CN111832524A - Video learning behavior analysis method in SPOC environment based on time dimension - Google Patents

Video learning behavior analysis method in SPOC environment based on time dimension Download PDF

Info

Publication number
CN111832524A
CN111832524A CN202010716232.2A CN202010716232A CN111832524A CN 111832524 A CN111832524 A CN 111832524A CN 202010716232 A CN202010716232 A CN 202010716232A CN 111832524 A CN111832524 A CN 111832524A
Authority
CN
China
Prior art keywords
students
learning
video
time
watching
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010716232.2A
Other languages
Chinese (zh)
Inventor
刘聪
陆婷
何华
张立晔
王绍卿
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shandong University of Technology
Original Assignee
Shandong University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shandong University of Technology filed Critical Shandong University of Technology
Priority to CN202010716232.2A priority Critical patent/CN111832524A/en
Publication of CN111832524A publication Critical patent/CN111832524A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G06Q50/205—Education administration or guidance

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Educational Technology (AREA)
  • Tourism & Hospitality (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Educational Administration (AREA)
  • Multimedia (AREA)
  • Software Systems (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • General Business, Economics & Management (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a video learning behavior analysis method in an SPOC environment based on time dimension, which comprises the following steps: 1) acquiring basic data including student data and video learning data; 2) classifying the students according to the student data; 3) classifying the video learning data according to the classification of the students; 4) performing statistical analysis on the video learning data of different types of students and performing visual presentation; 5) and analyzing the learning behaviors of different types of students in different time dimensions according to the statistical result. The invention classifies and counts the video learning data based on the time dimension, analyzes the difference of different types of students on the video learning data under different time factors, finds out the time factors influencing the learning effect of the students, provides feedback for teachers and students, helps the teachers to formulate personalized teaching schemes and assists the students in improving the learning effect.

Description

Video learning behavior analysis method in SPOC environment based on time dimension
Technical Field
The invention relates to the technical field of video learning data analysis, in particular to a video learning behavior analysis method in an SPOC environment based on time dimension.
Background
The application of Small-scale restrictive Online Course (SPOC) innovates the teaching mode and promotes the reform and development of education. In the SPOC learning environment, video resources are one of the most important learning resources, and learning videos are the most basic and important activities of learners. Through the analysis of the video learning data of the students, the learning characteristics of the students of different types can be obtained, potential factors influencing the learning effect of the students can be found, and auxiliary support is provided for self-regulation learning of the students and personalized teaching of teachers.
Most studies of current learning behavior analysis analyze various learning data in an online platform, such as login time, online duration, number of video comments, number of video views, number of forums posts, and the like. Most of the current work researches the relation between different learning behaviors and learning effects, but there is no difference of the learning behaviors of students under the influence of different time factors by fine-grained analysis, and the learning behaviors of different types of students are not compared. Meanwhile, the video learning data of learners are not deeply mined, each video learning data is not discussed in detail, and different types of students are not distinguished in learning behavior characteristics.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, and provides a video learning behavior analysis method in an SPOC environment based on time dimension.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a video learning behavior analysis method in an SPOC environment based on a time dimension comprises the following steps:
1) acquiring basic data including student data and video learning data;
2) classifying the students according to the student data;
3) classifying the video learning data according to the classification of the students;
4) performing statistical analysis on the video learning data of different types of students and performing visual presentation;
5) and analyzing the learning behaviors of different types of students in different time dimensions according to the statistical result of the step 4).
In the step 1), the student data refers to the basic information of students, including student numbers, names, classes and scores; the video learning data is data generated by students in the video learning process and comprises student numbers, video lengths, watching time and watching times, and the video learning data can be acquired through an SPOC platform.
In step 2), the students are classified according to their scores, and are finally classified into four categories, namely "excellent", "good", "medium" and "poor", wherein the score range corresponding to each category of students is: "excellent" [85-100], "good" [75-84], "medium" [60-74], and "poor" [0-59 ].
In the step 3), the video learning data are classified according to the numbers of the students of the four types of "excellent", "good", "medium" and "poor", so that each type of student corresponds to one group of video learning data.
In step 4), for four types of students, namely "excellent", "good", "medium" and "poor", the video learning data is statistically analyzed and visually presented from the following 5 time dimensions, specifically as follows:
a. video learning data statistical analysis based on total number of views: the total watching number is the total number of times that the students watch videos in the whole learning period, and is the most direct data, and the data can intuitively reflect the time and energy put into by the students in the video learning;
b. video learning data statistical analysis based on viewing intervals: the time interval of watching videos of students in the video learning process is counted and calculated, the learning habits of the students can be found according to the watching intervals in the video learning of the students, and the learning behavior characteristics of the students can be analyzed according to different video learning habits, and the video learning method is as follows:
let txDenotes a viewing interval of the video, x ═ 1,2,3, where t1Is the time difference between the starting time of the course and the time when the student first viewed the video, t2Is the time difference between two adjacent viewing behaviors of the student, t3The time difference between the time when the student watches the video for the last time and the end time of the course is obtained; viewing interval T ═ l, r), l<r, r is a real number greater than 1, viewing interval tx∈T，1≤tx<r, x ═ 1,2, 3; in order to find out the video learning habit of students, the number of watching interval intervals T and the range of each watching interval are set according to the video content and the length, and the watching interval T of the students is countedxThe number of times N falling within each viewing interval T; since the number N of the students with a large total number of viewing intervals may be greater than the number of the students with a small total number of viewing intervals, and the direct comparison N cannot analyze the difference in viewing habits among the students, the ratio of the number N of the viewing intervals in the viewing interval T to the total number of the viewing intervals is calculated by using the following formula:
Figure BDA0002598216470000031
wherein M represents the total number of the watching intervals, W represents the proportion of the watching intervals, and the proportion of the watching intervals W represents the continuity of the student learning; the larger the value of W is, the stronger the learning continuity of the student is, and the higher the learning efficiency is; conversely, the smaller the W value is, the poorer the student learning continuity is, and the lower the learning efficiency is;
c. video learning data statistical analysis based on the number of views in different time periods: the two time periods of working day/rest day and morning/afternoon/evening are combined to form the following 6 time period combinations: the method comprises the following steps of { working day + morning }, { working day + afternoon }, { working day + evening }, { resting day + morning }, { resting day + afternoon }, { resting day + evening }, and analyzing the learning behaviors and the learning preferences of students in different time periods;
the average number of views on weekdays is calculated as follows:
Figure BDA0002598216470000032
wherein, NWAijDenotes the average number of views, NWT, of the jth student during the working day period iijRepresents the total number of views of the jth student in the working day time period i, and NW represents the number of days of the working day;
the average number of views on weekdays is calculated as follows:
Figure BDA0002598216470000041
wherein, NRAijDenotes the average number of views, NRT, of the jth student during the holiday period iijRepresents the total number of views of the jth student in the holiday period i, and NR represents the holiday days;
d. video learning data statistical analysis based on the number of monthly views: with the change of months, the watching times of students also change, the total times of the students watching videos in each month are counted, and the learning behavior habits of the students in different stages can be found;
e. video learning data statistical analysis based on weekly watching number: counting the total times of the students watching the videos in each week, more finely describing the change of the learning behaviors of the students according to the week times, and discovering the influence of the learning enthusiasm of the students, the contents of the learned videos and the dominable time factors on the behaviors of the students;
and visually presenting the result in a mode of a bar chart and a line chart according to the statistical analysis result of the 5 time dimensions.
In step 5), the statistical results and the visual presentation of different types of students in different time dimensions are analyzed as follows:
a. according to the analysis result of the total watching amount, comparing the time invested by different types of students in the video learning;
b. finding out the video learning habits of different types of students according to the watching interval analysis result;
c. according to the viewing analysis results of different time periods, the learning preferences of different types of students in different time periods are compared;
d. according to the analysis result of the number of the watched months, learning behavior habits of different types of students in different stages are found, and the learning behavior habits comprise: the initial stage, the middle stage and the final stage;
e. according to the analysis result of the number of the weekly watched students, the change of the learning state of different types of students in a short period is found.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention distinguishes the difference of different types of students in learning behavior characteristics for the first time and performs video behavior analysis with pertinence to the characteristics of different types of students.
2. The invention analyzes the difference of the learning behaviors of students under the influence of different time factors for the first time and analyzes the difference of video learning data under different time factors.
3. The video learning data are classified and counted based on the time dimension and visually presented by combining the difference and sameness of the learning behaviors of different types of students under different time factors for the first time.
4. According to the learning characteristics of students at different time, the invention provides feasible suggestions for teachers to improve teaching strategies and students to improve learning modes, and provides technical support for students to self-regulate learning and teachers to perform personalized teaching.
5. The method has wide use space in the aspect of video learning data analysis, is simple to operate, has strong adaptability and has wide prospect in the aspect of student behavior analysis.
Drawings
FIG. 1 is a logic flow diagram of the present invention.
Fig. 2 is a histogram of the total number of video views of each type of student.
Fig. 3 is a histogram of video viewing interval ratios of students of each type.
Fig. 4 is a graph of the monthly video frequency line of each class of students.
Fig. 5 is a video frequency line graph of each type of student viewed weekly.
Detailed Description
The present invention will be further described with reference to the following specific examples.
As shown in fig. 1, the method for analyzing video learning behavior in an SPOC environment based on a time dimension provided by this embodiment includes the following steps:
1) acquiring basic data including student data and video learning data; specifically, the student data refers to the basic information of students, including student numbers, names, classes and scores; the video learning data is data generated by students in the video learning process, comprises student numbers, video lengths, watching time and watching times, and can be acquired through an SPOC platform. Learning data of software engineering 2016-level 1 and 2 classes of student object oriented programming (Java) courses in a SPOC platform of a school are selected. The curriculum has 11 chapters, is provided with 115 videos, has the lesson opening time of 10 months and 9 days to 12 months and 31 days in 2017, has the video learning data of 98 students, and comprises information shown in table 1.
TABLE 1 data description
Figure BDA0002598216470000061
2) Classifying the students according to the student scores, and finally classifying the students into four categories: "excellent", "good", "medium" and "poor", the score range corresponding to each class of students is: "excellent" [85-100], "good" [75- "84 ]," medium "[ 60-74], and" poor "[ 0-59 ]. The number of students per category and average performance are shown in table 2.
TABLE 2 student Classification results
Student type Number of people Average performance
Superior food 29 92.4
Good wine 23 76.2
In 27 65.6
Difference (D) 19 31.7
3) The video learning data are classified according to the numbers of the students of the four types of the students of 'excellent', 'good', 'medium', 'poor', so that each type of student corresponds to one group of video learning data, and four groups of video learning data are obtained.
4) For four types of students, namely 'excellent', 'good', 'medium', 'poor', the video learning data are subjected to statistical analysis and visualized presentation from the following 5 time dimensions, specifically as follows:
a. video learning data statistical analysis based on total number of views: the total number of views is the total number of times that the students watch the video in the whole learning period, and is the most direct type of data, which can visually reflect the time and energy put into the video learning by the students, in this case, the total number of views of each type of students is shown in fig. 2. Wherein the total number of video viewings decreases in sequence as the types of students go from "good" to "bad". And the total number of views of the students of "excellent", "good" and "medium" is closer, and the total number of views of the students of "poor" is more different than that of the students of other types. This is because students with high performance invest more learning time, while students with low performance invest less learning time.
b. Video learning data statistical analysis based on viewing intervals: the time interval of watching videos of students in the video learning process is counted and calculated, the learning habits of the students can be found according to the watching intervals in the video learning of the students, and the learning behavior characteristics of the students can be analyzed according to different video learning habits, and the video learning method is as follows:
let txDenotes a viewing interval of the video, x ═ 1,2,3, where t1Is the time difference between the starting time of the course and the time when the student first viewed the video, t2Is the time difference between two adjacent viewing behaviors of the student, t3The time difference between the time when the student watches the video for the last time and the end time of the course is obtained; viewing interval T ═ l, r), l<r, r is a real number greater than 1, viewing interval tx∈T，1≤tx<r, x ═ 1,2, 3; in order to find out the video learning habit of students, the number of watching interval intervals T and the range of each watching interval are set according to the video content and the length, and the watching interval T of the students is countedxThe number of times N falling within each viewing interval T; since the number N of the students with a large total number of viewing intervals may be greater than the number of the students with a small total number of viewing intervals, and the direct comparison N cannot analyze the difference in viewing habits among the students, the ratio of the number N of the viewing intervals in the viewing interval T to the total number of the viewing intervals is calculated by using the following formula:
Figure BDA0002598216470000071
where M denotes the total number of viewing intervals and W denotes the viewing interval ratio.
In the case of the video length within 20 minutes, 4 viewing intervals are used, respectively T1Not [0,20 min ]), T2Not [ < 20 min, 1 day) ], T31 day, 3 days), T43 days, + ∞) and counts the number of times the student's viewing interval falls in each intervalN1、N2、N3And N4。T1Intervals represent the behavior of a student watching a video continuously, T2Intervals represent the behavior of students watching video at short intervals, T3Intervals represent the behavior of students watching video at longer intervals, T4Intervals represent the behavior of the student without watching the video for a long time, and the visualization of the results is shown in fig. 3 as the percentage W of the number of watching intervals to the total number of watching intervals.
c. Video learning data statistical analysis based on the number of views in different time periods: the two time periods of working day/rest day and morning/afternoon/evening are combined to form the following 6 time period combinations: the method comprises the following steps of { working day + morning }, { working day + afternoon }, { working day + evening }, { resting day + morning }, { resting day + afternoon }, { resting day + evening }, and analyzing the learning behaviors and the learning preferences of students in different time periods;
the average number of views on weekdays is calculated as follows:
Figure BDA0002598216470000081
wherein, NWAijDenotes the average number of views, NWT, of the jth student during the working day period iijRepresents the total number of views of the jth student in the weekday period i, and NW represents the number of days of the weekday.
The average number of views on weekdays is calculated as follows:
Figure BDA0002598216470000082
wherein, NRAijDenotes the average number of views, NRT, of the jth student during the holiday period iijRepresents the total number of views of the jth student during the holiday period i, and NR represents the number of holiday days.
In this case, the number of each type of student viewed in each time period is calculated, and the result is shown in table 3.
TABLE 3 number of views per time period for each type of student
Figure BDA0002598216470000083
Figure BDA0002598216470000091
d. Video learning data statistical analysis based on the number of monthly views: with the change of months, the watching times of students also change, and the total times of the students watching videos in each month are counted, so that the learning behavior habits of the students in different stages can be found.
In this case, the variation trend of the number of students watched per month is counted, as shown in fig. 4. Wherein, the number of the students is increased from 10 months to 11 months. Students of "good" and "good" watched numbers higher than 11 months at 12 months, while students of "medium" and "poor" started watching numbers sliding down at 11 months.
e. Video learning data statistical analysis based on weekly watching number: the total times of the students watching the videos in each week are counted, the change of the learning behaviors of the students can be more finely described according to the weeks, and the influence of the learning enthusiasm of the students, the contents of the learned videos and the dominable time factors on the behaviors of the students can be found.
The change trend of the weekly watching number of the students in the example is shown in the attached figure 5. The number of the students watched is the most in 3 rd week and 12 th week, and the number of the students watched is the least in the first week; the number of the students who are poor is stable after week 5, and the numbers of the students who are poor slide down greatly in week 6 and week 9; after the number of views declined at week 6 and week 9, the number of views by "good" students rose rapidly at week 7 and week 10, while the number of views by "good" and "medium" students rose slowly.
And visually presenting the result in a mode of a bar chart and a line chart according to the statistical analysis result of the 5 time dimensions.
5) For the statistical results and visual presentation of different types of students in different time dimensions, the following analysis is performed:
a. according to the analysis result of the total watching amount, students with higher scores can be found, and the number of the watched videos is larger.
b. From the viewing interval analysis results, it can be found that: comparing the learning behaviors of different types of students, finding out students who learn poorly, W is compared with other types of students2Lower, and W3And W4Higher; students of "excellent", "good" and "middle" are in W1、W2And W3Not much different, W4And increases in turn. The watching habits of students are analyzed, and due to the fact that the front-back correlation exists among the video contents, knowledge points can be better mastered in a short time of continuous learning, and the learning efficiency is improved, so that the continuous watching learning mode is the main learning mode. And students with poor performance learn with higher proportion at longer intervals, and because the continuity of learning is interrupted when the students learn with good intervals for several days, the learning efficiency is lower, and the students do not learn during the interruption of learning, the learning time is also reduced.
Further, the following conclusions are drawn: students are more inclined to a continuous learning mode; the proportion that students with high performance can not see videos for a long time is lower, and the learning discontinuous period is shorter; and students with low performance do not see videos for a long time in a higher proportion, and have longer learning intermission period.
c. According to the different time periods to watch the analysis results, it can be found that: all students watch more video on weekdays than on holidays; and the video watching frequency is the most at night no matter on working days or rest days; on the holidays, the number of views in the afternoon is higher than in the morning; on weekdays, the number of views in the morning and afternoon is comparable.
Different types of students have different numbers of video views in each time period of the working day and the resting day. The number of the 'excellent' students watching in the afternoon on the rest day is higher than that of the working day, and the watching number of other students in each time period on the rest day is lower than that of the working day; the number of good students watched in the morning, afternoon and evening of the working day is close to that of good students, but the number of good students watched in the morning, afternoon and evening of the resting day is lower than that of good students; the number of the students in 'middle' is close to that of the students in 'good' on the rest day, but the number of the students in each time period on the working day is lower than that of the students in 'good'; the number of "bad" students is lowest whether on weekdays or weekdays. Analysis shows that the number of the 'excellent' students watching in the afternoon of the rest day is higher than that of the working day in the afternoon of the rest day, because the students still keep higher learning enthusiasm in the afternoon of the rest day; the number of good students watched on the working day is close to that of good students, and the number of good students watched on the rest day is lower than that of good students, because the good students pay more time to study at ordinary times, but the study enthusiasm is reduced by the rest day; the learning enthusiasm of the students of "middle" and "poor" is low both on weekdays and on weekdays.
d. According to the analysis result of the number of the watched per month, students with higher watching number in the initial learning stage can be found, and the watching number in the middle and the final learning stage is also higher, and the score is higher; on the contrary, students who have a small number of views in the initial stage of learning are also small in the number of views in the subsequent learning, and the achievement is low. Because the initial learning period is the period of the base of the students, the learning quality of the students can influence the final learning result, and therefore, the teacher can perform teaching intervention according to the initial video watching condition of the students. The middle period is a period when the learning power of students is high and is also a key period of teaching, and the number of videos watched by the students in the middle period is the largest and is also an important period for teaching intervention. In the end period, the video watching number of students with high performance and low performance changes differently, the students with high performance can still ensure sufficient learning time, and the learning enthusiasm of the students with low performance is reduced.
Based on the monthly views analysis, the conclusion that can be drawn is: the number of the video watched by the students with high performance in each learning period is high, the change trend of the number of the watched videos keeps a growing trend, and the learning enthusiasm is strong. The number of the video watched by students with low performance in each learning period is low, and in the end period, the number of the videos watched tends to decline, and the learning enthusiasm is reduced.
e. According to the weekly watching number analysis result, the influence of the teaching progress on the video watching number of the students can be found, when new knowledge is learned, the students can actively put into the learning process, and when the number of videos available for learning is large, the watching number of the students is also high. Conversely, if there is no new video for learning, the progress of the student is slowed down.
Based on weekly viewings analysis, one can conclude that: students with excellent performance, good performance and moderate performance have certain similarity in the change trend of the learning times and are related to the teaching progress. Students with low performance have poor autonomous learning ability, and only invest certain learning time in a few weeks before starting the course.
In conclusion, after the scheme is adopted, the method provides a new method for analyzing the video learning behavior in the SPOC environment, considers the time dimension of the students in the video learning process, can discover more potential learning behavior characteristics, provides relevant suggestions for teachers and students, provides help for teachers to improve teaching policies and students to self-regulate learning in the SPOC learning environment, has practical popularization value, and is worthy of popularization.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereby, and all changes made in the concept and principle of the present invention should be covered within the scope of the present invention.

Claims (6)

1. A video learning behavior analysis method under an SPOC environment based on a time dimension is characterized by comprising the following steps:
1) acquiring basic data including student data and video learning data;
2) classifying the students according to the student data;
3) classifying the video learning data according to the classification of the students;
4) performing statistical analysis on the video learning data of different types of students and performing visual presentation;
5) and analyzing the learning behaviors of different types of students in different time dimensions according to the statistical result of the step 4).
2. The method according to claim 1, wherein the analysis method for video learning behavior under SPOC environment based on time dimension is characterized in that: in the step 1), the student data refers to the basic information of students, including student numbers, names, classes and scores; the video learning data is data generated by students in the video learning process and comprises student numbers, video lengths, watching time and watching times, and the video learning data can be acquired through an SPOC platform.
3. The method according to claim 1, wherein the analysis method for video learning behavior under SPOC environment based on time dimension is characterized in that: in step 2), the students are classified according to their scores, and are finally classified into four categories, namely "excellent", "good", "medium" and "poor", wherein the score range corresponding to each category of students is: "excellent" [85-100], "good" [75-84], "medium" [60-74], and "poor" [0-59 ].
4. The method according to claim 1, wherein the analysis method for video learning behavior under SPOC environment based on time dimension is characterized in that: in the step 3), the video learning data are classified according to the numbers of the students of the four types of "excellent", "good", "medium" and "poor", so that each type of student corresponds to one group of video learning data.
5. The method according to claim 1, wherein the analysis method for video learning behavior under SPOC environment based on time dimension is characterized in that: in step 4), for four types of students, namely "excellent", "good", "medium" and "poor", the video learning data is statistically analyzed and visually presented from the following 5 time dimensions, specifically as follows:
a. video learning data statistical analysis based on total number of views: the total watching number is the total number of times that the students watch videos in the whole learning period, and is the most direct data, and the data can intuitively reflect the time and energy put into by the students in the video learning;
b. video learning data statistical analysis based on viewing intervals: the time interval of watching videos of students in the video learning process is counted and calculated, the learning habits of the students can be found according to the watching intervals in the video learning of the students, and the learning behavior characteristics of the students can be analyzed according to different video learning habits, and the video learning method is as follows:
let txDenotes a viewing interval of the video, x ═ 1,2,3, where t1Is the time difference between the starting time of the course and the time when the student first viewed the video, t2Is the time difference between two adjacent viewing behaviors of the student, t3The time difference between the time when the student watches the video for the last time and the end time of the course is obtained; viewing interval T ═ l, r), l<r, r is a real number greater than 1, viewing interval tx∈T，1≤tx<r, x ═ 1,2, 3; in order to find out the video learning habit of students, the number of watching interval intervals T and the range of each watching interval are set according to the video content and the length, and the watching interval T of the students is countedxThe number of times N falling within each viewing interval T; since the number N of the students with a large total number of viewing intervals may be greater than the number of the students with a small total number of viewing intervals, and the direct comparison N cannot analyze the difference in viewing habits among the students, the ratio of the number N of the viewing intervals in the viewing interval T to the total number of the viewing intervals is calculated by using the following formula:
Figure FDA0002598216460000021
wherein, M represents the total number of the watching intervals, W represents the proportion of the watching intervals, the proportion of the watching intervals W represents the learning continuity of students, the larger the value of W, the stronger the learning continuity of students and the higher the learning efficiency, and conversely, the smaller the value of W, the poorer the learning continuity of students and the lower the learning efficiency;
c. video learning data statistical analysis based on the number of views in different time periods: the two time periods of working day/rest day and morning/afternoon/evening are combined to form the following 6 time period combinations: the method comprises the following steps of { working day + morning }, { working day + afternoon }, { working day + evening }, { resting day + morning }, { resting day + afternoon }, { resting day + evening }, and analyzing the learning behaviors and the learning preferences of students in different time periods;
the average number of views on weekdays is calculated as follows:
Figure FDA0002598216460000031
wherein, NWAijDenotes the average number of views, NWT, of the jth student during the working day period iijRepresents the total number of views of the jth student in the working day time period i, and NW represents the number of days of the working day;
the average number of views on weekdays is calculated as follows:
Figure FDA0002598216460000032
wherein, NRAijDenotes the average number of views, NRT, of the jth student during the holiday period iijRepresents the total number of views of the jth student in the holiday period i, and NR represents the holiday days;
d. video learning data statistical analysis based on the number of monthly views: with the change of months, the watching times of students also change, the total times of the students watching videos in each month are counted, and the learning behavior habits of the students in different stages can be found;
e. video learning data statistical analysis based on weekly watching number: counting the total times of the students watching the videos in each week, more finely describing the change of the learning behaviors of the students according to the week times, and discovering the influence of the learning enthusiasm of the students, the contents of the learned videos and the dominable time factors on the behaviors of the students;
and visually presenting the result in a mode of a bar chart and a line chart according to the statistical analysis result of the 5 time dimensions.
6. The method according to claim 1, wherein the analysis method for video learning behavior under SPOC environment based on time dimension is characterized in that: in step 5), the statistical results and the visual presentation of different types of students in different time dimensions are analyzed as follows:
a. according to the analysis result of the total watching amount, comparing the time invested by different types of students in the video learning;
b. finding out the video learning habits of different types of students according to the watching interval analysis result;
c. according to the viewing analysis results of different time periods, the learning preferences of different types of students in different time periods are compared;
d. according to the analysis result of the number of the watched months, learning behavior habits of different types of students in different stages are found, and the learning behavior habits comprise: the initial stage, the middle stage and the final stage;
e. according to the analysis result of the number of the weekly watched students, the change of the learning state of different types of students in a short period is found.
CN202010716232.2A 2020-07-23 2020-07-23 Video learning behavior analysis method in SPOC environment based on time dimension Pending CN111832524A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010716232.2A CN111832524A (en) 2020-07-23 2020-07-23 Video learning behavior analysis method in SPOC environment based on time dimension

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010716232.2A CN111832524A (en) 2020-07-23 2020-07-23 Video learning behavior analysis method in SPOC environment based on time dimension

Publications (1)

Publication Number Publication Date
CN111832524A true CN111832524A (en) 2020-10-27

Family

ID=72925082

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010716232.2A Pending CN111832524A (en) 2020-07-23 2020-07-23 Video learning behavior analysis method in SPOC environment based on time dimension

Country Status (1)

Country Link
CN (1) CN111832524A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112732784A (en) * 2020-12-31 2021-04-30 山东理工大学 Online learning behavior model mining system based on process mining and mining method thereof

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107945600A (en) * 2017-12-07 2018-04-20 李成斌 A kind of individualized video learning system and method
CN108182489A (en) * 2017-12-25 2018-06-19 浙江工业大学 Method is recommended in a kind of individualized learning based on on-line study behavioural analysis

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107945600A (en) * 2017-12-07 2018-04-20 李成斌 A kind of individualized video learning system and method
CN108182489A (en) * 2017-12-25 2018-06-19 浙江工业大学 Method is recommended in a kind of individualized learning based on on-line study behavioural analysis

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张峰，刘迪，高莹: "基于SPOC视频学习数据的学习行为特征分析方法", 《软件》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112732784A (en) * 2020-12-31 2021-04-30 山东理工大学 Online learning behavior model mining system based on process mining and mining method thereof
CN112732784B (en) * 2020-12-31 2022-05-10 山东理工大学 Online learning behavior model mining system based on process mining and mining method thereof

Similar Documents

Publication Publication Date Title
Chen et al. DropoutSeer: Visualizing learning patterns in Massive Open Online Courses for dropout reasoning and prediction
Kost et al. Characterizing the gender gap in introductory physics
Conger et al. Explaining race, poverty, and gender disparities in advanced course‐taking
Lee et al. Academic behaviors among high-achieving African-American students
McIntosh et al. The influence of family political discussion on youth civic development: Which parent qualities matter?
Garrison The 1987 distinguished lecture: A constructionist approach to mass media and public opinion
Dalton et al. From Track to Field: Trends in Career and Technical Education across Three Decades.
CN111832524A (en) Video learning behavior analysis method in SPOC environment based on time dimension
Recker et al. Analyzing learner and instructor interactions within learning management systems: Approaches and examples
DURAK et al. School principals’ technological leadership self-efficacies and 21 st century teacher skills
Dusen et al. Influencing students’ relationships with physics through culturally relevant tools
Anisef et al. Differential effects of university and community college education on occupational status attainment in Ontario
Chudnovskaya Educational expansion and educational homogamy among the highly educated in Sweden
Mozhaeva et al. Correlation of MOOC students’ behavior patterns and their satisfaction with the quality of the course
Amreen Emerging adult career pathways: understanding aspirations and switching
Blaushild et al. The Design and Impact of Remote Professional Development on Computer Science Instructional Coaching and Teaching in Chicago Public Schools During COVID-19 October 29, 2022
Douglas et al. Comparison of live, late and archived mode learner behavior in an advanced engineering MOOC
de Gendre et al. From subsidies to loans: The effects of a national student finance reform on the choices of secondary school students
Holzman et al. Inequalities in Postsecondary Attainment by English Learner Status: The Role of College-Level Course-Taking. Research Brief for the Houston Independent School District. Volume 9, Issue 2.
Wyness Mismatch in Higher Education
Torrance Data & Analytics for Instructional Designers
Yin A progressively focused qualitative study of teacher thinking in English for academic purposes (EAP) classroom language assessment
Aretoulis et al. GREEK UNIVERSITY STUDENTS'PREFERRED LEARNING STYLES IN THE CONTEXT OF DISTANCE EDUCATION
Beaudin et al. Predicting freshman persistence in economics: A gender comparison
Bidjerano EXPLORING THE IMPACT OF THE PANDEMIC ON INTEREST IN TEACHING AS A CAREER PATH

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20201027

WD01 Invention patent application deemed withdrawn after publication