JP2006260095A - Lecture support system, lecture support method and lecture-supporting computer program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a lecture support system exhibiting a proper lecture attendance state of a student to an instructor without requiring always monitoring of the system to the instructor. <P>SOLUTION: This lecture support system has: a means detecting the state of the student, and classifying the detected lecture attendance state of the student into one of a plurality of lecture attendance state levels; a comparison means comparing the lecture attendance state level of each student at first time and the lecture attendance state level of each student at second time; and an output means generating prescribed output when a difference between a first distribution of the lecture attendance state levels of the respective students at the first time and a second distribution of the lecture attendance state levels of the respective students at the second time becomes out of a predetermined threshold value in a comparison result of the comparison means. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a lecture support system, a lecture support method, and a computer program for lecture support.

A system disclosed in Patent Document 1 is known as a system for supporting a lecture by grasping the attendance state of a student.
In this lecture support system, the attendance status of a plurality of students is determined based on the biometric information of the students. As a result of the judgment, for example, the ratio of the students who are concentrated in the lecture and the students who are not concentrated is output, and the lecturer is made aware of the situation of the student.
In addition, please refer to Patent Documents 2 to 4 that disclose techniques related to the present invention.
JP 2004-199506 A JP 2004-229948 A JP 2004-129703 A Japanese Patent Laid-Open No. 10-78743

According to the above-mentioned conventional lecture support system, the status of the student is presented to the lecturer, so the lecturer can accent the lecture content, repeat the lecture content or change it according to the student's situation. it can. As an accent, for example, when the lecture is unidirectional, it is possible to ask the student a question and call attention, or for a long lecture, take a short break.
However, in order to grasp the state of the student by the system, the instructor must always monitor the state of the student using the system, and there is a possibility that the concentration power to be poured into the lecture may be dispersed.

Accordingly, an object of the present invention is to provide a lecture support system that does not require the lecturer to constantly monitor the situation of the student.
Another object of the present invention is to provide a lecture support system that provides appropriate information to the lecturer so that the lecture can be concentrated on the basis of changes in the situation of the student.

The first aspect of the present invention has been made to achieve the above object. That is, means for detecting a student's state and classifying the detected student's attendance state into one of a plurality of predetermined attendance state levels;
A comparing means for comparing the attendance state level of each student at a first time with the attendance state level of each student at a second time;
In the comparison result of the comparison means, the difference between the first distribution of the attendance state level of each student at the first time and the second distribution of the attendance state level of each student at the second time Output means for generating a predetermined output when is deviated from a predetermined threshold;
Lecture support system comprising

  According to the lecture support system configured as described above, the first distribution of the attendance level of each of the students at the first time and the second distribution of the attendance level of each of the students at the second time. When the difference from the distribution is predetermined and deviates from the threshold value, a predetermined output such as an alarm is issued. Thus, the instructor can grasp a large change in the attendance state from the output without always monitoring the attendance state of the student. Thus, the burden on the instructor is reduced.

The second aspect of the present invention is defined as follows. That is, means for detecting a student's state and classifying the detected student's attendance state into one of a plurality of predetermined attendance state levels;
A comparing means for comparing the attendance state level of each student at a first time with the attendance state level of each student at a second time;
In the comparison result of the comparison means, the first distribution of the attendance state level of each student at the first time is compared with the second distribution of the attendance state level of each student at the second time. Lecture characteristics identification means for identifying lecture characteristics,
Means for displaying the identified lecture characteristics;
Lecture support system comprising

  According to the lecture support system of the second aspect configured as described above, the first distribution of the attendance state level of each student at the first time and the attendance state of each student at the second time Lecture characteristics by comparing with the second distribution of level (characteristics related to lecture contents, such as difficulty of lecture contents, redundancy, classroom brightness, classroom size, classroom temperature / humidity, board writing and slides (Including the lecture environment such as the size of the character and the volume of the lecturer) is specified, and the lecture characteristics are displayed. By grasping the lecture characteristics, the lecturer can correct the lecture contents and the lecture environment so that the students can concentrate more.

Hereafter, each said element is demonstrated in detail.
(Student attendance status detection)
The attendance state of the student can be obtained by detecting the biometric information of the student by a well-known method. A sensor that contacts the student can detect an electroencephalogram, heartbeat, electrocardiogram, skin potential, body temperature, and the like. Also. The position of the head or back can also be detected by a position sensor. An acceleration sensor that differentiates the output from the position sensor can also be used. Furthermore, the biometric information of the student can be detected by imaging the student's posture and processing the image.
In order to more accurately identify the attendance state of the student, it is preferable to use the detection results of the two or more pieces of biological information.

The attendance status of the students is, for example, (1) a state where concentration on the lecture is substantial (attendance state level I), (2) a state where concentration on the lecture is insufficient (attendance state level II), (3) Other than the lecture, the state is focusing (learning state level III), (4) sleeping (learning state level IV), (5) abnormal state due to some other reason (learning level V) be able to.
The detection result of each said biometric information in each said attendance state level (I)-(V) is beforehand preserve | saved as standard biometric information about a standard student. Then, the student's attendance state level is classified in light of the stored standard biometric information on the actual biometric information detection result obtained for the student when the system of the present invention is executed. At this time, it is preferable to process a plurality of types of detection results to identify the attendance state level.

  In this way, by classifying the attendance state of each student for each attendance state level, a list of attendance state levels of all the learners who have been detected at a specific time is formed. That is, the distribution of the attendance state level of each student at a specific time is obtained. In other words, in the present invention, a group of attendance state levels of a plurality of students (groups) is regarded as an information unit to be processed.

Furthermore, in this invention, the change of the processing information unit which consists of a group of attendance state levels of a plurality of students at a specific time is verified.
For example, the ratio of students in the attendance state level I at a specific time can be used as the processing information unit. Then, the attendance ratio of the attendance state level I at the second time after the elapse of a predetermined time exceeds the predetermined threshold (for example, 60%) with respect to the attendance ratio of the attendance state level I at the first time. When it falls, it can alert the lecturer that the student is starting to lack overall focus. In addition, when any student at the attendance level V is detected, a warning can be issued immediately.

Changes in processing information units can be associated with lecture characteristics. Here, the lecture characteristics include characteristics related to the lecture environment such as the brightness and darkness of the classroom, the sound volume, and the temperature of the lecture room in addition to the characteristics related to the lecture contents such as difficulty and redundancy.
When various lecture characteristics change between the first time and the second time for a standard group of students in advance, the distribution of the attendance state level (first distribution) and the first at the first time A change pattern of the attendance state level distribution (second distribution) at time 2 is recorded as a standard distribution change pattern in association with a change in lecture characteristics. Then, when the system of the present invention is operated, the first distribution and the second distribution are compared to identify the change pattern, and compared with the standard distribution change pattern recorded in advance. Estimate changes. Then, by outputting the change in the lecture characteristic (image output, audio output, etc.), the instructor can specifically grasp the change in the lecture characteristic.

Examples of the present invention will be described below.
FIG. 1 is a block diagram showing the configuration of a lecture support system 1 according to an embodiment of the present invention.
As shown in FIG. 1, the lecture support system 1 according to the embodiment is roughly composed of a terminal device 2 on the lecturer side and a sensor device 20 on the student side.
The instructor-side terminal device 2 includes a central processing unit 3, an input device 5, a display device 6, a network interface 7, and a storage device 8 (memory A to memory C, hard disk type recording device 9). The central processing unit 3 and other elements are connected by a general-purpose bus line. A control program is stored in the memory A, and the central processing unit 3 reads the control program from the memory A and controls the entire system.
The instructor inputs various parameters necessary for operating the lecture support system 1 using the input device 5. For example, the interval between the first time and the second time as the timing for forming the distribution of the attendance state level is determined, the detection target person of the attendance state is specified, or the threshold parameter of the attendance state transition is changed.
The display device 6 is a display composed of liquid crystal or CRT including a small device such as a PDA, and displays the output result of the lecture support system. A printer or a sound output device can be used instead of or in combination with the display device 6.

As shown in Table 1, the memory B stores the relationship between the attendance state level and the standard biometric information. In this embodiment, body temperature, blinking state, and amount of sweat are detected as biological information.
The student side sensor device 20 includes a smart chair 21. The smart chair 21 includes various sensors, a headset, and a network interface on the chair body. A thermometer 22, a CCD camera 23, and an electrode 24 are provided as sensors. The thermometer 22 and the electrode 24 are brought into contact with the student, and the body temperature and the amount of perspiration are detected. Since the resistance of the entire body changes when the amount of sweating changes, the amount of sweating can be specified by measuring the resistance of the entire body using the electrode 24. The state of blinking can be specified by imaging a student's face, particularly around the eyes, with a CCD camera provided in the headset, and performing image processing using a known method. The brain wave of the student is measured by the electroencephalograph 25, and the movement of the student on the chair is detected by the position sensor 26. The personal authentication device 27 authenticates who is sitting on the smart chair. As this personal authentication device, a headset CCD camera can be used to authenticate the student from the photographed face and retina data. The pulse rate sensor 28 measures the heart rate of the student, and the blood sensor 29 measures the blood saturated oxygen concentration (SpO ₂ ) to measure the degree of fatigue of the student.
In the lecture room, all seats where the students sit may be smart chairs, and these sensors 22 to 29 may be attached, or only specific seats may be smart chairs.

体温については検出された温度がＴ１（温度幅があるものとする、Ｔ２〜Ｔ４についても同じ）のとき受講状態レベルはレベル１にあるもと標準化されている。
他方、瞬きの回数（単位時間当たり）がＮ１（幅があるものとする、Ｎ２〜Ｎ４についても同じ）のとき受講状態レベルはレベル１にあるものと標準化されている。
発汗量は受講者の体全体の電気抵抗（インピーダンス）がＲ１（幅があるものとする、Ｒ２〜Ｒ４についても同じ）のとき受講状態レベルはレベル１にあるものとする。
なお、実際の測定にあたっては検出対象の優先順位を付すことが好ましい。例えば、体温がＴ１であるにもかかわらず、瞬きの回数がＮ２となることがあるからである。優先順位を付した場合には、優先順位の高い生体情報の検出結果に基づき受講状態レベルが特定されることとなる。
この実施例では１つの生体情報の検出値と受講状態レベルとを関連付けているが、複数の生体情報の検出値の組合わせに受講状態レベルを関連付けることもできる。 In Table 1, the standard biometric information and the attendance state level are associated with each other.

Regarding the body temperature, when the detected temperature is T1 (assuming that there is a temperature range, the same applies to T2 to T4), the attendance state level is standardized as being at level 1.
On the other hand, when the number of blinks (per unit time) is N1 (assuming there is a width, the same applies to N2 to N4), the attendance state level is standardized as being at level 1.
As for the amount of sweating, the attendance state level is assumed to be level 1 when the electrical resistance (impedance) of the entire body of the student is R1 (assuming there is a width, the same applies to R2 to R4).
In the actual measurement, it is preferable to give priority to detection targets. For example, even if the body temperature is T1, the number of blinks may be N2. When priorities are assigned, the attendance state level is specified based on the detection result of the biological information having a high priority.
In this embodiment, the detection value of one biological information and the attendance state level are associated with each other, but the attendance state level may be associated with a combination of detection values of a plurality of biological information.

この例では、標準的な受講者としてＡ〜Ｆの計６名の受講状態レベルの分布がひとつのまとまった情報として処理されている。
例えば、パターン１では時刻ｔ１において集中状態の全受講者がその後の時刻ｔ２において集中力を低下させている。かかる状況が生じた原因として講義内容が退屈であることが考えられる。
同様に、パターン２は講義が冗長になっている場合、パターン３は講義室が暗い場合、パターン４は講義室が寒い場合を示唆している。
なお、表２の例は標準分布変化パターンの一例を示すものであり、かかる標準分布変化パターンをより多く蓄積することにより、講義特性をより正確に特定できることとなる。 In the memory C, as shown in Table 2, from the distribution of the attendance level of each student at time 1 (first distribution) to the distribution of the attendance state level of each student at time 2 (second distribution) The relationship between changes and lecture characteristics is compiled and stored.

In this example, the distribution of attendance state levels of a total of six students A to F as standard students is processed as a single piece of information.
For example, in pattern 1, all students who are concentrated at time t1 reduce their concentration at subsequent time t2. The reason for this situation is that the lecture content is boring.
Similarly, pattern 2 suggests that the lecture is redundant, pattern 3 indicates that the lecture room is dark, and pattern 4 indicates that the lecture room is cold.
The example in Table 2 shows an example of a standard distribution change pattern. By accumulating more standard distribution change patterns, lecture characteristics can be specified more accurately.

The operation of the lecture support system 1 according to the embodiment of the present invention will be described with reference to the flowchart shown in FIG.
In step 1, the biometric information of a student sitting on a predetermined chair 21 at an arbitrary time t 1 is detected using the sensors 22 to 24. The detected biometric information is compared with the relationship shown in Table 1 stored in the memory 2, and the attendance state level is specified for each student (step 3). In this embodiment, each standard biological information is weighted equally. For example, when the body temperature of the student A is T1, the blink is N2, and the sweating amount R1, the attendance state level is I. Similarly, when the student's temperature is T3, the blink is N2, and the sweating amount is R2, the attendance state level is II. Note that no data is formed as noise when the body temperature of the trainee is T1, the blink is N2, and the sweating amount is R3.

In step S5, the ratio r1 of the students whose attendance state level is I among the learners whose attendance state level is specified is calculated.
Then, when the time elapses and the time reaches t2, the same processing as in step 1, step 3 and step 5 is executed to calculate the ratio r2 of persons whose attendance state level is I at time t2 ( Steps 11, 13, 15).
In step 21, the ratio between the ratio r1 of persons having the attendance state level I at time t1 and the ratio r2 of persons having the attendance state level I at time t2 is calculated, and the ratio falls below a predetermined threshold value. Then, a warning is output to the display device 6 (step 30), and the lecturer is alerted. As a result, the instructor can grasp the state in which the student's concentration on the lecture has decreased without constantly monitoring the lecture support system.

The other operation | movement aspect of the lecture assistance system of an Example is shown in the flowchart of FIG. In addition, the same code | symbol is attached | subjected to the same step of FIG. 2, and the description is partially abbreviate | omitted.
In the example of FIG. 3, a distribution (first distribution) of attendance state levels for each student at time t1 is created in step 6 and similarly, a distribution of attendance state levels (first distribution) for each student at time t2 in step 16. 2 distribution). Then, the mode of change between the first distribution and the second distribution is compared with the relationship in Table 2 stored in the memory C. When the mode of change between the first distribution and the second distribution matches or approximates the standard distribution change pattern in Table 2, the corresponding lecture characteristics are specified (steps 23 and 25). Then, the specified lecture characteristic is displayed on the display device 6 (step 27). This allows the instructor to specifically understand the characteristics of the lecture (for example, the lecture content is boring), and to respond to it, accent the lecture content (question the student and call attention) Or give a short break for a long lecture, etc.) or make corrections.

  The present invention is not limited to the description of the embodiments and examples of the invention described above. Various modifications are also included in the present invention as long as those skilled in the art can easily conceive without departing from the scope of the claims. For example, it can be applied as a training system for companies or the like, or as a real-time remote conference system.

FIG. 1 is a block diagram showing the configuration of a lecture support system according to an embodiment of the present invention. FIG. 2 is a flowchart showing the operation of the lecture support system according to the embodiment. FIG. 3 is a flowchart illustrating another operation mode of the lecture support system according to the embodiment.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Lecture support system 2 Lecturer side terminal device 3 Central processing unit 20 Student side sensor device 21 Chair 22 Thermometer 23 CCD
24 electrodes

Claims (8)

Means for detecting the state of the student and classifying the detected student's attendance state into one of predetermined attendance state levels;
A comparing means for comparing the attendance state level of each student at a first time with the attendance state level of each student at a second time;
In the comparison result of the comparison means, the difference between the first distribution of the attendance state level of each student at the first time and the second distribution of the attendance state level of each student at the second time Output means for generating a predetermined output when is deviated from a predetermined threshold;
Lecture support system comprising Means for detecting the state of the student and classifying the detected student's attendance state into one of predetermined attendance state levels;
A comparing means for comparing the attendance state level of each student at a first time with the attendance state level of each student at a second time;
In the comparison result of the comparison means, the first distribution of the attendance state level of each student at the first time is compared with the second distribution of the attendance state level of each student at the second time. Lecture characteristics identification means for identifying lecture characteristics,
Means for outputting the specified lecture characteristics;
Lecture support system comprising The lecture characteristic specifying means includes a storage means for preliminarily storing a relationship between a change in attendance state level distribution and lecture characteristics at two times,
Identifying the lecture characteristics in light of the comparison result between the first distribution and the second distribution in relation to the change in the distribution of the attendance state level stored in the storage means and the lecture characteristics; The lecture support system according to claim 2, wherein the system is a lecture support system. The lecture support system according to claim 1, wherein the state of the student is detected based on a plurality of pieces of biological information related to the student. Detecting the state of the student and classifying the detected student's attendance state into one of predetermined attendance state levels;
A comparison step of comparing the attendance state level of each student at a first time with the attendance state level of each student at a second time;
In the comparison result of the comparison means, the difference between the first distribution of the attendance state level of each student at the first time and the second distribution of the attendance state level of each student at the second time An output step for producing a predetermined output when is deviated from a predetermined threshold;
A lecture support method comprising: Detecting the state of the student and classifying the detected student's attendance state into one of predetermined attendance state levels;
A comparison step of comparing the attendance state level of each student at a first time with the attendance state level of each student at a second time;
In the comparison result of the comparison means, the first distribution of the attendance state level of each student at the first time is compared with the second distribution of the attendance state level of each student at the second time. Lecture characteristic identification step for identifying lecture characteristics,
Outputting the identified lecture characteristics;
Lecture support method comprising Computer equipment,
Means for detecting the state of the student and classifying the detected student's attendance state into one of predetermined attendance state levels;
A comparing means for comparing the attendance state level of each student at a first time with the attendance state level of each student at a second time;
In the comparison result of the comparison means, the difference between the first distribution of the attendance state level of each student at the first time and the second distribution of the attendance state level of each student at the second time Output means for generating a predetermined output when is deviated from a predetermined threshold;
A computer program for supporting lectures characterized by Computer equipment,
Means for detecting the state of the student and classifying the detected student's attendance state into one of predetermined attendance state levels;
A comparing means for comparing the attendance state level of each student at a first time with the attendance state level of each student at a second time;
In the comparison result of the comparison means, the first distribution of the attendance state level of each student at the first time is compared with the second distribution of the attendance state level of each student at the second time. Lecture characteristics identification means for identifying lecture characteristics,
A computer program for supporting lectures characterized by

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