Nuclear medicine clinical and continuing education system and method based on structured instructional resources
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XIEHE HOSPITAL ATTACHED TO TONGJI MEDICAL COLLEGE HUAZHONG SCI & TECH UNIV
- Filing Date
- 2024-01-22
- Publication Date
- 2026-07-24
AI Technical Summary
Current clinical and continuing education in nuclear medicine cannot accurately assess trainees' knowledge acquisition, and the teaching content is monotonous and cannot be adjusted in real time, resulting in poor teaching quality.
Design a nuclear medicine training and education system based on structured teaching resources, including a structured teaching resource system module, a learning evaluation standard module, and a personalized multi-level learning model module. By designing course elements from multiple dimensions, collecting student data for learning evaluation, and generating personalized teaching resources.
It enables the provision of personalized, multi-level teaching content based on students' needs, improves teaching quality, meets the needs of different learning stages, combines imaging and clinical needs, enhances students' participation, and integrates medical humanities education.
Smart Images
Figure CN122453073A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of educational technology, and in particular to a system and method for clinical and continuing education in nuclear medicine based on structured teaching resources. Background Technology
[0002] The core of medical talent training lies in competency-based education and job skills development. In recent years, medical imaging, especially nuclear medicine, has seen rapid growth, particularly in the last decade. However, many medical schools have not included nuclear medicine in their curricula, and medical students often do not receive dedicated training in it during their internships. Furthermore, the highly specialized nature of nuclear medicine within medical imaging, coupled with a lack of understanding among many clinicians, makes it difficult to master in a short period. As a discipline that emphasizes both theory and practice, teaching should highlight its advanced and unique aspects, focusing on cultivating students' learning and critical thinking abilities while simultaneously enhancing their practical and comprehensive skills. Unlike general medical courses, many resource-constrained medical schools are unable to establish nuclear medicine departments for residency training.
[0003] Therefore, the nuclear medicine training and education system and method based on structured teaching resources proposed in this invention can effectively break through the limitations of geography and time, greatly expand the audience of high-quality medical education resources, alleviate the bottleneck of standardized training of nuclear medicine professionals in my country and the current serious uneven distribution of educational resources. Summary of the Invention
[0004] The purpose of this invention is to provide a nuclear medicine clinical and continuing education system and method based on structured teaching resources, which addresses the current situation in nuclear medicine clinical and continuing education practices where the student's mastery of relevant knowledge and experience cannot be accurately assessed, the teaching content cannot be adjusted in real time, and the teaching content is monotonous and outdated, ultimately leading to poor teaching quality.
[0005] According to one aspect of the present invention, a nuclear medicine clinical and continuing education system based on structured teaching resources is provided, comprising:
[0006] (1) Nuclear Medicine Structured Teaching Resource System Module: Based on the actual clinical business and research needs of nuclear medicine, the course elements are designed in multiple dimensions according to the course objectives, course content, and course implementation. The teaching resources refer to all course elements, including five types: text, illustrations, audio, video, and tests. According to the difficulty of the course elements, four progressively higher difficulty levels are marked: Level 1, Level 2, Level 3, and Level 4. All course elements are labeled with objectives: job objectives, clinical objectives, and research objectives.
[0007] (2) Learning evaluation standard module: collect data on students' use of the system, analyze the data, and evaluate the students' learning effect. The data used includes login value RW, course value LW, difficulty factor λ, adjustment factor β, focus value FW and test score SW.
[0008] (3) Personalized multi-level learning mode module, including student data collection module, student profile self-updating module and teaching resource self-organizing module; the student data collection module is used to collect student learning information, historical learning records, learning evaluation indicators and determine the student's learning performance in various dimensions; the student profile self-updating module combines the multi-dimensional learning evaluation of each student to plan the corresponding learning profile of the student; the teaching resource self-organizing module automatically generates personalized teaching resources according to the pre-scoring of teaching resources and student profile.
[0009] Furthermore, in the structured teaching resource system module for nuclear medicine, the job target labels are physicist, chemist, technician, physician, and nurse; the clinical target labels are associate degree, bachelor's degree, associate degree training, and continuing education; and the research target labels are bachelor's degree, master's degree, doctoral degree, and postdoctoral training.
[0010] Furthermore, in the structured teaching resource system module of nuclear medicine, the course elements have three structured dimensions: type, difficulty, and objective. Based on these three dimensions, teaching resources are combined for each student to form a personalized course body.
[0011] Furthermore, in the learning evaluation criteria module, the total login time and total number of times a student's account is logged in are collected and recorded as login time RT and login count RC, respectively. The login time RT (minutes) and login count RC are then converted into... The login value RW is calculated using the formula RW=a1*RT+a2*RC, where a1 and a2 are the system's preset weight coefficients, with a1=0.02 and a2=0.3 respectively. The course value LW for each part of the course is calculated based on the structured teaching resources, specifically the number of text characters W, the number of illustrations F, the audio duration Vc (minutes), and the video duration Vd (minutes) of the teaching resources. The calculation formula is LW=b1*W+b2*F+b3*Vc+b4*Vd, where b1, b2, b3, and b4 are the system's preset weight coefficients, with b1=0.03, b2=0.6, b3=0.3, and b4=1.0 respectively. The difficulty factor λ for each part of the course is calculated based on the structured teaching resources, derived from the total test difficulty coefficients corresponding to that part of the course in the teaching resources. The calculation formula is: ,in Sn represents the total number of students who completed a certain test, and Xn represents the total number of students who correctly completed the test.
[0012] Furthermore, in the learning evaluation criteria module, the difficulty coefficient of all tests is initialized uniformly to [value missing]. Based on the student's job title, work experience, and learning evaluation scores from each login, the adjustment factor β is calculated using the following formula: ,in The job coefficient is 1 for physicists, chemists, and technicians, and 0.8 for doctors and nurses. The coefficient for work experience is 1 for more than 5 years, 0.8 for less than 5 years but more than 2 years, and 0.5 for less than 2 years. The learning evaluation criteria obtained from a certain test;
[0013] When the teaching resources used by students are videos, tests with corresponding tags are displayed during the video playback. The time when the test appears and the time when the student selects the test answer are collected, and the time difference TD (minutes) between the two is recorded to calculate the focus value FW.
[0014] After completing each course section, all tests corresponding to that section of the course in the teaching resources are displayed. All student answers to these tests are collected and compared with preset standard answers to obtain a test score SW. Each time a student logs into the system, the login value RW, course value LW, difficulty factor λ, adjustment factor β, focus value FW, and test score SW are summed to obtain the learning evaluation standard G, calculated using the following formula: Where g1, g2, g3, and g4 are the preset weighting factors for login value RW, course value LW, focus value FW, and test score SW, respectively, and g4>g3>g2>g1>1.2.
[0015] Furthermore, the self-organizing module of teaching resources includes teaching review units and expectation units. The review units randomly arrange the tests with incorrect answers based on the learners' learning process. The expectation units arrange the teaching resources that have not been learned by type, difficulty, and goal based on the learners' training objectives and previous test scores. The review units and expectation units together form the learners' individualized teaching resources and automatically organize and iterate after each learning session.
[0016] Furthermore, in the personalized multi-level learning model module, the nuclear medicine training and education for trainees is based on structured teaching resources and achieves personalized, multi-level learning for trainees through scientifically designed learning evaluation standards.
[0017] According to another aspect of the present invention, a learning method for a nuclear medicine clinical and continuing education system based on structured teaching resources is provided. The method is as follows: (1) Initialize the student profile. When a new student logs in for the first time, he first selects the training objective and then conducts a competency test to obtain the initial student profile.
[0018] (2) Collect the total duration and total number of logins of student accounts, and record them as login duration RT and login count RC respectively. Calculate the login value RW from the login duration RT (minutes) and login count RC.
[0019] (3) Calculate the course value LW for each part of the course based on the structured teaching resources;
[0020] (4) Calculate the difficulty factor λ for each part of the course based on the structured teaching resources;
[0021] (5) Calculate the adjustment factor β based on the student's job position, work experience, and learning evaluation standard scores during each login to the system;
[0022] (6) When the teaching resource used by the student is a video, the test with the corresponding label is displayed during the video playback. The time when the test appears and the time when the student selects the test answer are collected. The time difference TD (minutes) between the two is recorded and the focus value FW is calculated.
[0023] (7) After completing each part of the course, display all the tests corresponding to that part of the course in the teaching resources, collect all the students' answers to the test, compare them with the preset standard answers, and obtain the test score SW;
[0024] (8) After each student logs into the system to learn, the learning evaluation standard G is obtained by summing the login value RW, course value LW, difficulty factor λ, adjustment factor β, focus value FW, and test score SW. The calculation formula is as follows: .
[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0026] 1. This nuclear medicine training and education system and method based on structured teaching resources uses multi-dimensional and structured teaching resources to design course evaluation standards, providing appropriate course elements for medical staff or physicians at different learning stages, realizing individualized instruction, and teaching nuclear medicine cases in a multi-level and phased manner, reflecting the different levels of needs of resident trainees at different grades in the learning process.
[0027] 2. In this nuclear medicine training and education system and method based on structured teaching resources, nuclear medicine imaging characteristics are organically integrated with ideological and political education in the "Internet+" and "smart teaching" environment, according to clinical needs and combined with the development of imaging, laboratory diagnostics and other technologies.
[0028] 3. This nuclear medicine training and education system and method based on structured teaching resources enhances trainees' participation, avoids the rote learning of traditional textbook courses, and incorporates medical humanities. Through enrichment, it helps trainees establish medical ethics and teaches them how to be good doctors with both moral integrity and professional competence. Attached Figure Description
[0029] Figure 1 A flowchart illustrating the structure of the nuclear medicine structured teaching resource system modules;
[0030] Figure 2 A flowchart illustrating the structure of the learning evaluation criteria module;
[0031] Figure 3 The flowchart shows the structure of the personalized multi-level learning mode module. Detailed Implementation
[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0033] See Figure 1 In the structured teaching resource system module, teaching resources refer to all course elements, including five types: text, illustrations, audio, video, and tests. Based on the difficulty of the course elements, four difficulty levels are marked: Level 1 (easy), Level 2 (moderate), Level 3 (difficult), and Level 4 (advanced). All course elements are tagged with target objectives: job objective, clinical objective, and research objective. Job objective tags are categorized as physicist, chemist, technician, physician, and nurse; clinical objective tags are categorized as associate degree, bachelor's degree, associate degree training, and continuing education (advanced training); and research objective tags are categorized as bachelor's degree, master's degree, doctoral degree, and postdoctoral training.
[0034] The course elements within the system have three structured dimensions: type, difficulty, and objective. Based on these three dimensions, teaching resources are combined for each student to form a personalized course body.
[0035] See Figure 2 In the learning evaluation criteria module, when a new student logs in for the first time, they first select a training objective, and then take an ability test to obtain an initial student profile.
[0036] The total login time and total number of logins for student accounts are collected and recorded as login time RT and login count RC, respectively. The login value RW is calculated from the login time RT (minutes) and login count RC using the formula RW = a1*RT + a2*RC, where a1 and a2 are system-preset weighting coefficients (a1 = 0.02, a2 = 0.3). The course value LW for each part of the course is calculated based on the structured teaching resources. The text count W, number of illustrations F, audio duration Vc (minutes), and video duration Vd (minutes) of the teaching resources are calculated using the formula LW = b1*W + b2*F + b3*Vc + b4*Vd, where b1, b2, b3, and b4 are system-preset weighting coefficients (b1 = 0.03, b2 = 0.6, b3 = 0.3, b4 = 1.0). The difficulty factor λ for each part of the course is calculated based on the total test difficulty coefficients corresponding to that part of the course in the teaching resources using the formula: ,in Sn represents the total number of students who completed a certain test, and Xn represents the total number of students who correctly completed the test.
[0037] In the learning evaluation criteria module, the difficulty level of all tests is initialized uniformly as follows: Based on the student's job title, work experience, and learning evaluation scores from each login, the adjustment factor β is calculated using the following formula: ,in The job coefficient is 1 for physicists, chemists, and technicians, and 0.8 for doctors and nurses. The coefficient represents the work experience level: 1 for more than 5 years, 0.8 for less than 5 years but more than 2 years, and 0.5 for less than 2 years; Gn represents the learning evaluation standard obtained from a certain test.
[0038] When students use video as the teaching resource, a test with corresponding tags is displayed during video playback. The time when the test appears and the time when the student selects a test answer are recorded, and the time difference TD (minutes) between the two is recorded. The focus value FW is calculated using the following formula: If the test answer is correct, C=1; otherwise, C=0.
[0039] After completing each course section, all tests corresponding to that section of the course in the teaching resources are displayed. All student answers to these tests are collected and compared with preset standard answers to obtain a test score SW. Each time a student logs into the system, the login value RW, course value LW, difficulty factor λ, adjustment factor β, focus value FW, and test score SW are summed to obtain the learning evaluation standard G, calculated using the following formula: Where g1, g2, g3, and g4 are the preset weighting factors for login value RW, course value LW, focus value FW, and test score SW, respectively, and g4>g3>g2>g1>1.2.
[0040] See Figure 3 The personalized multi-level learning mode module includes a student data collection module, a student profile self-updating module, and a teaching resource self-organizing module. The student data collection module collects student learning information, historical learning records, and learning evaluation indicators to determine students' learning performance across various dimensions. The student profile self-updating module combines each student's multi-dimensional learning evaluations to plan their corresponding learning profile. The teaching resource self-organizing module automatically generates personalized teaching resources based on the pre-scoring of teaching resources and the student profile.
[0041] The self-organizing module for instructional resources includes review units and prospective units. The review units randomly arrange incorrectly answered tests based on the learner's learning process. The prospective units arrange unlearned instructional resources by type, difficulty, and objective based on the learner's training objectives and previous test scores. Together, the review and prospective units form individualized instructional resources for each learner, which then iterate and organize themselves after each learning session.
[0042] Nuclear medicine training for trainees is based on structured teaching resources and uses scientifically designed learning evaluation standards to achieve personalized, multi-level learning for trainees.
[0043] In some implementations, a learning method for a nuclear medicine clinical and continuing education system based on structured teaching resources is disclosed. The method is as follows: (1) Initialize the student profile. When a new student logs in for the first time, he / she first selects the training objective and then performs a competency test to obtain the initial student profile.
[0044] (2) Collect the total duration and total number of logins of student accounts, and record them as login duration RT and login count RC respectively. Calculate the login value RW from the login duration RT (minutes) and login count RC.
[0045] (3) Calculate the course value LW for each part of the course based on the structured teaching resources;
[0046] (4) Calculate the difficulty factor λ for each part of the course based on the structured teaching resources;
[0047] (5) Calculate the adjustment factor β based on the student's job position, work experience, and learning evaluation standard scores during each login to the system;
[0048] (6) When the teaching resource used by the student is a video, the test with the corresponding label is displayed during the video playback. The time when the test appears and the time when the student selects the test answer are collected. The time difference TD (minutes) between the two is recorded and the focus value FW is calculated.
[0049] (7) After completing each part of the course, display all the tests corresponding to that part of the course in the teaching resources, collect all the students' answers to the test, compare them with the preset standard answers, and obtain the test score SW;
[0050] (8) After each student logs into the system to learn, the learning evaluation standard G is obtained by summing the login value RW, course value LW, difficulty factor λ, adjustment factor β, focus value FW, and test score SW. The calculation formula is as follows: .
[0051] The above description of the embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.
Claims
1. A nuclear medicine clinical and continuing education system based on structured teaching resources, characterized in that: include: (1) Nuclear Medicine Structured Teaching Resource System Module: Based on the actual clinical business and research needs of nuclear medicine, the course elements are designed in multiple dimensions according to the course objectives, course content, and course implementation. The teaching resources refer to all course elements, including five types: text, illustrations, audio, video, and tests. According to the difficulty of the course elements, four progressively higher difficulty levels are marked: Level 1, Level 2, Level 3, and Level 4. All course elements are labeled with objectives: job objectives, clinical objectives, and research objectives. (2) Learning Evaluation Standards Module: This module collects data on student system usage, analyzes the data, and evaluates student learning outcomes. The data used includes login value RW, course value LW, difficulty factor λ, adjustment factor β, focus value FW, and test score SW. It also collects the total login time and total number of logins for each student account, recording them as login time RT and login count RC, respectively. The login value RW is calculated from the login time RT (minutes) and login count RC using the formula RW = a1*RT + a2*RC. These are the system's preset weight coefficients, taken as follows: Based on the structured teaching resources, the course value LW for each part of the course is calculated. The text count W, the number of illustrations F, the audio duration Vc (minutes), and the video duration Vd (minutes) of the teaching resources are calculated using the following formula: ,in These are the system's preset weight coefficients, taken as follows: The difficulty factor λ for each part of the course is calculated based on the structured teaching resources. It is derived from the difficulty coefficients of all tests corresponding to that part of the course in the teaching resources. The calculation formula is as follows: ,in Sn is the total number of students who completed a certain test, and Xn is the total number of students who correctly completed the test. (3) Personalized multi-level learning mode module, including student data collection module, student profile self-updating module and teaching resource self-organization module; the student data collection module is used to collect student learning information, historical learning records, learning evaluation indicators and determine the student's learning performance in various dimensions. The student profile self-updating module combines multi-dimensional learning evaluations for each student to plan a corresponding learning profile; the teaching resource self-organizing module automatically generates personalized teaching resources based on the pre-scoring of teaching resources and student profiles.
2. The nuclear medicine clinical and continuing education system based on structured teaching resources according to claim 1, characterized in that: In the structured teaching resource system module of nuclear medicine, the job target labels are physicist, chemist, technician, physician, and nurse; the clinical target labels are junior college, undergraduate, junior college training, and continuing education; and the research target labels are undergraduate, master's, doctoral, and postdoctoral training.
3. The nuclear medicine clinical and continuing education system based on structured teaching resources according to claim 1, characterized in that: In the structured teaching resource system module of nuclear medicine, the course elements have three structured dimensions: type, difficulty, and objective. Based on these three dimensions, teaching resources are combined for each student to form a personalized course body.
4. The nuclear medicine clinical and continuing education system based on structured teaching resources according to claim 1, characterized in that: In the learning evaluation criteria module, the difficulty coefficient of all tests is initialized uniformly as follows: Based on the student's job title, work experience, and learning evaluation scores from each login, the adjustment factor β is calculated using the following formula: ,in The job coefficient is 1 for physicists, chemists, and technicians, and 0.8 for doctors and nurses. The coefficient represents the work experience level: 1 for more than 5 years, 0.8 for less than 5 years but more than 2 years, and 0.5 for less than 2 years; Gn represents the learning evaluation standard obtained from a certain test. When the learning resource used by the student is video, a test with corresponding tags is displayed during video playback. The time when the test appears and the time when the student selects a test answer are recorded, and the time difference TD (minutes) between the two is recorded. The focus value FW is calculated using the following formula: If the test answer is correct, then Conversely ; After completing each course section, all tests corresponding to that section of the course in the teaching resources are displayed. All student answers to these tests are collected and compared with preset standard answers to obtain a test score SW. Each time a student logs into the system, the login value RW, course value LW, difficulty factor λ, adjustment factor β, focus value FW, and test score SW are summed to obtain the learning evaluation standard G, calculated using the following formula: Where g1, g2, g3, and g4 are the preset weighting factors for login value RW, course value LW, focus value FW, and test score SW, respectively, and g4>g3>g2>g1>1.
2.
5. The nuclear medicine clinical and continuing education system based on structured teaching resources according to claim 1, characterized in that: The self-organizing module of teaching resources includes a review unit and a prospective unit. The review unit randomly arranges the tests with incorrect answers based on the learner's learning process. The prospective unit arranges the teaching resources that have not been learned by type, difficulty, and objective based on the learner's training objectives and test scores. The review unit and the prospective unit together form the learner's individualized teaching resources and automatically organize and iterate after each learning session.
6. The nuclear medicine clinical and continuing education system based on structured teaching resources according to claim 1, characterized in that: In the personalized, multi-level learning model module, the nuclear medicine training and education for trainees is based on structured teaching resources and uses scientifically designed learning evaluation standards to achieve personalized, multi-level learning for trainees.
7. The learning method of the nuclear medicine clinical and continuing education system based on structured teaching resources according to any one of claims 1 to 6, characterized in that: (1) Initialize student profile: When a new student logs in for the first time, they first select the training objective and then conduct a competency test to obtain the initial student profile. (2) Collect the total duration and total number of logins of student accounts, and record them as login duration RT and login count RC respectively. Calculate the login value RW from the login duration RT (minutes) and login count RC. (3) Calculate the course value LW for each part of the course based on the structured teaching resources; (4) Calculate the difficulty factor λ for each part of the course based on the structured teaching resources; (5) Calculate the adjustment factor β based on the student's job position, work experience, and learning evaluation standard scores during each login to the system; (6) When the teaching resource used by the student is a video, the test with the corresponding label is displayed during the video playback. The time when the test appears and the time when the student selects the test answer are collected. The time difference TD (minutes) between the two is recorded and the focus value FW is calculated. (7) After completing each part of the course, display all the tests corresponding to that part of the course in the teaching resources, collect all the students' answers to the test, compare them with the preset standard answers, and obtain the test score SW; (8) After each student logs into the system to learn, the learning evaluation standard G is obtained by summing the login value RW, course value LW, difficulty factor λ, adjustment factor β, focus value FW, and test score SW. The calculation formula is as follows: .