Grading computer graphics programming assessments and generating formative and summative feedback can require significant effort on the part of human experts.Since these assessments generate visual outputs that can be static or animated, determining correctness may be subjective.For feedback to be effective, it must be delivered in a timely manner.This can be a challenge for introductory computer graphics-based courses since cohort size can be substantial, errors in visual output can be subtle, and causes of errors are often not obvious.In this paper, we explore the feasibility of an automated system for marking visual output and providing program implementation feedback for learners in an introductory computer graphics-based design course in three short programming assessments, including static and animated scenes.To assess the effectiveness of our approach, we compare the marks generated by our tool with those assigned by a human expert.We show that it is possible to automate marking, providing both a grade based on the visual output and formative feedback on source code in the style of a human marker.This can improve objective consistency, grade reproducibility, and reduce marking time, enabling a course to scale to support large cohorts without the need for more resourcing for human markers.We describe lessons learnt and potential pitfalls to assist educators with introducing automated marking for their courses.Finally, we identify areas for future refinement and development of our automated system.

Teaching large cohorts in upper-level computing courses is challenging, as providing rapid feedback and marking at scale is difficult without significant resources. Many institutions lack funds to employ a large number of skilled markers or such markers are simply not available.

The domain of teaching medicine involves the mastery of many complex skills that almost always need to be performed in real life situations following very high professional standards. However, this training is not always possible for various reasons such as ethics, safety and costs. Virtual reality (VR) and augmented reality (AR) have started to be widely used as alternative medical teaching practices. Our proposed AR system works online, so no installation is required on a users device; it only requires a generic colour web-cam to track a pictorial AR tag which contains a hidden QR code. These QR codes contain data, such as the ID of a three-dimensional (3D) model, and merge it with text so that it can be used as both an identity and tag pattern in the AR marker. The system can then show the corresponding computer-generated 3D anatomical models of organs; relevant text information about the subject is displayed above the AR Tag. The tag is numerically encrypted and decrypted and detectable by shape and orientation. Different from other similar techniques, our AR Tag is both a bar-code and a template marker, QR code is used to load a previously setup website, and then the detail of that QR code is used as a template to identify the border and orientation of the marker. The system is thus faster and more robust that allows users to control and navigate the 3D environment by zooming in and out and rotating left and right. It is hoped that this virtual environment will help reduce the need for real-life surgical practice, instead of increasing intuition, the direct 3D perception of the human body and other 3D medical imaging data (mimesis). This system could even be further developed to present the framework of a patient's anatomy.