instructed to verbalize their learning activities concurrently while learning. In the three analyses of this dissertation, the coded think aloud data were examined in detail by using frequency-based methods as well as Process Mining techniques.
The first analysis addressed the comparison of the learning activities between the experimental and control groups during the first learning session. This study concentrated on the research questions whether metacognitive prompting increases the number of metacognitive learning activities, whether a higher number of these learning activities corresponds with learning outcome (mediation), and which differences regarding the sequential structure of learning activities can be revealed. The second analysis investigated the impact of the individual prompts as well as the conditions of their effectiveness on the micro level. In addition to Process Mining, we used a data mining approach to compare the findings of both analysis methods. More specifically, we classified the prompts by their effectiveness, and we examined the learning activities preceding and following the presentation of instructional support. Finally, the third analysis considered the long-term effects of metacognitive prompting on the learning process during another learning session without support. It was the key objective of this study to examine which fostered learning activities and process patterns remained stable during the second learning session.
Overall, all three analyses indicated the additional value of Process Mining in comparison to a frequency-based analysis. Especially when conceptualizing the learning process as a dynamic sequence of multiple activities, Process Mining allows identifying regulatory loops and crucial routing points of the process. These findings might contribute to optimizing intervention strategies. However, before drawing conclusions for the design of instructional support based on the revealed process patterns, additional analyses need to investigate the generalizability of results. Moreover, the application of Process Mining remains challenging because guidelines for analytical decisions and parameter settings in technology-enhanced learning context are currently missing. Therefore, future studies need to examine further the potential of Process Mining as well as related analysis methods to provide researchers with concrete recommendations for use. Nevertheless, the application of Process Mining techniques can already contribute to advance the understanding of the impact of instructional support through the use of fine-grained process data.
We provide a literature overview of 30 years of research on the amount of invested mental effort (AIME, Salomon, 1984), illuminating relevant literature in this field. Since the introduction of AIME, this concept appears to have vanished. To obtain a clearer picture of where the theory of AIME has diffused, we conducted a literature search focusing on the period 1985–2015. We examined scientific articles (N = 244) that cite Salomon (1984) and content-analyzed their keywords. Based on these keywords, we identified seven content clusters: affect and motivation, application fields, cognition and learning, education and teaching, media technology, learning with media technology, and methods. We present selected works of each content cluster and describe in which research field the articles had been published. Results indicate that AIME was most commonly (but not exclusively) referred to in the area of educational psychology indicating its importance regarding learning and education, thereby investigating print and TV, as well as new media. From a methodological perspective, research applied various research methods (e.g., longitudinal studies, experimental designs, theoretical analysis) and samples (e.g., children, college students, low income families). From these findings, the importance of AIME for further research is discussed.
Ownership and usage of personal voice assistant devices like Amazon Echo or Google Home have increased drastically over the last decade since their market launch. This thesis builds upon existing computers are social actors (CASA) and media equation research that is concerned with humans displaying social reactions usually exclusive to human-human interaction when interacting with media and technological devices. CASA research has been conducted with a variety of technological devices such as desktop computers, smartphones, embodied virtual agents, and robots. However, despite their increasing popularity, little empirical work has been done to examine social reactions towards these personal stand-alone voice assistant devices, also referred to as smart speakers. Thus, this dissertation aims to adopt the CASA approach to empirically evaluate social responses to smart speakers. With this goal in mind, four laboratory experiments with a total of 407 participants have been conducted for this thesis. Results show that participants display a wide range of social reactions when interacting with voice assistants. This includes the utilization of politeness strategies such as the interviewer-bias, which led to participants giving better evaluations directly to a smart speaker device compared to a separate computer. Participants also displayed prosocial behavior toward a smart speaker after interdependence and thus a team affiliation had been induced. In a third study, participants applied gender stereotypes to a smart speaker not only in self-reports but also exhibited conformal behavior patterns based on the voice the device used. In a fourth and final study, participants followed the rule of reciprocity and provided help to a smart speaker device that helped them in a prior interaction. This effect was also moderated by subjects’ personalities, indicating that individual differences are relevant for CASA research. Consequently, this thesis provides strong empirical support for a voice assistants are social actors paradigm. This doctoral dissertation demonstrates the power and utility of this research paradigm for media psychological research and shows how considering voice assistant devices as social actors lead to a more profound understanding of voice-based technology. The findings discussed in this thesis also have implications for these devices that need to be carefully considered both in future research as well as in practical design.