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Background: Augmented reality (AR) is receiving increasing interest as a tool to create an interactive and motivating learning environment. Yet, it is unclear how instructional support affects performance in AR. Objectives: This study sought to explore how varying the instructional support in AR can affect performance-related behaviours of students with low cognitive abilities during assembly work. Methods: A total of 90 Belgian secondary school students repeatedly executed four different realistic assembly tasks. Three levels of instructional support (low, medium, and high) in AR as well as a control condition with paper instructions with a high level of detail were systematically varied across tasks and participants. Results and Conclusions: Multilevel regression analyses showed that AR instructions yielded lower assembly times and a lower perceived physical effort than paper instructions. Additionally, participants perceived tasks as less complex when given AR instructions with a high or a medium level of detail than when given a low level of detail. No effects of instructional support were established for other performance-related behaviours, namely necessary assistance, error-making, cognitive load, competence frustration, and stress. Effect sizes were small, at least among the instructional support conditions studied, yielding a limited base for adaptivity. Presumably, tailoring the instructional support in AR is only beneficial for highly complex tasks. The results might be useful for the design and implementation of AR in educational settings.

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.

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.