Amanda P. Goodwin

Eye-tracking offers rich insights into student cognition and engagement, but remains underutilized in classroom-facing educational technology due to challenges in data interpretation and accessibility. In this paper, we present the iterative design and evaluation of a gaze-based learning analytics dashboard for English Language Arts (ELA), developed through five studies involving teachers and students. Guided by user-centered design and data storytelling principles, we explored how gaze data can support reflection, formative assessment, and instructional decision-making. Our findings demonstrate that gaze analytics can be approachable and pedagogically valuable when supported by familiar visualizations, layered explanations, and narrative scaffolds. We further show how a conversational agent, powered by a large language model (LLM), can lower cognitive barriers to interpreting gaze data by enabling natural language interactions with multimodal learning analytics. We conclude with design implications for future EdTech systems that aim to integrate novel data modalities in classroom contexts.

As technology continues to shape how students read and write, digital literacy practices have become increasingly multimodal and complex—posing new challenges for researchers seeking to understand these processes in authentic educational settings. This paper presents three qualitative studies that use multimodal analyses and visual modeling to examine digital reading and writing across age groups, learning contexts, and literacy activities. The first study introduces collaborative composing snapshots, a method that visually maps third graders’ digital collaborative writing processes and highlights how young learners blend spoken, written, and visual modes in real-time online collaboration. The second study uses digital reading timescapes to track the multimodal reading behaviors of fifth graders—such as highlighting, re-reading, and gaze patterns—offering insights into how these actions unfold over time to support comprehension. The third study explores multimodal composing timescapes and transmediation visualizations to analyze how bilingual high school students compose across languages and modes, including text, image, and sounds. Together, these innovative methods illustrate the power of multimodal analysis and visual modeling for capturing the complexity of digital literacy development. They offer valuable tools for designing more inclusive, equitable, and developmentally responsive digital learning environments—particularly for culturally and linguistically diverse learners.

With advancements in AI, new gaze estimation methods are exceeding state-of-the-art (SOTA) benchmarks, but their real-world application reveals a gap with commercial eye-tracking solutions. Factors like model size, inference time, and privacy often go unaddressed. Meanwhile, webcam-based eye-tracking methods lack sufficient accuracy, in particular due to head movement. To tackle these issues, we introduce We bEyeTrack, a framework that integrates lightweight SOTA gaze estimation models directly in the browser. It incorporates model-based head pose estimation and on-device few-shot learning with as few as nine calibration samples (k < 9). WebEyeTrack adapts to new users, achieving SOTA performance with an error margin of 2.32 cm on GazeCapture and real-time inference speeds of 2.4 milliseconds on an iPhone 14. Our open-source code is available at https://github.com/RedForestAi/WebEyeTrack.

Reading assessments are essential for enhancing students' comprehension, yet many EdTech applications focus mainly on outcome-based metrics, providing limited insights into student behavior and cognition. This study investigates the use of multimodal data sources -- including eye-tracking data, learning outcomes, assessment content, and teaching standards -- to derive meaningful reading insights. We employ unsupervised learning techniques to identify distinct reading behavior patterns, and then a large language model (LLM) synthesizes the derived information into actionable reports for educators, streamlining the interpretation process. LLM experts and human educators evaluate these reports for clarity, accuracy, relevance, and pedagogical usefulness. Our findings indicate that LLMs can effectively function as educational analysts, turning diverse data into teacher-friendly insights that are well-received by educators. While promising for automating insight generation, human oversight remains crucial to ensure reliability and fairness. This research advances human-centered AI in education, connecting data-driven analytics with practical classroom applications.

Digital reading is ubiquitous, yet understanding digital reading processes and links to comprehension remains underdeveloped. Guided by new literacies and active reading theories, this study explored the reading behaviors and comprehension of thirteen fifth graders who read static digital texts. We coded for the quantity and quality of digital reading behaviors and employed action path diagrams to connect behaviors to comprehension. We used timescape analyses to visualize how behaviors were orchestrated differently across readers. Findings showed no single behavior was related directly to comprehension, indicating varying pathways to digital reading success. Occasional rereading seemed to support active reading and improved comprehension. Instances of students subverting expected digital tools were observed. Minor distractions like mind-wandering did not link to poor performance. This research deepens our understanding of self-monitoring and active reading in static digital contexts, offering insights for future study of more complex digital reading contexts like reading on the internet.

This study explores the roles of morphological skills (Morphological Awareness, Morphological-Syntactic-Knowledge, Morphological-Semantic-Knowledge, and Morphological-Orthographic/Phonological-Know...