Students in undergraduate premedical anatomy courses may experience suboptimal and superficial learning experiences due to large class sizes, passive lecture styles, and difficultto-master concepts. This study introduces an innovative, hands-on activity for human musculoskeletal system education with the aim of improving students' level of engagement and knowledge retention. In this study, a collaborative learning intervention using the REFLECT (augmented reality for learning clinical anatomy) system is presented. The system uses the augmented reality magic mirror paradigm to superimpose anatomical visualizations over the user's body in a large display, creating the impression that she sees the relevant anatomic illustrations inside her own body. The efficacy of this proposed system was evaluated in a large-scale controlled study, using a team-based muscle painting activity among undergraduate premedical students (n = 288) at the Johns Hopkins University. The baseline knowledge and post-intervention knowledge of the students were measured before and after the painting activity according to their assigned groups in the study. The results from knowledge tests and additional collected data demonstrate that the proposed interactive system enhanced learning of the musculoskeletal system with improved knowledge retention (F (10,133) = 3.14, P < 0.001), increased time on task (F (1,275) = 5.70, P < 0.01), and a high level of engagement (F (9,273) = 8.28, P < 0.0001). The proposed REFLECT system will be of benefit as a complementary anatomy learning tool for students.Anat Sci Educ 12: 599-609.

Research in learning analytics and educational data mining has recently become prominent in the fields of computer science and education. Most scholars in the field emphasize student learning and student data analytics; however, it is also important to focus on teaching analytics and teacher

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Screen-based Augmented Reality (AR) systems can be built as a window into the real world as often done in mobile AR applications or using the Magic Mirror metaphor, where users can see themselves with augmented graphics on a large display. Such Magic Mirror systems have been used in digital clothing environments to create virtual dressing rooms, to teach human anatomy, and for collaborative design tasks. The term Magic Mirror implies that the display shows the users enantiomorph, i.e. the mirror image, such that the system mimics a real-world physical mirror. However, the question arises whether one should design a traditional mirror, or instead display the true mirror image by means of a non-reversing mirror? This is an intriguing perceptual question, as the image one observes in a mirror is not a real view, as it would be seen by an external observer, but a reflection, i.e. a front-to-back reversed image. In this paper, we discuss the perceptual differences between these two mirror visualization concepts and present a first comparative study in the context of Magic Mirror anatomy teaching. We investigate the ability of users to identify the correct placement of virtual anatomical structures in our screen-based AR system for two conditions: a regular mirror and a non-reversing mirror setup. The results of our study indicate that the latter is more suitable for applications where previously acquired domain-specific knowledge plays an important role. The lessons learned open up new research directions in the fields of user interfaces and interaction in non-reversing mirror environments and could impact the implementation of general screen-based AR systems in other domains.

Movement synchrony refers to the dynamic temporal connection between the motions of interacting people. The automatic measurement of movement synchrony is worth studying for social behavior analysis applications, for instance, in play therapy of children in the autism spectrum. Existing approaches based on motion energy analysis are strongly reliant on the region of interest, and thus limit the interaction between individuals, especially for highly engaging activities like play therapy. Inspired by action quality assessment, a task to assess how well an action has been performed, in this paper, we propose an end-to-end deep learning method to integrate the following major tasks: (1) the automatic assessment of children's performance in play therapy, and (2) the automatic estimation of movement synchrony between children and therapists, facilitated by an auxiliary task of intervention activity recognition. This multi-task paradigm generally improves the performance of our model across all tasks. Furthermore, when annotations are subjective, the typical exclusive annotation strategy may reduce tagging quality. As a result, we explored applying distribution learning to mitigate human bias in movement synchrony estimation. We allowed the second and third labels for each instance, namely the uncertainty-preserved annotation approach. We tested our method on Play Therapy 13 (PT13), a dataset collected from video recordings of play therapy interventions. The findings of the experiments indicated that our framework can accurately quantify movement synchronization and assess the quality of children's actions in play therapy. Moreover, the uncertainty-preserved annotation approach produced a comparable outcome to standard methods at a far reduced cost, demonstrating its efficacy in mitigating biases.