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Instructional support can be implemented in learning environments to pseudo-modify the difficulty or time intensity of items presented to persons. This support can affect both the response accuracy of persons towards items as well as the time persons require to complete items. This study proposes a framework to model response time in learning environments as a function of instructional support. Moreover, it explores the effect of instructional support on response time in assembly tasks training using Virtual Reality. Three models are fitted with real-life data collected by a project that involves both industry and academic partners from Belgium. A Bayesian approach is followed to implement the models, where the Bayes factor is used to select the best fitting model.

Over the last decade, Higher Education has focused more of its attention toward soft skills compared to traditional technical skills. Nevertheless, there are not many studies concerning the relation between the courses followed within an academic program and the development of soft skills. This work presents a practical approach to model the effects of courses on soft skills proficiency. Multiple Membership Ordinal Logistic Regression models are trained with real data from students of the 2021, 2022, and 2023 cohorts from the general engineering program in a French Higher Education institution. The results show that attending a postgraduate course in average increases the odds of being more proficient in terms of soft skills. Nonetheless, there is considerable variability in the individual effect of courses, which suggest there can be huge differences between courses. Moreover, the data also suggest great dispersion in the students' initial soft skill proficiency.

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.