This pioneering study explored the eye movement classifications among expert and novice anatomists in a gross anatomy cadaver lab. Participants wore eye-tracking glasses while identifying tagged anatomical structures in a prosected donor body. As expected, our initial findings indicated that participants with greater expertise had shorter completion times. Also, the normalized fixation features of participants showed that as expertise levels increased, there were fewer fixations with shorter durations. This suggests that participants with greater expertise employed distinctive visual attention strategies. We navigated several challenges related to the authentic environment, highlighting the need to explore further improving methodological and data analysis techniques. These preliminary findings have implications for informing pedagogical approaches leveraging digitization without sacrificing the 3D environment.

In this study, we utilize a concurrent mixed methods design to explore the attention patterns and processing strategies of young people as they evaluate socioscientific information on social media. We utilized the screen-based Tobii eye tracker to capture ten 8th and twenty 9th grade student's fixations while they evaluated if climate change information was believable or not. The students then completed a retrospective think aloud to explain their process. We find that it is possible to categorize young people's gaze patterns into heuristic and systematic processing categories, and the think aloud data converges with these categories. Further, while most studies are done with adults in a laboratory, we demonstrate that using an eye tracker with a relatively low sampling frequency makes it possible to capture valid and reliable eye tracking in an ecologically valid setting.

This study presents a novel framework to evaluate the usability and user experience of e-learning platforms, focusing on the Duolingo language learning app. By integrating advanced Natural Language Processing (NLP) techniques such as Large Language Models (LLMs), Latent Dirichlet Allocation (LDA), and sentence embedding models, we identified thirteen key usability and user experience topics from a large dataset of user reviews. Our findings reveal that Duolingo is highly rated since users value its enjoyable learning experiences and effectiveness. However, technical issues, lack of explanations, and frustrating features were identified as areas for improvement. The study demonstrates the effectiveness of our framework in analyzing user feedback at scale, offering valuable insights for improving user experience in e-learning platforms and other domains.

This paper presents a set of qualitative and quantitative scores designed to assess performance of any eye movement classification algorithm. The scores are designed to provide a foundation for the eye tracking researchers to communicate about the performance validity of various eye movement classification algorithms. The paper concentrates on the five algorithms in particular: Velocity Threshold Identification (I-VT), Dispersion Threshold Identification (I-DT), Minimum Spanning Tree Identification (MST), Hidden Markov Model Identification (I-HMM) and Kalman Filter Identification (I-KF). The paper presents an evaluation of the classification performance of each algorithm in the case when values of the input parameters are varied. Advantages provided by the new scores are discussed. Discussion on what is the "best" classification algorithm is provided for several applications. General recommendations for the selection of the input parameters for each algorithm are provided.

In today's industry, intuitive gesture recognition, as manifested in numerous consumer electronics devices, becomes a main issue of HMI device research. Although finger-tip touch based user interface has paved a main stream in mobile electronics, we envision touch-less HMI as a promising technology in futuristic applications with higher potential in areas where sanity or outdoor operation become of importance. In this paper, we introduce a novel HMI device for non-contact gesture input for intuitive HMI experiences. The enabling technology of the proposed device is the IPA (infrared Proximity Array) sensor by which realtime 3 dimensional depth information can be captured and realized for machine control. For the usability study, two different operating modes are adopted for hand motion inputs: one is a finger tip control mode and the other is a palm control mode. Throughput of the proposed device has been studied and compared to a traditional mouse device for usability evaluation. During the human subject test, the proposed device is found to be useful for PC mouse pointer control. The experimental results are shared in the paper as well.