CN115689820B - Bidirectional learning quality assessment method and continuous medical education closed-loop management system - Google Patents
Bidirectional learning quality assessment method and continuous medical education closed-loop management system Download PDFInfo
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Abstract
The invention discloses a learning quality evaluation method based on two directions and a continuous medical education closed-loop management system, and belongs to the technical field of education information management. The method comprises the following steps: creating a learning index judging model, obtaining learning quality indexes of students based on learning process records and test results, and arranging the students meeting the conditions according to the learning quality indexes based on the score rules in a descending order; creating a teaching index judgment model, generating a teaching quality index about courses based on the teaching quality feedback record, and continuously optimizing an education management mode and a course teaching mode based on the teaching quality index. The invention adopts a closed-loop management mode, improves the management efficiency, improves the teaching quality, saves the time cost, the site, the traffic, the manpower, the management and other costs, is favorable for continuously pushing the medical education work to be continued to be carried out healthily and orderly in epidemic prevention and control normalization, and has important popularization significance.
Description
Technical Field
The invention belongs to the technical field of education information management, and particularly relates to a bidirectional learning quality assessment method and a continuous medical education closed-loop management system.
Background
The continuous medical education (ContinuingMedical Education, CME) mainly refers to post-academic medical education with knowledge updating as main content, and is a source of continuous knowledge updating, experience enrichment and self-improvement for medical staff. Continuing medical education generally adopts an offline meeting and professor mode, lacks intelligent system support, and lacks a unified and standardized process for management. The remote on-line education mode gradually enters the CME field, and has remarkable advantages. In recent years, technologies such as Internet video conference and network live broadcast are applied to medical academic conferences, traditional scientific and teaching activity modes are broken, and the method has the characteristics of openness, high efficiency, real-time, interaction and the like, and has outstanding advantages.
At present, continuous medical education mainly passes through an offline mode, so that the method is time-consuming and labor-consuming, has low efficiency, and is unfavorable for continuously pushing continuous medical education to work healthily and orderly. Meanwhile, the online conference software mainly uses third party conference software, only provides conference and live broadcast functions, and cannot completely cover planning options, expert offers, course making, chapter management, registration, check-in and card punching, online learning, interactive answering, online ward, online evaluation, credit distribution, learning quality analysis, teaching quality feedback, online and offline fusion and the like to continue the whole medical education flow. Many links are almost manually operated, a great deal of work is performed before, during and after each training, and careless mistakes are easy to occur.
Disclosure of Invention
The invention provides a bidirectional-based learning quality assessment method and a continuous medical education closed-loop management system for solving the technical problems in the background technology.
The invention is realized by adopting the following technical scheme: the learning quality assessment method based on two directions at least comprises the following steps:
presetting an education management mode according to a student or teaching requirement, and configuring a corresponding course teaching mode based on the education management mode;
the students complete online learning based on the course teaching mode, and store learning process records of each student, and meanwhile obtain test results of each student; generating at least one teaching quality feedback record by a learner in the learning process;
creating a learning index judging model, obtaining learning quality indexes of students based on the learning process records and the test results, arranging the students meeting the conditions according to the learning quality indexes based on a score rule in a descending order, granting scores to the first n students, wherein n is the number of the students which can be granted to the current course;
and creating a teaching index judgment model, generating a teaching quality index about courses based on the teaching quality feedback record, and continuously optimizing an education management mode and a course teaching mode based on the teaching quality index.
In a further embodiment, the learning process record includes at least:
the learner learns the check-in of the corresponding course, the online learning time and the online learning mode; the online learning mode comprises, but is not limited to, live lessons, on-demand lessons, online wards and online conferences.
In a further embodiment, the calculation flow of the learning quality index is as follows:
step 101, recording a self-defined learning process basic quality factor R based on the learning process i ， R i ＝(r i1 ,r i2 ,…,r ip ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein i represents the number of the learner, p represents the behavior category during learning course, r ip The number of times that the behaviour p is completed when the learner i learns the course is represented;
step 102, performing statistical analysis on the historical data of the behavior p to obtain a threshold value of the behavior p, and making a learning course for a learnerNon-dimensionality processing is carried out on the behavior category of the learning process to obtain a basic quality factor R related to the learning process i Is a learning dimensionless factor R' i ，R’ i ＝(r ’i1 ,r ’i2 ,…,r ’ip )；
Step 103, determining a determination auxiliary judgment factor beta, beta= (beta) by adopting an AHP analytic hierarchy process based on the basic quality factor of the learning process 1 ,β 2 ,…β p ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein, 0 is less than or equal to beta l ≤1，1≤q≤p，
104, calculating to obtain the learning process quality factor K of the learner i through the following formula i ：
K i ＝R’ i T β；
Step 105, performing dimensionless processing on the test results of the trainee i about the courses to obtain a score evaluation quality factor S ’i ：
Wherein S is the score set of the course learner, S i The test result of the student i;
step 106, based on the learning process quality factor R i Score evaluation quality factor S ’i Setting a score auxiliary evaluation factor θ, θ= (θ) 1 ,θ 2 ) Wherein 0.ltoreq.θ 1 ≤1,0≤θ 2 ≤1,θ 1 +θ 2 ＝1；
Step 107, calculating the learning quality index V of the learner i by the following formula i ：
V i ＝(K i ,S ’i ) T θ。
Because the magnitude order and the measurement unit of each collected index are different, the non-dimensionality treatment is required to be carried out on each parameter, the data is normalized, and the data analysis is convenient.
In a further embodiment, said R i ＝(r i1 ,r i2 ,r i3 ,r i4 ,r i5 ,r i6 ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is i1 On-line learning time length r for learning course for learner i2 Representing the offline times r i3 For the interaction times r i4 Indicating the time length r for reading learning materials i5 Indicating the number of times of ineffective learning detection hits, r i6 Learning the video clip times for playback; corresponding R' i ＝(r ’i1 ,r ’i2 ,r ’i3 ,r ’i4 ,r ’i5 ,r ’i6 )；
Wherein r is ’i1 For learning time length r online i1 Is not dimensionalized in (3):
wherein T is c For the total lessons of the corresponding lessons +.>m is a chemokines for learning duration;
r ’i2 for the offline times r i2 Is not dimensionalized in (3):
wherein O is max The maximum offline times of the students in the corresponding courses are obtained;
r ’i3 for the interaction times r i3 Is not dimensionalized in (3):
wherein K is int The maximum interaction times of the students in the corresponding courses are obtained;
r ’i4 for reading learning material duration r i4 Is not dimensionalized in (3):
wherein K is mat The maximum time length for reading learning materials appearing to the students in the corresponding courses;
r ’i5 detecting hit number r for ineffective learning i5 Is not dimensionalized in (3):
wherein K is inv Detecting the maximum number of hits for ineffective learning in the corresponding course;
r ’i6 representing the number r of playback learning video clips i6 Is not dimensionalized in (3):
wherein K is rev The video clip is learned a maximum number of times for playback.
The magnitude order and the measurement unit of each index are different, the dimensionless treatment is carried out, the data are normalized, and the data analysis is convenient.
In a further embodiment, the score rule includes at least the following assessment indicators: course learning duration, test score, ineffective learning detection hit times, check-in and check-out;
if at least one of the assessment indexes is not satisfied, the students belonging to the non-conforming condition will not be awarded with the score.
In a further embodiment, the teaching quality feedback record includes: reaction records, learning records, behavior records, and outcome records;
the response records are satisfaction of students and teaching teachers, and participation of online interaction and remote answering;
learning records are the theoretical and skill improvement conditions after the students participate in courses and the completion conditions of training targets;
behavior is recorded as the change of behavior in practice after the learner participates in the course;
the result is recorded as the performance change condition of the organization where the student is and the unit caused by the study of the student.
In a further embodiment, the calculation flow of the teaching quality index is as follows:
step 201, recording a custom teaching quality feedback factor Q based on teaching quality feedback i ， Q i ＝(q i1 ,q i2 ,…,q if ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein f represents a record item in the teaching quality feedback record, q if Representing feedback content about record item f, which is embodied by student i in the teaching quality feedback record;
step 202, feedback factor Q to teaching quality i Performing dimensionless treatment to obtain a corresponding teaching dimensionless factor Q i’ ，Q i’ ＝(q i1’ ,q i2’ ,…,q if ’)；
Step 203, dimensionless factor Q for teaching i ' mean processing:1≤t≤f；
step 204, calculating standard deviation of each record item:obtaining a standard deviation set sigma= (sigma) of the record items 1 ,σ 2 ,…,σ f )；
Step 205, determining feedback auxiliary judgment factors omega, omega= (omega) by adopting an AHP analytic hierarchy process based on the record item f 1 ,ω 2 ,…ω f ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein, 0.ltoreq.ω t ≤1，
Step 206, calculating a course k teaching quality feedback index: c (C) k ＝σ T ω；
Step 207, calculating a learner learning quality average value of course k:in->The learning quality index mean value of all students is obtained;
step 208, setting teaching assistance based on teaching quality feedback indexes and learning quality average valuesAuxiliary judging factor gamma, gamma= (gamma) 1 ,γ 2 )，0≤γ 1 ≤1，0≤γ 2 ≤1，γ 1 +γ 2 ＝1；
Step 209, calculating the teaching quality index T of course k by the following formula k ：
T k ＝(C k ,V i ’) T γ。
The continuous medical education closed-loop management system is used for realizing the learning quality assessment method, and comprises the following steps:
a planning module, configured to preset an education management mode according to a learner or a teaching requirement;
the course development module is set to configure a corresponding course teaching mode based on the education management mode;
a learning module configured to provide a learning platform for a learner; the learner performs on-line learning based on the course teaching mode on the learning platform,
the learning collection module is used for collecting and storing learning process records of each student and simultaneously obtaining test results of each student;
the teaching feedback module is used for collecting and storing at least one teaching quality feedback record generated by a learner in the learning process;
the learning quality analysis module is used for creating a learning index judging model, obtaining learning quality indexes of students based on the learning process records and the test results, arranging the students meeting the conditions according to the learning quality indexes based on the score rules in a descending order, granting scores to the first n students, wherein n is the number of the students which can be granted to the current course;
the teaching quality analysis module is used for creating a teaching index judgment model, generating teaching quality indexes about courses based on the teaching quality feedback records, and continuously optimizing an education management mode and a course teaching mode based on the teaching quality indexes.
In a further embodiment, the education management mode is a course establishment, an organization planning work, a course system establishment, a course information release, and a registration channel, a payment form, a sign-in and a score release requirement of a student for continuing medical education;
the course teaching mode is information of a lecturer, teaching contents, teaching forms, teaching materials and evaluation modes about courses.
In a further embodiment, the teaching quality analysis module comprises at least: a reaction layer, a learning layer, a behavior layer and a result layer;
the reaction layer is used for collecting and recording satisfaction of students and teaching teachers and participation of online interaction and remote answering;
the learning layer is used for collecting the theoretical and skill improvement conditions and the completion conditions of training targets of the students after participating in courses;
the behavior layer is used for collecting the behavior change condition in practice after the learner participates in the course;
the result layer is set to collect performance change conditions of the organization where the student is located and the unit caused by the study of the student.
The invention has the beneficial effects that: the invention forms bidirectional teaching quality assessment by evaluating the learning quality of the students and evaluating the teaching quality of the teaching teacher, and solves the problem that the teaching quality is only measured from a single direction of the students in the prior art. The closed-loop type continuous medical education management system is formed by the aspects of early-stage organization planning, course planning, learning conditions, teaching feedback and the like, so that the teaching quality is improved, and the learning efficiency is improved from multiple aspects.
The management mode of offline teaching is not suitable for online teaching any more, and the learning state of a learner and the teaching quality of a teaching teacher are not well monitored, so that the invention realizes the full-flow closed-loop management of continuous medical education activities such as continuous medical education activity planning, organization registration, learning, quality assessment, score distribution, result archiving and the like. Provides an integrated platform for an organizer, a teaching expert, a student and a educational administration staff to carry out comprehensive management, the organizer can plan a project, issue a learning course, manage and popularize courses by using the system, the teaching expert can make courseware, give lessons on line and answer questions remotely by using the system, through the platform, students can report the names, learn, interact and check online, obtain the scores, and the manager can patrol online, check learning and evaluation records, grant the scores, and analyze the teaching quality and continuously improve the project according to the data and teaching feedback information provided by the quality module. The system can also remind teachers to make courseware and students to learn online according to rules at regular time, and can import offline learning data. Not only can the learning condition of a single course be checked, but also a plurality of teaching data such as time intervals, multiple dimensions, comprehensive whole year and the like are provided. By adopting the closed-loop management mode, the management efficiency is improved, the teaching quality is improved, the time cost, the site, the traffic, the manpower, the management and other costs are saved, the continuous promotion of the health and orderly development of the continuous medical education work is facilitated, and the method has important popularization significance.
Drawings
Fig. 1 is a flowchart showing the operation of a closed-loop management system for continuing medical education according to embodiment 1 of the present invention.
Fig. 2 is a business framework diagram of a closed-loop management system for continuing medical education in embodiment 2 of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples of the specification.
In the conventional teaching system, the learning effect of a learner on a certain course is generally judged only by the evaluation result of the learner, but the judgment has certain unilateral performance, and the reason is as follows: the learning situation of a learner depends on the learning attitude or learning ability of the learner, but other reasons cannot be denied: for example, the course arrangement is unreasonable, the teaching time is insufficient, or the teaching teacher explains the way, the course difficulty and the like.
Based on the above problems, the existing teaching management cannot check other factors except students, and therefore, the teaching optimization of applicability is not given.
Example 1
The embodiment discloses a learning quality evaluation method based on two directions, which comprises the following steps:
firstly, presetting an education management mode according to requirements, and configuring a corresponding course teaching mode based on the education management mode;
step two, the students complete online learning based on the course teaching mode, and the learning process record of each student is stored, and meanwhile, the test score of each student is obtained; generating at least one teaching quality feedback record by a learner in the learning process;
creating a learning index judging model, obtaining learning quality indexes of students based on the learning process record and the test result, arranging the students meeting the conditions according to the learning quality indexes based on a score rule in a descending order, granting scores to the first n students, wherein n is the number of the students which can be granted to the current course;
and step four, creating a teaching index judgment model, generating a teaching quality index related to courses based on the teaching quality feedback record, and continuously optimizing an education management mode and a course teaching mode based on the teaching quality index.
In this embodiment, the education management mode in the first step is expressed as: items, i.e., information about the set-up project topic, training objectives, training programs, objective trainees, development forms, existing foundations, costs, and expected benefits.
The target trainees including the home staff, the inpainting trainees, the witnessing students, the interns, the community medical staff and the like are determined, and the current situation investigation can be carried out on the target training group simultaneously according to the information of the target trainees such as the training direction, the departments, the grouping and the like so as to know the learning requirement of the trainees.
Corresponding culture plans are set to adapt to different training requirements and targets. The sponsor sets the stand information and submits the stand information to the related responsible person for approval, the approval progress reminds the sponsor and the approver in the form of short messages and public number messages, and the approver can reject and agree or approve step by step.
After the course approval is passed, course content planning is carried out, a course system, a course theme, a learning form, a presenter and an agenda are planned, and then course basic information is determined: sponsors, underwriters, sponsors, time, place, cost, assessment requirements, and credit distribution criteria. After course content is determined, the expert is contacted with the expert in time to invite the expert, and a record is formed for each offer. For the specialists receiving the offers, the collection of specialist information is started and recorded into a specialist database in the system. If the offer fails, the system automatically reminds the sponsor of timely processing according to the emergency degree of the task, and a standby scheme is started if necessary.
The registration channel is generally in the form of a poster, is convenient to publicize, and supports online design of the registration channel in the forms of a poster, an H5 page and the like. The attractive poster can be designed in a dragging and pasting mode without additional development, and can be supported to be shared into communities, short messages and mailboxes by one key, and can also be downloaded and printed. The student enters the registration channel through a two-dimensional code entry on the poster, registers the registration, and the system collects basic information such as the work unit, department, name, sex, age, title, certificate type, certificate number, student number, contact mode and the like of the student. After the information is input by the students, the system can automatically judge and prompt the error information, and the system submits the verification of the sponsor after judging that the error is free, and the verification is successful after passing the registration.
In a further embodiment, the course teaching mode in the step one includes the following procedures: courseware making and course publishing: after the expert accepts the offer, firstly, the expert is organized to carry out discussion and determination of the training outline, and then, courseware is produced according to the training outline. The course mainly has three forms of meeting, live broadcast and recorded broadcast. The conference and live courseware is mainly PPT, and the courseware PPT can be uploaded to platform organization specialists for review after being manufactured. The recorded courseware is mainly in a video form, can be recorded through the system, can also upload recorded courseware to the system, and the system comprises a video editing function and can carry out online editing on the courseware.
After the courseware is manufactured, the course data is uploaded and the course is released through the system. Course information is extracted, and a learning entry is generated by one key according to the course form and the registration information. Setting course chapters and learning duration requirements in the system. The completion degree of online learning can be set, for example, 90% of the video is watched to complete learning, and the video can be set to disable fast forward and multiple play, and the mobile/computer terminal synchronously switches to log in according to the requirement.
For a recorded lesson, the video is set under the corresponding chapter. For conferences and live broadcasting courses, the system provides a preview function, and can be debugged in advance and color-coded. Test questions can be recorded and test papers can be made in the system, and the test papers are associated to corresponding courses, so that evaluation requirements are set. And sets the credit issuing rule.
The traditional continuous education activity mode of the off-line training can be used for realizing high frequency of occurrence in some scenes and difficult complete on-line realization in a short period, and off-line sign-in and sign-out information can be imported into a learning platform for unified management, so that learning duration can be automatically counted. Meanwhile, due to the limitation of sites and time, the system can also combine live broadcast and cloud classroom functions in the system to realize an online and offline fusion training mode, so that students who cannot arrive at the site can learn online.
Based on streaming media technology, the live lesson supports a computer end and a mobile end, and achieves multi-end coverage. The method supports RMTP and FLV formats, supports multiple definition modes and adapts to different network states: the system has high definition, standard and smooth modes, and can automatically degrade the technology according to the network quality, so that the user under the weak network can participate in learning better. The students can enter learning by clicking course links, public number menus or scanning course two-dimension codes, and each time the students enter and exit, the students can have complete records. The system can be internally provided with picture layout to support teaching operations such as sharing, whiteboard, labeling and the like.
The connection ward is mainly used for live-action teaching and remote ward checking in a video connection mode. The live pictures can be projected to a classroom or a large screen of a meeting place, and can be pushed to a live broadcast class. When the multi-party connection is carried out, a host can remotely control the camera and the microphone by utilizing a control function in the system, and the camera with consistent communication protocol can also remotely control the angle, the focal length and the like.
The cloud class mainly records and plays courses, and can learn according to chapters according to course setting.
Meanwhile, an online interaction and answering channel is configured, and when a learner encounters a problem in the learning process, the learner can ask questions to the expert in time, and the expert can answer online. The question and answer content is displayed to all students in the course, other students can answer and supplement, and the students can exchange and actively discuss each other through the question and answer channel.
In a further embodiment, the learning process record in the second step includes: the learner learns the check-in of the corresponding course, the online learning time and the online learning mode; the online learning mode comprises, but is not limited to, live lessons, on-demand lessons, online wards and online conferences.
Based on the learning process record, the calculation flow of the learning quality index is as follows:
step 101, recording a self-defined learning process basic quality factor R based on the learning process i ， R i ＝(r i1 ,r i2 ,…,r ip ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein i represents the number of the learner, p represents the behavior category during learning course, r ip The number of times that the behaviour p is completed when the learner i learns the course is represented;
step 102, performing statistical analysis on historical data of the behavior p to obtain a threshold value of the behavior p, and performing dimensionless processing on behavior categories made by a learner in course learning to obtain a basic quality factor R related to a learning process i Is a learning dimensionless factor R' i ，R’ i ＝(r ’i1 ,r ’i2 ,…,r ’ip )；
Step 103, determining an auxiliary judgment factor beta, beta= (beta) by adopting an AHP analytic hierarchy process based on the basic quality factor of the learning process 1 ,β 2 ,…β p ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein, 0 is less than or equal to beta l ≤1，1≤q≤p，
104, calculating to obtain the learning process quality factor K of the learner i through the following formula i ：
K i ＝R’ i T β；
Step 105, test the trainee i about courseThe score is processed in a dimensionless way to obtain a score evaluation quality factor S ’i ：
Wherein S is the score set of the course learner, S i The test result of the student i;
step 106, based on the learning process quality factor R i Score evaluation quality factor S ’i Setting a score auxiliary evaluation factor θ, θ= (θ) 1 ,θ 2 ) Wherein 0.ltoreq.θ 1 ≤1,0≤θ 2 ≤1,θ 1 +θ 2 ＝1；
Step 107, calculating the learning quality index V of the learner i by the following formula i ：
V i ＝(K i ,S ’i ) T θ。
In a further embodiment, p has a value of 6, that is, six learning courses are included, and the learning courses are respectively: the student online learning time length, offline behavior, interactive behavior, learning material browsing behavior, ineffective learning detection hit behavior, and playback learning video clip behavior. Correspondingly, R i ＝(r i1 ,r i2 ,r i3 ,r i4 ,r i5 ,r i6 ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is i1 On-line learning time length r for learning course for learner i2 Representing the offline times r i3 For the interaction times r i4 Indicating the time length r for reading learning materials i5 Indicating the number of times of ineffective learning detection hits, r i6 Learning the video clip times for playback; r's' i ＝(r ’i1 ,r ’i2 ,r ’i3 ,r ’i4 ,r ’i5 ,r ’i6 )。
Based on the above embodiment, the manner of non-dimensionalization processing of the quality factor for each learning process is as follows:
r ’i1 for learning time length r online i1 Is not dimensionalized in (3):
wherein, in the formula, T c For the total lessons of the corresponding lessons +.>m is a chemokines for learning duration; in this embodiment, <' > a->m＝2。
r ’i2 For the offline times r i2 Is not dimensionalized in (3):
wherein O is max The maximum offline times of the students in the corresponding courses are obtained;
r ’i3 for the interaction times r i3 Is not dimensionalized in (3):
wherein K is int The maximum interaction times of the students in the corresponding courses are obtained; default to 3.
r ’i4 For reading learning material duration r i4 Is not dimensionalized in (3):
wherein K is mat The maximum time length for reading learning materials appearing to the students in the corresponding courses;
r ’i5 detecting hit number r for ineffective learning i5 Is not dimensionalized in (3):
wherein K is inv Detecting the maximum number of hits for ineffective learning in the corresponding course;
r ’i6 representing the number r of playback learning video clips i6 Is not dimensionalized in (3):
wherein K is rev The video clip is learned a maximum number of times for playback.
It should be noted that, the score rule in the third step at least includes the following assessment indexes: course learning duration, test score, ineffective learning detection hit times, check-in and check-out; and are shown in table 1.
| Rules of | Requirements for |
| Duration of course learning | Generally, course online learning is performed for no less than 8 hours in one day. Specifically determined by course form |
| Evaluation of results | The accuracy is more than 80 percent |
| Ineffective learning detection number of hits | The accumulation is not more than 5 times. Adjusting according to course duration |
| Check-in and check-out | Completed sign-in and sign-out within a prescribed time |
That is, if a certain learner does not meet the requirement corresponding to one rule in the table, the learner who belongs to the non-conforming condition will not grant the learner a score.
In a further embodiment, the teaching quality feedback record in step two includes: reaction records, learning records, behavior records, and outcome records; the response records are satisfaction of students and teaching teachers, and participation of online interaction and remote answering; learning records are the theoretical and skill improvement conditions after the students participate in courses and the completion conditions of training targets; behavior is recorded as the change of behavior in practice after the learner participates in the course; the result is recorded as the performance change condition of the organization where the student is and the unit caused by the study of the student. The teaching quality feedback record is generated in the form of a questionnaire, the content of which may include: student satisfaction, teacher satisfaction, interaction answering participation, theoretical skill level improvement, practice behavior change and the like. Different options, corresponding to different scores.
In a further embodiment, the calculation flow of the teaching quality index is as follows:
step 201, recording a custom teaching quality feedback factor Q based on teaching quality feedback i ， Q i ＝(q i1 ,q i2 ,…,q if ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein f represents a record item in the teaching quality feedback record, q if Representing feedback content about record item f, which is embodied by student i in the teaching quality feedback record;
step 202, feedback factor Q to teaching quality i Performing dimensionless treatment to obtain a corresponding teaching dimensionless factor Q i ’，Q i ’＝(q i1 ’,q i2 ’,…,q if ’)；
Step 203, dimensionless factor Q for teaching i ' mean processing:1≤t≤f；
step 204, calculating standard deviation of each record item:obtaining a standard deviation set sigma= (sigma) of the record items 1 ,σ 2 ,…,σ f ) The method comprises the steps of carrying out a first treatment on the surface of the In this embodiment, the value of f is 4;
step 205, determining feedback auxiliary judgment factors omega, omega = based on the record item f by using an AHP analytic hierarchy process(ω 1 ,ω 2 ,…ω f ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein, 0.ltoreq.ω t ≤1，
Step 206, calculating a course k teaching quality feedback index: c (C) k ＝σ T ω；
Step 207, calculating a learner learning quality average value of course k:in->The learning quality index mean value of all students is obtained; step 208, setting teaching auxiliary judgment factors gamma, gamma= (gamma) based on teaching quality feedback indexes and learning quality average values 1 ,γ 2 )，0≤γ 1 ≤1，0≤γ 2 ≤1，γ 1 +γ 2 ＝1；
Step 209, calculating the teaching quality index T of course k by the following formula k ：
T k ＝(C k ,V i ’) T γ。
Compared with the traditional teaching mode, the bidirectional learning quality assessment method disclosed by the embodiment is based on the following steps:
the traditional mode is broken through and limited by time, space, number of students, scientific and teaching conditions and the like, and an education pattern of complementation of coexistence of sites and networks is formed. Continuously promote the medical education work to be continued to be carried out healthily and orderly.
The semi-manual and semi-automatic management mode of the continuous education work is finished, the full-flow closed loop of the continuous education activity is realized, and the full-period management of the continuous education activity is completed through a unified platform. And the emergency situation can be dealt with, the conference and live broadcast training can be rapidly carried out, and the emergency training plays an important role.
The data acquisition and the mining are carried out on the whole process of the continuous educational activity on line, and the multi-dimensional analysis of the whole process on-line supervision and the teaching quality (the learning quality of a student and the teaching quality of a teacher) of the continuous educational activity is creatively realized. The method provides decision support for optimizing courses, improving teaching quality and perfecting continuous education activity management.
The embodiment is suitable for continuing medical education, and clinical staff is busy in daily work no matter from a tertiary comprehensive hospital or a community hospital. The closed-loop management system for continuing medical education provides flexible, elastic and convenient learning resources and conditions for medical staff, and can complete continuing medical education by utilizing fragmentation time so as to update and enrich own medical knowledge and skills.
Example 2
The embodiment discloses a continuous medical education closed-loop management system for implementing the bidirectional-based learning quality assessment method described in embodiment 1, which comprises the following steps: the system comprises a planning module, a course development module, a learning acquisition module, a teaching feedback module, a teaching quality analysis module and an optimization module.
Further, the planning module is set to preset an education management mode according to requirements;
the course development module is set to configure a corresponding course teaching mode based on the education management mode;
the learning module is arranged to provide a learning platform for a learner, and the learner completes online learning on the learning platform based on the course teaching mode;
the learning collection module is used for collecting and storing learning process records of each student and simultaneously obtaining test results of each student;
the teaching feedback module is used for collecting and storing at least one teaching quality feedback record generated by a learner in the learning process;
the learning quality analysis module is used for creating a learning index judging model, obtaining learning quality indexes of students based on the learning process records and the test results, arranging the students meeting the conditions according to the learning quality indexes based on the score rules in a descending order, granting scores to the first n students, wherein n is the number of the students which can be granted to the current course; and comprehensively evaluating the learning quality of the students through summarized data according to click and stay records, effective learning duration, examination results, interactive answering participation conditions and the like. The method can not only aim at single course, but also comprehensively study records for comparison analysis, and provide the comparison analysis for management staff in the form of reports.
The teaching quality analysis module is used for creating a teaching index judgment model and generating a teaching quality index about courses based on the teaching quality feedback record; the method comprises the steps of collecting teaching quality related data and teaching feedback, wherein the teaching quality related data comprise basic information of students, the number of people participating in learning per time period/class, the peak learning time period, the learning time record of each class/person, click and stay records, the course learning time arrangement, the learning progress, the midway offline times, the reading learning data time duration, the ineffective learning detection hit times, the number of replay learning video clips, the number of people participating in examination, the answer accuracy of each question, the number of passing people, the score acquisition condition, the answer details, the interactive answer participation records and the like, and further comprise basic information of teaching experts, teaching forms, teaching time duration and the like. Teaching feedback is divided into four aspects according to the koch model: reaction layer: the satisfaction degree of students and teaching teachers and the participation degree of online interaction and remote answering are collected; learning layer: the method mainly collects whether theoretical, skill and the like are improved or not after teaching of a student and the completion condition of a training target; behavior layer: the behavior change condition in practice after training of students is mainly collected; the result layer: and collecting performance change conditions of the organization where the student is located and the unit caused by the study of the student. The data acquisition module penetrates through the whole learning process and provides data support for teaching quality assessment.
And the optimizing module is used for continuously optimizing the education management mode and the course teaching mode based on the teaching quality index.
Claims (7)
1. The learning quality assessment method based on two directions is characterized by comprising the following steps:
presetting an education management mode according to requirements, and configuring a corresponding course teaching mode based on the education management mode;
the students complete online learning based on the course teaching mode, and store learning process records of each student, and meanwhile obtain test results of each student; generating at least one teaching quality feedback record by a learner in the learning process;
creating a learning index judging model, obtaining learning quality indexes of students based on the learning process records and the test results, arranging the students meeting the conditions according to the learning quality indexes based on a score rule in a descending order, granting scores to the first n students, wherein n is the number of the students which can be granted to the current course;
creating a teaching index judgment model, generating a teaching quality index about courses based on the teaching quality feedback record, and continuously optimizing an education management mode and a course teaching mode based on the teaching quality index;
the calculation flow of the learning quality index is as follows:
step 101, recording a self-defined learning process basic quality factor R based on the learning process i ，R i ＝(r i1 ,r i2 ,…,r ip ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein i represents the number of the learner, p represents the behavior category during learning course, r ip The number of times that the behaviour p is completed when the learner i learns the course is represented;
step 102, performing statistical analysis on historical data of the behavior p to obtain a threshold value of the behavior p, and performing dimensionless processing on behavior categories made by a learner in course learning to obtain a basic quality factor R related to a learning process i Is a learning dimensionless factor R' i ，R’ i ＝(r’ i1 ,r’ i2 ,…,r’ ip )；
Step 103, based on the learning process basic quality factor R i Determining auxiliary judgment factor beta, beta= (beta) by using AHP analytic hierarchy process 1 ,β 2 ,…β p ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein, 0 is less than or equal to beta l ≤1，1≤q≤p，
104, calculating to obtain the learning process quality factor K of the learner i through the following formula i ：
Step 105, performing dimensionless processing on the test results of the trainee i about the courses to obtain a score evaluation quality factor S' i ：
Wherein S is the score set of the course learner, S i The test result of the student i;
step 106, based on the learning process quality factor R i Score evaluation quality factor S' i Setting a score auxiliary evaluation factor θ, θ= (θ) 1 ,θ 2 ) Wherein 0.ltoreq.θ 1 ≤1,0≤θ 2 ≤1,θ 1 +θ 2 ＝1；
Step 107, calculating the learning quality index V of the learner i by the following formula i ：
The R is i ＝(r i1 ,r i2 ,r i3 ,r i4 ,r i5 ,r i6 ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is i1 On-line learning time length r for learning course for learner i2 Representing the offline times r i3 For the interaction times r i4 Indicating the time length r for reading learning materials i5 Indicating the number of times of ineffective learning detection hits, r i6 Learning the video clip times for playback; corresponding R' i ＝(r’ i1 ，r’ i2 ,r’ i3 ，r’ i4 ，r’ i5 ,r’ i6 )；
Wherein r 'is' i1 For learning time length r online i1 Is not dimensionalized in (3):
wherein T is c For the total lessons of the corresponding lessons +.>m is a chemokines for learning duration;
r’ i2 for the offline times r i2 Is not dimensionalized in (3):
wherein O is max The maximum offline times of the students in the corresponding courses are obtained;
r’ i3 for the interaction times r i3 Is not dimensionalized in (3):
wherein K is int The maximum interaction times of the students in the corresponding courses are obtained;
r’ i4 for reading learning material duration r i4 Is not dimensionalized in (3):
wherein K is mat The maximum time length for reading learning materials appearing to the students in the corresponding courses;
r’ i5 detecting hit number r for ineffective learning i5 Is not dimensionalized in (3):
wherein K is inv Detecting the maximum number of hits for ineffective learning in the corresponding course;
r’ i6 representing the number r of playback learning video clips i6 Is not dimensionalized in (3):
wherein K is rev Learning video clips for playbackMaximum number of times;
the calculation flow of the teaching quality index is as follows:
step 201, recording a custom teaching quality feedback factor Q based on teaching quality feedback i ，Q i ＝(q i1 ,q i2 ,…,q if ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein f represents a record item in the teaching quality feedback record, q if Representing feedback content about record item f, which is embodied by student i in the teaching quality feedback record;
step 202, feedback factor Q to teaching quality i Performing dimensionless treatment to obtain a corresponding teaching dimensionless factor Q i’ ，Q i’ ＝(q i1’ ,q i2’ ,…,q if’ )；
Step 203, dimensionless factor Q for teaching i’ And (3) carrying out mean value treatment:
step 204, calculating standard deviation of each record item:obtaining a standard deviation set sigma= (sigma) of the record items 1 ,σ 2 ,…,σ f )；
Step 205, determining feedback auxiliary judgment factors omega, omega= (omega) by adopting an AHP analytic hierarchy process based on the record item f 1 ,ω 2 ,…ω f ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein, 0.ltoreq.ω t ≤1，
Step 206, calculating a course k teaching quality feedback index:
step 207, calculating a learner learning quality average value of course k:in->The learning quality index mean value of all students is obtained;
step 208, setting teaching auxiliary judgment factors based on teaching quality feedback indexes and learning quality average values
Step 209, calculating the teaching quality index T of course k by the following formula k ：
2. The bi-directional based learning quality assessment method of claim 1, wherein the learning process record comprises at least:
the learner learns the check-in of the corresponding course, the online learning time and the online learning mode; the online learning mode comprises, but is not limited to, live lessons, on-demand lessons, online wards and online conferences.
3. The bi-directional based learning quality assessment method of claim 1, wherein the score rules include at least the following assessment criteria: course learning duration, test score, ineffective learning detection hit times, check-in and check-out;
if at least one of the assessment indexes is not satisfied, the students belonging to the non-conforming condition will not be awarded with the score.
4. The bi-directional based learning quality assessment method of claim 1, wherein the teaching quality feedback record comprises: reaction records, learning records, behavior records, and outcome records;
the response records are satisfaction of students and teaching teachers, and participation of online interaction and remote answering;
learning records are the theoretical and skill improvement conditions after the students participate in courses and the completion conditions of training targets;
behavior is recorded as the change of behavior in practice after the learner participates in the course;
the result is recorded as the performance change of the organization where the learner is located and the unit caused by the learning of the learner.
5. A continuing medical education closed-loop management system for implementing the method of any one of claims 1-4, comprising:
a planning module, configured to preset an education management mode according to a learner or a teaching requirement;
the course development module is set to configure a corresponding course teaching mode based on the education management mode;
a learning module configured to provide a learning platform for a learner; the learner completes online learning on the learning platform based on the course teaching mode;
the learning collection module is used for collecting and storing learning process records of each student and simultaneously obtaining test results of each student;
the teaching feedback module is used for collecting and storing at least one teaching quality feedback record generated by a learner in the learning process;
the learning quality analysis module is used for creating a learning index judging model, obtaining learning quality indexes of students based on the learning process records and the test results, arranging the students meeting the conditions according to the learning quality indexes based on the score rules in a descending order, granting scores to the first n students, wherein n is the number of the students which can be granted to the current course;
the teaching quality analysis module is used for creating a teaching index judgment model and generating a teaching quality index about courses based on the teaching quality feedback record;
and the optimizing module is used for continuously optimizing the education management mode and the course teaching mode based on the teaching quality index.
6. The closed-loop management system for continuing medical education according to claim 5, wherein the education management mode is a course setting, an organization planning work, establishment of a course system, release of course information, and a registration channel, a payment form, a check-in and a credit issuing requirement of a learner;
the course teaching mode is information of a lecturer, teaching contents, teaching forms, teaching materials and evaluation modes about courses.
7. The continued medical education closed-loop management system of claim 5,
the teaching quality analysis module at least comprises: a reaction layer, a learning layer, a behavior layer and a result layer;
the reaction layer is used for collecting and recording satisfaction of students and teaching teachers and participation of online interaction and remote answering;
the learning layer is used for collecting the theoretical and skill improvement conditions and the completion conditions of training targets of the students after participating in courses;
the behavior layer is used for collecting the behavior change condition in practice after the learner participates in the course;
the result layer is used for collecting performance change conditions of the organization where the student is located and the unit caused by the study of the student; the learning quality analysis module includes:
the course inspection unit is used for checking course states, teaching progress, student states, learning records, answering records, examination and score records in real time;
the data acquisition unit is arranged for acquiring data related to teaching quality and feedback of the teaching quality;
the learning quality analysis unit is used for analyzing the learning quality of the students and comprehensively evaluating the learning quality of the students through summarized data according to clicking and stay records, effective learning duration, examination results and interactive answering participation conditions.
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