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Zusammenfassung

ntroduction: Reducing household energy consumption through behavioral changes is a key strategy in addressing the emissions driving the climate crisis. Behavioral changes in affluent households toward more sustainable practices can have a significant positive impact. Prior research highlighted the role of individual values and motivational factors in shaping sustainable clusters. A more personalized approach toward encouraging the resulting clusters of people to adopt more sustainable strategies seems promising. Such an approach could incorporate aligned feedback, which has been proven to be a powerful mechanism throughout learning processes. Method: Over 9 weeks, a pilot study with 50 participants investigated the impact of different types of feedback on washing behavior. The within-subjects design included (1) a baseline condition, (2) feedback on energy consumption (kWh), and (3) feedback on monetary costs per cycle (EUR). Data collection encompassed pre- and post-condition surveys, a final comprehensive survey, and a diary-formatted table. The primary objective was to evaluate the potential for individualization. Asynchronous structured interviews were conducted at the end to explore participants' perceptions and washing behaviors. Results: While we found effects for the feedback manipulation, we found no differences between user clusters in individual washing behaviors. Furthermore, participants qualitatively reported habitual changes, feeling more knowledgeable about the monetary impacts of specific washing programs and temperatures, and wished for a more accessible preset time function. Most participants expressed willingness to switch to a dynamic energy price if it translated to significant cost savings. Discussion: Our findings may support the notion that individualized behavior change strategies are promising. In general, these strategies should be easily applicable, cost-effective, and promote habits to be exerted regularly. Arising methodological limitations suggest further research in this domain. From an applied perspective, our research provides valuable insights for designing products, services, and regulations by governments and companies, empowering them to develop more effective strategies for reducing energy consumption.

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Considering teachers’ poor state of health, we conducted a literature review to investigate, whether resilience, as a professional competence, has a health-promoting effect on teachers. Re-directing the seemingly inevitable trajectory from stress to illness towards health, the effects should be reflected in the improvement of teachers’ negative strain ratios. Hence, our review investigated the effect of resilience and specific resilience resources regarding teachers’ risk of burnout, stress perception and coping, general well-being, attrition rates, and effectiveness. Our results indicate that resilience supports teachers’ health, by consistently showing a positive impact on the investigated aspects. Furthermore, our catalog of identified resilience resources supports the concept of teacher-specific resilience. As effective programs for promoting teachers’ resilience are lacking, this informs new intervention approaches to promote teachers’ health.

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This paper presents a novel adaptive training system that integrates a Virtual Reality (VR) environement, real-time neurofeedback (NF) via functional near-infrared spectroscopy (fNIRS), and tactile stimulation, specifically designed to enhance cognitive skills in autistic adults. Central to this system is an integration platform that enables real-time, synchronised interplay between user behaviours and neurophysiological data. The presented neuroadaptivity loop employs a feedback mechanism that continuously assesses users’ cognitive states through fNIRS, allowing the system to dynamically adjust environmental complexity and send tactile cues based on real-time mental state indicators. This multimodal approach fosters cognitive executive functions relevant for managing everyday situations. By prioritising user-centred design through iterative feedback from autistic individuals, the platform ensures usability and safety while promoting skill transfer from structured training to real-world applications. The implications of this system extend to personalised, adaptive technologies in workplace training, highlighting important considerations regarding user empowerment.

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Cognitive diversity has driven societal progress, but it can provide challenges in daily workplace interactions. Hence, we developed a modular training paradigm using virtual reality (VR) and structured cognitive exercise to build transferable skills and enhance awareness of cognitive differences. The training strengthens core executive functions, which are subsequently applied to simulated and real-life scenarios. To evaluate performance impacts and technology acceptance, 17 neurodivergent adults explored our VR-based spatial working memory training module. Descriptive results for task performance and the UTAUT dimensions showed performance improvements, low anxiety levels, and high perceived self-efficacy. Participants further expected high effort and low performance gains in work situations, leading to low engagement intentions. These findings suggest that the abstract nature of the training module may have hindered its perceived applicability to practical tasks. While the training demonstrates potential for skill enhancement, refining its relevance to workplace scenarios is crucial for sustained use.

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Given the ever-evolving demands of the professional world, higher education plays a vital role in equipping students with strategies for self-organized and sustainable skill development. This enables students to quickly and independently adapt to new knowledge and skills throughout their careers. Therefore, it is of the essence to integrate methods that enhance self-regulation skills into our study programs, alongside the instruction of specific subject-matter expertise. Addressing these demands, we introduce a focused training that embeds the assistive software tool focUS into a structured seminar concept, leveraging eduScrum elements and accompanying learning communities. After introducing the results of a pilot evaluation with a small group of interdisciplinary doctoral students, we discuss possibilities of technical and conceptual integration in course curricula and learning counseling in higher education. Taken together, our approach indicates value for empowering future professionals across various domains to unleash their full potential.

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Introduction The persistent prevalence of distractions challenges people's capacities to study and work productively. Digital tools can support focusing on meaningful tasks with features like time tracking, feedback, or rewards. Evidence exists for benefits regarding behavioral focus, distraction management, and motivation, but also for hesitation to use digital support at all. Building on the Unified Theory of Acceptance and Use of Technology (UTAUT), this research explores which factors can foster or hinder the intention to use an exemplary software to support self-regulation. Methods A sample of 96 adult volunteers watched a short introductory video explaining how the software focUS fosters self-regulated studying and working. Subsequently, participants completed an online survey to capture their willingness to use focUS, expected gains and challenges, and individual characteristics. Results Participants expressed stronger intentions to use focUS when they expected increased benefits in performance. By trend, lower levels of expected effort also hinted on stronger intentions to use focUS. Contrary to expectations, participants lacking previous experience with software in the scope described, who anticipated higher effort when using focUS, tended to express stronger intentions to use it. Discussion Taken together, our evidence suggests that highlighting specific performance improvements may encourage the use of digital support for tasks that require self-regulation. Ensuring the use of digital support to be as effortless as possible could provide yet another compelling argument to use it. Particularly for inexperienced users, sparking curiosity for the challenges of the unknown might be a worthwhile strategy to reduce hesitation towards emerging technologies.

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Eye movements during reading offer a window into cognitive processes and language comprehension, but the scarcity of reading data with interruptions – which learners frequently encounter in their everyday learning environments – hampers advances in the development of intelligent learning technologies. We introduce InteRead – a novel 50-participant dataset of gaze data recorded during self-paced reading of real-world text. InteRead further offers fine-grained annotations of interruptions interspersed throughout the text as well as resumption lags incurred by these interruptions. Interruptions were triggered automatically once readers reached predefined target words. We validate our dataset by reporting interdisciplinary analyses on different measures of gaze behavior. In line with prior research, our analyses show that the interruptions as well as word length and word frequency effects significantly impact eye movements during reading. We also explore individual differences within our dataset, shedding light on the potential for tailored educational solutions. InteRead is accessible from our datasets web-page: https://www.ife.uni-stuttgart.de/en/llis/research/datasets/.

Zusammenfassung

Distractions are omnipresent and can derail our attention, which is a precious and very limited resource. To achieve their goals in the face of distractions, people need to regulate their attention, thoughts, and behavior; this is known as self-regulation. How can self-regulation be supported or strengthened in ways that are relevant for everyday work and learning activities? To address this question, we introduce and evaluate a desktop application that helps people stay focused on their work and train self-regulation at the same time. Our application lets the user set a goal for what they want to do during a defined period of focused work at their computer, then gives negative feedback when they get distracted, and positive feedback when they reorient their attention towards their goal. After this so-called focus session, the user receives overall feedback on how well they focused on their goal relative to previous sessions. While existing approaches to attention training often use artificial tasks, our approach transforms real-life challenges into opportunities for building strong attention control skills. Our results indicate that optimal attentional feedback can generate large increases in behavioral focus, task motivation, and self-control---benefitting users to successfully achieve their long-term goals.

Zusammenfassung

<jats:title>Abstract</jats:title><jats:p>Given the urgency of climate change action and the significant climate impact of household emissions, understanding the drivers of individuals’ sustainable behavior patterns is more important than ever. Consequently, we investigate whether different clusters of individual users can be distinguished regarding sustainability-related values, attitudes, and intentions. If these diverse clusters exist, we can explore tailored approaches to promote sustainable behavior patterns among them based on their unique needs and targets. For this purpose, we employ a mixed-method approach combining qualitative interviews with a quantitative survey. The obtained insights help us identify core factors that drive sustainable behavior, develop representations of different user groups, and suggest individualized interventions for supporting sustainable behavior patterns. The qualitative part comprised interviews with ten participants, resulting in the development of qualitative personas. Emerging differences could subsequently be used to select validated psychological scales for the quantitative part to confirm the differences. Applying data-driven clustering, we identify five intention-based clusters that vary regarding factors such as belief in climate change, collaboration, or skepticism concerning sustainability. Building on both qualitative and quantitative insights, five validated personas are created for research and practical use. These personas include Socially Sustainable, Responsible Savers, Unconcerned Spenders, Comfort-Oriented, and Skeptical Consumers. Individuals corresponding to the selected persona may, for example, respond positively to sustainability benefits, while others may be more receptive to hedonistic benefits. Addressing related varying motivational factors raises the demand for individualized interventions. These could be achieved by incorporating the personas’ needs with more individualized products and services to promote sustainable behavior.</jats:p>

Zusammenfassung

Select one of your emails You have multiple emails registered with Frontiers: Notify me on publication Please enter your email address: Email If you already have an account, please login You don't have a Frontiers account ? You can register here Interruptions are often pervasive and require attentional shifts from the primary task. Limited data are available on the factors influencing individuals' efficiency in resuming from interruptions during digital reading. The reported investigation -conducted using the InteRead dataset -examined whether individual differences in visuo-spatial working memory capacity (vsWMC) and prior knowledge could influence resumption lag times during interrupted reading. Participants' vsWMC capacity was assessed using the symmetry span (SSPAN) task, while a pre-test questionnaire targeted their background knowledge about the text. While reading an extract from a Sherlock Holmes story, they were interrupted six times and asked to answer an opinion question. Our analyses revealed that the interaction between vsWMC and prior knowledge significantly predicted the time needed to resume reading following an interruption. The results from our analyses are discussed in relation to theoretical frameworks of task resumption and current research in the field.

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Introduction: Significant threats to our environment tremendously affect biodiversity and related gains. Particularly wild bees actively contribute by pollinating plants and trees. Their increasing extinction comes with devastating consequences for nutrition and stability of our ecosystem. However, most people lack awareness about those species and their living conditions, preventing them to take on responsibility. Methods: We introduce an intervention consisting of a mobile app and related project workshops that foster responsibility already at an early stage in life. Drawing on principles from multimedia learning and child-centered design, six gamified levels and accompanying nature-based activities sensitize for the importance of wild bees and their role for a stable and diverse ecosystem. A pilot evaluation across three schools, involving 44 children aged between 9 and 12, included a pre-, post-, and delayed post-test to inspect app usability and learning gains. Results: Most children perceived the app as intuitive, engaging, and visually appealing, and sustainably benefited from our intervention in terms of retention performance. Teacher interviews following the intervention support the fit with the envisioned target group and the classroom setting. Discussion: Taken together, the obtained evidence emphasizes the benefits of our intervention, even though our sample size was limited due to dropouts. Future extensions might include adaptive instructional design elements to increase observable learning gains.

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Following recent technological developments, organizations and businesses seek to improve their effectiveness by increasing the use of artificial agents in the workplace. Previous research suggests that humans react to the adoption of artificial agents in three ways: 1) some humans appreciate algorithmic advice (algorithm appreciation); 2) some humans oppose algorithmic advice (algorithm aversion); and 3) some humans fully relinquish control to artificial agents (automation bias). Using tools and methods form the field of systems thinking, we analyze the existing literature on human-machine interactions in organizational settings and develop a conceptual model that provides an underlying structural explanation for the emergence of algorithm appreciation, algorithm aversion, and automation bias in various contexts. In doing so, we create a powerful visual tool that can be used to ground discussions about the responsible adoption of artificial agents in the workplace and the long-term impact they cause for organizations and humans within them. We use the model to hypothesize possible behavioral outcomes produced by the proposed structure.

Zusammenfassung

Given the urgency of climate change action and the significant climate impact of household emissions, understanding the drivers of individuals’ sustainable behavior patterns is more important than ever. Consequently, we investigate whether different clusters of individual users can be distinguished regarding sustainability-related values, attitudes, and intentions. If these diverse clusters exist, we can explore tailored approaches to promote sustainable behavior patterns among them based on their unique needs and targets. For this purpose, we employ a mixed-method approach combining qualitative interviews with a quantitative survey. The obtained insights help us identify core factors that drive sustainable behavior, develop representations of different user groups, and suggest individualized interventions for supporting sustainable behavior patterns. The qualitative part comprised interviews with ten participants, resulting in the development of qualitative personas. Emerging differences could subsequently be used to select validated psychological scales for the quantitative part to confirm the differences. Applying data-driven clustering, we identify five intention-based clusters that vary regarding factors such as belief in climate change, collaboration, or skepticism concerning sustainability. Building on both qualitative and quantitative insights, five validated personas are created for research and practical use. These personas include Socially Sustainable, Responsible Savers, Unconcerned Spenders, Comfort-Oriented, and Skeptical Consumers. Individuals corresponding to the selected persona may, for example, respond positively to sustainability benefits, while others may be more receptive to hedonistic benefits. Addressing related varying motivational factors raises the demand for individualized interventions. These could be achieved by incorporating the personas’ needs with more individualized products and services to promote sustainable behavior.

Zusammenfassung

Workers are increasingly embracing Artificial Intelligence (AI) to optimise various aspects of their operations in the workplace. While AI offers new opportunities, it also presents unintended challenges that they must carefully navigate. This paper aims to develop a deeper understanding of workers’ experiences with interactions with automated agents (AA) in the workplace and provide actionable recommendations for organisational leaders to achieve positive outcomes. We propose and test a simulation model that quantifies and predicts workers’ experiences with AA, shedding light on the interplay of diverse variables, such as workload, effort and trust. Our findings suggest that lower-efficiency AA might outperform higher-efficiency ones due to the constraining influence of trust on adoption rates. Additionally, we find that lower initial trust in AA could lead to increased usage in certain scenarios and that stronger emotional and social responses to the use of AA may foster greater trust but result in decreased AA utilisation. This interdisciplinary research blends a systems dynamics approach with management theories and psychological concepts, aiming to bridge existing gaps and foster the sustainable and effective implementation of AA in the workplace. Ultimately, our research endeavour contributes to advancing the field of human-AI interaction in the workplace.

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Humans’ performance varies due to the mental resources that are available to successfully pursue a task. To monitor users’ current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.

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Short-sighted decisions can have devastating consequences, and teaching people to make their decisions in a more far-sighted way is challenging. Previous research found that reflecting on one’s behavior can boost learning from success and failure. Here, we explore the potential benefits of guiding people to reflect on whether and how they thought about what to do (i.e., systematic metacognitive reflection). We devised a series of Socratic questions that prompt people to reflect on their decision-making and tested their effectiveness in a process-tracing experiment with a 5-step planning task ( $N=265$ ). Each participant went through several cycles of making a series of decisions and then either reflecting on how they made those decisions, answering unrelated questions, or moving on to the next decision right away. We found that systematic metacognitive reflection helps people discover adaptive, far-sighted decision strategies faster. Our results suggest that systematic metacognitive reflection is a promising approach to boosting people’s decision-making competence.

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Following recent technological developments in the artificial intelligence space, artificial agents are increasingly taking over organizational tasks typically reserved for humans. Studies have shown that humans respond differently to this, with some being appreciative of their advice (algorithm appreciation), others being averse toward them (algorithm aversion), and others still fully relinquishing control to artificial agents without adequate oversight (automation bias). Using systems thinking, we analyze the existing literature on these phenomena and develop a conceptual model that provides an underlying structural explanation for their emergence. In doing so, we create a powerful visual tool that can be used to ground discussions about the impact artificial agents have on organizations and humans within them.

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Affective Tutoring Systems (ATS) detect and mitigate critical emotional learner states with the aim of providing individualized support. In tutoring systems for safety-critical work environments, students are trained to achieve and maintain high performance, therefore an ATS should be capable of identifying critical emotional states hindering performance. Interindividual differences in the emotion-performance-relationship can be considered by using the ARC categorization system. The present contribution aims at developing a questionnaire-based method of classifying new learners to the categories. To that end, we investigated differences in personality traits between the different categories. In an airspace surveillance task, we measured performance, emotional valence, emotional arousal, and personality traits in N = 50 subjects. Results showed that a positive valence-performance-relationship, compared to a negative valence-performance-relationship, is associated with higher Neuroticism, lower Conscientiousness, and lower Openness to experience. There were no significant differences in the traits Agreeableness and Extraversion. Based on these results, a future ATS for safety-critical work environments could classify new learners in the ARCs using self-report data and thus dispense with physiological sensors. Thereby, user state diagnosis and evaluation for high performance is possible, setting the ground for an ATS adapting to critical emotional learner states.

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Stereotypical assumptions associating high levels of giftedness and outstanding performance with maladaptive behavioral characteristics and personality traits (cf. disharmony stereotype) are rather prevalent in the school context as well as in the musical domain. Such preconceptions among teachers can influence student assessment and corresponding performance expectations, which might, in turn, impact future lesson planning. In an experiment using a controlled vignette approach, the current study, with Nþinspace=þinspace211 (prospective) German music school teachers, investigated how background information, combined with a manipulated music recording, affected (prospective) music school teachers' assessment of a fictive student's performance, behavioral characteristics, personality traits, and teachers'  consequential lesson planning. Experimental variations included the fictive student's supposed level of giftedness, social interaction, age, and duration of instrumental lessons. Results indicated that music school teachers' preconceptions of students assumed to be musically gifted were a high level of intellectual and musical abilities with behavioral characteristics and personality traits rated at least equivalent to those of students assumed to have average giftedness. Teachers' lesson planning was not influenced by any of the manipulated background information. Taken together, the observed pattern of effects contradicts the disharmony stereotype but tends to align more with the harmony stereotype as music school teachers' prevailing preconceptions about students supposed to be musically gifted.

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Introduction We investigated brain activation patterns of interacting emotional distractions and cognitive processes in a close-to-naturalistic functional near-infrared spectroscopy (fNIRS) study. Methods Eighteen participants engaged in a monitoring-control task, mimicking common air traffic controller requirements. The scenario entailed experiencing both low and high workload, while concurrently being exposed to emotional speech distractions of positive, negative, and neutral valence. Results Our investigation identified hemispheric asymmetries in prefrontal cortex (PFC) activity during the presentation of negative and positive emotional speech distractions at different workload levels. Thereby, in particular, activation in the left inferior frontal gyrus (IFG) and orbitofrontal cortex (OFC) seems to play a crucial role. Brain activation patterns revealed a cross-over interaction indicating workload-dependent left hemispheric inhibition processes during negative distractions and high workload. For positive emotional distractions under low workload, we observed left-hemispheric PFC recruitment potentially associated with speech-related processes. Furthermore, we found a workload-independent negativity bias for neutral distractions, showing brain activation patterns similar to those of negative distractions. Discussion In conclusion, lateralized hemispheric processing, regulating emotional speech distractions and integrating emotional and cognitive processes, is influenced by workload levels and stimulus characteristics. These findings advance our understanding of the factors modulating hemispheric asymmetries during the processing and inhibition of emotional distractions, as well as the interplay between emotion and cognition. Moreover, they emphasize the significance of exploring emotion-cognition interactions in more naturalistic settings to gain a deeper understanding of their implications in real-world application scenarios (e.g., working and learning environments).

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Bees are significant contributors to our diverse flora, as they pollinate plants and trees. In addition to honeybees, their “wild sisters” provide further support with this task. There are more than 560 different species of wild bees known in Germany alone. Unlike honeybees, they lack protective beehives and the care of beekeepers. More than half of them is endangered or already became extinct. Potential consequences of this development are far reaching and devastating for the diversity of our ecosystem. As wild bees play such a crucial role, the question emerges why we do not protect them adequately. The answer is rather simple: Most people lack knowledge about these species, their living conditions, and the tremendous impact on our lives. In this talk, we introduce a project that has the goal to change this situation and build a sense of responsibility already at an early stage in life. A mobile application for classroom settings is developed and integrated into project workshops, inviting students on an insightful journey into the world of wild bees. By combining hands-on environmental actions with virtual game elements, this approach strives to sensitize students at a young age to the dramatic consequences if wild bees were to become extinct. The project follows a child-centered design process and builds on findings from multimedia learning research, for example, on intelligent agents that accompany learning and provide feedback and support. The talk will present results from an evaluation of the combined concept in school contexts and discuss potential future directions.

Zusammenfassung

Affect-adaptive tutoring systems detect the current emotional state of the learner and are capable of adequately responding by adapting the learning experience. Adaptations could be employed to manipulate the emotional state in a direction favorable to the learning process; for example, contextual help can be offered to mitigate frustration, or lesson plans can be accelerated to avoid boredom. Safety-critical situations, in which wrong decisions and behaviors can have fatal consequences, may particularly benefit from affect-adaptive tutoring systems, because accounting for affecting responses during training may help develop coping strategies and improve resilience. Effective adaptation, however, can only be accomplished when knowing which emotions benefit high learning performance in such systems. The results of preliminary studies indicate interindividual differences in the relationship between emotion and performance that require consideration by an affect-adaptive system. To that end, this article introduces the concept of Affective Response Categories (ARCs) that can be used to categorize learners based on their emotion-performance relationship. In an experimental study, N = 50 subjects (33% female, 19-57 years, M = 32.75, SD = 9.8) performed a simulated airspace surveillance task. Emotional valence was detected using facial expression analysis, and pupil diameters were used to indicate emotional arousal. A cluster analysis was performed to group subjects into ARCs based on their individual correlations of valence and performance as well as arousal and performance. Three different clusters were identified, one of which showed no correlations between emotion and performance. The performance of subjects in all other clusters benefitted from negative arousal and differed only in the valence-performance correlation, which was positive or negative. Based on the identified clusters, the initial ARC model was revised. We then discuss the resulting model, outline future research, and derive implications for the larger context of the field of adaptive tutoring systems. Furthermore, potential benefits of the proposed concept are discussed and ethical issues are identified and addressed.

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The omnipresence of distracting information constantly challenges our limited attentional resources. Consequently, approaching educational goals effectively and concentrating on them often becomes difficult for learners. Looking at existing training tools to strengthen executive functions, we often notice the use of artificial tasks and settings that limit the transferability of the acquired skills to everyday life situations. Addressing this gap, the talk introduces an example of how people can be supported to set goals, implement periods of focused work, and integrate a meaningful break management by using an attention training software during regular study and work activities. The computer-based intervention influences the entire lifecycle of self-regulated learning and can easily become part of daily computer use. It enables users to redefine their existing goals into training tasks and thus motivates them to achieve sustainable training results. As its core, the software has an intelligent feedback mechanism that visually discloses the value of staying focused against getting distracted, building on a model of the expected value of cognitive control. Results from field experiments already indicate the benefit of this approach over a control condition, in particular related to task-specific learning. The talk will take a more fine-grained look at patterns of distractibility across different task settings and discuss related benefits of embedding this novel approach to executive functions training across formal and informal learning contexts and workplace scenarios.

Zusammenfassung

Mind wandering (MW) is defined as a shift of attention to task-unrelated internal thoughts that is pervasive and disruptive for learning performance. Current state-of-the-art gaze-based attention-aware intelligent systems are capable of detecting MW from eye movements and delivering interventions to mitigate its negative effects. However, the beneficial functions of MW and its trait-level tendency, defined as the content of MW experience, are still largely neglected by these systems. In this pilot study, we address the questions of whether different MW trait-level tendencies can be detected through off-screen fixations’ frequency and duration and blink rate during a lecture viewing task. We focus on prospective planning and creative problem-solving as two of the main MW trait-level tendencies. Despite the non-significance, the descriptive values show a higher frequency and duration of off-screen fixations, but lower blink rate, in the creative problem-solving MW condition. Interestingly, we do find a highly significant correlation between MW level and engagement scores in the prospective planning MW group. Potential explanations for the observed results are discussed. Overall, these findings represent a preliminary step towards the development of more accurate and adaptive learning technologies, and call for further studies on MW trait-level tendency detection.

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Twenty-first century learning is characterized by an increasing number of augmented learning environments (e.g., multimedia, educational games, virtual reality, intelligent companions). The general idea is to support learning by integrating elements or mechanisms to optimize learning outcomes and experiences. While these augmented learning scenarios seem promising, effects on learning appear heterogeneous. Therefore, a better understanding of underlying mechanisms is crucial. For instance, new interactive features and technologies might increase learners’ motivation and interest in the topic but at the same time they are prone to overstrain their cognitive resources. Importantly, we can investigate these learning situations by augmenting them with process measures (e.g., EEG, eye tracking, fMRI, log files) or leverage data mining techniques to analyze large educational datasets, allowing a more fine-grained analysis of the learning process. Modern technologies such as virtual reality devices or game engines advance how learning content can be presented. Computer-based technology enables us to augment learning contexts and bring intelligent companions into individual learners’ daily lives. In the current symposium, we address important factors of learning, i.e., working memory, attention, emotions, and self-regulation, with augmented measures, materials, and contexts. In particular, studies on multimedia, educational games, virtual reality, and intelligent companions will be presented. Consequently, our symposium provides insights into late-breaking research innovations in the field of augmented learning. It also critically discusses potential limitations and downsides of this field of research to pave the way for a future research agenda.

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Over the last years, we could observe increasing awareness for sustainability and climate change in society. Individual sustainable behavior emerges by various influencing factors, resulting in different degrees of sustainable behavior. An important factor is the intention behind pro-environmental behavior, which can be goal-directed, motivated by other goals, or habitual. At the same time, good intentions do not always translate into sustainable actions. To develop interventions that promote pro-environmental behavior, we need to shed light on cognitive mechanisms underneath sustainable thoughts and how they stimulate actions. We conducted ten semi-structured interviews with representative individuals asking about their intentions, influencing factors of sustainability and examples from everyday life. Based on their scope of reflection, knowledge and predominant intention, five different sustainability personas were identified: sustainability-oriented, open-minded, opportunistic, careless and dismissive. We present personas, discuss the validation process and investigate cognitive mechanisms of reflection in the context of sustainable behavior.

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Learning requires a multitude of metacognitive activities that support knowledge acquisition and direct the learning process. These include selecting and planning goals, applying, observing and evaluating learning strategies as well as regulatory efforts required for goal achievement. We introduce an example of how learners can be supported to set goals, implement periods of focused work and integrate a meaningful break management by using the AI-based training software ACTrain. Metacognitive feedback based on principles of machine learning conveys the value of staying focused on goal-related activities. Pilot results already indicate positive effects of the presented approach and suggest further investigation. We will explore potentials for integrating such approach in school settings and how this could affect the role of teachers.

Zusammenfassung

Bienen gehören zu den wichtigsten Nutztieren und tragen durch die Bestäubung der Pflanzen und Bäume maßgeblich zum Erhalt der Vielfalt unserer Flora bei. Neben den Honigbienen helfen insbesondere deren „Wilde Schwestern“ entscheidend bei dieser wichtigen Aufgabe – im Gegensatz zu Honigbienen jedoch ohne den Schutz eines Bienenstocks und die Pflege durch Hobby- oder Berufsimkerinnen und -imker. Wie zahlreiche Berichte zeigen, sind die Lebensräume von Wildbienen nachweislich bedroht, einige Arten bereits ausgestorben und die entstehenden Konsequenzen für den Ernteertrag kritisch. Umweltpsychologische Modelle der Bewusstseinsbildung können eine fundierte Basis für die Entwicklung nachhaltig wirksamer Interventionen zum Schutz von Wildbienen bilden. Besonders in einer jungen Altersgruppe lässt sich ihre Wirkung durch die gezielte Verbindung mit digitalen Medien steigern. So könnten durch umsichtiges Handeln in einer mobilen App gute Lebensbedingungen für eine Wildbiene erreicht und die Konsequenzen des eigenen realen Handelns in Projektwerkstätten über die virtuelle Welt direkt erfahrbar werden. Eine solche Intervention könnte spätere Landwirtinnen und -wirte dazu animieren, einen achtsamen Umgang mit Pestiziden zu pflegen oder angehende Hobbygärtnerinnen und -gärtner zu einer insektenfreundlichen Blühpflanzenwahl bewegen. Auf diese Weise birgt die Verbindung von Umwelt und App ein hohes Potential zum Anstoß weitreichender gesellschaftlicher Veränderungen.