Preparing the next generation for an ever-changing environment is of utmost importance in Higher Education. Though cultural diversity is highly prevalent in modern societies, (implicit) biases, stereotypes and racism maintain a persistent yet mostly overlooked part of everyday life. Many students are not aware of their biases. Hence, it is imperative for them to undergo a critical self-reflection process about their beliefs and (unconscious) biases. To induce this process, raise awareness and confront them with potential (unconscious) biases and stereotypes, 404 university students in teacher training and vocational education completed an Implicit Association Test (IAT) on skin color before watching a lecture discussing biases and stereotypes. The preliminary results show a variety of reactions after taking the test, ranging from denial and anger to discomfort and surprise. We will discuss possibilities to support students in leveraging the resulting cognitive dissonance as an opportunity to begin their individual self-reflection process.

Behavior plays a crucial role in promoting sustainability. In semi-structured user-interviews, ten subjects provided information on their laundry habits. Most participants had little understanding of the unit kilowatt-hours and rarely used the eco-program. Some participants were unaware if their machine has an eco-option. They learned washing from their mothers or taught themselves. However, since the mothers did not know the eco-program, they could not pass on its use. Confronted with resource consumption of modern washing machines, some participants indicated plans for behavioral adjustment. The idea of displaying the price per washing cycle directly on the machine was evaluated positively by most participants. In conclusion, the majority had no conception of their electricity consumption or the eco-program. Results indicate that sustainability is hardly tangible for most people. Hence, making behavioral consequences accessible provides a good starting point for behavioral change.

We present a dynamic process model for workload, developedaccording to a conducted experiment, which recorded the pupildilation during an air traffic controller simulation. We describehow we built such a dynamic system based on the collecteddata. Logged events that happened in our simulation were usedas system input and the recorded pupil dilation as output. Af-terwards, we used the MATLAB system identification toolboxto identify the transfer function between input and output. Theidentified model is validated with a validation data set that hasbeen excluded from the identification process. Results showthat we are able to explain nearly 50% of the variance of therecorded pupil dilation data in the air traffic controller simu-lation. Moreover, the model explains some contrary results ofthe statistical analysis from our experiment.

Over the last years, we could observe increasing awareness of 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, we could identify five different sustainability personas: sustainability-oriented, open-minded, opportunistic, careless, and dismissive. We present characteristics of these personas, discuss the validation process, and investigate cognitive mechanisms of reflection in the context of sustainable behavior.

There are plenty of opportunities for life-long learning but peo-ple rarely seize them. Game elements are an increasingly pop-ular tool to keep students engaged in learning. But gamifica-tion only works when it is done properly. Here, we introducethe first principled approach to gamifying learning environ-ments. Our feedback mechanism rewards students’ efforts andstudy choices according to how beneficial they are in the longrun. The rewards are conveyed by game elements that we call“optimal brain points”. In our experiment, these optimal brainpoints significantly increased the proportion of participantswho attempted to learn a difficult skill, persisted through fail-ure, and succeeded to master it. Our method provides a princi-pled approach to designing incentive structures and feedbackmechanisms for both educational games and online courses.We are optimistic that this can help people overcome the moti-vational obstacles to self-directed life-long learning.

Advanced digitization and related information overload foster the prevalence of disruptive stimuli that constantly challenge people in learning and working contexts. The high variety of potential distractions increasingly reduces attention and subsequently minimizes capacities for productive study and work habits. Addressing this challenge, intelligent training software can leverage metacognitive feedback to support people with resisting situational temptations and keeping focused on meaningful tasks. While pilot studies indicate benefits to strengthen attention control skills, they also show hesitation towards using such software at all. Hence, the present study investigates users’ acceptance towards an AI-based software to promote attention control in more detail. A sample of 71 volunteers completed a survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) to identify factors determining people’s willingness to use the software. Results indicate that performance expectancy is an important predictor to consider when introducing the software to potential target groups.

There is major potential for using electroencephalography (EEG) in brain decoding that has been untapped due to the need for large amounts of data. Advances in machine learning have mitigated this need through data augmentation techniques, such as Generative Adversarial Networks (GANs). Here, we gauged the extent to which GANs can augment EEG data to enhance classification performance. Our objectives were to determine which classifiers benefit from GAN-augmented EEG and to estimate the impact of sample sizes on GAN-enhancements. We investigated three classifiers---neural networks, support vector machines, and logistic regressions---across seven sample sizes ranging from 5 to 100 participants. GAN-augmented EEG enhanced classification for neural networks and support vector machines, but not logistic regressions. Further, GAN-enhancements diminished as sample sizes increased---suggesting it is most effective with small samples, which may facilitate research that is unable to collect large amounts of data.

Based on the established framework of Cognitive Load Theory, the presented research focuses on the inspection of cognitive load factors in an interrupted learning task. The task itself is inspired from basic cognitive research and demands participants to learn abstract symbol combinations of varying complexity. In addition, they have to deal with interruptions while performing the task. Experimental results indicate the influence of task complexity on how interruptions effect learning performance. However, questions on underlying learner cognition persist, rising the need for a more in-depth way of examination. For this purpose, a cognitive model within the cognitive architecture ACT-R is developed to clarify cognitive processes and mechanisms within different conditions of the task. Preliminary results from a first model for the easy task condition already indicate some fit between human and model data. Modeling work continues with adjusting the current model and implementing a model for the difficult task condition.

Learning constitutes an essential part of human experienceover the life course. Independent of the domain, it ischaracterized by changes in performance. But what cognitivemechanisms are responsible for these changes and how dosituational features affect the dynamics? To inspect that inmore detail, this paper introduces a cognitive modelingapproach that investigates performance-related changes inlearning situations. It leverages the cognitive architectureACT-R to model learner behavior in an interrupted learningtask in two conditions of task complexity. Comparisons withthe original human dataset indicate a good fit in terms of bothaccuracy and reaction times. Although interruption effects aremore obvious in the human data, they are prevalent as well inthe model. Furthermore, the model can map the learningeffects, particularly in the easy task condition. Based on theexisting mapping of ACT-R module activity with fMRI data,simulated neural activity is computed to investigate underlyingcognitive mechanisms in more detail. The resulting evidenceconnects learning and interruption effects in both taskconditions with activation-related patterns to explain changesin performance.