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This paper explores whether students' learning outcomes can be improved through the use of self-assessment rubrics. Students on a computer programming module in a Higher Education Institution were required to complete a self-assessment using the same rubric as the assessors. Observing discrepancies between the grades the students were receiving, and the grades the students thought they should be receiving, the lecturers made improvements to the pedagogical approaches taken for some elements of the course by changing the format and focus of classroom activities. This resulted in both improved grades and improved self-regulation by students. The process was facilitated through a system created by the authors of the paper called SAFE (Self-Assessment Feedback and Evaluation Learner Lifecycle), which greatly enhances the learner feedback lifecycle of an assignment. The research corroborates existing studies around the importance of revisiting feedback both for assessor and student.

The algorithm presented in this paper is designed to detect people in real-time from 3D footage for use in Augmented Reality applications. Techniques are discussed that hold potential for a detection system when combined with stereoscopic video capture using the extra depth included in the footage. This information allows for the production of a robust and reliable system. To utilise stereoscopic imagery, two separate images are analysed, combined and the human region detected and extracted. The greatest benefit of this system is the second image, which contains additional information to which conventional systems do not have access, such as the depth perception in the overlapping field of view from the cameras. We describe the motivation behind using 3D footage and the technical complexity of human detection. The system is analysed for both indoor and outdoor usage, when detecting human regions. The developed system has further uses in the field of motion capture, computer gaming and augmented reality. Novelty comes from the camera not being fixed to a single point. Instead, the camera is subject to six degrees of freedom (DOF). In addition, the algorithm is designed to be used as a first filter to extract feature points in input video frames faster than real-time.

This paper presents a method for non-computationally expensive automatic alignment of cameras that utilises stereoscopic imagery separated at varying distances just below that of the intraocular distance. Here, automatic stereoscopic alignment in real-time is a non-trivial process that relies on calculating the best virtual alignment of camera lenses through image overlaying. This is important as retail 3D camera lenses are typically not sufficiently calibrated for accurate estimates of distance. The alignment of images allows the filtering of background objects and focuses on points of interest. Imprecision in camera lens calibration leads to problems with the required alignment of images and consequent filtering of background objects. The algorithm presented in this paper allows virtual calibration within non-calibrated cameras to provide a real-time filtering of images and the consequent identification of points of interest. The proposed method is capable of generating the best alignment setup at a reasonable computational expense in natural environments with partial background occlusion.

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