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Industrial settings will be characterized by far-reaching production automation brought about by advancements in robotics and artificial intelligence. As a consequence, human assembly workers will need to adapt quickly to new and more complex assembly procedures, which are most likely to increase cognitive workload, or potentially induce overload. Measurement and optimization protocols need to be developed in order to be able to monitor workers’ cognitive load. Previous studies have used electroencephalographic (EEG, measuring brain activity) and electrooculographic (EOG, measuring eye movements) signals, using basic computer-based static tasks and without creating an experience of overload. In this study, EEG and EOG data was collected of 46 participants performing an ecologically valid assembly task while inducing three levels of cognitive load (low, high and overload). The lower individual alpha frequency (IAF) was identified as a promising marker for discriminating between different levels of cognitive load and overload. ispartof: Applied Ergonomics: human factors in technology and society vol:102 pages:1-9 ispartof: location:England status: Published online

Based on current societal transitions and in the context of lifelong learning, both upper secondary education, higher education and vocational training are invited to think about how to enable people, at any stage of their life, to take part in stimulating learning experiences. The expectancy to be present at one core location is getting more and more difficult in this (inter)national society. In addition, the student population is changing and balancing work and family life becomes an important point of priority. Therefore, it is important to make education less dependent on location and time and improve flexibility within the learning trajectory (Lakhal, De Sherbrooke, & Bateman, 2017). Next to this, there are growing insights about the need to collaborate. ispartof: pages:310-319 ispartof: Proceedings of the European Distance and E-Learning Network pages:310-319 ispartof: EDEN 2019 Annual Conference - Connecting through Educational Technology. To produce effective learning environments. location:Bruges, Belgium date:16 Jun - 19 Jun 2019 status: published

Computer vision has shown great accomplishments in a wide variety of classification, segmentation and object recognition tasks, but tends to encounter more difficulties when tasks require more contextual assessment. Measuring the engagement of students is an example of such a complex task, as it requires a strong interpretative component. This research describes a methodology to measure students’ engagement, taking both an individual (student-level) and a collective (classroom) approach. Results show that students’ individual behaviour, such as note taking or hand-raising, is challenging to recognise, and does not correlate with students’ self reported engagement. Interestingly, students’ collective behaviour can be quantified in a more generic way using measures for students’ symmetry, reaction times and eye-gaze intersections. Nonetheless, the evidence for a connection between these collective measures and engagement is rather weak. Although this study does not succeed in providing a proxy of students’ self-reported engagement, our approach sheds light on the needs for future research. More concretely, we suggest that not only the behavioural, but also the emotional and cognitive component of engagement should be captured. ispartof: Mathematics vol:9 issue:3 pages:1-20 status: Published online

This study aims to compare the effects of oral, paper and augmented reality (AR) instructions during assembly tasks. 44 assembly operators, the majority of which have cognitive or motor disabilities, participate in a within-subjects experiment in which they perform three different assembly tasks. In doing so, the different instructional media are alternated. The outcome variables under study are productivity, quality, stress, help-seeking behaviour, perceived complexity, physical effort and competence frustration. In addition, this study examines the effects of characteristics of operators by taking into account their cognitive skills and experience with each task type as predictor variables. Finally, as operators perform one of the assembly tasks four times, learning curves are studied as well. Results of a multilevel analysis show that operators using AR instructions outperform their colleagues using more traditional instructional media with regard to quality. In addition, AR instructions give rise to less help-seeking behavior as compared to oral instructions. Finally, there is some evidence for AR instructions to yield a lower perceived complexity than oral instructions. Furthermore, the results indicate that the operators’ cognition and experience play a significant role in terms of many outcome variables. Learning curves reveal that operators using AR instructions experience less stress from the first assembly attempt onwards, whereas for operators using oral or paper instructions, it takes several attempts before being equally relaxed. ispartof: International Journal Of Human-Computer Studies vol:143C status: Published online

Cognitive load theory is one of the most influential theoretical explanations of cognitive processing during learning. Despite its success, attempts to assess cognitive load during learning have proven difficult. Therefore, in the current study, students’ self-reported cognitive load after the problem- solving process has been combined with measures of physiological data, namely, electrodermal activity (EDA) and skin temperature (ST) during the problem-solving process. Data was collected from 15 students during a high and low complex task about learning and teaching geometry. This study first investigated the differences between subjective and physiological data during the problem- solving process of a high and low complex task. Additionally, correlations between subjective and physiological data were examined. Finally, learning behavior that is retrieved from log-data, was related with EDA. Results reveal that the manipulation of task complexity was not reflected by physiological data. Nevertheless, when investigating individual differences, EDA seems to be related to mental effort. ispartof: Frontline Learning Research vol:7 issue:2 pages:57-74 status: published

This exploratory study challenges the current practices in cognitive load measurement by using multichannel data to investigate cognitive load affordances during online complex problem solving. Moreover, it is an attempt to investigate how cognitive load is related to strategy use. Accordingly, in the current study a well- and an ill-structured problem were developed in a virtual learning environment. Online support was provided. Participants were 15 students from the teacher training program. This study incorporated subjective measurements of students’ cognitive load (i.e., intrinsic, extraneous, germane load and their mental effort) combined with physiological data containing galvanic skin response (GSR) and skin temperature (ST). A first aim was to investigate whether there was a significant difference for the subjective measurements, physiological data and consultation of support between the well- and ill-structured problem. Secondly this study investigated how individual differences of subjective measurements are related to individual differences of physiological data and consultation of support. Results reveal significant differences for intrinsic load, mental effort between a well- and ill-structured problem. Moreover, when investigating individual differences, findings reveal that GSR might be related to mental effort. Additionally, results indicate that cognitive load influences strategy use. Future research with larger sample sizes should verify these findings in order to have more insight into how we can measure cognitive load and how its related to self-directed learning. These insights should allow us to provide adaptive support in virtual learning environments. ispartof: pages:61-70 ispartof: https://dl.acm.org/citation.cfm?doid=3303772.3303778 pages:61-70 ispartof: 9th International Conference on Learning Analytics & Knowledge (LAK19) location:Tempe (AZ), USA date:4 Mar - 8 Mar 2019 status: Published online