On the development of an AI performance and behavioural measures for teaching and classroom management

• URL: http://arxiv.org/abs/2506.11143v2
• Date: Mon, 14 Jul 2025 12:41:39 GMT
• Title: On the development of an AI performance and behavioural measures for teaching and classroom management
• Authors: Andreea I. Niculescu, Jochen Ehnes, Chen Yi, Du Jiawei, Tay Chiat Pin, Joey Tianyi Zhou, Vigneshwaran Subbaraju, Teh Kah Kuan, Tran Huy Dat, John Komar, Gi Soong Chee, Kenneth Kwok,
• Abstract summary: 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.
• Score: 29.68201271068342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a two-year research project focused on developing AI-driven measures to analyze classroom dynamics, with particular emphasis on teacher actions captured through multimodal sensor data. We applied real-time data from classroom sensors and AI techniques to extract meaningful insights and support teacher development. Key outcomes include a curated audio-visual dataset, novel behavioral measures, and a proof-of-concept teaching review dashboard. An initial evaluation with eight researchers from the National Institute for Education (NIE) highlighted the system's clarity, usability, and its non-judgmental, automated analysis approach -- which reduces manual workloads and encourages constructive reflection. Although the current version does not assign performance ratings, it provides an objective snapshot of in-class interactions, helping teachers recognize and improve their instructional strategies. Designed and tested in an Asian educational context, this work also contributes a culturally grounded methodology to the growing field of AI-based educational analytics.

Related papers

• AI-Educational Development Loop (AI-EDL): A Conceptual Framework to Bridge AI Capabilities with Classical Educational Theories [8.500617875591633]
  This study introduces the AI-Educational Development Loop (AI-EDL), a theory-driven framework that integrates classical learning theories with human-in-the-loop artificial intelligence (AI)<n>The framework emphasizes transparency, self-regulated learning, and pedagogical oversight.
  arXiv  Detail & Related papers  (2025-08-01T15:44:19Z)
• Research on Comprehensive Classroom Evaluation System Based on Multiple AI Models [22.997376359616208]
  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.
  arXiv  Detail & Related papers  (2025-06-29T04:06:55Z)
• A Practical Guide for Supporting Formative Assessment and Feedback Using Generative AI [0.0]
  Large-language models (LLMs) can help students, teachers, and peers understand "where learners are going," "where learners currently are," and "how to move learners forward"<n>This review provides a comprehensive foundation for integrating LLMs into formative assessment in a pedagogically informed manner.
  arXiv  Detail & Related papers  (2025-05-29T12:52:43Z)
• Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics [0.0]
  This paper assesses the impact of new pedagogical incentives implemented in a first-year undergraduate computing module at University College London. We employ a mixed methods approach, combining learning analytics with qualitative data to evaluate the effectiveness of these incentives on increasing student engagement. Our paper introduces an interpretable and actionable model for student engagement, which integrates objective, data-driven analysis with students' perspectives.
  arXiv  Detail & Related papers  (2024-03-13T16:39:38Z)
• Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts [13.617709093240231]
  We examine how artificial intelligence (AI) and machine learning (ML) methods can analyze educational content, teacher discourse, and student responses to foster instructional improvement. We identify key areas where AI/ML integration offers significant advantages, including teacher coaching, student support, and content development. This paper emphasizes the importance of aligning AI/ML technologies with pedagogical goals to realize their full potential in educational settings.
  arXiv  Detail & Related papers  (2024-03-06T18:29:18Z)
• Revisiting Self-supervised Learning of Speech Representation from a Mutual Information Perspective [68.20531518525273]
  We take a closer look into existing self-supervised methods of speech from an information-theoretic perspective. We use linear probes to estimate the mutual information between the target information and learned representations. We explore the potential of evaluating representations in a self-supervised fashion, where we estimate the mutual information between different parts of the data without using any labels.
  arXiv  Detail & Related papers  (2024-01-16T21:13:22Z)
• 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)
• Towards Goal-oriented Intelligent Tutoring Systems in Online Education [69.06930979754627]
  We propose a new task, named Goal-oriented Intelligent Tutoring Systems (GITS) GITS aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment. We propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI)
  arXiv  Detail & Related papers  (2023-12-03T12:37:16Z)
• Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
  This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs) The system is into three inter-connected core processes-interaction, reflection, and reaction. Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
  arXiv  Detail & Related papers  (2023-09-15T02:42:03Z)
• Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble [56.705249154629264]
  Self-training teacher-student frameworks are proposed to improve the robustness of NER models. In this paper, we propose an adaptive teacher learning comprised of two teacher-student networks. Fine-grained student ensemble updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise.
  arXiv  Detail & Related papers  (2022-12-13T12:14:09Z)
• 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)
• Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation [67.52229938775294]
  In past years, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, we propose to conduct further exploration to imitate the human learning process from different aspects.
  arXiv  Detail & Related papers  (2021-11-28T01:14:29Z)
• 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)

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.