KR20140087700A - System and method for analyzing data related to study consulting - Google Patents
System and method for analyzing data related to study consulting Download PDFInfo
- Publication number
- KR20140087700A KR20140087700A KR1020120158274A KR20120158274A KR20140087700A KR 20140087700 A KR20140087700 A KR 20140087700A KR 1020120158274 A KR1020120158274 A KR 1020120158274A KR 20120158274 A KR20120158274 A KR 20120158274A KR 20140087700 A KR20140087700 A KR 20140087700A
- Authority
- KR
- South Korea
- Prior art keywords
- study
- user
- data
- time
- efficiency
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 94
- 238000007405 data analysis Methods 0.000 claims description 80
- 238000012360 testing method Methods 0.000 claims description 52
- 230000006870 function Effects 0.000 claims description 22
- 230000004622 sleep time Effects 0.000 claims description 14
- 210000004556 brain Anatomy 0.000 claims description 13
- 230000006399 behavior Effects 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 10
- 238000002599 functional magnetic resonance imaging Methods 0.000 claims description 7
- 230000004913 activation Effects 0.000 claims description 6
- 238000001134 F-test Methods 0.000 claims description 2
- 238000007689 inspection Methods 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 40
- 238000010586 diagram Methods 0.000 description 54
- 230000008569 process Effects 0.000 description 35
- 230000003442 weekly effect Effects 0.000 description 12
- 238000011156 evaluation Methods 0.000 description 10
- 238000002600 positron emission tomography Methods 0.000 description 8
- 230000001568 sexual effect Effects 0.000 description 7
- 230000036578 sleeping time Effects 0.000 description 7
- 210000001652 frontal lobe Anatomy 0.000 description 3
- 238000001228 spectrum Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 210000001320 hippocampus Anatomy 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000007958 sleep Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 210000003926 auditory cortex Anatomy 0.000 description 1
- 230000017531 blood circulation Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 230000002354 daily effect Effects 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000002595 magnetic resonance imaging Methods 0.000 description 1
- 230000007595 memory recall Effects 0.000 description 1
- 238000009206 nuclear medicine Methods 0.000 description 1
- 210000000977 primary visual cortex Anatomy 0.000 description 1
- 229940121896 radiopharmaceutical Drugs 0.000 description 1
- 239000012217 radiopharmaceutical Substances 0.000 description 1
- 230000002799 radiopharmaceutical effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 210000003478 temporal lobe Anatomy 0.000 description 1
Images
Classifications
-
- 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
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
Description
The present invention relates to a data analysis system and method for providing study consulting such as suitability of a user's current study mode or reachability to target data based on a study time of a user.
In the past, it was difficult for students to see how much effort they had to make to get to the college they are currently pursuing. Students simply grasped unconditional learning as a way to increase their study time and reduce sleep time to pass their desired college.
However, unconditional increases in study time and reduced sleep time do not guarantee that they will pass to the desired university. In addition, statistically, it is less effective to increase study time and to reduce sleep time, because the university has a certain level of study time and sleep time for students who pass.
In the end, we need a way to improve the efficiency of students' study by suggesting a study method that matches the target university that the students set.
The present invention provides a system and method for determining whether there is a possibility of reaching target data set by a user and providing a variety of study consulting data such as study time, sleep time, study efficiency, etc. so as to reach target data do.
The present invention provides a system and method for measuring the relative percentile of study time for reaching target data set by a user, and comparing the measured percentile with reference data, which is statistical data, to provide objective relative percentiles.
In the present invention, when user data over a predetermined standard is accumulated, the possibility of university acceptance is provided based only on relative data between users.
The present invention provides a system and method for providing consulting data by automatically creating plans related to learning such as subject-specific study methods and time-by-subject time allocations.
The present invention provides a system and method for providing study consulting data including study time, study efficiency, and study amount corresponding to sector data based on user's sexual data or target data set by a user.
A data analysis system according to an embodiment of the present invention includes a reference database storing reference data including study efficiency, study time, and study method extracted from a plurality of statistical groups; An input database for receiving and storing user data according to a time interval from a user device; A first analyzer for analyzing a user's study amount using the study efficiency and the study time of the user, comparing the study amount with reference data, and outputting user level data; A second analyzer for comparing the target data of the user with the user data of the user and outputting consulting data related to a study time, a study efficiency and a study amount necessary for a user; And a third analyzer for outputting check data indicating the possibility of reaching the target data using the study amount and the study time of the user.
According to an embodiment of the present invention, by providing customized study consulting data to a user, it is possible to accurately and easily grasp a study method for reaching target data set by a user.
According to an embodiment of the present invention, object-based study consulting data can be provided by providing statistical-based reference data in consideration of target data set by a user or sector data based on user's sexual data.
1 is a view for explaining operations of a user apparatus and a data analysis system according to an embodiment of the present invention.
2 is a diagram illustrating user data input to a data analysis system in a user equipment according to an embodiment of the present invention.
3 is a diagram illustrating user data output to a user apparatus in a data analysis system according to an embodiment of the present invention.
4 is a detailed block diagram of a data analysis system according to an embodiment of the present invention.
5 is a detailed block diagram of a reference database according to an embodiment of the present invention.
6 is a diagram illustrating brain activation associated with the method of studying according to an embodiment of the present invention.
7 is a diagram illustrating a process of storing a user database according to an embodiment of the present invention and determining an evaluation criterion according to the number of user data.
8 is a diagram illustrating a process of determining sector data of a user according to sexual data in user data of a user according to an embodiment of the present invention.
9 is a diagram illustrating an example of sector data according to an embodiment of the present invention.
10 is a view illustrating a detailed operation of the first analyzing unit according to an embodiment of the present invention.
11 is a diagram illustrating a process of pointing a study efficiency according to an embodiment of the present invention.
FIG. 12 is a diagram illustrating a process of determining sector data to which learning efficiency belongs according to an embodiment of the present invention.
13 is a diagram illustrating a process of pointing a study time according to an embodiment of the present invention.
FIG. 14 is a diagram illustrating a process of determining points of a study amount using pointed study time and study efficiency according to an embodiment of the present invention.
15 is a diagram illustrating a process of determining level data based on a user's study amount according to an embodiment of the present invention.
FIG. 16 illustrates a process of determining a percentage to be used according to a result of a test between user data and reference data according to an exemplary embodiment of the present invention. Referring to FIG.
17 is a diagram illustrating a process of adjusting spectrum between user data and reference data according to an embodiment of the present invention.
FIG. 18 is a diagram showing percentiles of relative user data according to user sector data and target data through a first analyzing unit according to an embodiment of the present invention. FIG.
19 is an illustration showing the percentile of user data according to an embodiment of the present invention.
20 is a diagram illustrating an operation of a second analyzing unit according to an embodiment of the present invention.
FIG. 21 is a diagram illustrating a process of deriving an insufficient study time corresponding to a target data of a user through a second analyzing unit according to an embodiment of the present invention.
22 is a diagram illustrating a process of deriving insufficient study efficiency in accordance with a target data of a user through a second analysis unit according to an embodiment of the present invention.
23 is a diagram illustrating a process of deriving an insufficient amount of study corresponding to a target data of a user through a second analyzing unit according to an embodiment of the present invention.
FIG. 24 is an illustration showing suitability of study time, study efficiency, and study amount corresponding to target data according to an embodiment of the present invention.
25 is a diagram illustrating a process of generating consulting data related to study time, study efficiency, and study method based on target data and sector data according to an embodiment of the present invention.
26 is a diagram showing an example of consulting data according to an embodiment of the present invention.
27 is a diagram showing another example of consulting data according to an embodiment of the present invention.
28 is a diagram illustrating an operation of a third analyzer according to an embodiment of the present invention.
29 is a diagram illustrating a process of determining whether there is a possibility of reaching a top group of target data through a third analyzing unit according to an embodiment of the present invention.
Figure 30 is an illustration showing the likelihood of reaching the highest group of target data in accordance with an embodiment of the present invention.
31 is a diagram illustrating a process of tracking sector data according to an embodiment of the present invention.
32 is an illustration showing the result of tracking sector data according to an embodiment of the present invention.
FIG. 33 is a diagram illustrating a concentration mode using a mobile device according to an embodiment of the present invention. FIG.
FIG. 34 is a diagram for explaining a friend-making function based on a specific area and a school in accordance with an embodiment of the present invention.
FIG. 35 is a view illustrating a detailed operation of the affiliate system according to an embodiment of the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
1 is a view for explaining operations of a user apparatus and a data analysis system according to an embodiment of the present invention.
The user device can input user data including user's user profile data and target data to a data analysis system via a web, a touch sensor, a GPS sensor, a photo recognition device (camera, etc.). Here, the user equipment may be a wired or wireless type terminal, and the user data may be input to the data analysis system automatically or manually via the user equipment. The target data may mean a university, a company, a test, etc. that the user is aiming at.
Then, the data analysis system makes it possible to objectively determine the situation in which the current study mode corresponds to the user based on the statistical data related to the target data set by the user. The evaluation result according to the study method of the user can be provided through the consulting data expressed by the study time, the study efficiency, the study amount, and the like. The data analysis system can provide various data analyzed according to various output methods such as web, display, sound, vibration, and pop-up to the user device.
2 is a diagram illustrating user data input to a data analysis system in a user equipment according to an embodiment of the present invention.
As shown in FIG. 2, the user data may be input to the data analysis system via the user device according to the time interval. For example, the data analysis system may receive and store the grade, average study time, target data, gender, recent baseline test scores, and area from the user device first. And, the data analysis system can receive and store the daily study time, sleeping time, and use place of the user device from the user device.
Also, the data analysis system can receive and store the reference test scores, the questionnaire data related to the study efficiency, the test time, the vacation period, the type and the number of the institute of the learning from the user device every quarter. On the other hand, the data analysis system can receive and store the tracking function setting (spy setting) and the target data confirmation request (request to check whether or not it belongs to the top group of the target data) from the user device.
3 is a diagram illustrating output data output to a user apparatus in a data analysis system according to an embodiment of the present invention. Referring to FIG. 3, output data, which is an analysis result of each component of the data analysis system, may be output to the user device.
For example, the data analysis system may be configured to analyze the percentages of weeks, months, and yearly study hours, percentile of weekly study, percentile of level data, percentile of study efficiency, percentile of study time, The percentile of the whole level, and the percentile of the overall study efficiency. At this time, the user data is evaluated by the reference database when the data is below the set reference, and when the user data is more than the set reference, the relative percentile of the user data alone is output. On the other hand, the first analyzing unit can output the percentile of the study time, the percentile of the weekly study amount, the percentile of the level data, and the percentile of the study efficiency based on the target data.
Then, it is possible to determine whether or not the target data set by the user can be reached through the Simple Adviser of the second analysis unit of the data analysis system. Thereafter, when the user's study time is less than the study time of each target data extracted from the reference data, the second analysis unit may calculate the study time required for the user to reach the target data. If the study efficiency of the user is less than the study efficiency of the target data extracted from the reference data, the second analysis unit may calculate the study efficiency required for the user to reach the target data. In addition, when the user's study amount is smaller than the study amount of each target data extracted from the reference data, the second analysis unit may calculate the study amount required for the user to reach the target data.
In another example, the second analyzing unit determines the relative study time, study amount, and study efficiency based on the sector data and the level data based on the user's reference test scores through the Smart Planning System, and determines the relative study time, To provide consulting data for reaching the target data of the user.
The third analysis unit of the data analysis system can provide the result of tracking the sector data (Spy setting) and whether the user can reach the top target group of the target data (Sky line result).
4 is a detailed block diagram of a data analysis system according to an embodiment of the present invention.
Referring to FIG. 4, the data analysis system may include a reference database, an input database, a result database, a first analysis unit, a second analysis unit, and a third analysis unit.
The reference database may store reference data including study efficiency, study time, and study method extracted from a plurality of statistical groups. For example, the reference database is a study efficiency based on the average sleeping time and the distribution ratio of statistical subjects per target data; Study time of statistical subject related to target data; And a study method based on brain activation of the statistical subject.
Here, the study method is determined by a learning pattern that combines the learning behavior of the statistical subject for each specific subject. Also, the learning behavior of the statistical subject can be determined by the area where the subject's brain is activated through fMRI or PET scanning.
The input database may receive and store user data according to a time interval from the user device. Specifically, the input database may receive and store the grade, average study time, target data, sex, recent reference test scores, and area from the user first. Then, the input database can receive and store the study time for each subject, the sleeping time, and the use place of the user apparatus from the user. In addition, the input database can receive and store the reference test scores, the questionnaire data related to the study efficiency, the test time, the vacation period, and the type and number of the studying institutes from the user every quarter. On the other hand, the input database can arbitrarily receive and store the tracking function setting and the target data confirmation request from the user.
The result database may store the analysis results of the first analysis unit, the second analysis unit, and the third analysis unit.
The first analyzing unit analyzes the user's study amount using the study efficiency and the study time of the user, and outputs the level data of the user by comparing the study amount with the reference data. For example, the first analyzing unit may calculate the study efficiency based on the sleep time of the user, and may calculate the study amount of the user by combining the study time and the study efficiency.
At this time, the first analyzing unit may use the percentage of the user data or the percentage of the reference data based on the T test or F test result of the user data and the reference data related to the user's study amount, study time, and study efficiency Can be determined.
In addition, the first analyzing unit may determine a user's study time, study efficiency, study amount, and degree of user's level data with respect to sector data based on the user's reference test score. Then, the first analyzing unit can determine the user's study time, study efficiency, study amount, and degree of user's level data with respect to the target data of the user.
For example, the first analyzing unit of the data analysis system may be configured to analyze the percentages of week, month, and yearly study hours, percentile of weekly study, percentile of level data, percentile of study efficiency, percentile of study time, The percentile of the level, and the percentile of the overall study efficiency. On the other hand, the first analyzing unit can output the percentile of the study time, the percentile of the weekly study amount, the percentile of the level data, and the percentile of the study efficiency based on the target data.
The second analyzing unit may compare the target data of the user with the user data of the user, and output the consulting data related to the study time, study efficiency, and study amount necessary for the user.
For example, the second analyzing unit may compare the study time, the study amount, or the study efficiency of the user with the study time, the study amount, or the study efficiency for each target data extracted from the reference data, and output the possibility of reaching the target data. If the study time of the user is less than the study time of the target data extracted from the reference data, the second analysis unit may calculate the study time required for the user to reach the target data.
If the study efficiency of the user is less than the study efficiency of the target data extracted from the reference data, the second analysis unit may calculate the study efficiency required for the user to reach the target data. On the other hand, when the user's study amount is smaller than the study amount per target data extracted from the reference data, the second analysis unit can calculate the amount of study required for the user to reach the target data.
In another example, the second analyzing unit may determine a relative study time, a study amount, and a study efficiency based on the sector data data and the level data based on the user's reference test scores, and determine the relative study time, the study amount, It is possible to provide consulting data for reaching the target data. In this case, the consulting data may include a study time for each subject and a study method for reaching the target data of the user.
The second analysis unit of the data analysis system can determine whether or not the target data set by the user can be reached through the Simple Adviser. Thereafter, when the user's study time is less than the study time of each target data extracted from the reference data, the second analysis unit may calculate the study time required for the user to reach the target data. If the study efficiency of the user is less than the study efficiency of the target data extracted from the reference data, the second analysis unit may calculate the study efficiency required for the user to reach the target data. In addition, when the user's study amount is smaller than the study amount of each target data extracted from the reference data, the second analysis unit may calculate the study amount required for the user to reach the target data.
In another example, the second analyzing unit determines the relative study time, study amount, and study efficiency based on the sector data and the level data based on the user's reference test scores through the Smart Planning System, and determines the relative study time, To provide consulting data for reaching the target data of the user.
The third analyzing unit can output check data indicating the possibility of reaching the target data by using the study amount and the study time of the user. For example, the third analyzing unit may provide the check data indicating whether there is a possibility of reaching the highest group of the target data using the study amount or the study time. In another example, the third analysis unit may determine the sector data to be tracked by the data analysis system, and provide the study time or the study time of each user belonging to the sector data.
The third analysis unit of the data analysis system can provide the results of tracking the sector data and whether the user can reach the top group of target data.
5 is a detailed block diagram of a reference database according to an embodiment of the present invention.
Referring to FIG. 5, the reference database may include a study efficiency database, a study time database, and a study method database.
Study efficiency, study time, and study method can be defined as follows.
<Study efficiency>
The average characteristics of the test takers' average sleeping time, listening attitude during study, etc., were determined through a questionnaire. The tendency was a certain tendency (50% or more (Ie, when the data is concentrated on one item), the results are extracted from the statistics and stored in the study efficiency database. The efficiency of the user's study is judged based on the stored data. In particular, study efficiency is related to sleep time.
The sleeping time can be determined as follows. The data processing system may set a sleep mode after setting an alarm time to a terminal of a user who wants to measure the sleep time. If the alarm time is reached, the user can determine whether the user operates the terminal, such as touching the terminal or turning off the terminal. Then, the data processing system can determine the sleeping time from the time when the sleep mode is set to the time when the terminal is operated.
The statistical data of the study period of one year before the examination test of the specific test applicants are stored and classified by the university. When there are statistically significant statistical data of 30 or more (standard of social statistics) . It is a statistical data on the results of the questionnaire about the life of one year before the examination for the life pattern for the persons who received the results within the top 5% of the specific test candidates. Statistical data on life patterns have a standard distribution (T test, F test), and only the results below the standard deviation are stored as meaningful data, which is called study efficiency.
For example, the results of the questionnaire surveying the characteristics of candidates within the top 5% of the test scores with more than 100,000 candidates between 16 and 23 years old (T test, F test) Study efficiency can be determined. Or the study efficiency can be expressed as a point having a tendency of being concentrated more than 50% to a specific value. Specifically, if the average sleep time during the X college students' intake is more than 70% between 6 and 7 hours, this average sleep time can be determined as the study efficiency of the X college students. And the more difference between the standard average sleep time, the lower the efficiency of study.
<Study time>
The statistical results are extracted through a questionnaire on the average study time according to the test results of the specific test applicants, and they are stored in the study time database. It is possible to compare the study time stored in the future study time database with the study time of the user so as to determine whether the study time is large or small according to the target test result of the user.
For example, if the average student study time is over the average study time per student during the college entrance examination period of more than 1,000 students within 3 years of taking the test, the average and statistical data can be obtained and used as the reference data when the user data is insufficient. Is not used as the reference data at the moment when the number exceeds 10,000. Data from college students are verified annually by questionnaire. If the user data becomes more than the reference, only the user data will be used from this point, and average and statistics will be used and used as the result value.
<How to study>
The process of modularizing the learning behaviors of specific test takers and combining them and observing the activation level of the brain using fMRI and PET scanning. Based on the results of activation observation of the brain, we classify the combined learning behaviors and consult the user with the learning behavior process based on them.
Specifically, it stores data on the subject-specific learning methods of those who have received scores within the top 5% of the specific test takers. The learning method is to modularize the learning behaviors of the specific test candidates by the educational psychologists and divide them into the criteria of MATE through fMRI and PET scanning. do.
FUNRI (Funtional Magnetic Resonance Imaging) and PET scanning (Positron-Emission Tomography) are used to examine 10 or more brains in males and females, respectively. At this time, it can be classified into M, A, T, and E according to the brain area (hippocampus, frontal lobe, Al, Vl) activated according to each study method, If the number of users of the user does not satisfy the preset number of users, it becomes a criterion of the user's study time and percentage of life pattern, and can act as a reference for calibrating user data even when the number of users is satisfied. In addition, a reference database is used to advise on how to learn by subject.
6 is a diagram illustrating brain activation associated with the method of studying according to an embodiment of the present invention.
The study method means that educational psychologists modularize and combine learning behaviors when more than 10 people in each subject within 3 years of taking a specific test take the course of learning activities for each subject over 2 hours each time do. For example, when a specific test includes mathematics, a tester studying mathematics can be modularized into 8 types by classifying the learning activities as follows by observing the course of 2 hours or more. This is classified by expert advice Can be created as an action module.
Behavior modules: 'Read', 'View', 'Memory Recall', 'Combine Memory', 'Draw Graph', 'Draw Shape', ' 'Moment Break'
By combining the following 8 modular methods, you can define various learning behaviors as follows.
① 'Read' → 'Rest' → 'Read' → 'Rest' → 'Read'
② 'Reading' → 'Remember the concept' → 'Calculate'
③ 'Read' → 'Conceptual memory' → 'Read' → 'Combine memory' → 'Calculate'
④ 'Read' → 'View' → 'Remember Concept' → 'Draw Graph' → 'Calculate'
... The learning pattern can be defined by combining the modulated learning actions as described above. These modularized and defined learning behaviors are called study methods. These modularization and combination are meaningful only when the educational psychologist has completed the direct or final verification and has used fMRI or PET scanning to determine whether the brain is activated. Can be stored as a study method.
fMRI is a measure of changes in the blood flow of the brain and is a tool to determine which parts of the brain are activated. Positron-Emission Tomography (PET) scanning is a nuclear medicine testing method that can display the physiological, chemical, and functional images of the human body in three dimensions using radiopharmaceuticals that emit positron. It can be judged. In FIG. 6, AI corresponds to the primary auditory cortex, and VI corresponds to the primary visual cortex.
If the temporal lobe (hippocampus) is activated, it is determined as "M", when the frontal lobe or AI is activated "A", when the frontal lobe or VI is activated, "I" MATE can be divided as follows.
<Table 1>
The MATE evaluated through fMRI and PET scanning can be converted into data, and the resultant MATE data can be converted into evaluation sites. The MATE evaluation paper can be provided to users on the web, etc. to provide a suitable study method for the user. In particular, by comparing the user's target data with the user's result data for each of the MATE evaluation results M, A, T, and E, it is possible to advise the user about the study method for the area in which the result data is insufficient.
7 is a diagram illustrating a process of matching user data to be stored in an input database with a reference database according to an embodiment of the present invention.
The data analysis system can identify data classification and statistics. Meanwhile, the data analysis system can determine whether the user's sexual data belongs to which sector data by using the reference test data (evaluation source report card) received from the user apparatus. If the data analysis system can determine whether the number of user data exceeds a preset number. If exceeded, the data analysis system may use a percentage of the user data. Conversely, if not, the data analysis system can match the user DB with the reference DB.
8 is a diagram illustrating a process of determining sector data of a user according to sexual data in user data of a user according to an embodiment of the present invention.
Referring to FIG. 8, the user can match the report card, which is the standard test data, to the picture frame. If the report card is fitted to the picture frame and is in focus, the user device can take the report card and transmit it to the data analysis system. After analyzing the user's personal information related to the report card, the data analysis system can average the grade of each subject included in the report card. Thereafter, the data analysis system may calculate a percentage relative to the grade of each subject averaged, and then determine the sector data to which the percentage calculated according to the preset criteria belongs.
9 is a diagram illustrating an example of sector data according to an embodiment of the present invention.
Referring to FIG. 9, the sector data is used to determine in which position the user's sexual data belongs in a statistical manner. Referring to FIG. 9, the sector data may be divided into six pieces, and the sexual data may be matched for each sector data.
10 is a view illustrating a detailed operation of the first analyzing unit according to an embodiment of the present invention.
The first analysis unit of the data analysis system can determine whether the input DB DB I and the reference DB satisfy predetermined conditions. If the conditions are met, the data analysis system can point to study efficiency and study time. The data analysis system can then convert points of study efficiency to percentages and convert points of study time into percentages in units of days / weeks / months / years.
On the other hand, the data analysis system can convert the point of study time and the point of study efficiency to the point of study amount, and then the level data of the user can be calculated by using the point of study amount through the level system. Sector data, points of study, points of study time, points of study efficiency and level data can be entered into the calibration system.
11 is a diagram illustrating a process of pointing a study efficiency according to an embodiment of the present invention.
The first analyzing unit can calculate the standard distribution of each element of the study efficiency from the reference DB. Then, the first analyzing unit can set the reference distance d to 50% based on the average of the study efficiency. Also, the first analyzing unit may assign the user data included in the DB I, which is the input DB, to the standard deviation of the study efficiency derived from the reference DB to be pointed. Then, the first analyzing unit can calculate the total score of each element according to the
Data analysis systems can point to study efficiency by entering A and B in
FIG. 12 is a diagram illustrating a process of determining sector data to which learning efficiency belongs according to an embodiment of the present invention.
Referring to FIG. 12, the data analysis system can set the reference distance d1 to 50% based on the average for the study efficiency. Then, the data analysis system can convert the total score of each sector according to
Study efficiency measures most of the factors on a quarterly basis. Here, the study efficiency factor to be measured every day is determined according to the current sleeping time. The results of the questionnaire for the students within the top 4% of the existing sample are converted to the standard distribution for the elements showing similarity to the standard distribution curve (T distribution, F distribution verification).
And the average is set to 25% d by 50% on both sides, and the score is reduced by x in the full score every time it goes out of d. The calculated points can be added up to a point of 100 points, and the relative position can be confirmed by calculating the percentile.
Core Function2 =
For example, assuming that the user's score in
13 is a diagram illustrating a process of pointing a study time according to an embodiment of the present invention.
Referring to FIG. 13, the data analysis system can extract the lecture time of the user from DB I, which is the input DB, and convert the extracted lecture time by minutes. The data analysis system can then point to the results of the conversion in minutes.
FIG. 14 is a diagram illustrating a process of determining points of a study amount using pointed study time and study efficiency according to an embodiment of the present invention.
Referring to FIG. 14, the data analysis system can set the point of study efficiency to A and set the point of study time to B. After that, the data analysis system can apply points A and B to
15 is a diagram illustrating a process of determining level data based on a user's study amount according to an embodiment of the present invention.
Referring to FIG. 15, the data analysis system can compare the study amount according to the level of the user's study amount and the level. Here, points of the user's study amount can be accumulated over time. The data analysis system can compare the study amount of the user with the study amount according to the level and output the level data of the user.
FIG. 16 illustrates a process of determining a percentage to be used according to a result of a test between user data and reference data according to an exemplary embodiment of the present invention. Referring to FIG.
The data analysis system can perform a T-test or an F-test with the reference data derived from the reference database using the points and percentages of the user's study amount, the points and percentages of the study time, and the points and percentages of the study efficiency.
If the test result according to the test result is more than the preset test value, the data analysis system uses the percentage of the user data. Conversely, if the test result according to the test result is less than the preset test value, the data analysis system uses the percentage of the reference data of the reference DB.
17 is a diagram illustrating a process of adjusting spectrum between user data and reference data according to an embodiment of the present invention.
The process shown in FIG. 17 is performed through a calibration system. The calibration system adjusts the spectrum of the user data through the reference DB when it is determined that the distribution of the user data is not constant through the T test and the F test with the standard distribution. This is to grasp the exact relative position of the user's data using the reference DB.
For example, when User Data is input as shown below, the relative position of only User Data is included in the upper 10 ~ 20%, but when it is rearranged by the reference DB, it can be corrected to the upper data of 35 ~ 40% . This is an effort to increase the reliability of the relative percentages.
FIG. 18 is a diagram showing percentiles of relative user data according to user sector data and target data through a first analyzing unit according to an embodiment of the present invention. FIG.
Through the first analysis unit, user's sector data, points of study time, points of study amount, points of study efficiency, and level data of the user can be derived. Then, the percentage of study time, the percentage of study efficiency, the percentage of study volume, and the percentage of level data associated with the user's sector data can be determined. Then, the percentage of study time, the percentage of study efficiency, the percentage of study volume, and the percentage of level data associated with the user's target data can be determined. Regardless, the percentage of total study time, the percentage of study efficiency, the percentage of study volume, and the percentage of level data can be determined. Results derived through the first analysis unit may be stored in DB II.
19 is an illustration showing the percentile of user data according to an embodiment of the present invention.
Through the analysis result of the first analyzing unit, it is possible to determine in which percentage the user's study time corresponds to the total / sector data / target data. Likewise, the analysis result of the first analyzing unit can be used as a percentage to determine in which position the user's study efficiency corresponds to the total / sector data / target data. In addition, it can be determined as a percentage how much the user's study amount corresponds to the total / sector data / target data through the analysis result of the first analysis unit. The analysis result of the first analyzing unit can be used as a percentage to determine the position of the user's level data in relation to the whole / sector data / target data. That is, it can be objectively determined through the first analysis unit which position the user's study state corresponds to when compared with the statistical user's study state.
20 is a diagram illustrating an operation of a second analyzing unit according to an embodiment of the present invention.
Referring to FIG. 20, the second analyzing unit can determine whether DB I, DB II, and DB, which are input DBs, satisfy predetermined conditions, and then can operate the Simple Adviser and the Smart Planning System.
FIG. 21 is a diagram illustrating a process of deriving an insufficient study time corresponding to a target data of a user through a second analyzing unit according to an embodiment of the present invention.
The second analyzing unit can extract the target data and the study time of the user from
If the value of (2) - (1) / (1) = T is equal to or greater than a predetermined value X, the learning time of the current user can be determined as an excellent section A. If the value of (2) - (1) / (1) = T is equal to or greater than Y and less than X, the current user's study time may be determined as an appropriate interval B. Further, when the value of (2) - (1) / (1) = T is less than the predetermined Y, it can be determined as the interval C in which the study time of the current user is short
At this time, if the current study time of the user is determined as C, the data analysis system can determine and output the insufficient study time from the average time of the target data through the points of the study time.
22 is a diagram illustrating a process of deriving insufficient study efficiency in accordance with a target data of a user through a second analysis unit according to an embodiment of the present invention.
The second analyzing unit can extract the target data and the study efficiency of the user in
If the value of (2) - (1) / (1) = T is equal to or greater than the preset X, it can be determined that the current user's study efficiency is excellent. If the value of (2) - (1) / (1) = T is equal to or greater than Y and less than X, the current user's study efficiency can be determined as an appropriate interval B. Further, when the value of (2) - (1) / (1) = T is less than the predetermined Y, it can be determined as the interval C in which the current user's learning efficiency is insufficient
At this time, if the current study efficiency of the user is determined as C, the data analysis system can determine and output the insufficient study efficiency from the average time of the target data through the point of study efficiency.
23 is a diagram illustrating a process of deriving an insufficient amount of study corresponding to a target data of a user through a second analyzing unit according to an embodiment of the present invention.
The second analyzing unit can extract the target data and the study amount of the user in
If the value of (2) - (1) / (1) = T is equal to or greater than a predetermined value X, the current user's study amount can be determined as an excellent segment A. If the value of (2) - (1) / (1) = T is equal to or greater than Y and less than X, the current user's study amount can be determined as a suitable interval B. Further, when the value of (2) - (1) / (1) = T is less than the preset Y, it may be determined as the interval C in which the current user's study amount is short
At this time, if the current study amount of the user is determined as C, the data analysis system can determine and output the insufficient study amount from the average time of the target data through the point of the study amount.
21 to 23 illustrate a case where the user compares the study time / study efficiency / study amount per target data derived from the
FIG. 24 is an illustration showing suitability of study time, study efficiency, and study amount corresponding to target data according to an embodiment of the present invention.
FIG. 24 shows a result of comparing the study time, the study efficiency, and the study amount, which are derived for each week, with the study time, the study efficiency, and the study amount related to the target data when the target data of the user is the SNU. The user related to FIG. 24 is excellent in studying time, lacks study efficiency, and is determined to be appropriate for the study amount. In addition, the data analysis system can numerically express the degree of insufficient study efficiency.
25 is a diagram illustrating a process of generating consulting data related to study time, study efficiency, and study method based on target data and sector data according to an embodiment of the present invention.
The data analysis system can output the weekly average study time of the user in
On the other hand, the data analysis system can calculate the target study time and study efficiency for each subject from the reference data related to the target data. The data analysis system can calculate the difference by comparing the target study time with the accumulated study time, and then generate the consulting data through the planning system.
Then, the data analysis system can derive the study method classified as reference study time, MATE for each sector data in the reference DB. Then, the data analysis system can output the study method to the user according to the MATE evaluation paper by using the study method classified as the reference study time for each sector data, MATE.
In addition, the data analysis system can determine the time allocation data for each subject. The determined time allocation data for each subject can be used to calculate the target study time for each subject.
The Planning System of FIG. 25 can output the following results.
Planning System outputs the following results.
- Current sector data, level data
- The target study time between work (d), week (w)
- Study time completeness so far,% relative study time among users,% relative study load among users,
- Study time tendency and study efficiency tendency
- Objective study time and actual study time by subjects last week
- The following consulting results can be derived by using the data derived from the first analysis department on the way to improve in the future.
(i) Relatively short subjects
(ii) subjects that have studied the most compared to the target, and subjects that have studied less
(iii) Study time by subject
(iv) Proposal of study method
In other words, the planning system presents the study method and the time allocation according to the subject sector data and target data. Then, a study time tendency, target sector data, study time difference for target data, and specific subject study method are provided according to sector data to date.
26 is a diagram showing an example of consulting data according to an embodiment of the present invention.
Referring to FIG. 26, the consulting data may include an evaluation result for a specific period (week / month / year). Specifically, the consulting data may include the relative completeness of the user's study time absolute / relative completeness and study amount. The consulting data may include sector or level data to which the user belongs.
On the other hand, the consulting data includes insufficient subjects by comparing the target amount in the cycle and the result obtained by the user, subjects to be further studied by the user in order to reach the upper sector, study time by subject, can do.
27 is a diagram showing another example of consulting data according to an embodiment of the present invention.
Referring to FIG. 27, the consulting data may include a user's MATE evaluation result and a user's customized recommendation study method. These results are derived from the objective MATE evaluation results, so that more accurate and quick processing is possible.
28 is a diagram illustrating an operation of a third analyzer according to an embodiment of the present invention.
The third analysis unit can operate the skyline system and the spy system when the DB I (input DB), DB II (the results of the first analysis unit and the second analysis unit) and the reference DB are satisfied. Here, the Skyline system determines whether the user is likely to reach the top group of the target data, and the Spy System can display the result of tracking the sector data.
29 is a diagram illustrating a process of determining whether there is a possibility of reaching a top group of target data through a third analyzing unit according to an embodiment of the present invention.
The data analysis system can determine the weekly studying time and the weekly studying time of the user through the results (DB II) of the first analyzing unit and the second analyzing unit. And data analysis system can convert weekly study and weekly study time into annual study and annual study time.
In addition, the data analysis system can determine the reference amount of the annual study amount and the annual study amount per target data (target university) from the reference data in the reference DB.
If the user data exceeds the preset number, the data analysis system can use only the reference percentage of the reference data for the judgment process. Conversely, when the user data is less than or equal to the preset number, the data analysis system can utilize both the annual study amount of the reference data and the reference percentage in the judgment process. Thereafter, the data analysis system judges whether there is a possibility of reaching the highest group of the target data by comparing the annual study amount of the user and the annual study time derived from the reference data to the annual study amount, and outputs the determination result .
Figure 30 is an illustration showing the likelihood of reaching the highest group of target data in accordance with an embodiment of the present invention.
Referring to Figure 30, the probability of reaching the highest group of target data for the user is expressed as a percentage. Specifically, when the target data is a university that the user wants to aim at, one of the highest ranking groups is designated as SNU. Then, the data analysis system can express the possibility that a user passes to SNU as a study time and a study amount. Then, the user can objectively determine whether or not the user can pass the top group of target data.
That is, FIG. 30 compares the user's (annual, weekly) study amount, the (annual, weekly) study time with the target university's (annual, weekly) study amount, can do.
31 is a diagram illustrating a process of tracking sector data according to an embodiment of the present invention.
Referring to FIG. 31, the data analysis system can determine sector data to be tracked in the input database. The data analysis system can then randomly determine what to track in the determined sector data. Thereafter, the data analysis system can extract and output the study time and the grading data for the day / week / month for the object to be tracked.
32 is an illustration showing the result of tracking sector data according to an embodiment of the present invention.
32 shows the result of tracking for sector data. After the data analysis system determines the sector data to be tracked, the data analysis system can randomly determine an object to be tracked in the sector data, and then output the study time, study efficiency, and study amount of the object.
32, the average study time was 9 hours and 29 minutes, the average study time was 8 hours and 53 minutes after the second day, and the average study time was 3 hours. As a result, 10 hours and 13 minutes. This sector data can be set by the user at will.
FIG. 33 is a diagram illustrating a concentration mode using a mobile device according to an embodiment of the present invention. FIG.
Referring to FIG. 33, there is shown an operation when a concentrated mode is selected through a mobile device that is a user apparatus. Specifically, the mobile device may inquire of the user whether or not the user selects the convergence mode. At this time, when the user does not select the convergence mode, there is no limitation in using all functions of the mobile device. Then, the mobile device can perform calculation processing by multiplying the measured time of the user by 0 <X <100% weighted value.
On the other hand, when the user selects the centralized mode, the mobile device can restrict the operation of the specific function. Then, the mobile device can multiply the study time of the measured user by a weight of 100%, perform calculation processing, and transmit the calculated study time to a predetermined database. That is, the fact that the intensive mode is selected indicates that the user has concentrated purely on studying, so that the weight is 100% as the study time of the user.
I will explain limiting the operation to the functions of the mobile device. Specifically, when the user selects the convergence mode, it notifies that only a telephone or character of at most n registered telephone numbers (parent, family, etc.) has a telephone or a character by sound or vibration, The call or the text of the call can be silenced / rejected. That is, in the concentrated mode, the mobile device can notify the user that a telephone call or a character has been received only for a telephone number that the user has designated in advance, so that the user can concentrate on study while concentrating on only necessary telephone numbers.
On the other hand, the mobile device performs only the functions of the present invention described above with reference to FIG. 33, and the operation for the remaining programs is limited. Alternatively, the restricted mode may be divided into steps so that only selected programs, such as a music player or an Internet lecture, may be activated. On the other hand, a messenger program running on a mobile device may also have limited functionality.
FIG. 34 is a diagram for explaining a friend-making function based on a specific area and a school in accordance with an embodiment of the present invention.
Referring to FIG. 34, the user can request a function of searching a school of interest, a friend search function in a current / interest area, and a friend search function in a target university. When the user device performs the "interest school search function" according to the user's request, it can store up to n schools of interest set by the user. Then, the user device can find a friend corresponding to the school of interest set by the user through the friendship system.
On the other hand, when the user device performs the "friend search function belonging to the current / area of interest" according to the request of the user, the user can set the specific location or area range through the GPS or map of the user device. Then, the user device can search for a friend corresponding to the location or area range set by the user through the friend-joining system.
When the user device performs the "friend search function to the target university" according to the user's request, the friend device can search for friends corresponding to the target university set by the user.
FIG. 35 is a view illustrating a detailed operation of the affiliate system according to an embodiment of the present invention.
When the user requests to find a friend corresponding to the "school of interest "," current / area of interest ", and "target university ", the friend system can provide the user with a friend list corresponding to the request of the user. Then, the user can select a desired friend from the friend list and apply for friend. The friend establishing system can transmit a friend request requested by the user to the corresponding friend, and receive a response to the friend making. If the friend requested by the user accepts the friend application, the user and the friend are established. At this time, the user and the friend having the friend relationship can know the position of the other partner. Also, by checking the study time, study efficiency and study amount of the other party, it is possible to grasp the learning result of the friend. Conversely, if the friend requested by the user refuses the friend application, the function ends.
The methods according to embodiments of the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the medium may be those specially designed and constructed for the present invention or may be available to those skilled in the art of computer software.
While the invention has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. This is possible.
Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined by the equivalents of the claims, as well as the claims.
Claims (15)
An input database for receiving and storing user data according to a time interval from a user device;
A first analyzer for analyzing a user's study amount using the study efficiency and the study time of the user, comparing the study amount with reference data, and outputting user level data;
A second analyzer for comparing the target data of the user with the user data of the user and outputting consulting data related to a study time, a study efficiency and a study amount necessary for a user; And
A third analyzing unit for outputting check data indicating the possibility of reaching the target data by using the study amount and the study time of the user,
The data processing system comprising:
Wherein the reference database comprises:
Study efficiency based on average sleep time and distribution ratio of statistical subjects per target data; Study time of statistical subject related to target data; And a data processing system for storing a study method based on brain activation of a statistical subject.
The above-
It is determined by the learning pattern that combines the learning behavior of the statisticians by specific subjects,
The learning behavior of the statistical subject is,
wherein the data processing system is determined according to a site where the subject's brain is activated through fMRI or PET scanning.
Wherein the input database comprises:
The user receives and stores the grade, the average study time, the target data, the sex, the recent reference test score, and the area from the user first,
A sleep time, and a use place of the user device from the user,
The user receives and saves the reference test score, the questionnaire data related to the study efficiency, the test time, the vacation period, the type and number of the learning institute every quarter,
And a data processing system for receiving and storing a tracking function setting and a target data check request arbitrarily from the user.
Wherein the input database comprises:
And stores the sector data to which the user belongs based on the reference test score of the user.
Wherein the first analyzer comprises:
Calculating a study efficiency based on the sleep time of the user, and calculating the study amount of the user by combining the study time and the study efficiency.
Wherein the first analyzer comprises:
Determining whether to use the percentage of the user data or the percentage of the reference data based on the T-test or F-test result of the user data related to the user's study amount, study time, and study efficiency and the reference data Data processing system.
Wherein the first analyzer comprises:
The studying time, the study efficiency, the amount of study, and the level of the user's level data with respect to the sector data based on the reference test results of the user,
And determines the degree of the user's study time, study efficiency, study amount, and level data of the user with respect to the target data of the user.
Wherein the second analyzing unit comprises:
And comparing the study time, the study amount, or the study efficiency of the user with the study time, the study amount, or the study efficiency for each target data extracted from the reference data, and outputting the possibility of reaching the target data.
Wherein the second analyzing unit comprises:
Calculating a study time required for the user to reach the target data when the study time of the user is less than the study time of each target data extracted from the reference data,
Calculating a study efficiency required for the user to reach the target data when the study efficiency of the user is less than the study efficiency for each target data extracted from the reference data,
And calculates a study amount required for the user to reach the target data when the user's study amount is smaller than the study amount for each target data extracted from the reference data.
Wherein the second analyzing unit comprises:
A studying time, a studying amount, and a studying efficiency in accordance with the sector data and the level data based on the user's reference test results, and for consulting data for reaching the target data of the user by using the determined relative studying time, The data analysis system comprising:
The consulting data,
A study time for each subject to reach the target data of the user, and a study method.
Wherein the third analyzing unit comprises:
And provides inspection data indicating whether there is a possibility of reaching a top group of target data by using the study amount or the study time of the user.
Wherein the third analyzing unit comprises:
A data analysis system for determining sector data to be tracked by the data analysis system and providing a study time or a study time for a subject belonging to the sector data,
Receiving and storing user data according to a time interval from a user device;
Analyzing the user's study amount using the study efficiency and the study time of the user, comparing the study amount with the reference data, and outputting the user's level data;
Comparing the target data of the user with the user data of the user and outputting consulting data related to the study time, study efficiency and study amount necessary for the user; And
Outputting check data indicating the possibility of reaching the target data using the study amount and the study time of the user
≪ / RTI >
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020120158274A KR20140087700A (en) | 2012-12-31 | 2012-12-31 | System and method for analyzing data related to study consulting |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020120158274A KR20140087700A (en) | 2012-12-31 | 2012-12-31 | System and method for analyzing data related to study consulting |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| KR20140087700A true KR20140087700A (en) | 2014-07-09 |
Family
ID=51736579
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020120158274A KR20140087700A (en) | 2012-12-31 | 2012-12-31 | System and method for analyzing data related to study consulting |
Country Status (1)
| Country | Link |
|---|---|
| KR (1) | KR20140087700A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016089057A1 (en) * | 2014-12-01 | 2016-06-09 | 주식회사 탐생 | Method and system for compensating study time using mobile terminal, and computer program and recording medium for implementing same in computer |
| JP2017176250A (en) * | 2016-03-28 | 2017-10-05 | Ｎｅｃソリューションイノベータ株式会社 | Sleep log support apparatus, sleep log support method, and program |
-
2012
- 2012-12-31 KR KR1020120158274A patent/KR20140087700A/en not_active Application Discontinuation
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016089057A1 (en) * | 2014-12-01 | 2016-06-09 | 주식회사 탐생 | Method and system for compensating study time using mobile terminal, and computer program and recording medium for implementing same in computer |
| JP2017176250A (en) * | 2016-03-28 | 2017-10-05 | Ｎｅｃソリューションイノベータ株式会社 | Sleep log support apparatus, sleep log support method, and program |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Crawford et al. | The impact of placements on the academic performance of UK and international students in higher education | |
| Caricati et al. | Real and perceived employability: a comparison among Italian graduates | |
| Lenkeit | Effectiveness measures for cross-sectional studies: A comparison of value-added models and contextualised attainment models | |
| JP2017522676A (en) | Talent data-driven identification system and method | |
| CN111887867A (en) | Method and system for analyzing character formation based on expression recognition and psychological test | |
| Brese | TEDS-M 2008 User Guide for the International Database. | |
| KR20170137514A (en) | Personal credit rating device, method and computer programs based on psychometric data | |
| CN112598184B (en) | Method and device for predicting repeated air suction risk of drug addict | |
| Hossler et al. | The role of institutional practices in college student persistence | |
| Hill et al. | Education and Certification Qualifications of Departmentalized Public High School-Level Teachers of Selected Subjects: Evidence from the 2011-12 Schools and Staffing Survey. NCES 2015-814. | |
| CN107784379A (en) | A kind of application for studying abroad forecasting system and method | |
| KR20160109575A (en) | Method and apparatus for job mentoring | |
| Šorgo et al. | Biology Content and Classroom Experience as Predictors of Career Aspirations. | |
| Morton et al. | Investigating the factor structure of the South African Personality Inventory-English version | |
| CN111461949A (en) | Community correction scheme intelligent generation system based on big data | |
| Dahlberg et al. | Introduction to research and evaluation basics | |
| KR20140087700A (en) | System and method for analyzing data related to study consulting | |
| CN114758778A (en) | Student mental health management system and method | |
| Adalian | General considerations about data and selection of statistical approaches | |
| Callander et al. | Health and functional capacity–capturing capability limitations in measures of health | |
| Kyrgidou et al. | Identifying tomorrow’s entrepreneurs: entrepreneurship education in Greece | |
| Gonçalves et al. | X3S: A multi-modal approach to monitor and assess stress through human-computer interaction | |
| CN110991924A (en) | Structural equation model-based high-level thesis publication number influence factor evaluation method | |
| Zahner et al. | Comparing Longitudinal and Cross-Sectional School Effect Estimates in Postsecondary Education. | |
| Wang et al. | Application of Biographical Data in Student’s Major Selection |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| WITN | Withdrawal due to no request for examination |