Method, device and system for providing previous and later capability assessment solutions for stepwise capability assessment and customized curriculum recommendation

Abstract

The invention provides a method, a device and a system for providing previous and later capability assessment solutions for gradual capability assessment and customized course recommendation. The method comprises the following steps: receiving a pre-competency diagnosis request from a first user terminal; providing the first diagnostic question to the terminal of the first user to diagnose a priori capability of the first user; generating a first diagnosis result of the previous ability of the first user according to the answer of the first user to the first diagnosis question; selecting a lecture required by the first user as a first recommended lecture according to a result of the first diagnosis; providing a first-period recommended lecture for the first user terminal; receiving a post-capability diagnosis request from the first user terminal; providing a second diagnostic problem for diagnosing post-capabilities of the first user to the first user terminal; generating a second diagnosis result according to the answer of the first user to the second diagnosis question, and diagnosing the post-ability of the first user according to the result; and selecting a lecture required by the first user as a second recommended course according to a result of the second diagnosis.

Description

TECHNICAL FIELD

[0001] The following embodiments relate to a technology for providing a pre-competency and post-competency diagnostic solution for a step-by-step competency assessment and customized course recommendation. BACKGROUND

[0002] In recent years, as the importance of competencies to job seekers and workers has been highlighted, interest in competency diagnosis has rapidly increased. Here, competency is an individual's inherent trait that leads to effective and excellent performance in a specific situation or job, and can be classified into various types of competencies such as leadership, communication, management awareness, document writing, and cognitive flexibility.

[0003] In other words, like a personality test, competency diagnosis can be performed to identify people's competencies through diagnosis results.

[0004] However, conventionally, competency diagnosis has only been used to identify people's competencies without providing additional services using diagnosis results.

[0005] Therefore, there is an increasing demand for competency diagnosis that utilizes diagnosis results to provide customized course recommendations and diagnostic solutions, and research into related technologies is needed.

[0006] PRIOR ART DOCUMENT

[0007] PATENT DOCUMENT

[0008] (Patent Document 1) Korean Registered Patent No. 10-2672162

[0009] (Patent Document 2) Korean Registered Patent No. 10-2610634

[0010] (Patent Document 3) Korean Registered Patent No. 10-2470007

[0011] (Patent Document 4) Korean Patent No. 10-2023-0057896 SUMMARY

[0012] Problems to be Solved by the Invention

[0013] According to one embodiment, an object is to provide a method, apparatus, and system for providing a pre-competency and post-competency diagnostic solution for a step-by-step competency assessment and customized course recommendation.

[0014] The object of the present invention is not limited to the above-mentioned object, and other unmentioned objects can be clearly understood from the following description.

[0015] Means for Solving the Problems

[0016] According to one embodiment, in the method for providing pre-competency and post-competency diagnosis solutions for step-by-step competency assessment and customized course recommendation, a pre-competency diagnosis request is received from a first user terminal by a device; a first diagnostic question is provided to the first user's terminal to diagnose the first user's prior competency; a first diagnostic result of the first user's prior competency is generated according to the first user's answer to the first diagnostic question; a lecture required by the first user is selected as a first recommended lecture according to the result of the first diagnosis; the first recommended lecture is provided to the first user terminal; a post-competency diagnosis request is received from the first user terminal; a second diagnostic question is provided to diagnose the first user's post-competency; a second diagnostic result is generated according to the first user's answer to the second diagnostic question, which diagnoses the first user's post-competency; a lecture required by the first user is selected as a second recommended lecture according to the result of the second diagnosis; and a method for providing pre- and post-competency diagnosis solutions for step-by-step competency assessment and customized course recommendation is provided, comprising the step of providing the second recommended lecture to the first user terminal.

[0017] The step of selecting a lecture required by the first user as the first recommended lecture is a step of encoding the first diagnostic result and generating a first input signal; the first input signal is input into a first AI model that has been trained to evaluate the user's competency level by analyzing the diagnostic result; when a first evaluation result evaluating the first user's competency level is generated by the first input signal, a first output signal indicating the first evaluation result is obtained; if the result of the first evaluation is confirmed by the first output signal, the competency rated as excellent is classified into a first group and the competency rated as insufficient is classified into a second group according to the result of the first evaluation; and the step of selecting the first recommended lecture can be included according to the classification results of the first group and the second group.

[0018] The step of selecting a lecture required by the first user as the second recommended lecture is a step of encoding the second diagnostic result and generating a second input signal; the second input signal is input into the first artificial intelligence model; when a second evaluation result evaluating the first user's competency level is generated by the second input signal, a second output signal indicating the second evaluation result is obtained; if the result of the second evaluation is confirmed by the second output signal, the competency rated as excellent is classified into a third group and the competency rated as insufficient is classified into a fourth group according to the result of the second evaluation; and the step of selecting the second recommended lecture can be included according to the classification results of the third group and the fourth group.

[0019] On the basis of the classification results of the first and second groups, the step of selecting the first recommended course is to confirm the number of the second group classified abilities as the number of the first competencies; if it is found that the number of the first competencies is zero, the ability with the lowest score of the second diagnostic problem evaluation among the first group classified abilities is determined as the first competency, and a lecture for developing the first competency is selected as the first recommended lecture; if it is confirmed that the number of the first competencies is one, the ability classified in the second group is determined as the second competency, and a lecture for developing the second competency is selected as the first recommended lecture; if it is confirmed that the number of the first competencies is greater than one, an important score of each ability of the first user is calculated according to the desired occupation of the first user, and the ability with the highest important score among the abilities classified in the second group is determined as a third competency, and a lecture for developing the third competency can be selected as the first recommended lecture.

[0020] According to the classification results of the third and fourth groups, the step of selecting the second recommended course is to confirm the number of the fourth group classified abilities as the number of the second competencies; if it is found that the number of the second competencies is zero, the ability with the lowest score of the second diagnostic problem evaluation among the abilities classified in the third group is determined as a fourth competency, and a lecture for developing the fourth competency is selected as the second recommended lecture; if it is confirmed that the number of the second competencies is one, the ability classified in the fourth group is determined as a fifth competency, and a lecture for developing the fifth competency is selected as the second recommended lecture; if it is confirmed that the number of the second competencies is greater than one, an attendance score of each ability of the first user is calculated according to the attendance history of the first user, and the ability with the lowest attendance score among the abilities classified in the fourth group is determined as a sixth competency, and a lecture for developing the sixth competency is selected as the second recommended lecture.

[0021] Inventive Effects

[0022] According to one embodiment, by providing a competency pre-post diagnosis solution for stepwise competency assessment and customized course recommendation, there is an effect of providing a customized training course by recommending competency assessment and customized lectures for each stage through competency pre-diagnosis and competency post-diagnosis.

[0023] On the other hand, the effects of the embodiments are not limited to the above-described effects, and those skilled in the art can clearly understand other effects not mentioned from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 FIG. 1 is a diagram summarizing a system configuration according to a diaphragm.

[0025] Figure 2 FIG. 5 is a flowchart for explaining a process of providing a pre-post competency diagnosis solution according to a daily embodiment for stepwise competency assessment and customized course recommendation.

[0026] Figure 3 FIG. 1 is a flowchart illustrating a process of selecting a first recommended lecture according to an evaluation result of each ability level according to an exemplary embodiment.

[0027] Figure 4 FIG. 2 is a flowchart illustrating a process of selecting a second recommended lecture according to an evaluation result of each ability level according to an exemplary embodiment.

[0028] Figure 5 FIG. 3 is a flowchart illustrating a process of selecting a first recommended lecture according to a result of classification by ability according to an exemplary embodiment.

[0029] Figure 6 FIG. 4 is a flowchart illustrating a process of selecting a second recommended lecture according to a result of classification by ability according to an exemplary embodiment.

[0030] Figure 7 FIG. 5 is a flowchart illustrating a process of providing a first recommended lecture according to an exemplary embodiment.

[0031] Figure 8 FIG. 6 is a flowchart illustrating a process of providing a second recommended lecture according to an exemplary embodiment. DETAILED DESCRIPTION

[0032] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the embodiments can be modified in various ways, and the scope of the patent application is not limited or restricted by the embodiments. Any modification, equivalence, or substitution of the embodiments should be understood to be included in the scope of the claims.

[0033] The detailed structure or function description of the embodiments is for illustrative purposes only, and can be changed and implemented in various forms. Therefore, the embodiments are not limited to the specific form disclosed, and the scope of the specification includes changes, unifications, or substitutions included in the descriptive idea.

[0034] Terms such as "first" or "second" can be used to describe various components, but the interpretation of these terms should be used only to distinguish one component from another component. For example, a first component can be named a second component, and likewise, a second component can be named a first component.

[0035] When a component is referred to as being "connected" to another component, it should be understood that it can be directly connected to the other component or connected to the other component with another component therebetween.

[0036] The terms used in the embodiments are used only for illustrative purposes and should not be construed as intentionally limiting. The singular expression includes the plural expression, unless the context clearly indicates otherwise. In the present specification, the terms "include" or "have" or the like are intended to designate the presence of the features, numbers, steps, actions, components, parts or combinations thereof, and should not be construed as excluding the possibility of existence or addition of the other features or numbers, steps, actions, components, parts or combinations thereof.

[0037] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant description, and should not be interpreted in an idealistic or overly formal sense.

[0038] In addition, in describing the drawings, the same reference numerals are given to the same parts regardless of the drawing codes, and the same repeated description is omitted. In describing the embodiments, if it is judged that a detailed description of the related notification technology can unnecessarily obscure the gist of the embodiments, detailed explanation should be omitted.

[0039] The embodiments can be implemented in various types of products, including personal computers, notebook computers, tablet computers, smartphones, televisions, smart home appliances, smart cars, self-service terminals, and wearable devices.

[0040] In the embodiments, an artificial intelligence (AI) system is a computer system that realizes human-level intelligence, unlike existing rule-based intelligent systems, and is a system that learns by itself and makes judgments. As the recognition rate of the artificial intelligence system improves and the understanding of user preferences becomes more accurate, existing rule-based intelligent systems are gradually being replaced by artificial intelligence systems based on deep learning.

[0041] The artificial intelligence technology is composed of machine learning and element technology using machine learning. Machine learning is an algorithm technology that classifies / learns the characteristics of input data by itself, and element technology is a technology that simulates functions such as human brain cognition and judgment using machine learning algorithms such as deep learning, and is composed of technical fields such as language understanding, visual understanding, reasoning / prediction, knowledge representation, and motion control.

[0042] Various fields to which artificial intelligence technology is applied are as follows. Language understanding is a technology to recognize, adapt, and process human language / text, including natural language processing, machine translation, dialog systems, question answering, and speech recognition / synthesis. Visual understanding is a technology to recognize and process objects like human vision, including object recognition, object tracking, image search, human identification, scene understanding, spatial understanding, and image improvement. Reasoning / prediction is a technology to logically infer and predict information by judging information, including knowledge / probability-based reasoning, optimization prediction, preference-based planning, and recommendation. Knowledge representation is a technology to automatically process human experience information into knowledge data, including knowledge building (data generation / classification) and knowledge management (data utilization). Motion control is a technology to control autonomous driving of vehicles and motion of robots, including motion control (navigation, collision, driving) and operation control (behavior control).

[0043] In general, in order to apply a machine learning algorithm to real life, training is performed in a trial-and-error manner due to the nature of the basic method of machine learning. In particular, deep learning requires hundreds of thousands of iterations. Since this is not possible in an actual physical external environment, the actual physical external environment is virtually implemented on a computer and learns through simulation.

[0044] Figure 1 is a diagram that outlines a system configuration according to a septum.

[0045] Referring to Figure 1 A system according to an embodiment can include a plurality of user terminals 100 and a device 200, which can communicate with each other through a communication network.

[0046] First, a communication network can be configured regardless of a communication method, such as wired and wireless, and can be implemented in various forms, thereby enabling communication between servers and communication between servers and terminals.

[0047] The plurality of user terminals 100 are terminals used by students and can include a first user terminal 110 used by a first user, a second user terminal 120 used by a second user, and the like.

[0048] Each of the plurality of user terminals 100 can be configured to perform all or part of a computing function, a storage / referencing function, an input / output function, and a control function of a general computer. The plurality of user terminals 100 can be configured to perform wired or wireless communication with the device 200.

[0049] Each of the plurality of user terminals 100 accesses a web page operated by an individual or an organization that uses the device 200 to provide services, or a web page developed by an individual or an organization that uses the device 200 to provide services. A deployed application can be installed. Each of the plurality of user terminals 100 can be linked to the device 200 through the web page or the application.

[0050] Each of the plurality of user terminals 100 can access the device 200 through the web page or the application provided by the device 200.

[0051] For convenience of explanation hereinafter, the operation of the first user terminal 110 is mainly explained, but of course the operation of the first user terminal 110 can be performed in other user terminals such as the second user terminal 120.

[0052] The device 200 can be its own server, owned by an individual or an organization that uses the device 200 to provide services, or it can be a cloud server, or it can be a point-to-point (P2P) distributed node set. The device 200 can be configured to perform all or part of the computing function, storage / reference function, input / output function, and control function of a general-purpose computer. The device 200 can be equipped with at least one artificial intelligence model that performs an inference function.

[0053] The device 200 can be configured to communicate with a plurality of user terminals 100 in a wired or wireless manner, and can control the operation of each of the plurality of user terminals 100 and control which information is displayed on each screen of the plurality of user terminals 100.

[0054] On the other hand, for convenience of explanation, in Figure 1 , only the first user terminal 110 and the second user terminal 120 among the plurality of user terminals 100 are shown, but the number of terminals can vary depending on the embodiment. There is no particular limitation on the number of terminals as long as the processing capacity of the device 200 allows.

[0055] According to one embodiment, the device 200 can gradually perform competency assessment according to time through pre-diagnosis of competency and post-diagnosis of competency, and recommend a customized course through the competency assessment result, and a detailed description thereof will be described later with reference to Figure 2 .

[0056] According to one embodiment, the device 200 can assess the user's ability level based on artificial intelligence, and a detailed description thereof will be described later with reference to Figures 3-4 . To this end, the device 200 can include a number of pre-trained AI models for performing a machine learning algorithm.

[0057] In the present disclosure, artificial intelligence (AI) refers to a technology that imitates learning, reasoning, and perception capabilities of a human being and implements them in a computer, and can include concepts such as machine learning and symbolic logic. Machine learning (ML) is an algorithmic technique that classifies or learns features of input data by itself. The artificial intelligence technology is a machine learning algorithm that analyzes input data, learns the result of the analysis, and can make a judgment or prediction based on the learned result. In addition, a technology that uses a machine learning algorithm to imitate functions of a human brain, such as cognition and judgment, can also be understood as a scope of artificial intelligence. For example, it can include technical fields such as language understanding, visual understanding, reasoning / prediction, knowledge representation, and motion control.

[0058] Machine learning can refer to a process of training a neural network model using experience of processing data. Machine learning can mean that computer software can improve its ability to process data by itself. The neural network model is constructed by modeling correlations between data, which can be expressed in a plurality of parameters. The neural network model extracts features from given data, analyzes them, and derives correlations between the data, and it can be said that machine learning repeats this process to optimize the parameters of the neural network model. For example, the neural network model can learn a mapping (correlation) between input and output given as an input / output pair. Or, the neural network model can derive regularity between a given data set and learn these relationships even if only input data is given.

[0059] The artificial intelligence learning model or the neural network model can be designed to replicate the structure of the human brain on a computer and can include a plurality of network nodes that simulate and weight neurons of a human neural network. The plurality of network nodes can simulate synaptic activity in which neurons transmit and receive signals through synapses and are connected to each other. In the AI learning model, the plurality of network nodes can be located in layers at different depths and transmit and receive data according to convolutional connections thereof. For example, the AI learning model can be an artificial neural network, a convolutional neural network (CNN), or the like. As an embodiment, the AI learning model can be subjected to machine learning according to methods such as supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms for performing machine learning can include decision trees, Bayesian networks, support vector machines, artificial neural networks, Ada-boost, perceptrons, genetic programming, and clustering.

[0060] A CNN is a type of multilayer perceptron designed to use minimal preprocessing. A CNN consists of one or more convolutional layers and typical neural network layers on top of it with additional weight and pooling layers. Due to this structure, CNNs can make full use of input data from two-dimensional structures. CNNs perform well in both video and audio domains compared to other deep learning structures. CNNs can also be trained through standard backpropagation. CNNs are easier to train than other feedforward neural network techniques and have the advantage of using fewer parameters.

[0061] Convolutional networks are neural networks that contain a set of nodes with tied parameters. The increase in the size of available training data and the availability of computing power, coupled with advances in algorithms such as discriminative linear units and dropout training, have greatly improved many computer vision tasks. In large data sets, such as the ones currently available for many tasks, overfitting is not a concern, and increasing the size of the network can improve test accuracy. The optimal use of computing resources is a limiting factor. For this, decentralized, scalable deep neural network implementations can be used.

Claims

1. A method for providing pre- and post-competency assessment solutions for stepwise competency assessment and customized curriculum recommendation, in terms of how to provide pre- and post-competency diagnostic solutions for stepwise competency assessment and customized curriculum recommendation, both performed by a device, wherein, The method includes the following steps: Receive a pre-competence diagnostic request from the first user terminal; Provide the first diagnostic problem to the first user's terminal to diagnose the first user's prior capabilities; Based on the first user's answer to the first diagnostic question, generate the first diagnostic result of the first user's previous capabilities; Based on the results of the first diagnosis, select the lecture needed by the first user as the first recommended lecture stage; Provide the first recommended lecture for the first user terminal; Receive capability diagnostic request from the first user terminal; Provide a second diagnostic question for diagnosing the first user's post-user terminal capabilities; Based on the first user's answer to the second diagnostic question, a second diagnostic result is generated, which diagnoses the first user's subsequent abilities. Based on the results of the second diagnosis, the lectures required by the first user were selected as the stage for the second recommended course; and A second recommended lecture will be provided to the first user terminal.

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