## 1 Introduction  
In recent years, consumer-level head-mounted displays (HMDs) are being adopted more to deploy engaging and educational immersive experiences, potentially offering a high sense of presence to final users. The demand for such technologies is growing, in particular, they are starting to have an impact within the education sector (see [1, 2]), due to their costs becoming more affordable compared to two or three years ago. Recent findings prove consumers tend to see the opportunity in HMDs to _explore_ virtual places and, in general, a positive attitude towards hedonic applications such as panoramic content and immersive VR games [3]. During a limited amount of time, users (such as visitors of an exhibit, virtual students, etc.) explore and perform interactions in a 3D virtual environment. Given an immersive virtual environment (IVE), an in-depth investigation of users' sessions--including for instance spatial behaviors, visual attention and so on--can be really useful to understand users' interaction patterns and discover unexpected attention toward specific scene elements. Capturing whole VR sessions and rich user states can in fact provide valuable insights to analysts regarding spatial analysis or specific assessments. Within the education sector, _recording_ immersive VR sessions as students interact with the 3D scene can provide valid support for learning assessment [4]. Regarding distance learning technologies and education, when such records can be easily exchanged in networked contexts, they enable online teachers or instructors to remotely investigate learners' interactions. In general, recording per-user fine-grained data (interaction states) is a strong requirement to understand an immersive VR session from both quantitative and qualitative perspectives. For analysts, having tools to directly map and playback captured records (such as locomotion data) can support their interpretation. Furthermore, within methodical processes such as assurance of learning (AoL) [5] remote teachers/instructors could be interested into inspecting learners' sessions while the VR applications, such as desktop-based or immersive web-apps, are up and running. In networked scenarios (e.g., virtual classrooms) the exchange of such records between students and teachers endpoints, especially over the Internet, can be challenging or involve computationally-intensive routines (for instance, decoding compressed data). This is even more delicate if we take into account completely web-based tools--i.e., using a common web browser (desktop or mobile) without any additional software required by users to inspect the data. Furthermore, compressed interaction states transported between peers can not be manipulated (e.g., interactively edited) until the client (analyst) decompress the data into something that can be analyzed and inspected at runtime. A single student session may contain large amounts of interaction states and attributes captured over time, such as gaze, visual attention, locomotion or more complex data, thus posing serious challenges for exchanging these states in networked contexts. In this paper, we investigate and present a few applications of a developed image-based encoding model and the implementation of a framework (called \"PRISMIN\") that provides scalable, compact methods and accessories to capture, compare and manipulate user VR sessions and interaction states in networked contexts. Tracked learners attributes are encoded into special images and layouts offering:  
* lightweight transmission of captured data in networked environments and over Internet connections. * small computational load for encoding/decoding routines. * direct manipulation of complex records on GPU hardware and offline image processing. * basic image-based operations to compare or assess specific patterns. We present a few results obtained using the framework, encoding previously recorded locomotion data during public VR installations and applying it to networked scenarios (virtual classrooms).