CN114282755A - Student behavior data analysis method - Google Patents
Student behavior data analysis method Download PDFInfo
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- CN114282755A CN114282755A CN202111361126.8A CN202111361126A CN114282755A CN 114282755 A CN114282755 A CN 114282755A CN 202111361126 A CN202111361126 A CN 202111361126A CN 114282755 A CN114282755 A CN 114282755A
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Abstract
The invention relates to the technical field of student management, in particular to a method for analyzing student behavior data, which comprises the steps of collecting student position data and physiological data, analyzing attendance, interests and movement of students, respectively outputting analysis results of the attendance, interests and movement of the students, transversely comparing and analyzing the analysis results of the attendance, interests and movement in S3, quantizing the comparison results, reflecting student behavior characteristics, and carrying out targeted teaching according to the analysis results of the behavior characteristics in S4, and also provides a system for analyzing the student behavior data. And the quality of teaching in the course is improved by data driving after double subtraction.
Description
Technical Field
The invention relates to the technical field of student management, in particular to a method for analyzing student behavior data.
Background
The school is an organization mechanism for performing systematic education in a planned and organized manner, the school education is purposeful, systematic and organized by full-time staff and special institutions to influence the physical and mental development of educated school educators into direct-target social activities, the school is a place for student learning, and due to the fact that the number of students in the school is large, monitors are required to be installed in the school to monitor the classroom behaviors, outdoor behaviors and the behaviors of going in and out of the school in real time, the school is convenient to manage the students, and normal behavior activities of the students in the school are guaranteed. However, the existing student behavior analysis system cannot accurately obtain attendance, interest, and dynamic and static data of students, and cannot perform proportional calculation on the obtained data and perform transverse comparison on the obtained data to assist in making a teaching method according with characteristics of the students.
Disclosure of Invention
The invention aims to solve the defects in the background art and provides a method for analyzing student behavior data.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows: a method for analyzing student behavior data comprises the following main steps:
s1, collecting student position data and physiological data;
s2, analyzing attendance, interests and movement and stillness of students;
s3, respectively outputting analysis results on attendance, interest, movement and stillness of students;
s4, performing transverse comparison analysis on the attendance checking, interest, dynamic and static analysis results in the S3, quantifying the comparison results, and reflecting behavior characteristics of students;
and S5, performing targeted teaching according to the behavior feature analysis result in the S4.
Preferably, the attendance analysis in step S2 includes calculating the number of normal attendance and abnormal attendance, and the ratio of the number of normal attendance and abnormal attendance to the total attendance number.
Preferably, the interest analysis of step S2 includes calculating the cumulative time of interest over different interests and the ratio of the cumulative time of interest to the total interest time activity.
Preferably, the dynamic and static analysis in step S2 includes the cumulative time of the dynamic and static states, and the ratio of the cumulative time of the dynamic and static states to the total time.
Still provide a student behavior data analysis's system for cooperate the use of foretell method, including data acquisition module, hardware module and data high in the clouds, its characterized in that: the data acquisition module is connected with the data analysis module, the data analysis module includes student's attendance analysis module, student interest analysis module and student's sound analysis module, the data analysis module is connected with horizontal contrast module, horizontal contrast module is connected with the result display module, the result display module is connected and is pointed the teaching and guide module, wherein:
the data acquisition module is used for collecting position data and physiological data of students;
the data cloud is used for remotely transmitting data information;
the data analysis module is used for analyzing attendance, interest, movement and stillness of students;
the transverse comparison module is used for transversely comparing the acquired attendance, interest, dynamic and static analysis results;
the result display module is used for outputting the student characteristics obtained by the transverse comparison analysis;
the targeted teaching guidance module is used for guiding teaching modes adopted by different students.
Preferably, the transverse analysis module comprises a transverse result comparison module, a quantitative data sorting module and a student behavior characteristic analysis module which are connected in sequence, the transverse result comparison module is used for comparing and transversely comparing the acquired attendance, interest, dynamic and static data, the quantitative data sorting module is used for quantitatively sorting the compared data, and the student behavior characteristic analysis module is used for analyzing the result.
Preferably, student's attendance analysis module is including work and rest time setting module, record module constantly, unusual proportion calculation module, work and rest time setting module is used for setting up work and rest time, record module constantly is used for recording student's time of registering, unusual proportion calculation module is used for calculating the proportion of opening unusual account-for-attendance total, work and rest time setting module includes time setting module and course time setting module from beginning to end, the time of going to end is used for setting up the attendance time of going to end, the course time setting module is used for setting up the attendance time of course.
Preferably, the student interest analysis module comprises an interest place input module, a place check-in module, a cumulative time counting module and an interest proportion calculation module, wherein the interest place input module is used for inputting different types of interest places, the place check-in module is used for checking in students who are in the interest places, the cumulative time counting module is used for counting the cumulative time of the students in different interest places, and the interest proportion calculation module is used for calculating the proportion of the cumulative time of the students in different interest places to the total interest time.
Preferably, the student dynamic and static analysis module comprises a heart rate recording module, a step number recording module and a dynamic and static state proportion calculation module, the heart rate recording module is used for recording the heart rate of the student, the step number recording module is used for recording the step number of the student, the dynamic and static states of the student are obtained by combining the heart rate recording module and the step number recording module, and the dynamic and static state proportion calculation module is used for calculating the dynamic state to static state to dynamic state total time ratio.
Preferably, the data cloud end comprises a data storage module and a calling module, and the hardware module comprises a host end, a sign-in device, a satellite or indoor positioning module, a step number detection module and a heart rate detection module.
Compared with the prior art, the invention has the following beneficial effects:
the position of a student, physiological data and teaching related data are collected and analyzed, the data sources are combined for use in the analysis process, the analysis result can be quantized and used for reflecting the behavior characteristics of the student so as to assist the student in making a corresponding teaching mode, the supervision of the student is more comprehensive and scientific, and the teaching quality in courses is improved through data driving after double subtraction.
Drawings
FIG. 1 is a schematic diagram of a system for student behavioral data analysis in accordance with the present invention;
FIG. 2 is a schematic diagram of a student attendance analysis module of the student behavior data analysis system of the present invention;
FIG. 3 is a schematic diagram of a student interest analysis module of a system for student behavior data analysis according to the present invention;
FIG. 4 is a schematic diagram of a student activity and statics analysis module of the system for student behavior data analysis according to the present invention;
fig. 5 is a schematic diagram of a data cloud and hardware module of the student behavior data analysis system according to the present invention;
FIG. 6 is a diagram showing the results of a transverse contrast analysis according to a method for analyzing student behavior data according to the present invention;
FIG. 7 is a diagram showing the results of a transverse comparative analysis of a method for analyzing student behavior data according to the present invention;
fig. 8 is a diagram showing attendance analysis results of the student behavior data analysis method of the present invention.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art.
The method for analyzing the student behavior data comprises the following main steps:
s1, collecting student position data and physiological data;
s2, analyzing attendance, interests and movement and stillness of students;
s3, respectively outputting analysis results on attendance, interest, movement and stillness of students;
s4, performing transverse comparison analysis on the attendance checking, interest, dynamic and static analysis results in the S3, quantifying the comparison results, and reflecting behavior characteristics of students;
and S5, performing targeted teaching according to the behavior feature analysis result in the S4.
The attendance analysis in step S2 includes calculating the normal and abnormal attendance times and the ratio of the normal and abnormal attendance times to the total attendance time.
The interest analysis in step S2 includes calculating the cumulative time over different interests and the ratio of the cumulative time over interests to the total interest time activity.
The dynamic and static analysis in step S2 includes the ratio of the dynamic and static cumulative time to the total time.
Fig. 6 is a diagram showing a lateral contrast analysis result of the method, fig. 7 is a diagram showing another lateral contrast analysis result of the method, and fig. 8 is a diagram showing an attendance analysis result of the method.
Based on the method for analyzing the student behavior data, as shown in fig. 1-5, a system for analyzing the student behavior data is provided, which comprises a data acquisition module, a hardware module and a data cloud end, wherein the data acquisition module is connected with a data analysis module, the data analysis module comprises a student attendance analysis module, a student interest analysis module and a student dynamic and static analysis module, the data analysis module is connected with a transverse comparison module, the transverse comparison module is connected with a result display module, and the result display module is connected with a targeted teaching guidance module, wherein:
the data acquisition module is used for collecting the position data and the physiological data of the students;
the data cloud is used for remotely transmitting data information;
the data analysis module is used for analyzing attendance, interest, movement and stillness of students;
the transverse comparison module is used for transversely comparing the acquired attendance, interest and dynamic and static analysis results;
the result display module is used for outputting the student characteristics obtained by the transverse comparison analysis;
the targeted teaching guidance module is used for guiding teaching modes adopted by different students.
The transverse analysis module comprises a transverse result comparison module, a quantitative data sorting module and a student behavior characteristic analysis module which are sequentially connected, wherein the transverse result comparison module is used for comparing and transversely comparing attendance, interest and dynamic and static data which are obtained more, the quantitative data sorting module is used for quantitatively sorting the compared data, and then the student behavior characteristic analysis module is used for analyzing a result.
Student's attendance analysis module is including the work and rest time setting module, record module constantly, unusual proportion calculation module, the work and rest time setting module is used for setting up the work and rest time, record module constantly is used for recording student's time of registering, unusual proportion calculation module is used for calculating the proportion of opening unusual account-up attendance total, the work and rest time setting module includes that the time of going to school sets up module and course time setting module, the time of going to school is used for setting up the attendance time of going to school, the course time setting module is used for setting up the attendance time of course.
The student interest analysis module comprises an interest place input module, a place check-in module, an accumulated time counting module and an interest proportion calculation module, wherein the interest place input module is used for inputting interest places of different types, the place check-in module is used for checking in students who are interested in places, the accumulated time counting module is used for counting accumulated time of the students in the different interest places, and the interest proportion calculation module is used for calculating proportion of the accumulated time of the students in the different interest places to total interest time.
The student dynamic and static analysis module comprises a heart rate recording module, a step number recording module and a dynamic and static state proportion calculation module, the heart rate recording module is used for recording the heart rate of students, the step number recording module is used for recording the step number of the students, the dynamic and static states of the students are obtained by combining the step number and the step number, and the dynamic and static state proportion calculation module is used for calculating the dynamic state total time ratio and the static state total time ratio.
Data high in the clouds includes data storage module and retrieval module, the hardware module includes the host computer end, the equipment of registering, satellite or indoor orientation module, step number detection module, rhythm of the heart detection module, the host computer end is used for providing mutual processing channel for managers, the equipment of registering arranges in attendance places such as interest place, class that correspond, satellite or indoor orientation module, step number detection module, rhythm of the heart detection module can be for intelligent bracelet, wear at every student's hand, real-time supervision student's position, rhythm of the heart, the step number.
The student position, physiological data and teaching related data are collected and analyzed, the data sources are combined in the analysis process, the analysis result can be quantized and used for reflecting the behavior characteristics of the student so as to assist the student in making a corresponding teaching mode, and the supervision of the student is more comprehensive and scientific.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A method for analyzing student behavior data is characterized by comprising the following steps: the method comprises the following main steps:
s1, collecting student position data and physiological data;
s2, analyzing attendance, interests and movement and stillness of students;
s3, respectively outputting analysis results on attendance, interest, movement and stillness of students;
s4, performing transverse comparison analysis on the attendance checking, interest, dynamic and static analysis results in the S3, quantifying the comparison results, and reflecting behavior characteristics of students;
and S5, performing targeted teaching according to the behavior feature analysis result in the S4.
2. The method for student behavior data analysis according to claim 1, wherein: the attendance analysis in step S2 includes calculating the normal and abnormal attendance times and the ratio of the normal and abnormal attendance times to the total attendance time.
3. The method for student behavior data analysis according to claim 1, wherein: the interest analysis in step S2 includes calculating the cumulative time over different interests and the ratio of the cumulative time over interests to the total interest time activity.
4. The method for student behavior data analysis according to claim 1, wherein: the dynamic and static analysis in step S2 includes the ratio of the dynamic and static cumulative time to the total time.
5. A system for student behavioural data analysis, for use with the method of any one of claims 1 to 4, comprising a data acquisition module, a hardware module and a data cloud, characterized in that: the data acquisition module is connected with the data analysis module, the data analysis module includes student's attendance analysis module, student interest analysis module and student's sound analysis module, the data analysis module is connected with horizontal contrast module, horizontal contrast module is connected with the result display module, the result display module is connected and is pointed the teaching and guide module, wherein:
the data acquisition module is used for collecting position data and physiological data of students;
the data cloud is used for remotely transmitting data information;
the data analysis module is used for analyzing attendance, interest, movement and stillness of students;
the transverse comparison module is used for transversely comparing the acquired attendance, interest, dynamic and static analysis results;
the result display module is used for outputting the student characteristics obtained by the transverse comparison analysis;
the targeted teaching guidance module is used for guiding teaching modes adopted by different students.
6. The system for student behavior data analysis according to claim 5, wherein: the transverse analysis module comprises a transverse result comparison module, a quantitative data sorting module and a student behavior characteristic analysis module which are sequentially connected, wherein the transverse result comparison module is used for transversely comparing more acquired attendance, interest and dynamic and static data, the quantitative data sorting module is used for quantitatively sorting the compared data, and then the student behavior characteristic analysis module analyzes the result.
7. The system for student behavior data analysis according to claim 5, wherein: the student attendance analysis module comprises a work and rest time setting module, a time recording module and an abnormal proportion calculation module, wherein the work and rest time setting module is used for setting work and rest time, the time recording module is used for recording the attendance time of students, the abnormal proportion calculation module is used for calculating the proportion of the opened abnormal attendance total number, the work and rest time setting module comprises an upper and lower school time setting module and a course time setting module, the upper and lower school time is used for setting the attendance time of the upper and lower schools, and the course time setting module is used for setting the attendance time of the courses.
8. The system for student behavior data analysis according to claim 5, wherein: the student interest analysis module comprises an interest place input module, a place check-in module, an accumulated time counting module and an interest proportion calculation module, wherein the interest place input module is used for inputting different types of interest places, the place check-in module is used for checking in students who carry out the interest places, the accumulated time counting module is used for counting the accumulated time of the students in different interest places, and the interest proportion calculation module is used for calculating the proportion of the accumulated time of the students in different interest places to the total interest time.
9. The system for student behavior data analysis according to claim 5, wherein: the student dynamic and static analysis module comprises a heart rate recording module, a step number recording module and a dynamic and static state proportion calculation module, wherein the heart rate recording module is used for recording the heart rate of students, the step number recording module is used for recording the step number of the students, the dynamic and static states of the students are obtained by combining the heart rate recording module and the step number recording module, and the dynamic and static state proportion calculation module is used for calculating the ratio of the dynamic state to the total time and the ratio of the static state to the total time.
10. The system for student behavior data analysis according to claim 5, wherein: the data cloud end comprises a data storage module and a calling module, and the hardware module comprises a host end, a sign-in device, a satellite or indoor positioning module, a step number detection module and a heart rate detection module.
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