This study recognizes the need for providing a tool to facilitate learning British Sign Language (BSL). Virtual reality coupled with gamification holds exciting prospects to accommodate this need. This paper presents a work-in-progress study that focuses on evaluating the impact of combining scaffolded instruction with gamification to design a 3D interactive game to support learning the BSL alphabet. The paper outlines the project motivations, its aims and objectives, the proposed research methodology, and the expected contributions to knowledge.

Authentic assessment is the concept of using creative learning experiences to assess students’ skills and knowledge in conditions that simulate reality. The importance of authentic assessment in higher education is recognized as it helps measure student performance and attainment of skills related to the expected learning outcomes of the degree program, they study that map the skills required in their profession in the related industry. There is also growing evidence that authentic assessment can enhance student engagement and improve the student experience and learning outcomes. VR coupled with gamification offers the authentic context for students to apply their knowledge and skills while solving real-world problems. This paper presents a work-in-progress study that focuses on the creation of an educational resource that effectively supports teaching and learning crime-scene analysis and evidenced-based inferences in Criminology through simulated crime scenes. The paper describes a participatory design process involving students and academics as co-creators of a VR game, the CrimOPS, it presents the initial prototype derived from this collaboration and preliminary study results.

This special session will provide an active forum for presentations and discussions on projects and vision related to the use of video games for addressing Sustainable Development Goals (UN SDG's). The projects are the outcome of the latest development game jam projects from the Games Immersion Conference (G.I.CON) 2021 hosted in April 2021 in iLRN Virtual Campus. All projects were developed by group of university students with reference point to address “Life of the Future” using dedicated UN SDG's. The panelists leading the event have extensive experience in immersive technologies and SDG's applied within games. The workshop will follow a mixed format. Including learning outcomes form latest student G.I.CON conference, project presentation, awards for best game concepts and hand-on activity for exploiting future trends behind student immersion engagement activities.

There is a growing literature addressing the potential of Serious Games (SGs) in supporting Deep Learning (DL). DL in pedagogical context goes beyond basic training as it encourages students to refine, broaden and deepen their understanding, develop advanced processes and skills, and apply learning to new, novel and authentic situations. The aim of this doctoral colloquium research is to effect and quantify DL using SGs. This paper presents a methodology that addresses this aim. This comprises of the development of a framework called the Game ELC+ to test the strength of adult teaching (andragogy) as well as facilitate DL. The Game ELC+ framework fuses the Octalysis Framework, Bloom's Taxonomy, Cognitive Theory of Multimedia Learning and Ruskov's four evidences of DL. A use case of judgment and decision making in recruitment and selection - through a SG build following the Game ELC+ is proposed and tested with users.

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. This model is used to the representation of motion in image sequences by initialising a suitable segmentation. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.

Automatic sports video segmentation is a fast growth area of research in the visual information retrieval field. This paper presents a semi-parametric algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus performs a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental results shows that the proposed football video segmentation algorithm performs with high accuracy.

We propose the design of a real-time system to recognize and interpret hand gestures. The acquisition devices are low cost 3D sensors. 3D hand pose segmentation, characterization and tracking will be implemented using the growing neural gas (GNG) structure. The capacity of the system to obtain information with a high degree of freedom allows the encoding of many gestures and a very accurate motion capture. The use of hand pose models combined with motion information provided with GNG permits to deal with the problem of the hand motion representation. A natural interface applied to a virtual mirror writing system and a module to estimate hand pose have been designed to demonstrate the validity of the system.