Article: Evaluation of teaching quality in accounting smart education classrooms driven by student expression feature recognition Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2025 Vol.17 No.9 pp.1 - 11 Abstract: The extensive use of artificial intelligence is making traditional quality evaluation methods progressively unfit for modern education's personalisation, intelligence, and real-time needs. Especially in accounting, students' expressiveness level and classroom participation influence their learning. For accounting smart education, student expression feature recognition technology is applied to offer a classroom teaching quality rating system. Two tests of the system were carried out in this work. First employing a confusion matrix, the model effectively detects the seven basic emotional states. The second experiment tracked participants' expression variations over a designated period and matched the manually annotated results with the outcomes of the expression recognition system. All experimental results show that the framework effectively and consistently detects student's expression states and catches dynamic classroom emotions, so optimising classroom teaching and learning. This paper presents fresh quality evaluation techniques in smart education and supports smart accounting education. Inderscience Publishers - linking academia, business and industry through research

Title: Evaluation of teaching quality in accounting smart education classrooms driven by student expression feature recognition

Authors: Shu Chen

Addresses: College of Accounting, Zhanjiang University of Science and Technology, Zhanjiang 524094, China

Abstract: The extensive use of artificial intelligence is making traditional quality evaluation methods progressively unfit for modern education's personalisation, intelligence, and real-time needs. Especially in accounting, students' expressiveness level and classroom participation influence their learning. For accounting smart education, student expression feature recognition technology is applied to offer a classroom teaching quality rating system. Two tests of the system were carried out in this work. First employing a confusion matrix, the model effectively detects the seven basic emotional states. The second experiment tracked participants' expression variations over a designated period and matched the manually annotated results with the outcomes of the expression recognition system. All experimental results show that the framework effectively and consistently detects student's expression states and catches dynamic classroom emotions, so optimising classroom teaching and learning. This paper presents fresh quality evaluation techniques in smart education and supports smart accounting education.

Keywords: expression feature recognition; convolutional neural networks; CNN; accounting smart education; classroom teaching quality evaluation.

DOI: 10.1504/IJRIS.2025.147654

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.9, pp.1 - 11

Received: 06 May 2025
Accepted: 31 May 2025
Published online: 24 Jul 2025 *

Title: Evaluation of teaching quality in accounting smart education classrooms driven by student expression feature recognition

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