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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
AutoGrad: Automated Grading Software for Mobile Game Assi...
Prince Steven Annor, Samuel Boateng, Edwin Pelpuo Kayang, George · 2021-07-26 · via cs.HC updates on arXiv.org

Automatic grading systems have been in existence since the turn of the half-century. Several systems have been developed in the literature with either static analysis and dynamic analysis or a hybrid of both methodologies for computer science courses. This paper presents AutoGrad, a novel portable cross-platform automatic grading system for graphical Processing programs developed on Android smartphones during an online course. AutoGrad uses Processing, which is used in the emerging Interactive Media Arts, and pioneers grading systems utilized outside the sciences to assist tuition in the Arts. It also represents the first system built and tested in an African context across over thirty-five countries across the continent. This paper first explores the design and implementation of AutoGrad. AutoGrad employs APIs to download the assignments from the course platform, performs static and dynamic analysis on the assignment to evaluate the graphical output of the program, and returns the grade and feedback to the student. It then evaluates AutoGrad by analyzing data collected from the two online cohorts of 1000+ students of our SuaCode smartphone-based course. From the analysis and students' feedback, AutoGrad is shown to be adequate for automatic assessment, feedback provision to students, and easy integration for both cloud and standalone usage by reducing the time and effort required in grading the 4 assignments required to complete the course.