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1. User Identification Utilizing Minimal Eye-Gaze Features in Virtual Reality Applications

   https://doi.org/10.3390/virtualworlds1010004  

   Asish, Sarker Monojit; Kulshreshth, Arun K.; Borst, Christoph W. (December 2022, Virtual Worlds)

   Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work. 

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2. How to Support ML End-User Programmers through a Conversational Agent

   https://doi.org/10.1145/3597503.3608130  

   Garcia, E; Pimentel, J; Feng, Z; Gerosa, M; Steinmacher, I; Sarma, A (February 2024, Proceedings of the International Conference on Software Engineering)

   Machine Learning (ML) is increasingly gaining significance for end-user programmer (EUP) applications. However, machine learning end-user programmers (ML-EUPs) without the right background face a daunting learning curve and a heightened risk of mistakes and flaws in their models. In this work, we designed a conversational agent named “Newton” as an expert to support ML-EUPs. Newton’s design was shaped by a comprehensive review of existing literature, from which we identified six primary challenges faced by ML-EUPs and five strategies to assist them. To evaluate the efficacy of Newton’s design, we conducted a Wizard of Oz within-subjects study with 12 ML-EUPs. Our findings indicate that Newton effectively assisted ML-EUPs, addressing the challenges highlighted in the literature. We also proposed six design guidelines for future conversational agents, which can help other EUP applications and software engineering activities. 

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3. Learning from Irreproducibility: Introducing Data Leakage Case Studies for Machine Learning Education

   Fund, Fraida; Saeed, Mohamed; Malik, Shaivi; Ishak, Kyrillos (July 2025, ACM)

   Data leakage remains a pervasive issue in machine learning (ML), especially when applied to science, leading to overly optimistic performance estimates and irreproducible findings. Despite its prevalence, data leakage receives limited attention in ML education, in part due to the lack of accessible, hands-on teaching resources. To address this gap, we developed interactive learning modules in which students reproduce examples from academic publications that are affected by data leakage, then repeat the evaluation without the data leakage error to see how the finding is affected. These modules were deployed by the authors in two introductory machine learning courses, enabling students to explore common forms of leakage and their impact on model reliability. Following their engagement with these materials, student feedback highlighted increased awareness of subtle pitfalls that can compromise machine learning workflows. 

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4. Distributing epistemic functions and tasks—A framework for augmenting human analytic power with machine learning in science education research

   https://doi.org/10.1002/tea.21803  

   Kubsch, Marcus; Krist, Christina; Rosenberg, Joshua M. (August 2022, Journal of Research in Science Teaching)

   Abstract Machine learning (ML) has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human‐driven codes of students' work. Despite this promise, we and other scholars argue that machine learning has not yet achieved its transformational potential. We argue that this is because our field is currently lacking frameworks for supporting creative, principled, and critical endeavors to use machine learning in science education research. To offer considerations for science education researchers' use of ML, we present a framework, Distributing Epistemic Functions and Tasks (DEFT), that highlights the functions and tasks that pertain to generating knowledge that can be carried out by either trained researchers or machine learning algorithms. Such considerations are critical decisions that should occur alongside those about, for instance, the type of data or algorithm used. We apply this framework to two cases, one that exemplifies the cutting‐edge use of machine learning in science education research and another that offers a wholly different means of using machine learning and human‐driven inquiry together. We conclude with strategies for researchers to adopt machine learning and call for the field to rethink how we prepare science education researchers in an era of great advances in computational power and access to machine learning methods. 

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5. Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research

   https://doi.org/10.1093/biosci/biaa044  

   Pearson, Katelin D; Nelson, Gil; Aronson, Myla F; Bonnet, Pierre; Brenskelle, Laura; Davis, Charles C; Denny, Ellen G; Ellwood, Elizabeth R; Goëau, Hervé; Heberling, J Mason; et al (May 2020, BioScience)

   Abstract Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth. 

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