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- Institut Mensch - Computer - Medien (16) (remove)
Emotional shifts are often a fundamental part of the narrative experience and engrained into the schematic structures of stories. Recent theoretical work suggests that these shifts are key for narrative influence and are interconnected with transportation, a known mechanism of narrative effects. Empirical research examining this proposition is still scarce, inconclusive, and lacking measures that assess the experience of emotional shifts throughout a narrative to explain effects. This thesis aims to contribute to this research lacuna and investigates the link between emotional shifts, transportation, and story-consistent outcomes using different methods to measure emotional shifts in the moment they occur (Manuscript #1 and #2), and using various narrative stimuli (audiovisual, written, auditive).
Manuscript #1 uses real-time-response (RTR) measurement to examine the relationship of valence shifts experienced during film viewing with transportation and post-exposure self-reported emotional flow. Manuscript #2 reports a pilot study and two experiments in which a self-probed emotional retrospection task is used to measure the number and intensity of emotional shifts during reading. I investigate the effect of reviews on transportation, the link between transportation and emotional shifts, and their respective associations with story-consistent attitudes, social sharing intentions, and donation behavior. In Manuscript #3, narrative structures are manipulated. Two experiments examine the effects of audio stories with shifting (positive-negative-positive) vs. positive-only emotional trajectories on the experience of happiness- and sadness-shifts, transportation, and post-exposure emotional flow.
Transportation was positively linked to valence shifts (M#1), and the number and intensity of emotional shifts (M#2), and emotional flow (M#1, M#3). In M#3, transportation was predicted by shifts in happiness, but not sadness. Emotional flow was linked to shifts in happiness, sadness, and RTR valence (M#1, M#3). Emotional shifts and transportation were associated with social sharing intentions, but only transportation was linked to some story-consistent attitudes (affective attitudes in particular).
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.
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.
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.
Errors in Prospective Memory
(2019)
Prospective memory is the ability to implement intentions at a later point in time in response to a specified cue. Such prospective memory tasks often occur in daily living and workplace situations. However, in contrast to retrospective memory there has been relatively little research on prospective memory. The studies by Harris (1984) and Einstein and MacDaniel (1990) served as a starting point for a now steadily growing area of research. Based on this emerging field of study this dissertation presents and connects and five journal articles, which further explore prospective memory by focusing on its potential errors.
The first article addresses the question if additional cognitive resources are needed after a prospective memory cue occurs to keep the intention active until it is implemented. The theory by Einstein, McDaniel, Williford, Pagan and Dismukes (2003), which suggested this active maintenance, could not be replicated. The second article demonstrated that interruptions between cue and the window of opportunity to implement the intention reduce prospective memory performance, especially if the interruption is tied with a change of context. Article three to five were focused on the erroneous implementation of a no longer active prospective memory task, so called commission errors. The suggested mechanism for their occurrence, the dual-mechanism account (Bugg, Scullin, & Rauvola, 2016), was not suited to explain the present results. A modification for the dual-mechanism account was formulated, which can account for prior work, as well as for the present data.
The results of all five articles also indicate that the moment of cue retrieval is even more relevant for prospective memory and its errors than previously accounted for.
In this thesis, metacognition research is connected with fluency research. Thereby, the focus lies on how disfluency can be used to improve metacognitive monitoring (i.e., students` judgments during the learning process). Improving metacognitive monitoring is important in educational contexts in order to foster performance. Theories about metacognition and self-regulated learning suppose that monitoring affects control and performance. Accurate monitoring is necessary to initiate adequate control and better performance. However, previous research shows that students are often not able to accurately monitor their learning with meaningful text material. Inaccurate monitoring can result in inadequate control and low performance.
One reason for inaccurate monitoring is that students use cues for their judgments that are not valid predictors of their performance. Because fluency might be such a cue, the first aim of this thesis is to investigate under which conditions fluency is used as a cue for judgments during the learning process. A fluent text is easy to process and, hence, it should be judged as easy to learn and as easy to remember. Inversely, a disfluent text is difficult to process, for example because of a disfluent font type (e.g., Mistral) or because of deleted letters (e.g., l_tt_rs). Hence, a disfluent text should be judged as difficult to learn and as difficult to remember. This assumption is confirmed when students learn with both fluent and disfluent material. When fluency is manipulated between persons, fluency seems to be less obvious as a cue for judgments. However, there are only a few studies that investigated the effects of fluency on judgments when fluency is manipulated between persons. Results from Experiment 1 (using deleted letters for disfluent text) and from Experiment 4 (using Mistral for disfluent text) in this thesis support the assumption that fluency is used as a cue for judgments in between-person designs. Thereby, however, the interplay with the type of judgment and the learning stage seems to matter.
Another condition when fluency affects judgments was investigated in Experiment 2 and 3. The aim of these experiments was to investigate if disfluency leads to analytic monitoring and if analytic monitoring sustains for succeeding fluent material. If disfluency activates analytic monitoring that remains for succeeding fluent material, fluency should no longer be used as a cue for judgments. Results widely support this assumption for deleted letters (Experiment 2) as well as for the font type Mistral (Experiment 3). Thereby, again the interplay between the type of judgment and the learning stage matters.
Besides the investigation of conditions when fluency is used as a cue for different types of judgments during the learning process, another aim of this thesis is to investigate if disfluency leads to accurate monitoring. Results from Experiment 3 and 4 support the assumption that Mistral can reduce overconfidence. This is the case when fluency is manipulated between persons or when students first learn with a fluent and then with a disfluent text. Dependent from the type of judgment and the learning stage, disfluency can lead even to underconfidence or to improved relative monitoring accuracy (Experiment 4).
Improving monitoring accuracy is only useful when monitoring is implemented into better control and better performance. The effect of monitoring accuracy on control and performance was in the focus of Experiment 4. Results show that accurate monitoring does not result in improved control and performance. Thus, further research is required to develop interventions that do not only improve monitoring accuracy but that also help students to implement accurate monitoring into better control and performance.
Summing up, the aim of this thesis is to investigate under which conditions fluency is used as a cue for judgments during the learning process, how disfluency can be used to improve monitoring accuracy, and if improved monitoring accuracy leads to improved performance. By connecting metacognition research and fluency research, further theories about metacognition and theories about fluency are specified. Results show that not only the type of fluency and the design, but also the type of judgment, the type of monitoring accuracy, and the learning stage should be taken into account. Understanding conditions that affect the interplay between metacognitive processes and performance as well as understanding the underlying mechanisms is necessary to enable systematic research and to apply findings into educational settings.
Computer games are highly immersive, engaging, and motivating learning environments. By providing a tutorial at the start of a new game, players learn the basics of the game's underlying principles as well as practice how to successfully play the game. During the actual gameplay, players repetitively apply this knowledge, thus improving it due to repetition. Computer games also challenge players with a constant stream of new challenges which increase in difficulty over time. As a result, computer games even require players to transfer their knowledge to master these new challenges. A computer game consists of several game mechanics. Game mechanics are the rules of a computer game and encode the game's underlying principles. They create the virtual environments, generate a game's challenges and allow players to interact with the game. Game mechanics also can encode real world knowledge. This knowledge may be acquired by players via gameplay. However, the actual process of knowledge encoding and knowledge learning using game mechanics has not been thoroughly defined, yet. This thesis therefore proposes a theoretical model to define the knowledge learning using game mechanics: the Gamified Knowledge Encoding. The model is applied to design a serious game for affine transformations, i.e., GEtiT, and to predict the learning outcome of playing a computer game that encodes orbital mechanics in its game mechanics, i.e., Kerbal Space Program. To assess the effects of different visualization technologies on the overall learning outcome, GEtiT visualizes the gameplay in desktop-3D and immersive virtual reality. The model's applicability for effective game design as well as GEtiT's overall design are evaluated in a usability study. The learning outcome of playing GEtiT and Kerbal Space Program is assessed in four additional user studies. The studies' results validate the use of the Gamified Knowledge Encoding for the purpose of developing effective serious games and to predict the learning outcome of existing serious games. GEtiT and Kerbal Space Program yield a similar training effect but a higher motivation to tackle the assignments in comparison to a traditional learning method. In conclusion, this thesis expands the understanding of using game mechanics for an effective learning of knowledge. The presented results are of high importance for researches, educators, and developers as they also provide guidelines for the development of effective serious games.
Affective states in the context of learning and achievement can influence the learning process essentially. The impact of affective states can be both directly on the learning performance and indirectly mediated via, for example, motivational processes. Positive activating affect is often associated with increased memory skills as well as advantages in creative problem solving. Negative activating affect on the other hand is regarded to impair learning outcomes because of promoting task-irrelevant thinking. While these relationships were found to be relatively stable in correlation studies, causal relationships have been examined rarely so far. This dissertation aims to investigate the effects of positive and negative affective states in multimedia learning settings and to identify potential moderating factors. Therefore, three experimental empirical studies on university students were conducted. In Experiment 1, N = 57 university students were randomly allocated to either a positive or negative affect induction group. Affects were elicited using short film clips. After a 20-minute learning phase in a hypertext-based multimedia learning environment on “functional neuroanatomy” the learners’ knowledge as well as transfer performance were measured. It was assumed that inducing positive activating affect should enhance learning performance. Eliciting negative activating affect on the other hand should impair learning performance. However, it was found that the induction of negative activating affect prior to the learning phase resulted in slight deteriorations in knowledge. Contrary to the assumptions, inducing positive activating affect before the learning phase did not improve learning performance. Experiment 2 induced positive activating affect directly during learning. To induce affective states during the entire duration of the learning phase, Experiment 2 used an emotional design paradigm. Therefore, N = 111 university students were randomly assigned to learn either in an affect inducing multimedia learning environment (use of warm colours and round shapes) or an affectively neutral counterpart (using shades of grey and angular shapes) on the same topic as in Experiment 1. Again, knowledge as well as transfer performance were measured after learning for 20 minutes. In addition, positive and negative affective states were measured before and after learning. Complex interaction patterns between the treatment and initial affective states were found. Specifically, learners with high levels of positive affect before learning showed better transfer performance when they learned in the affect inducing learning environment. Regarding knowledge, those participants who reported high levels of negative activating affect prior to the learning period performed worse. However, the effect on knowledge did not occur for those students learning in the affect inducing learning environment. For knowledge, the treatment therefore protected against poorer performance due to high levels of negative affective states. Results of Experiment 2 showed that the induction of positive activating affect influenced learning performance positively when taking into account affective states prior to the learning phase. In order to confirm these interaction effects, a conceptual replication of the previous experiment was conducted in Experiment 3. Experiment 3 largely retained the former study design, but changed the learning materials and tests used. Analogous to Experiment 2, N = 145 university students learning for 20 minutes in either an affect inducing or an affectively neutral multimedia learning environment on “eukaryotic cell”. To strengthen the treatment, Experiment 3 also used anthropomorphic design elements to induce affective states next to warm colours and round shapes. Moreover, in order to assess the change in affective states more exactly, an additional measurement of positive and negative affective states after half of the learning time was inserted. Knowledge and transfer were assessed again to measure learning performance. The learners’ memory skills were used as an additional learning outcome. To control the influence of potential confounding variables, the participants’ general and current achievement motivation as well as interest, and emotion regulation skills were measured. Contrary to the assumptions, Experiment 3 could not confirm the interaction effects of Experiment 2. Instead, there was a significant impact of positive activating affect prior to the learning phase on transfer, irrespective of the learners’ group affiliation. This effect was further independent of the control variables that were measured. Nevertheless, the results of Experiment 3 fit into the picture of findings regarding “emotional design” in hypermedia learning settings. To date, the few publications that have used this approach propose heterogeneous results, even when using identical materials and procedures.
The rising use of new media has given rise to public discussions about their possible negative consequences. The social sciences have answered these concerns, providing many studies investigating different media types (e.g., social media, video games) and different related variables (e.g., psychological well-being, academic achievement). Within this big body of research, some research results have confirmed negative associations with frequent media use; other studies have found no or even positive relationships. With heterogeneous results, it is difficult to obtain a clear picture of the relationships and causalities of new media. The method of meta-analysis allows a synthesis of all existing data, providing an overall effect size as well as moderator and mediator analyses which might explain the heterogeneity. Three manuscripts present meta-analytic evidence related to a) the relationship between social media use and academic achievement, b) the relationship between video gaming and overweight, and c) the relationship between social media and psychological correlates. Manuscript #1 found small relationships which depend on the usage pattern of social media. The relationship is positive, as long as social media use is related to school. Manuscript #2 showed that children’s and adolescents’ video gaming is independent from their body mass, while adults who play more have a higher body mass. Manuscript #3 summarized existing meta-analytic evidence that links social media with psychological wellbeing, academic achievement, and narcissism with small to moderate effect sizes. All three manuscripts underscore the potential of meta-analyses to synthesize previous research and to identify moderators. Although meta-analyses are not necessarily superior to other approaches because of their limitations (e.g. limited information or quality of primary studies) they are very promising for media psychology. Meta-analyses can reduce complexity and might be helpful for the communication of research results to the general public.
Using color in user interface design is both art and science. Often, designers focus on aesthetic properties of color, but neglect that it also carries meaning and entails profound psychological consequences. Color psychology, filling this gap, is in its infancy, and lacks a theoretical approach that predicts and explains color-meaning associations shared by a large group of people in a large variety of contexts.
To amend this situation, this work develops Conceptual Metaphor Theory of Color (CMToC), which predicts and explains cross-cultural and experience-based semantic color associations. The theory is based on the idea from cognitive linguistics that the study of metaphorical language provides valuable insights into our mental models involving color. A discussion of three types of metaphors that cover associations with physical and abstract concepts in light of existing empirical evidence provides the basis for deriving empirical research questions.
The first research question addresses the use of color for conveying physical information like weight in user interfaces. The results of four online surveys involving a total of 295 German and Japanese participants show the relative impact of hue, saturation and brightness for associations with 16 physical properties. Two thirds of these color associations were correctly predicted by CMToC. Participants frequently matched physical properties to colors based on sensorimotor correspondences and participants of both cultures did not considerably vary in their performance.
The second research question addresses the use of color for conveying abstract information like importance in user interfaces. In one experimental study, a total of 75 German and Japanese participants validated color-to-abstract mappings in form of color population stereotypes like important is dark. The majority of these color associations (86%) were correctly predicted by CMToC. Again, participants of both cultures did not considerably vary in their performance.
The third research question addresses whether predicted color associations with physical and abstract information are processed automatically as a precondition for intuitive use. The results of three studies involving a total of 85 German and Japanese participants show on the example of temperature that color automatically influences the identification speed of related physical properties, but not vice versa. Color and abstract information were not automatically associated.
As a result of these studies it can be concluded that predictions of CMToC are cross-culturally valid for user interface design. Derived implicit associations with physical properties and explicit associations with abstract concepts can inform design decisions in both hard- and software user interface design.