KR102636581B1 - Diagnosis and prescription service provision system for each type of self-directed learning - Google Patents

Abstract

The present invention relates to a system that remotely connects a learner's first terminal and a teacher's second terminal to select a self-directed learning type in a non-face-to-face environment and provides diagnosis and prescription services for each type, wherein the first and second terminals and the web , acquires learning event information about the learner's self-directed learning activities from the first terminal while communicating by building a network with an external server, extracts arbitrary learning analysis data, and selects a pre-designated type for the extracted learning analysis data. One type among a plurality of self-directed learning types is selected as the first type of the learner by mutual matching with the data, and in response to the selected first type, type-specific introduction information to introduce the first type and , Generate customizing information in which type-specific diagnostic information to explain the learning tendency for the first type and type-specific guide information to guide future learning strategies and action prescriptions for the first type are mapped, and the generated customization A control unit that outputs a preset notification signal according to the information; and a notification unit provided in the first and second terminals and visually displaying the notification signal output from the control unit on an arbitrary screen,
It has the effect of contributing to the activation of metacognition through improved intimacy and at the same time inspiring the desire to learn.

Description

Diagnostic and prescription service provision system for each type of self-directed learning {DIAGNOSIS AND PRESCRIPTION SERVICE PROVISION SYSTEM FOR EACH TYPE OF SELF-DIRECTED LEARNING}

The present invention makes the selection of various self-directed learning types and diagnosis and prescription according to type more interesting based on learning analysis data related to the learner's self-directed learning, and the highest index level among the index levels in the cognitive domain and behavioral domain. It is about a system for providing diagnosis and prescription services for each type of self-directed learning that can contribute to the activation of metacognition with improved intimacy through a configuration that guides the recommended path to the final goal type, while also encouraging the desire to learn. .

As non-face-to-face education becomes active due to COVID-19, learning services based on smart learning, where learners individually use smart devices (PC, tablet PC, mobile, etc.) to learn in an online environment, are attracting attention.

The educational value of smart device-based learning is emerging in that it allows learning beyond time and place and facilitates learning through interaction. Teaching and learning methods are needed to enjoy the advantages of smart learning and obtain appropriate learning effects. Research is needed.

In particular, as described above, smart learning has the advantage of not only being limited by location and time, but also allowing learners to have autonomy in their learning.

Previous studies (Artino, 2008; Dabbagh & Kitsantas, 2004; Gunawardena & McIssac, 2003; Hartley & Bendixen, 2001; Shapley, 2000; Song & Hill, 2007) show that learners need to learn successfully in a smart learning environment. It is revealed that self-directed learning ability is required, and here, the main factors that can identify the characteristics of self-directed learning can be analyzed through learning analysis data.

In this regard, the field of Learning Analytics research is emerging, which collects data on learners' actual learning behavior through smart devices and provides customized learning prescriptions based on the analysis results, and in artificial intelligence tutoring systems, Based on a series of data collected through smart devices, we can provide learners with personalized learning tailored to their individual needs and provide instructors with more accurate and convenient teaching and learning support.

The artificial intelligence tutoring system can be expected to play a role in solving problems in the field of education, such as not only supporting personalized learning and teaching and learning, but also socially increasing the efficiency of non-face-to-face learning and resolving educational gaps. However, the artificial intelligence tutoring system has no utility. In order to establish itself in the educational field, data-based teaching and learning design is necessary, and for this, data definition and analysis must be prerequisite from the perspective of learning analytics.

Accordingly, studies have been continuously conducted to explore the components of self-directed learning in online education environments and examine their relevance to academic achievement. Recently, AI-based learning and data-based educational design have been attracting attention, and practical studies have been conducted from a learning analytics perspective. There are increasing attempts to analyze the data left behind by learners while performing activities and derive meaningful results.

Here, learning analytics is a useful academic approach that stores and analyzes related data to understand the overall environment that occurs in the learner's learning process and optimize it. It observes and analyzes the learner's behavior to more objectively understand and analyze the learning process. It is being recognized for its value in that it can be interpreted, identifies learner characteristics, predicts future learning results, and suggests appropriate teaching and learning prescriptions.

However, most of the existing research was conducted in online and LMS environments limited to video lecture platforms, and research targeting learning activities based on smart devices such as tablet PCs and mobile devices is still lacking, as well as the learners' own There is a lack of technology to easily identify self-directed learning types and their diagnosis and prescription.

In this situation, the actual data of 70,000 elementary school learners from learning services such as 'Ice Cream Home Run' are expected to create a useful research environment for exploring data and deriving meaningful results in a smart device-based learning environment. In addition, type indicators such as 'MBTI type', which are commercialized to help understand the other person's personality, are expected to be meaningfully applied to constructing self-directed learning types in a more interesting and friendly way.

Republic of Korea Patent Publication No. 10-2056822 (Title of invention: Method and device for providing learning services)

Therefore, the present invention was created to solve the above problems, and makes the selection of various self-directed learning types and diagnosis and prescription according to type more interesting based on learning analysis data related to the learner's self-directed learning. , through a configuration that guides the recommended path to the final goal type with the highest index level among the index levels of the cognitive and behavioral domains, it can contribute to activating metacognition with improved intimacy and at the same time inspire the desire to learn. The purpose is to provide a system for providing diagnosis and prescription services for each type of self-directed learning.

However, the technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly apparent to those skilled in the art from the description below. It will be understandable.

According to an embodiment of the present invention, which is a technical means for achieving the above objective, the learner's first terminal and the teacher's second terminal are remotely connected to select the type of self-directed learning in a non-face-to-face environment, and diagnose and prescribe each type. In a system that provides a service, a network is established and communicated with the first and second terminals and a web and external server, and learning event information about the learner's self-directed learning activities is acquired from the first terminal to perform arbitrary learning analysis. Data is extracted, and the extracted learning analysis data is matched with pre-designated type selection data to select one type among a plurality of self-directed learning types as the first type of the learner, and the selected first type In response, introductory information for each type to introduce the first type, diagnostic information for each type to explain the learning tendency for the first type, and guide future learning strategies and action prescriptions for the first type. a control unit that generates customization information to which guide information for each type is mapped, and outputs a preset notification signal according to the generated customization information; and a notification unit provided in the first and second terminals to visually display the notification signal on an arbitrary screen according to the notification signal output from the control unit.

In addition, the learning analysis data divides the self-directed learning activity into a cognitive domain and a behavioral domain and assigns a score based on an arbitrary learning analysis standard model provided from the web or an external server, and according to the assigned score. It may be data on a leveled (graded) index, and the type selection data assigns the index for the cognitive domain and the index for the behavioral domain according to preset classification criteria, and each is different in combination according to the assignment. This may be data on multiple self-directed learning types with indices.

In addition, the self-directed learning type can be expressed as animal characters with representative characteristic points that are commonly recognized and associated with at least one observation element of appearance and behavior, and each of the self-directed learning types is the animal character. Each highlighted characteristic point can be configured by relating it to the combination index, and the highlighted characteristic point of each animal character can be reflected in the introduction information for each type.

In addition, the customizing information may be further mapped to roadmap information for displaying a roadmap in which the self-directed learning types are arranged in a random arrangement through the screen of the notification unit, and the roadmap information may be mapped to the roadmap on the roadmap. First roadmap information for displaying a path between the first type and the second type selected as the existing first type; And it may include second roadmap information for displaying a path on the roadmap starting from the first type to the third type with the highest combination index.

In addition, the path indicated by the second roadmap information may include a first optimal path that can reach the third type most quickly when an index improvement for the cognitive domain is preferred; and a second optimal path that can reach the third type most quickly when an index improvement for the action area is preferred.

In addition, the self-directed learning type can be expressed as nine animal characters, and the roadmap graphs the relationship between the index for the cognitive domain and the index for the behavioral domain, with the third type at the highest vertex. It can be displayed in the form of a square with a 3×3 array structure tilted to position.

Additionally, the learning analytics standard model may be the IMS Caliper Analytics standard of the IMS Global Learning Consortium, which defines arbitrary learning activities as behavior models, and the IMS Caliper Analytics standard may be applied to the learning event information.

Additionally, the control unit may include a data generation unit that converts the learning event information into data; a database unit that collects and records data of the learning event information generated by the data generation unit, and stores and manages type selection data predetermined by the second terminal; The learning analysis data is extracted based on the data of the learning event information recorded in the database unit, and the extracted learning analysis data is matched with the type selection data stored in the database unit to perform a plurality of self-directed learning. a data analysis unit that selects one of the types as the first type of the learner; and generate customization information to which the introduction information for each type, diagnosis information for each type, guide information for each type, and roadmap information corresponding to the first type selected by the data analysis unit are mapped, and customize information in advance according to the generated customization information. It may include a notification signal output unit that controls the output of the set notification signal.

The system for providing diagnosis and prescription services for each self-directed learning type according to the present invention makes the selection of various self-directed learning types and diagnosis and prescription according to type more interesting based on learning analysis data related to the learner's self-directed learning, Through a configuration that guides the recommended path to the final goal type with the highest index level among the index levels of the cognitive and behavioral domains, it can contribute to the activation of metacognition with improved intimacy and at the same time inspire the desire for learning. It works.

However, the effects that can be obtained from the present invention are not limited to the effects mentioned above, and other effects not mentioned above will be clearly understood by those skilled in the art from the description below. You will be able to.

1 is a diagram showing the external configuration and use example of a system for providing diagnosis and prescription services for each type of self-directed learning according to an embodiment of the present invention.
Figure 2 is a block diagram schematically showing the electronic configuration of the diagnosis and prescription service provision system for each self-directed learning type.
Figure 3 is a block diagram showing the configuration of the control unit in the system for providing diagnosis and prescription services for each self-directed learning type.
Figure 4 is an image illustrating a learning strategy and behavioral data-based model for measuring and identifying self-directed learning types in relation to learning event information and learning analysis data of the present invention.
Figure 5 is a usage example showing an example in which arbitrary guidance information is displayed in the notification section of the diagnosis and prescription service providing system for each self-directed learning type when learning event information of the present invention is not collected.
Figure 6 is an image showing an example of a roadmap in which self-directed learning types represented by 9 animal characters are arranged in a 3×3 array by applying a relationship graph between the index for the cognitive domain and the index for the behavioral domain.
Figure 7 is a table illustrating matching of learning analysis data and type selection data in relation to the present invention.
Figure 8 is a usage example showing an example in which customized information by the control unit of the diagnosis and prescription service providing system for each self-directed learning type is displayed in the notification unit.
Figure 9 is a usage example showing another example in which customized information by the control unit of the diagnosis and prescription service provision system for each self-directed learning type is displayed on the notification unit.
Figure 10 is a usage example showing another example in which customized information by the control unit of the diagnosis and prescription service providing system for each self-directed learning type is displayed in the notification unit.
Figure 11 is an image illustrating the actual application of the system for providing diagnosis and prescription services for each self-directed learning type according to Figure 1 through an example of its use.

Hereinafter, with reference to the attached drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the present invention. However, since the description of the present invention is only an example for structural or functional explanation, the scope of the present invention should not be construed as limited by the examples described in the text. In other words, since the embodiments can be modified in various ways and can have various forms, the scope of rights of the present invention should be understood to include equivalents that can realize the technical idea. In addition, the purpose or effect presented in the present invention does not mean that a specific embodiment must include all or only such effects, so the scope of the present invention should not be understood as limited thereby.

The meaning of terms described in the present invention should be understood as follows.

Terms such as “first” and “second” are used to distinguish one component from another component, and the scope of rights should not be limited by these terms. For example, a first component may be named a second component, and similarly, the second component may also be named a first component. When a component is referred to as being “connected” to another component, it should be understood that it may be directly connected to the other component, but that other components may also exist in between. On the other hand, when a component is referred to as being “directly connected” to another component, it should be understood that there are no other components in between. Meanwhile, other expressions that describe the relationship between components, such as "between" and "immediately between" or "neighboring" and "directly neighboring" should be interpreted similarly.

Singular expressions should be understood to include plural expressions, unless the context clearly indicates otherwise, and terms such as “comprise” or “have” refer to the specified features, numbers, steps, operations, components, parts, or them. It is intended to specify the existence of a combination, and should be understood as not excluding in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

All terms used herein, unless otherwise defined, have the same meaning as commonly understood by a person of ordinary skill in the field to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as consistent with the meaning they have in the context of the related technology, and cannot be interpreted as having an ideal or excessively formal meaning unless clearly defined in the present invention.

Figure 1 is a diagram showing the external configuration and use example of a diagnosis and prescription service provision system for each self-directed learning type according to an embodiment of the present invention, and Figure 2 shows the electronic configuration of the diagnosis and prescription service provision system for each self-directed learning type. It is a block diagram schematically shown, and Figure 3 is a block diagram showing the configuration of the control unit in the diagnosis and prescription service provision system for each type of self-directed learning, and Figure 4 is a block diagram showing the configuration of the control unit in relation to the learning event information and learning analysis data of the present invention. It is an image illustrating a learning strategy and behavioral data-based model for measuring and identifying learning types, and Figure 5 shows that when learning event information of the present invention is not collected, arbitrary guidance information is provided for diagnosis and diagnosis by self-directed learning type. It is an example usage diagram showing an example displayed in the notification section of the prescription service provision system, and Figure 6 is a self-directed learning type expressed in 9 animal characters by applying a relationship graph between the index for the cognitive domain and the index for the behavioral domain. This is an image illustrating a roadmap arranged in a 3×3 array, Figure 7 is a table illustrating the matching of learning analysis data and type selection data in relation to the present invention, and Figure 8 is a table showing the self-directed data. This is an example usage diagram showing an example in which customization information by the control unit of the diagnosis and prescription service provision system for each learning type is displayed in the notification unit, and Figure 9 shows customization information by the control unit of the diagnosis and prescription service provision system for each self-directed learning type. 10 is a usage example diagram showing another example displayed in the notification unit, and FIG. 10 is a usage example diagram showing another example in which customized information by the control unit of the diagnosis and prescription service providing system for each self-directed learning type is displayed in the notification unit. 11 is an image illustrating the actual application of the diagnosis and prescription service provision system for each self-directed learning type according to FIG. 1 through an example of its use.

As shown in Figures 1 and 2, the system 100 for providing diagnosis and prescription services for each self-directed learning type according to the present invention remotely connects the learner terminal and the teacher terminal to select the self-directed learning type in a non-face-to-face environment. In the system that provides diagnosis and prescription services by type, it includes a first terminal 110, a second terminal 120, a web 130, an external server 140, a control unit 150, and a notification unit 160. It can be configured as follows.

However, the components shown in FIGS. 1 and 2 are only the minimum configuration necessary for the present invention, and an evaluation service providing system with more additional components may be implemented.

The first terminal 110 is configured to include various known input devices (not shown) such as a keyboard, pad, mouse, etc. According to a preferred embodiment of the present invention, it is located at the learner (L) to use any learning tool. An input environment is provided that allows learning event information about the self-directed learning activities of the learner (L) to be input to the web 130, the external server 140, and the control unit 150.

At this time, the learning tool may be learning content supported by an external server 140, which will be described later, including various known learning servers, for example, AI currently being commercialized on the web 130 or an external server 140, which will be described later. The analysis service program "Homerun AI life record" can be applied, but it is not limited to this, and of course, various changes and designs can be made by those skilled in the relevant field within the technical scope of the present invention.

The Home Run AI life record is a learning analysis service provided by Ice Cream Edu Co., Ltd., and has been certified by the IMS Caliper Analytics 1.1 standard from the IMS (Instructional Management System) Global Learning Consortium, and includes the learner's (L) learning status, learning patterns, and study. This is a technology that analyzes habits, etc., summarizes them on a monthly basis, and provides visual data in the form of an e-portfolio to learners (L) and teachers (T).

The second terminal 120 is configured to enable remote communication with the above-described first terminal 110, and may have the same components as the first terminal 110. According to the present invention, the teacher ( It is located to T and provides an input environment where the teacher (T) can input type selection data, which will be described later, into the web 130, the external server 140, and the control unit 150.

Here, the first and second terminals 110 and 120 may be composed of a plurality of terminals, and are preferably tablets, but are not limited thereto and may be PCs, smartphones, etc.

Since the configuration of the first and second terminals 110 and 120 corresponds to a typical terminal device that is generally published, the detailed configuration is not shown in the drawings and a more detailed description thereof will be omitted.

The web 130 is an Internet service configured to support various multimedia such as text, graphics, images, sounds, and videos. According to the present invention, the web 130 is connected to the first and second terminals 110 and 120 to provide the first and second terminals 110 and 120. 2 It can be configured to provide various known educational content to learners (L) and teachers (T) who use the terminals 110 and 120, respectively.

The external server 140 is preferably configured to correspond to the above-described web 130 to form a network with the first and second terminals 110 and 120, the web 130, and the control unit 150, and the web It performs the function of servicing a preset program to the control unit 150 in conjunction with 130.

Here, the program may be an educational program composed of various data related to education, and as this is a known technology, it goes without saying that it can be freely changed and designed by a person skilled in the art.

The control unit 150 actively reacts according to the event information while forming an educational program corresponding to the web 130 and the external server 140, so that the learner (L) using the first terminal 110 can learn A learning management control server for supporting a teaching/instruction guide environment for a teacher (T) using the second terminal 120, preferably the first and second terminals 110 and 120. , It is desirable to establish a network with the web 130 and the external server 140 to communicate with each other.

According to the present invention, the control unit 150 obtains learning event information input from the first terminal 110 and served through the web 130 and the external server 140 and extracts arbitrary learning analysis data. And, the extracted learning analysis data is matched with pre-designated type selection data to select one type among a plurality of self-directed learning types as the first type of the learner, and in response to the selected first type, 8, introduction information (2) for each type to introduce the first type, diagnostic information (3) for each type to explain the learning tendency for the first type, and information for the first type. It generates customized information to which guide information (4) for each type is mapped to guide future learning strategies and action prescriptions, and performs the function of outputting a preset notification signal according to the generated customized information.

At this time, the learning analysis data divides the self-directed learning activity into a cognitive area related to learning strategy and a behavioral area related to effort, and gives a score based on an arbitrary learning analysis standard model provided from the web or external server, It may be data about an index leveled (grade) according to the assigned score, and the type selection data assigns the index for the cognitive domain and the index for the behavioral domain according to preset classification criteria, and the allocation It may be data on a plurality of self-directed learning types, each with a different combination index.

In addition, referring to FIG. 7, the self-directed learning type can be expressed as animal characters with representative characteristic points that are commonly recognized and associated with at least one observation element of appearance and behavior, and each of the self-directed learning types can be configured by associating the salient feature points of each of the animal characters with the combination index, and the salient feature points of each of the animal characters can be reflected in the introduction information 2 for each type of FIG. 8 .

In addition, the customizing information includes roadmap information 5 for displaying a roadmap in which the self-directed learning types are arranged in a random arrangement through the screen of the notification unit 160, which will be described later, with reference to FIGS. 8 to 10. It can be further mapped, and the roadmap information 5 includes first roadmap information 5_1 for displaying a path between a first type on the roadmap and a second type selected as the existing first type; And it may include second roadmap information (5_2) on the roadmap for displaying a path starting from the first type to the third type with the highest combination index, and the first roadmap information (5_1). ) may further include history information (5_3) to display detailed history.

Furthermore, the path indicated by the second roadmap information (5_2) includes a first optimal path (R1) that can reach the third type most quickly when an index improvement for the cognitive domain is preferred; And it may include a second optimal path (R2) that can reach the third type most quickly when an index improvement for the action area is preferred.

Here, the self-directed learning type can be more preferably expressed as nine animal characters, as shown in Figure 6, and the roadmap is the relationship between the index for the cognitive domain and the index for the behavioral domain. When graphing, the third type can be displayed in the form of a square with a 3×3 array structure tilted so that it is located at the highest vertex.

Meanwhile, according to the present invention, the learning analysis standard model is preferably the IMS Caliper Analytics standard of the IMS Global Learning Consortium, which defines arbitrary learning activities as behavior models.

More specifically, the IMS Caliper Analytics standard is a behavioral model for learning activities by the Instructional Management System Global Learning Consortium (IMS GLC), a non-profit international membership organization that defines international standards for e-learning systems. It is a standard that allows educational institutions to collect learning data from digital materials to better understand and visualize learning activities.

According to the present invention, the IMS Caliper Analytics standard can be applied to the learning event information.

Meanwhile, more specifically, with reference to FIG. 3, the control unit 150 includes a data generation unit 151 that converts the learning event information into data; a database unit 152 that collects and records data of the learning event information generated by the data generation unit, and stores and manages type selection data predetermined by the second terminal; The learning analysis data is extracted based on the data of the learning event information recorded in the database unit, and the extracted learning analysis data is matched with the type selection data stored in the database unit to perform a plurality of self-directed learning. a data analysis unit 153 that selects one of the types as the first type of the learner; and generate customizing information to which the type-specific introduction information, type-specific diagnosis information, type-specific guide information, and roadmap information corresponding to the first type selected by the data analysis unit are mapped, and customize information in advance according to the generated customization information. It may be configured to include a notification signal output unit 154 that controls the output of the set notification signal.

The notification unit 160 visually displays preset text and images on an arbitrary screen according to the notification signal output from the notification signal output unit 154 of the control unit 150. Preferably, the learner It is desirable to configure it to be provided in the first terminal 110 of (L) and the second terminal 120 of the teacher (T).

For example, according to the present invention, when the above-described learning event information is not collected, the notification unit 160 displays guidance information 1 indicating that 'collection of learning event information is necessary', as shown in FIG. It could be.

The notification unit 160 may be used for a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), or a flexible display. It may be configured to include at least one of a flexible display and a 3D display.

A detailed description of preferred embodiments of the invention disclosed above is provided to enable any person skilled in the art to make or practice the invention. Although the present invention has been described above with reference to preferred embodiments, those skilled in the art will understand that various modifications and changes can be made to the present invention without departing from the scope of the present invention. For example, a person skilled in the art may use each configuration described in the above-described embodiments by combining them with each other. Accordingly, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The present invention may be embodied in other specific forms without departing from the spirit and essential features of the present invention. Accordingly, the above detailed description should not be construed as restrictive in all respects and should be considered illustrative. The scope of the present invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention. The present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. In addition, claims that do not have an explicit reference relationship in the patent claims can be combined to form an embodiment or included as a new claim through amendment after filing.

100: Diagnosis and prescription service provision system for each type of self-directed learning
110: first terminal
120: second terminal
130: web
140: external server
150: control unit
151: data generation unit
152: database section
153: Data analysis department
154: Notification signal output unit
160: Notification unit
L: learner
T: teacher

Claims (8)

In a system that remotely connects the learner's first terminal and the teacher's second terminal to select the type of self-directed learning in a non-face-to-face environment and provides diagnosis and prescription services for each type,
While establishing and communicating with the first and second terminals and the web and external servers, learning event information about the learner's self-directed learning activities is acquired from the first terminal to extract arbitrary learning analysis data, and the extracted Learning analysis data is matched with pre-designated type selection data to select one type among a plurality of self-directed learning types as the first type of the learner, and in response to the selected first type, the first type Customized information in which introductory information by type to introduce the first type, diagnostic information by type to explain the learning tendency for the first type, and guide information by type to guide future learning strategies and action prescriptions for the first type are mapped. A control unit that generates and outputs a preset notification signal according to the generated customization information; and
A notification unit provided in the first and second terminals and visually displayed on an arbitrary screen according to a notification signal output from the control unit,
The learning analysis data is,
The self-directed learning activity is divided into a cognitive domain and a behavioral domain, a score is assigned based on an arbitrary learning analysis standard model provided from the web or an external server, and the index is leveled (graded) according to the assigned score. Characterized by being data,
The data for type selection is,
Characterized in that the index for the cognitive domain and the index for the behavioral domain are assigned according to preset classification criteria, and are data on a plurality of self-directed learning types each having a different combination index by the assignment,
The above self-directed learning type is,
Characterized by being expressed as animal characters with representative features that are commonly recognized and associated with at least one observation element of appearance and behavior,
Each of the above self-directed learning types is:
Characterized by constructing the salient feature points of each of the animal characters in relation to the combination index,
The salient features of each of the animal characters are,
Characterized by being reflected in the introduction information for each type,
The customization information is,
Through the screen of the notification unit, roadmap information is further mapped to display a roadmap in which the self-directed learning types are arranged in a random arrangement,
The above roadmap information is,
First roadmap information for displaying a path between the first type and a second type selected as the existing first type on the roadmap; and
Characterized in that the roadmap includes second roadmap information for displaying a path starting from the first type to the third type with the highest combination index,
The route indicated by the second roadmap information is,
a first optimal path that can reach the third type most quickly when an index improvement for the cognitive domain is preferred; and
Characterized by including a second optimal path that can reach the third type most quickly when an index improvement for the action area is preferred,
The above self-directed learning type is,
It is characterized by being expressed as 9 animal characters,
The roadmap is,
By type of self-directed learning, characterized in that the relationship between the index for the cognitive domain and the index for the behavioral domain is displayed in the form of a square in a 3×3 array structure tilted so that the third type is located at the highest vertex while graphing the relationship between the index for the cognitive domain and the index for the behavioral domain. Diagnosis and prescription service provision system.
delete delete delete delete delete According to claim 1,
The learning analysis standard model is,
It is characterized by the IMS Caliper Analytics standard of the IMS Global Learning Consortium, which defines arbitrary learning activities as behavioral models.
The IMS Caliper Analytics standard is,
A system for providing diagnosis and prescription services for each type of self-directed learning, characterized in that it is applied to the learning event information.
According to any one of claims 1 and 7,
The control unit,
a data generator that converts the learning event information into data;
a database unit that collects and records data of the learning event information generated by the data generation unit, and stores and manages type selection data predetermined by the second terminal;
The learning analysis data is extracted based on the data of the learning event information recorded in the database unit, and the extracted learning analysis data is matched with the type selection data stored in the database unit to perform a plurality of self-directed learning. a data analysis unit that selects one of the types as the first type of the learner; and
Generate customization information to which the introduction information for each type, diagnosis information for each type, guide information for each type, and roadmap information corresponding to the first type selected by the data analysis unit are mapped, and preset according to the generated customization information. A system for providing diagnosis and prescription services for each type of self-directed learning, comprising a notification signal output unit that controls the output of a notification signal.