Research on Comprehensive Classroom Evaluation System Based on Multiple AI Models
- URL: http://arxiv.org/abs/2506.23079v1
- Date: Sun, 29 Jun 2025 04:06:55 GMT
- Title: Research on Comprehensive Classroom Evaluation System Based on Multiple AI Models
- Authors: Cong Xie, Li Yang, Daben Wang, Jing Xiao,
- Abstract summary: This paper develops a comprehensive evaluation system that automatically generates evaluation reports and optimization suggestions from two dimensions: teacher teaching ability and classroom teaching effectiveness.<n>It meets the requirements of all-round and process-oriented classroom evaluation in the era of digital education.
- Score: 22.997376359616208
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The promotion of the national education digitalization strategy has facilitated the development of teaching quality evaluation towards all-round, process-oriented, precise, and intelligent directions, inspiring explorations into new methods and technologies for educational quality assurance. Classroom teaching evaluation methods dominated by teaching supervision and student teaching evaluation suffer from issues such as low efficiency, strong subjectivity, and limited evaluation dimensions. How to further advance intelligent and objective evaluation remains a topic to be explored. This paper, based on image recognition technology, speech recognition technology, and AI large language models, develops a comprehensive evaluation system that automatically generates evaluation reports and optimization suggestions from two dimensions: teacher teaching ability and classroom teaching effectiveness. This study establishes a closed-loop classroom evaluation model that comprehensively evaluates student and teaching conditions based on multi-dimensional data throughout the classroom teaching process, and further analyzes the data to guide teaching improvement. It meets the requirements of all-round and process-oriented classroom evaluation in the era of digital education, effectively solves the main problems of manual evaluation methods, and provides data collection and analysis methods as well as technologies for relevant research on educational teaching evaluation.
Related papers
- On the development of an AI performance and behavioural measures for teaching and classroom management [29.68201271068342]
This paper presents a two-year research project focused on developing AI-driven measures to analyze classroom dynamics.<n>Key outcomes include a curated audio-visual dataset, novel behavioral measures, and a proof-of-concept teaching review dashboard.
arXiv Detail & Related papers (2025-06-11T04:52:50Z) - A Survey of Automatic Evaluation Methods on Text, Visual and Speech Generations [58.105900601078595]
We present a comprehensive review and a unified taxonomy of automatic evaluation methods for generated content across all three modalities.<n>Our analysis begins by examining evaluation methods for text generation, where techniques are most mature.<n>We then extend this framework to image and audio generation, demonstrating its broad applicability.
arXiv Detail & Related papers (2025-06-06T11:09:46Z) - Monocle: Hybrid Local-Global In-Context Evaluation for Long-Text Generation with Uncertainty-Based Active Learning [63.531262595858]
Divide-and-conquer approach breaks comprehensive evaluation task into localized scoring tasks, followed by a final global assessment.<n>We introduce a hybrid in-context learning approach that leverages human annotations to enhance the performance of both local and global evaluations.<n>Finally, we develop an uncertainty-based active learning algorithm that efficiently selects data samples for human annotation.
arXiv Detail & Related papers (2025-05-26T16:39:41Z) - EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework [9.76455227840645]
Large language models (LLMs) increasingly serve as educational tools, yet evaluating their teaching capabilities remains challenging.<n>We introduce EducationQ, a multi-agent dialogue framework that efficiently assesses teaching capabilities through simulated dynamic educational scenarios.
arXiv Detail & Related papers (2025-04-21T07:48:20Z) - An Exploration of Higher Education Course Evaluation by Large Language Models [4.943165921136573]
Large language models (LLMs) within artificial intelligence (AI) present promising new avenues for enhancing course evaluation processes.
This study explores the application of LLMs in automated course evaluation from multiple perspectives and conducts rigorous experiments across 100 courses at a major university in China.
arXiv Detail & Related papers (2024-11-03T20:43:52Z) - Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition [70.60872754129832]
First NeurIPS competition on unlearning sought to stimulate the development of novel algorithms.
Nearly 1,200 teams from across the world participated.
We analyze top solutions and delve into discussions on benchmarking unlearning.
arXiv Detail & Related papers (2024-06-13T12:58:00Z) - Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education [0.2812395851874055]
This research study explores the conceptualization, development, and deployment of an innovative learning analytics tool.
By analyzing critical data points such as students' stress levels, curiosity, confusion, agitation, topic preferences, and study methods, the tool provides a comprehensive view of the learning environment.
This research underscores AI's role in shaping personalized, data-driven education.
arXiv Detail & Related papers (2023-12-15T06:00:26Z) - A Hierarchy-based Analysis Approach for Blended Learning: A Case Study
with Chinese Students [12.533646830917213]
This paper proposes a hierarchy-based evaluation approach for blended learning evaluation.
The results show that cognitive engagement and emotional engagement play a more important role in blended learning evaluation.
arXiv Detail & Related papers (2023-09-19T00:09:00Z) - Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach [40.06500618820166]
This paper presents an approach to deriving a learner model directly from an assessment rubric.
We illustrate how the approach can be applied to automatize the human assessment of an activity developed for testing computational thinking skills.
arXiv Detail & Related papers (2022-09-07T10:09:12Z) - Image Quality Assessment in the Modern Age [53.19271326110551]
This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA)
We will first revisit several subjective quality assessment methodologies, with emphasis on how to properly select visual stimuli.
Both hand-engineered and (deep) learning-based methods will be covered.
arXiv Detail & Related papers (2021-10-19T02:38:46Z) - The Challenges of Assessing and Evaluating the Students at Distance [77.34726150561087]
The COVID-19 pandemic has caused a strong effect on higher education institutions with the closure of classroom teaching activities.
This short essay aims to explore the challenges posed to Portuguese higher education institutions and to analyze the challenges posed to evaluation models.
arXiv Detail & Related papers (2021-01-30T13:13:45Z) - Neural Multi-Task Learning for Teacher Question Detection in Online
Classrooms [50.19997675066203]
We build an end-to-end neural framework that automatically detects questions from teachers' audio recordings.
By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions.
arXiv Detail & Related papers (2020-05-16T02:17:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.