## 6 Conclusions and Future Developments  
We described applications of an image-based encoding model to capture immersive VR sessions for events in public or shared spaces, virtual classrooms or online experiences, producing compact and lightweight data (images) for interactive, remote visual inspection. The developed open-source PRISMIN framework did prove to be suitable for networked scenarios (online classrooms, distance learning VR, etc.) and could be particularly useful for remote analysts, teachers and other professionals to support learning assessment, spatial analysis and to easily detect interaction patterns emerging from large amounts of users consuming educational immersive VR environments (IVEs). Within IVEs targeting education (including online WebVR/XR scenes) the captured data can offer valid support for learning assessment as the students explore and interact with the immersive 3D environment. _Session Volumes_ offer simple accessories to volumetrically capture interaction states and attributes at runtime. Once deployed in a 3D scene (IVE) they encode users' states over time into lightweight and compact image atlases (\(QSA\)). Thanks to basic color-space mappings and layouts, small computational resources are used for encoding and decoding routines. Furthermore, the image-based approach allow direct manipulation of captured data on GPU hardware and by means of basic 2D image processing, including easier comparison between different datasets. _Interaction Prisms_ offered by the PRISMIN framework allow to flexibly define and customize how incoming user states are consumed and _refracted_ into image atlases. Advantages of the framework are highlighted storage-wise (QSA and saliency tables) and in terms of scalable approaches, including deployment of several session volumes in the IVE, tracking of custom attributes and expandable architectures for the Web. We also discuss how extents and bit-depth of such volumes may impact accuracy for spatial attributes (quantization). Figure 10: Basic image operations to extract proximity to target locations and performance over the session (portions of QSAs are shown for the sake of clarity). Image subtraction (**A**); temporal resampling (**B**) and normalization (**C**). The compactness of the image-based encoding allows for the integration of the PRISMA framework with distance learning technologies and remote analytics, performed by a common web browser. Regarding locomotion data for instance, we obtained compression ratios of 12.77% using QSA (image-based encoding) implemented in PRISMA, that outperforms other lossless encoding schemes like [38] reporting 16.10% as best scenario. Such a compression ratio is explained by the lexicographically sorted layout of QSA (see Section 3.2) that considerably reduce the size of encoded locations on the PNG image (see [23]). Furthermore, encoded 3D locations in our model do not introduce dependencies on previous values, thus can be accessed and decoded with ease. The image-based approach allows in fact offline or direct 2D image manipulation to extract, combine or compare user interactions (see Section 5.2). Such operations are indeed more complex to obtain when dealing with raw binary data, especially when such data is in a compressed form. The QSA layout also allows analysts, teachers and other professionals to observe patterns at first glance by just observing produced image atlases (human-readable). Regarding limitations, the model presents quantization errors affecting accuracy of 3D locations (more in general spatial attributes in the volume) introducing a controlled error--see Section 3.1.1. As discussed, such quantization error depends on volume extents and color bit-depth adopted for QSA. The presented problem can be mitigated (or solved) by arranging multiple session volumes in the IVE, focusing on smaller areas to capture fine-grained and more accurate spatial data.