Growth type thinking model training method

Abstract

The invention discloses a thinking mode training method, and relates to the technical field of growth thinking, and the system comprises the following components: S1, a virtual situation creation module, S2, a participant immersion experience module, S3, a growth thinking excitation and training module, and S4, a data recording and analysis module. According to the method, participants are enabled to perform deep interaction in diversified virtual situations, face complex and changeable problems and challenges, continuously adjust and apply growing thinking to think, so that the participants face the challenges and absorb teaching training from failures to realize continuous development and improvement ability; according to the method, internal power of the growth type thinking model can be enhanced, participants can be helped to know that the ability can be improved through acquired effort, and the participants can be stimulated to continuously learn and positively face challenges through timely feedback and precise assistance, and finally, systematic training of the growth type thinking model is achieved through the method, and the development of the growth type thinking model is facilitated. And an effective path is provided for postnatal culture.

Description

Technical Field

[0001] The present invention relates to the technical field of thinking training, in particular to a growth mindset training method. Background Art

[0002] In today's rapidly developing society, the various problems and challenges people face are becoming increasingly complex and changeable. They need to have the ability to continuously develop and improve through unremitting efforts, continuous learning, actively facing challenges and learning lessons from failures. However, traditional thinking training methods have significant shortcomings in cultivating a growth mindset.

[0003] First of all, traditional thinking model training methods are often limited to fixed teaching models and lack close connection with real-life situations. This single training model is difficult to comprehensively and deeply exercise and improve the thinking ability of participants, and it is even more difficult to effectively guide participants to form a growth mindset. Participants often just passively accept knowledge and are unable to flexibly apply what they have learned in practice, resulting in rigid thinking and lack of flexibility and adaptability.

[0004] Secondly, traditional thinking training methods lack personalized guidance and feedback. Everyone’s thinking style and cognitive characteristics are unique, but traditional training methods often ignore individual differences and adopt a one-size-fits-all training method. This not only fails to accurately meet everyone’s learning needs, but may also cause learners to feel frustrated and resistant during the learning process, further hindering the formation of a growth mindset.

[0005] In addition, traditional thinking training methods often lack an effective evaluation mechanism. Due to the lack of scientific evaluation standards and means, it is difficult for trainers to accurately understand the participants' learning situation and the change process of growth mindset, and thus they are unable to adjust training strategies and feedback content in a timely manner. This not only affects the improvement of training effects, but may also cause participants to lack a clear understanding of their own progress and growth.

[0006] To sum up, traditional thinking training methods have many shortcomings in cultivating growth thinking and cannot meet the needs of modern society for improving the thinking ability of talents. Therefore, it is particularly important to develop a training method that can accurately guide and strengthen the formation of growth thinking patterns and significantly improve the flexibility and adaptability of growth thinking. Summary of the invention

[0007] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide a growth mindset training method, which can significantly improve the flexibility and adaptability of growth mindset by allowing participants to interact deeply in a variety of virtual situations, face complex and changing problems and challenges, and constantly adjust and use growth mindset to think, thereby facing challenges and learning lessons from failures to continuously develop and improve their abilities. Secondly, the method helps to strengthen the intrinsic motivation of the growth mindset, and through timely feedback and precise assistance, helps participants realize that abilities can be improved through acquired efforts, and stimulates their motivation to continue learning and actively face challenges. Finally, the method realizes the systematic training of the growth mindset and provides an effective path for acquired training.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: a growth mindset training method, the specific steps of the method are:

[0009] S1, virtual situation creation module: According to the goals and needs of growth mindset training, determine the virtual situation theme with the core of improving growth mindset, and build it around a variety of basic cognitive training tasks;

[0010] S1.1, using high-precision 3D modeling technology to construct scene elements in a virtual scenario, assuming that the scene contains n elements of different types, for the i-th element (1≤i≤n), the construction of its 3D model is based on the following algorithm formula:

[0011] M i =f(S i ,T i ,R i )

[0012] Among them, M i represents the three-dimensional model of the i-th element, S i represents the shape feature vector of the element in the context of growth mindset training, T i Represents the material feature vector of the element, R i The spatial position feature vectors representing the elements will be used as carriers for stimulus presentation in growth mindset training;

[0013] S1.2, add environmental sound effects to the virtual situation. Assume that the sound effects include m different types of sounds. For the jth sound (1≤j≤m), its sound effect generation is based on the following algorithm formula:

[0014] E j =g(A j ,V j ,D j )

[0015] Among them, E jrepresents the sound effect of the jth sound, A j The audio feature vector representing the sound, V j Represents the propagation characteristic vector of sound, D j A trigger condition vector representing the sound;

[0016] S1.3, setting lighting and shadow effects that match the virtual situation, assuming that the lighting and shadow effects include k different types of lighting and shadow effects, for the lth effect (1≤l≤k), its lighting and shadow effect generation is based on the following algorithm formula:

[0017] L l =h(P l ,I l ,C l )

[0018] Among them, L l Indicates the first light and shadow effect, P i Represents the physical characteristic vector of the light, I l Represents the interactive characteristic vector of lighting, C l A scene adaptation feature vector representing the lighting;

[0019] S1.4, create virtual characters that match the virtual situation. For each virtual character, set its appearance characteristics, behavior logic, and language ability. Suppose the appearance characteristics of the virtual character are composed of p different aspects. For the qth aspect (1≤q≤p), its appearance characteristics are set based on the following algorithm formula:

[0020] O q = r(F q ,S q ,E q )

[0021] Among them, O q represents the appearance feature of the qth aspect, F q The basic feature vector representing the appearance, S q The context-adapted feature vector representing the appearance, E q = 0, indicating the emotional expression feature vector of appearance, which guides participants to complete the growth mindset task through the character's language prompts;

[0022] S2, participant immersion experience module: provides adapted VR / AR equipment to enable participants to fully immerse themselves in the virtual situation, and captures participants' movements, eye contact, and voice interaction information in real time through the device's sensors;

[0023] S3, Growth Mindset Stimulation and Training Module: During the interaction between participants and the virtual environment and characters, a series of guiding tasks and questions are set to encourage participants to think in a growth mindset;

[0024] S4, data recording and analysis module: records the participants' thinking process, decision-making behavior, and data on interaction with the virtual environment and characters in real time in virtual situations, analyzes these data through the background data analysis system, and evaluates the participants' use of growth mindset in different situations.

[0025] Furthermore, in the construction of the scenario elements of the virtual scenario creation module S1.1, for the scenario elements constructed by the three-dimensional modeling technology, when determining the shape feature vector S i When calculating the weights of each component, it is also necessary to adjust the participants' cognitive feedback on different shape features and recalculate the weights of each shape feature using the weighted average method. The formula is as follows:

[0026]

[0027] Among them, S i(new) represents the recalculated shape feature vector of the i-th element, S ij represents the shape feature vector of the i-th element evaluated by the j-th participant, w 2j Represents the evaluation weight of the jth participant. In this way, the shape characteristics of the scene elements are more in line with the cognitive characteristics of the participants, improving their information processing efficiency and immersion in the basic cognitive training for growth thinking.

[0028] Furthermore, in the construction of the situation elements of the virtual situation creation module S1.2, for the generation of the environmental sound effects, when determining the audio feature vector A j When weighting the components of , we also need to consider the differences in the playback effects of different audio devices. We test different types of audio devices and record the changes in the audio features when playing the same sound effect. Suppose there are q3 types of audio devices tested. For the k3th audio device (1≤k3≤q3), its influence vector on the audio features of the jth sound is Then redetermine the audio feature vector A j The formula for each component weight is as follows:

[0029]

[0030] Among them, A j(new) represents the recalculated audio feature vector of the jth sound, represents the audio feature vector of the jth sound under the k3th audio device, Represents the test weight of the k3th audio device, which helps ensure that the sound effects can achieve good results on different devices and enhance the environmental realism of basic cognitive training.

[0031] Furthermore, in the construction of the scenario elements of the virtual scenario creation module S1.3, for the generation of light and shadow effects, after determining the physical characteristic vector P of the illumination, i When calculating the weights of each component, it is also necessary to adjust them in combination with the current ambient light conditions. When the participant enters the virtual situation, the current ambient light conditions are obtained through the device's sensor. Suppose the current ambient light condition vector is G4, then the physical characteristic vector P of the illumination is re-determined. i The formula for each component weight is as follows:

[0032]

[0033] Among them, P l(new) Represents the physical characteristic vector of the illumination of the lth lighting effect after recalculation, P li Represents the physical characteristic vector of the original lighting effect of the lth lighting effect, y 4i Represents the physical characteristic vector P of the current ambient light condition vector G4 and the original light li For example, when the known condition of the current ambient light is highly correlated with the intensity in the physical characteristic vector of the original illumination, the weight y corresponding to this characteristic 4i The value is larger. In this way, the light and shadow effects can better adapt to the ambient light conditions of the participants, improve the visual realism of the virtual situation, and provide a more suitable visual environment for basic cognitive training.

[0034] Furthermore, in the virtual character setting of the virtual situation creation module S1.4, for the appearance feature setting of the virtual character, in determining the basic feature vector F q When calculating the weights of each component, we also need to consider the participants’ aesthetic preferences for different appearance features, and use the weighted average method to calculate the basic feature vector F of each appearance feature. q The weights of each component are recalculated, and the formula is as follows:

[0035]

[0036] Among them, F q(new) represents the basic feature vector of the qth aspect of appearance after recalculation, F qj The basic feature vector representing the qth aspect of appearance evaluated by the jth participant, z 5j represents the evaluation weight of the jth participant. In this way, the appearance characteristics of the virtual character are more in line with the aesthetic needs of the participants, which increases the enthusiasm of the participants to interact with the virtual character, thereby enhancing their participation in basic cognitive training.

[0037] Furthermore, in the S2 participant immersion experience module, when the device sensors capture the participant's movements, eyes, and voice interaction information, and pre-process this information to achieve better interaction with the virtual environment and characters, an adaptive data filtering algorithm is used. Suppose the amount of captured interaction information data is N, for the i-th data point (1≤i≤N), its original data value is D i , the data value after preprocessing is D i(new) , the adaptive data filtering algorithm is based on the following formula:

[0038]

[0039] Among them, M avg It represents the average maximum value of this type of interaction information in a predefined time period, which is obtained through statistical analysis of multiple test data in the early stage. cur Indicates the maximum value of this type of interaction information currently captured, m avg Indicates the average minimum value of this type of interaction information within a predefined time period, m cur It represents the minimum value of this type of interactive information currently captured. Through this adaptive data filtering algorithm, the data can be reasonably adjusted according to the extreme values ​​of the interactive information currently captured, and the data deviation caused by the participants' accidental excessive actions and abnormal loud voices can be removed, so that the interactive information transmitted to the virtual environment and characters is more accurate and stable, thereby improving the fluency and authenticity of the interaction between the participants and the virtual environment and characters, and optimizing the participants' immersive experience in basic cognitive training.

[0040] Furthermore, in the stimulation and training of the S3 growth mindset, when a series of guiding tasks and questions are set to encourage participants to use the growth-oriented model to think, a weighted algorithm of task context relevance and thinking mode difficulty coefficient is adopted. Assuming that there are T9 tasks or questions in total, for the k9th task or question (1≤k≤T), its guiding weight for the j9th thinking mode is The weighted algorithm is based on the following formula:

[0041]

[0042] Among them, S kj represents the relevance between the kth task or problem and the situational element corresponding to the jth thinking mode, D jIt represents the difficulty coefficient of the j-th thinking mode, and its value is determined according to the assessment of the difficulty of applying the thinking mode in different situations. For example, the difficulty coefficient of the growth mindset of ability may be relatively high in complex ethical dilemmas. Through this weighted algorithm based on the task-context relevance and the difficulty coefficient of the thinking mode, the guiding role of each task on the growth mindset of ability can be set more accurately, so that participants can use the growth mindset to think more specifically in the process of interacting with the virtual environment and roles, thereby improving the efficiency and quality of the growth mindset training, especially guiding participants to use the growth mindset to cope with various challenges in basic cognitive training.

[0043] Furthermore, in the S3 growth mindset stimulation and training module, the difficulty and type of subsequent training tasks are dynamically adjusted according to the performance and feedback data of the participants in the previous training tasks, as follows:

[0044] When the success rate of participants reaches the set threshold and the average completion time is lower than the predetermined standard in several consecutive tasks, the difficulty level of subsequent tasks is increased. The difficulty increase can be achieved by increasing the complexity of the task, introducing more variables or constraints, and in problem-solving tasks, increasing the difficulty of the problem or adding interference factors;

[0045] If participants make multiple mistakes in a specific type of task, and the error types are mainly concentrated in a certain thinking link, then in the subsequent training, add special training tasks for this thinking link. These special tasks can be questions with similar logical structures but moderate difficulty, aiming to strengthen the thinking ability of participants in weak links. For participants who perform poorly in reaction speed tasks, the proportion of time-limited training will be appropriately increased in subsequent training. The time limit of time-limited training can be dynamically set according to the current average reaction time of the participants.

[0046] Regularly conduct comprehensive assessments of the growth mindset development of participants. The assessment criteria include but are not limited to performance in different types of tasks, flexibility of thinking, and improvement in adaptability. Based on the assessment results, develop a personalized training plan for each participant, adjust the focus of the training content and the progress of the training, ensure the targetedness and effectiveness of the training, and better promote the cultivation and development of the growth mindset of participants.

[0047] Compared with the existing technology, the growth mindset training method has the following beneficial effects:

[0048] 1. The present invention creates highly realistic virtual scenarios, covering a wide range of topics from historical periods to future technology, allowing participants to interact deeply with diverse environments and characters. This interaction involves a series of guiding tasks and questions, prompting participants to constantly adjust and use a growth mindset to think. Through continuous training, participants can significantly improve the flexibility and adaptability of their growth mindset, enabling them to better cope with complex and ever-changing problems and challenges in real life, and form a growth mindset that actively faces difficulties and dares to explore new knowledge.

[0049] 2. The present invention adopts a weighted algorithm of task context relevance and thinking mode difficulty coefficient to accurately set the guiding role of each task or question on the growth mindset. In the process of participants' interaction with the virtual environment and characters, a series of guiding tasks and questions are used to encourage them to use the growth mindset to think. At the same time, multiple forms and timely feedback are provided to provide targeted feedback based on the error types of participants' tasks, helping them to recognize the problems and take improvement measures. This mechanism of precise guidance and timely feedback continuously strengthens the growth mindset of participants in acquired training, enabling them to gradually develop the ability to continuously improve their abilities through unremitting efforts, continuous learning and actively facing challenges.

[0050] 3. The present invention dynamically adjusts the participants' cognitive feedback and aesthetic preferences on different shape features, environmental sound effects, light and shadow effects, and virtual character settings, providing participants with an immersive sense of immersion and reality. This refined scene construction and personalized setting not only enhances the realism and credibility of thinking training, but also significantly optimizes the training environment. In acquired training, participants can conduct thinking training in a more realistic and credible virtual environment, thereby more actively engaging in the training process and improving the training effect. At the same time, by real-time recording of participants' thinking processes, decision-making behaviors, and data on interactions with virtual environments and characters in virtual situations, and conducting in-depth analysis, the training strategies can be further evaluated and optimized to ensure the effectiveness and pertinence of growth mindset training. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 Growth mindset training method operation flow chart;

[0053] Figure 2Operational flow chart of the virtual scenario creation module. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Embodiment 1

[0056] This embodiment describes a classroom full of historical charm and sparks of wisdom, in which the teacher skillfully uses virtual reality (VR) technology to bring students into vivid and realistic ancient war scenes. In these scenes, students play key historical figures or characters of their own creation, and through simulation, they solve difficult problems in ancient wars, thereby exercising and gradually forming a growth mindset.

[0057] A virtual scenario theme is built around "Ancient War Strategy", and representative and challenging historical battles are selected as the background. The scenario elements include but are not limited to ancient battlefields, city walls, weapons, and historical figures. High-precision 3D modeling technology is used to build detailed 3D models of battlefields, city walls, and weapon scene elements based on historical data and imagination. Assume that the scene contains n different types of elements. For the i-th element (1≤i≤n), the construction of its 3D model is based on the following algorithm formula:

[0058] M i =f(S i ,T i ,R i )

[0059] Among them, M i represents the three-dimensional model of the i-th element, S i represents the shape feature vector of the element in the n-back training context, T i Represents the material feature vector of the element, R i The spatial position feature vector represents the element. These elements will be used as the carrier of stimulus presentation in n-back training. In the construction process, the shape characteristics, material characteristics and spatial position characteristics of the elements are fully considered to ensure the authenticity and credibility of the model. The sound effects of war drums, horse hooves and shouting are added. Assume that the sound effects contain m different types of sounds. For the jth sound (1≤j≤m), its sound effect generation is based on the following algorithm formula:

[0060] E j =g(A j ,V j ,D j )

[0061] Among them, E j represents the sound effect of the jth sound, A j The audio feature vector representing the sound, whose components are derived from the actual sound samples and simulation settings related to n-back training in the corresponding historical or future contexts, V j Represents the propagation characteristic vector of sound, D j The trigger condition vector represents the sound to enhance the immersion and sense of substitution of the situation. The audio feature vector of the sound effect should be derived from the collected samples and simulation settings of the actual historical scene. At the same time, the propagation characteristics and trigger conditions of the sound are considered, and the light and shadow effects that conform to the atmosphere of the ancient battlefield are set. The light and shadow effects are assumed to include k different types of lighting and shadow effects. For the lth effect (1≤l≤k), the light and shadow effect generation is based on the following algorithm formula:

[0062] L l =h(P l ,I l ,C l )

[0063] Among them, L l Indicates the first light and shadow effect, P i Represents the physical characteristic vector of the light, I l represents the interactive characteristic vector of illumination, whose components are derived from research data and physical simulations of actual illumination conditions in corresponding historical or future situations and the effects on n-back training vision, C l The scene adaptation feature vector representing the lighting, such as the afterglow of the setting sun and the light of torches, creates a tense and fierce war atmosphere. The generation of light and shadow effects should be based on the physical characteristics, interactive characteristics and scene adaptation characteristics of the lighting to ensure the perfect integration of the light and shadow effects with the overall situation. Create a virtual character, set its appearance characteristics, behavioral logic, and language ability. Suppose the appearance characteristics of the virtual character are composed of p different aspects. For the qth aspect (1≤q≤p), its appearance feature setting is based on the following algorithm formula:

[0064] O q = r(F q ,S q ,E q )

[0065] Among them, O q represents the appearance feature of the qth aspect, F q The basic feature vector representing the appearance, S q The context-adaptive feature vector representing appearance, whose components are determined by considering the overall context atmosphere, role function, and interactivity with n-back training, E q=0, indicating the emotional expression feature vector of appearance. The participants were guided to complete the n-back task through the language prompts of the characters, including the roles played by historical figures and students. The appearance characteristics, behavioral logic and language ability of the characters were set. The appearance characteristics of the characters should comprehensively consider the basic characteristics, situational adaptation characteristics and emotional expression characteristics to ensure the authenticity and credibility of the characters.

[0066] Students wear appropriate VR devices, enter virtual situations, play historical figures or characters of their own creation, and use the device's sensors to capture students' movements, eye expressions, and voice interaction information in real time so that they can interact with the virtual environment and characters. Adaptive data filtering algorithms are used to pre-process the captured interaction information data to achieve better interaction with the virtual environment and characters. The pre-processed data will be used to drive the movements and dialogues of virtual characters, as well as to adjust the parameters of the virtual environment.

[0067] In the virtual situation, set up a series of tasks and problems related to war strategies, such as formulating offensive plans, analyzing the enemy situation, and deploying troops. The difficulty and complexity of the tasks should be gradually increased to stimulate students' spirit of challenge and growth motivation, guide students to use a growth mindset, and think about how to overcome difficulties and complete tasks through hard work and learning. When students encounter difficulties, virtual characters can help students find solutions to problems through asking questions, guiding, and analyzing. Provide timely and accurate feedback, including scores, text explanations, and training results change charts. The feedback content should emphasize that abilities can be improved through hard work, and encourage students to maintain a positive learning attitude. Through multiple trainings, gradually help students form a growth mindset. During the training process, teachers should pay attention to the students' thinking changes, as well as their acceptance and response to feedback information, so as to adjust the training strategies and feedback content in a timely manner.

[0068] The students’ thinking process, decision-making behavior, and data on their interactions with the virtual environment and characters in real time in virtual situations should be recorded. The data records should include students’ movements, eye expressions, voice interaction information, and their performance in the tasks. Through the background data analysis system, the recorded data can be deeply analyzed to analyze the students’ application of the growth mindset in different situations, as well as their changes in thinking when facing tasks of different difficulty levels.

[0069] Embodiment 2

[0070] This embodiment describes organizing growth mindset training for employees within an enterprise to enhance their growth capabilities and proactive problem-solving mindset when facing new problems at work.

[0071] Construct a virtual scenario with the theme of "Future Innovation Factory". When constructing scene elements in 3D modeling, assume that the scene contains n different types of elements. For the i-th element (1≤i≤n), the construction of its 3D model is based on the following algorithm formula:

[0072] M i =f(S i ,T i ,R i )

[0073] Among them, M i represents the three-dimensional model of the i-th element, S i represents the shape feature vector of the element in the context of growth mindset training, T i Represents the material feature vector of the element, R i The spatial position feature vector represents the element. These elements will be used as carriers of stimulus presentation in growth mindset training. For example, for elements representing innovative tools, the weights are adjusted according to employees' cognitive feedback on their shape features. If the initial appearance shape weight of the 3D printer element is 0.4, and the functional component shape weight is 0.6, after employee trial evaluation, it is found that the appearance shape has a certain influence on employees' understanding of its innovative application scenarios. In this case, the appearance shape weight is adjusted to 0.5, and the functional component shape weight is adjusted to 0.5.

[0074] Add sound effects such as creative inspiration and difficulty. Suppose the sound effects include m different types of sounds. For the jth sound (1≤j≤m), its sound effect generation is based on the following algorithm formula:

[0075] E j =g(A j ,V j ,D j )

[0076] Among them, E j represents the sound effect of the jth sound, A j The audio feature vector representing the sound, V j Represents the propagation characteristic vector of sound, D j The trigger condition vector represents the sound. Taking into account the differences in playback effects of different audio devices in the corporate training room, the audio feature vector weights are re-determined after testing. For the sound effects that appear in creative inspiration, we hope to create a grand and uplifting atmosphere when playing them on the audio system, while paying more attention to clear and smart effects when playing them in headphones. Accordingly, the audio feature weights of the sound effects on different devices are adjusted.

[0077] Set the light and shadow effects with bright workspace and moderate lighting in regular areas. Assume that the light and shadow effects include k different types of lighting and shadow effects. For the lth effect (1≤l≤k), the light and shadow effect generation is based on the following algorithm formula:

[0078] L l =h(P l ,I l ,C l )

[0079] Among them, L l Indicates the first light and shadow effect, P i Represents the physical characteristic vector of the light, I l Represents the interactive characteristic vector of lighting, C l Represents the scene adaptation characteristic vector of lighting. When employees start training, the physical characteristic vector weight of lighting is adjusted according to the ambient light conditions of the training room. If the brightness weight of the innovation work area in the original light and shadow effect is 0.7, the weight is appropriately increased to 0.8 according to the darker environment to highlight the innovation work area and guide employees to focus on key areas.

[0080] Create a virtual character of "growth mindset mentor". Assume that the appearance features of the virtual character are composed of p different aspects. For the qth aspect (1≤q≤p), its appearance feature setting is based on the following algorithm formula:

[0081] O q = r(F q ,S q ,E q )

[0082] Among them, O q represents the appearance feature of the qth aspect, F q The basic feature vector representing the appearance, S q The context-adapted feature vector representing the appearance, E q =0, indicating the emotional expression feature vector of appearance. The language prompts of the characters are used to guide participants to complete the growth mindset task. When determining the weight of the basic appearance feature vector components, the employees' aesthetic preferences for the mentor image are taken into consideration. For example, the weight of the wise eyes feature of the "growth mindset mentor" was initially set to 0.5, and the weight of the professional dress feature was 0.5. After employee feedback, it was found that employees valued the wisdom conveyed by the eyes more, so the weight of the wise eyes feature was adjusted to 0.7, and the weight of the professional dress feature was adjusted to 0.3. The "growth mindset mentor" guides employees to complete innovative thinking tasks through language, such as "When faced with product improvement issues, what new perspectives can we think from?"

[0083] Employees use adapted AR devices to enter virtual situations. Device sensors capture employee movements, eye expressions, and voice interaction information, and use adaptive data filtering algorithms to process this information. For example, when capturing the force data of employees operating virtual tools, if the data value is less than the average minimum value of this type of interaction information within a predefined time period, the data value is multiplied by the ratio of the average minimum value to the currently captured minimum value to make adjustments, so that the virtual environment and characters can accurately respond to employee operations, thereby improving the realism and participation of training.

[0084] Set up tasks and questions such as "design a new product packaging solution" and "improve work processes to increase efficiency", and use a weighted algorithm based on the task context relevance and the difficulty coefficient of the thinking mode. For example, for the task of "designing a new product packaging solution", the correlation with the contextual elements of the divergent thinking mode is 0.7, and the difficulty coefficient of this thinking mode is 0.5. Calculate the guidance weight to encourage employees to use a growth mindset. When employees encounter difficulties in designing packaging solutions, the "growth mindset mentor" will ask guidance questions, such as "What successful packaging cases can we learn from? How can we combine the characteristics of our products to innovate?" Let employees understand that in the process of thinking and consideration, they can gain positive experiences and find the best strategies, so that they believe that abilities can be acquired.

[0085] The employee's score will be displayed immediately after the task is completed. If the employee lacks creative elements in the product packaging design, the "growth mindset mentor" will guide the employee to expand his or her thinking. If the employee's design plan does not conform to actual production, the mentor will guide the employee to re-evaluate the plan and make adjustments based on actual cases. If the employee's design time is too long, the mentor will encourage the employee and provide methods to improve efficiency, such as determining the core design concept first and then expanding the details, and adjusting the time limit later. According to the characteristics of the growth mindset training task, the mentor will flexibly adjust the guidance strategy and feedback method.

[0086] Record employees' thinking processes, decision-making behaviors, and interaction data in virtual situations, and analyze employees' application of growth mindset in different task situations, such as changes in thinking when faced with product packaging and workflow improvement tasks, as well as their acceptance and response to feedback information, to provide a reference for optimizing corporate innovative thinking training.

[0087] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. The growth mindset training method is characterized by: The specific steps of this method are: S1, virtual situation creation module: According to the goals and needs of growth mindset training, determine the virtual situation theme with the core of improving growth mindset, and build it around a variety of basic cognitive training tasks; S1.1, using high-precision 3D modeling technology to construct scene elements in a virtual scenario, assuming that the scene contains n elements of different types, for the i-th element (1≤i≤n), the construction of its 3D model is based on the following algorithm formula: M i =f(S i ,T i ,R i ) Among them, M i represents the three-dimensional model of the i-th element, S i represents the shape feature vector of the element in the context of growth mindset training, T i Represents the material feature vector of the element, R i The spatial position feature vectors representing the elements will be used as carriers for stimulus presentation in growth mindset training; S1.2, add environmental sound effects to the virtual situation. Assume that the sound effects include m different types of sounds. For the jth sound (1≤j≤m), its sound effect generation is based on the following algorithm formula: E j =g(A j ,V j ,D j ) Among them, E j represents the sound effect of the jth sound, A j The audio feature vector representing the sound, V j Represents the propagation characteristic vector of sound, D j A trigger condition vector representing the sound; S1.3, setting lighting and shadow effects that match the virtual situation, assuming that the lighting and shadow effects include k different types of lighting and shadow effects, for the lth effect (1≤l≤k), its lighting and shadow effect generation is based on the following algorithm formula: L l =h(P l ,I l ,C l ) Among them, L l Indicates the first light and shadow effect, P i Represents the physical characteristic vector of the light, I l Represents the interactive characteristic vector of lighting, C l A scene adaptation feature vector representing the illumination; S1.4, create virtual characters that match the virtual situation. For each virtual character, set its appearance characteristics, behavior logic, and language ability. Suppose the appearance characteristics of the virtual character are composed of p different aspects. For the qth aspect (1≤q≤p), its appearance characteristics are set based on the following algorithm formula: O q =r(F q ,S q ,E q ) Among them, O q represents the appearance feature of the qth aspect, F q The basic feature vector representing the appearance, S q The context-adapted feature vector representing the appearance, E q = 0, indicating the emotional expression feature vector of appearance, which guides participants to complete the growth mindset task through the character's language prompts; S2, participant immersion experience module: provides adapted VR / AR equipment to enable participants to fully immerse themselves in the virtual situation, and captures participants' movements, eye contact, and voice interaction information in real time through the device's sensors; S3, Growth Mindset Stimulation and Training Module: During the interaction between participants and the virtual environment and characters, a series of guiding tasks and questions are set to encourage participants to think in a growth mindset; S4, data recording and analysis module: records the participants' thinking process, decision-making behavior, and data on interaction with the virtual environment and characters in real time in virtual situations, analyzes these data through the background data analysis system, and evaluates the participants' use of thinking patterns in different situations.

2. The growth mindset training method according to claim 1, characterized in that: In the construction of the scenario elements of the virtual scenario creation module S1.1, for the scenario elements constructed by the three-dimensional modeling technology, after determining the shape feature vector S i When calculating the weights of each component, it is also necessary to adjust the participants' cognitive feedback on different shape features and recalculate the weights of each shape feature using the weighted average method. The formula is as follows: Among them, S i(new) represents the recalculated shape feature vector of the i-th element, S ij represents the shape feature vector of the i-th element evaluated by the j-th participant, w 2j represents the evaluation weight of the jth participant.

3. The growth mindset training method according to claim 1, characterized in that: In the construction of the situation elements of the virtual situation creation module S1.2, for the generation of the environmental sound effect, after determining the audio feature vector A j When weighting the components of , we also need to consider the differences in the playback effects of different audio devices. We test different types of audio devices and record the changes in the audio features when playing the same sound effect. Suppose there are q3 types of audio devices tested. For the k3th audio device (1≤k3≤q3), its influence vector on the audio features of the jth sound is Then redetermine the audio feature vector A j The formula for each component weight is as follows: Among them, A j(new) represents the recalculated audio feature vector of the jth sound, represents the audio feature vector of the jth sound under the k3th audio device, Indicates the test weight of the k3th audio device.

4. The growth mindset training method according to claim 1, characterized in that: In the construction of the scenario elements of the virtual scenario creation module S1.3, for the generation of light and shadow effects, after determining the physical characteristic vector P of the illumination, i When calculating the weights of each component, it is also necessary to adjust them in combination with the current ambient light conditions. When the participant enters the virtual situation, the current ambient light conditions are obtained through the device's sensor. Suppose the current ambient light condition vector is G4, then the physical characteristic vector P of the illumination is re-determined. i The formula for each component weight is as follows: Among them, P l(new) Represents the physical characteristic vector of the illumination of the lth lighting effect after recalculation, P li Represents the physical characteristic vector of the original lighting effect of the lth lighting effect, y 4i Represents the physical characteristic vector P of the current ambient light condition vector G4 and the original light li For example, when the known condition of the current ambient light is highly correlated with the intensity in the physical characteristic vector of the original illumination, the weight y corresponding to this characteristic 4i The value is larger.

5. The growth mindset training method according to claim 1, characterized in that: In the virtual character setting of the virtual situation creation module S1.4, for the appearance feature setting of the virtual character, after determining the basic feature vector F of the appearance, q When calculating the weights of each component, we also need to consider the participants’ aesthetic preferences for different appearance features, and use the weighted average method to calculate the basic feature vector F of each appearance feature. q The weights of each component are recalculated, and the formula is as follows: Among them, F q(new) represents the basic feature vector of the qth aspect of appearance after recalculation, F qj The basic feature vector representing the qth aspect of appearance evaluated by the jth participant, z 5j represents the evaluation weight of the jth participant.

6. The growth mindset training method according to claim 1, characterized in that: In the S2 participant immersion experience module, the device sensors capture the participant's movements, eyes, and voice interaction information. When preprocessing this information to achieve better interaction with the virtual environment and characters, an adaptive data filtering algorithm is used. Suppose the amount of captured interaction information data is N. For the i-th data point (1≤i≤N), its original data value is D i , the data value after preprocessing is D i(new) , the adaptive data filtering algorithm is based on the following formula: Among them, M avg It represents the average maximum value of this type of interaction information in a predefined time period, which is obtained through statistical analysis of multiple test data in the early stage. cur Indicates the maximum value of this type of interaction information currently captured, m avg Indicates the average minimum value of this type of interaction information within a predefined time period, m cur Indicates the minimum value of this type of interaction information currently captured.

7. The growth mindset training method according to claim 1, characterized in that: In the stimulation and training of the S3 growth mindset, when a series of guiding tasks and questions are set to encourage participants to use the growth-oriented model to think, a weighted algorithm of task context relevance and thinking mode difficulty coefficient is adopted. Assuming that there are T9 tasks or questions in total, for the k9th task or question (1≤k≤T), its guiding weight for the j9th thinking mode is The weighted algorithm is based on the following formula: Among them, S kj represents the relevance between the kth task or problem and the situational element corresponding to the jth thinking mode, D j Represents the difficulty coefficient of the j-th thinking mode.

8. The growth mindset training method according to claim 1, characterized in that: In the S3 growth mindset stimulation and training module, the difficulty and type of subsequent training tasks are dynamically adjusted according to the performance and feedback data of the participants in the previous training tasks. The specific methods are as follows: When the success rate of participants reaches the set threshold and the average completion time is lower than the predetermined standard in several consecutive tasks, the difficulty level of subsequent tasks is increased. The difficulty increase can be achieved by increasing the complexity of the task, introducing more variables or constraints, and in problem-solving tasks, increasing the difficulty of the problem or adding interference factors; If participants make multiple mistakes in a specific type of task, and the error types are mainly concentrated in a certain thinking link, then in the subsequent training, add special training tasks for this thinking link. These special tasks can be questions with similar logical structures but moderate difficulty, aiming to strengthen the thinking ability of participants in weak links. For participants who perform poorly in reaction speed tasks, the proportion of time-limited training will be appropriately increased in subsequent training. The time limit of time-limited training can be dynamically set according to the current average reaction time of the participants. Regularly conduct comprehensive assessments of the growth mindset development of participants. The assessment criteria include but are not limited to performance in different types of tasks, flexibility of thinking, and improved adaptability. Based on the assessment results, develop a personalized training plan for each participant and adjust the focus and progress of the training content.

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