KR102189334B1 - Learning Management System and method for medical - Google Patents

Abstract

A medical learning management method is disclosed. A medical learning management method of a medical learning management system including a haptic device and a server, wherein the server receives first continuous motion information of a haptic device manipulated by an expert, and the server is the haptic device manipulated by students. Receiving each of the second continuous motion information, the server comparing each of the first continuous motion information and the second continuous motion information, the server is the result of the comparison to each of the students Transmitting the result of the comparison to the electronic device so as to be displayed on the display of the electronic device of, the server providing learning materials composed of a plurality of slides in a streaming method to each electronic device of the students, the server Receiving time information for the students manipulating the learning material from each of the electronic devices, and calculating a time spent by the students for each of the plurality of slides, the server having a plurality of Generating probability distribution maps, and the server determining concentration for each of the students according to the plurality of probability distribution maps. The step of comparing each of the first continuous motion information and the second continuous motion information by the server may include information on any one of the first continuous motion information and the second continuous motion information And transmitting feedback information to the haptic device so that the haptic device feels a tactile sense to any of the students when the information of any one of them does not match. The server determining the concentration for each of the students according to the plurality of probability distribution maps, the server selecting at least one or more students whose test scores are within the top 30 percent, the server Determining whether the selected student belongs to a probability that the highest probability is 0.5 or more in at least one of the plurality of probability distribution maps, and the server has the highest probability in at least one of the plurality of probability distribution maps When it belongs to the probability of 0.5 or more, determining that the student has high concentration.

Description

{Learning Management System and method for medical}

An embodiment according to the concept of the present invention relates to a medical learning management system and method, and in particular, medical learning management that can objectively evaluate whether students learning medicine properly acquire a medical practice process and students have properly acquired a medical practice process. It relates to systems and methods.

Medical practice is important for students who want to become experts in the medical field, such as doctors and dentists. However, it is difficult for students to judge whether they are practicing properly. For example, a dental student who wants to become a dentist must know and understand each process accurately in order to learn the implant surgery method, but it is not easy to properly learn the implant surgery method only with the professor's class and textbook. Also, professors do not have an objective way to evaluate medical practice, although it is important.

The learning management system refers to a system that manages students' grades and progress, as well as attendance and absences, and other matters across the university through online. The conventional learning management system is used as a means for simply uploading a lesson plan or learning materials, or inputting test scores. The conventional learning management system did not provide any meaningful information regarding medical practice.

Unexamined Patent Publication No. 10-2014-0107062 (2014.09.04.) Unexamined Patent Publication No. 10-2016-0102464 (2016.08.30.)

The technical task to be achieved by the present invention is to provide a medical learning management system and method capable of objectively evaluating whether students have properly acquired a medical practice course and students have properly acquired a medical practice course in order to become experts in the medical field.

The medical learning management method according to an embodiment of the present invention includes receiving first continuous motion information of a haptic device manipulated by an expert, and receiving second continuous motion information of the haptic device manipulated by students, respectively. Comparing each of the first continuous motion information and the second continuous motion information, and displaying a result of the comparison so as to display a result of the comparison on a display of each electronic device of the students. And transferring to.

Comparing each of the first continuous motion information and the second continuous motion information may include any one of the first continuous motion information and the second continuous motion information. And when is not matched, transmitting feedback information to the haptic device so that the haptic device feels a tactile sense to any one of the students.

A medical learning management system according to an embodiment of the present invention receives a haptic device and first continuous motion information of the haptic device manipulated by an expert, and second continuous motion information of the haptic device manipulated by students. The electronic device displays the result of the comparison so as to receive each of the signals, compare each of the first continuous motion information with the second continuous motion information, and display the result of the comparison on a display of each electronic device of the students. Contains a server that transmits to the device.

The medical learning management system according to an embodiment of the present invention has an effect that students who want to become experts in the medical field can be compared by providing motion information of the haptic device manipulated by the expert so that the students can properly learn the medical practice process. .

In addition, the medical learning management system according to an embodiment of the present invention has an effect of allowing professors to objectively determine whether students are properly learning the medical practice process.

In addition, the medical learning management system according to another embodiment of the present invention has an effect of determining how much the students are focusing on class by calculating the time spent in the learning material and generating a probability distribution map.

A detailed description of each drawing is provided in order to more fully understand the drawings cited in the detailed description of the present invention.
1 is a block diagram of a medical learning management system according to an embodiment of the present invention.
FIG. 2 illustrates an embodiment of an interface displayed on the virtual reality device illustrated in FIG. 1.
3 is a block diagram of a medical learning management system according to another embodiment of the present invention.
FIG. 4 shows a probability distribution diagram of time of each slide of learning material staying on the display of the electronic devices shown in FIG. 3.
5 is a flowchart illustrating the operation of the medical learning management system shown in FIG. 3.

Specific structural or functional descriptions of the embodiments according to the concept of the present invention disclosed in the present specification are exemplified only for the purpose of describing the embodiments according to the concept of the present invention, and the embodiments according to the concept of the present invention are It may be implemented in various forms and is not limited to the embodiments described herein.

Since the embodiments according to the concept of the present invention can apply various changes and have various forms, the embodiments will be illustrated in the drawings and described in detail in the present specification. However, this is not intended to limit the embodiments according to the concept of the present invention to specific disclosed forms, and includes all changes, equivalents, or substitutes included in the spirit and scope of the present invention.

Terms such as first or second may be used to describe various elements, but the elements should not be limited by the terms. The terms are only for the purpose of distinguishing one component from other components, for example, without departing from the scope of the rights according to the concept of the present invention, the first component may be referred to as the second component, and similarly The second component may also be referred to as a first component.

When a component is referred to as being "connected" or "connected" to another component, it is understood that it may be directly connected or connected to the other component, but other components may exist in the middle. Should be. On the other hand, when a component is referred to as being "directly connected" or "directly connected" to another component, it should be understood that there is no other component in the middle. Other expressions describing the relationship between components, such as "between" and "just between" or "adjacent to" and "directly adjacent to" should be interpreted as well.

The terms used in this specification are used only to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present specification, terms such as "comprise" or "have" are intended to designate the presence of a set feature, number, step, action, component, part, or combination thereof, but one or more other features or numbers It is to be understood that the possibility of addition or presence of, steps, actions, components, parts, or combinations thereof is not preliminarily excluded.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms as defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in this specification. Does not.

Hereinafter, the present invention will be described in detail by describing a preferred embodiment of the present invention with reference to the accompanying drawings.

1 is a block diagram of a medical learning management system according to an embodiment of the present invention, and FIG. 2 shows an embodiment of an interface displayed on the virtual reality device shown in FIG. 1.

1 and 2, the medical learning management system 200 is a learning management system for students who want to become experts in the medical system to properly learn a medical practice process and evaluate it. The medical learning management system 200 includes a haptic device 30, a virtual reality device 40, and a server 50. The haptic device 30, the virtual reality device 40, and the server 50 can communicate with each other through a network.

The haptic device 30 is a device for medical practice, and is a device for giving a tactile sense to a user (eg, a student) by using vibration. For example, the haptic device 30 is used for medical practice such as implant surgery on an artificial tooth.

The haptic device 30 includes a sphere of this shape, a pedestal for supporting the sphere, a stick capable of moving the surface of the sphere, and a controller for controlling the haptic device 30. When a user (eg, an expert or a student) manipulates the stick, the controller transmits continuous motion information to the virtual reality device 40 and the server 50. For example, an expert such as a professor or an experienced dentist may manipulate the stick implemented in the haptic device 30 for medical practice of students. For example, the manipulation of the stick is the same as that of a medical tool used by an expert during an actual implant surgery. According to an embodiment, the haptic device 30 may be implemented in various forms.

Continuous motion information of the haptic device 30 according to the manipulation of the stick is transmitted to the virtual reality device 40 and the server 50 through the controller.

The virtual reality device 40 may be in the form of a wearable device that can be used by the experts or students. A screen as shown in FIG. 2 is displayed to experts or students through the virtual reality device 40. It is also possible to change the medical tools in the virtual reality device 40. The screen displayed on the virtual reality device 40 changes according to the manipulation of the stick.

The server 50 receives first continuous motion information of the haptic device 30 manipulated by an expert, and receives second continuous motion information of the haptic device 30 manipulated by students, respectively. Each of the first continuous motion information and the second continuous motion information is compared. The server 50 transmits the result of the comparison to the electronic device so as to display the result of the comparison on the display of each electronic device (not shown) of the students.

When any one of the first continuous motion information and any one of the second continuous motion information do not match, the server 50 causes the haptic device 30 to cause any one of the students The feedback information is transmitted to the haptic device 30 in order to make the child feel tactile.

In order to make classes more efficient, professors help students learn by uploading learning materials such as PowerPoint or PDF format to the learning management system. However, many students do not focus on the professor's class and do other things. When evaluating students' grades, it is difficult for professors to grasp how much they focus on class, so they have no choice but to evaluate students' grades only with tests that can be judged quantitatively. Some students do not concentrate on the class, but study only with the teaching materials and take the test well. Therefore, it is necessary to judge how much the students concentrate during class.

3 is a block diagram of a medical learning management system according to another embodiment of the present invention.

Referring to FIG. 3, the medical learning management system 100 may include a plurality of electronic devices 10-1 to 10-N; N is a natural number, and a server 20. The medical learning management system 100 is a system for determining the class concentration of students using a plurality of electronic devices 10-1 to 10-N. The medical learning management system 200 described in FIGS. 1 to 2 and the medical learning management system 100 described in FIGS. 3 to 5 may be implemented as a single management system. When the medical learning management system 200 described in FIGS. 1 to 2 and the medical learning management system 100 to be described in FIGS. 3 to 5 are implemented as one management system, the server 50 and FIG. 3 In the server 20 may be implemented as one server.

The plurality of electronic devices 10-1 to 10-N may be a smart phone, a tablet PC, or a laptop computer. The plurality of electronic devices 10-1 to 10-N include a display.

The server 20 includes a learning data providing module 21, a time calculation module 23, a probability distribution map generation module 25, and a concentration determination module 27.

The server 20 provides learning materials composed of a plurality of slides to the electronic devices 10-1 to 10-N of each student in a streaming method. The learning material providing module 21 provides the learning material uploaded by the professor to each of the electronic devices 10-1 to 10-N in a streaming method.

The plurality of electronic devices 10-1 to 10-N may display the learning material stored in the server 20 on the display through streaming. The learning material is not downloaded and displayed on the electronic devices 10-1 to 10-N. That is, since the learning materials are displayed in real time on the electronic devices 10-1 to 10-N, if the electronic devices 10-1 to 10-N and the server 20 do not communicate, the electronic devices ( 10-1 to 10-N), slides of non-streamed learning materials are not displayed. The plurality of slides displayed on the electronic devices 10-1 to 10-N are moved from one slide to the next slide under the control of the students. For example, student A may touch the display of the first electronic device 10-1 or a keyboard of the first electronic device 10-1 to move from the first slide to the second slide.

The server 20 can communicate with the electronic devices 10-1 to 10-N. Since the server 20 provides learning materials by streaming to the electronic devices 10-1 to 10-N, students who are owners of each of the electronic devices 10-1 to 10-N You can calculate how long you stay for. For example, student A may stay for 10 minutes on the first slide, 5 minutes on the second slide, and 3 minutes on the third slide. Also, student B may stay for 5 minutes on the first slide, 20 minutes on the second slide, and 5 minutes on the third slide. That is, the time calculation module 23 transmits the staying time to the server 20, so that all students participating in the class can calculate the staying time for each of the plurality of slides. The staying time refers to a time between a point when a student touches each of the electronic devices 10-1 to 10-N to turn one slide and a point when the student touches to turn the next slide.

The server 20 receives information about the time the students manipulate the learning material from each of the electronic devices 10-1 to 10-N, and calculates the time the students stay for each of the plurality of slides. And, a plurality of probability distribution maps are generated for the dwell time. The probability distribution map generation module 25 calculates each probability distribution map for a plurality of slides. The manipulation means that the student touches the display of the electronic devices 10-1 to 10-N in order to flip one of a plurality of slides that are learning materials in each of the electronic devices 10-1 to 10-N. , Means pressing the keyboard.

FIG. 4 shows a probability distribution diagram of time of each slide of learning material staying on the display of the electronic devices shown in FIG. 3.

3 and 4, in the case of the first slide (SD1), the probability of students staying within 5 minutes is the highest at 0.7, and students staying for 5 minutes to 10 minutes, 10 minutes to 15 minutes, and 15 minutes or more The probabilities are 0.1, 0.1, and 0.1, respectively.

In the case of slide 2 (SD2), the probability of students staying within 5 minutes and between 5 minutes and 10 minutes is 0.1 and 0.2, respectively, and the probability of students staying between 10 and 15 minutes is 0.5 and students who stay for 15 minutes or more The probability of is 0.2.

In the case of the third slide (SD3), the probability of students staying within 5 minutes, 5 minutes to 10 minutes, 10 minutes to 15 minutes, and 15 minutes or more are 0.21, 0.24, 0.3, and 0.25, respectively. According to an embodiment, the interval of time corresponding to the X-axis in the probability distribution map may vary.

The professor does not conduct the class at the same time for all slides of the study material, but the dwell time of the slides of the study material created by the professor differs according to the class of the professor. For example, the first slide corresponding to the introductory part can be taught briefly, and the middle slides explaining important contents can be taught longer.

Students who focus on class will flip the slides displayed on their electronic devices (10-1 to 10-N) according to the professor's class, and students who do not focus on class will be able to use their electronic devices regardless of the professor's class. It will flip through the slides displayed in the fields (10-1 to 10-N). Also, students who do not focus on class will have a relatively long time to stay on one slide regardless of the professor's class.

The server 20 determines concentration for each of the students according to the plurality of probability distribution maps. The concentration determination module 27 determines concentration based on whether the highest probability belongs among the plurality of probability distribution maps.

The server 20 determines whether the highest probability in any one of the plurality of probability distribution maps is greater than or equal to a predetermined probability value (eg, 0.5). For example, in FIG. 4, the highest probability in the probability distribution map for the first slide SD1 is 0.7, and thus is equal to or greater than the predetermined probability value (eg, 0.5). In addition, since the highest probability in the probability distribution diagram for the second slide SD2 in FIG. 4 is 0.5, it is equal to or greater than the predetermined probability value (eg, 0.5). On the other hand, in FIG. 4, the highest probability in the probability distribution diagram for the third slide SD3 is 0.3, and thus is equal to or less than the predetermined probability value (0.5).

When the highest probability is greater than or equal to the predetermined probability value (for example, 0.5), the server 20 determines that the concentration of students corresponding to the highest probability is high. For example, when student A belongs to 0.7, which is the highest probability in the probability distribution map for the first slide SD1, the server 20 determines that the concentration for student A is high. The server 20 determines the concentration of the students on the premise that the majority of students listen to the class with concentration. According to an embodiment, the predetermined probability value may be set to a number other than 0.5.

According to an embodiment, the server 20 determines whether the highest probability of at least two or more of the plurality of probability distribution maps is equal to or greater than the predetermined probability value (eg, 0.5).

When the highest probability of at least two or more of the plurality of probability distribution maps is greater than or equal to the predetermined probability value (eg, 0.5), the server 20 commonly corresponds to the highest probability of the two or more probability distribution maps. It is judged that the students' concentration is high. For example, student A and student B belong to the highest probability of 0.7 in the probability distribution map for the first slide (SD1), and student A belongs to the highest probability of 0.5 in the probability distribution map for the second slide (SD2). , When student B does not belong to the highest probability of 0.5 in the probability distribution map for the second slide SD2, the server 20 determines that the concentration power is high only for student A. The server 20 may increase the reliability of determination of concentration by determining that only students who commonly correspond to the highest probability in the two or more probability distribution maps have high concentration.

According to an embodiment, the server 20 may determine the concentration of the students by using the test scores of the students. The server 20 selects students whose test score falls within a certain percentage (eg, top 30 percent) among students, and students who fall within the random percent (eg, top 30 percent) from one of a plurality of probability distribution maps. Determine whether this belongs to the highest probability. When the students belong to the highest probability, the server 20 determines that the students have high concentration. When the majority (eg, 2/3 or more) of the students falling within the random percentage (eg, 30 percent) belong to the highest probability, and at least one or more do not belong to the highest probability, the server 20 is the most It can be determined that the at least one person who does not belong to a high probability did not concentrate on the class and took the test well only with learning materials.

The server 20 selects students whose test score falls within the random percentage (eg, the 30 percent) among students, and the random percentage (eg, the 30 percent) from at least two of a plurality of probability distribution maps. Determine whether the students who fall within are in the highest probability.

Among the students, the test score is within the random percentage (e.g., the 30 percent), and students who fall within the random percentage (e.g., the 30 percent) in at least two of the plurality of probability distributions are the highest probability. When belonging, the server 20 determines that the students included in the highest probability have high concentration.

Referring to FIG. 4, in the probability distribution map for the third slide SD3, the highest probability is 0.3, and there is no significant difference from the remaining probabilities (0.25, 0.24, and 0.21). This means that only a handful of students focused on the class, while the rest of the students did not. It may also mean that the professor's class did not attract students' interest. Therefore, the server 20 said that when the highest probability does not exceed a predetermined probability value (eg, 0.5), and the difference from the second highest probability is less than a reference value (eg, 0.1), the professor's class did not attract students' interest. Judge. That is, the server 20 may also be used to evaluate a professor's lecture by using a probability distribution map.

In addition, when the time spent on a specific slide is more than 15 minutes, the probability is highest, and when the time spent on other slides is between 0 and 5 minutes, the server 20 determines that the majority of students did not concentrate on the professor's class. .

The server 20 can also evaluate the professor's class for each parking. When any one of the plurality of probability distribution maps in the lecture of any one week of weekly class does not have the highest probability of more than a predetermined probability value (e.g., 0.5), the server 20 Evaluate by lecture. For example, when any one of the plurality of probability distribution maps for a plurality of slides in a 10 week lecture does not have a predetermined probability value (eg, 0.5) or more, the server 20 takes the 10 week lecture. Evaluate this as an unfocused lecture.

5 is a flowchart illustrating the operation of the medical learning management system shown in FIG. 3.

Referring to FIGS. 3 to 5, the server 20 provides learning materials composed of a plurality of slides to each of the electronic devices 10-1 to 10-N of students in a streaming method (S10).

The server 20 calculates the time spent by the students for each of the plurality of slides (S20). The staying time refers to the time between the time when the students touch the electronic devices 10-1 to 10-N to flip one slide and the time when the students touch to flip the next slide. do.

The server 20 generates a plurality of probability distribution maps for the staying time (S30). Each of the plurality of probability distribution maps is a probability distribution map for each slide, and the X axis indicates time and the Y axis indicates probability. The time unit of the X-axis may be readjusted in units of 5 minutes or by the server 20.

The server 20 determines concentration for each of the students according to the probability distribution maps (S40). The server 20 determines whether the highest probability in any one of the plurality of probability distribution maps is greater than or equal to a predetermined probability value (eg, 0.5), and when the highest probability is greater than or equal to the predetermined probability value (eg, 0.5) , It is determined that the concentration of the students corresponding to the highest probability is high.

According to an embodiment, the server 20 selects at least one student (eg, student A) whose test scores are within a certain percentage (eg, top 30 percent). The server 20 determines that the selected student (student A) has the highest probability in at least one of the plurality of probability distribution maps (eg, probability distribution map for the first slide SD1), the predetermined probability value (eg, It is judged whether it belongs to the probability of 0.5) or higher. In addition, the server 20 is the selected student (student A) at least two or more of the plurality of probability distribution maps (e.g., a probability distribution diagram for the first slide (SD1), a probability distribution diagram for the second slide (SD2)) ), it may be determined whether the highest probability belongs to a probability equal to or greater than the predetermined probability value (eg, 0.5).

When the selected student (student A) belongs to a probability that the highest probability in at least one of the plurality of probability distribution maps is equal to or greater than a predetermined probability value (eg, 0.5), the server 20 is assigned to the student (student A). It is judged that the concentration is high. According to an embodiment, when the selected student (student A) belongs to a probability that the highest probability of at least two or more of the plurality of probability distribution maps is equal to or greater than the predetermined probability value (eg, 0.5), the server 20 It can be judged that the concentration is high for the student (student A).

When the selected student (student A) belongs to a probability that the highest probability in at least one of the plurality of probability distribution maps is equal to or greater than a predetermined probability value (for example, 0.5), the server 20 returns all students belonging to the probability ( For example, students B and C) are judged to have high concentration. According to an embodiment, when the selected student (student A) belongs to a probability that the highest probability of at least two or more of the plurality of probability distribution maps is equal to or greater than the predetermined probability value (eg, 0.5), the server 20 It can be judged that concentration is high for all students (student B, student C) belonging to the probability. The server 20 may determine that the student B and the student C have test scores that do not fall within a certain percentage (eg, top 30 percent), but have high concentration.

The server 20 selects at least one student (student A) whose test score is within a certain percentage (eg, the top 30 percent), and the selected student (student A) is among the plurality of probability distribution maps. After determining whether the highest probability in at least one (probability distribution diagram for the first slide (SD1)) belongs to a probability that is equal to or greater than a predetermined probability value (e.g., 0.5), and determines that the selected student has high concentration, the server A student (e.g., student D) whose test score selected by (20) falls within a certain percentage (e.g., top 30 percent) has the highest probability (e.g., 0.5) among the plurality of probability distribution maps. When it does not belong to the above probability, the server 20 determines that the concentration for the student (student D) is not high.

According to an embodiment, the server 20 may further include a professor evaluation module 29. The faculty evaluation module 29 is also capable of evaluating a professor's class for each parking.

The server 20 determines whether there is a case in which the highest probability of any one of the plurality of probability distribution maps does not have a predetermined probability value (eg, 0.5) or more in any one of the weekly lessons. When any one of the plurality of probability distribution maps does not have a predetermined probability value (e.g., 0.5) or more in any one of the weekly lessons, the server 20 is unable to concentrate on the lecture. Evaluate by lecture. According to an embodiment, the server 20 determines whether a deviation of each probability is equal to or less than a reference value (eg, 0.2) in any one of a plurality of probability distribution maps in a lecture of one of the weekly classes. When the deviation of each probability in any one of a plurality of probability distribution maps in one of the classes for each week is less than a reference value (for example, 0.2), the server 20 does not focus on the class. It can be evaluated as a poor class.

The server 20 calculates whether there is a case in which the highest probability among the plurality of probability distribution maps does not have more than a predefined probability value (eg, 0.5) in how many lessons among weekly classes. When there is a case in which the highest probability does not have a pre-defined probability value (e.g., 0.5) or higher in any of the plurality of probability distribution maps in a random number of lessons (e.g., 3 times) or more in the weekly class, the server ( 20) gives an unfavorable score for the professor's class evaluation. For example, the server 20 may assign a professor's class evaluation as a failure.

The medical learning management system according to an embodiment of the present invention may determine how much the students are focusing on the class by calculating the time spent in the learning material and generating a probability distribution map.

Although the present invention has been described with reference to the exemplary embodiment shown in the drawings, this is only exemplary, and those of ordinary skill in the art will appreciate that various modifications and other equivalent embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the attached registration claims.

100: system;
10-1, 10-2, 10-3, 10-N: electronic devices;
20: server;
30: haptic device;
40: virtual reality device;

Claims (3)

In the medical learning management method of a medical learning management system including a haptic device and a server,
The server receiving first continuous motion information of a haptic device manipulated by an expert;
The server receiving, respectively, second continuous motion information of the haptic device manipulated by students;
The server comparing each of the first continuous motion information and the second continuous motion information;
Transmitting, by the server, a result of the comparison to the electronic device to display the result of the comparison on a display of each electronic device of the students;
Providing, by the server, learning materials composed of a plurality of slides to each electronic device of the students in a streaming method;
The server receiving time information for the students to manipulate the learning material from each of the electronic devices, and calculating a time spent by the students for each of the plurality of slides;
Generating, by the server, a plurality of probability distribution maps for the dwell time; And
The server includes the step of determining concentration for each of the students according to the plurality of probability distribution maps,
The server comparing each of the first continuous motion information and the second continuous motion information,
When any one of the first continuous motion information and any one of the second continuous motion information do not match, the server causes the haptic device to send to any one of the students. And transmitting feedback information to the haptic device in order to feel a tactile sense,
The server determining concentration for each of the students according to the plurality of probability distribution maps,
The server selecting at least one or more students whose test scores are within the top 30 percent;
Determining, at the server, whether the selected student belongs to a probability that the highest probability is 0.5 or more in at least one of the plurality of probability distribution maps; And
And determining, at the server, that the student has high concentration power when the selected student belongs to a probability that the highest probability in at least one of the plurality of probability distribution maps is 0.5 or more. delete Haptic devices; And
Receive first continuous motion information of the haptic device manipulated by an expert, and second continuous motion information of the haptic device manipulated by students, respectively, and the first continuous motion information and the first 2Compare each of the continuous motion information, transmit the result of the comparison to the electronic device so as to display the result of the comparison on the display of each of the students' electronic devices, and stream learning materials composed of a plurality of slides Provided to the electronic devices of each of the students, receiving information about the time the students manipulate the learning material from each of the electronic devices, calculating the time the students stay for each of the plurality of slides, And a server that generates a plurality of probability distribution maps for the staying time, and determines concentration for each of the students according to the plurality of probability distribution maps,
In the comparison between the first continuous motion information and the second continuous motion information, any one of the first continuous motion information and any one of the second continuous motion information match When not, the server transmits feedback information to the haptic device to make the haptic device feel tactile to any one of the students,
In determining concentration for each of the students according to the plurality of probability distribution maps, the server selects at least one or more students whose test scores are within the top 30 percent, and the selected student is among the plurality of probability distribution maps. It is determined whether at least one of the highest probability belongs to a probability of 0.5 or more, and when the selected student belongs to a probability that the highest probability is 0.5 or more from at least one of the plurality of probability distribution maps, the student has high concentration Medical learning management system that is determined to be.

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