CN112506855A - Question bank construction method based on cognitive hierarchy - Google Patents
Question bank construction method based on cognitive hierarchyDownload PDFInfo
- Publication number
- CN112506855A CN112506855ACN202011551539.8ACN202011551539ACN112506855ACN 112506855 ACN112506855 ACN 112506855ACN 202011551539 ACN202011551539 ACN 202011551539ACN 112506855 ACN112506855 ACN 112506855A
- Authority
- CN
- China
- Prior art keywords
- exercise
- question bank
- grade
- equal
- level
- Prior art date
- Legal status (The legal status 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 status listed.)
- Pending
Links
Images
Classifications
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/11—File system administration, e.g. details of archiving or snapshots
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
- G09B7/04—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Educational Administration (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Educational Technology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
Technical Field
The invention relates to the field of big data, in particular to a question bank construction method based on cognitive hierarchy.
Background
With the continuous development of computer technology and network era and the continuous fusion with education industry, online education products are increasingly applied in the life and learning process of people, intelligent analysis based on big data provides possibility for intelligent learning, online learning becomes a new trend in learning mode at present, and the online education method has important significance for the change of teaching mode and the improvement of teaching effect;
the problem bank is used as basic data and has an important position in the application of intelligent learning, generally speaking, the problem bank is a set of a large number of problems, the current common problem banks are all based on problem types, knowledge structures or chapters and sections to knowledge points, and the problem difficulty in the current problem bank products is also judged and input subjectively by problem bank input personnel, so that the problem bank does not necessarily accord with the actual experience of students, and therefore, the problem to be solved by technical personnel in the related field is to design a problem bank construction method based on cognitive hierarchy.
Disclosure of Invention
In order to solve the technical problems, the technical scheme provided by the invention is as follows: a question bank construction method based on cognitive hierarchy comprises the following steps:
(1) establishing a temporary question bank: collecting temporary exercise files comprising exercise numbers, exercise names, exercise contents, exercise knowledge points and exercise answers;
(3) and (3) assigning answers to the exercises: selecting target student users, randomly extracting the exercises in the temporary exercise library to the target students for answering, collecting answer data of answering a certain target exercise by a plurality of student users, and collecting corresponding answering data of each student user;
(4) collecting response data: after the answering is finished, the answering content of the student users is compared with the answers of the questions, the correct and wrong data and the correct rate of each target exercise are collected, and the scores of each answering of each student user are collected;
(5) establishing a grade question bank: collecting exercise files which are answered by students for n times (n is more than or equal to 100), wherein the exercise files comprise the correct and wrong data and the correct rate of each answering of the exercise files;
(6) grading: determining the difficulty level of each exercise according to the answer accuracy of the student user to each exercise and the accuracy interval; determining the cognitive grade of each student user according to the correct answer rate interval of each student user;
(7) establishing a personal question bank: and collecting error exercises of the student users according to answer data of the student users to the exercises, and collecting the exercises which are pushed by the grade exercise library and accord with the current cognitive grade of the student users.
Preferably, the process for determining the cognitive grade of the student user comprises the following steps:
(1) the question bank system pushes the question bank with the lowest difficulty level, the user selects the questions and answers the questions, and the question bank system records the correct and wrong data of each question of the user and the overall correctness of answering the questions;
(2) the question bank system judges the cognitive grade corresponding to the user accuracy, pushes the question bank corresponding to the grade to the user for answering, and records the answering data of the user;
(3) if the answer data of the user does not meet the cognition grade corresponding to the current exercise library, the exercise library system pushes a low-grade exercise library and updates the current cognition grade; if the answer data of the user exceeds the cognition level corresponding to the current exercise library, the exercise library system pushes the exercise library with a higher level and updates the current cognition level.
Preferably, when the accuracy of a target question is different from the recording accuracy by m% (m is larger than or equal to 10), the target question is returned to the temporary question bank, and the answering data is collected after the target question is re-answered and distributed.
Preferably, the problem with the problem accuracy rate of more than or equal to 80% and more than or equal to 100% is collected in the first-level problem library; collecting the exercises with the exercise correctness more than 80% and more than or equal to 60% in a first-level exercise library; collecting the exercises with the exercise accuracy more than 60% and more than or equal to 40% in a second-level exercise library; collecting the exercises with the exercise accuracy more than 40% and more than or equal to 20% in a third-level exercise library; the exercises with the exercise accuracy more than or equal to 0% in the 20% exercise accuracy are collected in the fourth-level exercise library.
Preferably, the student users with the exercise correctness rate of more than 20% and more than or equal to 0% are divided into a first cognition level; dividing student users with exercise correctness more than or equal to 20% by 40% into a second cognition level; dividing student users with exercise correctness more than or equal to 40% into a third cognition level when the exercise correctness is more than 60%; dividing student users with exercise correctness more than or equal to 60% into a fourth cognition level when the exercise correctness is more than 80%; and dividing the student users with the exercise correctness rate of more than or equal to 80% and more than or equal to 100% into a fifth cognitive grade.
After the scheme is adopted, the invention has the following advantages: according to the invention, through collecting multiple answers of the student user to the temporary exercise file, the difficulty degree of the target temporary exercise can be more accurately determined, the target exercise can be conveniently graded according to the cognitive level of the student user, and the cognitive level of the student user can be determined through each answer of the student user, so that the exercise according with the cognitive level is pertinently pushed, the study of the student user is facilitated, and when the target exercise is not matched with the current grade, the target exercise is graded again, and the accuracy of the exercise library is increased.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of the problem bank construction method based on cognitive hierarchy of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Examples
A question bank construction method based on cognitive hierarchy comprises the following steps:
(1) establishing a temporary question bank: collecting temporary exercise files comprising exercise numbers, exercise names, exercise contents, exercise knowledge points and exercise answers;
(3) and (3) assigning answers to the exercises: selecting target student users, randomly extracting the exercises in the temporary exercise library to the target students for answering, collecting answer data of answering a certain target exercise by a plurality of student users, and collecting corresponding answering data of each student user;
(4) collecting response data: after the answering is finished, the answering content of the student users is compared with the answers of the questions, the correct and wrong data and the correct rate of each target exercise are collected, and the scores of each answering of each student user are collected;
(5) establishing a grade question bank: collecting exercise files which are answered by students for n times (n is more than or equal to 100), wherein the exercise files comprise the correct and wrong data and the correct rate of each answering of the exercise files;
(6) grading: determining the difficulty level of each exercise according to the answer accuracy of the student user to each exercise and the accuracy interval; determining the cognitive grade of each student user according to the correct answer rate interval of each student user;
(7) establishing a personal question bank: and collecting error exercises of the student users according to answer data of the student users to the exercises, and collecting the exercises which are pushed by the level exercise library and accord with the current cognitive level of the student users and the exercises pushed by the temporary exercise library.
As a preferred embodiment of this embodiment, the process for determining the cognitive level of the student user includes the following steps:
(1) the question bank system pushes the question bank with the lowest difficulty level, the user selects the questions and answers the questions, and the question bank system records the correct and wrong data of each question of the user and the overall correctness of answering the questions;
(2) the question bank system judges the cognitive grade corresponding to the user accuracy, pushes the question bank corresponding to the grade to the user for answering, and records the answering data of the user;
(3) if the answer data of the user does not meet the cognition grade corresponding to the current exercise library, the exercise library system pushes a low-grade exercise library and updates the current cognition grade; if the answer data of the user exceeds the cognition level corresponding to the current exercise library, the exercise library system pushes the exercise library with a higher level and updates the current cognition level.
As a preferred embodiment of this embodiment, when the accuracy of a certain target problem is different from the recording accuracy by m% (m ≧ 10), the target problem is returned to the temporary problem library, re-answer distribution is performed, and answer data is collected.
As a preferred embodiment of the embodiment, the problem with the correct rate of 100% or more problem than 80% is collected in the first-level problem library; collecting the exercises with the exercise correctness more than 80% and more than or equal to 60% in a first-level exercise library; collecting the exercises with the exercise accuracy more than 60% and more than or equal to 40% in a second-level exercise library; collecting the exercises with the exercise accuracy more than 40% and more than or equal to 20% in a third-level exercise library; the exercises with the exercise accuracy more than or equal to 0% in the 20% exercise accuracy are collected in the fourth-level exercise library.
As a preferred embodiment of the embodiment, the student users with 20% of exercise correctness more than or equal to 0% are divided into a first cognition level; dividing student users with exercise correctness more than or equal to 20% by 40% into a second cognition level; dividing student users with exercise correctness more than or equal to 40% into a third cognition level when the exercise correctness is more than 60%; dividing student users with exercise correctness more than or equal to 60% into a fourth cognition level when the exercise correctness is more than 80%; and dividing the student users with the exercise correctness rate of more than or equal to 80% and more than or equal to 100% into a fifth cognitive grade.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include such modifications and variations.
Claims (5)
1. A question bank construction method based on cognitive hierarchy is characterized by comprising the following steps:
(1) establishing a temporary question bank: collecting temporary exercise files comprising exercise numbers, exercise names, exercise contents, exercise knowledge points and exercise answers;
(3) and (3) assigning answers to the exercises: selecting target student users, randomly extracting the exercises in the temporary exercise library to the target students for answering, collecting answer data of answering a certain target exercise by a plurality of student users, and collecting corresponding answering data of each student user;
(4) collecting response data: after the answering is finished, the answering content of the student users is compared with the answers of the questions, the correct and wrong data and the correct rate of each target exercise are collected, and the scores of each answering of each student user are collected;
(5) establishing a grade question bank: collecting exercise files which are answered by students for n times (n is more than or equal to 100), wherein the exercise files comprise the correct and wrong data and the correct rate of each answering of the exercise files;
(6) grading: determining the difficulty level of each exercise according to the answer accuracy of the student user to each exercise and the accuracy interval; determining the cognitive grade of each student user according to the correct answer rate interval of each student user;
(7) establishing a personal question bank: and collecting error exercises of the student users according to answer data of the student users to the exercises, and collecting the exercises which are pushed by the grade exercise library and accord with the current cognitive grade of the student users.
2. The question bank construction method based on the cognitive hierarchy as claimed in claim 1, wherein the process of determining the cognitive level of the student user comprises the following steps:
(1) the question bank system pushes the question bank with the lowest difficulty level, the user selects the questions and answers the questions, and the question bank system records the correct and wrong data of each question of the user and the overall correctness of answering the questions;
(2) the question bank system judges the cognitive grade corresponding to the user accuracy, pushes the question bank corresponding to the grade to the user for answering, and records the answering data of the user;
(3) if the answer data of the user does not meet the cognition grade corresponding to the current exercise library, the exercise library system pushes a low-grade exercise library and updates the current cognition grade; if the answer data of the user exceeds the cognition grade corresponding to the current question bank, the question bank system pushes a higher-grade question bank, and updates the current cognition grade and the questions pushed by the temporary question bank.
3. The method as claimed in claim 1, wherein when the correctness of a target question is different from the correctness of the description by m% (m is greater than or equal to 10), the target question is returned to the temporary question bank, and the assignment of answers is performed again and answer data is collected.
4. The method for constructing a question bank based on a cognitive hierarchy as claimed in claim 1, wherein questions with a correct rate of 100% or more and 80% or more are collected in the first-level question bank; collecting the exercises with the exercise correctness more than 80% and more than or equal to 60% in a first-level exercise library; collecting the exercises with the exercise accuracy more than 60% and more than or equal to 40% in a second-level exercise library; collecting the exercises with the exercise accuracy more than 40% and more than or equal to 20% in a third-level exercise library; the exercises with the exercise accuracy more than or equal to 0% in the 20% exercise accuracy are collected in the fourth-level exercise library.
5. The method for constructing the question bank based on the cognitive hierarchy as claimed in claim 1, wherein student users with 20% of question correctness more than or equal to 0% are divided into a first cognitive level; dividing student users with exercise correctness more than or equal to 20% by 40% into a second cognition level; dividing student users with exercise correctness more than or equal to 40% into a third cognition level when the exercise correctness is more than 60%; dividing student users with exercise correctness more than or equal to 60% into a fourth cognition level when the exercise correctness is more than 80%; and dividing the student users with the exercise correctness rate of more than or equal to 80% and more than or equal to 100% into a fifth cognitive grade.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011551539.8ACN112506855A (en) | 2020-12-24 | 2020-12-24 | Question bank construction method based on cognitive hierarchy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011551539.8ACN112506855A (en) | 2020-12-24 | 2020-12-24 | Question bank construction method based on cognitive hierarchy |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112506855Atrue CN112506855A (en) | 2021-03-16 |
Family
ID=74923264
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011551539.8APendingCN112506855A (en) | 2020-12-24 | 2020-12-24 | Question bank construction method based on cognitive hierarchy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112506855A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106547909A (en)* | 2016-11-23 | 2017-03-29 | 广东小天才科技有限公司 | Error-prone question bank construction management method and device based on big data |
| US20170147934A1 (en)* | 2015-11-25 | 2017-05-25 | International Business Machines Corporation | Method and system for quantitatively evaluating the confidence in information received from a user based on cognitive behavior |
| CN106781785A (en)* | 2017-01-04 | 2017-05-31 | 广东小天才科技有限公司 | Topic difficulty construction method and device based on big data and service equipment |
| CN109523439A (en)* | 2018-11-21 | 2019-03-26 | 杭州博世数据网络有限公司 | Online exam pool difficulty intelligence stage division and system |
- 2020
- 2020-12-24CNCN202011551539.8Apatent/CN112506855A/enactivePending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170147934A1 (en)* | 2015-11-25 | 2017-05-25 | International Business Machines Corporation | Method and system for quantitatively evaluating the confidence in information received from a user based on cognitive behavior |
| CN106547909A (en)* | 2016-11-23 | 2017-03-29 | 广东小天才科技有限公司 | Error-prone question bank construction management method and device based on big data |
| CN106781785A (en)* | 2017-01-04 | 2017-05-31 | 广东小天才科技有限公司 | Topic difficulty construction method and device based on big data and service equipment |
| CN109523439A (en)* | 2018-11-21 | 2019-03-26 | 杭州博世数据网络有限公司 | Online exam pool difficulty intelligence stage division and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109903617B (en) | Personalized exercise method and system | |
| CN111858906B (en) | Problem recommendation method and device, electronic equipment and computer readable storage medium | |
| CN110941723A (en) | Method, system and storage medium for constructing knowledge graph | |
| CN111274411A (en) | Course recommendation method and device, electronic equipment and readable storage medium | |
| CN106126524B (en) | Information pushing method and device | |
| JP4996608B2 (en) | Apparatus, method, and computer program for editing an exam, and apparatus, method, and computer program for testing an examinee | |
| CN105512214A (en) | Knowledge database, construction method and learning situation diagnosis system | |
| CN113851020A (en) | Self-adaptive learning platform based on knowledge graph | |
| Mu et al. | Towards Suggesting Actionable Interventions for Wheel-Spinning Students. | |
| CN111563156B (en) | Knowledge point intelligent diagnosis method and device, electronic equipment and storage medium | |
| KR101041672B1 (en) | Intelligent customized learning service method | |
| CN107977708A (en) | The student's DNA identity informations recommended towards individualized learning scheme define method | |
| US20120329028A1 (en) | Method for intelligent personalized learning service | |
| CN110929020B (en) | Knowledge point mastering degree analysis method based on test results | |
| CN116796802A (en) | Learning recommendation methods, devices, equipment and storage media based on wrong question analysis | |
| CN112348725A (en) | Knowledge point difficulty grading method based on big data | |
| Adjei et al. | Seven-year longitudinal implications of wheel spinning and productive persistence | |
| JP2002072856A (en) | Training course selection device, training course selection method, and storage medium | |
| CN114461786B (en) | Learning path generation method and system | |
| CN114202978A (en) | Intelligent training method and system based on big data | |
| CN115358897B (en) | Student management method, system, terminal and storage medium based on electronic student identity card | |
| CN115599989B (en) | A topic recommendation method, device and equipment | |
| CN111127271A (en) | Teaching method and system for studying situation analysis | |
| US20060234200A1 (en) | Computer based method for self-learning and auto-certification | |
| CN112506855A (en) | Question bank construction method based on cognitive hierarchy |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | Application publication date:20210316 | |
| RJ01 | Rejection of invention patent application after publication |