CN116844389B - A method to improve the teaching effectiveness of simulator operation through sampling tests - Google Patents
A method to improve the teaching effectiveness of simulator operation through sampling testsInfo
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- CN116844389B CN116844389B CN202310662443.6A CN202310662443A CN116844389B CN 116844389 B CN116844389 B CN 116844389B CN 202310662443 A CN202310662443 A CN 202310662443A CN 116844389 B CN116844389 B CN 116844389B
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- 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
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- 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
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Abstract
本发明提出了一种通过抽测提升模拟器操作教学效果的方法，用于提升教学效果。具体的，利用有向无环图描述模拟器操作教学中的知识体系；知识体系由多条知识路径构成，每条知识路径由理论知识节点和技能节点交替出现，知识路径的方向代表学习的先后顺序；每个节点包含多道试题；依据节点重要性和前次节点抽测状态确定本次的抽测策略；完成本次抽测后，更新所有节点的抽测状态，为下次制定抽测策略做准备。本发明能够在多轮抽测中，根据学习者的答题结果、知识点的重要程度以及历史抽测情况，及时调整抽测策略，通过试题评价，持续剔除不能有效检测知识点掌握情况的试题，优化试题库，增强教学的针对性，从而提高模拟器操作使用教学的效率。
This invention proposes a method to improve the teaching effectiveness of simulator operation through sampling tests. Specifically, it utilizes a directed acyclic graph (DAG) to describe the knowledge system in simulator operation teaching. The knowledge system consists of multiple knowledge paths, each alternating between theoretical knowledge nodes and skill nodes. The direction of the knowledge path represents the learning sequence. Each node contains multiple test questions. The sampling strategy for the current test is determined based on the importance of the node and the previous sampling status. After completing the current test, the sampling status of all nodes is updated to prepare for the next sampling strategy. This invention can adjust the sampling strategy in a timely manner during multiple rounds of testing based on learners' answers, the importance of knowledge points, and historical sampling data. Through question evaluation, it continuously eliminates questions that cannot effectively test knowledge point mastery, optimizes the question bank, enhances the relevance of teaching, and thus improves the efficiency of simulator operation teaching.
Description
Technical Field
The invention belongs to the technical field of teaching, and particularly relates to a method for improving the operation teaching effect of a simulator through spot test.
Background
In operation and use teaching, a simulator is usually used as a teaching means, all knowledge points cannot be covered at one time when theoretical and operation skill teaching is implemented, and learning conditions of learners on the knowledge points are difficult to evaluate, so that poor learning effect is caused. The prior simulator operation teaching can not dynamically adjust teaching test contents according to actual mastering conditions of students, so that the effect of the simulator operation teaching is difficult to effectively improve. Therefore, a method for improving the learning effect of the learner by the spot test method is needed.
Disclosure of Invention
Aiming at the problem that the simulator operation teaching can not improve the teaching effect in a spot test mode, the invention provides a method for improving the simulator operation teaching effect through spot test. According to the invention, in multiple rounds of spot test, the spot test strategy can be timely adjusted according to the answer result of a learner, the importance degree of a knowledge point and the history spot test condition, the subsequent spot test content can be dynamically changed, the defects existing in the early teaching can be improved, the pertinence of the teaching can be enhanced, and the efficiency of the simulator in operation and use teaching can be improved.
The invention is realized by the following technical scheme:
Describing a knowledge system in simulator operation teaching by using a directed acyclic graph, wherein the knowledge system is composed of a plurality of knowledge paths, each knowledge path is alternately formed by theoretical knowledge nodes and skill nodes, and the direction of the knowledge path represents the learning sequence;
determining a current spot-test strategy according to the importance of the node and the spot-test state of the previous node;
After the current spot test is completed, the spot test states of all nodes are updated, and preparation is made for the next spot test strategy formulation.
Further, the method comprises the steps of,
The node importance is equal to the number of edges associated with the node in the knowledge base divided by the number of all edges in the knowledge base.
Further, the method comprises the steps of,
The node spot test state comprises whether the node is drawn or not and the spot test result of the node.
Further, the method comprises the steps of,
The current spot-test strategy includes,
Firstly, extracting nodes which are not extracted in the previous sampling test;
then extracting nodes with high importance and low previous sampling test results;
And finally randomly selecting other nodes.
Further, the method comprises the steps of,
The sampling test result of the node comprises sampling test results of the sampling node and sampling test results of the non-sampling node, wherein the sampling test results of the sampling node are actual examination results, and the sampling test results of the non-sampling node are obtained by calculation of sampling test results of the sampling node in the current sampling test and historical sampling test results of the non-sampling node.
Further, the method comprises the steps of,
The calculation process of the extraction and measurement result of the non-extraction node is that,
Acquiring a sampling test result of a leading node of a node which is not sampled in the current sampling test;
Multiplying the sampling result of the leading node by the proportional relation P Positive direction between the non-sampled node and the leading node to obtain a first estimated value Est1;
Acquiring a sampling test result of a next node of the node which is not sampled in the current sampling test;
Multiplying the extraction and measurement result of the next-stage node by the proportional relation P Reverse-rotation between the node which is not extracted and the next-stage node to obtain a second estimated value Est2;
Acquiring a historical sampling test result of a node which is not sampled in the current sampling test;
And carrying out weighted summation on the first estimated value Est1, the second estimated value Est2 and the history sampling test result of the non-sampled node to obtain the sampling test result of the non-sampled node in the current sampling test, wherein the sampling test result is updated into the history sampling test result of the corresponding node.
Further, the method comprises the steps of,
The proportional relation P Positive direction is equal to the ratio of the sampling test result of the node i in the history sampling test to the sampling test result of the leading node in the history sampling test;
The proportional relation P Reverse-rotation is equal to the ratio of the sampling test result of the node i in the history sampling test to the sampling test result of the next node in the history sampling test.
Further, the method comprises the steps of,
In the calculation process of the extraction and measurement result of the node which is not extracted, if the leading node or the next-stage node is not extracted and measured as well, the calculation is carried out only according to the leading node or the next-stage node with the extraction and measurement result;
when a node belongs to a plurality of knowledge paths, one or more leading nodes exist in the node, one or more next-stage nodes exist in the node, and at the moment, all leading nodes and all next-stage nodes are needed to be included when the sampling test result of the node which is not sampled is calculated.
Advantageous effects
1) Comprehensively describing a knowledge system by adopting a directed acyclic graph, arranging theories and skills used by simulator operation into an orderly set which are mutually related, forming an orderly relation between theoretical knowledge points and skill points, and laying a foundation for accurately evaluating teaching effects corresponding to achievement of learner capability targets;
2) The sampling test result of the nodes without sampling is evaluated through the sampling test result of the nodes in sampling, so that the comprehensive estimation of the mastering condition of the complete knowledge system is realized;
3) Continuously updating the history spot test record, dynamically adjusting the proportional relations P Positive direction and P Reverse-rotation for describing the knowledge mastering situation association relation among the nodes, and being beneficial to truly reflecting the actual situation of the teaching effect;
4) And for the non-extracted nodes, the previous-stage nodes, the next-stage nodes and the historical extraction test results are adopted to jointly estimate the non-extracted nodes, so that the continuity and the relevance of knowledge point learning are fully followed, and the estimation result is more accurate.
5) On the basis of accurately estimating the existing teaching effect, the importance and comprehensive coverage of knowledge points are combined, the follow-up teaching spot measurement content is dynamically adjusted, the pertinence of spot measurement is enhanced, and the teaching efficiency is improved.
6) And continuously removing test questions which cannot effectively detect knowledge point mastering conditions through test question evaluation, optimizing a test question library, enhancing the pertinence of teaching, and optimizing the efficiency of simulator operation and teaching.
Drawings
FIG. 1, a directed acyclic graph representation of a knowledge system;
fig. 2, a flow chart of an implementation of the present invention.
Detailed Description
The method comprises knowledge system description, knowledge system evaluation and knowledge point spot sampling strategy establishment.
Description of (one) knowledge System
The purpose of describing the knowledge system is to sort the theories and skills used by the simulator operation into an orderly set of interrelations for forming orderly relations between theoretical knowledge points and skill points, corresponding to achievement of learner competency goals.
In the teaching content used by simulator operation, the knowledge system can be described as a directed acyclic graph by utilizing the interrelationship between theoretical knowledge points and skill points. The rule of using teaching design for simulator operation is that the teaching design is performed step by step from theory to practice, so that the nodes representing theoretical knowledge and the nodes representing skill points in the directed acyclic graph are alternately appeared, the initial node set of the graph is the theoretical knowledge node, and all the terminal nodes are the skill nodes. The path from the starting point to a skill point represents a "knowledge" corresponding to the learning rule of the operation skill, and each node corresponds to multiple questions, as shown in fig. 1, wherein a rectangle represents a knowledge node and a circle represents a skill node. The knowledge N11 represented by a vector of N11 to T21 is the theoretical basis for the skill T21.
The grasping of the theoretical basis is a precondition for grasping the subsequent skills, but since there is a gap from the grasping of the theory to the practice, there is a certain probability that the grasping of the subsequent skills is estimated from the grasping of the leading knowledge, as shown in fig. 1,On the premise of representing the first knowledge N11 of the first layer, the mastery degree of the subsequent first skill T21 is estimated.
(II) knowledge System assessment
After the examination of the questions, the data of the learner on the answer conditions of all the questions can be obtained and can be used as a sample for evaluating the knowledge and skill grasping degree of the current batch of trainees on the simulator operation. The set of all samples is the history of the assessment, expressed by a symbol as mem, and the history samples are updated once every time the measurement is performed. mem represents an a priori estimate of the learner's performance obtained by simulator operation using the assessment by the trainee. As in FIG. 1, mem (N 11) is used to represent a priori knowledge of knowledge point N11, i.e., the probability of a learner answering a question based on N11 in a historical examination.
The proportional relation P Positive direction between the extraction result of the node T21 and the extraction result of the leading node N11 is equal to the ratio of the extraction result of the node T21 in the history extraction test to the extraction result of the leading node N11 in the history extraction test, wherein P Positive direction is shown in the figure
The proportional relation P Reverse-rotation between the extraction result of the node T21 and the extraction result of the next node N31 is equal to the ratio of the extraction result of the node T21 in the history extraction test to the extraction result of the next node N31 in the history extraction test, wherein P Reverse-rotation is shown in the figure
After the examination of the question is completed once, firstly calculating the correct answer rate number of the learner in the knowledge points in the question extraction, and then estimating the result of the non-extracted knowledge points in the current question extraction by using the knowledge points in the question extraction according to the proportional relation P Positive direction 、P Reverse-rotation .
And estimating the extraction test result of the non-extracted knowledge point according to the extraction test results of the leading knowledge point and the subsequent knowledge point, wherein both the extraction test results of the non-extracted knowledge point and the extraction test result of the non-extracted knowledge point generate deviation from the real extraction test result of the non-extracted knowledge point. In addition, the result of the historical extraction test can also be used to determine the mastering condition of the corresponding knowledge points, for example, for simple knowledge points, the learner usually can easily master the knowledge points, but for difficult knowledge points, the learner usually cannot easily master the knowledge points, so for the non-extracted node T21, the first estimated value obtained by the leading node N11 is obtainedThe second estimated value obtained by the node N31 at the next stageAnd weighting and summing the historical sampling test result mem (T21) of the T21 to obtain a sampling test result mem (T21) new of the T21 in the sampling test, and updating the historical sampling test result mem (T21) of the T21 into mem (T21) new.
The formula of the above process is expressed as follows, knowing that the results of the current suction Test N11 and N31 are Test (N11) and Test (N31), respectively, two new estimates are obtained for T21 according to the suction Test results: And The estimation of the result of the spot T21 of skill knowledge is updated as:
wherein α+β+γ=1, the calculation of (2) is performed on all the non-retested nodes, and the history test record is updated.
If the leading node or the next level node is not detected, the calculation is only carried out according to the leading node or the next level node with the extraction detection result, when one node belongs to a plurality of knowledge paths, one or more leading nodes exist in the node, one or more next level nodes exist in the node, and at the moment, all the leading nodes and all the next level nodes are needed to be included when the extraction detection result of the node which is not detected is calculated.
Third, a knowledge point spot sampling strategy is formulated
In order to improve teaching efficiency, an effective knowledge point spot sampling strategy needs to be specified. Based on the knowledge architecture established in fig. 1, the spot test of the learning effect of the learner is generally based on test questions, which are divided into theoretical test questions and operation test questions, and belong to corresponding knowledge points and skill points in the knowledge system respectively.
Based on the operation teaching characteristics of the simulator, the design of the question drawing scheme is based on two indexes, namely knowledge point importance and previous question drawing state.
1. Knowledge point importance
The importance of the corresponding knowledge point is calculated from the number of edges associated with a certain node in the knowledge hierarchy. If the set of all edges is E, then the number of all edges in the knowledge hierarchy is card (E). For a node n, the number of edges associated with it is adj (n), then the score based on knowledge point importance is adj (n)/card (E).
2. The previous question drawing state
The node spot test state comprises whether the node is drawn or not and the spot test result of the node. The method is characterized in that questions are extracted in a knowledge system, the comprehensiveness of the questions on the extraction test is concerned, the 'knowledge' in a knowledge point system is covered as much as possible, the importance of nodes and the mastery degree of students are considered, and rules for dynamically adjusting the extraction strategies according to the importance of the knowledge points and the previous extraction states are designed.
The current spot-test strategy includes,
Firstly, extracting nodes which are not extracted in the previous sampling test;
Then extracting nodes with high importance and low previous extraction test results, wherein the extraction test results of the nodes comprise extraction test results of extraction nodes and extraction test results of non-extraction nodes;
And finally randomly selecting other nodes.
In addition, the emphasis of the question extraction strategy can be adjusted in a weighting coefficient mode according to the actual situation, or strategy adjustment can be performed by other prior art means.
Thus, the teaching effect is improved.
(IV) test question evaluation and optimization of question bank
In order to further improve teaching efficiency, test questions need to be evaluated, and a question bank is optimized according to evaluation results. The level of making test questions is limited by the subjective ability level of a teaching worker, the test questions with different performances are not suitable for being assigned to the same knowledge point for the learner, the purpose of test question evaluation is to evaluate the test question quality of the current knowledge point, and compared with the same batch of learners, good test questions are set to have similar answer scores.
The test question evaluation and optimization question bank further comprises that the test question set of the node T21 comprises 200 questions, a Mean (Item T21) and a variance Var (Item T21) of the correctness of the 200 questions in the sampling test history are calculated, the historical correctness of each question in the 200 questions is calculated, for example, a 10 th question is sampled 8 times in the historical sampling test, the correctness obtained by the answer result of the 8 times is in a { Mean (Item T21)-2Var(Item T21),Mean(Item T21)+2Var(Item T21) } interval, the 10 th question is an acceptable question, otherwise, replacement is carried out, and all the test questions of all the nodes are traversed to complete the question bank optimization. The interval range can be adjusted according to actual conditions.
The test question evaluation and optimization question bank can be performed after each time of sampling test, or after a certain sampling test times are separated.
The method of the invention is implemented by sequentially executing knowledge system description, knowledge point spot test, knowledge system evaluation, test question evaluation and optimizing a question bank.
And the first step is to construct a teaching knowledge system. And combing knowledge points according to theory and skill according to the teaching outline, using a directed acyclic graph as a knowledge system description method, and layering the arranged knowledge points according to the sequence from theoretical knowledge to operation skill.
And secondly, perfecting the question bank resource. For each knowledge point in the knowledge system, setting the question belonging to the knowledge point, requiring to distribute the identification for each newly added question, and recording the record of the question spot test in the database, including spot test frequency, correct number and the like.
And thirdly, checking and implementing, namely, knowledge point spot sampling test. The operation skill assessment is implemented on a batch of learners by the aid of the simulator. According to the specification of the knowledge point extraction and measurement method, extracting a batch of questions, pushing the questions to a simulator to be tested, answering the questions by a learner, and recording an answer result.
And (5) after each time of the sampling test, evaluating the batch number, and if the batch requirement is not met, continuing to sample the next batch of questions.
And fourthly, evaluating a knowledge system. After the learner in the batch finishes the examination, updating the prior according to the examination result, and specifically updating the formula (2).
And fifthly, evaluating and optimizing the question bank, namely evaluating and optimizing the question bank after 20 times of test drawing according to the setting.
Therefore, the application of the method in simulator operation teaching examination is realized, the full knowledge system suction test effect evaluation is realized, and the efficiency of skill teaching implementation by using the operation simulator is improved.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105761567A (en) * | 2016-05-04 | 2016-07-13 | 北京新唐思创教育科技有限公司 | Method and device for dynamically recommending exercises |
| CN110413973A (en) * | 2019-07-26 | 2019-11-05 | 浙江蓝鸽科技有限公司 | Computer automatically generates the method and its system of set volume |
| CN113282765A (en) * | 2021-06-17 | 2021-08-20 | 东莞市亚太未来软件有限公司 | Dynamic review test question generation method and system |
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| CN111090751A (en) * | 2018-10-08 | 2020-05-01 | 上海风创信息咨询有限公司 | Teaching recommendation method, system, storage medium and terminal based on knowledge graph |
| CN110992227B (en) * | 2019-12-02 | 2024-01-30 | 中船舰客教育科技(北京)有限公司 | School enterprise and professional skill talent combining culture system and method |
| CN111125342B (en) * | 2019-12-17 | 2023-06-27 | 深圳市鹰硕技术有限公司 | Problem test data generation method and device |
| CN111444423B (en) * | 2020-03-25 | 2023-08-25 | 上海乂学教育科技有限公司 | Learning resource intelligent pushing method |
| US20220237548A1 (en) * | 2021-01-28 | 2022-07-28 | Right-Hand Cyber Security Pte. Ltd. | Gamified real-time artificial intelligence based individualized adaptive learning |
| CN114065040A (en) * | 2021-11-18 | 2022-02-18 | 扬州大学 | Individual learning path and learning resource recommendation method based on discipline knowledge graph |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105761567A (en) * | 2016-05-04 | 2016-07-13 | 北京新唐思创教育科技有限公司 | Method and device for dynamically recommending exercises |
| CN110413973A (en) * | 2019-07-26 | 2019-11-05 | 浙江蓝鸽科技有限公司 | Computer automatically generates the method and its system of set volume |
| CN113282765A (en) * | 2021-06-17 | 2021-08-20 | 东莞市亚太未来软件有限公司 | Dynamic review test question generation method and system |
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