Self-regulated learning depends on task difficulty and on learners’ resources and cognitive load, as described by an inverted U-shaped relationship in Seufert’s (2018) model: for easy tasks, resources are high and load is low, so there is no need to regulate, whereas for difficult tasks, load is too high and resources are too low to regulate. Only at moderate task difficulty do learners regulate, as resources and load are in equilibrium. The purpose of this study is to validate this model, i.e., the inverted U-shaped relationship between task difficulty and self-regulatory activities, as well as learner resources and cognitive load as mediators. In the within-subject study, 67 participants reported their cognitive and metacognitive strategy use for four exams of varying difficulty. For each exam task difficulty, cognitive load, and available resources (such as prior knowledge, interest, etc.) were assessed. Multilevel analysis revealed an inverted U-shaped relationship between task difficulty and the use of cognitive strategies. For metacognitive strategies, only a linear relationship was found. Increasing cognitive load mediated these relationship patterns. For learner resources we found a competitive mediation, indicating that further mediators could be relevant. In future investigations a broader range of task difficulty should be examined.

As domestic service robots become more prevalent and act autonomously, conflicts of interest between humans and robots become more likely. Hereby, the robot shall be able to negotiate with humans effectively and appropriately to fulfill its tasks. One promising approach could be the imitation of human conflict resolution behaviour and the use of persuasive requests. The presented study complements previous work by investigating combinations of assertive and polite request elements (appeal, showing benefit, command), which have been found to be effective in HRI. The conflict resolution strategies each contained two types of requests, the order of which was varied to either mimic or contradict human conflict resolution behaviour. The strategies were also adapted to the users’ compliance behaviour. If the participant complied after the first request, no second request was issued. In a virtual reality experiment ( $$N = 57$$N=57 ) with two trials, six different strategies were evaluated regarding user compliance, robot acceptance, trust, and fear and compared to a control condition featuring no request elements. The experiment featured a human-robot goal conflict scenario concerning household tasks at home. The results show that in trial 1, strategies reflecting human politeness and conflict resolution norms were more accepted, polite, and trustworthier than strategies entailing a command. No differences were found for trial 2. Overall, compliance rates were comparable to human-human-requests. Compliance rates did not differ between strategies. The contribution is twofold: presenting an experimental paradigm to investigate a human-robot conflict scenario and providing a first step to developing acceptable robot conflict resolution strategies based on human behaviour.

One challenge while learning scientific concepts is to select relevant information and to integrate different representations of the learning content into one coherent mental model. Virtual reality learning environments (VRLEs) offer new possibilities to support learners and foster learning processes. Whether learning in VR is successful, however, depends to a large extent on the design of the VRLE and the learners themselves. Hence, adding supportive elements in VRLEs, such as annotations, might facilitate the learning process by guiding attention and supporting the selection of relevant information. Additionally, the mapping of pictorial and verbal information is eased by these annotations. The beneficial effect of annotations is highly dependent on learners' intrinsic motivation as intrinsic motivation while learning also affects the information selection and visual search patterns. In our experimental study (N = 61), we compared two conditions: learning in a VRLE with or without annotations. We measured the learning outcome on three different levels (knowledge, comprehension, and application). Additionally, we investigated intrinsic motivation as a moderator for the effect of annotations on learning outcome. We found no significant main effect of annotations on learning outcome. The moderating effect of intrinsic motivation for annotations on the overall learning outcome was significant. Our results imply that learners are either intrinsically motivated or need additional support by annotations as these support the selection of relevant information in the VRLE and therefore enable them to learn successfully. Which type or quantity of annotations supports learning processes best needs to be explored in future research.