Instructors who teach digital literacy skills are increasingly faced with the challenges that come with larger student populations and online courses. We asked an educator how we could support student learning and better assist instructors both online and in the classroom. To address these challenges, we discuss how behavioral signals collected from eye tracking and mouse tracking can be combined to offer predictions of student performance. In our preliminary study, participants completed two image masking tasks in Adobe Photoshop based on real college-level course content. We then trained a machine learning model to predict student performance in each task based on data from other students, as a step towards offering automated student assistance and feedback to instructors. We reflect on the challenges and scalability issues to deploying such a system in-the-wild, and present some guidelines for future work.

Share on A gaze-based study design to explore how competency evolves during a photo manipulation task Authors: Nora Castner Human-Computer Interaction/ Wilhelm-Schickard-Institute, University of Tübingen, Germany Human-Computer Interaction/ Wilhelm-Schickard-Institute, University of Tübingen, GermanyView Profile , Bela Umlauf Human - Computer Interaction Group, University of Tübingen, Germany Human - Computer Interaction Group, University of Tübingen, GermanyView Profile , Ard Kastrati Computer Engineering and Networks Laboratory, ETH Zurich, Switzerland Computer Engineering and Networks Laboratory, ETH Zurich, SwitzerlandView Profile , Martyna Beata Płomecka Methods of Plasticity Reasearch, University of Zurich, Switzerland Methods of Plasticity Reasearch, University of Zurich, SwitzerlandView Profile , William Schaefer University of Texas at San Antonio, United States University of Texas at San Antonio, United StatesView Profile , Enkelejda Kasneci University of Tubingen, Germany University of Tubingen, GermanyView Profile , Zoya Bylinskii Adobe Research, United States Adobe Research, United StatesView Profile Authors Info & Claims ETRA '22: 2022 Symposium on Eye Tracking Research and ApplicationsJune 2022 Article No.: 37Pages 1–3https://doi.org/10.1145/3517031.3531634Online:08 June 2022Publication History 0citation30DownloadsMetricsTotal Citations0Total Downloads30Last 12 Months30Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The collection of eye gaze information provides a window into many critical aspects of human cognition, health and behaviour. Additionally, many neuroscientific studies complement the behavioural information gained from eye tracking with the high temporal resolution and neurophysiological markers provided by electroencephalography (EEG). One of the essential eye-tracking software processing steps is the segmentation of the continuous data stream into events relevant to eye-tracking applications, such as saccades, fixations, and blinks. Here, we introduce DETRtime, a novel framework for time-series segmentation that creates ocular event detectors that do not require additionally recorded eye-tracking modality and rely solely on EEG data. Our end-to-end deep-learning-based framework brings recent advances in Computer Vision to the forefront of the times series segmentation of EEG data. DETRtime achieves state-of-the-art performance in ocular event detection across diverse eye-tracking experiment paradigms. In addition to that, we provide evidence that our model generalizes well in the task of EEG sleep stage segmentation.

Integer sequences are of central importance to the modeling of concepts admitting complete finitary descriptions. We introduce a novel view on the learning of such concepts and lay down a set of benchmarking tasks aimed at conceptual understanding by machine learning models. These tasks indirectly assess model ability to abstract, and challenge them to reason both interpolatively and extrapolatively from the knowledge gained by observing representative examples. To further aid research in knowledge representation and reasoning, we present FACT, the Finitary Abstraction Comprehension Toolkit. The toolkit surrounds a large dataset of integer sequences comprising both organic and synthetic entries, a library for data pre-processing and generation, a set of model performance evaluation tools, and a collection of baseline model implementations, enabling the making of the future advancements with ease.

Classical protocols for reliable broadcast and consensus provide security guarantees as long as the number of corrupted parties f is bounded by a single given threshold t. If f > t, these protocols are completely deemed insecure. We consider the relaxed notion of multi-threshold reliable broadcast and consensus where validity, consistency and termination are guaranteed as long as f ≤ t_v, f ≤ t_c and f ≤ t_t respectively. For consensus, we consider both variants of (1-ε)-consensus and almost-surely terminating consensus, where termination is guaranteed with probability (1-ε) and 1, respectively. We give a very complete characterization for these primitives in the asynchronous setting and with no signatures: - Multi-threshold reliable broadcast is possible if and only if max{t_c,t_v} + 2t_t < n. - Multi-threshold almost-surely consensus is possible if max{t_c, t_v} + 2t_t < n, 2t_v + t_t < n and t_t < n/3. Assuming a global coin, it is possible if and only if max{t_c, t_v} + 2t_t < n and 2t_v + t_t < n. - Multi-threshold (1-ε)-consensus is possible if and only if max{t_c, t_v} + 2t_t < n and 2t_v + t_t < n.