Classroom seat layout dynamic optimization method and system

Abstract

The invention discloses a classroom seat layout dynamic optimization method and system, and belongs to the technical field of education, and the method comprises the steps: obtaining the structured score data and associated behavior data of students; analyzing the structured score data and the associated behavior data to obtain score trend prediction data and learning ability portrait data; and processing the score trend prediction data and the learning ability portrait data based on a preset optimization strategy to obtain seat layout optimization data. By obtaining the structured score data and the associated behavior data of the students, appropriate judgment data can be provided for subsequent analysis; the structured score data and the associated behavior data are analyzed to obtain score trend prediction data and learning ability portrait data, and education attribute data of students can be obtained to provide materials for subsequent optimization; and based on a preset optimization strategy, processing the score trend prediction data and the learning ability portrait data to obtain seat layout optimization data, so that seats can be reasonably arranged.

Description

A method and system for dynamic optimization of classroom seating layout Technical Field

[0001] This invention relates to the field of educational technology, and in particular to a method and system for dynamic optimization of classroom seating layout. Background Technology

[0002] Current classroom seating arrangement management mainly relies on the following traditional methods, the core of which is static planning and human experience-driven. This seating arrangement method relies on experience and is difficult to promote. On the other hand, due to the decline in teachers' energy and planning ability, it may neglect students who need attention, resulting in a situation where the good students get even better, which does not conform to the original intention of education.

[0003] With the development of the education industry and the implementation of specific educational measures, scientific and reproducible seating planning is needed. Therefore, manual planning methods are gradually unable to meet the requirements of the industry. Summary of the Invention

[0004] This invention proposes a method and system for dynamic optimization of classroom seating layout to address the limitations of existing technologies.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for dynamically optimizing classroom seating layout includes: acquiring structured academic performance data and related behavioral data of students; parsing the structured academic performance data and the related behavioral data to obtain academic performance trend prediction data and learning ability profile data; and processing the academic performance trend prediction data and the learning ability profile data based on a preset optimization strategy to obtain seating layout optimization data.

[0007] Furthermore, the structured performance data includes mock exam scores, quiz scores, homework completion rates, and quiz results; the related behavioral data includes attendance records and course participation; obtaining students' structured performance data and related behavioral data also includes: establishing a time-series database of student performance data, storing it by subject and knowledge point; and eliminating biases caused by differences in subject difficulty through data cleaning and normalization.

[0008] Furthermore, the step of parsing the structured performance data and the associated behavioral data to obtain performance trend prediction data and learning ability profile data includes: processing the structured performance data using a time series model to obtain the performance trend prediction data; parsing the structured performance data to obtain performance fluctuation data, processing the performance fluctuation data and the associated behavioral data based on a random forest algorithm to obtain individual correlations; constructing a multidimensional scoring system to process the structured performance data to quantify students' knowledge mastery; processing the performance trend prediction data through cluster analysis to segment student groups; and labeling the individual correlations, knowledge mastery quantification values, and student group segmentation results to obtain the learning ability profile data.

[0009] Furthermore, the optimization strategy includes: pairing theoretically strong students with practically strong students in the student group segmentation results to promote collaborative learning; adjusting the seating areas of corresponding students based on recent performance trends in the performance trend prediction data; processing the recent performance trends based on a reinforcement learning model to prioritize allocating high-interaction area seats to students with large performance fluctuations; and setting fairness constraints to prevent high-achieving students from occupying front-row resources for extended periods.

[0010] Furthermore, the associated behavioral data includes: head orientation, gaze duration, blink frequency, number of hand raises, body movement amplitude during group discussions, facial expression recognition, head-down frequency, number of times students leave their seats, and interaction interval duration. Correspondingly, the acquisition of students' structured grade data and associated behavioral data includes: using OpenPose to detect head key points to determine the head orientation; using the ResNet-50 model to recognize facial expressions; and detecting the head-down frequency, the number of times students leave their seats, and the interaction interval duration based on an independent forest model.

[0011] A dynamic optimization system for classroom seating layout includes: a first module for acquiring structured academic performance data and related behavioral data of students; a second module for parsing the structured academic performance data and the related behavioral data to obtain academic performance trend prediction data and learning ability profile data; and a third module for processing the academic performance trend prediction data and the learning ability profile data based on a preset optimization strategy to obtain seating layout optimization data.

[0012] Furthermore, the structured performance data includes mock exam scores, quiz scores, homework completion rates, and quiz results; the related behavioral data includes attendance records and course participation; obtaining students' structured performance data and related behavioral data also includes: establishing a time-series database of student performance data, storing it by subject and knowledge point; and eliminating biases caused by differences in subject difficulty through data cleaning and normalization.

[0013] Furthermore, the step of parsing the structured performance data and the associated behavioral data to obtain performance trend prediction data and learning ability profile data includes: processing the structured performance data using a time series model to obtain the performance trend prediction data; parsing the structured performance data to obtain performance fluctuation data, processing the performance fluctuation data and the associated behavioral data based on a random forest algorithm to obtain individual correlations; constructing a multidimensional scoring system to process the structured performance data to quantify students' knowledge mastery; processing the performance trend prediction data through cluster analysis to segment student groups; and labeling the individual correlations, knowledge mastery quantification values, and student group segmentation results to obtain the learning ability profile data.

[0014] Furthermore, the optimization strategy includes: pairing theoretically strong students with practically strong students in the student group segmentation results to promote collaborative learning; adjusting the seating areas of corresponding students based on recent performance trends in the performance trend prediction data; processing the recent performance trends based on a reinforcement learning model to prioritize allocating high-interaction area seats to students with large performance fluctuations; and setting fairness constraints to prevent high-achieving students from occupying front-row resources for extended periods.

[0015] Furthermore, the associated behavioral data includes: head orientation, gaze duration, blink frequency, number of hand raises, body movement amplitude during group discussions, facial expression recognition, head-down frequency, number of times students leave their seats, and interaction interval duration. Correspondingly, the acquisition of students' structured grade data and associated behavioral data includes: using OpenPose to detect head key points to determine the head orientation; using the ResNet-50 model to recognize facial expressions; and detecting the head-down frequency, the number of times students leave their seats, and the interaction interval duration based on an independent forest model.

[0016] By adopting the above technical solution, the present invention has the following beneficial effects:

[0017] 1. This invention obtains structured academic performance data and related behavioral data of students, which can provide suitable judgment materials for subsequent analysis; by analyzing the structured academic performance data and the related behavioral data, academic performance trend prediction data and learning ability profile data are obtained, which can provide materials for subsequent optimization; based on a preset optimization strategy, the academic performance trend prediction data and the learning ability profile data are processed to obtain seat layout optimization data, which can reasonably arrange seats. Attached Figure Description

[0018] Figure 1 is a flowchart of a dynamic optimization method for classroom seating layout proposed in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Figure 1 illustrates a method for dynamic optimization of classroom seating layout, which includes:

[0021] S1. Obtain students' structured academic performance data and related behavioral data;

[0022] S2. Analyze the structured performance data and the related behavioral data to obtain performance trend prediction data and learning ability profile data;

[0023] S3. Based on the preset optimization strategy, process the performance trend prediction data and the learning ability profile data to obtain seat layout optimization data.

[0024] Structured academic performance data refers to data that has been organized and formatted to meet specific data storage and processing requirements. This type of data facilitates systematic processing and storage, making it suitable for the standardization and professionalization of education. Related behavioral data comprises various student behaviors collected, primarily including behaviors describing the student's own characteristics, interactions between the student and the outside world, and data obtained by digitally describing these behaviors.

[0025] By using a specified data processing algorithm, the structured grade data and the associated behavioral data are parsed to obtain predicted grade values, i.e., grade trend prediction data, and values ​​used to describe the personal image formed by students during the learning process, i.e., learning ability profile data.

[0026] By using a preset optimization strategy (i.e., a preset data processing algorithm), the performance trend prediction data and the learning ability profile data are processed to obtain seat layout optimization data.

[0027] The structured performance data includes mock exam scores, quiz scores, homework completion rates, and quiz results; the related behavioral data includes attendance records and course participation; the acquisition of students' structured performance data and related behavioral data also includes: establishing a time-series database of student performance data, storing it according to subject and knowledge point dimensions; and eliminating biases caused by differences in subject difficulty through data cleaning and normalization.

[0028] Homework completion includes the number of times assignments are completed and the percentage of each assignment completed. In-class quizzes include short questions and tests given in class. Course participation mainly includes the number of times questions are answered.

[0029] Establishing a time-series database of student grades, categorized and stored by subject and knowledge point, allows for data classification and facilitates accurate analysis. Data cleaning and normalization processes eliminate biases caused by differences in subject difficulty, enabling comprehensive analysis of grades.

[0030] The process of parsing the structured performance data and the associated behavioral data to obtain performance trend prediction data and learning ability profile data includes: processing the structured performance data using a time series model to obtain the performance trend prediction data; parsing the structured performance data to obtain performance fluctuation data, processing the performance fluctuation data and the associated behavioral data based on a random forest algorithm to obtain individual correlations; constructing a multidimensional scoring system to process the structured performance data to quantify students' knowledge mastery; processing the performance trend prediction data through cluster analysis to segment student groups; and labeling the individual correlations, knowledge mastery quantification values, and student group segmentation results to obtain the learning ability profile data.

[0031] Performance fluctuation data refers to sudden increases or decreases in performance data, where the difference meets a certain threshold. Personality correlation describes the probability and frequency of various behaviors occurring during the period corresponding to performance fluctuation data, used to determine the correlation between the two.

[0032] The optimization strategy includes: pairing theoretically strong students with practically strong students from the student group segmentation results to promote collaborative learning; adjusting the seating areas of corresponding students based on recent performance trends in the performance trend prediction data; processing the recent performance trends based on a reinforcement learning model to prioritize allocating high-interaction area seats to students with large performance fluctuations; and setting fairness constraints to prevent high-achieving students from occupying front-row resources for a long time.

[0033] The associated behavioral data includes: head orientation, gaze duration, blink frequency, number of hand raises, body movement amplitude during group discussions, facial expression recognition, head-down frequency, number of times students leave their seats, and interaction interval duration. Correspondingly, the acquisition of students' structured grade data and associated behavioral data includes: using OpenPose to detect head key points to determine the head orientation; using the ResNet-50 model to recognize facial expressions; and detecting the head-down frequency, the number of times students leave their seats, and the interaction interval duration based on an independent forest model.

[0034] A dynamic optimization system for classroom seating layout includes: a first module for acquiring structured academic performance data and related behavioral data of students; a second module for parsing the structured academic performance data and the related behavioral data to obtain academic performance trend prediction data and learning ability profile data; and a third module for processing the academic performance trend prediction data and the learning ability profile data based on a preset optimization strategy to obtain seating layout optimization data.

[0035] The structured performance data includes mock exam scores, quiz scores, homework completion rates, and quiz results; the related behavioral data includes attendance records and course participation; the acquisition of students' structured performance data and related behavioral data also includes: establishing a time-series database of student performance data, storing it according to subject and knowledge point dimensions; and eliminating biases caused by differences in subject difficulty through data cleaning and normalization.

[0036] The process of parsing the structured performance data and the associated behavioral data to obtain performance trend prediction data and learning ability profile data includes: processing the structured performance data using a time series model to obtain the performance trend prediction data; parsing the structured performance data to obtain performance fluctuation data, processing the performance fluctuation data and the associated behavioral data based on a random forest algorithm to obtain individual correlations; constructing a multidimensional scoring system to process the structured performance data to quantify students' knowledge mastery; processing the performance trend prediction data through cluster analysis to segment student groups; and labeling the individual correlations, knowledge mastery quantification values, and student group segmentation results to obtain the learning ability profile data.

[0037] The optimization strategy includes: pairing theoretically strong students with practically strong students from the student group segmentation results to promote collaborative learning; adjusting the seating areas of corresponding students based on recent performance trends in the performance trend prediction data; processing the recent performance trends based on a reinforcement learning model to prioritize allocating high-interaction area seats to students with large performance fluctuations; and setting fairness constraints to prevent high-achieving students from occupying front-row resources for a long time.

[0038] The associated behavioral data includes: head orientation, gaze duration, blink frequency, number of hand raises, body movement amplitude during group discussions, facial expression recognition, head-down frequency, number of times students leave their seats, and interaction interval duration. Correspondingly, the acquisition of students' structured grade data and associated behavioral data includes: using OpenPose to detect head key points to determine the head orientation; using the ResNet-50 model to recognize facial expressions; and detecting the head-down frequency, the number of times students leave their seats, and the interaction interval duration based on an independent forest model.

[0039] Existing classroom seating layout management mainly relies on the following traditional methods, the core of which is static planning and human experience-driven approaches:

[0040] 1. Manual seating arrangement

[0041] Operation method: Teachers manually arrange seats based on experience. Common strategies include arranging by height, grouping by academic performance, and gender balance.

[0042] Typical scenarios: In elementary school classrooms, seating is arranged according to height to avoid obstructing the view; in middle school classrooms, students are divided into "high-achieving student area" and "students needing improvement area" based on their academic performance.

[0043] 2. Software assistance based on simple rules

[0044] Tool type: Use Excel spreadsheets, class management software (such as ClassDojo), or lightweight seating arrangement tools.

[0045] Algorithm logic: Generates a static seating chart according to preset rules (such as random allocation or sorting by the first letter of the surname), without dynamic adjustment capability.

[0046] 3. Fixed layout mode

[0047] Common layouts:

[0048] The paddy field arrangement (row and column arrangement) is suitable for lecture-style teaching, but has poor interactivity.

[0049] Small group format: Fixed groups of 4-6 people, suitable for discussion but lacking flexibility.

[0050] Application limitations: Once the layout is fixed, it is difficult to dynamically adjust it according to the course content.

[0051] The corresponding issues include:

[0052] 1. Data has only one dimension, resulting in superficial analysis.

[0053] Problem: Relying on structured data such as grades, height, and attendance, while ignoring unstructured data such as classroom behavior (e.g., attention and interaction frequency).

[0054] Consequence: The inability to capture students' real-time status leads to biased decision-making.

[0055] 2. Decision-making relies on subjective experience and lacks objectivity.

[0056] Problem: Teachers manually assigning seats is susceptible to implicit biases (such as stereotypes about "active students"), and the effectiveness is difficult to quantify.

[0057] Case study: Placing lower-achieving students in the back row may exacerbate the learning gap.

[0058] 3. Adjustments are delayed, failing to respond to demands in real time.

[0059] Problem: Seating layout adjustments are done in a long period (usually on a monthly basis), making it difficult to adapt to dynamic changes in the classroom (such as switching from lectures to group discussions).

[0060] Data comparison: Traditional methods take hours to days to adjust, while AI-driven solutions can achieve a response time in minutes.

[0061] 4. Insufficient personalization and fairness

[0062] question:

[0063] Homogeneous grouping ignores individual differences among students.

[0064] Lack of fair standards (such as unfair allocation of front-row seats)

[0065] 5. Poor scene adaptability

[0066] Problem: Fixed layouts are difficult to adapt to diverse teaching scenarios. For example:

[0067] Theoretical lessons need a "rice paddy field" approach to focus the teacher's attention;

[0068] Experimental classrooms need a flexible circular layout to support equipment sharing;

[0069] Dual-teacher classrooms need to take into account the eye contact and interaction between local and remote students.

[0070] Consequence: Teachers are forced to compromise between "layout adaptation" and "teaching efficiency".

[0071] 6. Low level of technology integration and lack of closed-loop optimization.

[0072] Problem: Existing tools are isolated and fragmented (such as attendance systems and seating arrangement software being independent), data cannot be integrated, and there is no closed loop of "behavior collection-analysis-optimization-verification".

[0073] Case: A school uses cameras to record behavior, but the data is not linked to the seating arrangement system and is only used for post-event discipline management.

[0074]

[0075] The core objective of this invention is to construct a classroom seating layout optimization system based on multimodal data fusion and artificial intelligence dynamic decision-making, addressing the systemic shortcomings of traditional methods such as single data dimension, high decision-making subjectivity, delayed response, and poor scenario adaptability. Specific objectives include:

[0076] Multi-source heterogeneous data fusion: Integrating student behavior data (attention span, interaction frequency, learning simulation test scores, student daily grades), course content characteristics (theory / experiment / dual-teacher model), and other multimodal information to break through the limitations of structured data.

[0077] Dynamic real-time control capability: Through reinforcement learning and digital twin simulation, seat layout optimization can be achieved at the minute level, responding to instantaneous switching of teaching scenarios (such as lecture → group discussion → experimental operation).

[0078] Personalization and fairness are optimized in tandem: differentiated seating schemes are matched based on student behavior profiles, while the fairness of front-row seat allocation is quantified through algorithms.

[0079] End-to-end closed-loop verification system: Construct a closed loop for the entire process of "data collection → behavior modeling → layout generation → effect feedback", and support A / B testing to verify the effectiveness of optimization.

[0080] 1: Behavioral analysis based on academic performance

[0081] Data collection and integration

[0082] Data source:

[0083] Structured performance data includes mock exam scores, quiz scores, homework completion rates, and quiz results.

[0084] Related behavioral data: Combining attendance records (frequency of lateness / early departure), course participation (number of questions asked, level of discussion activity), etc.

[0085] Data processing:

[0086] Establish a time-series database of student grades and store it categorized by subject and knowledge point.

[0087] Data cleaning and normalization processes eliminate biases caused by differences in subject difficulty.

[0088] Behavioral modeling and analysis

[0089] Performance Trend Prediction:

[0090] Time series models (such as LSTM) are used to predict trends in student performance and to identify the "potential improvement group" and the "intervention-required group".

[0091] We combined the random forest algorithm to analyze the correlation between performance fluctuations and other behaviors (such as absenteeism rate and homework submission delay).

[0092] Learning ability profile:

[0093] Construct a multi-dimensional scoring system to quantify students' knowledge mastery (such as formula derivation ability and experimental operation proficiency).

[0094] Students can be grouped using cluster analysis (such as K-means), for example, into groups with "theoretical strengths" or "practical weaknesses".

[0095] Layout optimization strategy

[0096] Differential grouping rules:

[0097] Complementary grouping based on academic performance: Pairing students with theoretical strengths with students with practical strengths to promote collaborative learning;

[0098] Dynamic promotion and demotion mechanism: Adjust seating areas based on recent performance trends (e.g., the "core improvement area" should be close to the teacher's podium).

[0099] Resource allocation algorithm:

[0100] Based on a reinforcement learning model, priority is given to assigning seats in high-interaction areas to students whose grades fluctuate greatly.

[0101] Introduce fairness constraints to prevent high-achieving students from occupying top-ranking resources for extended periods.

[0102] Behavioral Process and Example 1

[0103] Data preprocessing and feature engineering

[0104] Data cleaning: Remove outliers (such as exam absences or cheating records) and fill missing values ​​with the median.

[0105] Normalization: Standardizing scores across different subjects (Z-score formula): ( and italics The average score for the subject. and italics (Standard deviation)

[0106] Feature extraction: Construct time-series features (such as the rate of change of scores in the last 3 times) and knowledge point correlations (such as the score correlation between "trigonometric functions" and "vectors").

[0107] Time series forecasting model (LSTM)

[0108] Input data: A sequence of students' historical scores S={s1,s2,...,st}, and another student's score sequence (italicized). and italics ) Course-related behavioral data (number of days of delayed homework submission, number of times questions were asked in class).

[0109] LSTM cell formula:

[0110] (The Gate of Oblivion);

[0111] (Input Gate);

[0112] ;

[0113] (Memory unit);

[0114] (Output gate);

[0115] ;

[0116] Output: Predict the trend of performance in the next 3 tests. And mark potential risks (such as continuous decline).

[0117] K-means clustering analysis

[0118] Feature vector: F = [theoretical score, practical score, interaction frequency, performance fluctuation variance];

[0119] Clustering objective function: Minimize the sum of squared intra-cluster distances.

[0120]

[0121] Output: Students are divided into four categories (e.g., "theoretical strength type", "practical weakness type", "balanced development type", "intervention required type").

[0122] Dynamic optimization algorithm (reinforcement learning)

[0123] State space: student category, classroom layout pattern (row-shaped / circular / group-shaped), course type;

[0124] Movement space: Adjusting seating arrangements and allocating teaching resources (such as experimental equipment);

[0125] Reward function: R = α•(performance improvement rate) + β•(interaction efficiency) − γ•(fairness deviation) (α, β, γ are weighting coefficients, and fairness deviation is quantified by the Gini coefficient).

[0126] Implementation process:

[0127] Scenario: A high school math classroom, with students falling into two categories: those with "theoretical strengths" and those with "practical weaknesses".

[0128] Data input:

[0129] Grade sequence: S=[85,82,78] (theoretical grades show a downward trend), while practical grades remain stable at 60 points.

[0130] Time series prediction:

[0131] The LSTM prediction score will continue to drop to 75 points, triggering an "intervention required" warning.

[0132] Clustering and grouping:

[0133] Students with strong theoretical knowledge are paired with those with weak practical skills to form complementary groups.

[0134] Layout adjustments:

[0135] The reinforcement learning model generates a circular layout, and students with weaker practical skills are assigned to the area near the teacher's demonstration table.

[0136] Feedback on results:

[0137] In the next cycle, practical scores improved by an average of 12%, and theoretical scores rebounded from a downward trend to 80 points.

[0138] 2: Hardware Behavior Analysis

[0139] Data Acquisition and Processing

[0140] Hardware configuration:

[0141] Deploy edge computing cameras (supporting face recognition and skeletal key point detection);

[0142] Install infrared sensors to help capture micro-behaviors such as looking down and turning around.

[0143] Behavior recognition:

[0144] Real-time analysis of computer vision algorithms:

[0145] Attention indicators: head orientation, duration of eye focus, blinking frequency;

[0146] Interaction metrics: number of hands raised, range of body language during group discussions;

[0147] Emotional state: Facial expression recognition (focused, confused, tired).

[0148] Real-time analysis and decision making

[0149] Behavioral quantification model:

[0150] Attention score: Calculated by weighting based on the duration of eye contact (>80% is considered high focus);

[0151] Dynamic heatmap: Generates a real-time map of classroom attention distribution and marks areas of low engagement.

[0152] Anomaly detection:

[0153] The isolated forest algorithm is used to identify abnormal behaviors (such as persistently slumping over a desk or frequently leaving one's seat) and trigger warnings on the teacher's end.

[0154] Layout optimization mechanism

[0155] Adjust strategies in real time:

[0156] Students with low focus are automatically assigned to high-interaction areas (such as the center of the teacher's line of sight or the seat next to the group leader).

[0157] The corridor space can be dynamically expanded / contracted to adapt to the current teaching mode (e.g., in lecture mode, the corridor can be narrowed to concentrate seats).

[0158] Adaptive feedback loop:

[0159] The layout suggestions are updated every 5 minutes, and implementation is carried out after the feasibility is verified through digital twin simulation.

[0160] Behavioral Process and Example 2

[0161] Behavior recognition algorithms (computer vision)

[0162] Head pose estimation:

[0163] OpenPose was used to detect key points on the head and calculate the yaw angle. and (Direction of gaze):

[0164] ;

[0165] .

[0166] Attention score: Percentage of time the eyes are focused on the blackboard

[0167] .

[0168] Emotion recognition:

[0169] Classify facial expressions (focus / confusion / fatigue) using a ResNet-50 model, and output the probability distribution. .

[0170] Real-time heatmap generation:

[0171] Spatial grid division: Divide the classroom into a 10×10 grid and calculate the average attention score for each grid.

[0172] Gaussian smoothing filter:

[0173] ( For grid attention values, (for weight)

[0174] Anomaly Detection (Isolated Forest):

[0175] Feature inputs: frequency of looking down, number of times one leaves their seat, and duration of interaction intervals;

[0176] Anomaly detection: If the sample path length is significantly shorter than that of normal data, it is marked as an anomaly.

[0177] Dynamic layout optimization strategy:

[0178] Priority rules:

[0179] Students with low attention should be assigned to the high-interaction zone (±30° from the teacher's line of sight).

[0180] Students experiencing anxiety were assigned to areas near windows or in corners.

[0181] Real-time adjustment formula:

[0182] ( To adjust the step size, (This refers to the gradient direction in the heatmap).

[0183] Implementation process

[0184] Scenario: A university English debate class, which requires frequent switching between individual speeches and group discussions.

[0185] Data collection:

[0186] The camera detected that two students were constantly looking down (attention score <20%), and three students raised their hands 5 times per minute.

[0187] Heatmap analysis:

[0188] The average attention span in the front row was 85%, while in the back row it was only 40%.

[0189] Layout adjustments:

[0190] Move the students who were looking down to the front row "focus area" and form a debate group with the students who raised their hands;

[0191] The desks and chairs were rearranged into a horseshoe shape to shorten the distance between teachers and the back row to 2 meters.

[0192] Effect verification:

[0193] Attention in the back row increased to 65%, and students who raised their hands had 50% more opportunities to speak.

[0194] Technical Point 1: Multimodal Data Fusion and Behavioral Modeling

[0195] Technical content: Integrating individual student behavior data (such as real-time attention span, interaction frequency, emotional state, learning simulation test scores, and student's daily grades) and course characteristics (such as theoretical courses / experimental courses / dual-teacher classroom models), the system uses computer vision (behavior recognition), natural language processing (course content analysis), and IoT sensing technology to collect and fuse multi-source heterogeneous data.

[0196] Beneficial effects:

[0197] Data Dimension Expansion: Breaking through the limitations of traditional structured data (grades, height), we introduce unstructured behavioral data and environmental parameters to build a more comprehensive student behavior profile.

[0198] Foundation for accurate decision-making: Based on multi-dimensional data analysis, identify students' cognitive styles (such as visual / auditory learners) and real-time status, providing a quantitative basis for differentiated layout.

[0199] Enhanced scenario adaptation: Through the course content analysis module, the teaching mode requirements are automatically identified (such as the need for high interactivity in group discussions), and the layout generation logic is dynamically adapted.

[0200] Technical Point 2: Dynamic Optimization Algorithm Based on Reinforcement Learning

[0201] Technical Content: A dynamic decision-making model is constructed using a reinforcement learning framework, combined with digital twin technology to perform real-time simulation and effect prediction of classroom layout. The algorithm incorporates multi-objective optimization constraints, including maximizing learning efficiency, ensuring fairness in seat allocation (such as balanced resources in the front rows), and adapting to special needs.

[0202] Beneficial effects:

[0203] Minute-level response capability: Optimizes decision-making time from hours in traditional manual solutions to minutes, supporting instant switching of teaching scenarios (such as lecture mode → experiment mode).

[0204] Multi-objective collaborative optimization: balancing individual needs with collective fairness, for example, using a "fairness weight" algorithm to prevent front-row seats from being occupied by specific groups for a long time.

[0205] Risk prediction and avoidance: Use digital twin simulation to preview different layout schemes and avoid conflicts in physical adjustments (such as table and chair collisions, and corridor blockages).

[0206] Technical Point 3: Personalized Matching and Adaptive Grouping Mechanism

[0207] Technical content: A dynamic grouping model is built based on student behavioral profiles. By analyzing and identifying groups with similar learning styles, differentiated seating arrangements are generated in conjunction with course objectives. Special needs annotation is supported.

[0208] Beneficial effects:

[0209] Precise and personalized matching: Breaking away from the traditional homogeneous grouping model, improving student participation and collaboration efficiency.

[0210] Adaptive scenario adjustment: Automatically switch grouping strategies based on course content (e.g., grouping theoretical classes by focus and experimental classes by complementary skills).

[0211] Mental health care: Provide low-stress seating areas (such as quiet seats by the window) for anxious students through environmental perception and behavioral warning.

[0212] Technical Point 4: End-to-End Closed-Loop Verification and Continuous Optimization

[0213] Technical content: Construct a closed-loop system covering the entire process of "data collection → behavior modeling → layout generation → effect feedback". It collects teaching effect data (such as knowledge mastery rate and classroom participation) in real time through multi-source sensors such as cameras and wearable devices, and uses A / B testing to compare the advantages and disadvantages of different layout schemes.

[0214] Beneficial effects:

[0215] The effects can be quantified and verified: the layout optimization effect is objectively evaluated through multi-dimensional indicators (such as attention curve and test score improvement rate), replacing the traditional subjective experience judgment.

[0216] Continuous iteration capability: Dynamically update AI model parameters based on feedback data to adapt to long-term changes in the teaching environment (such as changes in class personnel and curriculum reforms).

[0217] Optimize teaching strategies: Provide teachers with data-driven decision-making suggestions (e.g., if the participation of students in the back row decreases by 20% under a certain layout, the interaction format needs to be adjusted).

[0218] Technical Point 5: Fairness Constraints and Ethical Protection Mechanisms

[0219] Technical content: Embed fairness constraints (such as differential privacy protection and front-row seat rotation rules) into the algorithm and establish an ethical review module to prevent discriminatory allocation (such as gender and academic bias) caused by data abuse.

[0220] Beneficial effects:

[0221] Transparent resource allocation: Blockchain technology is used to record seating adjustment logs to ensure that the decision-making process is traceable and auditable.

[0222] Eliminating implicit bias: Constraining algorithms to avoid associating specific labels (such as poor grades) with seating areas, thus promoting educational equity.

[0223] Privacy and security protection: Sensitive behavioral data (such as records of distraction) is anonymized and used only for anonymized group analysis.

[0224] Summary of overall beneficial effects

[0225] 1. Improved teaching efficiency: By dynamically optimizing the layout to adapt to diverse teaching models, classroom interaction efficiency is increased by more than 40%.

[0226] 2. Improved learning outcomes: Based on personalized matching, students master knowledge points faster by 25%.

[0227] 3. Reduced management costs: Fully automated adjustments reduce teachers' time spent on seating arrangements by 80%, allowing them to focus on core teaching activities.

[0228] 4. Promote educational equity: Through algorithmic quantitative constraints, classroom participation of students in the back row increased from 35% to 60%.

[0229] The above description is a detailed description of the preferred embodiments of the present invention. However, the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modifications made under the technical spirit of the present invention should fall within the patent scope covered by the present invention.

Claims

1. A method for dynamic optimization of classroom seating layout, characterized in that, include: Obtain students' structured academic performance data and related behavioral data; The structured performance data and the associated behavioral data are analyzed to obtain performance trend prediction data and learning ability profile data. Based on a preset optimization strategy, the performance trend prediction data and the learning ability profile data are processed to obtain seat layout optimization data.

2. The classroom seating layout dynamic optimization method according to claim 1, characterized in that, The structured performance data includes mock exam scores, quiz scores, homework completion rates, and quiz results; the related behavioral data includes attendance records and course participation; the acquisition of students' structured performance data and related behavioral data also includes: establishing a time-series database of student performance data, storing it according to subject and knowledge point dimensions; and eliminating biases caused by differences in subject difficulty through data cleaning and normalization.

3. The classroom seating layout dynamic optimization method according to claim 2, characterized in that, The process of parsing the structured performance data and the associated behavioral data to obtain performance trend prediction data and learning ability profile data includes: processing the structured performance data using a time series model to obtain the performance trend prediction data; parsing the structured performance data to obtain performance fluctuation data, processing the performance fluctuation data and the associated behavioral data based on a random forest algorithm to obtain individual correlations; constructing a multidimensional scoring system to process the structured performance data to quantify students' knowledge mastery; processing the performance trend prediction data through cluster analysis to segment student groups; and labeling the individual correlations, knowledge mastery quantification values, and student group segmentation results to obtain the learning ability profile data.

4. The method for dynamic optimization of classroom seating layout according to claim 3, characterized in that, The optimization strategy includes: pairing theoretically strong students with practically strong students from the student group segmentation results to promote collaborative learning; adjusting the seating areas of corresponding students based on recent performance trends in the performance trend prediction data; processing the recent performance trends based on a reinforcement learning model to prioritize allocating high-interaction area seats to students with large performance fluctuations; and setting fairness constraints to prevent high-achieving students from occupying front-row resources for a long time.

5. The classroom seating layout dynamic optimization method according to claim 4, characterized in that, The associated behavioral data includes: head orientation, gaze duration, blink frequency, number of hand raises, body movement amplitude during group discussions, facial expression recognition, head-down frequency, number of times students leave their seats, and interaction interval duration. Correspondingly, the acquisition of students' structured grade data and associated behavioral data includes: using OpenPose to detect head key points to determine the head orientation; using the ResNet-50 model to recognize facial expressions; and detecting the head-down frequency, the number of times students leave their seats, and the interaction interval duration based on an independent forest model.

6. A dynamic optimization system for classroom seating layout, characterized in that, include: The first module is used to acquire students' structured academic data and related behavioral data; The second module is used to parse the structured performance data and the associated behavior data to obtain performance trend prediction data and learning ability profile data. The third module is used to process the performance trend prediction data and the learning ability profile data based on a preset optimization strategy to obtain seat layout optimization data.

7. The classroom seating layout dynamic optimization system according to claim 6, characterized in that, The structured performance data includes mock exam scores, quiz scores, homework completion rates, and quiz results; the related behavioral data includes attendance records and course participation; the acquisition of students' structured performance data and related behavioral data also includes: establishing a time-series database of student performance data, storing it according to subject and knowledge point dimensions; and eliminating biases caused by differences in subject difficulty through data cleaning and normalization.

8. The classroom seating layout dynamic optimization system according to claim 7, characterized in that, The process of parsing the structured performance data and the associated behavioral data to obtain performance trend prediction data and learning ability profile data includes: processing the structured performance data using a time series model to obtain the performance trend prediction data; parsing the structured performance data to obtain performance fluctuation data, processing the performance fluctuation data and the associated behavioral data based on a random forest algorithm to obtain individual correlations; constructing a multidimensional scoring system to process the structured performance data to quantify students' knowledge mastery; processing the performance trend prediction data through cluster analysis to segment student groups; and labeling the individual correlations, knowledge mastery quantification values, and student group segmentation results to obtain the learning ability profile data.

9. The classroom seating layout dynamic optimization system according to claim 8, characterized in that, The optimization strategy includes: pairing theoretically strong students with practically strong students from the student group segmentation results to promote collaborative learning; adjusting the seating areas of corresponding students based on recent performance trends in the performance trend prediction data; processing the recent performance trends based on a reinforcement learning model to prioritize allocating high-interaction area seats to students with large performance fluctuations; and setting fairness constraints to prevent high-achieving students from occupying front-row resources for a long time.

10. The classroom seating layout dynamic optimization system according to claim 9, characterized in that, The associated behavioral data includes: head orientation, gaze duration, blink frequency, number of hand raises, body movement amplitude during group discussions, facial expression recognition, head-down frequency, number of times students leave their seats, and interaction interval duration. Correspondingly, the acquisition of students' structured grade data and associated behavioral data includes: using OpenPose to detect head key points to determine the head orientation; using the ResNet-50 model to recognize facial expressions; and detecting the head-down frequency, the number of times students leave their seats, and the interaction interval duration based on an independent forest model.

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