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Multimodal deep learning for sports teacher behavior analysis: design and evaluation of a personalized continuing education recommendation system - PMC

This study addresses the limitations of traditional continuing education approaches for sports teachers by developing a personalized recommendation system based on multimodal deep learning analysis of teaching behaviors. The system implements a ...

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Multimodal deep learning for sports teacher behavior analysis: design and evaluation of a personalized continuing education recommendation system

Zhongli Chen

Zhongli Chen

1
College of General Education, Chongqing City Vocational College, Yongchuan, Chongqing, 402160 China

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1
College of General Education, Chongqing City Vocational College, Yongchuan, Chongqing, 402160 China

✉
Corresponding author.

Received 2025 May 6; Accepted 2025 Dec 2; Collection date 2026.

© The Author(s) 2025

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Abstract

This study addresses the limitations of traditional continuing education approaches for sports teachers by developing a personalized recommendation system based on multimodal deep learning analysis of teaching behaviors. The system implements a comprehensive framework that captures video, audio, and motion data from teaching sessions to analyze instruction quality across multiple dimensions. A hierarchical classification system categorizes teaching behaviors while a multidimensional quality assessment model evaluates performance. The personalized recommendation algorithm integrates teacher ability profiles with resource characteristics through a multi-objective optimization approach that balances development needs, interests, and learning preferences. System evaluation with 124 physical education teachers demonstrated superior recommendation accuracy (F1 = 0.85) compared to traditional methods and significant improvements in teaching behaviors for the intervention group across instructional clarity (d = 0.68), demonstration quality (d = 0.72), and feedback specificity (d = 0.59). The findings indicate that multimodal behavior analysis can effectively identify specific development needs and generate targeted continuing education recommendations that significantly enhance sports teaching quality and professional development.

Keywords:
Multimodal deep learning, Sports teacher behavior analysis, Personalized recommendation system, Continuing education, Teacher professional development, Teaching quality assessment
Subject terms:
Computer science, Scientific data
Introduction

The continuing education of sports teachers plays a crucial role in enhancing teaching quality and promoting professional development in physical education. With the rapid evolution of educational theories and practices, sports teachers face increasing demands to update their knowledge and refine their teaching methodologies
1
. Before proceeding, it is essential to clarify key terminologies used throughout this study. Sports Teacher Teaching Behaviors refer to all observable and measurable instructional activities performed by physical education teachers to achieve teaching objectives, including movement demonstrations, verbal explanations, organizational management, feedback provision, and motivational interactions. Within this framework, Organizational Behaviors specifically denote management-oriented teaching actions such as learning environment arrangement, equipment setup, student grouping, and activity transitions, aimed at maximizing effective learning time while minimizing management disruptions. Motivational Behaviors encompass psychological support strategies including verbal encouragement, nonverbal reinforcement, challenge establishment, and achievement recognition, designed to enhance student engagement, learning persistence, and self-efficacy in physical education contexts. Continuing education serves as a vital mechanism for sports teachers to adapt to emerging pedagogical approaches and technological innovations in physical education delivery. Research indicates that effective continuing education significantly impacts teachers’ instructional effectiveness, classroom management, and student engagement in physical education settings
2
. Despite widespread recognition of its importance, current continuing education practices for sports teachers often suffer from standardization issues, failing to address the diverse needs and varied skill levels across the teaching population.

Traditional continuing education models for sports teachers typically employ a one-size-fits-all approach, characterized by uniform training content and standardized delivery methods
3
. These conventional approaches frequently disregard individual teachers’ distinct teaching styles, pedagogical strengths, classroom dynamics, and specific improvement areas. Consequently, many sports teachers find standard continuing education programs either too elementary or excessively advanced, resulting in limited practical application of acquired knowledge to their specific teaching contexts
4
. Furthermore, traditional assessment methods in continuing education programs often fail to capture the nuanced characteristics of sports teaching behaviors, which are inherently multimodal and context-dependent.

Multimodal deep learning offers promising solutions to these challenges by enabling comprehensive analysis of sports teachers’ classroom behaviors through simultaneous processing of diverse data types
5
. This advanced computational approach can integrate and analyze visual data (capturing movement demonstrations and spatial arrangements), audio information (including verbal instructions and feedback), and contextual cues (such as student responses and environmental factors). The multidimensional analysis capabilities of these technologies provide unprecedented opportunities to develop sophisticated understanding of teaching effectiveness in physical education settings
6
.

The integration of multimodal deep learning with personalized recommendation systems represents a significant advancement in continuing education approaches for sports teachers. Such systems can process complex classroom interaction data to identify specific behavioral patterns, teaching strengths, and areas requiring improvement for individual educators
7
. This granular analysis enables the development of highly customized professional development pathways that align precisely with each teacher’s unique professional development needs, teaching context, and career aspirations.

The significance of this research lies in its potential to fundamentally transform continuing education practices for sports teachers through technological innovation. By leveraging multimodal deep learning algorithms to analyze teaching behaviors comprehensively, the proposed system promises to deliver unprecedented personalization in professional development recommendations. The innovation extends beyond technological application to include the development of new frameworks for understanding teaching quality in physical education
8
. Furthermore, this research addresses critical gaps in existing literature regarding the application of artificial intelligence in sports education professional development contexts.

This study aims to design, implement, and evaluate a personalized continuing education recommendation system for sports teachers based on multimodal deep learning analysis of teaching behaviors. The research employs a mixed-methods approach combining quantitative analysis of teaching behavior data with qualitative assessment of user experience and implementation outcomes. The paper is structured to first establish theoretical foundations, followed by detailed system design methodology, implementation procedures, evaluation results, and concluding with implications for physical education professional development practices and policies.
Literature review and theoretical foundation

Current status of research on sports teachers’ teaching behavior analysis

Research on sports teachers’ teaching behavior analysis has evolved significantly over the past two decades, transitioning from primarily qualitative observational methods to increasingly sophisticated quantitative and mixed-methods approaches. Traditional classroom observation protocols have served as fundamental tools in identifying and categorizing key instructional behaviors in physical education settings
9
. These protocols typically involve trained observers documenting teacher actions, instructional strategies, and classroom management techniques using standardized coding schemes. Internationally, systematic observation instruments such as the Physical Education Teacher Assessment Instrument (PETAI) and the System for Observing Fitness Instruction Time (SOFIT) have gained widespread adoption in analyzing sports teaching behaviors across different educational contexts and cultural settings
10
. These instruments have provided valuable frameworks for understanding the complexity of physical education instruction through direct observation and documentation of teacher-student interactions.

The methodological landscape of sports teaching behavior analysis has expanded considerably with technological advancements. Video-based analysis emerged as a significant improvement over real-time observation, allowing researchers to review teaching episodes multiple times with enhanced reliability and capturing details that might be missed in live observations
11
. This approach facilitated more nuanced coding of teacher behaviors and enabled cross-analysis by multiple researchers to establish stronger validity. Further technological integration has introduced wearable sensors and motion capture systems that provide objective measurements of teacher movements, spatial positioning, and physical demonstrations during instruction
12
. These quantitative metrics complement traditional qualitative assessments, offering multi-dimensional insights into teaching effectiveness and instructional patterns in physical education.

Traditional teaching behavior analysis methods exhibit distinct advantages and limitations that significantly impact their utility in sports education research. Direct observation methods offer contextual richness and capture the nuanced interactions between teachers and students in authentic learning environments
13
. However, these approaches suffer from observer bias, limited scope of observation focus, and substantial resource requirements for training observers and conducting observations. Self-reporting instruments like teaching logs and reflective journals provide valuable insights into teachers’ decision-making processes and pedagogical reasoning not observable through external methods
14
. Nevertheless, these self-report measures are susceptible to subjectivity, social desirability bias, and often lack standardization, complicating cross-study comparisons.

Questionnaire-based assessment of teaching behaviors enables efficient data collection from larger teacher samples and structured quantification of behavioral patterns
15
. The primary limitations of questionnaire approaches include recall inaccuracies, limited granularity in behavioral descriptions, and potential disconnection between teachers’ perceptions and actual classroom practices. Video analysis methods offer enhanced reliability through repeated viewing and permanent documentation of teaching episodes, but face challenges related to camera positioning limitations, Hawthorne effects on participants, and labor-intensive coding procedures
16
. Each traditional method presents inherent trade-offs between comprehensiveness, objectivity, efficiency, and scalability in analyzing sports teaching behaviors.

Several significant challenges persist in current sports teaching behavior analysis research. The multidimensional nature of physical education instruction, which encompasses verbal instruction, movement demonstrations, spatial organization, and nonverbal communication, creates considerable complexity in developing comprehensive analysis frameworks
17
. Existing research methodologies often focus on isolated aspects of teaching behavior rather than capturing the integrated, holistic nature of effective sports instruction. The context-dependent nature of effective teaching behaviors presents another substantial challenge, as instructional strategies that prove effective in certain educational environments may be less successful in others
18
. Cultural variations in physical education pedagogical expectations further complicate the development of universally applicable analysis frameworks.

Methodological limitations represent another significant challenge in this research domain. Traditional behavior coding systems frequently reduce complex teaching behaviors to simplistic categories, potentially missing important qualitative aspects of instruction
19
. The labor-intensive nature of manual coding processes has historically limited sample sizes and longitudinal analyses in sports teaching behavior research
20
. Furthermore, existing research approaches often struggle to establish definitive connections between specific teaching behaviors and student learning outcomes in physical education contexts, complicating efforts to identify and promote evidence-based instructional practices.
Application of multimodal deep learning technologies in education

Multimodal deep learning represents a specialized approach within artificial intelligence that integrates and processes information from multiple data modalities simultaneously, enabling comprehensive analysis of complex phenomena like teaching behaviors. The fundamental principle underlying multimodal deep learning involves concurrent analysis of heterogeneous data types—such as visual, audio, textual, and sensor data—to extract complementary information that would be unavailable through single-modality analysis
21
. This approach mimics human perceptual integration, where understanding is enhanced by synthesizing multiple sensory inputs rather than processing them in isolation. The technical framework typically employs specialized neural network architectures that extract modality-specific features before integrating them through various fusion mechanisms. Early fusion combines raw or minimally processed data from different modalities before feature extraction, as represented in Eq. 1:

where
represents input from the
-th modality,
denotes concatenation, and
is a neural network with parameters
22
. Alternatively, late fusion first extracts modality-specific features independently before integration, as shown in Eq. 2:

where
represents modality-specific feature extractors and
is an integration function with parameters
23
. More advanced hybrid approaches like attention-based fusion selectively weight modalities based on their relevance to the task, as in Eq. 3:

where
represents attention weights determined dynamically based on input characteristics
24
.

Educational assessment and behavioral analysis have increasingly leveraged multimodal deep learning approaches to capture the multifaceted nature of teaching and learning processes. Classroom discourse analysis applications have employed combined audio-textual models to evaluate teacher-student interactions, identifying patterns in questioning strategies, wait time, and feedback quality that correlate with enhanced student engagement
25
. These systems transcend traditional observational methods by processing natural language features alongside acoustic properties like tone, pace, and emphasis that convey pedagogical intent. Student engagement monitoring systems have successfully integrated visual attention tracking with physiological signals to provide real-time indicators of cognitive involvement and emotional states during learning activities
26
. The multimodal approach has proven particularly effective in capturing engagement dimensions that manifest through different channels simultaneously, such as visual attention, verbal participation, and emotional expression.

Automated assessment of teaching quality has advanced significantly through multimodal frameworks that analyze instructional videos to evaluate multiple dimensions of teaching practice
27
. These systems can assess aspects ranging from content clarity and instructional coherence to classroom management techniques and student interaction patterns by processing visual, audio, and contextual information streams simultaneously. Performance skill assessment in physical education contexts has benefited from systems that combine motion capture data with expert demonstrations to provide detailed feedback on movement quality and skill execution
28
. Recent applications have extended to the analysis of teacher professional development interactions, where multimodal systems analyze collaborative discussions among educators to identify effective knowledge-sharing patterns and professional growth indicators
29
. Learning analytics platforms increasingly incorporate multimodal data sources to develop more comprehensive understanding of educational processes, integrating traditional academic metrics with behavioral and interaction data to create multidimensional student and teacher profiles
30
.

Applying multimodal deep learning to sports teaching behavior analysis offers distinctive advantages over traditional single-modality approaches. The inherently physical and demonstrative nature of sports instruction involves simultaneous verbal explanation, physical demonstration, spatial positioning, and nonverbal communication, making it ideally suited for multimodal analysis techniques
31
. This technology enables comprehensive capture of teaching behaviors across multiple dimensions, addressing the limitations of traditional observation methods that typically focus on selected aspects while neglecting others
32
. The objective measurement capabilities of multimodal systems reduce the subjectivity inherent in human observation, providing standardized metrics that facilitate reliable comparison across different teaching contexts and temporal points
33
. Additionally, these systems can process large volumes of instructional data efficiently, enabling analysis at scales previously unattainable through manual methods. The temporal analysis capabilities of deep learning architectures are particularly valuable for sports instruction analysis, as they can track behavioral patterns and pedagogical strategies as they evolve during lessons and across teaching episodes
34
.

Despite these advantages, significant challenges remain in applying multimodal deep learning to sports teaching behavior analysis. Technical challenges include the complexity of synchronizing heterogeneous data streams with different sampling rates and structural characteristics from classroom environments
35
. The substantial computational resources required for processing high-dimensional multimodal data present implementation barriers, particularly for real-time analysis applications in educational settings with limited technological infrastructure. Data annotation represents another substantial challenge, as developing training datasets requires extensive expert labeling of teaching behaviors across multiple modalities—a process that is both time-intensive and requires specialized knowledge in both education and data science domains. Interpretability concerns also arise with complex multimodal models, as the “black box” nature of deep neural networks can complicate efforts to provide transparent, actionable feedback to educators based on model outputs. Privacy and ethical considerations present additional challenges, as comprehensive multimodal data collection in educational settings raises significant concerns regarding student and teacher privacy, informed consent, and appropriate data governance frameworks
36
.
Research progress in personalized education recommendation systems

Personalized recommendation systems in education have evolved through distinct developmental phases that reflect broader technological and pedagogical advancements. Early educational recommendation systems emerged in the late 1990s and early 2000s, primarily focusing on content-based filtering approaches to suggest learning materials based on explicit user preferences and predefined content attributes
37
. These initial systems operated within relatively constrained digital environments like learning management systems and relied heavily on manual content tagging and explicit user feedback mechanisms. The evolution continued with the introduction of collaborative filtering techniques in educational contexts during the mid-2000s, enhancing recommendation capabilities by identifying patterns across learner behaviors and preferences without requiring extensive content annotation
38
. This advancement enabled more dynamic recommendations based on similarities between user profiles and interaction histories, significantly expanding the scalability and adaptability of educational recommendation systems beyond what was possible with purely content-based approaches.

The field underwent substantial transformation with the integration of machine learning techniques in the 2010s, which enabled hybrid recommendation models combining multiple filtering approaches and incorporating increasingly diverse data sources
39
. These systems began leveraging implicit behavioral data alongside explicit preferences, developing more nuanced learner profiles that reflected engagement patterns, learning progress, and contextual factors. The current generation of educational recommendation systems represents a significant advancement through the incorporation of deep learning architectures, knowledge graphs, and multimodal data processing capabilities that enable context-aware, adaptive recommendations responsive to complex learning needs and trajectories
40
. This evolution reflects a progressive movement toward increasingly personalized, data-driven approaches to educational resource recommendation that align with individual learning characteristics, preferences, and goals.

Contemporary educational recommendation systems employ diverse algorithmic models with varying theoretical foundations and operational mechanisms. Content-based filtering algorithms in educational contexts typically utilize natural language processing and semantic analysis techniques to establish meaningful relationships between learning resources and learner profiles based on content features, skill taxonomies, and learning objectives
41
. These approaches excel at recommending resources with similar characteristics to those previously accessed but often struggle with recommendation diversity and adapting to evolving user preferences. Collaborative filtering approaches address some of these limitations by generating recommendations based on behavioral patterns across user groups, identifying resources utilized by learners with similar profiles or learning trajectories
42
. While powerful for identifying non-obvious resource connections, these methods frequently encounter cold-start problems with new users or content and may perpetuate existing resource utilization patterns without introducing beneficial novel materials.

Matrix factorization and latent factor models have gained prominence in educational recommendation systems, decomposing user-item interaction matrices to identify latent features that predict resource relevance across dimensions not explicitly defined in content metadata
43
. These approaches effectively balance personalization with generalization but require substantial interaction data for robust performance. Knowledge-based recommendation strategies incorporate domain-specific educational frameworks and ontologies to generate recommendations aligned with pedagogical principles, learning progressions, and competency frameworks
44
. Evaluation methodologies for educational recommendation systems have expanded beyond traditional accuracy metrics to include educational impact measures such as learning gain, engagement duration, knowledge retention, and skill development, acknowledging that recommendation quality in educational contexts cannot be assessed through interaction metrics alone
45
. This evolution toward comprehensive evaluation frameworks reflects growing recognition that educational recommendation systems must optimize for learning outcomes rather than merely user satisfaction or interaction frequency.

The application of personalized recommendation systems in teacher continuing education represents an emerging field with significant potential for enhancing professional development effectiveness. Current implementations primarily focus on recommending formal learning resources such as courses, workshops, and scholarly articles based on teachers’ subject areas, grade levels, and explicitly stated interests
46
. These systems typically operate within institutional learning management systems or professional development platforms, with limited integration of teachers’ classroom practice data or performance indicators. More advanced applications have begun incorporating competency-based approaches that align recommendations with professional teaching standards and career development frameworks, enabling more structured professional growth pathways
47
. These systems utilize skill gap analysis to identify areas for development based on self-assessment, supervisor evaluation, or performance data, then recommend resources specifically targeted to address identified needs.

The potential for personalized recommendation systems in teacher continuing education extends considerably beyond current implementations, particularly through integration with teaching behavior analysis technologies
48
. By incorporating multimodal classroom observation data, these systems could provide recommendations responsive to observed teaching patterns, strengths, and areas for development rather than relying solely on self-reported preferences or standardized assessments. Collaborative filtering approaches utilizing data from teacher communities of practice show promise for identifying effective professional development pathways based on the experiences of educators with similar teaching contexts, challenges, and career trajectories
49
. Despite this potential, significant research gaps remain regarding the effectiveness of recommendation systems in supporting teacher professional growth, appropriate balance between algorithmic recommendation and teacher autonomy, and integration of these systems within broader professional development ecosystems
50
.

III. Multimodal Sports Teaching Behavior Analysis Methods
.
Multimodal data collection and preprocessing framework

The comprehensive analysis of sports teaching behaviors necessitates a multimodal data collection framework capable of capturing the multidimensional nature of physical education instruction. Our proposed framework employs a synchronized multi-sensor approach integrating high-definition video recordings, directional audio capture, wearable inertial measurement units (IMUs), and infrared motion tracking systems positioned strategically throughout the teaching environment
51
. Video data collection utilizes three camera perspectives—wide-angle classroom view, teacher-focused tracking camera, and student interaction zone—recording at 60 frames per second with 1080p resolution to capture detailed visual information on teacher demonstrations, spatial positioning, and student-teacher interactions. Audio data is simultaneously collected through a wireless lavalier microphone worn by the teacher and strategically positioned ambient microphones that capture both instructional dialogue and environmental context. The temporal synchronization of these heterogeneous data streams is achieved through a centralized data acquisition system that aligns all modalities using common timestamp references, ensuring coherent multimodal analysis despite varying sampling rates across different sensor types.

Video preprocessing follows a structured pipeline beginning with frame normalization and stabilization to compensate for camera movement and lighting variations. Teacher detection and tracking is accomplished using a modified YOLOv5 architecture that implements the following detection function:

where
represents the video frame at time
,
denotes bounding box coordinates,
indicates the detected class (teacher, student, equipment), and
represents the confidence score
52
. Spatial-temporal feature extraction employs a 3D convolutional neural network to capture motion dynamics through the function:

where
represents a video segment of duration
starting at time
.

Audio preprocessing begins with environmental noise reduction using spectral subtraction techniques, followed by speaker diarization to isolate teacher instructions from student responses and ambient sounds. Acoustic feature extraction encompasses both time-domain features (energy, zero-crossing rate) and frequency-domain characteristics (MFCCs, spectral centroid) represented as:

where
represents the audio segment at time
53
. Speech recognition transforms verbal instructions into text representation using a domain-adapted automatic speech recognition model trained specifically for physical education terminology and instruction patterns. The resulting text undergoes natural language processing to extract linguistic features including instruction clarity, terminology usage, and feedback patterns.

Motion data processing integrates information from wearable IMUs attached to key body segments and infrared motion capture systems tracking reflective markers on the teacher’s body. Kinematic feature extraction calculates joint angles, movement velocity, and acceleration profiles through the following transformation:

where
represents joint angles,
denotes velocity, and
indicates acceleration derived from motion data
at time
54
. Movement quality assessment employs dynamic time warping to compare teacher demonstrations against reference models of optimal technique execution using the distance metric:

where
and
represent the teacher and reference movement sequences respectively, and
denotes the optimal warping path
55
.

The multimodal representation model integrates these heterogeneous features through a hierarchical fusion architecture that preserves modality-specific characteristics while enabling cross-modal interaction analysis. Initial modality-specific encoding transforms raw feature vectors into embedded representations through specialized neural networks optimized for each data type. Cross-modal attention mechanisms then generate integrated representations that capture the complementary information across modalities according to:

where
represents the feature representation from modality
, and
indicates the attention weight determined by cross-modal relevance
56
. To validate the superiority of the hierarchical attention-based fusion approach, we conducted ablation experiments comparing three fusion strategies: early fusion (concatenating raw features before processing), late fusion (combining modality-specific outputs), and attention-based fusion (dynamically weighting modalities). As shown in Table
1
, the attention-based fusion achieved the highest F1 score (0.85) and behavior classification accuracy (88.3%), outperforming early fusion (F1 = 0.79, accuracy = 83.1%) and late fusion (F1 = 0.82, accuracy = 85.7%). The attention mechanism’s superior performance stems from its ability to adaptively emphasize relevant modalities for different behavior categories. For demonstration behaviors, attention weights were calibrated as α_visual = 0.60, α_audio = 0.20, α_motion = 0.20, prioritizing visual information. Conversely, for instructional explanation behaviors, weights shifted to α_audio = 0.70, α_visual = 0.20, α_motion = 0.10, emphasizing linguistic content. These weights were validated through expert-labeled data comprising 2,500 annotated teaching behavior segments, ensuring optimal modality balance for each behavior category.

Table 1.

Ablation analysis of multimodal fusion Strategies.

Fusion strategy

Precision

Recall

F1 Score

Classification accuracy

Early fusion

0.81

0.77

0.79

83.1%

Late fusion

0.84

0.80

0.82

85.7%

Attention-based fusion (Proposed)

0.87

0.83

0.85

88.3%

Open in a new tab
This multimodal representation framework preserves temporal alignment across modalities while capturing the complex interrelationships between visual demonstrations, verbal instructions, and physical movements that characterize effective sports teaching behaviors. The resulting integrated representation serves as the foundation for subsequent teaching behavior analysis, enabling identification of behavioral patterns that would be imperceptible through single-modality analysis approaches.
Deep learning-based teaching behavior recognition and classification

Effective analysis of sports teaching behaviors requires a structured classification system that comprehensively captures the multidimensional nature of physical education instruction. Based on extensive literature review and expert consultation, we developed a hierarchical classification framework that categorizes sports teaching behaviors into four primary domains: instructional, feedback, organizational, and motivational behaviors
57
. This classification system, detailed in Table
2
, establishes a taxonomic structure that enables systematic analysis of teaching behaviors while maintaining sufficient granularity to identify nuanced pedagogical patterns. Each behavior category encompasses multiple specific behavioral instances that manifest through different modalities, necessitating multimodal analysis approaches for accurate detection and classification. The classification framework incorporates both observable physical actions and communicative behaviors, providing a comprehensive foundation for subsequent deep learning model development.

Table 2.

Classification of sports teacher teaching Behaviors.

Behavior category

Behavior description

Typical examples

Corresponding teaching effect

Instructional behaviors

Behaviors focused on knowledge and skill transmission through demonstration and explanation

Technique demonstrations, concept explanations, movement analysis

Enhanced student comprehension and skill acquisition efficiency

Feedback behaviors

Behaviors providing information about student performance to reinforce or correct actions

Error correction, performance evaluation, improvement suggestions

Improved performance accuracy and reduced skill acquisition time

Organizational behaviors

Behaviors managing learning environment, equipment, and student activities

Grouping strategies, equipment arrangement, transition management

Maximized active learning time and minimized management disruptions

Motivational behaviors

Behaviors encouraging student engagement and effort through psychological support

Encouragement, challenge establishment, achievement recognition

Increased student participation intensity and learning persistence

Open in a new tab
The multimodal deep learning architecture for teaching behavior recognition integrates specialized neural network components optimized for processing each data modality while enabling cross-modal information fusion. The visual behavior analysis module employs a hybrid CNN-Transformer architecture that processes sequential video frames to identify visual teaching behaviors through spatial-temporal convolution operations
58
. All deep learning models were trained using a custom sports teaching dataset comprising 15,000 annotated video segments (45 h total) collected from 180 physical education classes, with pre-training performed on the Kinetics-400 action recognition dataset to leverage transfer learning. Training employed the Adam optimizer with an initial learning rate of 0.001 (exponentially decayed by 0.95 every 10 epochs), batch size of 32, and 150 training epochs with early stopping based on validation loss. The visual CNN utilized ResNet-50 as the backbone with 3D convolutional layers (kernel size 3 × 3 × 3), while the audio analysis employed a Convolutional Recurrent Neural Network with 4 convolutional layers (64-128-256-512 filters) followed by 2 bidirectional LSTM layers (256 hidden units each). Model robustness was validated through 5-fold cross-validation, achieving mean accuracy of 86.7% (SD = 2.1%) across folds. Learning curves demonstrated stable convergence without overfitting, with training accuracy plateauing at 89.2% while validation accuracy stabilized at 86.7% after epoch 120. Hyperparameters were optimized through grid search over learning rates (0.0001-0.01), batch sizes (16–64), and dropout rates (0.2–0.5), with final configurations selected based on validation set performance. Table
3
presents comprehensive model training specifications.

Table 3.

Deep learning model training Specifications.

Component

Specification

Dataset

Training samples

12,000 video segments (36 h)

Validation samples

1,500 video segments (4.5 h)

Test samples

1,500 video segments (4.5 h)

Pre-training dataset

Kinetics-400 (240,000 videos)

Annotation type

Expert-labeled behavior categories (4 main classes, 16 sub-classes)

Model architecture

Visual module

ResNet-50 + 3D CNN (5 layers)

Audio module

CRNN (4 conv layers + 2 BiLSTM layers)

Motion module

Graph Convolutional Network (3 layers)

Fusion module

Hierarchical attention mechanism

Training hyperparameters

Optimizer

Adam (β₁=0.9, β₂=0.999)

Initial learning rate

0.001

Learning rate decay

Exponential (γ = 0.95 every 10 epochs)

Batch size

32

Training epochs

150 (early stopping at epoch 120)

Dropout rate

0.3

Weight decay

0.0001

Cross-validation results

5-fold CV mean accuracy

86.7% ± 2.1%

Training accuracy (final)

89.2%

Validation accuracy (final)

86.7%

Test accuracy

88.3%

Computational resources

Hardware

NVIDIA Tesla V100 GPU (32GB)

Training time

36 h (GPU)

Inference time per video

8.5 s (1-hour video)

Open in a new tab
This module captures both static postures and dynamic movements through a dual-stream approach represented as:

where
represents the current frame,
denotes the temporal sequence of previous frames, and
indicates feature concatenation. The spatial stream employs residual convolutional blocks to extract postural features while the temporal stream utilizes 3D convolutions to capture motion dynamics.

Audio behavior analysis incorporates parallel processing pathways for linguistic content and acoustic characteristics. The linguistic analysis pathway employs a bidirectional LSTM network operating on word embeddings derived from transcribed speech:

where
represents word embeddings of the transcribed speech sequence
59
. The acoustic analysis pathway processes raw audio features through a convolutional recurrent network architecture:

where
represents the audio feature sequence extracted from the teaching episode.

Kinematic behavior analysis processes motion capture data through a graph convolutional network that models the human body as an articulated structure with joints as nodes and limb connections as edges:

where
represents the graph structure derived from joint position data at time
60
. This approach effectively captures the spatial relationships between body segments while accommodating the varying movement patterns characteristic of different teaching behaviors.

Cross-modal feature fusion implements a hierarchical attention mechanism that dynamically weights modality-specific features based on their relevance to specific behavior categories:

where
represents the attention weight for modality
when classifying behavior category
61
. This approach enables the model to emphasize different modalities when identifying behaviors that manifest predominantly through specific channels, such as prioritizing visual features for demonstration behaviors or linguistic features for explanatory behaviors.

The behavior classification module employs a temporal convolutional network operating on the fused feature representations to model sequential dependencies in teaching behavior patterns:

where
represents the sequence of behavior labels across time steps
through
62
. This formulation enables the model to consider contextual relationships between sequential behaviors rather than treating each behavior instance independently.

Teaching behavior sequence analysis extends beyond individual behavior classification to identify meaningful pedagogical patterns that emerge over time. We propose a temporal pattern mining approach that identifies recurring behavior sequences through a modified sequential pattern mining algorithm:

where
represents the set of significant behavior sequences,
denotes a specific behavior sequence,
is the minimum frequency threshold, and
is the minimum sequence length
63
. This approach enables identification of characteristic teaching routines that may not be apparent through analysis of isolated behaviors.

Sequential behavior effectiveness analysis employs a sliding window approach to evaluate the pedagogical impact of different behavior sequences through correlation with student engagement and performance metrics. The effectiveness score for a behavior sequence is calculated as:

where
represents the
-th student outcome measure and
is the corresponding weight reflecting the relative importance of each outcome dimension. This framework enables the identification of optimal teaching behavior sequences for specific instructional objectives, providing a foundation for subsequent personalized recommendation generation based on observed teaching patterns and their demonstrated effectiveness.
Teaching behavior quality assessment model

Developing a comprehensive evaluation framework for sports teaching behaviors requires integrating pedagogical expertise with quantitative assessment methodologies. Through a Delphi study involving 18 physical education experts, we established a multidimensional quality assessment indicator system for sports teaching behaviors
64
. The expert panel consisted of 10 experienced K-12 physical education teachers (minimum 10 years teaching experience, including 4 national-level master teachers), 5 university sport pedagogy researchers with expertise in teaching behavior analysis and teacher education, and 3 provincial-level physical education teacher trainers specializing in professional development program design. Expert selection criteria included demonstrated expertise through publications or recognized professional achievements, diverse representation across educational levels (primary, secondary, and tertiary), and geographic distribution across urban and rural contexts. The Delphi process comprised three rounds of structured questionnaires administered between January and March 2023. In Round 1, experts independently proposed quality indicators and assessment dimensions; Round 2 involved rating the importance of consolidated indicators on 5-point Likert scales; Round 3 sought consensus on final indicator weights and assessment standards. Consensus was defined as ≥ 80% agreement among experts, achieved for all retained indicators by Round 3. Indicator weights were determined through the Analytic Hierarchy Process (AHP), with pairwise comparisons aggregated using geometric mean. The final indicator system demonstrated strong internal consistency (Kendall’s coefficient of concordance W = 0.82,
p
< 0.001), confirming expert consensus.

This expert-derived framework incorporates both process-oriented measures focusing on teaching behavior characteristics and outcome-oriented metrics addressing instructional effectiveness. The resulting hierarchical evaluation system, presented in Table
4
, encompasses five primary assessment dimensions with corresponding indicators and evaluation standards that provide a structured foundation for quantitative behavior quality assessment. Each dimension captures distinct aspects of teaching quality while collectively providing a comprehensive evaluation framework that aligns with established physical education pedagogical principles.

Table 4.

Quality assessment indicator system for sports teaching Behaviors.

Assessment dimension

Assessment indicators

Assessment standards

Technical accuracy

Demonstration precision, Biomechanical correctness, Safety adherence

Clear visual presentation, Biomechanically optimal execution, Emphasis on critical technique elements

Instructional clarity

Verbal explanation coherence, Terminology appropriateness, Visual-verbal synchronization

Concise explanations, Age-appropriate terminology, Synchronized demonstration and explanation

Adaptability

Individual differentiation, Progression modification, Environmental adjustment

Tailored instruction for different ability levels, Appropriate task progression, Effective adaptation to spatial/equipment constraints

Feedback quality

Specificity, Timeliness, Constructiveness

Targeted performance elements, Immediate response to performance, Balance of corrective and reinforcing feedback

Student engagement

Motivational effectiveness, Active participation facilitation, Learning environment creation

Visible student motivation, Maximized activity time, Positive and inclusive atmosphere

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The multimodal quality assessment algorithm integrates features extracted from different data modalities to generate comprehensive quality scores across multiple assessment dimensions. Technical accuracy assessment primarily leverages visual and kinematic data through a comparative analysis between teacher demonstrations and expert reference models
65
. The technical accuracy score is calculated using a weighted similarity metric:

where
represents the teacher’s demonstration feature vector for the
-th technique component,
denotes the corresponding reference model feature vector, and
indicates the relative importance of each component as determined by expert weighting. Instructional clarity assessment integrates linguistic and acoustic features extracted from verbal instruction, incorporating metrics such as speech rate, vocabulary complexity, and prosodic emphasis
66
. The methodology employs natural language processing techniques to analyze explanation coherence and terminology appropriateness, while cross-modal synchronization analysis evaluates temporal alignment between verbal explanations and physical demonstrations.

Adaptability assessment implements a temporal analysis framework that detects variations in teaching behaviors in response to student performance and environmental conditions. The adaptability score is computed through a pattern variation metric:

where
represents the appropriate variation in behavior pattern
conditioned on context
across
instructional episodes
67
. This formulation enables quantification of a teacher’s responsive adjustments to student needs, spatial constraints, and equipment availability throughout the teaching session.

Feedback quality assessment employs a multi-aspect analysis framework incorporating feedback timing, specificity, and constructiveness. The temporal relationship between student performance and teacher feedback is quantified through a response time metric:

where minimal values indicate more immediate feedback provision. Feedback specificity is assessed through linguistic analysis of verbal content, while constructiveness is evaluated through sentiment analysis and motivational content identification
68
.

Student engagement assessment leverages computer vision techniques to analyze student behavioral indicators including activity levels, attentional focus, and interactive participation. The engagement score integrates multiple visual indicators through a weighted function:

where
represents activity level,
denotes attentional focus,
indicates interactive participation, and
,
, and
are weighting coefficients determined through correlation with learning outcomes
69
.

The correlation analysis between teaching behavior quality and student learning outcomes employs a multi-level structural equation modeling approach to identify relationships between specific teaching behavior dimensions and various learning metrics
70
. Student learning assessment incorporates both immediate performance measures captured during the instructional session and delayed retention tests administered following appropriate intervals. Motor skill acquisition is evaluated through standardized skill assessments comparing pre-instruction and post-instruction performance levels, while cognitive understanding is assessed through knowledge tests covering relevant conceptual content
71
. Affective outcomes including enjoyment, motivation, and self-efficacy are measured through validated psychological instruments administered following instructional episodes.

Statistical mediation analysis reveals that the relationship between teaching behavior quality and learning outcomes is mediated by student engagement levels and practice quality. High-quality instructional behaviors lead to enhanced student engagement, which subsequently facilitates more effective practice and improved learning outcomes across skill acquisition, knowledge development, and affective domains
72
. To establish construct validity of behavior metrics, we conducted correlation analysis between teaching behavior quality dimensions and objective student learning outcomes measured through standardized skill tests, knowledge assessments, and engagement observations. As presented in Table
5
, instructional clarity demonstrated strong positive correlation with student skill test scores (
r
= 0.68,
p
< 0.001) and knowledge retention rates measured two weeks post-instruction (
r
= 0.62,
p
< 0.001). Demonstration quality showed the strongest association with student movement execution accuracy (
r
= 0.74,
p
< 0.001), while feedback specificity correlated significantly with skill improvement rate over the intervention period (
r
= 0.61,
p
< 0.001). Student engagement levels, measured through systematic observation protocols, correlated moderately with all teaching behavior dimensions (
r
= 0.52–0.67, all
p
< 0.01). Structural equation modeling confirmed that student engagement partially mediated the relationship between teaching behaviors and learning outcomes (indirect effect β = 0.43,
p
< 0.001), with significant direct effects remaining (β = 0.38,
p
< 0.001), supporting the proposed theoretical framework linking teaching quality to student achievement.

Table 5.

Correlation matrix between teaching behavior quality and student learning Outcomes.

Teaching behavior dimension

Skill test scores

Knowledge retention

Movement accuracy

Skill improvement rate

Student engagement

Instructional clarity

0.68***

0.62***

0.59***

0.54**

0.63***

Demonstration quality

0.71***

0.48**

0.74***

0.66***

0.58**

Feedback specificity

0.64***

0.57**

0.61***

0.61***

0.67***

Adaptability

0.52**

0.51**

0.48**

0.59***

0.52**

Student engagement facilitation

0.69***

0.63***

0.66***

0.64***

0.81***

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Note: *
p
< 0.05, **
p
< 0.01, ***
p
< 0.001.
N
= 124 teachers, student outcomes averaged across classes (total
n
= 3,876 students).
Factor analysis indicates that different teaching behavior dimensions exhibit varying importance depending on specific learning objectives and student characteristics. Technical accuracy and instructional clarity demonstrate stronger associations with motor skill acquisition, while adaptability and feedback quality show stronger relationships with cognitive understanding and problem-solving capacity
73
. These findings provide valuable insights for developing personalized teacher development recommendations that target specific teaching behavior dimensions based on individual instructional goals and student populations.
Design and implementation of personalized continuing education recommendation system

Teacher ability model construction

The development of an effective personalized continuing education recommendation system requires a comprehensive teacher ability model that accurately represents the multidimensional competencies of sports educators. Based on the multimodal teaching behavior analysis framework described in Sect. 3, we constructed a three-dimensional sports teacher ability model that integrates technical proficiency, pedagogical competence, and professional adaptation capabilities
74
. This model, detailed in Table
6
, establishes the foundation for generating personalized continuing education recommendations by identifying specific ability dimensions and corresponding indicators that can be quantitatively assessed through multimodal teaching behavior analysis. The ability model was validated through consultation with physical education experts and alignment with established professional teaching standards to ensure comprehensive coverage of essential competencies for effective sports instruction.

Table 6.

Sports teacher ability model dimensions.

Ability dimension

Ability indicators

Assessment methods

Technical proficiency

Movement demonstration accuracy, Sport-specific knowledge, Biomechanical understanding

Visual movement analysis, Knowledge assessment, Demonstration-reference comparison

Pedagogical competence

Instructional clarity, Feedback effectiveness, Class organization skills

Verbal instruction analysis, Student-teacher interaction patterns, Temporal efficiency metrics

Professional adaptation

Differentiation capabilities, Environmental adjustment, Progressive development

Context-response pattern analysis, Environmental adaptation metrics, Longitudinal improvement tracking

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The multi-dimensional representation of teacher abilities employs a vector-based approach that quantifies performance across multiple competency indicators through normalized numerical values. Each teacher’s ability profile is represented as a high-dimensional vector where individual elements correspond to specific ability indicators derived from multimodal teaching behavior analysis
75
. This representation enables quantitative comparison between teachers, identification of relative strengths and development needs, and measurement of longitudinal growth across different ability dimensions. The vector-based representation supports both global ability assessment through composite metrics and granular analysis of specific competency elements, facilitating targeted continuing education recommendations that address precisely identified development needs.

The ability representation incorporates both absolute performance metrics and relative positioning within peer comparison groups to provide contextual interpretation of individual ability profiles. Absolute performance metrics quantify specific teaching behaviors against established benchmarks derived from expert performances and evidence-based best practices
76
. Complementing these absolute measures, relative positioning metrics situate individual teachers within appropriate comparison groups based on teaching experience, institutional context, and student demographic characteristics. This dual representation approach ensures that ability assessments and subsequent recommendations account for contextual factors that influence teaching effectiveness rather than applying universal standards without appropriate contextualization.

The dynamic update mechanism for teacher ability profiles implements a temporal integration approach that incrementally updates ability representations as new teaching behavior data becomes available. The updating function employs an exponentially weighted moving average that balances historical stability with responsiveness to recent performance changes:

where
represents the updated ability vector at time
,
denotes the previous ability vector,
indicates the newly observed performance vector, and
is a weighting parameter that controls the update rate
77
. The optimal value of α = 0.7 was determined through empirical validation using longitudinal teaching behavior data from 30 teachers over 12 months. We compared three candidate values: α = 0.5 (equal weighting of historical and new data), α = 0.7 (prioritizing stability), and α = 0.9 (strong historical emphasis). Cross-validation results demonstrated that α = 0.7 achieved the highest recommendation accuracy (F1 = 0.85) compared to α = 0.5 (F1 = 0.79) and α = 0.9 (F1 = 0.81). This configuration provides 70% weight to historical ability assessments (ensuring profile stability against temporary performance fluctuations) while incorporating 30% weight for new observations (maintaining responsiveness to genuine skill development), thereby optimally balancing long-term consistency with adaptive updating based on recent professional growth.

This formulation enables the ability model to evolve as teachers develop new skills and refine existing competencies through continuing education and classroom experience, ensuring that recommendations remain aligned with current ability profiles rather than historical assessments.

The ability model incorporates both observed behavioral indicators and self-reported competency assessments to develop comprehensive teacher profiles that integrate objective performance metrics with subjective professional insights. This multi-source approach addresses potential limitations of purely behavior-based assessment by incorporating teachers’ metacognitive understanding of their professional strengths and development needs
78
. The integration mechanism employs a weighted fusion approach that combines behavioral observations and self-assessments while accounting for potential discrepancies between these information sources. This comprehensive ability modeling approach provides the foundation for generating personalized continuing education recommendations that address specific development needs identified through multimodal teaching behavior analysis while respecting teachers’ professional self-understanding and development goals.
Personalized recommendation algorithm design

The effectiveness of continuing education for sports teachers depends significantly on the alignment between provided resources and individual development needs. To optimize this alignment, we propose a multidimensional matching algorithm that quantifies the relevance between teacher ability profiles and continuing education resources through vector similarity computation. Each continuing education resource is represented as a multidimensional vector
indicating its coverage across different ability dimensions, while teacher development needs are represented as a corresponding vector
derived from the ability model described in Sect.
4.1
79
. The recommendation relevance score is calculated using a weighted cosine similarity function:

where
represents the importance weight assigned to each ability dimension based on institutional priorities and professional development frameworks. This approach prioritizes resources that address specific development needs identified through multimodal teaching behavior analysis while accounting for varying importance across different ability dimensions.

The multi-objective recommendation strategy extends beyond simple needs-resource matching to incorporate teacher interests, learning preferences, and professional development trajectories. This approach formulates recommendation generation as a multi-objective optimization problem balancing potentially competing objectives including development need alignment, interest satisfaction, learning style compatibility, and career advancement potential
80
. The multi-objective utility function is defined as:

where
represents a specific teacher,
denotes a continuing education resource, and
,
,
, and
are weighting parameters that control the relative importance of different objectives. These parameters can be adjusted based on teacher preferences, institutional priorities, or specific professional development contexts to generate recommendations aligned with varying objective priorities.

To address the “exploration-exploitation” tension in recommendation systems, we implement a dynamic recommendation diversity mechanism that intentionally introduces varied resources alongside targeted recommendations. The exploration component is incorporated through a controlled randomization function:

where
controls the exploration-exploitation balance and
measures the dissimilarity between resource
and the previously recommended resource set
81
. This approach ensures that teachers receive predominantly relevant recommendations while periodically introducing novel resources that might address emerging development needs or expose teachers to innovative instructional approaches not captured by their current ability profiles.

The cold start problem represents a significant challenge for personalized recommendation systems when limited information is available about new teachers or recently added continuing education resources.

To address this challenge, we implemented a hybrid initialization approach combining content-based analysis with collaborative filtering techniques
82
. For new teachers with limited behavioral data, initial ability profiles are constructed through a combination of self-assessment instruments, credential information, and demographic similarities with existing teacher profiles according to:

where
represents the set of similar teachers identified through demographic and contextual attributes. Similar teachers are defined as those matching on multiple criteria: teaching experience within ± 3 years, same educational level (primary/middle/high school), same school location type (urban/suburban/rural), related sport specialization (e.g., basketball and volleyball both categorized as ball sports), and class size within ± 10 students. The matching algorithm employs weighted Euclidean distance across these dimensions, identifying the 5 most similar existing teachers for profile initialization. To validate this cold-start solution, we conducted a retrospective experiment with 10 new teachers who joined the system during the evaluation period. Teachers initialized using the hybrid approach achieved recommendation acceptance accuracy of F1 = 0.78 during their first month, compared to F1 = 0.52 for new teachers without initialization (baseline using only self-assessment), representing a 50% improvement in initial recommendation quality. Component contribution analysis revealed that similar teacher profiles contributed 50% to initialization effectiveness, self-assessment 30%, and credential information 20%, justifying the hybrid weighting scheme (γ = 0.5, α = 0.3, β = 0.2).

For new continuing education resources, content analysis techniques extract structured feature representations that enable preliminary matching with teacher profiles even before accumulating significant usage data
83
.

Feedback adjustment mechanisms ensure continuous refinement of both teacher ability profiles and recommendation algorithms based on interaction data and explicit feedback. The recommendation utility function undergoes periodic recalibration through Bayesian optimization that adjusts weighting parameters to maximize agreement with observed teacher selections and feedback:

where
represents the set of teacher-resource pairs with associated feedback
84
. Additionally, resource representations are dynamically updated based on accumulated teacher interactions and outcomes through a reinforcement learning framework that adjusts resource feature vectors to better predict observed utility.

Comparative evaluation of different recommendation algorithms, summarized in Table
7
, demonstrates the superior performance of the proposed multi-objective approach across accuracy, recall, F1 score, and user satisfaction metrics
85
. Content-based filtering achieves reasonable accuracy but demonstrates limited recall, suggesting difficulty in identifying all relevant resources for specific development needs. Collaborative filtering exhibits stronger recall but reduced accuracy, indicating a tendency to recommend broadly relevant resources that may not precisely target individual development needs. The proposed hybrid matching algorithm balances accuracy and recall more effectively than single-strategy approaches, while the full multi-objective optimization system achieves the highest performance across all metrics.

Table 7.

Comparison of recommendation algorithm Performance.

Algorithm type

Accuracy

Recall

F1 Score

User Satisfaction

Content-based filtering

0.76

0.68

0.72

3.8/5.0

Collaborative filtering

0.72

0.81

0.76

4.0/5.0

Hybrid matching (proposed)

0.83

0.79

0.81

4.3/5.0

Multi-objective optimization

0.85

0.82

0.83

4.5/5.0

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Sequential recommendation evaluation reveals that performance improves significantly as the system accumulates interaction data and refines both teacher ability profiles and resource representations. Longitudinal analysis of recommendation acceptance rates and subsequent teaching behavior improvements demonstrates that teachers who consistently followed system recommendations showed greater developmental progress than those who selected resources without system guidance
86
. This empirical validation confirms the effectiveness of the proposed multi-objective recommendation approach in supporting personalized continuing education that addresses specific development needs identified through multimodal teaching behavior analysis.
System implementation and deployment

The personalized continuing education recommendation system adopts a microservice-based architecture that ensures modularity, scalability, and maintainability across diverse educational environments. The architecture consists of four primary layers: data acquisition and storage, behavior analysis and modeling, recommendation generation, and user interaction
87
. Each layer operates independently with well-defined interfaces, enabling flexible deployment configurations ranging from cloud-based implementations for large educational institutions to lightweight on-premises installations for resource-constrained environments. The system architecture implements event-driven communication patterns between components, facilitating real-time data processing and recommendation updates while maintaining loose coupling between functional modules. This architectural approach enables incremental system evolution and component-level scaling to accommodate varying deployment scenarios and institutional requirements.

The functional modules comprising the system are detailed in Table
8
, which outlines each module’s primary responsibilities and technical implementation approach. The modular design enables selective deployment based on specific institutional requirements, with core recommendation functionality maintained regardless of which supplementary modules are implemented. Each module employs standardized data formats and communication protocols to ensure interoperability across heterogeneous educational technology ecosystems.

Table 8.

System functional module design.

Functional module

Primary functions

Technical implementation

Data collection

Multimodal teaching data acquisition, preprocessing, and storage

Containerized preprocessing pipelines, time-series database, S3-compatible object storage

Behavior analysis

Video/audio/motion analysis, behavior classification, quality assessment

TensorFlow-based deep learning pipeline, NVIDIA TensorRT optimization, distributed processing

Ability modeling

Teacher ability profile generation, strength/weakness identification, temporal tracking

Knowledge graph representation, Neo4j database, Bayesian profile updating

Resource management

Continuing education resource indexing, attribute extraction, representation modeling

Elasticsearch engine, NLP-based attribute extraction, content embedding generation

Recommendation engine

Personalized recommendation generation, diversity management, explanation production

Multi-objective optimization algorithms, reinforcement learning feedback integration, containerized deployment

User interface

Personalized dashboard, recommendation visualization, feedback collection

Progressive web application, React framework, responsive design, accessibility compliance

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The system data flow follows a cyclic pattern that continuously improves recommendations through feedback integration and model updating. The process begins with multimodal data collection from teaching sessions, which undergoes preprocessing and feature extraction before entering the behavior analysis pipeline
88
. Analysis results feed into the teacher ability modeling module, which generates or updates comprehensive ability profiles. These profiles are matched against the resource representation database through the recommendation engine to generate personalized continuing education suggestions. User interactions with recommendations generate feedback data that flows back into the system for continuous refinement of both ability models and recommendation algorithms. This closed-loop architecture ensures that the system evolves with accumulating data while adapting to changing educational contexts and teacher development trajectories.

The technical implementation employs containerized microservices orchestrated through Kubernetes to ensure deployment flexibility and operational resilience. Containerization enables consistent execution across diverse infrastructure environments while facilitating horizontal scaling during peak usage periods
89
. System performance benchmarks, presented in Table
9
, demonstrate practical viability for real-world educational deployment. Processing time for a typical 1-hour teaching video averages 12 min in GPU-accelerated environments (NVIDIA Tesla V100) and 45 min using CPU-only processing (Intel Xeon E5-2680), making the system suitable for both well-resourced institutions and resource-constrained schools. Recommendation generation occurs rapidly, with mean response time of 0.8 s (SD = 0.2 s) from query submission to display of personalized suggestions, ensuring smooth user experience. The system supports up to 200 concurrent users without performance degradation, adequate for institutional deployments serving medium to large teacher populations. Data storage requirements average 5GB per teacher per semester, including multimodal teaching behavior data, processed features, and recommendation history. System availability during the evaluation period reached 99.2% uptime, with planned maintenance accounting for most downtime. These performance metrics confirm the system’s readiness for operational deployment in diverse educational settings.

Table 9.

System performance Benchmarks.

Performance metric

Value

Video processing time

1-hour video (GPU environment)

12 min

1-hour video (CPU-only environment)

45 min

Recommendation generation

Mean response time

0.8 ± 0.2 s

95th percentile response time

1.2 s

System scalability

Maximum concurrent users

200

Peak load processing capacity

500 videos per day

Storage requirements

Per teacher per semester

5 GB

Video data (compressed)

3.2 GB

Processed features and metadata

1.8 GB

System reliability

Uptime during evaluation period

99.2%

Mean time between failures

720 h

Mean time to recovery

15 min

Computational resources

GPU environment

NVIDIA Tesla V100 (32GB)

CPU environment

Intel Xeon E5-2680 (16 cores)

RAM requirement

64 GB (GPU), 128 GB (CPU)

Network bandwidth

100 Mbps minimum

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The deep learning inference pipeline leverages GPU acceleration where available, with graceful degradation to CPU-only processing in resource-constrained environments. Data persistence layers implement a polyglot approach with specialized databases selected for different data types: MongoDB for document storage, Neo4j for knowledge graph representation, and TimescaleDB for time-series behavioral data. This approach optimizes query performance while maintaining data integrity through distributed transaction management.

The deployment strategy follows a graduated implementation model beginning with pilot deployments in controlled educational environments before expanding to broader institutional adoption. The initial deployment phase focuses on data collection and behavior analysis functionality, enabling the system to accumulate sufficient training data for effective ability modeling and recommendation generation
90
. Subsequent phases introduce recommendation functionality with increasing sophistication, progressing from basic content-based matching to full multi-objective optimization as user interaction data accumulates. Each deployment phase incorporates comprehensive evaluation protocols to assess system performance, user satisfaction, and educational impact before proceeding to broader implementation. This phased approach minimizes implementation risks while ensuring that recommendation quality improves progressively as the system matures.

The system implements comprehensive security and privacy protections to safeguard sensitive educational data. All teaching behavior data undergoes anonymization during preprocessing, with personally identifiable information segregated from behavioral analytics through secure tokenization. Student faces in teaching videos are automatically anonymized using a two-stage process: first, the MTCNN (Multi-task Cascaded Convolutional Networks) face detection algorithm identifies facial regions with 99.1% accuracy; second, detected faces are de-identified through Gaussian blurring (σ = 15) or pixelation (8 × 8 pixel blocks), rendering individuals unrecognizable while preserving spatial context for classroom behavior analysis. Teacher voices are separated from student audio through speaker diarization, with student utterances excluded from linguistic analysis to protect privacy. Access control implements role-based permissions with granular control over data visibility: teachers access only their own behavioral data and recommendations; school administrators view aggregated institutional statistics without individual identifiers; system administrators manage technical infrastructure without access to raw teaching videos. All data transmission employs end-to-end encryption using TLS 1.3 protocol with forward secrecy, while stored data is protected through AES-256 encryption-at-rest. Cryptographic keys are managed through Hardware Security Modules (HSMs) with automatic key rotation every 90 days. The system fully complies with the Personal Information Protection Law of the People’s Republic of China (2021), implementing lawful processing bases, data minimization principles, purpose limitation, and explicit consent mechanisms. Users exercise rights to access, correct, and delete their personal data through system interfaces, with deletion requests processed within 72 h. Video data retention follows institutional policies (5 years for research purposes) before secure destruction through cryptographic key deletion and multi-pass data overwriting. Regular security audits and penetration testing ensure ongoing protection against emerging threats. These comprehensive privacy protections enable ethical deployment while maintaining data utility for personalized recommendation generation.
System evaluation and validation

The evaluation of the personalized continuing education recommendation system employed a mixed-methods approach combining quantitative performance metrics with qualitative assessment of user experience and professional impact. All experimental procedures were conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. The study protocol was reviewed and approved by the Human Research Ethics Committee at Chongqing City Vocational College (approval number: CCVC-HREC-2023-015). Written informed consent was obtained from all participating teachers prior to data collection. Additional consent was obtained from school administrators for conducting research within their institutions.

The experimental design followed a quasi-experimental methodology with pre-post measurements of teaching behaviors and a comparison between an intervention group using the recommendation system and a control group following traditional continuing education selection approaches
91
. Participants included 124 physical education teachers (68 female, 56 male) from 28 schools across diverse educational contexts. By educational level, the sample comprised 46 primary school teachers (37.1%), 42 middle school teachers (33.9%), and 36 high school teachers (29.0%). School locations included 15 urban schools (53.6%), 8 suburban schools (28.6%), and 5 rural schools (17.9%). Teaching experience ranged from 2 to 25 years (M = 8.7, SD = 6.2). Educational qualifications included 89 teachers with bachelor’s degrees (71.8%) and 35 with master’s degrees or higher (28.2%). All participants held valid physical education teaching certifications, with sport specializations covering ball sports, track and field, gymnastics, and other domains. Table
10
presents the detailed demographic characteristics of participants.

Table 10.

Demographic characteristics of study participants (
N
= 124).

Characteristic

Category

n

Percentage

Gender

Female

68

54.8%

Male

56

45.2%

Educational Level

Primary School

46

37.1%

Middle School

42

33.9%

High School

36

29.0%

School location

Urban

67

54.0%

Suburban

38

30.6%

Rural

19

15.4%

Teaching experience

2–5 years

42

33.9%

6–10 years

35

28.2%

11–15 years

28

22.6%

> 15 years

19

15.3%

Educational qualification

Bachelor’s Degree

89

71.8%

Master’s Degree or Higher

35

28.2%

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All data collection procedures respected participant privacy and confidentiality, with video recordings anonymized using participant ID codes and personally identifiable information stored separately from behavioral data. The intervention group (
n
= 62) received access to the full recommendation system for six months, while the control group (
n
= 62) utilized a simplified platform containing identical continuing education resources but without personalized recommendations. Data collection was conducted between April 2023 and October 2023 following ethics approval granted on March 15, 2023. Both groups participated in three video-recorded teaching sessions at the beginning, middle, and end of the evaluation period to assess changes in teaching behaviors. Participants were informed of their right to withdraw from the study at any time without penalty, and consent forms specified that video data would be retained for 5 years for research purposes and then securely destroyed.

Recommendation accuracy evaluation employed traditional information retrieval metrics including precision, recall, and F1 score, calculated by comparing system recommendations against expert-identified optimal resources for each teacher based on their observed development needs. The mean precision across all participants reached 0.87 (SD = 0.08), indicating that approximately 87% of recommended resources were relevant to teachers’ specific development needs as determined by expert evaluation
92
. Recall performance averaged 0.83 (SD = 0.09), demonstrating the system’s capability to identify most relevant resources from the available catalog. The integrated F1 score was calculated as:

This performance substantially exceeded baseline methods including random recommendation (F1 = 0.21), popularity-based recommendation (F1 = 0.39), and content-based filtering without multimodal behavior analysis (F1 = 0.62)
93
.

Personalization effectiveness was assessed through recommendation diversity across participants and alignment with individual development needs. Inter-user recommendation diversity, measured as the average Jaccard distance between recommendation sets for different teachers, averaged 0.76 (SD = 0.11), indicating substantial personalization rather than generic recommendations across participants. Within-user recommendation relevance, assessed through post-activity surveys using a 5-point Likert scale, yielded a mean score of 4.41 (SD = 0.38), demonstrating strong alignment between recommendations and perceived development needs
94
. Temporal analysis of recommendation patterns revealed progressive refinement in personalization as the system accumulated interaction data, with the average relevance score increasing from 3.82 in the first month to 4.53 in the final month. The personalization impact was calculated using a utility gain metric:

where
represents the utility of personalized recommendations and
represents the utility of non-personalized recommendations for teacher
. The average utility gain across participants was 0.42 (SD = 0.09), indicating substantial benefit from personalization
95
.

User satisfaction evaluation employed both quantitative surveys and qualitative interviews to assess teachers’ experiences with the recommendation system. The System Usability Scale (SUS) yielded a mean score of 84.6 (SD = 7.3), substantially exceeding the established threshold of 68 for above-average usability. Feature-specific satisfaction assessment indicated highest satisfaction with recommendation relevance (4.6/5.0) and explanation clarity (4.5/5.0), with somewhat lower satisfaction for system responsiveness (3.9/5.0) and initial setup complexity (3.7/5.0)
96
. Subgroup analysis revealed variations in user satisfaction across demographic categories, as illustrated in Fig.
1
. Urban teachers reported slightly higher SUS scores (M = 86.2, SD = 6.8) compared to rural teachers (M = 83.1, SD = 7.9), though this difference was not statistically significant (t = 2.01,
p
= 0.08). Educational level showed no significant effect on satisfaction, with primary (M = 84.3, SD = 7.5), middle (M = 85.1, SD = 7.0), and high school teachers (M = 84.4, SD = 7.6) reporting comparable SUS scores (F = 0.32,
p
= 0.73). However, teaching experience significantly influenced satisfaction ratings (F = 4.28,
p
= 0.01), with early-career teachers (< 5 years experience) reporting lower scores (M = 81.9, SD = 8.2) than experienced teachers (6–15 years: M = 85.8, SD = 6.4; >15 years: M = 86.5, SD = 6.9), possibly reflecting greater comfort with technology adoption among more established professionals. These findings suggest the system performs consistently across most user segments while highlighting opportunities for enhanced onboarding support for novice teachers.

Fig. 1.

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User satisfaction (SUS Scores) by demographic subgroups. Note: Error bars represent standard deviations. Dashed line indicates SUS threshold of 68 for above-average usability.
Qualitative interviews revealed that teachers particularly valued the system’s ability to identify specific development needs through teaching behavior analysis rather than relying on self-assessment alone. As one participant noted: “The system identified patterns in my feedback approach I wasn’t consciously aware of, helping me select resources that addressed specific weaknesses rather than general topics I thought I needed.”

The impact on professional development was evaluated through comparative analysis of teaching behavior changes between the intervention and control groups. Multimodal behavior analysis of pre-post teaching sessions revealed significantly greater improvements in the intervention group across multiple dimensions including instructional clarity (d = 0.68,
p
< 0.01), demonstration quality (d = 0.72,
p
< 0.01), and feedback specificity (d = 0.59,
p
< 0.01)
97
. Student engagement metrics, assessed through automated analysis of classroom videos, showed corresponding improvements in the intervention group compared to the control group (d = 0.64,
p
< 0.01).

Longitudinal analysis demonstrated acceleration in improvement rates as the system accumulated data and refined its recommendations, with the most substantial gains occurring during the latter half of the evaluation period. Follow-up assessments three months after the formal evaluation period indicated maintenance of improved teaching behaviors in the intervention group, suggesting lasting impact rather than temporary changes during the evaluation period. Sample retention at the 3-month follow-up was 85% in the intervention group (53 of 62 teachers) and 82% in the control group (51 of 62 teachers), with no significant difference in retention rates between groups (χ²=0.19,
p
= 0.66). Attrition analysis comparing baseline characteristics between retained and dropped participants revealed no significant differences in teaching experience (t = 1.23,
p
= 0.22), educational level distribution (χ²=2.14,
p
= 0.34), initial teaching behavior quality scores (t = 0.87,
p
= 0.39), or school location type (χ²=1.56,
p
= 0.46), suggesting that dropout was random and unlikely to bias follow-up results. Intention-to-treat analysis using last-observation-carried-forward imputation for missing data confirmed the robustness of main findings, with intervention effects remaining significant for instructional clarity (d = 0.65,
p
< 0.01), demonstration quality (d = 0.70,
p
< 0.01), and feedback specificity (d = 0.57,
p
< 0.01).

The system demonstrated several notable advantages including: (1) ability to identify specific development needs that teachers were not consciously aware of through objective multimodal behavior analysis; (2) capacity to match continuing education resources precisely with individual development needs rather than general teaching domains; and (3) adaptability to changing development needs as teachers progressed through their professional growth trajectories
98
. However, several limitations were also identified: (1) initial data collection requirements created a substantial entry barrier for new users; (2) computational requirements limited real-time recommendation in some resource-constrained educational environments; and (3) the recommendation algorithm occasionally prioritized addressing weaknesses over leveraging and enhancing existing strengths.

Future development directions include: expanding the multimodal analysis capabilities to incorporate additional sensor technologies for more comprehensive behavior assessment; developing transfer learning approaches to reduce initial data requirements for new users; implementing explainable AI techniques to increase transparency in the recommendation process; and extending the system to facilitate collaborative professional development through community-based recommendation features. Additionally, longitudinal studies are needed to assess the system’s impact on teacher retention, career satisfaction, and student learning outcomes over extended periods
99
. These enhancements would address current limitations while extending the system’s capabilities to support broader aspects of teacher professional development beyond individual continuing education resource recommendations.
Author contributions

Zhongli Chen conceived and designed the study, developed the methodological framework, conducted all data collection and analysis, implemented the technical systems, performed the statistical evaluations, and wrote the manuscript. The author has read and approved the final manuscript.
Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Declarations

Competing interests

The authors declare no competing interests.
Ethics approval and consent to participate

All experimental procedures were conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. The study protocol was reviewed and approved by the Human Research Ethics Committee at Chongqing City Vocational College (approval number: CCVC-HREC-2023-015). Written informed consent was obtained from all participating teachers and school administrators prior to data collection.
Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Data Availability Statement

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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</reference>

<statements>
1. Recent ITS research introduces automatic construction of multimodal knowledge graphs from course videos and textbooks and Retrieval-Augmented Generation (RAG) to ground large language models, improving factuality and explainability of tutoring dialogue.
2. Systems such as CAM-Vtrans use multimodal visual–text fusion to provide real-time action feedback, while others use multimodal deep learning to analyze sports teachers’ behavior and recommend continuing education resources. Together, these works motivate an integrated sports intelligent tutoring system that can reason over both symbolic knowledge and sensor-derived signals.
3. Multimodal data fusion integrates information from multiple sensor or content modalities—visual, audio, textual, kinematic, and physiological—into a unified representation for downstream analysis and decision-making.
4. Audio and text for instruction clarity and feedback analysis.
5. Empirical studies in physical education and sports show that multimodal fusion consistently outperforms single-modality approaches for behavior classification, anomaly detection, and recommendation accuracy.
6. Chen (Sci Rep 2025) develops a multimodal deep learning framework to analyze sports teachers’ classroom behaviors using synchronized video, audio, wearable inertial measurement units (IMUs), and infrared motion tracking. The system defines a hierarchical classification of teaching behaviors (instructional, feedback, organizational, motivational) and a multimodal representation model capturing visual, audio, and motion features.
7. Ablation experiments show that attention-based fusion achieves an F1 score of 0.85 and behavior classification accuracy of 88.3%, surpassing early fusion (F1 0.79, accuracy 83.1%) and late fusion (F1 0.82, accuracy 85.7%). On top of this, a personalized recommendation engine uses a multi-objective optimization algorithm and multimodal behavior analysis to recommend continuing education resources, achieving F1 0.85 and high teacher satisfaction. This supports the idea that multimodal analysis can drive intelligent tutoring of coaches and instructors themselves.
8. Sensing layer: wearable devices, cameras, microphones, and environmental sensors capture raw multimodal data.
9. Core analysis layer: deep learning models (CNN, LSTM, Transformer, GCN, cross-attention, hierarchical attention) perform action recognition, anomaly detection, behavior classification, and state estimation.
10. Knowledge and tutoring layer: knowledge graphs, recommendation engines, RAG-based LLMs, and rule-based logic perform tutoring, explanation, and resource recommendations.
11. Application layer: user interfaces deliver feedback—visual overlays, textual explanations, audio prompts, and vibration alerts—and record interactions for system refinement.
12. Hierarchical attention fusion: Chen dynamically weights visual, audio, and motion modalities according to behavior categories, yielding higher F1 and accuracy than early/late fusion baselines.
13. Empirical ablations consistently show that attention-based and hybrid fusion strategies outperform simple concatenation and late fusion, particularly in complex tasks like anomaly detection, behavior analysis, and multimodal tutoring.
14. Coach/teacher professional development: analyzing teaching behaviors, recommending targeted resources, and tracking teaching quality improvements.
15. Cameras (RGB or depth) and motion capture track body posture and movement; microphones capture verbal instructions and feedback.
16. Synchronize video, sensor, and audio streams using timestamp alignment and dynamic time warping.
17. Apply hierarchical attention fusion to combine video, audio, and motion for teaching behavior analysis and quality assessment for sports educators, following Chen.
18. Offer teachers and coaches behavior analytics, quality indicators, and personalized professional development recommendations.
19. Higher recommendation and tutoring quality: Multimodal analysis and multi-objective optimization (knowledge graphs + behavioral data) can yield recommendation F1 scores around 0.85 and user satisfaction scores above 4.5/5 in teacher development contexts.
20. Federated learning and on-device processing, as used by Xi, can mitigate privacy concerns by keeping raw sensor data local while sharing only model updates. Ethical considerations include informed consent, safe training policies, and transparent explanation of AI-driven guidance, especially for youth and novice athletes.
21. Studies of sports teachers and coaches emphasize that AI systems should complement, not replace, human expertise, providing objective measurements, pattern detection, and personalized recommendations while leaving final decisions and nuanced motivational strategies to humans. Interfaces should be designed to support rapid inspection, override, and reconfiguration, and to incorporate user feedback into continuous learning loops, similar to Deng’s human-in-the-loop knowledge graph review.
22. Future work will need to expand datasets, refine fusion strategies, and deepen human–AI collaboration, but the empirical evidence already supports practical deployment of multimodal sports intelligent tutoring and guidance systems in real-world settings.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.