StuArt: Individualized Classroom Observation of Students with Automatic Behavior Recognition and Tracking
Each student matters, but it is hardly for instructors to observe all the students during the courses and provide helps to the needed ones immediately. In this paper, we present StuArt, a novel automatic system designed for the individualized classroom observation, which empowers instructors to concern the learning status of each student. StuArt can recognize five representative student behaviors (hand-raising, standing, sleeping, yawning, and smiling) that are highly related to the engagement and track their variation trends during the course. To protect the privacy of students, all the variation trends are indexed by the seat numbers without any personal identification information. Furthermore, StuArt adopts various user-friendly visualization designs to help instructors quickly understand the individual and whole learning status. Experimental results on real classroom videos have demonstrated the superiority and robustness of the embedded algorithms. We expect our system promoting the development of large-scale individualized guidance of students. More information is in https://github.com/hnuzhy/StuArt.
Code (1)
Similar Papers 제목 키워드 기반
Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course
Using large language models (LLMs) for automatic evaluation has become an important evaluation method in NLP research. However, it is unclear whether these LLM-based evaluators can be applied in real-world classrooms to …
Instruction FollowingLanguage ModelingLanguage ModellingLarge Language ModelRevealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis
Learning analytics research increasingly studies classroom learning with AI-based systems through rich contextual data from outside these systems, especially student-teacher interactions. One key challenge in leveraging …
Siamese Neural Networks for Class Activity Detection
Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms. A CAD solution helps teachers get instant feedback on their pedagogical instructions. However, CA…
Action DetectionActivity DetectionContinued Pretraining for Domain Adaptation of Wav2vec2.0 in Automatic Speech Recognition for Elementary Math Classroom Settings
Creating Automatic Speech Recognition (ASR) systems that are robust and resilient to classroom conditions is paramount to the development of AI tools to aid teachers and students. In this work, we study the efficacy of c…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Domain AdaptationMath+2Students' Perceived Roles, Opportunities, and Challenges of a Generative AI-powered Teachable Agent: A Case of Middle School Math Class
Ongoing advancements in Generative AI (GenAI) have boosted the potential of applying long-standing learning-by-teaching practices in the form of a teachable agent (TA). Despite the recognized roles and opportunities of T…
Math