KR20160095543A - Method and system for analyzing learning activities - Google Patents

Method and system for analyzing learning activities Download PDF

Info

Publication number
KR20160095543A
KR20160095543A KR1020150016939A KR20150016939A KR20160095543A KR 20160095543 A KR20160095543 A KR 20160095543A KR 1020150016939 A KR1020150016939 A KR 1020150016939A KR 20150016939 A KR20150016939 A KR 20150016939A KR 20160095543 A KR20160095543 A KR 20160095543A
Authority
KR
South Korea
Prior art keywords
learning
evaluation
learning activity
user
user terminal
Prior art date
Application number
KR1020150016939A
Other languages
Korean (ko)
Inventor
김태현
정현옥
성대훈
Original Assignee
주식회사 다우인큐브
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 주식회사 다우인큐브 filed Critical 주식회사 다우인큐브
Priority to KR1020150016939A priority Critical patent/KR20160095543A/en
Priority to PCT/KR2015/001150 priority patent/WO2016125930A1/en
Publication of KR20160095543A publication Critical patent/KR20160095543A/en

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education

Abstract

The present invention relates to a learning activity analyzing method and a system thereof. The method analyzes a learning activity in the learning activity analyzing system in which a user terminal and an analysis server are connected through a network. The method comprises the following steps of: configuring an evaluation index matrix for a learning activity analysis of learning content; selecting learning activity data in correspondence with each item of the evaluation index matrix; and analyzing a learning activity of the learning content based on the selected learning activity data. Therefore, the method can modify a preset evaluation index matrix configuration in accordance with a need of a user.

Description

[0001] METHOD AND SYSTEM FOR ANALYZING LEARNING ACTIVITIES [0002]

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

Electronic publications are electronic books, digital magazines, digital catalogs, and digital textbooks that record and display information in a digitized form. These electronic publications are created as media on which various kinds of contents such as text and images are packaged in consideration of off-line.

This type of learning using electronic publications is introduced as a form of e-learning. In the case of learning using electronic publications, a learner reproduces a digital textbook using a viewer installed in a personal computer, a mobile communication terminal, an electronic dictionary, and a dedicated terminal to conduct learning.

At this time, the evaluation of the learner 's learning activities provides problems related to the contents of the learning and evaluates the learning ability by checking the answers inputted by the learner. In evaluating these learning activities, various evaluation criteria must be applied according to the school, subject, etc., and other evaluation standards should be applied according to the teaching method of the professor even in the same school subject.

However, conventional learning activity analysis techniques use fixed analysis algorithms based on uniformized analysis (evaluation) criteria, so it is difficult to derive meaningful analysis results from the analysis data.

SUMMARY OF THE INVENTION The present invention has been made in order to solve the problems of the prior art described above, and it is an object of the present invention to provide a learning activity analysis method and system that can constitute an evaluation index matrix and analyze an evaluation result according to a teaching method of a user.

The present invention also provides a learning activity analysis method and system that can modify a predetermined evaluation index matrix configuration according to a user's need.

According to an embodiment of the present invention, there is provided a method of analyzing a learning activity in a learning activity analysis system connected to a network by a user terminal and an analysis server, And a step of analyzing the learning activity of the learning contents based on the selected learning activity data, and a step of analyzing the learning activity of the learning contents based on the selected learning activity data .

Further, the evaluation index matrix is characterized by being selected by the user in the evaluation criteria candidate group DB.

In addition, the evaluation reference candidate group DB stores the evaluation reference items of the learning participation time, the number of assignments, the number of answer correct answers, and the number of questions in class.

The evaluation criterion item is characterized in that a weight value is given by the user.

Further, the learning activity data is collected based on a metric index matrix for measuring the learning activity.

 Meanwhile, a learning activity analysis system according to an embodiment of the present invention is a learning activity analysis system in which a user terminal and an analysis server are connected to each other via a network. The analysis system includes a communication unit, a user input unit, a memory, an output unit, DB, an evaluation criteria candidate group DB, and an evaluation index matrix. If the evaluation index matrix for analyzing the learning activities of the learning contents is configured using the matrix generation unit evaluation criteria candidate group DB, The learning activity data corresponding to each item of the evaluation index matrix is selected and the learning activity is analyzed based on the selected learning activity data.

The evaluation index matrix may include at least one item selected by a user through the user terminal in the evaluation criteria candidate group DB.

In addition, the evaluation reference candidate group DB stores the evaluation reference items of the learning participation time, the number of assignments, the number of answer correct answers, and the number of questions in class.

The evaluation criteria items are weighted according to user inputs inputted from the user terminal.

Further, the learning activity data is collected based on a metric index matrix for measuring the learning activity.

As described above, according to the present invention, since the evaluation index matrix can be configured to analyze the evaluation result according to the teaching method of the user, it can be utilized as the data 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;
FIG. 2 is a block diagram of the user terminal shown in FIG. 1. FIG.
FIG. 3 is a block diagram of a database (DB) for collecting learning activity data of the analysis server shown in FIG. 1;
FIG. 4 is a block diagram of a database for learning evaluation of the analysis server shown in FIG. 1; FIG.
5 is a flowchart illustrating a method of analyzing a learning activity according to an embodiment of the present invention.
6 to 7 are diagrams for explaining a learning activity analysis process according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, it should be understood that the following embodiments are provided so that those skilled in the art will be able to fully understand the present invention, and that various modifications may be made without departing from the scope of the present invention. It is not.

The terms "comprises", "comprising", "having", and the like are used herein to mean that a component can be implanted unless otherwise specifically stated, Quot; element ".

Also, the terms " part, "" module, "and" module ", as used herein, refer to a unit that processes at least one function or operation and may be implemented as hardware or software or a combination of hardware and software . It is also to be understood that the articles "a", "an", "an" and "the" Can be used.

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

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

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

The user terminal 100 is a dedicated terminal through which an application, such as a viewer, can be installed and operated so that a user (learner or a teacher) can read learning contents (e.g., a learning electronic document). Such a user terminal 100 may be a tablet computer, a mobile terminal, a smart phone, a personal computer and a personal digital assistant (PDA), an e-book reader ), Navigation, and the like.

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

Also, the user terminal 100 may access the learning support system (not shown) to perform learning on the learning contents. The learning support system (not shown) includes an analysis server 200.

The user terminal 100 extracts data on a learning activity based on learning activity measurement index metrics and transmits the data to the analysis server 200 when the learning contents are learned. At this time, the learning activity measurement index matrix may be provided from the analysis server 200.

The user terminal 100 accesses the analysis server 200 according to a user input, and constructs a learning activity evaluation index matrix for each learning content. In other words, when the user is a teacher, the user terminal 100 constructs an evaluation index matrix as a learning activity evaluation standard for evaluating learning activities according to teaching activities according to user input. The metrics metrics can be modified by the user.

The analysis server 200 provides electronic documents (electronic publications) for learning such as electronic books, digital magazines, and digital textbooks. The analysis server 200 transmits the learning content to the user terminal 100 when the user terminal 100 requests download of the specific learning content. At this time, the analysis server 200 may retrieve and read the learning content selected by the user from the learning content database (not shown) after user authentication to the user terminal 100, and transmit the retrieved learning content to the user terminal 100.

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

Fig. 2 shows a block diagram of the user terminal shown in 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 (e.g., learning contents) by performing data communication with the user terminal 100 or the analysis server 200 by the user terminal 100. [ The communication unit 100 may be implemented as a mobile communication module, a wired / wireless Internet module, a short distance communication module, a broadcasting communication module, or the like.

The user input unit 120 generates input data for controlling the operation of the terminal 100 by a user. 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 and viewer applications and the like.

The output unit 140 outputs one or more signals such as a text signal, an audio signal, a video signal, an alarm signal, and a warning signal, and may include a display unit 141 and an audio output unit 143 .

The display unit 141 displays information to be processed by the user terminal 100. For example, the user terminal 100 displays a GUI (Graphic User Interface), an electronic document, contents, and the like. Such a display device may be a liquid crystal display, a thin film transistor-liquid crystal display, an organic light-emitting diode, a flexible display, a 3D display ), A transparent display, a touch screen, and an electronic paper display (EPD). Here, when the display unit 141 is implemented as a touch screen, the display unit 141 may 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 by the user terminal 100. The sound output unit 143 may include a speaker, a buzzer, and the like.

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

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

The analysis server 200 collects the learning activity data based on the metric index matrix for collecting the learning activity data shown in FIG. 3 and stores the collected data in a database (DB). At this time, the analysis server 200 utilizes a teaching / learning support platform to collect external data such as a digital textbook service platform, a learning community service platform, and various other services as well as internal data produced in teaching and learning processes. Learning activity metrics are broadly defined as task performance and platform operation processes.

3, a database (DB) for collecting learning activity data includes a digital textbook learning activity information database 310, a learning community activity information DB 320, a dashboard activity information DB 330, (340), a curriculum and a textbook information DB (350), and the like.

Information on digital textbook learning is stored in the digital learning activity information DB 310. The digital learning activity information DB 310 includes information on utilization such as reading a digital textbook and moving a page, media information such as a moving picture playing and audio playing, adding a highlight, Such as learning activity information, formative evaluation, and unit-by-unit evaluation of the learning result.

The learning community activity information DB 320 stores information about the 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, Etc.).

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

The external learning data information DB 340 stores information about learning activities using external learning data. That is, the external learning data information includes meta information such as an external educational application and personal authoring content information, and sharing / utilization frequency information.

The curriculum and textbook information DB 350 stores curriculum information, textbook information, and formation evaluation information. The curriculum information includes the curriculum information, the textbook information, and the contents information of the textbook, and the textbook information includes the time information, the target information for each hour, and the learning information for each hour. The formation evaluation information includes the textbook formation evaluation meta information, Information.

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

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

The user profile database 410 stores information about a user, and may store user personal information such as age and gender of a user, education level, and learning history. The user's personal information may be an item directly input by the user at the time of registering the learning content registration.

The evaluation criteria candidate group DB 420 stores various evaluation criteria items for constructing the evaluation index metrics for the learning evaluation according to the learning activities of the learner. Evaluation criteria items may include time for participation in learning, number of correct answers, number of assignments, number of questions during class, and so on. The evaluation criterion items that constitute the evaluation index metrics depend on the purpose of analyzing the learning activities.

As a result of the analysis, the DB 430 stores the evaluation results based on the evaluation index metrics and the analysis results of the learning activities based on the evaluation results. As a result of the analysis, the DB 430 stores the evaluation results such as creativity and logic.

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

5, an evaluation index matrix for analyzing learning activities is configured by the user terminal 100 in a learning activity analysis system connected to the user terminal 100 and the analysis server 200 via a network (S110). The evaluation index matrix may be configured in accordance with user selection in the evaluation criteria candidate group DB 420 by the matrix generation unit. The evaluation criteria candidate DB includes evaluation criteria items such as learning participation time, the number of assignments, the number of correct answers, and the number of questions in the class. The user selects the evaluation index matrix, Activity can be assessed. In particular, the user can assign a weight to each evaluation criterion item according to the characteristics of the evaluation, or form an evaluation index matrix by setting the evaluation order.

Next, the analysis server 200 selects the corresponding learning activity data for each item of the evaluation index matrix from the database storing the learning activity data (S120). The learning activity data is a measure of the data generated by the learners' learning activities based on the metric metrics.

Next, the analysis server 200 analyzes the learning activity based on the selected learning activity data based on the evaluation index 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 learning activity analysis result to the learner and the instructor as audiovisual information.

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

6, in the case of performing the creativity evaluation, the analysis server 200 confirms whether the task evaluation of level 1 (level 1) is an item included in the evaluation index matrix, and if it is the item, And outputs an image or an image as an analysis result. On the other hand, if the task evaluation of level 1 is not an item included in the evaluation index matrix, the analysis server 200 outputs a result of analysis through the aggressiveness evaluation of the task and the " lower "

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

Referring to FIG. 7, the analysis server 200 evaluates the participation participation time, the correct answer number, and the question number based on the predetermined evaluation index matrix in order to perform logical evaluation, analyzes the evaluation result, Output.

100: user terminal 110:
120: user input unit 130: memory
140: output unit 141: display unit
143: Acoustic output unit 150:
200: Analysis server 410: User profile DB
420: Evaluation standard candidate group DB 430: Analysis result DB

Claims (10)

A method for analyzing a learning activity in a learning activity analysis system connected to a network by a user terminal and an analysis server,
A step of constructing an evaluation index matrix for analyzing learning activities of learning contents,
Selecting learning activity data corresponding to each item of the evaluation index matrix;
And analyzing the learning activity of the learning content based on the selected learning activity data.
The method according to claim 1,
Wherein the evaluation index matrix is configured by user selection in an evaluation criteria candidate group DB.
3. The method of claim 2,
In the evaluation criterion group DB,
Wherein the evaluation items of the learning participation time, the number of assignments, the number of correct answers, and the number of questions in the class are stored.
The method of claim 3,
The evaluation criterion item includes:
And a weight is assigned by a user.
The method according to claim 1,
Wherein the learning activity data comprises:
Wherein the learning activity is collected based on a metric matrix for measuring learning activities.
A learning activity analysis system connected to a user terminal and an analysis server via a network,
A user terminal including a communication unit, a user input unit, a memory, an output unit, and a control unit,
A user profile DB, an evaluation criterion group DB, and a metric generating unit for generating an evaluation metric matrix,
When the matrix generator generates the evaluation index matrix for analyzing the learning activities of the learning contents 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 And analyzing the learning activity based on the learning activity.
The method according to claim 6,
Wherein the evaluation index matrix comprises:
And one or more items selected by a user through the user terminal in the evaluation criterion group DB.
8. The method of claim 7,
In the evaluation criterion group DB,
Wherein the evaluation items of the learning participation time, the number of assignments, the number of correct answers, and the number of questions in the class are stored.
9. The method of claim 8,
In the evaluation criteria item,
And a weight is assigned according to a user input inputted from a user terminal.
The method according to claim 6,
Wherein the learning activity data comprises:
Wherein the learning activity is collected based on a metric matrix for measuring learning activities.
KR1020150016939A 2015-02-03 2015-02-03 Method and system for analyzing learning activities KR20160095543A (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
KR1020150016939A KR20160095543A (en) 2015-02-03 2015-02-03 Method and system for analyzing learning activities
PCT/KR2015/001150 WO2016125930A1 (en) 2015-02-03 2015-02-04 Learning activity analysis method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
KR1020150016939A KR20160095543A (en) 2015-02-03 2015-02-03 Method and system for analyzing learning activities

Publications (1)

Publication Number Publication Date
KR20160095543A true KR20160095543A (en) 2016-08-11

Family

ID=56564258

Family Applications (1)

Application Number Title Priority Date Filing Date
KR1020150016939A KR20160095543A (en) 2015-02-03 2015-02-03 Method and system for analyzing learning activities

Country Status (2)

Country Link
KR (1) KR20160095543A (en)
WO (1) WO2016125930A1 (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107240046A (en) * 2017-04-28 2017-10-10 深圳前海易维教育科技有限公司 A kind of Learning behavior analyzing method and system
KR20180045202A (en) * 2016-10-25 2018-05-04 김경식 Smart electronic book platform system
KR102089725B1 (en) * 2018-09-28 2020-03-16 주식회사 또가배 Method and apparatus for mutual learning based on image using learning motivation index
KR102297708B1 (en) * 2020-11-26 2021-09-03 (주)웅진씽크빅 System and method for supporting a learning using handwriting recognition

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110674464B (en) * 2019-08-27 2023-04-18 湖南科技学院 Computer teaching rating system based on Internet of things

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20060117828A (en) * 2005-05-14 2006-11-17 인제대학교 산학협력단 The system which manages the process which appraise learning outcome based on the on-line network
KR100878588B1 (en) * 2007-03-16 2009-01-15 충북대학교 산학협력단 Information Competency Evaluation System and Method
KR20110062255A (en) * 2009-12-03 2011-06-10 한국전자통신연구원 Method and system for personalized learning
KR20120001987A (en) * 2010-06-30 2012-01-05 에스케이 텔레콤주식회사 Learning management service system and method thereof
US20140099624A1 (en) * 2012-05-16 2014-04-10 Age Of Learning, Inc. Mentor-tuned guided learning in online educational systems

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20180045202A (en) * 2016-10-25 2018-05-04 김경식 Smart electronic book platform system
CN107240046A (en) * 2017-04-28 2017-10-10 深圳前海易维教育科技有限公司 A kind of Learning behavior analyzing method and system
KR102089725B1 (en) * 2018-09-28 2020-03-16 주식회사 또가배 Method and apparatus for mutual learning based on image using learning motivation index
KR102297708B1 (en) * 2020-11-26 2021-09-03 (주)웅진씽크빅 System and method for supporting a learning using handwriting recognition

Also Published As

Publication number Publication date
WO2016125930A1 (en) 2016-08-11

Similar Documents

Publication Publication Date Title
Hsu Examining EFL teachers’ technological pedagogical content knowledge and the adoption of mobile-assisted language learning: A partial least square approach
Tortorella et al. Considering learning styles and context-awareness for mobile adaptive learning
Martin et al. Here and now mobile learning: An experimental study on the use of mobile technology
Ennouamani et al. A context-aware mobile learning system for adapting learning content and format of presentation: design, validation and evaluation
US8483606B2 (en) Automatic determination of user alignments and recommendations for electronic resources
US9575616B2 (en) Educator effectiveness
KR102013955B1 (en) Smart education system for software expert practical affairs education and estimation and method thereof
CN105184520A (en) Evaluation method and device for professional abilities of teachers
CN108664649B (en) Knowledge content pushing method and device and pushing server
US20190385471A1 (en) Assessment-based assignment of remediation and enhancement activities
KR20160095543A (en) Method and system for analyzing learning activities
Louhab et al. Considering mobile device constraints and context-awareness in adaptive mobile learning for flipped classroom
BR112020003468A2 (en) learning system with measurable progress based on assessment
Martínez-Torres et al. Identification of the design variables of eLearning tools
TWI598855B (en) Online teaching and action learning system
JP6855541B2 (en) Portable information processing device, test support system and test support method
CN111046852A (en) Personal learning path generation method, device and readable storage medium
Ahn et al. Exploring issues of implementation, equity, and student achievement with educational software in the DC public schools
US20150125844A1 (en) Server and method for managing learning
KR102343385B1 (en) Apparatus and Method for Managing Curriculum based on Job Competence which is corresponded to Society Demand based on ontology and deep-learning
CN112507679A (en) Method and device for automatically generating curriculum schedule, electronic equipment and storage medium
Yen et al. Systematic design an intelligent simulation training system: from learn-memorize perspective
KR20150129496A (en) System for providing personally custom made study service
Brata et al. An Idea of Interactive Japanese Language M-Learning Application to Support Listening and Speaking Exercise
Mittal et al. Development and validation of teachers mobile learning acceptance scale for higher education teachers

Legal Events

Date Code Title Description
A201 Request for examination
E902 Notification of reason for refusal
E601 Decision to refuse application