Emotional design of pedagogical agents: the influence of enthusiasm and model-observer similarity

Pedagogical agents were found to enhance learning but studies on the emotional effects of such agents are still missing. While first results show that pedagogical agents with an emotionally positive design might especially foster learning, these findings might depend on the gender of the agent and the learner. This study investigated whether emotional expressions performed by an on-screen instructor were able to increase learning outcomes while considering differences the gender of the agent and the learner. In a 2 (neutral vs. enthusiastic expressions) × 2 (female vs. male agent) between-subject design with additional consideration of the gender of the learner, data of 129 participants was collected. Results revealed that the manipulation of enthusiasm lead to higher perceptions of positive emotions. In addition, a pedagogical agent who performed enthusiastic expressions led to a higher retention but not transfer performance. In terms of the gender of the agent and the learner, male learners retained knowledge better when they watched the agent performing enthusiastic expression irrespective of the persona gender. Female learners, however, retained knowledge only better when a female agent performed enthusiastic expressions. Results are discussed in the light of the positivity principle, model-observer similarity hypotheses and current theories on social cues in multimedia learning

Energy efficiency quo vadis? : The role of energy efficiency in a 100 % renewable future

Following the decisions of the Paris climate conference at the end of 2015 as well as similar announcements e.g. from the G7 in Elmau (Germany) in the summer of 2015, long-term strategies aiming at (almost) full decarbonisation of the energy systems increasingly move into the focus of climate and energy policy. Deep decarbonisation obviously requires a complete switch of energy supply towards zero GHG emission sources, such as renewable energy. A large number of both global as well as national climate change mitigation scenarios emphasize that energy efficiency will likewise play a key role in achieving deep decarbonization. However, the interdependencies between a transformation of energy supply on the one hand and the role of and prospects for energy efficiency on the other hand are rarely explored in detail. This article explores these interdependencies based on a scenario for Germany that describes a future energy system relying entirely on renewable energy sources. Our analysis emphasizes that generally, considerable energy efficiency improvements on the demand side are required in order to have a realistic chance of transforming the German energy system towards 100 % renewables. Efficiency improvements are especially important if energy demand sectors will continue to require large amounts of liquid and gaseous fuels, as the production of these fuels are associated with considerable energy losses in a 100 % renewables future. Energy efficiency on the supply side will therefore differ considerably depending on how strongly the use of liquid and gaseous fuels in the various demand sectors can be substituted through the direct use of electricity. Apart from a general discussion of the role of energy efficiency in a 100 % renewable future, we also look at the role of and prospects for energy efficiency in each individual demand sector

Deep decarbonisation pathways for the industrial cluster of the Port of Rotterdam

The Port of Rotterdam is an important industrial cluster mainly comprising of oil refining, chemical manufacturing and power and steam generation. In 2015, the area accounted for 18 % of the Netherlands' total CO2 emissions. The Port of Rotterdam Authority is aware that the port's economy is heavily exposed to future global and EU decarbonization policies, as the bulk of its activities focuses on trading, handling, converting and using fossil fuels. Based on a study for the Port Authority, our paper explores possible pathways of how the industrial cluster can keep its strong market position in Europe and still reduce its CO2 emissions by 98 % by 2050. The "Biomass and CCS" scenario assumes that large amounts of biomass can be supplied sustainably and will be used in the port for power generation as well as for feedstock for refineries and the chemical industry. Fischer-Tropsch fuel generation plays an important role in this scenario, allowing the port to become a key cluster for the production of synthetic fuels and feedstocks in Western Europe. The "Closed Carbon Cycle" scenario assumes that renewables-based electricity will be used at the port to supply heat and hydrogen for the synthetic generation of feedstock for the chemical industry. The carbon required for the chemicals will stem from recycled waste. Technologies particularly needed in this scenario are water electrolysis and gasification or pyrolysis to capture carbon from waste, as well as technologies for the production of base chemicals from syngas. The paper compares both scenarios with regard to their respective technological choices and infrastructural changes. The scenarios’ particular opportunities and challenges are also discussed. Using possible future pathways of a major European petrochemical cluster as an example, the paper illustrates options for deep decarbonisation of energy intensive industries in the EU and beyond

Influence of measurement uncertainty on machine learning results demonstrated for a smart gas sensor

Humans spend most of their lives indoors, so indoor air quality (IAQ) plays a key role in human health. Thus, human health is seriously threatened by indoor air pollution, which leads to 3.8 × 106 deaths annually, according to the World Health Organization (WHO). With the ongoing improvement in life quality, IAQ monitoring has become an important concern for researchers. However, in machine learning (ML), measurement uncertainty, which is critical in hazardous gas detection, is usually only estimated using cross-validation and is not directly addressed, and this will be the main focus of this paper. Gas concentration can be determined by using gas sensors in temperature-cycled operation (TCO) and ML on the measured logarithmic resistance of the sensor. This contribution focuses on formaldehyde as one of the most relevant carcinogenic gases indoors and on the sum of volatile organic compounds (VOCs), i.e., acetone, ethanol, formaldehyde, and toluene, measured in the data set as an indicator for IAQ. As gas concentrations are continuous quantities, regression must be used. Thus, a previously published uncertainty-aware automated ML toolbox (UA-AMLT) for classification is extended for regression by introducing an uncertainty-aware partial least squares regression (PLSR) algorithm. The uncertainty propagation of the UA-AMLT is based on the principles described in the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements. Two different use cases are considered for investigating the influence on ML results in this contribution, namely model training with raw data and with data that are manipulated by adding artificially generated white Gaussian or uniform noise to simulate increased data uncertainty, respectively. One of the benefits of this approach is to obtain a better understanding of where the overall system should be improved. This can be achieved by either improving the trained ML model or using a sensor with higher precision. Finally, an increase in robustness against random noise by training a model with noisy data is demonstrated

Smart Sirens - Civil Protection in Rural Areas

Germany carried out a nationwide “Alert Day” in 2020 to test its civil alarm systems. The test revealed some problems. Heterogeneous development structures and topography can be limiting factors for sound propagation. In consequence, sirens could be heard inadequately, depending on their location. Furthermore, the reason of warning remains unknown to the public. In terms of civil protection, warnings with the code of behavior by general available media is desired. Smart sirens can transmit additional spoken information and be installed on already-existing streetlights. In this study, we analyze how smart sirens could lead to an improved civil protection. Exemplarily, a detailed analysis is made for a different structured rural area, Dansenberg in Germany, whereas the influence of local conditions on the sound propagation is considered. We analyzed with the software CadnaA - a software for calculation, assessment and prediction of environmental sound - how the location and number of smart sirens can be optimized in order to produce a full coverage of the study area. We modeled the coverage in different scenarios and compared four scenarios: (a) current situation with two E57 type sirens; (b) replacing the existing sirens with two high-performance sirens; (c) one high-performance siren at the more central point; and (d) optimized network of smart sirens of the type Telegrafia Bono. The aim was to achieve a full coverage with a minimum of warning sirens. We could show that the current situation with two E57 type sirens fails to reach out to the whole population whereas the optimized network of smart sirens results in a better coverage. Therefore, a reconsideration of the existing warning system of civil protection with smart sirens could result in a better coverage and improved information of warning

The effect of microlevel and macrolevel signaling on learning with 360° videos

The application of 360° videos raised the attention of educators and researchers, as it appears to be an approachable option to mediate complete environments in educational settings. However, challenges emerge from the perspective of educational psychology. Learning irrelevant cognitive strains might be imposed because it is necessary to navigate through spherical material. However, these potential downsides could be compensated for using signaling techniques. In a two (macrolevel vs. no macrolevel signaling) × two (microlevel vs. no microlevel signaling) factorial between‐subjects design plus control group, 215 fifth‐and sixth‐grade students will watch a 360° video about visual and behavioral characteristics of animals. Learning outcomes, cognitive load, disorientation, and presence will be investigated. It is expected that macrolevel signaling will enhance learning and presence and reduce cognitive load and disorientation. Microlevel signaling will have comparable advantages, but these effects will be more pronounced when macrolevel signaling is implemented

Successful learning with whiteboard animations – a question of their procedural character or narrative embedding?

Although whiteboard animations are increasingly used for educational purposes, there is little empirical evidence as to why such animations can enhance learning. To specify essential elements, their dynamic visual presentation, as well as their narrative embedding, were found to be theortically important. In a first Experiment (N = 133) with a 2 (presentation mode: static pictures vs. progressive drawing) x 2 (narrative context: with vs. without a narrative) between-subject factorial design, motivational, cognitive, affective variables, as well as learning outcomes, of secondary school students were measured. Results revealed that progressive drawing, as well as a narrative context, are mostly associated with an increase in learning-relevant variables. In a second experiment with the same sample and the same experimental design but a different whiteboard animation, results from Experiment 1 generalize to another learning content. Again, a progressive drawing, as well as a narrative context within whiteboard animation, fostered learning relevant variables as well as learning outcomes. Results are discussed considering the cognitive theory of multimedia learning, the contiguity effect as well as the instructional design theory of anchored instruction

Analysing the Relationship Between Mental Load or Mental Effort and Metacomprehension Under Different Conditions of Multimedia Design

Cognitive load theory assumes effort may only lead to comprehension if the material-induced load leaves enough resources for learning processes. Therefore, multimedia materials should induce as little non-relevant load as possible. Metacognition research assumes that learners tap into their memory processes to generate a mental representation of their comprehension to regulate learning. However, when judging their comprehension, learners need to make inferences about actual understanding using cues such as their experienced mental load and effort during learning. Theoretical assumptions would assume both to affect understanding and its metacognitive representation (metacomprehension). However, the question remains how perceived effort and load are related to metacomprehension judgments while learning with multimedia learning material. Additionally, it remains unclear if this varies under different conditions of multimedia design. To better understand the relationship between perceived mental load and effort and comprehension and metacomprehension under different design conditions of multimedia material, we conducted a randomised between-subjects study (N = 156) varying the design of the learning material (text-picture integrated, split attention, active integration). Mediation analyses testing for both direct and indirect effects of mental load and effort on metacomprehension judgments showed various effects. Beyond indirect effects via comprehension, both mental load and effort were directly related to metacomprehension, however, this seems to vary under different conditions of multimedia design, at least for mental effort. As the direction of effect can only be theoretically assumed, but was not empirically tested, follow-up research needs to identify ways to manipulate effort and load perceptions without tinkering with metacognitive processes directly. Despite the limitations due to the correlative design, this research has implications for our understanding of cognitive and metacognitive processes during learning with multimedia

When do customers get what they expect? Understanding the ambivalent effects of customers’ service expectations on satisfaction

Extant research established that customers’ expectations play an ambivalent role in the satisfaction formation process: While higher expectations are more difficult to meet and thus cause dissatisfaction, they simultaneously increase satisfaction via customers’ perceived performance owing to a placebo effect. However, to date, knowledge is scarce on the question under which conditions either the positive or negative effect of expectations on satisfaction prevails. Building on information processing theory, the authors hypothesize that an essential contingency of the indirect, placebo-based effect is the degree to which customers are able and motivated to process a service experience. Three studies with a total of over 4,000 customers in different service contexts provide strong evidence for this hypothesis. Thus, managers are well advised to provide a realistic or even understated prospect if the service context favors customers’ ability or motivation to evaluate. Conversely, if customers are neither able nor motivated to evaluate the service, increasing customer expectations represents a viable strategy to enhance satisfaction. Relatedly, if customers hold low service expectations, managers should foster customers’ ability and motivation to evaluate the service. In contrast, if service expectations are high, managers may benefit from reducing the likelihood that customers overly focus on the service performance

The Cognitive-Affective-Social Theory of Learning in digital Environments (CASTLE)

For a long time, research on individuals learning in digital environments was primarily based on cognitive-oriented theories. This paper aims at providing evidence that social processes affect individual learning with digital materials. Based on these theories and empirical results, a social-processes-augmented theory is suggested: the Cognitive-Affective-Social Theory of Learning in digital Environments (CASTLE). This CASTLE postulates that social cues in digital materials activate social schemata in learners leading to enhanced (para-)social, motivational, emotional, and metacognitive processes. To substantiate this theory, socio-cognitive theories are used, which predict social influences on learning with digital materials. Besides, previous empirical findings are presented assuming that with a rising number of social cues in digital materials, the influence of social processes increases. Finally, consequences regarding the design of digital learning media are discussed