WO2016125930A1

WO2016125930A1 - Learning activity analysis method and system

Info

Publication number: WO2016125930A1
Application number: PCT/KR2015/001150
Authority: WO
Inventors: 김태현; 정현옥; 성대훈
Original assignee: 주식회사 다우인큐브
Priority date: 2015-02-03
Filing date: 2015-02-04
Publication date: 2016-08-11
Also published as: KR20160095543A

Abstract

The present invention relates to a learning activity analysis method and system, and the method for analyzing a learning activity in the learning activity analysis system, in which a user terminal and an analysis server are connected through a network, comprises the steps of: configuring an evaluation index matrix for a learning activity analysis of learning content; selecting learning activity data corresponding to each item of the evaluation index matrix; and analyzing a learning activity of the learning content on the basis of the selected learning activity data.

Description

Learning Activity Analysis Method and System

The present invention relates to a learning activity analysis method and system that can apply different analysis criteria according to a user by utilizing a dynamic profile for learning analysis.

Electronic publications record and present information in a digitized form, including electronic books, digital magazines, digital catalogs, and digital textbooks. Such an electronic publication is generated as a packaged medium in which various kinds of contents such as text and images are considered in consideration of off-line.

Learning forms using these electronic publications are introduced as a form of electronic learning. In the case of learning using an electronic publication, the learner proceeds by playing a digital textbook using a viewer installed in a personal computer, a mobile communication terminal, an electronic dictionary, and a dedicated terminal.

At this time, the evaluation of the learner's learning activity provides a problem related to the learning content and checks the answer input by the learner to evaluate the learning ability. When evaluating learning activities, various evaluation criteria should be applied according to the school and subjects, and different evaluation criteria should be applied according to the teaching method of the instructor even in the same school subjects.

However, since the conventional learning activity analysis technique uses a fixed analysis algorithm based on a uniform analysis (evaluation) criterion, it is difficult to derive meaningful analysis results from the analysis data.

The present invention has been made to solve the above problems of the prior art, and to provide a learning activity analysis method and system to configure the evaluation indicator matrix to analyze the evaluation results according to the user's teaching method.

In addition, the present invention is to provide a learning activity analysis method and system that can modify a predetermined evaluation indicator matrix configuration according to the user's needs.

Learning activity analysis method according to an embodiment of the present invention for solving the above problems in the method for analyzing the learning activity in the learning activity analysis system connected to the user terminal and the analysis server network, the learning activity analysis of the learning content Comprising the step of forming the evaluation indicator matrix for, the learning activity data corresponding to each item of the evaluation indicator matrix, and analyzing the learning activity of the learning content based on the selected learning activity data Characterized in that.

The evaluation index matrix may be configured by user selection in the evaluation criteria candidate group DB.

In addition, the evaluation criteria candidate group DB, the learning participation time, the number of submissions, the number of questions pooled correct answers, characterized in that the evaluation criteria items of the number of questions during the class is stored.

The evaluation criteria item is characterized by being weighted by the user.

In addition, the learning activity data is characterized in that collected based on a measurement indicator matrix for measuring the learning activity.

On the other hand, the learning activity analysis system according to an embodiment of the present invention is a learning activity analysis system in which the user terminal and the analysis server is connected to the network, a user terminal and a user profile including a communication unit, a user input unit, a memory, an output unit, a control unit DB, evaluation criteria candidate group DB, an analysis server including a matrix generation unit for generating the evaluation indicator matrix, wherein the matrix generation unit configures the evaluation indicator matrix for learning activity analysis of learning content using the evaluation criteria candidate DB, And learning activity data corresponding to each item of the evaluation indicator matrix, and analyzing the learning activity based on the selected learning activity data.

In addition, the evaluation indicator matrix, characterized in that composed of one or more items selected by the user through the user terminal in the evaluation criteria candidate group DB.

In addition, the evaluation criteria candidate group DB, characterized in that the evaluation criteria items of the learning participation time, the number of submissions, the number of questions pooled correct answers, the number of questions during the class is stored.

The evaluation criteria item may be weighted according to a user input received from a user terminal.

As described above, according to the present invention, it is possible to analyze the evaluation result according to the teaching method of the user by configuring the evaluation indicator matrix, it can be used as a material for diagnosing the teaching and learning situation.

1 is a block diagram of a learning activity analysis system according to a preferred embodiment of the present invention.

2 is a block diagram of the user terminal shown in FIG.

Figure 3 is a database (DB) configuration diagram for learning activity data collection of the analysis server shown in FIG.

4 is a configuration diagram of a database for learning evaluation of the analysis server shown in FIG. 1.

5 is a flowchart illustrating a learning activity analysis method according to an embodiment of the present invention.

6 to 7 is an example for explaining the learning activity analysis process according to an embodiment of the present invention.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the following embodiments are provided to those skilled in the art to fully understand the present invention, and may be modified in various forms, and the scope of the present invention is limited to the embodiments described below. It doesn't happen.

As used herein, the terms "comprise", "comprise" and "have" mean that a component may be included unless specifically stated to the contrary, and thus, do not exclude other components. That means you can include more elements.

In addition, the terms “… unit”, “… unit”, “module”, and the like described herein refer to a unit that processes at least one function or operation, which may be implemented by hardware or software or a combination of hardware and software. Can be. Also, the articles “a”, “an” and “the” are intended to include both the singular and the plural, unless the context clearly dictates otherwise in the specification, or is clearly contradicted by the context. Can be used.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

As shown in FIG. 1, the learning activity analysis system includes a user terminal 100 and an analysis server 200 connected through a network. Here, the network may be implemented by communication technologies such as mobile communication and wired / wireless internet, short-range communication, and broadcast communication.

The user terminal 100 is a dedicated terminal on which an application, for example, a viewer or the like, which allows a user (a learner or an instructor) to read learning content (eg, a learning electronic document), may be installed and run. The user terminal 100 may include a tablet computer, a mobile terminal, a smart phone, a personal computer, a personal digital assistant, and an e-book reader. ), Navigation, or the like.

The user terminal 100 accesses the analysis server 200 through a wired or wireless communication network, searches for and downloads specific learning content according to a user's control command. The user terminal 100 stores the downloaded learning content and displays it on the screen.

In addition, the user terminal 100 may access a learning support system (not shown) to perform learning about learning content. The learning support system (not shown) includes an analysis server 200.

When learning the learning content, the user terminal 100 extracts data on the learning activity based on the learning activity measurement matrixes and transmits the data to the analysis server 200. In this case, the learning activity measurement index matrix may be provided from the analysis server 200.

The user terminal 100 connects to the analysis server 200 according to a user input to configure a learning activity evaluation index matrix for each learning content. In other words, when the user is an instructor, the user terminal 100 configures the learning activity evaluation criteria for evaluating the learning activity according to the teaching activity as an evaluation index matrix according to the user input. The evaluation indicator matrix can be modified by the user.

The analysis server 200 provides an electronic document (electronic publication) for learning such as an electronic book, a digital magazine, a digital textbook, and the like. When the user terminal 100 requests the download of specific learning content, the analysis server 200 transmits the corresponding learning content to the user terminal 100. In this case, the analysis server 200 may search for and read the learning content selected by the user after the user authentication for the user terminal 100 from a learning content database (not shown) and transmit the same to the user terminal 100.

The analysis server 200 collects learning activity data generated in the learner's learning process based on the measurement matrix. The analysis server 200 selects learning activity data from the learning activity data collected based on the evaluation index matrix. The analysis server 200 evaluates and analyzes the learning activity using the selected learning activity data. At this time, the analysis server 200 analyzes the learning activity for each learner.

FIG. 2 is a block diagram of the user terminal illustrated in FIG. 1.

Referring to FIG. 2, the user terminal 100 includes a communication unit 110, a user input unit 120, a memory 130, an output unit 140, and a controller 150.

The communication unit 110 transmits and receives an electronic document and various contents (eg, learning content) by the user terminal 100 performing data communication with another user terminal 100 or the analysis server 200. The communication unit 100 may be implemented as a mobile communication module, a wired / wireless internet module, a short range communication module, a broadcast communication module, or the like.

The user input unit 120 generates input data for the user to control the operation of the terminal 100. The user input unit 120 may include a key pad, a dome switch, a touch pad, a jog wheel, a jog switch, and the like.

The memory 130 may store a program for controlling the operation of the user terminal 100, or may perform a function for temporarily storing input / output data. For example, the memory 130 stores learning content, a viewer application, and the like.

The output unit 140 is for outputting any one or more signals such as a text signal, an audio signal, an image signal, an alarm signal, a warning signal, and the like, and may include a display unit 141 and an audio output unit 143. .

The display unit 141 displays information processed by the user terminal 100. For example, the user terminal 100 displays a graphical user interface (GUI), an electronic document, content, and the like. Such displays include liquid crystal displays, thin film transistor-liquid crystal displays, organic light-emitting diodes, flexible displays, and 3D displays. ), A transparent display, a touch screen, and an electronic paper display (Electronic Paper Display, EPD) may include any one or more. Here, when the display unit 141 is implemented as a touch screen, the display unit 141 may also be used as an input device.

The sound output unit 143 outputs an audio signal, and outputs an audio signal (sound signal) related to a function performed in the user terminal 100. The sound output unit 143 may include a speaker, a buzzer, and the like.

The controller 150 controls the above-described components to control the overall operation of the user terminal 100. When the controller 150 operates as the learning content viewer, the controller 150 loads the learning content selected by the user from the memory 130 and displays the learning content on the screen of the display unit 141.

3 is a block diagram of a database (DB) for collecting learning activity data of the analysis server shown in FIG. 1.

The analysis server 200 collects learning activity data based on the measurement indicator matrix for collecting learning activity data shown in FIG. 3 and stores the learning activity data in a database DB. In this case, the analysis server 200 collects external data such as digital textbook service platform, learning community service platform, and various other services as well as internal data produced in the teaching and learning process by using the teaching / learning support platform. Learning activity metrics are broadly defined as task performance and platform operations.

Referring to Figure 3, the database for learning activity data (DB) is a digital textbook learning activity information DB (310), learning community activity information DB (320), dashboard activity information DB (330), external learning material information DB 340, curriculum and textbook information DB 350, and the like.

The digital learning activity information DB 310 stores information about digital textbook learning, and utilization information such as reading digital textbooks and page movement, media information such as video playback and audio playback, adding highlights, adding notes, setting bookmarks, and the like. Problem-solving information such as learning activity information, formative assessment and unit assessment.

The learning community activity information DB 320 stores information about a learning community activity of the user. Here, the learning community activity information includes class participation information (e.g., participation class, class writing, reply activity, etc.), network information (e.g. friend setting, reply activity, feed, etc.), learning activity information (e.g., Q & A registration and assignment submission). And the like).

The dashboard activity information DB 330 stores information related to the dashboard activity of the user. For example, the dashboard activity information includes dashboard utilization learning information including dashboard login history and utilization time log, and dashboard utilization recommendation information including content recommendation activity and learning tool recommendation activity.

The external learning material information DB 340 stores information on learning activities using the external learning material. That is, the external learning material information includes meta information such as external educational application and personal work content information and sharing / utilization frequency information.

Curriculum and textbook information DB 350 stores the curriculum information, textbook information, formation evaluation information. Curriculum information includes curriculum information, textbook information, textbook table of contents information, etc. Textbook information includes instructional information, instructional learning objective information by class, and linked instructional information by class. Contains information.

4 is a block diagram of a database for learning evaluation of the analysis server shown in FIG.

The database configuration for learning evaluation includes a user profile DB 410, evaluation criteria candidate group DB 420, analysis result DB 430.

The user profile DB 410 stores information about a user, and user personal information such as age, gender, and educational attainment of the user and learning history may be stored. The user's personal information may be an item directly input by the user when the user registers for the registration of the learning content course.

The evaluation criteria candidate group DB 420 stores various evaluation criteria items for constructing evaluation indicator metrics for learning evaluation according to learner's learning activities. The evaluation criteria items may include the time to participate in the study, the number of correct answering questions, the number of questions submitted, and the number of questions in class. The criteria items that make up the evaluation indicator metrics vary depending on the activity analysis objectives.

The analysis result DB 430 stores the evaluation result according to the evaluation indicator matrix and the learning activity analysis result based on the evaluation result. The analysis result DB 430 stores evaluation results such as creativity and logic.

Referring to FIG. 5, first, an evaluation index matrix for learning activity analysis is configured by the user terminal 100 in a learning activity analysis system in which the user terminal 100 and the analysis server 200 are connected to a network (S110). The evaluation index matrix may be configured according to a user selection in the evaluation candidate group DB 420 by the matrix generator. Evaluation Criteria Candidate DB includes evaluation criteria items such as learning time, number of assignments, correct answers, and number of questions in class. Since the evaluation indicator matrix is configured by user selection, users can learn according to their teaching methods. Evaluate the activity. In particular, the user may configure the evaluation index matrix by assigning a weight to each evaluation criteria item or determining the evaluation order according to the characteristics of the evaluation.

Next, the analysis server 200 selects the learning activity data corresponding to each item of the evaluation indicator matrix from the database storing the learning activity data (S120). Learning activity data is the data generated by the learner's learning activities based on the measurement indicator matrix.

Next, the analysis server 200 analyzes the learning activity based on the learning activity data selected based on the evaluation indicator matrix (S130). At this time, the analysis server 200 analyzes the learning activity for each learner using the user profile DB (410). The analysis server 200 provides the learner and instructor with the results of the learning activity analysis as audiovisual information.

6 to 7 are examples for explaining the learning activity analysis process according to an embodiment of the present invention.

As shown in FIG. 6, when the creativity evaluation is performed, the analysis server 200 checks whether the task evaluation of level 1 is included in the evaluation indicator matrix, and if it is an item, the aggressiveness of the task is determined. Evaluate and output phase or medium as an analysis result. On the other hand, the analysis server 200 outputs 'ha' as an analysis result after the evaluation of the task of the level 1 is not included in the evaluation index matrix through the aggressive evaluation of the task.

The analysis server 200 proceeds in the same manner as the learning activity analysis method for the level 2 learning activity analysis level 1.

Referring to FIG. 7, the analysis server 200 evaluates the learning participation time, the number of correct answers, and the number of questions based on a predetermined evaluation index matrix and analyzes the results of the analysis of the learning activity in order to perform the logic evaluation. Output

Claims

In the method of analyzing the learning activity in the learning activity analysis system networked by the user terminal and the analysis server,

A step of constructing an evaluation index matrix for learning activity analysis of learning content;

Selecting learning activity data corresponding to each item of the evaluation indicator matrix;

And analyzing the learning activity of the learning content based on the selected learning activity data.
The method of claim 1,

The evaluation indicator matrix is learning activity analysis method, characterized in that configured by the user selection in the evaluation criteria candidate group DB.
The method of claim 2,

In the evaluation criteria candidate group DB,

Learning activity analysis method characterized in that the evaluation criteria items of the learning participation time, the number of homework submissions, the number of correct answer questions, the number of questions during the class is stored.
The method of claim 3,

The evaluation criteria item,

Learning activity analysis method characterized in that the weight is given by the user.
The method of claim 1,

The learning activity data,

Learning activity analysis method characterized in that collected based on the measurement indicator matrix for measuring the learning activity.
In the learning activity analysis system in which a user terminal and an analysis server are connected to a network,

A user terminal including a communication unit, a user input unit, a memory, an output unit, a control unit,

Includes an analysis server including a user profile DB, evaluation criteria candidate group DB, the matrix generation unit for generating the evaluation indicator matrix,

When the matrix generator configures an evaluation index matrix for learning activity analysis of learning content using the evaluation criteria candidate group DB, the learning activity data corresponding to each item of the evaluation index matrix is selected, and the selected learning activity data. Learning activity analysis system, characterized in that for analyzing the learning activity based on.
The method of claim 6,

The evaluation indicator matrix,

Learning activity analysis system, characterized in that composed of one or more items selected by the user through the user terminal in the evaluation criteria candidate group DB.
The method of claim 7, wherein

In the evaluation criteria candidate group DB,

Learning activity analysis system, characterized in that the evaluation criteria items of the learning participation time, the number of homework submissions, the number of questions answered correctly, the number of questions in class.
The method of claim 8,

In the evaluation criteria item,

Learning activity analysis system, characterized in that the weight is given according to the user input input from the user terminal.
The method of claim 6,

The learning activity data,

Learning activity analysis system, characterized in that collected based on the measurement indicator matrix for measuring learning activity.