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Miguel Nacenta

Publications and source records attributed to Miguel Nacenta.

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Semantic Segmentation of Node and Edge Diagrams for Assistive Technology

In this paper, we present a novel set of related models for semantic segmentation of node-link diagrams. These diagrams are frequently used to represent mathematical graphs, relationships between concepts, and flowcharts. Such diagrams are difficult to access non-visually; while some assistive interfaces have been designed for node-link diagrams, they rely upon a machine-readable representation of the diagram, whereas such diagrams will generally be made available as bitmap images. Our compact deep learning models show excellent quantitative and qualitative performance on a large synthetic dataset of node-link diagrams, reaching per-pixel accuracy over 93\%.

cs.CV

Characterization and Effects of CS2 Learning with GenAI, Visualization, and Human Support

Generative AI (GenAI) is becoming a widely adopted learning support tool for both students and instructors, as it offers benefits such as personalized tutoring and scaffolded learning. However, recent research highlights potential drawbacks such as overreliance and metacognitive issues, especially in novice programmers. Most prior work focuses on introductory programming courses, and important questions remain about the underlying mechanisms behind the negative effects of GenAI and if findings can be generalized when students learn more advanced computer science concepts. To address this gap, we conducted a mixed-methods study comparing student interactions with GenAI to two traditional learning supports in a second-year algorithms course: algorithm visualization (AV) and human live tutoring (LT). Twelve students participated in three 90-minute study sessions focusing on sorting, tree, and graph algorithms. We recorded gaze and interaction data, and each session concluded with a test assessing their conceptual understanding of the topic. Our analysis classifies when during the problem-solving process participants sought help, and compares the interaction patterns across the three learning supports. Although GenAI produced a larger increase in self-efficacy compared to live tutoring, it was associated with noticeably lower results in learning outcomes. We found that participants did not use algorithm visualizations effectively, faced usage barriers when using GenAI to learn advanced topics, and that live tutoring yielded the highest learning outcomes.

cs.HC

The Impact of Visual Segmentation on Lexical Word Recognition

When a reader encounters a word in English, they split the word into smaller orthographic units in the process of recognizing its meaning. For example, "rough", when split according to phonemes, is decomposed as r-ou-gh (not as r-o-ugh or r-ough), where each group of letters corresponds to a sound. Since there are many ways to segment a group of letters, this constitutes a computational operation that has to be solved by the reading brain, many times per minute, in order to achieve the recognition of words in text necessary for reading. We hypothesized that providing segmentation information in the text itself could help the reading process by reducing its computational cost. In this paper we explore whether and how different visual interventions could communicate segmentation information for reading and word recognition. We ran a series of pre-registered lexical decision experiments with 192 participants that tested five types of visual segmentations: outlines, spacing, connections, underlines and color. The evidence indicates that, even with a moderate amount of training, these visual interventions always slow down word identification, but each to a different extent. These findings are important because they indicate that, at least for typical adult readers with a moderate amount of specific training in these visual interventions, accelerating the lexical decision task is unlikely. The results also offer an empirical measurement of the cost of a common set of visual manipulations of text, which can be useful for practitioners seeking to visualize alongside or within text without impacting reading performance. Finally, the interaction between typographically encoded information and visual variables presented unique patterns that deviate from existing theories, suggesting new directions for future inquiry.

cs.HC