Sensor-based methods such as eye tracking are promising for user modeling in adaptive educational systems, as they enable continuous, fine-grained assessment of learners’ learning processes. However, implementing gaze-based user models requires selecting from a large set of available gaze measures, posing a challenge for adaptive-system designers. To inform this design process, we conduct an exploratory analysis of the associations between gaze measures, learner engagement, and learning outcomes in a video-based learning context. An analysis of data collected from 18 learners indicates that fixation dispersion and fixation rate on active areas of interest are indicators of engagement, while pupil diameter is an indicator of learning outcomes. To support further research on gaze-based user modeling and adaptation, we introduce EnGazement, a dataset comprising over 1.3 million processed eye-tracking data points, enriched with subjective and objective measures of engagement and learning.

Hybrid board games (HBGs) augment their analog origins digitally (e.g., through apps) and are an increasingly popular pastime activity. Continuous world and character development and customization, known to facilitate engagement in video games, remain rare in HBGs. If present, they happen digitally or imaginarily, often leaving physical aspects generic. We developed DungeonMaker, a fabrication-augmented HBG bridging physical and digital game elements: 1) the setup narrates a story and projects a digital game board onto a laser cutter; 2) DungeonMaker assesses player-crafted artifacts; 3) DungeonMaker’s modified laser head senses and moves player- and non-player figures, and 4) can physically damage figures. An evaluation (n = 4 × 3) indicated that DungeonMaker provides an engaging experience, may support players’ connection to their figures, and potentially spark novices’ interest in fabrication. DungeonMaker provides a rich constellation to play HBGs by blending aspects of craft and automation to couple the physical and digital elements of an HBG tightly.

Personal fabrication is made more accessible through repositories like Thingiverse, as they replace modeling with retrieval. However, they require users to translate spatial requirements to keywords, which paints an incomplete picture of physical artifacts: proportions or morphology are non-trivially encoded through text only. We explore a vision of in-situ spatial search for (future) physical artifacts, and present ShapeFindAR, a mixed-reality tool to search for 3D models using in-situ sketches blended with textual queries. With ShapeFindAR, users search for geometry, and not necessarily precise labels, while coupling the search process to the physical environment (e.g., by sketching in-situ, extracting search terms from objects present, or tracing them). We developed ShapeFindAR for HoloLens 2, connected to a database of 3D-printable artifacts. We specify in-situ spatial search, describe its advantages, and present walkthroughs using ShapeFindAR, which highlight novel ways for users to articulate their wishes, without requiring complex modeling tools or profound domain knowledge.

Interacting with a group of people requires to direct the attention of the whole group, thus requires feedback about the crowd’s attention. In face-to-face interactions, head and eye movements serve as indicator for crowd attention. However, when interacting online, such indicators are not available. To substitute this information, gaze visualizations were adapted for a crowd scenario. We developed, implemented, and evaluated four types of visualizations of crowd attention in an online study with 72 participants using lecture videos enriched with audience’s gazes. All participants reported increased connectedness to the audience, especially for visualizations depicting the whole distribution of gaze including spatial information. Visualizations avoiding spatial overlay by depicting only the variability were regarded as less helpful, for real-time as well as for retrospective analyses of lectures. Improving our visualizations of crowd attention has the potential for a broad variety of applications, in all kinds of social interaction and communication in groups.

In online lectures, showing an on-screen instructor gained popularity amidst the Covid-19 pandemic. However, evidence in favor of this is mixed: they draw attention and may distract from the content. In contrast, using signaling (e.g., with a digital pointer) provides known benefits for learners. But effects of signaling were only researched in absence of an on-screen instructor. In the present explorative study, we investigated effects of an on-screen instructor on the division of learners´ attention; specifically, on following a digital pointer signal with their gaze. The presence of an instructor led to an increased number of fixations in the presenter area. This did neither affect learning outcomes nor gaze patterns following the pointer. The average distance between the learner's gaze and the pointer position predicts the student's quiz performance, independent of the presence of an on-screen instructor. This can also help in creating automated immediate-feedback systems for educational videos.

Webcam-based eye-tracking promises easy and quick data collection without the need for specific or additional eye-tracking hardware. This makes it especially attractive for educational research, in particular for modern formats, such as MOOCs. However, in order to fulfill its promises, webcam-based eye tracking has to overcome several challenges, most importantly, varying spatial and temporal resolutions. Another challenge that the educational domain faces especially, is that typically individual students are of interest in contrast to average values. In this paper, we explore whether an attention measure that is based on eye movement synchronicity of a group of students can be applied with unreliably-sampled data. Doing so we aim to reproduce earlier work that showed that, on average, eye movement synchronicity can predict performance in a comprehension quiz. We were not able to reproduce the findings with unreliably-sampled data, which highlights the challenges that lie ahead of webcam-based eye tracking in practice.

The accessibility of tools to model artifacts is one of the core driving factors for the adoption of Personal Fabrication. Subsequently, model repositories like Thingiverse became important tools in (novice) makers' processes. They allow them to shorten or even omit the design process, offloading a majority of the effort to other parties. However, steps like measurement of surrounding constraints (e.g., clearance) which exist only inside the users' environment, can not be similarly outsourced. We propose Mix&Match a mixed-reality-based system which allows users to browse model repositories, preview the models in-situ, and adapt them to their environment in a simple and immediate fashion. Mix&Match aims to provide users with CSG operations which can be based on both virtual and real geometry. We present interaction patterns and scenarios for Mix&Match, arguing for the combination of mixed reality and model repositories. This enables almost modelling-free personal fabrication for both novices and expert makers.