Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms
Abstract
:1. Introduction
2. Smart Sensor Technologies and Applications
2.1. Environmental Sensors
2.1.1. Climate Control
2.1.2. Lighting
2.1.3. Noise Control
2.2. Biometric Sensors
2.2.1. Engagement Analysis
Image-Based Biometric Sensors
No-Image-Based Biometric Sensors
Sensor Type | Monitored Feature | Monitoring Purpose | Typical Studies |
---|---|---|---|
EEG Device | Brain activity | Concentration and mental state analysis | [39,40] |
Acoustic Sensor | Voice modulation | Stress and emotional state analysis | [37,38] |
Galvanic Skin Response Sensor | Skin conductance | Stress and emotional arousal | [41,42] |
Heart Rate Monitor | Heart rate | Stress levels and engagement | [42,43] |
Blood Oxygen Sensor | Blood oxygen levels | Health monitoring in physical education | [43] |
Respiratory Rate Sensor | Respiratory rate | Stress levels and engagement | [40] |
2.2.2. Attendance
2.2.3. Accessibility Support
2.3. Overview of Sensor Technologies and Applications in Classroom
3. Software: Integration with Artificial Intelligence
3.1. Data Processing and Understanding
3.1.1. Image-Based Recognition Algorithm
3.1.2. Non-Image-Based Recognition Algorithms
- Spectral Entropy: a measure of the randomness and complexity of the sound signals, which is usually used to distinguish between voiced (such as speech) and unvoiced (such as breathing or background noise) sound.
- Formant Frequency: relates to the frequency characteristics of a sound and can reflect the quality of a vowel, helping to identify the content and intensity of speech.
- Autocorrelation: used to analyze the periodicity of a sound signal and helps to identify the rhythm, rate, and repetition pattern of a sound.
- Energy: the loudness of the sound signal, which reflects the level of activity and engagement of the students.
3.2. Data Analysis and Content Generation
3.2.1. Analysis and Prediction
3.2.2. Teaching Quality Assessment and Student Feedback
3.2.3. Personalized Learning and Instructional Support
3.3. Summary of AI Integrated Sensor Software Algorithms in Classroom
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sensor Type | Monitored Feature | Monitoring Purpose | Typical Studies |
---|---|---|---|
Thermal sensor | Temperature | Classroom climate temperature | [10] |
Acoustic sensor | Sound level meter | Detecting noise levels | [11,12] |
Infrared radio sensor | Infrared energy | Scouting for student activity in the classroom | [13] |
Carbon dioxide sensors | Carbon dioxide content | Monitoring of carbon dioxide levels in the air | [10,14] |
Photosensitive sensor | Lighting conditions | Monitoring light levels and uniformity | [15,16] |
Infrared camera | Infrared image | Student body temperature | [17] |
Sensor Type | Monitored Feature | Monitoring Purpose | Typical Studies |
---|---|---|---|
Facial recognition system | Facial features | Identification and emotional state analysis | [25] |
Posture and motion sensor | Body posture | Engagement analysis and classroom dynamics | [26] |
Thermal camera | Body temperature | Health monitoring and stress analysis | [17] |
Eye-tracking device | Eye movement and pupil dilation | Focus and engagement analysis | [27,28,29,30] |
Sensor Type | Monitored Data | Monitoring Purpose | Typical Studies |
---|---|---|---|
Camera | Facial or body image features | Facial features identification | [50] |
Fingerprint Sensor | Fingerprint patterns | Fingerprint features identification | [49] |
Acoustic Sensors | Voice characteristics | Voice features identification | [52] |
Pressure Sensor | The pressure applied to the chair | Detect if someone is sitting in the chair | [53] |
Infrared Camera | Infrared motion image | Detect movements | [54] |
Types of Recognition Sensors | Recognized Features | Recognition Purpose | Classifier | Accuracy | Typical Study |
---|---|---|---|---|---|
Computer camera | Student facial images | Emotion classification | SVM Regression | 99.16% | [68] |
Classroom wall-mounted camera | Student facial images | Dynamically evaluate classroom performance | CNN | 70.1% | [69] |
Classroom wall-mounted camera | Student facial images | Automated attendance | CNN | 99% | [70] |
Classroom wall-mounted camera | Student facial images | Facial feature recognition, emotion recognition, classroom sign-in | MTCNN | Facial recognition: 98% Emotion recognition: 92% | [71] |
Classroom wall-mounted camera | Student facial images | Student identification | KNN | 93.94% | [72] |
Classroom wall-mounted camera | Student facial images | Face recognition under blurry conditions | YOLOv5 | 81.42% | [73] |
Computer camera | Student facial images | Confused emotion recognition | CNN–SVM | 93.8% | [74] |
Computer camera | Student body movement images | Student action recognition | CNN | 89.5% | [75] |
Types of Recognition Sensors | Recognized Features | Recognition Purpose | Classifier | Accuracy | Typical Study |
---|---|---|---|---|---|
Acoustic Sensor | Student Voice | Understanding student interest levels | Adaboost M1 | 81.9% | [37] |
Acoustic Sensor | Teacher’s Voice | Reflecting student satisfaction | Random Forest | 70.7%~83.9% | [88] |
Acoustic Sensor | Teacher’s Voice | Identify emotional information in teachers’ voice during teaching | CNN and LSTM | 75.36% | [89] |
EEG Sensors | Student EEG signals | Identifying student emotions | DeepBi LSTM | Binary classification: 70.89% Multivariate classification: 90.33% | [44] |
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Zhang, X.; Ding, Y.; Huang, X.; Li, W.; Long, L.; Ding, S. Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms. Sensors 2024, 24, 5487. https://doi.org/10.3390/s24175487
Zhang X, Ding Y, Huang X, Li W, Long L, Ding S. Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms. Sensors. 2024; 24(17):5487. https://doi.org/10.3390/s24175487
Chicago/Turabian StyleZhang, Xiaochen, Yiran Ding, Xiaoyu Huang, Wujing Li, Liumei Long, and Shiyao Ding. 2024. "Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms" Sensors 24, no. 17: 5487. https://doi.org/10.3390/s24175487