Intelligent training course planning method based on education management

Abstract

The invention belongs to the field of training course planning, relates to a data analysis technology, is used for solving the problem that in the prior art, targeted course planning analysis cannot be carried out by combining the learning progress of historical students, examination data and basic information of training students, and particularly relates to a training course intelligent planning method based on education management. Comprising the following steps: marking students performing course training as planning objects, acquiring preliminary screening information of the planning objects, and marking the preliminary screening objects according to the preliminary screening information; marking the secondary screening object through the secondary screening information; training efficiency and effect analysis is carried out on the secondary screening object, and an effect matching object and an efficiency matching object are obtained; according to the method, the historical students closest to the current states of the training students can be screened, and the matching degree of the planning course and the current states of the training students can be improved by taking the secondary screening object as an object of efficiency and effect analysis.

Description

Technical Field

[0001] The present invention belongs to the field of training course planning, relates to data analysis technology, and specifically is an intelligent planning method for training courses based on educational management. Background Art

[0002] Intelligent planning of training courses is to achieve an automated and personalized decision support system for the entire process such as course design, resource allocation, and teaching implementation through the deep integration of artificial intelligence technology and educational management; its core lies in dynamically adapting to the needs of learners and the optimal allocation of educational resources.

[0003] The invention patent with the publication number CN113689126A discloses an intelligent customized service platform for examination training courses. This platform realizes the reasonable planning and formulation of learning plans according to the different time arrangements and learning tasks of each learner through the learning planning module. At the same time, it constantly detects the completion situation of students, dynamically adjusts the learning plan accordingly, and reminds students, enabling students to reasonably complete learning tasks and significantly improving the learning effect of students; however, this platform cannot conduct targeted curriculum planning analysis by combining the learning progress, examination data of historical students, and the basic information of training students, resulting in a low matching degree between the training plan and training students and poor training effects.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent planning method for training courses based on educational management, which is used to solve the problem that the prior art cannot conduct targeted curriculum planning analysis by combining the learning progress, examination data of historical students, and the basic information of training students; The technical problem that the present invention needs to solve is: how to provide an intelligent planning method for training courses based on educational management that can conduct targeted curriculum planning analysis by combining the learning progress, examination data of historical students, and the basic information of training students.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An intelligent planning method for training courses based on educational management includes the following steps: Step 1: Conduct primary screening and analysis of the basic information of students undergoing course training: Mark the students undergoing course training as planning objects, obtain the primary screening information of the planning objects, and mark the primary screening objects according to the primary screening information; Step 2: Conduct secondary screening and analysis of the basic information of students undergoing course training: Obtain the secondary screening information of the planning objects. The secondary screening information includes the age and graduation years of the planning objects, and mark the secondary screening objects through the secondary screening information; Step 3: Analyze the training efficiency and effect of the secondary screening objects to obtain the effect-matched objects and efficiency-matched objects; Step 4: Conduct targeted curriculum planning analysis on the trainees undergoing curriculum training: The planning object selects a planning mode, and the planning modes include the efficiency-first mode and the effect-first mode. When the planning object does not select a planning mode, a planning mode is randomly selected for curriculum planning; Step 5: Conduct optimization analysis on the training curriculum.

[0007] Furthermore, the specific process of marking the primary screening objects according to the primary screening information includes: The primary screening information includes the training curriculum, gender, first academic degree, and the major to which the first academic degree belongs of the planning object. Historical trainees with exactly the same primary screening information as the planning object are retrieved from the database and marked as primary screening objects.

[0008] Furthermore, the specific process of marking the secondary screening objects according to the secondary screening information includes: Mark the age and graduation years of the planning object as NL and NX respectively. Obtain the age high value NLmax and age low value NLmin through the formulas NLmax = t1×NL and NLmin = t2×NL, where t1 and t2 are both proportionality coefficients, and 1.15 ≤ t1 ≤ 1.25, 0.75 ≤ t2 ≤ 0.85.

[0009] Furthermore, an age range is formed by the age high value NLmax and age low value NLmin; Obtain the years high value NXmax and years low value NXmin through the formulas NXmax = t3×NX and NXmin = t4×NX, where t3 and t4 are both proportionality coefficients, and 1.25 ≤ t3 ≤ 1.35, 0.65 ≤ t4 ≤ 0.75. A years range is formed by the years high value NXmax and years low value NXmin. The primary screening objects whose age and graduation years are respectively within the age range and years range are marked as secondary screening objects.

[0010] Furthermore, the process of obtaining the efficiency-matched objects includes: Obtain the score data and class hour data of the secondary screening objects. The score data is the numerical value of the end-of-course exam score of the secondary screening object after completing the curriculum training, and the class hour data is the number of class hours of the secondary screening object when completing the curriculum training; Compare the score data with a preset score threshold: If the score data is less than the score threshold, mark the corresponding secondary screening object as a score-ignored object; If the score data is greater than or equal to the score threshold, mark the corresponding secondary screening object as a score-retained object; Mark the score-retained object with the smallest class hour data as the efficiency-matched object.

[0011] Further, the process of obtaining the effect matching object includes: comparing the class hour data with a preset class hour threshold: if the class hour data is greater than the class hour threshold, marking the corresponding secondary screening object as a class hour ignored object; if the class hour data is less than the class hour threshold, marking the corresponding secondary screening object as a class hour retained object; marking the class hour retained object with the largest score data as the effect matching object.

[0012] Further, the specific process of course planning for the planning object in the efficiency - priority mode includes: retrieving the course arrangement of the efficiency matching object during course training and marking it as the basic planned course of the planning object. At the same time, marking the L1 score - retained objects with the smallest class hour data as efficiency supplement objects, obtaining the knowledge points corresponding to the wrong questions in the end - of - course exam after the efficiency supplement objects complete the course training and marking them as efficiency supplement knowledge points, marking the course chapters corresponding to the efficiency supplement knowledge points as supplementary planned courses, and the optimized planned course of the planning object is composed of the basic planned course and the supplementary planned course; The specific process of course planning for the planning object in the effect - priority mode includes: retrieving the course arrangement of the effect matching object during course training and marking it as the basic planned course of the planning object. At the same time, marking the L1 class hour - retained objects with the largest score data as effect supplement objects, obtaining the knowledge points corresponding to the wrong questions in the end - of - course exam after the effect supplement objects complete the course training and marking them as effect supplement knowledge points, marking the course chapters corresponding to the effect supplement knowledge points as supplementary planned courses, and the optimized planned course of the planning object is composed of the basic planned course and the supplementary planned course.

[0013] Further, the specific process of optimizing and analyzing the training courses includes: generating an optimization period, classifying the training courses according to the training type, and obtaining the score performance value and class hour performance value of the training courses at the end of the optimization period; marking the L2 training courses with the smallest score performance value and the L2 training courses with the largest class hour performance value in the same training type as the effect - optimized courses and the efficiency - optimized courses respectively; sending the effect - optimized courses and the efficiency - optimized courses of each training type to the mobile terminal of the management personnel.

[0014] Further, the score performance value is the average value of the score data of all planning objects corresponding to the same training course whose training completion time is within the optimization period, and the class hour performance value is the average value of the class hour data of all planning objects corresponding to the same training course whose training completion time is within the optimization period.

[0015] The present invention has the following beneficial effects: 1. By conducting the initial screening and secondary screening analysis of the basic information of the trainees undergoing course training, historical trainees closest to the current status of the training trainees can be screened out. Using the secondary screening objects as the objects for efficiency and effect analysis can improve the matching degree between the planned courses and the current status of the training trainees; 2. By analyzing the training efficiency and effect of the secondary screening objects, efficiency matching objects and effect matching objects are obtained. The efficiency matching objects and effect matching objects respectively represent the best templates screened from the perspective of training efficiency and the best templates screened from the perspective of training effect. Therefore, based on the course planning of the efficiency matching objects and effect matching objects, the best templates in different directions can be provided for the training trainees, broadening the user's selectivity; 3. By conducting targeted course planning analysis on the trainees undergoing course training, different planning modes can be used to generate basic planning courses and supplementary planning courses for the training trainees. The supplementary planning courses incorporate the missing knowledge points in the completion exam of the corresponding templates, ensuring the training effect of the training trainees; 4. By conducting optimization analysis on the training courses, the performance values of the scores and class hours of all training courses corresponding to different training types within the optimization period are analyzed in the way of periodic optimization analysis, so as to screen the training courses that need to be optimized according to the performance values of the scores and class hours, improving the quality of the training courses. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is the flowchart of the method according to Embodiment 1 of the present invention; Figure 2 It is the system block diagram according to Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] Embodiment 1: As Figure 1 shown, a method for intelligent planning of training courses based on educational management includes the following steps: Step 1: Conduct initial screening and analysis of the basic information of the trainees undergoing course training: Mark the trainees undergoing course training as the planning objects, obtain the initial screening information of the planning objects, where the initial screening information includes the training courses, genders, first academic qualifications, and majors to which the first academic qualifications belong of the planning objects. Retrieve historical trainees with exactly the same initial screening information as the planning objects from the database and mark them as the initial screening objects; Step 2: Conduct secondary screening and analysis of the basic information of the trainees undergoing course training: Obtain the secondary screening information of the planning objects, where the secondary screening information includes the ages and graduation years of the planning objects; Mark the age and graduation year of the planning object as NL and NX respectively. Obtain the age high value NLmax and age low value NLmin through the formulas NLmax = t1 × NL and NLmin = t2 × NL, where t1 and t2 are both proportionality coefficients, and 1.15 ≤ t1 ≤ 1.25, 0.75 ≤ t2 ≤ 0.85. The age range is composed of the age high value NLmax and age low value NLmin; Obtain the year high value NXmax and year low value NXmin through the formulas NXmax = t3 × NX and NXmin = t4 × NX, where t3 and t4 are both proportionality coefficients, and 1.25 ≤ t3 ≤ 1.35, 0.65 ≤ t4 ≤ 0.75. The year range is composed of the year high value NXmax and year low value NXmin. Mark the initial screening objects whose ages and graduation years are respectively within the age range and year range as the secondary screening objects; Conducting initial screening and secondary screening analysis of the basic information of the trainees undergoing course training can screen out historical trainees who are closest to the current status of the training trainees. Using the secondary screening objects as the objects for efficiency and effect analysis can improve the matching degree between the planned courses and the current status of the training trainees.

[0020] Step 3: Analyze the training efficiency and effect of the secondary screening objects: Obtain the score data and class hour data of the secondary screening objects. The score data is the score value of the secondary screening objects in the final exam after completing the course training, and the class hour data is the number of class hours of the secondary screening objects when completing the course training. Compare the score data with the preset score threshold: If the score data is less than the score threshold, mark the corresponding secondary screening object as a score-ignored object; if the score data is greater than or equal to the score threshold, mark the corresponding secondary screening object as a score-retained object. Mark the score-retained object with the smallest class hour data as an efficiency-matched object. Compare the class hour data with the preset class hour threshold: If the class hour data is greater than the class hour threshold, mark the corresponding secondary screening object as a class hour-ignored object; if the class hour data is less than the class hour threshold, mark the corresponding secondary screening object as a class hour-retained object. Mark the class hour-retained object with the largest score data as an effect-matched object. Analyze the training efficiency and effect of the secondary screening objects to obtain the efficiency-matched object and the effect-matched object. The efficiency-matched object and the effect-matched object respectively represent the best templates selected from the perspective of training efficiency and the best templates selected from the perspective of training effect. Therefore, based on the course plans of the efficiency-matched object and the effect-matched object, the best templates in different directions can be provided for the training students, broadening the user's selectivity.

[0021] Step 4: Conduct targeted course plan analysis on the students undergoing course training: Let the planning object select a planning mode, and the planning modes include the efficiency-priority mode and the effect-priority mode. When the planning object does not select a planning mode, randomly select a planning mode for course planning. Conduct targeted course plan analysis on the students undergoing course training, and different planning modes can be used to generate the basic planning courses and supplementary planning courses for the training students. The supplementary planning courses incorporate the missing knowledge points of the corresponding templates in the final exam, ensuring the training effect of the training students.

[0022] The specific process of course planning for the planning object using the efficiency-priority mode includes: Retrieving the course arrangement of the efficiency-matched object during the course training and marking it as the basic planning course of the planning object. At the same time, mark the L1 score-retained objects with the smallest class hour data as efficiency supplementary objects. Obtain the knowledge points corresponding to the wrong questions of the efficiency supplementary objects in the final exam after completing the course training and mark them as efficiency supplementary knowledge points. Mark the course chapters corresponding to the efficiency supplementary knowledge points as supplementary planning courses. The optimized planning course of the planning object is composed of the basic planning course and the supplementary planning courses. The specific process of curriculum planning for the planned object using the effect - priority mode includes: retrieving the curriculum arrangement of the effect - matching object during curriculum training and marking it as the basic planned curriculum of the planned object. At the same time, marking the L1 lesson - retaining objects with the largest score data as effect - supplement objects, obtaining the knowledge points corresponding to the wrong questions in the final exam after the effect - supplement objects complete the curriculum training and marking them as effect - supplement knowledge points, and marking the curriculum chapters corresponding to the effect - supplement knowledge points as supplementary planned curriculums. The optimized planned curriculum of the planned object is composed of the basic planned curriculum and the supplementary planned curriculum; Step 5: Conduct optimization analysis on the training courses: Generate an optimization cycle, classify the training courses according to the training type, and obtain the score performance value and class - hour performance value of the training courses at the end of the optimization cycle. The score performance value is the average of the score data of all planned objects corresponding to the same training course whose training completion time is within the optimization cycle, and the class - hour performance value is the average of the class - hour data of all planned objects corresponding to the same training course whose training completion time is within the optimization cycle. Mark the L2 training courses with the smallest score performance value and the L2 training courses with the largest class - hour performance value in the same training type as the effect - optimized courses and the efficiency - optimized courses respectively. Send the effect - optimized courses and the efficiency - optimized courses of each training type to the mobile terminals of the management staff. Conduct optimization analysis on the training courses, and analyze the score performance value and class - hour performance value of all training courses corresponding to different training types within the optimization cycle in a periodic optimization - analysis manner, so as to screen the training courses that need to be optimized according to the score performance value and class - hour performance value, and improve the quality of the training courses.

[0023] Example 2: As Figure 2 shown, an intelligent training - course planning system based on educational management includes a historical screening module, a training analysis module, a curriculum planning module, and an optimization analysis module that are connected in sequence. The historical screening module, the training analysis module, and the curriculum planning module are all in communication connection with the database; The historical screening module is used to conduct primary screening and secondary screening of the basic information of the students undergoing curriculum training based on historical training data and obtain the sub - screened objects; The training analysis module is used to conduct training - efficiency and effect analysis on the sub - screened objects and obtain the effect - matching objects and efficiency - matching objects; The curriculum planning module is used to conduct targeted curriculum - planning analysis on the students undergoing curriculum training and obtain the targeted planned curriculums; The optimization analysis module is used to conduct optimization analysis on the training courses and mark the effect - optimized courses and the efficiency - optimized courses of each training type.

[0024] An intelligent planning method for training courses based on education management. During operation, the trainees undergoing course training are marked as planning objects, and the preliminary screening information and secondary screening information of the planning objects are obtained. Based on the preliminary screening information and secondary screening information, the basic information of the trainees undergoing course training is initially screened and secondarily screened to obtain the secondary screening objects; the performance data and class hour data of the secondary screening objects are obtained, and the efficiency matching objects and effect matching objects are marked according to the performance data and class hour data; the planning object selects a planning mode, and the planning modes include the efficiency - priority mode and the effect - priority mode. When the planning object does not select a planning mode, a planning mode is randomly selected for course planning; an optimization period is generated, the training courses are classified according to the training type, and at the end of the optimization period, the performance value and class hour value of the training courses are obtained, and the efficiency - optimized courses and effect - optimized courses of each training type are marked through the performance value and class hour value.

[0025] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

[0026] In the description of this specification, the descriptions referring to terms such as "an embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0027] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not elaborate all the details, nor do they limit the invention to only the specific implementation manners. Obviously, according to the content of this specification, many modifications and changes can be made. This specification selects and specifically describes these embodiments to better explain the principle and practical application of the present invention, so that those skilled in the art of this technology can well understand and utilize the present invention. The present invention is only limited by the claim book and its full scope and equivalents.

Claims

1. A training course intelligent planning method based on education management, characterized in that: The following steps are involved: Step 1: Conduct an initial screening and analysis of the basic information of the trainees who are taking the course training: mark the trainees who are taking the course training as planning objects, obtain the initial screening information of the planning objects, and mark the initial screening objects according to the initial screening information; Step 2: Conduct a secondary screening analysis of the basic information of the trainees taking the course training: obtain the secondary screening information of the planning object, which includes the age and graduation year of the planning object, and mark the secondary screening object through the secondary screening information; Step 3: Analyze the training efficiency and effect of the secondary screening objects and obtain the effect matching objects and efficiency matching objects; Step 4: Conduct targeted course planning analysis for trainees who are receiving course training: The planning object selects the planning mode, which includes efficiency priority mode and effect priority mode. If the planning object does not select a planning mode, a planning mode is randomly selected for course planning; Step 5: Optimize and analyze the training courses.

2. The method for intelligent planning of training courses based on education management according to claim 1, characterized in that: The specific process of marking the initial screening objects according to the initial screening information includes: the initial screening information includes the planning object's training courses, gender, first degree and the major of the first degree, and the historical students who have exactly the same initial screening information as the planning object are retrieved from the database and marked as initial screening objects.

3. The method for intelligent planning of training courses based on education management according to claim 2, characterized in that: The specific process of marking the secondary screening objects according to the secondary screening information includes: marking the age and graduation year of the planning object as NL and NX respectively, and obtaining the high age value NLmax and the low age value NLmin through the formulas NLmax=t1×NL and NLmin=t2×NL, where t1 and t2 are both proportional coefficients, and 1.15≤t1≤1.25, 0.75≤t2≤0.

85.

4. The method for intelligent planning of training courses based on education management according to claim 3 is characterized in that: The age range is composed of the upper age value NLmax and the lower age value NLmin; the upper age value NXmax and the lower age value NXmin are obtained by the formulas NXmax=t3×NX and NXmin=t4×NX, where t3 and t4 are proportional coefficients, and 1.25≤t3≤1.35, 0.65≤t4≤0.

75. The age range is composed of the upper age value NXmax and the lower age value NXmin, and the initial screening objects whose age and graduation year are within the age range and the age range respectively are marked as secondary screening objects.

5. The method for intelligent planning of training courses based on education management according to claim 4, characterized in that: The process of acquiring the efficiency matching object includes: acquiring the grade data and class data of the secondary screening object, the grade data is the final examination score value of the secondary screening object after completing the course training, and the class data is the number of class hours when the secondary screening object completes the course training; comparing the grade data with the preset grade threshold: if the grade data is less than the grade threshold, the corresponding secondary screening object is marked as a grade ignored object; if the grade data is greater than or equal to the grade threshold, the corresponding secondary screening object is marked as a grade retained object; and the grade retained object with the smallest class hour data is marked as an efficiency matching object.

6. The method for intelligent planning of training courses based on education management according to claim 5, characterized in that: The process of obtaining the effect matching object includes: comparing the class data with the preset class threshold: if the class data is greater than the class threshold, the corresponding sub-screening object is marked as a class ignored object; if the class data is less than the class threshold, the corresponding sub-screening object is marked as a class retained object; the class retained object with the largest score data is marked as an effect matching object.

7. The method for intelligent planning of training courses based on education management according to claim 6, characterized in that: The specific process of optimizing and analyzing training courses includes: generating an optimization cycle, classifying training courses according to training types, and obtaining the performance values ​​and class performance values ​​of the training courses at the end of the optimization cycle; marking the L2 training courses with the smallest performance values ​​and the L2 training courses with the largest class performance values ​​of the same training type as effect-optimized courses and efficiency-optimized courses respectively; and sending the effect-optimized courses and efficiency-optimized courses of each training type to the mobile phone terminals of managers.

8. The method for intelligent planning of training courses based on education management according to claim 7, characterized in that: The performance value of grades is the average of the performance data of all planned objects of the same training course whose training completion time is within the optimization cycle. The performance value of class hours is the average of the class hour data of all planned objects of the same training course whose training completion time is within the optimization cycle.

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