A distinct feature of educational games using augmented reality (AR) is that the game is played through physically interacting with the environment, whereas physical interaction is typically rather limited in other digital games. Understanding and performing the interactive game mechanics can be cognitively demanding. Adding pre-training could help students manage cognitive load during in-game performance. However, traditional approaches of pre-training (eg, paper-based or video sequences) might not be sufficient, given the crucial role of physical interactions in educational AR games. In the present study, primary and early secondary school students (N = 255) were randomly assigned to an active pre-training, which involved students practising the movements needed in the game or a passive pre-training, where students watched a video explaining the game. The aim was to investigate whether active pre-training reduces students' cognitive load and improves in-game performance. It was also examined whether these effects were dependent on students' visuospatial working memory capacity (WMC). Results showed no significant differences between the conditions regarding students' cognitive load and in-game performance. However, visuospatial WMC predicted students' dropout of the game. This suggests that observing the movements in the passive pre-training might be similarly effective as enacting the movements. Nevertheless, more research is needed to gain a better understanding of how different levels of WMC impact learning with educational AR games and how students with low visuospatial WMC can be efficiently supported.Practitioner notes What is already known about this topic Working memory capacity influences students' ability to successfully perform complex tasks. Not every learning medium is equally effective for students with different levels of working memory capacity. Pre-training in educational games can support learning and increases the academic performance of students. Educational games with AR can be cognitively demanding, since students need to understand the interactions with the used device in addition to the game content and mechanics. It is unclear how to sufficiently support students in AR environments. What this paper adds Both active and passive pre-training are equally effective. Students with lower working memory capacity were more likely to drop out during the educational AR game than students with high working memory capacity. Implications for practice and/or policy Both practising and watching movements may be helpful for students to learn the relevant game mechanics to interact with the AR environment. There are students who do not optimally benefit when AR games are implemented in education and may need additional support.

Educational games are designed to increase students’ motivation and persistence. Nevertheless, student dropout is a very common issue that many game interventions in education face, which can hamper students’ learning opportunities. Dropout can be manifested in different ways for different reasons, for instance quitting due to lack of interest or failing due to lack of abilities to succeed in the game. Thus far, little is known about the underlying factors that lead to student dropout. The present paper (N = 272 early secondary school students) investigates different types of dropout and whether students’ working memory capacity and situational interest are predictive of the likelihood of dropping out during an educational game using augmented reality (AR). Moreover, log data were used to explore differences in students’ in-game behavior (i.e., number, type, and patterns of mistakes) while playing the game. The results indicate that 16.9% of the students dropped out during the game, either because they quit or because they failed to reach the last level. Working memory capacity and situational interest were not predictive of students’ dropout. However, process maps of students’ in-game behavior showed that dropout students differed in the number of mistakes they made and also followed a different behavioral pattern than non-dropout students. In all, this study contributes to the knowledge base by revealing different types of dropout students who showed distinct behavioral patterns. More insights into these patterns could help to identify students at-risk of dropping out early on and to develop targeted interventions to prevent dropout.

Digital games are widely used in education to motivate students for science. Additionally, augmented reality (AR) is increasingly used in education. However, recent research indicates that these technologies might not be equally beneficial for students with different background characteristics. Moreover, students with different backgrounds may differ in their self‐efficacy and interest when playing games and this could lead to differences in performance. Given the increased use of games and immersive technologies in education, it is important to gain a better understanding of the effectiveness of games for different student groups. This study focused on the role of students' socio‐economic status (SES) and examined whether SES was associated with in‐game performance and whether interest and self‐efficacy mediated potential associations between SES and in‐game performance. Since log data are increasingly used to predict learning outcomes and can provide valuable insights into individual behaviour, in‐game performance was assessed with the use of log data. In total, 276 early secondary school students participated in this study. The results indicate that SES has no direct or indirect effect through self‐efficacy and interest on in‐game performance. However, a lower self‐efficacy increased the likelihood to drop out of the game. These findings suggest that students from different socio‐economic backgrounds are equally interested and self‐efficacious while playing the game and that their performance is not affected by their background. The affordances of AR as an immersive learning environment might be motivating enough to help mitigate possible SES differences in students. Practitioner notes What is already known about this topic Digital games are an effective tool to increase motivation and learning outcomes of students. Students' self‐efficacy and situational interest influence learning outcomes and in‐game performance. It is unclear whether digital games are equally effective for students with different socio‐economic status. What this paper adds Socio‐economic status (SES) of students does not affect in‐game performance. Students with different SES are equally interested and self‐efficacious. Lower self‐efficacy and lower school track influence the likelihood of dropout. Implications for practice and/or policy Socio‐economic status does not fortify the possible performance differences between students and games can be utilized as a learning tool that motivates all students equally. There are students who do not optimally benefit when games are implemented in education and who may need additional support.

BackgroundLearning analytics dashboards are increasingly being used to communicate feedback to learners. However, little is known about learner preferences for dashboard designs and how they differ depending on the self-regulated learning (SRL) phases the dashboards are presented (i.e., forethought, performance, and self-reflection phases) and SRL skills. Insight into design preferences for dashboards with different reference frames (i.e., progress, social, internal achievement and external achievement) is important because the effectiveness of feedback can depend upon how a learner perceives it.ObjectiveThis study examines workplace learner preferences for four dashboard designs for each SRL phase and how SRL skills relate to these preferences.MethodsSeventy participants enrolled in a chemical process apprenticeship program took part in the study. Preferences were determined using a method of adaptive comparative judgement and SRL skills were measured using a questionnaire. Preferences were tested on four dashboard designs informed by social and temporal comparison theory and goal setting theory. Multinomial logistic regressions were used to examine the relationship between dashboard preferences and SRL.Results and ConclusionsResults show that the progress reference frame is more preferred before and after task performance, and the social reference frame is less preferred before and after task performance. It was found that the higher the SRL skill score the higher the probability a learner preferred the progress reference frame compared to having no preference before task performance. The results are consistent with other findings, which suggest caution when using social comparison in designing dashboards which provide feedback. What is already known about this topic? Learning analytics dashboards use visualisations to provide feedback on learning tasks to optimise learning. Learners can better understand the meaning of their feedback if it is presented alongside a point of comparison, such as a prior level of performance, the performance level of their peers or how their performance level compares to an achievement goal. Learning analytics dashboards can support learning behaviours before, during, and after learners perform a task. Learners can acquire information to improve their learning via learning analytics dashboards, such as feedback on performance, which illustrates areas of a task learners are stronger and weaker at, which in turn can help inform future training efforts.What this paper adds? Workplace learners typically prefer dashboards which offer visualisations comparing their current performance level with past levels of performance. Comparison with peers is typically the least preferred point of comparison when offered in learning analytics dashboards. No clear preference emerged between reference frames containing assigned or self-set goals in dashboards presented before and after task performance.Implications for practice Designers should take into account learner preferences when designing learning analytics dashboard visualisations. Designers should consider presenting learning analytics dashboards before, during, and after task performance. Designers should gain more insight into how learners process learning analytic dashboards and act upon it.

Game-based learning has proven to be effective and is widely used in science education, but usually the heterogeneity of the student population is being overlooked. To examine the differential effects of game interventions in STEM (Science, Technology, Engineering and Mathematics) related subjects on diverse student groups, a meta-analysis has been conducted that included 39 studies that compared game-based learning interventions with traditional classrooms in primary and early secondary education. We found moderate positive effects on cognition (g = .67), motivation (g = .51), and behaviour (g = .93). Additionally, substantial heterogeneity between studies was found. Moderator analyses indicated that primary school students achieve higher learning outcomes and experience game interventions as more motivating than secondary school students, whereas gender did not have any moderating effect. There were too few studies reporting information on the remaining moderators (socioeconomic status, migration background, and special educational needs) to include them in a multiple meta-regression model. Therefore, we assessed their role by separate moderator analyses, but these results need to be interpreted with caution. Additional descriptive analyses suggested that game-based learning may be less beneficial for students with low socioeconomic status compared to students with high socioeconomic status.

To explore the benefits of scalable, interactive handheld Augmented Reality (AR) serious games for anatomical terminology learning, an AR learning game for German-Latin terminology of bone-structures and areas of the female pelvis was developed and evaluated. The evaluation of the game with 36 midwifery students was conducted with a focus on intrinsic motivation, usability and perceived usefulness of the app. The results indicated strong effect sizes of increased perceived competence and intrinsic motivation, and a medium effect size for increased pressure (Intrinsic Motivation Inventory) after completing the AR game. Furthermore, students perceived the usability of the game as "best imaginable" (contextualization of SUS scores) and strongly agreed that the app is very useful to them.

Digital games are widely used in education to motivate students for science. In parallel, the role of augmented reality (AR) in education is increasing. This paper introduces Marie’s ChemLab: a marker-less handheld AR application in which K-12 students learn about science. In Marie’s ChemLab, users perform interactions in their physical environment using virtual AR objects displayed on a handheld device. The utilized interaction concept is based on the TrainAR framework (Blattgerste et al, 2021), which originally was envisioned for procedural AR training and layered feedback mechanisms. The original framework has been enhanced to support gamified interactions with virtual objects, specifically for K-12 students. In total, 239 early secondary school students played the final application as part of a larger study. The usability score indicated marginally acceptable usability, and the application was successfully implemented in a classroom setting.