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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.

In this paper, we examine whether early warning signals from accounting courses (such as early engagement and early formative performance) are predictive of first-year progression outcomes, and whether this data is more predictive than personal data (such as gender and prior achievement). Using a machine learning approach, results from a sample of 609 first-year students from a continental European university show that early warnings from accounting courses are strongly predictive of first-year progression, and more so than data available at the start of the first year. In addition, the further the student is along their journey of the first undergraduate year, the more predictive the accounting engagement and performance data becomes for the prediction of programme progression outcomes. Our study contributes to the study of early warning signals for dropout through machine learning in accounting education, suggests implications for accounting educators, and provides useful pointers for further research in this area.