Article: Evaluation of mechanical engineering classroom teaching effectiveness based on improved MTCNN algorithm Journal: International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 2026 Vol.36 No.11 pp.23 - 49 Abstract: To address the challenge of monitoring student engagement in mechanical engineering classrooms, this study proposes an enhanced multi-task cascaded convolutional neural network combined with particle filtering and a support vector machine. The model integrates facial expression and head pose recognition to evaluate classroom attention. Experimental results show face detection accuracy reached 91% on the FDDB dataset, while expression recognition achieved 92% accuracy and 90% F1-score on CK+. The combined method improved behaviour recognition accuracy by 13.28% and increased frame rates by 13.6-25% compared to single-method approaches. Practical application demonstrated a 22% increase in homework completion, 28.46% rise in assignment scores, and doubled interaction frequency. The model effectively assesses teaching effectiveness and provides data-driven support for improving instructional content and student engagement. Inderscience Publishers - linking academia, business and industry through research

Title: Evaluation of mechanical engineering classroom teaching effectiveness based on improved MTCNN algorithm

Authors: Chengyu Xiang

Addresses: School of Elevator Engineering, Hunan Electrical College of Technology, Xiangtan, 411101, China

Abstract: To address the challenge of monitoring student engagement in mechanical engineering classrooms, this study proposes an enhanced multi-task cascaded convolutional neural network combined with particle filtering and a support vector machine. The model integrates facial expression and head pose recognition to evaluate classroom attention. Experimental results show face detection accuracy reached 91% on the FDDB dataset, while expression recognition achieved 92% accuracy and 90% F1-score on CK+. The combined method improved behaviour recognition accuracy by 13.28% and increased frame rates by 13.6-25% compared to single-method approaches. Practical application demonstrated a 22% increase in homework completion, 28.46% rise in assignment scores, and doubled interaction frequency. The model effectively assesses teaching effectiveness and provides data-driven support for improving instructional content and student engagement.

Keywords: classroom teaching effectiveness evaluation; facial detection; facial expression recognition; MTCNN algorithm; particle filter.

DOI: 10.1504/IJCEELL.2026.154666

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.11, pp.23 - 49

Received: 17 Sep 2025
Accepted: 28 Jan 2026
Published online: 09 Jul 2026 *

Title: Evaluation of mechanical engineering classroom teaching effectiveness based on improved MTCNN algorithm

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