A method and system for analyzing student learning behavior in applied education

Abstract

The present invention relates to the field of data processing technology, and specifically to a method and system for analyzing student learning behavior in applied education, including: obtaining students' course completion records and students' viewing records of each course video every day; obtaining students' learning autonomy based on the completion of each chapter and course; obtaining students' concentration based on the number of pauses and continuous viewing of each course video, and obtaining students' abnormal behavior probability based on their continuous learning; obtaining students' regular learning degree based on their continuous learning and interruption; obtaining each student's final learning behavior score based on their learning autonomy and regular learning degree, as well as abnormal behavior probability. The present invention analyzes students' learning behavior based on their course learning on an online education platform, accurately reflecting students' actual learning situation.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for analyzing student learning behavior in applied education. Background Art

[0002] In the context of modern education, applied education has gradually become an important model for colleges and universities to cultivate talents with practical ability and innovation. The learning method of online education platforms records a large amount of data on students' learning behavior. Through these data, students' learning behavior can be monitored and analyzed in multiple dimensions, helping teachers to accurately understand students' learning status and optimize teaching strategies in a timely manner, thereby improving teaching effectiveness and students' learning experience.

[0003] When studying courses on an online education platform, learning progress and related test scores are the main basis for final grade assessment. However, due to certain limitations in the supervision of online learning and flexible student schedules, some students take a cramming approach to complete the course when the course deadline is approaching. This results in insufficient understanding of the relevant course content, and it is difficult to truly reflect the students' actual learning situation in the final test scores. Summary of the invention

[0004] The present invention provides a method and system for analyzing student learning behavior in applied education to solve the existing problems.

[0005] The present invention provides a method and system for analyzing student learning behavior in applied education using the following technical solutions:

[0006] An embodiment of the present invention provides a method for analyzing student learning behavior in applied education, the method comprising the following steps:

[0007] According to the start time and stop time of each course video of each chapter watched by the students every day, as well as the total course duration of each course video of each chapter, obtain the course completion record of the students and the viewing record of each course video watched by the students every day;

[0008] The degree of students' learning autonomy is obtained based on the students' viewing of each chapter and the completion of the course in the students' course completion records, as well as the distribution of learning time in the students' viewing records of each course video every day;

[0009] According to the number of pauses and continuous viewing in the video records of each course watched by students every day, the degree of concentration of students in watching each course video is obtained; according to the continuous learning status in the video records of each course watched every day and the degree of concentration of students in watching each course video, the probability of abnormal behavior of students is obtained;

[0010] According to the continuous learning and interruption of students in the viewing records of each course video every day, the regular learning degree of students is obtained;

[0011] The final learning behavior score of each student is obtained based on the degree of learning autonomy and regular learning, as well as the probability of abnormal behavior.

[0012] Furthermore, the specific steps of obtaining the course completion record of the student and the viewing record of each course video watched by the student every day are as follows:

[0013] The sum of the differences between all start and stop times of all course videos of each student and the ratio of the total course duration is recorded as the course completion rate of each student;

[0014] The ratio of the number of days each student watched the course video to the number of days the course started and ended was recorded as the attendance rate of each student;

[0015] The number of consecutive days each student watches the course video is recorded as the number of consecutive learning days for each student;

[0016] The total number of course videos each student watched in a day was recorded as the number of course videos each student studied per day;

[0017] The course completion rate, attendance rate, consecutive study days and number of course videos studied every day of each student constitute the course completion record of each student;

[0018] The difference between the start time and the stop time of each course video watched by each student is recorded as the duration of each course video watched by each student in each chapter;

[0019] The number of stop times that occur in each course video watched by each student is recorded as the number of automatic pauses in each course video watched by each student in each chapter;

[0020] The difference between the last stop time and the next start time of each course video watched by each student is recorded as a pause interval time for each student to watch each course video;

[0021] The time interval between each start time and stop time of each course video watched by each student is recorded as a continuous viewing time of each course video watched by each student;

[0022] The duration of each student's viewing of each course video, the number of automatic pauses, all pause intervals, and all continuous viewing times constitute each student's viewing record of each course video every day.

[0023] Furthermore, the step of obtaining the degree of learning autonomy of students includes the following specific steps:

[0024]

[0025] In the formula, represents the degree of learning autonomy of the i-th student, represents the course completion rate of the i-th student, represents the attendance rate of the i-th student, Indicates the number of chapters, represents the sum of the duration of each course video of the nth chapter watched by the i-th student, Represents the sum of the duration of all the course videos in the nth chapter, Indicates the time from the preset deadline when the i-th student finishes watching the n-th chapter.

[0026] Furthermore, the step of obtaining the concentration level of students when watching each course video includes the following specific steps:

[0027]

[0028] In the formula, Indicates the concentration level of the i-th student watching the m-th course video; represents the number of automatic pauses when the i-th student watches the m-th course video. represents the sum of all pause intervals of the i-th student watching the m-th course video, represents the variance of all consecutive viewing times of the i-th student watching the m-th course video, represents the maximum variance of all consecutive viewing times of the i-th student watching the m-th course video; represents the linear normalization function.

[0029] Furthermore, the specific method of obtaining the maximum variance is as follows:

[0030] The maximum value of the partial sample variance of all students' continuous viewing time is recorded as the maximum variance of the continuous viewing time.

[0031] Furthermore, the specific steps of obtaining the student's abnormal behavior probability are as follows:

[0032]

[0033] In the formula, represents the probability of abnormal behavior of the i-th student; represents the number of consecutive study days of the i-th student; represents the number of course videos watched continuously by the i-th student on the p-th day; represents the sum of the concentration levels of all course videos watched by the i-th student on the p-th day; represents the linear normalization function.

[0034] Furthermore, the step of obtaining the regular learning degree of the students includes the following specific steps:

[0035]

[0036] In the formula, represents the regular learning degree of the i-th student; represents the maximum number of consecutive study days of the i-th student, represents the mean number of days that the i-th student interrupts his study, represents the kurtosis of the daily study time of the ith student; the daily study time is obtained by recording the sum of the duration of each student watching each course video of each chapter every day as each student’s daily study time, represents the linear normalization function.

[0037] Furthermore, the specific method of obtaining the number of days of interrupted learning is as follows:

[0038] The number of days each student did not study between two consecutive study days was recorded as interrupted study days.

[0039] Furthermore, the specific steps of obtaining the final learning behavior score of each student are as follows:

[0040]

[0041] In the formula, represents the final learning behavior score of the i-th student; represents the degree of learning autonomy of the i-th student; represents the regular learning degree of the i-th student; Indicates the behavioral grade of the preset course; represents the probability of abnormal behavior of the i-th student; represents the linear normalization function.

[0042] The present invention also proposes a student learning behavior analysis system for applied education, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned method for analyzing student learning behavior for applied education.

[0043] The beneficial effects of the technical solution of the present invention are: obtaining students' course completion records and students' viewing records of each course video every day; obtaining students' learning autonomy through students' completion of watching each chapter and course, and showing students' learning autonomy; obtaining students' concentration level according to the number of pauses and continuous viewing of each course video, and obtaining students' abnormal behavior probability according to their continuous learning situation, indicating that students have bad learning behaviors; obtaining students' regular learning level according to their continuous learning and interruption situations, reflecting students' learning behavior habits; obtaining each student's final learning behavior score according to their learning autonomy level and regular learning level, as well as abnormal behavior probability. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 The present invention is a flowchart of the steps of a method for analyzing student learning behavior in applied education. DETAILED DESCRIPTION

[0046] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the student learning behavior analysis method and system for applied education proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0047] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0048] The following is a detailed description of a method and system for analyzing student learning behavior in applied education provided by the present invention in conjunction with the accompanying drawings.

[0049] See also Figure 1 , which shows a flow chart of the steps of a method for analyzing student learning behavior in applied education provided by an embodiment of the present invention, the method comprising the following steps:

[0050] Step S001, according to the start time and stop time of each course video of each chapter watched by the student every day, and the total course duration of each course video of each chapter, obtain the student's course completion record and the student's viewing record of each course video watched every day.

[0051] The purpose of this embodiment is to analyze the learning behavior of students based on their learning situation on the online education platform. Therefore, firstly, the start time and stop time of each course video of each chapter watched by each student every day, as well as the total course duration of each course video of each chapter, are obtained through the system of the online education platform. The course completion record of the student and the viewing record of the student watching each course video every day are obtained.

[0052] The sum of the differences between all start and stop times of all course videos of each student and the ratio of the total course duration is recorded as the course completion rate of each student;

[0053] The ratio of the number of days each student watched the course video to the number of days the course started and ended was recorded as the attendance rate of each student;

[0054] The number of consecutive days each student watches the course video is recorded as the number of consecutive learning days for each student;

[0055] The total number of course videos each student watched in a day was recorded as the number of course videos each student studied per day;

[0056] The course completion rate, attendance rate, consecutive study days, and number of course videos studied per day of each student constitute each student’s course completion record;

[0057] The difference between the start time and the stop time of each course video watched by each student is recorded as the duration of each course video watched by each student in each chapter;

[0058] The number of stop times that occur in each course video watched by each student is recorded as the number of automatic pauses in each course video watched by each student in each chapter;

[0059] The difference between the last stop time and the next start time of each course video watched by each student is recorded as a pause interval time for each student to watch each course video;

[0060] The time interval between each start time and stop time of each course video watched by each student is recorded as a continuous viewing time of each course video watched by each student;

[0061] The duration of each student's viewing of each course video, the number of automatic pauses, all pause intervals, and all continuous viewing times constitute each student's viewing record of each course video every day.

[0062] Step S002: Obtain the degree of student learning autonomy based on the student's viewing of each chapter, the course completion status in the student's course completion record, and the distribution of learning time in the student's viewing record of each course video every day.

[0063] It should be noted that the behavior data of students on the online education platform reflects a student's learning behavior habits. Therefore, by analyzing the student's behavior data, the student's learning autonomy can be reflected. For a course, the student's learning autonomy is related to the student's participation in each chapter. The task completion rate, attendance rate, learning time of each chapter and each course video under each chapter, and the time from the end of the course completion to the deadline can all reflect the student's overall learning status of the course. The more time a chapter has to study, the higher the student's learning autonomy. The earlier the completion time of all courses is from the deadline, the higher the degree of student autonomy in learning the course and the less restricted by the deadline.

[0064] Specifically, the degree of learning autonomy of the i-th student is calculated as follows:

[0065]

[0066] In the formula, represents the degree of learning autonomy of the i-th student, represents the course completion rate of the i-th student, represents the attendance rate of the i-th student, Indicates the number of chapters, represents the sum of the duration of each course video of the nth chapter watched by the i-th student, Represents the total duration of all course videos in the nth chapter. Indicates the time from the preset deadline when the i-th student finishes watching the n-th chapter.

[0067] It means analyzing the student's learning autonomy from the overall course record. The higher the student's attendance rate and task completion rate, the higher the student's learning autonomy. It indicates the students' autonomy in watching a single chapter. The longer the watching time is and the earlier they finish learning, the better their learning autonomy is.

[0068] Step S003, obtaining the degree of concentration of each student in watching each course video according to the number of automatic pauses and continuous watching of each course video; obtaining the probability of abnormal behavior of each student according to the number of consecutive study days of each student, the number of course videos watched continuously every day, and the degree of concentration in watching each course video.

[0069] It should be noted that since it is difficult to effectively supervise students' learning status during online teaching, the students' concentration during a single use can be analyzed based on the continuity of the students' learning time recorded by the platform; when students watch a single course, the worse the continuity of the viewing time, the lower the students' concentration during the learning process. When the screen switches during video playback, the video will automatically pause. The more automatic pauses there are, the more screen switches there are during this learning process, and the worse the students' concentration during this learning process; the longer the interval time, the longer the time it takes to switch the screen, and the shorter the continuous viewing time between automatic pauses, the worse the corresponding concentration.

[0070] Specifically, for the mth lesson in a course, the calculation method for the concentration of the i-th student watching is:

[0071]

[0072] In the formula, Indicates the concentration level of the i-th student watching the m-th course video; represents the number of automatic pauses when the i-th student watches the m-th course video. represents the sum of all pause intervals of the i-th student watching the m-th course video, represents the variance of all consecutive viewing times of the i-th student watching the m-th course video, represents the maximum variance of all consecutive viewing times of the mth course video watched by the ith student, where the maximum variance is the maximum value of the partial sample variance of all consecutive viewing times when watching the video; represents the linear normalization function.

[0073] This means that the more times the automatic pauses occur and the longer the corresponding pauses are, the worse the corresponding concentration level is. It indicates the discrete degree of each viewing time of students in watching the video of the mth course. When the discrete degree is larger, it means that the single viewing time is longer and the degree of attention concentration is higher.

[0074] It should be further explained that due to the flexible class times of online courses, there are cases where students cheat on classes when the deadline is approaching. When students watch multiple course videos in a row, their concentration levels will decrease. Therefore, when students are studying normally, their concentration levels will tend to decrease. When students cheat on classes, their concentration levels will not change significantly.

[0075] Furthermore, the probability of abnormal behavior of the i-th student is calculated as:

[0076]

[0077] In the formula, represents the probability of abnormal behavior of the i-th student; Represents the number of consecutive study days of the i-th student. represents the number of course videos watched continuously by the i-th student on the p-th day; represents the sum of the concentration levels of all course videos watched by the i-th student on the p-th day; represents the linear normalization function.

[0078] It indicates the ratio of the degree of students' attention decline when watching multiple course videos continuously to the number of course videos watched. The larger the ratio, the more the student's attention declines, the smaller the probability of abnormal behavior. The smaller the degree of change in students' attention during the continuous viewing of courses, the greater the possibility of cheating and the greater the probability of abnormal behavior.

[0079] Step S004: Obtain the regular learning degree of the students according to the continuous learning and interruption situations in the viewing records of each course video watched by the students every day.

[0080] It should be noted that in the online learning model, students' learning patterns are different; some students tend to concentrate on learning and watching the entire course content in a relatively short period of time. This learning method is helpful for memory and understanding in the short term, but in the long run, it may lead to weak memory and incomplete understanding due to lack of deep learning and repeated consolidation, making it difficult to effectively digest and absorb the course content; another part of the students choose to evenly distribute their study time over multiple consecutive time periods. This learning method helps to disperse the pressure of study, enhance the in-depth understanding and absorption of the course content, and help cultivate students to develop good learning behavior habits; therefore, the longer the students study every day and the more evenly the daily study time is distributed, the more regular the students' learning behavior is, and the better the learning effect of the course is.

[0081] Specifically, the regular learning degree of the corresponding student is obtained by analyzing the continuous learning behavior and daily learning distribution of the i-th student. The calculation method of the regular learning degree of the i-th student is:

[0082]

[0083] In the formula, represents the regular learning degree of the i-th student; represents the maximum number of consecutive study days of the i-th student, represents the mean number of days that the i-th student interrupts his study, represents the kurtosis of the daily study time of the ith student; the daily study time is obtained by recording the sum of the duration of each student watching each course video of each chapter every day as each student’s daily study time; represents the linear normalization function.

[0084] It means that when the kurtosis of the i-th student's daily study time is larger, the i-th student's daily study time is more concentrated and the study time arrangement is more reasonable. When the kurtosis of the i-th student's daily study time is smaller, it means that the difference in students' daily study time is greater, there is a situation of concentrated study, and the degree of regular learning is worse. It means that when the maximum continuous learning time is more, the learning behavior of the i-th student is more regular, and when the corresponding mean of the number of interrupted days of continuous learning is larger, the degree of regular learning is worse.

[0085] Step S005: Obtain a final learning behavior score for each student based on the degree of learning autonomy of each student, the degree of regular learning of each student, and the probability of abnormal behavior of each student.

[0086] It should be noted that different learning behaviors and habits will affect students' mastery of knowledge, and in the process of online learning, teachers cannot fully understand and supervise students' learning situation. The final learning behavior score of students in the course is obtained through the degree of students' learning autonomy and regularity, as well as the proportion of behavioral scores in the total course score.

[0087]

[0088] In the formula, represents the final learning behavior score of the i-th student; represents the degree of learning autonomy of the i-th student; represents the regular learning degree of the i-th student; Indicates the behavioral grade of the preset course; represents the probability of abnormal behavior of the i-th student; represents a linear normalization function; in this embodiment, the behavioral score of the course is preset to 10 points, and other embodiments may set other values, which are not specifically limited in this embodiment.

[0089] It means that when the i-th student has a higher degree of learning regularity, a lower probability of abnormal behavior, and a higher degree of learning autonomy, the corresponding learning behavior is better and the final learning behavior score is higher.

[0090] At this point, the final learning behavior score of each student is obtained. According to the final learning behavior score, the student with the highest learning behavior score is encouraged, and the student with the lowest learning behavior score is warned.

[0091] So far, the present invention is completed.

[0092] The present invention also provides a student learning behavior analysis system for applied education, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned method for analyzing student learning behavior for applied education.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing student learning behavior in applied education, characterized in that: The method comprises the following steps: According to the start time and stop time of each course video of each chapter watched by the students every day, as well as the total course duration of each course video of each chapter, obtain the course completion record of the students and the viewing record of each course video watched by the students every day; The degree of students' learning autonomy is obtained based on the students' viewing of each chapter and the completion of the course in the students' course completion records, as well as the distribution of learning time in the students' viewing records of each course video every day; According to the number of pauses and continuous viewing in the video records of each course watched by students every day, the degree of concentration of students in watching each course video is obtained; according to the continuous learning status in the video records of each course watched every day and the degree of concentration of students in watching each course video, the probability of abnormal behavior of students is obtained; According to the continuous learning and interruption of students in the viewing records of each course video every day, the regular learning degree of students is obtained; Obtain each student's final learning behavior score based on their degree of learning autonomy and regular learning, as well as the probability of abnormal behavior; The specific steps of obtaining the course completion record of the student and the viewing record of each course video watched by the student every day are as follows: The sum of the differences between all start and stop times of all course videos of each student and the ratio of the total course duration is recorded as the course completion rate of each student; The ratio of the number of days each student watched the course video to the number of days the course started and ended was recorded as the attendance rate of each student; The number of consecutive days each student watches the course video is recorded as the number of consecutive learning days for each student; The total number of course videos each student watched in a day was recorded as the number of course videos each student studied per day; The course completion rate, attendance rate, consecutive study days and number of course videos studied every day of each student constitute the course completion record of each student; The difference between the start time and the stop time of each course video watched by each student is recorded as the duration of each course video watched by each student in each chapter; The number of stop times that occur in each course video watched by each student is recorded as the number of automatic pauses in each course video watched by each student in each chapter; The difference between the last stop time and the next start time of each course video watched by each student is recorded as a pause interval time for each student to watch each course video; The time interval between each start time and stop time of each course video watched by each student is recorded as a continuous viewing time of each course video watched by each student; The duration of each student watching each course video, the number of automatic pauses, all pause intervals, and all continuous viewing time constitute the viewing record of each student watching each course video every day; The specific steps of obtaining the degree of students' learning autonomy are as follows: In the formula, represents the degree of learning autonomy of the i-th student, represents the course completion rate of the i-th student, represents the attendance rate of the i-th student, Indicates the number of chapters, represents the sum of the duration of each course video of the nth chapter watched by the i-th student, Represents the sum of the duration of all the course videos in the nth chapter, It indicates the time from the preset deadline when the i-th student finishes watching the n-th chapter; The specific steps of obtaining the concentration level of students watching each course video are as follows: In the formula, Indicates the concentration level of the i-th student watching the m-th course video; represents the number of automatic pauses when the i-th student watches the m-th course video. represents the sum of all pause intervals of the i-th student watching the m-th course video, represents the variance of all consecutive viewing times of the i-th student watching the m-th course video, represents the maximum variance of all consecutive viewing times of the i-th student watching the m-th course video; represents the linear normalization function; The specific steps of obtaining the student's abnormal behavior probability are as follows: In the formula, represents the probability of abnormal behavior of the i-th student; represents the number of consecutive study days of the i-th student; represents the number of course videos watched continuously by the i-th student on the p-th day; represents the sum of the concentration levels of all course videos watched by the i-th student on the p-th day; represents the linear normalization function; The specific steps of obtaining the regular learning level of the students are as follows: In the formula, represents the regular learning degree of the i-th student; represents the maximum number of consecutive study days of the i-th student, represents the mean number of days that the i-th student interrupts his study, represents the kurtosis of the daily study time of the ith student; the daily study time is obtained by recording the sum of the duration of each student watching each course video of each chapter every day as each student’s daily study time, represents the linear normalization function; The specific steps of obtaining the final learning behavior score of each student are as follows: In the formula, represents the final learning behavior score of the i-th student; represents the degree of learning autonomy of the i-th student; represents the regular learning degree of the i-th student; Indicates the behavioral grade of the preset course; represents the probability of abnormal behavior of the i-th student; represents the linear normalization function.

2. The method for analyzing student learning behavior of applied education according to claim 1, characterized in that: The specific method of obtaining the maximum variance is as follows: The maximum value of the partial sample variance of all students' continuous viewing time is recorded as the maximum variance of the continuous viewing time.

3. The method for analyzing student learning behavior of applied education according to claim 1, characterized in that: The specific method of obtaining the number of days of interrupted learning is as follows: The number of days each student did not study between two consecutive study days was recorded as interrupted study days.

4. A student learning behavior analysis system for applied education, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of a method for analyzing student learning behavior in applied education as described in any one of claims 1 to 3 are implemented.

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