KR20190021752A - Method and apparatus of cognitive load decision using the prior knowledge and psychophysiological responses - Google Patents

Abstract

The present invention relates to a cognitive load decision method and a cognitive load decision apparatus using prior knowledge and psychophysiological response. More specifically, the cognitive load decision method uses the psychophysiological response of a learner who learns a learning video in accordance with prerequisite knowledge of the learner and assignment complexity of the learning video, and determines a cognitive load of the learner by a learning section of the learning video. In addition, the cognitive load decision method comprises the following steps: collecting the psychophysiological response on a first learning video from the learner; providing a second learning video to the learner; collecting the psychophysiological response on the second learning video from the learner; analyzing the psychophysiological response; and determining the cognitive load of the learner.

Description

[0001] METHOD AND APPARATUS FOR COGNITIVE LOAD DECISION USING THE PRIOR KNOWLEDGE AND PSYCHOPHYSIOLOGICAL RESPONSES [0002]

The present invention relates to a cognitive load judgment method and a cognitive load judgment apparatus using prior knowledge and physiological psychological reaction, and more particularly, to a cognitive load judgment method and a cognitive load judgment apparatus using learner's prior knowledge and learning complexity, And more particularly, to a method and apparatus for determining a cognitive load.

Learning analytic studies are based on data based on student information system (SIS) and learning management system (LMS) data, which are used to identify learners' learning behavior patterns and to predict academic achievement and dropout according to identified learning behavior patterns. Efforts have been made to improve learning outcomes through analysis and forecasting.

However, learning analytical research is somewhat difficult to grasp the learning psychology that acts on the basis of the learner 's behavior as a reason for the learning behavior pattern. In addition, in order for the results of learning analysis to lead to effective prescriptions in the actual learning context, the process of analysis must be closely related to the teaching design and be interpreted and fed back. The linkage or model that can interpret the collected data contextually There was an insufficient part to present.

In recent years, it has been emphasized the importance of understanding the learner's behavior and cognitive and emotional changes in the learning process, and attempts to analyze and analyze the learner's cognitive and psychological state in the learning process through the physiological response It is expanding. In particular, as in modern times, as social interest in public emphasis increases, and as the alternative learning resources that can be learned are overflowing, the environment in which the cognitive load due to information overload is increasing explosively increases the cognitive burden of the learner Measures for effective management are required.

In order to solve this problem, cognitive load theory has been actively applied as a basis to provide learner 's cognitive situation and instructional design to support it. Cognitive load theory ultimately emphasizes that the main goal of education lies in the construction and automation of the schema. The instructional design is based on the relationship between three types of cognitive loads and the limits of available cognitive abilities, Explain that it should be done to facilitate.

In other words, in order to promote the burdens perceived as the ultimate goal of learning, it is necessary to minimize the external cognitive load of the learner and effectively adjust the intrinsic cognitive load to provide an environment that maximizes the cognitive burden. We need to support the relics or the burden sufficiently.

However, previous researches on cognitive load theory have mainly focused on external cognitive load reduction. Research on intrinsic information processing of learners considering instructional design and learner characteristics is insufficient, There are some difficulties in objectively measuring learner's cognitive load.

Therefore, a method to objectively measure learner 's cognitive load in the course of actual video learning is needed.

The present invention provides a cognitive load determination method and apparatus for determining a cognitive load of a learner occurring in a learning process of a learning image by analyzing a player's knowledge and complexity of a learning learner's learning.

The present invention analyzes the learner's physiological psychological reaction to grasp the behavior, cognition, and emotional state of the learner changing during the process of learning the learning image, and thereby, learners' perceptions according to the learner's prior knowledge and the complexity of the tasks included in the learning contents The present invention provides a method and an apparatus for determining a cognitive load to determine whether a load occurs.

According to an embodiment of the present invention, there is provided a method of determining a cognitive load, the method comprising: collecting a physiological psychological reaction for a first learning image from a learner learning a first learning image based on player knowledge of the learner; And the learning of the first learning image is completed. Providing a second learning image having a difference in the complexity of the first learning image and the second learning image to the learner; Collecting a physiological psychological reaction for a second learning image from a learner learning the second learning image; Analyzing the physiological psychological reaction collected in the process of learning the first learning image and the second learning image according to the player knowledge and complexity of the task; And determining a cognitive load of the learner according to the analyzed learning period according to the physiological psychological reaction.

The step of collecting the physiological psychological reaction for the first learning image according to an embodiment may include a step of acquiring a physiological psychological reaction based on a learner's knowledge level of the learner through a pre-test composed of learning contents to be learned by the learner, Identifying; And classifying the learner into an upper group or a lower group corresponding to the athlete knowledge level.

The physiological psychological data according to an exemplary embodiment may include a heart rate variability indicating a variation of a pupil size and a heart rate indicating a change of the eye of the learner, and the step of analyzing the physiological response may include calculating the pupil size and heart rate variability And the physiological psychological reaction of the learner according to the learning section constituting the first learning image and the second learning image can be analyzed.

The analysis of the physiological psychological reaction according to an embodiment may include analyzing a physiological response according to a pupil size and a heart rate variability of a learner according to a learning interval in consideration of a task complexity that varies according to a time point of the first learning image and a second learning image, The physiological psychological reaction can be analyzed.

The step of determining the cognitive load of the learner according to the embodiment may include determining the cognitive load of the learner based on the prior knowledge level of the group to which the learner belongs among the upper group or the lower group according to the physiological response of the analyzed learner can do.

A cognitive load determination device according to an exemplary embodiment includes a first collection unit for collecting physiological psychological responses to a first learning image from a learner who learns a first learning image based on player knowledge of the learner; And the learning of the first learning image is completed. A providing unit for providing the learner with a second learning image in which there is a difference in the complexity between the first learning image and the task; A second collecting unit for collecting a physiological psychological reaction for a second learning image from a learner learning the second learning image; An analyzer for analyzing a physiological psychological reaction collected in the process of learning the first learning image and the second learning image according to the athletic knowledge and the complexity of the task; And a determination unit for determining a cognitive load of the learner for each learning interval according to the analyzed physiological response.

The first collecting unit according to an embodiment grasps the player's knowledge level of the learner based on the learner's prior learning ability before learning the first learning video and sets the learner to a higher group It can be classified into subgroups.

The physiological psychological data according to an exemplary embodiment may include a heart rate variation indicating a change in pupil size and a heart rate indicating a change in the eye of the learner, and the step of analyzing the physiological response may include: And the physiological psychological reaction of the learner according to the learning section constituting the first learning image and the second learning image can be analyzed.

The analyzer according to an embodiment analyzes the physiological response of the learner according to the pupil size and heart beat variability of the learner according to the learning interval considering the complexity of the task which changes according to the time of the first learning image and the second learning image can do.

The determination unit may determine the cognitive load of the learner based on the prior knowledge level of the group to which the learner belongs among the upper group or the lower group according to the analyzed physiological response of the learner.

A cognitive load determination method and apparatus according to an exemplary embodiment can determine a cognitive load of a learner occurring in a learning process of a learning image by analyzing a player's knowledge and task complexity of a learner learning a learning image.

The cognitive load determination method and apparatus according to an embodiment analyze the learner's physiological psychological reaction to grasp the changing learner's behavior, cognition, and emotion state in learning image learning, It is possible to determine whether the learner has a cognitive load in the process.

The method and apparatus for determining the cognitive load according to an embodiment of the present invention can be applied to a learning support system suitable for a learner's knowledge level displayed in each learning section by using the difference in learner's cognitive load through a learning section having a high complexity and a low complexity System can be provided.

1 is a diagram illustrating a cognitive load determination apparatus according to an embodiment.
2 is a diagram illustrating a detailed configuration of a cognitive load determination apparatus according to an embodiment.
FIG. 3 is a diagram for explaining a configuration for collecting prior knowledge of a learner and physiological psychological reaction of a learner according to an embodiment.
4 is a diagram for explaining a correlation between prior knowledge and task complexity according to an embodiment.
FIG. 5 is a diagram illustrating a change in a pupil size during a learner's physiological psychological reaction based on a learner's prior knowledge according to an embodiment.
FIG. 6 is a diagram illustrating a change in heart beat variability during a learner's physiological psychological reaction based on a learner's prior knowledge according to an embodiment.
7 is a flowchart for explaining a cognitive load determination method according to an embodiment.

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 diagram illustrating a cognitive load determination apparatus according to an embodiment.

1, the cognitive load determination apparatus 101 determines the cognitive load of the learner 102 in the process of learning the prior knowledge and the learning image 103 learned by the learner 102 before learning the learning image 103. [ The physiological response can be collected to determine the cognitive load of the learner 102 through the learner 102 prior knowledge and physiological response.

In other words, the cognitive load determination apparatus 101 can determine the cognitive load of the learner 102 in the learning process for the learning image 103 of the moving image learning environment. Here, a video-based learning environment can mean an environment for learning to create new learning experiences utilizing digital technologies including the Internet. For example, the cognitive load determination apparatus 101 determines the cognitive load of the learner 102 through a learning process in a moving image learning environment using a learning image 103 for a proposition subject of mathematics composed of text without any intervention of a teacher can do.

The learning image 103 may include one or more learning images in which there is a difference in the complexity of the task in order to determine the cognitive load of the learner 102. [ For example, the learning image 103 may include a first learning image and a second learning image that change according to a time point at which the image progresses, and the first learning image may include learning about the < RTI ID = 0.0 & And the second learning image may include a learning configuration related to the " dual proposition " in mathematics.

Specifically, the learning situation is followed by a follow-up learning content with a higher task complexity, followed by less complex player learning content. The first learning image and the second learning image represent the learning situation of the learner. Accordingly, the first learning image includes the learning contents corresponding to the player learning contents of the second learning image and having a low complexity of the task.

For example, the first learning image includes the contents of learning about the propositional proposition and existence proposition, and the propositional proposition and existence proposition are the player learning contents about the learning contents of the propositional proposition constructed in the second learning image, And the elements of the learning content. Thus, the bilingual proposition can include not only the learning contents included in the propositional propositions and existing propositions but also the section of new learning contents to the learner. In addition, complex propositions are a combination of propositional propositions and existential propositions, and the number of elements to be understood as learning contents or the interaction between each element can be implemented as a complicated process.

The cognitive load determination apparatus 101 can determine the cognitive load that occurs to the learner 102 according to the task complexity of each image while learning the first learning image and the second learning image.

Then, the cognitive load determination device 101 evaluates the learner's academic process by measuring, collecting, and analyzing data on the context of the learner in order to understand and optimize the environment occurring in the learning environment, Can be predicted. In this case, the learner 's learning outcomes can be different from each other depending on the type of the learning image and the learner' s prior knowledge level of learning the learning image.

To this end, the cognitive load determination device 101 can grasp the learner's prior knowledge level. In other words, the cognitive load determination apparatus 101 can execute a pre-test based on the learning content of the learning image to be learned by the learner and grasp the prior knowledge level of the learner according to the executed pre-test. For example, the cognitive load determination device 101 can confirm the level of the prior knowledge learned by the learner through a preliminary test on the propositional subject of mathematics. Then, the cognitive load determination apparatus 101 can classify the learner into an upper group or a lower group corresponding to the determined level.

The cognitive load determination apparatus 101 can collect the physiological psychological reaction of the learner changing during the process of learning the learning image. Here, the learner 's physiological response can show the cognitive response, emotional response, and behavioral response observed through the physiological principle and phenomenon of a person. The learner 's physiological response can be represented by the pupil size and the ratio of the sympathetic and parasympathetic nerves (LF / HF ratio) of heart rate variability (HRV). Pupil size means the size of the pupil's pupil diameter. The change in pupil size can be used as an index to estimate the cause of cognitive load and cognitive load. Heart rate variability is a change in heart rate with time. Heart rate can be a reaction caused by the interaction of sympathetic and parasympathetic nervous system.

The cognitive load determination apparatus 101 can determine the cognitive load of the learner through the physiological psychological reaction of the learner changing in the process of learning the learning image based on the prior knowledge level of the learner. In other words, according to the learner's prior knowledge about the learning contents constituting the learning image to be learned, the learner's cognitive ability in the process of learning the learning image can be grasped. That is, according to the present invention, a learning image is learned on the basis of a degree of content already known to the learner or a degree learned by studying, and the learning contents of the learning image are processed and stored in a storage place of the learner's brain, It is possible to determine the cognitive load that occurs to the user. Therefore, according to the present invention, the cognitive load shows that the learner's cognitive load in the course of learning image learning is lower as the learner's prior knowledge level is higher, and as the learner's prior knowledge level is lower, The learner's cognitive load may be high.

As a result, the present invention provides a method and apparatus for learning a learning image from a learning environment in which a learning image is learned through a physiological psychological reaction that occurs in a learning image based on a learner's prior knowledge and a task complexity of the learning image, Can be determined.

2 is a diagram illustrating a detailed configuration of a cognitive load determination apparatus according to an embodiment.

2, the cognitive load determination apparatus 201 includes a first collecting unit 202, a providing unit 203, a second collecting unit 204, an analyzing unit 205, and a determining unit 206 .

The first collecting unit 202 can collect the physiological psychological reaction for the first learning image from the learner who learns the first learning image based on the athlete knowledge of the learner. In detail, the first collecting unit 202 can grasp the player's knowledge level of the learner based on the learner's prior learning ability before learning the first learning image. In other words, the first collecting unit 202 can perform a pre-test for grasping the degree of learning formed for the learner in relation to the learning image to be learned. After the pre-test is completed, the first collecting unit 202 may classify the learner into an upper group or a lower group so as to correspond to the learner's prior knowledge level. In detail, the first collecting unit classifies the learner into a higher group when the learner is equal to or more than the median of the average of all the learner performing the pretest, and conversely, when the learner is less than the median, the learner can be classified into the lower group.

The first collecting unit 202 may collect the physiological psychological reaction of the learner who is learning the first learning image based on the learner's prior knowledge. Here, the physiological psychological reaction may include the pupil size and the heart rate variability of the learner stimulated from the first learning image. At this time, the present invention can collect psychophysiological responses to grasp the behavior, cognitive, and emotional changes appearing to learners in the process of learning.

The providing unit 203 completes the learning of the first learning image. It is possible to provide the learner with the second learning image in which the difference between the first learning image and the task complexity exists. Here, the providing unit 203 may include a first learning image having a low task complexity and a second learning image having a high task complexity for measuring a cognitive load according to a learner's knowledge level, while minimizing the external load on the learner And can be provided to learners sequentially.

Here, the task complexity of the first learning image and the second learning image can be determined on the basis of the average score of the learners of each learning section of the pre-test performed by the learner before learning the learning image. In other words, if the average score of each test section in the pre-test is less than the median of all the learners who have performed the pre-test, the task complexity of the corresponding learning section can be set to 'up'. If the average score of each learning section in the pre-test is a middle or higher learning interval, the complexity of the task in the corresponding learning interval can be set to be "low".

The second collecting unit 204 may collect the physiological psychological reaction for the second learning image from the learner learning the second learning image. Here, the second collecting unit 204 can collect the physiological psychological reaction of the learner who learns the second learning image having a higher task complexity than the first learning image.

The analysis unit 205 can analyze the physiological psychological reaction collected in the process of learning the first learning image and the second learning image according to the player knowledge and complexity of the task. Specifically, the analyzing unit 205 can distinguish between a learning interval exhibiting a high complexity of a task complexity existing between the first learning image and a second learning image and a learning interval indicating a low complexity of the task. The analyzing unit 205 can determine the physiological psychological reaction of the learner in each of the learning sections exhibiting high complexity and low complexity of the task.

The analysis unit 205 analyzes the physiological psychological reaction that changes according to the progress of the first learning image and the second learning image. As the degree of difficulty of the learning task according to the complexity of the task increases, the pupil size of the learner increases And the learner's heartbeat variability can be increased. As the complexity of the task becomes higher, the learning difficulty of the learning contents increases proportionally, and the learning difficulty of the learners may be different depending on the prior knowledge level.

For example, a learner with a high level of prior knowledge may experience a shorter response time to acquire learning content based on previously learned knowledge than a learner with a low knowledge level, even though the learning difficulty increases. In other words, a learner with a high level of prior knowledge may have a higher level of arousal level than a learner with a lower level of prior knowledge. The learner 's physiological psychological response can be a criterion for the learner to know whether the cognitive load on the learning contents has occurred or not.

Accordingly, the analyzer 205 can analyze the learner's physiological psychological reaction in order to check the cognitive load of the learner in the process of learning the learning image according to the player knowledge and complexity of the task.

The determination unit 206 can determine the cognitive load of the learner in the learning interval in which the difference of the task complexity according to the analyzed physiological response occurs. The determination unit 206 may display different arousal levels according to the complexity of the learning image based on the learner's prior knowledge. In other words, assuming that the amount of knowledge available to me is 100%, the learner learns the germane cognitive load, extraneous cognitive load, A cognitive load corresponding to an intrinsic cognitive load may occur.

- Intrinsic cognitive load is determined by the level of interaction of the elements that make up the information, and can be generated by the task complexity of the learning contents. Implicit cognitive load increases when intrinsic information of learning contents is complicated or information presentation is very fast, but the frequency of cognitive load can be changed according to learners' athletic knowledge or experience level. Therefore, a learner who has a lot of athletic knowledge in a situation where the same learning image is given experiences a low inherent cognitive load. On the other hand, a learner who has a low athletic knowledge experiences a high inherent cognitive load even in a learning image having a low task complexity have.

- External cognitive load may be an unnecessary cognitive load caused by the presentation style of learning materials, the method, and information not related to learning.

- This cognitive load may be a cognitive load resulting from the mental effort involved in learning to tackle a learner's problem in the process of integrating with the athlete's knowledge in order for the learner to understand new information. This cognitive load can occur in the solution process to generate performance for the learning task according to the learning contents.

Finally, the implicit cognitive load is the cognitive load caused by the complexity of the learning contents, and the cognitive load is the cognitive load that occurs in the cognitive process of the learning contents. The present invention analyzes the changes in the pupil size and the physiological psychological reaction as unconscious or conscious actions according to the cognitive load generated in the process of learning the learning image, thereby determining whether or not the cognitive load of each section of the learning image occurs .

In addition, the present invention distinguishes the upper group and the lower group classified according to the athletic knowledge to check whether or not the cognitive load is generated, so that the cognitive load of the learner is not simply measured, but the degree of the cognitive load And the occurrence interval can be more clearly confirmed. Based on this, it is possible to systematically provide the learning plan for each level.

More specifically, the present invention allows the learner to know whether or not a cognitive load has occurred in the learning process based on the following Table 1.

1 Type
(Task complexity / cognitive load) 2 Types
(Task complexity / cognitive load) Cognitive load ' 3 Types
(Task complexity / under cognitive load) 4 Types
(Task complexity / cognitive load) Cognitive load 'ha' Challenge complexity 'award' The task complexity 'ha'

The present invention can determine the perceived load state of each learner by classifying the type in which the cognitive load occurs as shown in Table 1. [ In this case, the type 1 and type 4 correspond to the level of the complexity of the task and the level of the cognitive load, while the type 2 is the type that appears when the user experiences unnecessary external cognitive load. It may be the type that appears when there is.

In detail, the type 1 is a case where the task complexity is the same as the cognitive load, and the learner is learning the relevant section correctly at the high level task complexity interval. Accordingly, the present invention may not provide a separate real-time prescription for pushing the learning process.

4 type is the interval in which the cognitive load appears at the same time as the task complexity. In other words, the cognitive load of the learner may be low in the low complexity interval. This situation may not provide a separate prescription for pushing the learning process as it is a general situation for learners learning the learning image.

2 Although the type is a combination of tasks (low), a high level of cognitive load experienced by a learner may indicate a condition. In this situation, if the learner is a learner with a high level of prior knowledge, it can be confirmed that the additional external cognitive load is generated due to the unnecessary learning. On the other hand, even if the learner is a learner with a low knowledge level, the learner has the same external cognitive load as the learner with a high knowledge level, or objectively, the complexity of the task is low. It can mean that the temperature is not low.

Therefore, the learner with a high level of prior knowledge is given the option to pass the corresponding learning section and learn, and to the learner with low knowledge level, ① to pass the learning section and learn ② to learn supplementary learning materials, You can choose whether to continue.

3 type can show low cognitive load even though the complexity of the task is high and it can be a type that appears when the learner is not immersed in learning. There is a possibility that the learners did not concentrate properly due to the high complexity of the learning section, or that the contents of the section were difficult to understand.

Therefore, in this case, after completion of the relevant section, it is possible to learn supplementary learning materials to all the learners of the athletic knowledge, and to continue the next section, so that if necessary, .

After the learning is completed, the type information of each video learning section of the video segment corresponding to the type 2 and the type 3 is automatically sent to the online learning operation tutor so that appropriate teaching support can be provided to the individual learner.

FIG. 3 is a diagram for explaining a configuration for collecting prior knowledge of a learner and physiological psychological reaction of a learner according to an embodiment.

Referring to FIG. 3 (a), the cognitive load determination device can present test items for evaluating the learner's prior knowledge. In detail, the cognitive load determination device can grasp player knowledge from a learner to a resource that has already been formed in order to solve a task according to learning contents of the learning image. In detail, the athlete knowledge can be defined in the same context with the schema that the learner has in relation to the learning contents, and it can be formed around the subject of learning contents of the learning image. In other words, the prior knowledge level is a starting point behavior that the learner should have in advance and can be a criterion for judging that the learner understands the structure and characteristics of the learning contents.

For example, the athlete knowledge is applied in the course of processing the learning contents collected through the learning image, and can indicate the degree of the contents already known by the learner in response to the learning contents, or the degree of learning by learning. Then, the cognitive load determination device can grasp the prior knowledge related to the learning contents of the learning image to be learned among the various knowledge accumulated by the learner.

For this purpose, the cognitive load determination device can provide the learner with a pre-test composed of the problems that are solved to grasp the accumulated knowledge of the learner. Here, the problems that are generated by the pre-test are formed into different difficulty levels according to the respective items, and different points can be set according to the difficulty levels. This is a test to be conducted prior to proceeding with the learning image, and the pre-test may be composed of the items based on the learning contents of the first learning image and the second learning image, respectively. For example, the first learning image includes a learning section related to the " proposition / existence proposition " in mathematics, and the second learning image includes a learning configuration related to the " And a total of 16 questions, each of which consists of eight items.

The cognitive load determination device can evaluate the level of the learner's prior knowledge based on the scoring result (score) of the pre-test. Then, the cognitive load determination apparatus can classify the learner into an upper group or a lower group corresponding to the level of the learner evaluated.

For example, if the cognitive load determination device sets the total score of the pre-test to 100 points, if the score is less than 50 points, the learner is classified as a subgroup. If the score is 50 or more, .

Referring to FIG. 3 (b), when the pre-test is completed, the cognitive load determination apparatus can reproduce the first learning image while the screen is switched from the image related to the pre-test. The first learning image is an image provided for confirming the general tendency of the cognitive load experienced by the learner in the course of learning, and may include learning contents composed of sections of task complexity lower than the second learning image.

The cognitive load determination device can collect the pupil size and heart beat variability of the learner learning the first learning image. Here, the cognitive load determination device can collect the physiological psychological reaction that changes during the course of learning in consideration of the group of learners classified through the pre-test.

Referring to FIG. 3C, when the learning of the first learning image is completed, the cognitive load determination apparatus can provide the second learning image with the difference between the first learning image and the task complexity to the learner. The cognitive load determination device can collect the physiological psychological reaction including the pupil size and heart beat variability of the learner with respect to the second learning image from the learner learning the second learning image.

Here, the pupil size changes when sensory, mental, and emotional events occur regardless of illumination, visual image, or distance from the retina, responding by sympathetic nerves to pupil dilation, and parasympathetic response to pupil dilation have. At this time, pupil enlargement reflects the cognitive process in human psychological activity, and the enlargement of pupil size can be interpreted as a mental workload during the cognitive process.

The present invention can calculate the pupil size as a change in the pupil size at the time of measurement of the pupil size in the base reaction section. Here, the pupil size is used as an indicator of the total amount of cognitive load experienced by the learner, and it can be seen that the cognitive load increases as the pupil size is greatly increased as compared with the base reaction time.

Heart rate variability (HRV) is a measure of the variation in heart rate. The change in heart rate is the physiological adaptation ability of the human body that the heart responds quickly to stimulation (learning image) It can be an element that can evaluate the degree of change of the nervous system. The sympathetic and parasympathetic nerves can be used as the factors, and the level of awakening in the learner 's learning environment can be confirmed through the ratio of sympathetic to parasympathetic nerves (LF / HF ratio). At this time, LF (Low Frequency) indicates the degree of activation by the sympathetic nerve, and sympathetic nerve can be caused by the learner's arousal, stress and excitement. HF (High Frequency) indicates the degree of activation by parasympathetic nerve, and parasympathetic nerve can be caused by learner's relaxation, stability, drowsiness.

In the present invention, if the index value by the ratio (LF / HF ratio) is large, it can be understood that the sympathetic nerve is activated compared to the parasympathetic nerve. The larger the index value according to the arousal level in the learning situation according to the index value, As shown in Fig.

Accordingly, the present invention utilizes the physiological psychological reaction according to the pupil size and the heartbeat variation among the physical characteristics changing in the learning environment of the learner, so that it is possible to grasp the occurrence of the more reliable learner in the cognitive load.

4 is a diagram for explaining the correlation between the level of prior knowledge and complexity of the task according to an embodiment.

Referring to FIG. 4, the cognitive load determination device should consider the complexity of learning tasks and instructional help of the learning image in order to check the learner's cognitive load. Here, complexity and pedagogical support should be provided considering the expertise of the learner's performance ability, ie, the level of the athlete's knowledge.

In learning the learning image, the learner may experience cognitive burden in the process of learning new contents according to the degree of difficulty and ease of the task. In other words, when the degree of difficulty or difficulty of the task according to the learning image is too difficult, the learner can not learn the contents of learning required for solving the task and the interaction with the memorized contents exceed the level of the learner's working memory capacity This can be difficult to happen properly. On the other hand, if the degree of difficulty or difficulty of the task according to the learning image is too easy, the learner may not be able to challenge the learning because it generates an imminent cognitive load that is too low compared to the learner's working memory capacity level in solving the task.

In addition, the learner's expertise, task complexity, and instructional support can affect the task difficulty experienced by the learner. Here, the task complexity is a concept that includes all things learned through learners' athletic knowledge or learning, and it can have a relation with learning components and interaction components as well as concepts and procedures related to learning contents.

More specifically, task complexity can be highly related to intrinsic cognitive load, meaning that the components of the task are highly correlated. Accordingly, the present invention defines a learning section having a large number of elements to be understood or a lot of information to be processed at the same time as a section having high complexity, and a learning section having a high complexity of tasks and a low complexity of tasks Can be analyzed separately.

Interactive components can have a logical association between each construct, meaning that they need to be processed simultaneously in the working memory, and one element means a schema, with elements that need to be processed. In the absence of interrelated discrete elements constituted by schemas, learning materials with a high level of element-interactivity have more working memory resources than those with low level content element interactivity You may need it.

In addition, the task complexity of the task in the learning image may affect the level of the athlete 's athletic knowledge (expertise), not the intrinsic or load effect of the learning contents. In other words, assuming that the learner is given the same task, the learner with low expertise (learner with low knowledge) is able to generate the intrinsic cognitive load by increasing the interactivity of the content element, Cognitive load may not be generated.

As a result, task complexity can be treated as a single factor for a professional learner because a series of interacting learning elements can be treated as a single element for a learner with low knowledge, because the level of the task complexity varies depending on the learner's prior knowledge .

FIG. 5 is a diagram illustrating a change in a pupil size during a learner's physiological psychological reaction based on a learner's prior knowledge according to an embodiment.

5, the cognitive load determination device uses a mean line of the pupil size of a learner belonging to a higher-order group and a lower-order group according to a player's knowledge to check the tendency of the learner's cognitive load that changes in the learning process, Can be confirmed. To this end, the cognitive load determination device may classify the learner into an upper group or a lower group according to the learner's level of the athlete's knowledge, before learning the learning image. The cognitive load determination apparatus can display the pupil size change of the learner changed in the learning process for each classified group through the graph as shown in FIG. 5 (a).

In the graph of FIG. 5 (a), a learning segment to be distinguished between the first learning image and the second learning image may be divided into a central dotted line. The graph on the left side of the dotted line (Time Index) shows the pupil size change of the learner in the section in which the first learning image is reproduced. The graph shown on the right side with respect to the point island shows a change in pupil size of the learner in the section in which the second learning image is reproduced.

The dotted line indicated in the second half region of the learning section of the first learning image and the second half region of the learning section of the second learning image indicates the total reproduction time of the first learning image and the total reproduction time of the second learning image, respectively. After the point indicated by the dotted line, the data (pupil size) appearing from learners who have learned additional learning beyond the previously set play time is displayed after the first learning image and the second learning image are all reproduced.

In order to more intuitively identify the pupil size which changes according to the learners' prior knowledge and task complexity, the present invention smoothed the circle mean and displayed once more in the center.

The pupil size according to the graph of FIG. 5 is not clearly expressed in the width difference between the upper group and the lower group, but the height of the average line can confirm that the player knowledge is expressed lower than the learner belonging to the upper group. In detail, the learner who has low knowledge about the learning image with the same task complexity has many factors to understand the learning image. As the information to be processed simultaneously increases, the pupil size can be expanded as one of the learner 's psychological changes. On the other hand, the pupil size can be reduced because the learner with high knowledge about the learning image having the same task complexity has a relatively lower factor to understand and information to process than the learner with low prior knowledge.

As a result, a high-level group of high-learner learners can form a graph with a mean line lower than the mean line of pupil size measured in a subgroup of low-learner learners, Is reproduced more clearly in the period in which the second learning image is reproduced. Here, in the subgroup, in the section where the first learning image is reproduced, the fluctuation width of the pupil size is shown as a small average line over the entire learning process, and in the section where the second learning image is reproduced, the average line is almost linear.

The cognitive load determination apparatus can determine the difference between the pupil sizes of the upper and lower groups according to the player's knowledge in the learning interval of the first learning image and the learning interval of the second learning image in which the complexity of the task is high . To this end, the cognitive load determination apparatus may calculate an average of the pupil sizes in the learning period of the first learning image and the pupil sizes in the learning period of the second learning image, for each of the upper and lower groups. Then, the cognitive load determination apparatus can confirm the difference in pupil size between the upper group and the lower group calculated by the average.

In the present invention, the pupil size difference according to the athlete's knowledge level in the second learning image (the propositional proposal) including the first learning image including the learning section with low complexity (full-name / existence proposition) The average comparison graph of FIG. 5 (b) is shown for more intuitive confirmation.

The average of the pupil sizes in each section of the first learning image and the second learning image is smaller than that of the lower group in the upper group and the pupil knowledge in the upper learning group in the learning period of the second learning image, It can be confirmed that the difference in size is largely widened.

Therefore, in the case of a learner belonging to a higher-level group having a higher prior knowledge, the learning image can be learned more stably than the learner belonging to the lower group even if the task complexity increases.

FIG. 6 is a diagram illustrating a change in heart beat variability during a learner's physiological psychological reaction based on a learner's prior knowledge according to an embodiment.

6, the cognitive load determination apparatus determines a tendency of a cognitive load of a learner who changes in a learning process by using a mean line of heartbeat variation of a learner belonging to a higher group and a lower group according to a player's knowledge, Can be confirmed. To this end, the cognitive load determination device may classify the learner into an upper group or a lower group according to the learner's level of the athlete's knowledge, before learning the learning image. The cognitive load determination apparatus can display the change in the heartbeat variation of the learner changed in the learning process for each classified group through the graph as shown in FIG. 6 (a).

The cognitive load determination device displays a mean line for each of the upper group and the lower group according to the player's knowledge as shown in FIG. 6 (a), using the heartbeat variation in the learning process, that is, the change in the ratio of the sympathetic nerve and the parasympathetic nerve. As shown in the graph, it can be seen that the ratio of the sympathetic nerve and the parasympathetic nerve is kept high in the upper group according to the player knowledge over the first learning image and the second learning image, as compared with the lower group.

In other words, the learner who belongs to the higher group has a tendency of descending while the ratio of the sympathetic nerve and the parasympathetic nerve is steadily rising at the beginning of the first learning image (the proposition / presence proposition), while the learner belonging to the lower group, The ratio of neurons has maintained a moderate rise pattern. In detail, in the learning section of the first learning image, which was low in task complexity, the subgroups showed a gentle shape and maintained a low level with time as the learners already knew. On the other hand, the upper group showed a rapid increase in heart rate variability at the beginning of the study and a sharp decrease after the middle level.

At this time, the learner belonging to the upper group and the learner belonging to the lower group differ in the magnitude of the value according to the heartbeat variation in the second learning image. The learner who belongs to the upper group can maintain a stable state as the learning image is progressed and the understanding of the learning contents is smoothly performed, thereby greatly reducing the heartbeat variation. On the contrary, the learners belonging to the subgroups showed a high rate of heart beat variability as the learning image progressed and the understanding of the learning contents became increasingly difficult.

In the present invention, a graph is shown in FIG. 6 (b) to more intuitively confirm the change in the ratio of the sympathetic nerve and the parasympathetic nerve between the two groups according to the athlete knowledge level.

According to the present invention, in the present invention, a first learning image (a proposition / existence proposition) including a learning section with a low complexity and a second learning image (a proposal with two sections) FIG. 5B shows an average comparison graph for more intuitively confirming the pupil size difference.

The heart rate variability in all the sections of the first learning image and the second learning image showed a higher ratio of sympathetic and parasympathetic nerves than that of the lower group having higher player knowledge, The ratio between the sympathetic nerves and parasympathetic nerves in the group increased significantly.

7 is a flowchart for explaining a cognitive load determination method according to an embodiment.

In step 701, the perceptual load determination device may collect physiological psychological responses to the first learning image from the learner who learns the first learning image based on the athlete knowledge of the learner. At this time, prior to learning the first learning image, the cognitive load determination device grasps the player's knowledge level of the learner through a pre-test composed of learning contents scheduled to be learned by the learner, It can be classified into groups or subgroups.

In step 702, when the learning of the first learning image is completed, It is possible to provide the learner with the second learning image in which the difference between the first learning image and the task complexity exists.

In step 703, the perceptual load determination device may collect the physiological psychological reaction for the second learning image from the learner learning the second learning image.

In step 704, the perceptual load determination device may analyze the physiological response collected during the process of learning the first learning image and the second learning image according to the player knowledge and complexity of the task. Here, the physiological psychological data may include a pupil size representing a change of the eye of the learner and a heartbeat variation representing a change due to a heartbeat. Then, the cognitive load determination apparatus generates an average value corresponding to each of the pupil size and the heartbeat variation, and analyzes the physiological psychological reaction of the learner according to the learning section constituting the first learning image and the second learning image.

In step 705, the cognitive load determination device can determine the cognitive load of the learner according to the analyzed physiological response. The cognitive load determination device can determine the cognitive load of the learner based on the prior knowledge level of the group to which the learner belongs among the upper group or the lower group according to the physiological response of the analyzed learner.

Implementations of the various techniques described herein may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or combinations thereof. Implementations may be implemented in a computer program product, such as an information carrier, e.g., a machine readable storage device, such as a computer readable storage medium, for example, for processing by a data processing apparatus, May be embodied as a computer program recorded on a device (computer readable medium). A computer program, such as the computer program (s) described above, may be written in any form of programming language, including compiled or interpreted languages, and may be stored as a stand-alone program or in a module, component, subroutine, As other units suitable for use in the present invention. A computer program may be deployed to be processed on one computer or multiple computers at one site or distributed across multiple sites and interconnected by a communications network.

Processors suitable for processing a computer program include, by way of example, both general purpose and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The elements of a computer may include at least one processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer may include one or more mass storage devices for storing data, such as magnetic, magneto-optical disks, or optical disks, or may receive data from them, transmit data to them, . ≪ / RTI > Information carriers suitable for embodying computer program instructions and data include, for example, semiconductor memory devices, for example, magnetic media such as hard disks, floppy disks and magnetic tape, compact disk read only memory A magneto-optical medium such as a floppy disk, an optical disk such as a DVD (Digital Video Disk), a ROM (Read Only Memory), a RAM , Random Access Memory), a flash memory, an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), and the like. The processor and memory may be supplemented or included by special purpose logic circuitry.

In addition, the computer-readable medium can be any available media that can be accessed by a computer, and can include both computer storage media and transmission media.

While the specification contains a number of specific implementation details, it should be understood that they are not to be construed as limitations on the scope of any invention or claim, but rather on the description of features that may be specific to a particular embodiment of a particular invention Should be understood. Certain features described herein in the context of separate embodiments may be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented in multiple embodiments, either individually or in any suitable subcombination. Further, although the features may operate in a particular combination and may be initially described as so claimed, one or more features from the claimed combination may in some cases be excluded from the combination, Or a variant of a subcombination.

Likewise, although the operations are depicted in the drawings in a particular order, it should be understood that such operations must be performed in that particular order or sequential order shown to achieve the desired result, or that all illustrated operations should be performed. In certain cases, multitasking and parallel processing may be advantageous. Also, the separation of the various device components of the above-described embodiments should not be understood as requiring such separation in all embodiments, and the described program components and devices will generally be integrated together into a single software product or packaged into multiple software products It should be understood.

It should be noted that the embodiments of the present invention disclosed in the present specification and drawings are only illustrative of specific examples for the purpose of understanding and are not intended to limit the scope of the present invention. It will be apparent to those skilled in the art that other modifications based on the technical idea of the present invention are possible in addition to the embodiments disclosed herein.

101: Cognitive load determination device
102: Learners
103: Learning video

Claims (10)

Collecting a physiological psychological reaction for a first learning image from a learner learning a first learning image based on player knowledge of the learner;
And the learning of the first learning image is completed. Providing a second learning image having a difference in the complexity of the first learning image and the second learning image to the learner;
Collecting a physiological psychological reaction for a second learning image from a learner learning the second learning image;
Analyzing the physiological psychological reaction collected in the process of learning the first learning image and the second learning image according to the player knowledge and complexity of the task; And
Determining a cognitive load of the learner according to a learning interval according to the analyzed physiological response;
And determining a cognitive load. The method according to claim 1,
Wherein the collecting of the physiological psychological reaction for the first learning image comprises:
A step of grasping the player's knowledge level of a learner through a pre-test composed of learning contents to be learned by the learner before learning the first learning image; And
Classifying the learner into an upper group or a lower group corresponding to the athlete knowledge level
And determining a cognitive load. The method according to claim 1,
The physiological psychological data includes:
A heartbeat variation indicating a change in pupil size and a heartbeat indicating a change in the eye of the learner,
The step of analyzing the physiological response comprises:
And generating a mean value corresponding to each of the pupil size and the heartbeat variation to analyze the physiological psychological reaction of the learner according to the learning section constituting the first learning image and the second learning image. The method of claim 3,
The step of analyzing the physiological response comprises:
A cognitive load judgment method for analyzing a physiological psychological reaction of a learner according to a pupil size and a heart beat variability of a learner according to a learning section considering a complexity of a task that changes depending on a time point at which the first learning image and a second learning image are progressed. 3. The method of claim 2,
The step of determining the cognitive load of the learner includes:
Wherein the perceived load of the learner is determined based on a prior knowledge level of a group to which the learner belongs among the upper group or the lower group according to the analyzed physiological response of the learner. A first collecting unit for collecting a physiological psychological reaction for a first learning image from a learner learning a first learning image based on player knowledge of the learner;
And the learning of the first learning image is completed. A providing unit for providing the learner with a second learning image in which there is a difference in the complexity between the first learning image and the task;
A second collecting unit for collecting a physiological psychological reaction for a second learning image from a learner learning the second learning image;
An analyzer for analyzing a physiological psychological reaction collected in the process of learning the first learning image and the second learning image according to the athletic knowledge and the complexity of the task; And
A determination unit for determining a cognitive load of the learner for each learning interval according to the analyzed physiological response,
And the cognitive load determination device. The method according to claim 6,
Wherein the first collecting unit comprises:
Prior to learning the first learning image, grasps the player's knowledge level of the learner through a pre-test composed of learning contents to be learned by the learner and classifies the learner into a higher group or a lower group corresponding to the player knowledge level The cognitive load determining device. The method according to claim 6,
The physiological psychological data includes:
A heartbeat variation indicating a change in pupil size and a heartbeat indicating a change in the eye of the learner,
The step of analyzing the physiological response comprises:
And generates a mean value corresponding to each of the pupil size and the heartbeat variation to analyze the physiological psychological reaction of the learner according to the learning section constituting the first learning image and the second learning image. 9. The method of claim 8,
The analyzing unit,
A cognitive load determining device for analyzing a physiological psychological reaction of a learner according to a pupil size and a heart beat variance of a learner according to a learning section in consideration of a complexity of a task that changes according to a time point at which the first learning image and the second learning image are progressed. The method according to claim 6,
Wherein,
Wherein the cognitive load determination unit determines the cognitive load of the learner based on a prior knowledge level of a group to which the learner belongs among the upper group or the lower group according to the physiological response of the analyzed learner.

Images