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cs.HC updates on arXiv.org

Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
Toward Finding and Supporting Struggling Students in a Pr...
Belinda Schantong, Dominik Gorgosch, Janet Siegmund · 2024-01-25 · via cs.HC updates on arXiv.org

Background: Programming skills are advantageous to navigate today's society, so it is important to teach them to students. However, failure rates for programming courses are high, and especially students who fall behind early in introductory programming courses tend to stay behind. Objective: To catch these students as early as possible, we aim to develop an early warning system, so we can offer the students support, for example, in the form of syntax drill-and-practice exercises. Method: To develop the early warning system, we assess different cognitive skills of students of an introductory programming course. On several points in time over the course, students complete tests that measure their ability to develop a mental model of programming, language skills, attention, and fluid intelligence. Then, we evaluated to what extent these skills predict whether students acquire programming skills. Additionally, we assess how syntax drill-and-practice exercises improve how students acquire programming skill. Findings: Most of the cognitive skills can predict whether students acquire programming skills to a certain degree. Especially the ability to develop an early mental model of programming and language skills appear to be relevant. Fluid intelligence also shows predictive power, but appears to be comparable with the ability to develop a mental model. Furthermore, we found a significant positive effect of the syntax drill-and-practice exercises on the success of a course. Implications: Our first suggestion of an early warning system consists of few, easy-to-apply tests that can be integrated in programming courses or applied even before a course starts. Thus, with the start of a programming course, students who are at high risk of failing can be identified and offered support, for example, in the form of syntax drill-and-practice exercises to help students to develop programming skills.