Design of real-time feedback system for physical education classroom under digital transformation(http://doi.org/10.63386/620873)

Yanan Liu 1 , Hongjie Liu 2, *

1.General Graduate School, Dongshin University, Naju-si ,Jeollanam-do

,58245，Republic of Korea

2. School of Physical Education and Health,Linyi University, Linyi,Shandong

,27600, China
*e-mail：hongjieliu8888@163.com

Abstract:In the context of educational digital transformation, traditional physical education (PE)
classrooms often face challenges such as delayed feedback, limited individualization, and
inefficient data utilization. To address these issues, this study designs and implements a real-time
feedback system tailored for PE instruction, integrating data collection, intelligent processing,
visual feedback, and instructional optimization into a unified framework. The system architecture
adopts a four-layer model—platform, core control, terminal, and data analysis—to ensure modular
coordination and extensibility.The system enables real-time sensing through wearable devices,
collects physiological and motion data, and processes these inputs using edge computing and
visual analysis techniques. Feedback is delivered to students via multi-modal outputs including
visual cues, voice prompts, and dashboard summaries, while teachers receive real-time diagnostic
support and post-class reports. The student interface is designed for intuitive interaction,
supporting engagement and self-correction during active movement sessions.A field experiment
was conducted in three junior high school classes, involving over 90 students and multiple PE
sessions. The results demonstrate the system’s effectiveness: average feedback response times
were under two seconds, and student satisfaction reached 84.3%, as shown in post-session
surveys. Teachers also reported increased efficiency and decision-making accuracy during
classroom instruction. However, challenges such as environmental signal interference and motion
recognition errors under complex movements were identified, highlighting areas for future
technical optimization.This study contributes to the integration of educational technology and
intelligent feedback in physical education, offering a practical solution for real-time, data-driven
teaching. The proposed system promotes timely interventions, improves learning engagement, and
supports evidence-based instruction. Future work will focus on adaptive feedback personalization,
scalability across diverse PE settings, and long-term impacts on student performance and
motivation.
Keywords: digital transformation, physical education, real-time feedback, wearable sensing,
instructional optimization, educational technology

1. Introduction
   With the continuous advancement of information technology, digital transformation has become a
   crucial driving force for educational reform. In basic education, digital technologies are
   profoundly reshaping classroom organization, content delivery methods, and teacher-student
   interaction pathways. As a vital component of quality-oriented education, physical education
   classes have long been constrained by factors such as delayed feedback mechanisms, one-way

teacher-student interactions, and insufficient personalized guidance[1]. In traditional PE classes,
teachers often struggle to obtain real-time data on students’ movement execution, physical
performance, and classroom engagement. This results in subjective and non-targeted feedback that
hinders continuous improvement in teaching effectiveness.
As a vital application in educational informatization, real-time feedback systems have been
widely implemented in subjects like mathematics and English. Their teaching-assistance
capabilities, based on real-time data collection and visual analysis, provide effective tools to
enhance classroom efficiency and optimize instructional practices[2-3]. However, research and
application of such systems in physical education remain in their infancy. Due to the open nature
of PE classrooms, dynamic student movements, and complex data collection challenges, most
existing systems struggle to meet the demands for real-time responsiveness, high precision, and
interactive feedback[4]. Therefore, developing an integrated real-time feedback system tailored to
PE classroom characteristics—featuring data acquisition, feedback generation, and teaching
optimization functions—holds significant academic value and practical significance.
This study proposes an instant feedback system design for digital physical education
classrooms, adopting a three-dimensional integration perspective of “technology-teaching
behaviors-learning outcomes”. The system integrates data acquisition modules (e.g., motion
recognition and heart rate monitoring), feedback generation mechanisms (e.g., visual feedback and
scoring systems), and teaching support terminals (e.g., teachers ‘mobile devices and large-screen
displays). Using a secondary school PE class as a case study, the system underwent experimental
implementation and effectiveness evaluation. Through its deployment, the system not only
enhanced teachers’ ability to make timely and scientific instructional adjustments but also
improved students’ perception of physical performance and classroom engagement.

2. Research methods and technical routes
   This study employs a systematic design methodology to develop an instant feedback system
   tailored for digital physical education classrooms. The research framework begins with needs
   analysis, leveraging modern information technology architecture to focus on four core
   components: “data collection, data processing, feedback generation, and instructional
   optimization”. By integrating modular design principles, the system achieves functional
   integration and application optimization[5-6]. Developed using object-oriented design principles,
   the system utilizes wearable devices and teaching terminals in synergy to enable real-time
   classroom data perception and interactive feedback.
   2.1 Technical architecture and module design
   The system adopts a four-layer structure architecture of “platform layer, core system layer,
   terminal execution layer and data analysis layer” (see Figure 1).
   The platform layer utilizes the school’s digital education platform as its supporting
   environment, responsible for stable system deployment and permission management. The core
   system layer serves as the control center of this system, integrating command scheduling, module

coordination, and resource allocation functions. The terminal execution layer includes teacher
feedback devices (such as tablets), student wearable devices (like smart bracelets and motion
capture trackers), and teaching display equipment (including large screens and projectors). The
data analysis layer processes collected data through modeling, featuring modules like “Classroom
Behavior Analysis”, “Student Movement Analysis”, and “Teaching Optimization Suggestions”.
These modules are interconnected via wireless networks, with internal data flows progressing
from the perception layer to the processing layer before being transmitted back to teachers through
feedback modules, enabling closed-loop control of teaching processes. The system interface
design adheres to the principles of “simplicity, intuitiveness, and efficiency”, ensuring teachers can
quickly access system feedback in complex teaching scenarios to assist in instructional decision-
making.
2.2 Data acquisition method
Data collection is the prerequisite for system operation, which is mainly carried out by the multi-
mode sensing devices worn by students. According to the activity characteristics of physical
education classes, the data collection content mainly includes [7-8]:
Physiological data: such as heart rate and body temperature, using Bluetooth heart rate belt
and infrared temperature measuring device;
Motion data: such as the frequency, amplitude and rhythm of running, jumping and throwing,
using three-axis accelerometer and gyroscope;
Location data: Determine the activity range of students through UWB positioning or camera
video analysis;
Classroom behavior data: such as action completion rate and participation heat, which are
automatically generated by image recognition and heat map algorithm.
The system is configured to collect data at 5-10 samples per second, ensuring immediate
generation of initial feedback. All raw data is transmitted via encrypted wireless networks to the
core system, where it undergoes cleaning and formatting before being analyzed in real-time by
processing modules. The system also incorporates edge computing frameworks, enabling partial
data preprocessing at terminal devices to reduce server workload.

3. System design and implementation
   Based on the principle of “task-driven + data-driven”, this system adopts modular,
   configurable and responsive design strategy to deeply integrate key links and technical functions
   in physical education classroom, so as to ensure that the system has real-time, interactive and
   educational adaptability [9].
   3.1 Function module design

Based on the principle of “task-driven + data-driven”, this system adopts modular,
configurable and responsive design strategy to deeply integrate key links and technical functions
in physical education classroom, so as to ensure real-time, interactive and educational adaptability
of the system.
3.1.1 Teacher terminal function
Teachers can access the backend through teaching tablets or laptops to monitor student data
in real time, control feedback outputs, receive instructional suggestions, and generate course
reports (see Table 2). The system provides multiple data visualization methods (graphics, charts,
metrics) to help teachers quickly understand students’ physical performance and engagement
levels[10-12].
3.1.2 Student interactive interface
After students wear the device, they can get feedback information through wristband,
electronic screen or voice prompt. The interactive interface module has graphical representation,
voice broadcast, autonomous click feedback and other functions to meet the perceptual needs of
students of different ages (see Table 3).
3.1.3 Real-time feedback mechanism
The feedback mechanism is the core part of the system. Through the system setting threshold
and behavior recognition algorithm, different types of feedback can be triggered, such as color
prompt, vibration reminder, voice broadcast, graphic warning and other forms (see Table 4). The
feedback mechanism supports both automatic triggering and manual sending by teachers.
3.2 Information visualization and interaction strategy
In the design of the system, teachers ‘operation efficiency and students’ perceptual ability are
fully considered to build a multi-level visual interaction system:
Teacher-end interaction strategy: Utilizes card layouts with visual comparisons, supporting
filtering by time, project, and student. Real-time data charts (e.g., line graphs, radar charts) enable
instant decision-making. Student-end interaction strategy: Transmits physical movement through
color gradients and animations, delivering concise, intuitive feedback. Feedback output format:
Multi-modal integration like “red icons + voice prompts + score updates” enhances accuracy and
acceptance. Visual presentation hierarchy: The system supports three display dimensions: real-
time data (live scenes), periodic data (per-class), and cumulative data (semester summaries),
meeting diverse teaching evaluation needs.
Table 2

Module Function Description
Real-time Monitoring Display student activity data in real-time (e.g., heart rate,

movement)

Student Performance
Overview Aggregate performance data by student or class
Feedback Control Trigger individual or group feedback mechanisms
Teaching
Recommendations

Generate suggestions based on class engagement and

performance

Class Report Export Export summary reports for teaching reflection and

assessment

Table 3

Interface Element Function Description
Motion Indicator Displays movement status using icon colors or meters
Visual Feedback Prompt Pop-up visuals when action is correct/incorrect
Voice Prompt Audio prompts indicating errors or encouragement
Performance Dashboard Shows cumulative scores and exercise stats
Interaction Button Used for active response, confirmation or self-assessment

Table 4

Feedback
Type

Description Application Scenario

Visual

Uses colors, icons or animations for
instant perception

In dynamic movement tasks (e.g.,
sprint, jump)

Auditory

Voice or sound alerts triggered by
data thresholds

In high noise environments or for fast-

paced guidance

Textual

Short phrases displayed on screen
(e.g., ‘Try Again’)

For students with visual impairments
or younger students

Data
Dashboar
d

Graphical summary of student
performance data

For teachers to view patterns and
trends after class

4. System test and application effect evaluation
   In order to verify the feasibility and effectiveness of the digital sports classroom real-time
   feedback system in real teaching situations, this study selected three classes from a middle school
   in a city to carry out system testing, and carried out quantitative and qualitative evaluation on
   feedback response speed, student satisfaction and classroom participation performance
   respectively, so as to comprehensively judge the practical application value of the system.
   4.1 Experimental Settings (e.g., physical education test in a school)
   In order to verify the feasibility and effectiveness of the constructed digital sports classroom
   real-time feedback system in real teaching situations, this study selected three classes in a middle
   school in a city to carry out system testing, and conducted quantitative and qualitative evaluation
   on feedback response speed, student satisfaction and classroom participation performance
   respectively, so as to comprehensively judge the practical application value of the system[13].
   4.2 Data collection (teaching satisfaction, student performance, feedback response time)
   This study focuses on the collection of data for three key indicators:
   (1) Feedback Response Time: The average duration from detecting abnormal behavior to
   generating feedback reflects the system’s real-time responsiveness and technical stability. Data is
   automatically recorded in backend logs, as shown in Figure 6. Results indicate that Class A and C
   maintain stable average response times between 1.5-1.7 seconds, while Class B shows
   significantly slower performance, which may be related to on-site equipment layout or network
   environment factors.
   (2) Teaching Satisfaction: A self-designed questionnaire was used to assess students’

subjective perceptions regarding system operation experience, feedback effectiveness, and usage
interest. A total of 96 questionnaires were distributed with a 100% response rate. As shown in
Figure 7, the proportion of “very satisfied” and “satisfied” responses reached 84.3%, indicating
that the majority of students maintained a positive attitude toward the system.
(3) Classroom Performance and Participation: By combining the system-generated classroom
scoring records with teachers ‘observational feedback, this study evaluated students’ exercise
completion quality, frequency of active interaction, and classroom concentration. The data showed
that after the system intervention, students’ average scores increased by 12.6%, and the ratio of
active questioning to feedback actions significantly improved.
4.3 Effectiveness analysis
The overall performance of the system in field teaching is good, which verifies the feasibility
of its design objectives:
The feedback mechanism operates efficiently with a response time under 2 seconds, meeting the
essential “real-time” feedback requirements for physical education classes. Students demonstrate
high acceptance, as diversified feedback formats (visual/auditory) enhance engagement and
participation. The system’s teaching support value is significant, enabling teachers to identify
instructional gaps more precisely through real-time data analysis and implement personalized
adjustments.
However, this round of testing also exposed some shortcomings, such as some devices still
have misjudgment in motion recognition, signal interference occasionally occurs in outdoor
environment and other problems. In the future, we will optimize hardware compatibility and data
processing fault tolerance mechanism.

5. Discussion and conclusion

This study addresses critical challenges in digital transformation within physical education
classrooms, including delayed feedback, insufficient personalization, and low data utilization
rates. We developed an integrated real-time feedback system combining “data collection, real-time
feedback, and teaching decision support”. Through technical architecture design, functional
module development, field application testing, and quantitative analysis, the system has
demonstrated significant improvements in enhancing classroom interaction efficiency and
feedback timeliness.
From a system architecture perspective, the four-tier framework (Platform Layer, Core Layer,
Terminal Layer, Analysis Layer) proposed in this study not only decouples and coordinates
module functionalities but also provides robust support for future system scalability. Key modules
such as real-time monitoring for teachers, visual feedback systems for students, automated data
processing, and instructional suggestion delivery cover critical components of physical education
classroom workflows, enabling rapid closed-loop integration between teaching practices and
learning outcomes. Field testing results demonstrate the system’s strong adaptability in data
accuracy, instant feedback delivery, and user acceptance. Average response times for student
feedback are kept under 2 seconds, with the system promptly providing individual movement
quality assessments to educators for timely corrections. Satisfaction surveys show over 84% of
responses rated “satisfied” or higher, indicating widespread recognition of the system’s practical
value and user-friendliness. Teacher evaluations highlight that the visual interface and scoring
mechanism enhance teaching management efficiency and scientific rigor. However, several areas
require optimization: signal fluctuations in outdoor sports field environments may affect data
transmission, while equipment still exhibits measurement errors during extreme motion capture.
Additionally, differences in perception preferences between age groups regarding feedback
formats (e.g., voice versus icons) suggest the need for deeper system adaptation development in
personalized feedback and multimodal sensing capabilities.

Theoretically, this study effectively integrates educational technology and artificial
intelligence perception mechanisms with physical education classroom scenarios, expanding the
research boundaries of data-driven teaching practices in sports education. Unlike traditional
teaching models that rely on teachers’ experiential judgments, this system utilizes real-time data to
enhance the objectivity, immediacy, and precision of classroom feedback and instructional
interventions, providing an actionable practical paradigm for intelligent sports education[14-15].
In conclusion, the real-time feedback system developed in this study demonstrates promising
potential for implementation in physical education classrooms, effectively enhancing classroom
interaction efficiency and instructional feedback quality. Future research could expand its
application to multiple grade levels and teaching scenarios by integrating artificial intelligence
recognition algorithms, edge computing, and learning analytics models, thereby driving the
evolution of physical education toward “intelligent, precise, and personalized” directions.
Additionally, it is crucial to focus on improving teachers’ professional competencies and providing
systematic training for system utilization, ensuring the continuous integration and practical
effectiveness of technology into educational processes.

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