Child reading ability improvement training method and system based on multi-dimensional evaluation of brain basic cognitive function
By conducting multidimensional assessments of basic cognitive functions, individualized training programs, and gamified design, the problem of personalized training for children with reading difficulties was solved. This enabled dynamic optimization of the training process and evaluation of its effectiveness, thereby improving training efficiency and children's participation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INSTITUTE OF BIOPHYSICS CHINESE ACADEMY OF SCIENCES
- Filing Date
- 2026-05-20
- Publication Date
- 2026-07-14
AI Technical Summary
In existing technologies, intervention programs for children with reading difficulties lack personalization and fun, resulting in fixed training tasks that cannot be dynamically adjusted, limited evaluation of training effectiveness, and insufficient persistence among children.
By conducting multidimensional assessments of basic cognitive functions of the brain, a multidimensional brain function profile of children is obtained, weak dimensions are identified, and individualized training programs are constructed. By combining gamification elements and adaptive difficulty adjustment, the difficulty of training tasks is dynamically adjusted, and behavioral data throughout the process is collected for quantitative evaluation.
It achieves a match between training tasks and children's abilities, improves the effectiveness and adaptability of training, enhances children's enthusiasm for participation and long-term persistence, and provides a continuous tracking and evaluation mechanism.
Smart Images
Figure CN122392827A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of neuromodulation technology, and in particular to a training method and system for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain. Background Technology
[0002] Reading ability is a fundamental core skill for children to acquire knowledge and engage in learning activities. However, a significant number of children face difficulties in reading, and the root cause is not simply insufficient vocabulary, but more likely stems from uneven development or weaknesses in underlying cognitive functions such as vision, hearing, attention, and memory.
[0003] In related technologies, intervention programs for children with reading difficulties often focus on training superficial reading skills such as literacy, phonics, reading fluency, and reading comprehension strategies. While such training is effective for some children, due to significant differences in the patterns of weakness in underlying brain functions such as perception, attention, and memory among different children, using a uniform training program has the following drawbacks: First, the training tasks are of fixed difficulty and cannot be dynamically adjusted based on children's real-time performance, easily leading to tasks that are either too difficult or too easy. Second, the training methods lack interest, resulting in insufficient persistence among children. Third, the use of training process data is limited, making continuous tracking and evaluation of training effectiveness difficult. Summary of the Invention
[0004] This application provides a method and system for improving children's reading ability based on a multidimensional assessment of basic cognitive functions of the brain. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general description, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.
[0005] In a first aspect, embodiments of this application provide a method for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain, the method comprising: Acquire multidimensional brain function profiles of children to be trained across multiple preset cognitive dimensions; Based on multidimensional brain function profiling data, the weak dimensions and their degree of deviation in multiple preset cognitive dimensions of the children to be trained were identified. Based on each weak dimension and its degree of deviation, an individualized training plan is constructed and implemented for the child to be trained; wherein, the individualized training plan includes training tasks corresponding to each weak dimension; During the training process, the difficulty parameters of the training task are dynamically adjusted based on the real-time performance data of the children being trained.
[0006] Optionally, multiple preset cognitive dimensions include at least perceptual organization, rhythm perception, memory span, visual crowding, visual search, and attention allocation; among them, Multidimensional brain function profile data is used to characterize the specific performance information of children to be trained in each preset cognitive dimension.
[0007] Optionally, based on each weak dimension and its degree of deviation, an individualized training program is constructed and implemented for the child to be trained, including: Based on each weak dimension and its degree of deviation, an individualized training plan is constructed for the child to be trained; Schedule training tasks within an individualized training program; The training tasks are presented to the children in a gamified, interactive format so that they can perform the training tasks; among them, Gamified interaction forms include at least story scenarios, level-based mechanisms, reward feedback, character growth, point accumulation, badge incentives, or stage unlocking.
[0008] Optionally, based on each weak dimension and its degree of deviation, an individualized training program can be constructed for the child to be trained, including: Based on each weak dimension and its degree of deviation, determine the quantitative deviation value of each weak dimension; Based on the magnitude of the quantization deviation value of each weak dimension, all identified weak dimensions are sorted to generate a training priority sequence; among them, the weak dimensions with larger quantization deviation values have higher training priority. Based on the training priority sequence and the preset dimension-task mapping relationship library, at least one preset training task is mapped and configured for each weak dimension; wherein, the preset dimension-task mapping relationship library defines one or more training task types corresponding to each cognitive weak dimension; Based on the training priority sequence and the training tasks configured by the mapping, an individualized training plan is generated for the child to be trained; the individualized training plan includes the combination of training dimensions, the sequence of training tasks, and the initial training parameters.
[0009] Optionally, the difficulty parameters of the training task can be dynamically adjusted based on the real-time performance data of the child to be trained, including: Based on real-time performance data, determine the core performance indicators of the children to be trained under the current training task within the preset assessment window; The core performance indicators are compared with the preset target performance range to determine the real-time training performance level of the child to be trained; the real-time training performance level includes at least performance better than expected, performance in line with expectations, and performance lower than expected. The difficulty parameters of the training task are dynamically adjusted based on the real-time training performance level.
[0010] Optional, core performance indicators may include at least the child’s accuracy, reaction time, number of consecutive correct answers, number of consecutive incorrect answers, recent training trend, completion time, interruption rate, or fatigue performance when performing the task.
[0011] Optionally, the difficulty parameters of the training task can be dynamically adjusted based on the real-time training performance level, including: When the real-time training performance level is "better than expected," a difficulty-increasing operation is performed. This operation includes at least: increasing the number of distractors, shortening stimulus presentation time, increasing beat speed, increasing task rule complexity, expanding the length of the memory sequence, increasing the number of targets, decreasing the distance between targets and distractors, or reducing the contrast between targets and distractors; or... When the real-time training performance level is below expectations, a difficulty reduction operation is performed. This operation includes at least reducing the number of distractors, extending stimulus presentation time, decreasing tempo, simplifying task rules, shortening the length of the memory sequence, reducing the number of targets, increasing the spacing between targets and distractors, or increasing the contrast between targets and distractors; or... When the real-time training performance level is "performance meets expectations", the difficulty parameter of the current training task remains unchanged.
[0012] Optionally, after dynamically adjusting the difficulty parameters of the training task, the following also includes: Collect and record the entire process of behavioral data of the children to be trained during the execution of training tasks; the entire process behavioral data includes at least reaction time, accuracy rate, completion status of each training level, trajectory of difficulty change, dimensional progress curve, and stage training performance; Based on the behavioral data throughout the entire process, the training effect on the weak dimensions of the children to be trained is quantitatively evaluated in stages, and the results of the quantitative evaluation in stages are obtained. Based on the phased quantitative assessment results, a training effectiveness report is created, which includes the progress curves of the children to be trained in at least one cognitive dimension.
[0013] Optionally, based on multidimensional brain functional profiling data, identify the weak dimensions and their degree of deviation in multiple preset cognitive dimensions of the child to be trained, including: Analyze multidimensional brain function profile data to obtain quantitative assessment values for each cognitive dimension of the child to be trained in multiple preset cognitive dimensions; where the quantitative assessment values are percentiles. The quantitative assessment value of each preset cognitive dimension is compared with the preset dimension ability threshold; Cognitive dimensions whose quantitative assessment values are lower than their corresponding preset dimensional ability thresholds are identified as the weak dimensions of children to be trained; whereby the dimensional ability thresholds are preset based on norm data or clinical experience and are used to distinguish between normal and weak abilities. For the weak dimensions of the children to be trained, calculate the difference between the quantitative assessment value of the weak dimension and the corresponding dimension ability threshold; The difference is converted into a scale value, which represents the degree of deviation of the weak dimension.
[0014] Secondly, embodiments of this application provide a training system for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain. The system includes: The multidimensional brain function profile data acquisition module is used to acquire multidimensional brain function profile data of children to be trained in multiple preset cognitive dimensions. Among them, the multiple preset cognitive dimensions include at least perceptual organization, rhythm perception, memory span, visual crowding, visual search, and attention allocation. The multidimensional brain function profile data is used to characterize the performance information of children to be trained in each preset cognitive dimension. The weak dimension and deviation degree identification module is used to identify the weak dimensions and deviation degree of the child to be trained in multiple preset cognitive dimensions based on multidimensional brain function profile data; The individualized training program construction module is used to build and execute an individualized training program for the child to be trained based on each weak dimension and its degree of deviation. The individualized training program includes training tasks corresponding to each weak dimension. The difficulty parameter dynamic adjustment module is used to dynamically adjust the difficulty parameters of the training task based on the real-time performance data of the child being trained during the execution of the training task.
[0015] The technical solutions provided in this application embodiment may include the following beneficial effects: In this embodiment, on the one hand, the adaptive difficulty adjustment mechanism dynamically adjusts the parameters of the training task by collecting children's real-time performance data, thereby achieving dynamic closed-loop optimization of the training process. This ensures that the difficulty of the training task always matches the child's immediate ability level, maintaining a moderately challenging state, guaranteeing the effectiveness of training, enhancing its adaptability and individuality, and effectively improving training efficiency. On the other hand, when generating training tasks targeting specific weak dimensions, training objectives are combined with gamification elements, transforming core cognitive training into an attractive interactive game. This approach not only reduces children's resistance to training but also combines extrinsic and intrinsic motivation, significantly increasing children's enthusiasm, initiative, and the likelihood of long-term adherence to training. Furthermore, by systematically collecting behavioral data throughout the training process and conducting phased quantitative assessments, a visualized training effect report is generated. This approach not only clearly demonstrates children's progress in specific cognitive dimensions but also enables continuous tracking and evaluation of training effects.
[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description
[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0018] Figure 1 This is a flowchart illustrating a training method for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain, provided in an embodiment of this application. Figure 2 This is a game scene illustration of a perceptual organization ability provided in this application; Figure 3 This is a game scene illustration of a rhythm perception capability provided in this application; Figure 4 This is a game scenario illustration of a memory span capability provided in this application; Figure 5 This is a schematic diagram of a game scene illustrating visual crowding capabilities provided in this application; Figure 6 This is a game scene illustration of a visual search capability provided in this application; Figure 7 This is a game scene illustration of attention allocation ability provided in this application; Figure 8 This is a schematic diagram of the structure of a children's reading ability improvement training system based on multidimensional assessment of basic cognitive functions of the brain, as provided in this application; Figure 9 This is a schematic diagram of another children's reading ability improvement training system based on multidimensional assessment of basic cognitive functions of the brain, provided in this application; Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0019] The following description and accompanying drawings fully illustrate specific embodiments of this application to enable those skilled in the art to practice them.
[0020] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.
[0021] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with some aspects of this application as detailed in the appended claims.
[0022] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.
[0023] This application provides a training method and system for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain, to solve the problems existing in the aforementioned related technologies. In the embodiments of this application, on the one hand, an adaptive difficulty adjustment mechanism dynamically adjusts the parameters of the training task by collecting real-time performance data of children, thereby achieving dynamic closed-loop optimization of the training process. This ensures that the difficulty of the training task always matches the child's immediate ability level, maintaining a moderately challenging state, guaranteeing the effectiveness of training, enhancing its adaptability and individuality, and effectively improving training efficiency. On the other hand, when generating training tasks targeting specific weak dimensions, training objectives are combined with gamification elements, transforming core cognitive training into an attractive interactive game. This approach not only reduces children's resistance to training but also combines external and internal motivation, significantly improving children's enthusiasm, initiative, and the likelihood of long-term adherence to training. Furthermore, by systematically collecting behavioral data throughout the training process and conducting phased quantitative assessments, a visualized training effect report is generated. This method not only clearly demonstrates children's progress in specific cognitive dimensions, but also enables continuous tracking and evaluation of training effectiveness. The following is a detailed description using exemplary embodiments.
[0024] The following will be combined with the appendix Figure 1 -Appendix Figure 7This application provides a detailed description of a training method for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain, as provided in the embodiments of this application. This method can be implemented using a computer program and can run on a children's reading ability improvement training system based on the von Neumann architecture and multidimensional assessment of basic cognitive functions of the brain. This computer program can be integrated into the application or run as a standalone tool application.
[0025] Please see Figure 1 This document provides a flowchart illustrating a training method for improving children's reading ability based on a multidimensional assessment of basic cognitive functions of the brain, as described in this application. Figure 1 As shown, the method in this application embodiment may include the following steps: S101, acquire multidimensional brain function profile data of the child to be trained in multiple preset cognitive dimensions; Among them, the multidimensional brain function profile data is a structured data set that comprehensively represents the cognitive function characteristics of children under training by measuring and quantifying several preset cognitive dimensions related to reading through a standardized assessment system. This data describes the functional level and relative strength patterns of children in different cognitive dimensions such as perception, attention, and memory.
[0026] Among them, the multiple preset cognitive dimensions include at least perceptual organization, rhythm perception, memory span, visual crowding, visual search, and attention allocation; among them, multidimensional brain function profile data is used to characterize the specific performance information of the children to be trained in each preset cognitive dimension.
[0027] In some embodiments of this application, a series of standardized, computerized cognitive assessment tasks are presented to the child to be trained through a human-computer interaction interface. During the child's completion of each assessment task, the system collects their behavioral response data. For each preset cognitive dimension, the system calculates a quantitative assessment score for that dimension based on the collected corresponding behavioral response data using a preset algorithm model. The algorithm model typically includes: calculating task accuracy, standardized reaction time, error pattern analysis, or ability parameters estimated through psychometric models such as item response theory. All quantitative assessment scores calculated for the child across multiple preset cognitive dimensions are structurally integrated and stored according to a unified dimensional framework, forming a dataset representing a profile of the child's overall cognitive function. This dataset is the multidimensional brain function profile data.
[0028] S102, based on multidimensional brain function profile data, identify the weak dimensions and their degree of deviation in multiple preset cognitive dimensions of the children to be trained; Among these, a weak dimension refers to a specific dimension in which the quantitative assessment value of the child to be trained is lower than a preset threshold among multiple preset cognitive dimensions. The degree of deviation is an indicator used to quantitatively characterize the severity or size of the gap between the child's ability level and the normal or expected level in the weak dimension.
[0029] In some embodiments of this application, the specific process of identifying the weak dimensions and their deviations in multiple preset cognitive dimensions of a child to be trained, based on multidimensional brain functional profiling data, includes: parsing the multidimensional brain functional profiling data to obtain the quantitative assessment value of each cognitive dimension of the child to be trained in multiple preset cognitive dimensions; wherein, the quantitative assessment value is a percentile; comparing the quantitative assessment value of each preset cognitive dimension with a preset dimension ability threshold; determining the cognitive dimension whose quantitative assessment value is lower than its corresponding preset dimension ability threshold as the weak dimension of the child to be trained; wherein, the dimension ability threshold is a boundary value preset based on norm data or clinical experience to distinguish between normal ability and weak ability; for the weak dimension of the child to be trained, calculating the difference between the quantitative assessment value of the weak dimension and the corresponding dimension ability threshold; converting the difference into a scaling value as the deviation of the weak dimension.
[0030] S103, Based on each weak dimension and its degree of deviation, construct and execute an individualized training plan for the child to be trained; wherein, the individualized training plan includes training tasks corresponding to each weak dimension; In some embodiments of this application, the specific process of constructing and executing an individualized training program for a child to be trained based on each weakness dimension and its degree of deviation includes: constructing an individualized training program for the child to be trained based on each weakness dimension and its degree of deviation; scheduling training tasks in the individualized training program; and presenting the training tasks to the child to be trained in a gamified interactive form to execute the training tasks; wherein the gamified interactive form includes at least story scenarios, level-based mechanisms, reward feedback, character growth, point accumulation, badge incentives, or stage unlocking.
[0031] Specifically, the process of constructing an individualized training plan for the child to be trained based on each weak dimension and its degree of deviation includes: determining the quantified deviation value of each weak dimension based on its degree of deviation; sorting all identified weak dimensions according to the magnitude of their quantified deviation values to generate a training priority sequence; where weak dimensions with larger quantified deviation values have higher training priority; mapping and configuring at least one preset training task for each weak dimension based on the training priority sequence and a preset dimension-task mapping relationship library; where the preset dimension-task mapping relationship library defines one or more training task types corresponding to each cognitive weak dimension; and generating an individualized training plan for the child to be trained based on the training priority sequence and the mapped and configured training tasks; where the individualized training plan includes a combination of training dimensions, a training task sequence, and initial training parameters.
[0032] For example, for children with significant visual crowding abnormalities, visual separation and target recognition training can be prioritized; for children with insufficient memory span, retention, reproduction and chunking training can be prioritized; and for children with insufficient visual search or attention allocation, target localization, continuous tracking and resource allocation training can be prioritized.
[0033] It should be noted that individualized training programs can include training in a single key dimension or a combination of training in multiple dimensions, to suit the cognitive profiles of different children. Training frequency, duration, sequence, and progression path can all be dynamically updated based on the child's training performance.
[0034] For example, to enhance children's motivation and long-term training adherence, this application can employ gamified training design. Training tasks can be embedded in interactive formats such as story scenarios, challenge mechanisms, reward feedback, character growth, point accumulation, badge incentives, and stage unlocking, making the cognitive training process more engaging and goal-oriented. Gamification design does not alter the core cognitive objectives of the training; rather, it optimizes the human-computer interaction experience while ensuring training effectiveness, reducing the monotony of repetitive training. Children completing cognitive training tasks in a gamified environment helps improve training sustainability and practical application feasibility in home and school settings.
[0035] In one possible implementation, perceptual organization ability determines a child's ability to integrate scattered local information into a stable overall structure within a complex visual scene. During reading, children need to quickly integrate strokes, components, and character shapes, and extract meaningful visual objects from a complex background. Improving perceptual organization ability helps enhance the efficiency of character structure recognition and the stability of overall visual processing. A training program for perceptual organization ability involves designing rich and engaging game scenarios, using games like "finding hidden shapes" or "assembling complete shapes." Target structures are embedded within random arrays of dots, lines, or similar shapes, and children use a keyboard or touchscreen to determine the category or location of the target shapes. The training difficulty can be gradually adjusted by increasing the number of distractions, decreasing the number of local elements composing the target shape, increasing the spacing between elements, increasing the randomness of local orientation, or shortening the stimulus presentation time, to train children's ability to extract the overall structure from local features.
[0036] Training of perceptual organization skills, for example Figure 2 As shown, the game scenario is: Back on Earth, children play the role of astronaut pilots, controlling the spaceship to find the circle (Earth). If they answer correctly, they can land back to their home planet; if they fail, they cannot land on Earth.
[0037] In another possible implementation, rhythm perception reflects an individual's ability to perceive and predict temporal structure, sequential relationships, and regular changes. During reading, children need to be sensitive to phonological rhythm, sequential order, and continuous processing beats. Good rhythm processing helps improve reading fluency, syllable segmentation, and temporal integration abilities. A training program for rhythm perception involves using rhythm-matching games similar to dance mats or Taiko no Tatsujin. The system continuously presents single or multiple cue signals on the screen according to a preset beat, and children must press designated buttons within the corresponding time window to achieve a "beat-match" response. The training difficulty can be graded by adjusting the beat speed, rhythm complexity, the number of simultaneous targets, the cue lead time, and the allowable time error range, gradually improving children's perception, prediction, and synchronization abilities regarding temporal structure. Game scenarios include... Figure 3 As shown.
[0038] In another possible implementation, memory span reflects an individual's ability to retain and reproduce sequences of items in a short period of time. It is a crucial foundation for maintaining word information, tracking sequential relationships, and performing sequential processing during reading. Improving memory span helps enhance children's ability to retain characters, syllables, and short-term sequences of information, thereby supporting the decoding and integration processes during reading. Training programs for memory span include designing rich and engaging game scenarios, using games such as "memory chests" or "sequence reproduction." The system presents numbers, shapes, letters, or common symbols in ascending order, and children must retain them briefly before reproducing them in sequence. The training difficulty can be adjusted by increasing the number of items, shortening the presentation time, shortening the retention interval, introducing distracting items, or transitioning from single-material to mixed-material learning. When children experience difficulty improving, color grouping, rhythmic cues, or spatial chunking can be used to help them develop effective memory strategies.
[0039] Game scenarios that require memory span, for example Figure 4 As shown in the image, the little bear needs to cross a river. By memorizing numbers (left image), it then assembles them into a bridge, allowing it to cross successfully (right image).
[0040] In another possible implementation, visual crowding reflects an individual's ability to separate and identify targets under conditions of densely packed adjacent stimuli. Reading materials naturally feature high-density character arrangement; if the visual crowding effect is strong, children are more easily distracted by adjacent information when recognizing letters, Chinese characters, or components, thus affecting reading speed and accuracy. Improving visual spatial discrimination helps reduce the impact of interference in dense text. Training programs for visual crowding ability involve designing rich and engaging game scenarios, using games like "quickly identifying directions" or "squeezing out shapes to distinguish," where a target shape and its adjacent distracting shapes are briefly presented on the screen under different levels of crowding. Children must quickly determine the direction, position, or category of the target. The training difficulty can be adjusted by reducing the distance between the target and distracting items, increasing the number of adjacent distracting items, shortening the stimulus presentation time, reducing contrast, or increasing the similarity between the target and distracting items, to gradually improve children's ability to separate and identify targets in complex visual environments.
[0041] Game scenarios with visual crowding ability, such as Figure 5 As shown in the image, three T-shapes facing different directions are quickly presented (left image). Children need to quickly identify the orientation of the middle T and select it from the options in the right image.
[0042] In another possible implementation, visual search ability reflects an individual's efficiency in quickly locating a target and suppressing distractions among multiple candidate items. During reading, children need to continuously perform visual localization, target word lookup, and key information filtering. Good visual search ability helps improve reading location efficiency, reduce missed words, and enhance overall processing speed. A training program for visual search ability involves designing rich and engaging game scenarios, such as "find the target pattern" or "treasure hunt" games. One or more targets are embedded within a set of highly similar graphics, letters, components, or symbols, and children must find the targets and respond within a limited time. Training difficulty can be graded by increasing the size of the search array, increasing the similarity between the target and distractors, setting multi-target conditions, shortening the response time, or introducing rule switching to train children's rapid scanning, target localization, and distraction suppression abilities.
[0043] Game scenarios with visual search capabilities, for example Figure 6 As shown. Visual search training style: Children are required to quickly identify the few different individuals among similar shapes. (Options include shapes, letters, words, etc.).
[0044] In another possible implementation, attention allocation ability reflects an individual's capacity to allocate and flexibly switch resources among multiple targets, locations, or task requirements. Reading requires not only focused attention on current information but also consideration of subsequent expectations, spatial positioning, and multi-cue integration. Therefore, improving attention allocation ability helps enhance information processing efficiency in complex reading situations. Training programs for attention allocation ability include designing rich and engaging game scenarios, employing "multi-target tracking" or "multi-cue protection" games. Multiple movable targets and distracting targets appear simultaneously on the screen, requiring children to continuously track a designated target or simultaneously determine color, location, or shape during the tracking process. The training difficulty can be adjusted by increasing the number of targets, increasing movement speed, adding trajectory intersections, shortening the reaction window, or introducing additional judgment tasks to enhance children's ability to process and flexibly transfer information from multiple sources in complex environments.
[0045] Pay attention to game scenarios where abilities are allocated, such as... Figure 7 As shown in the image, in multi-target tracking, a few target balls initially appear with a yellow halo (top image), then the halo disappears (bottom image). The balls move and collide on the screen, and after a period of time, the child needs to report which ball was previously marked by the halo.
[0046] S104: During the execution of the training task, the difficulty parameters of the training task are dynamically adjusted based on the real-time performance data of the child to be trained.
[0047] In some embodiments of this application, the specific process of dynamically adjusting the difficulty parameters of the training task based on the real-time performance data of the child to be trained includes: determining the core performance indicators of the child to be trained within a preset evaluation window under the current training task based on the real-time performance data; comparing the core performance indicators with a preset target performance range to determine the real-time training performance level of the child to be trained; wherein the real-time training performance level includes at least performance better than expected, performance in line with expectations, and performance lower than expected; and dynamically adjusting the difficulty parameters of the training task based on the real-time training performance level.
[0048] Specifically, core performance indicators should include at least the accuracy rate, reaction time, number of consecutive correct answers, number of consecutive incorrect answers, recent training trends, completion time, interruption rate, or fatigue performance of the child being trained when performing the task.
[0049] Specifically, the process of dynamically adjusting the difficulty parameters of the training task based on the real-time training performance level includes: when the real-time training performance level is better than expected, performing a difficulty increase operation, which includes at least increasing the number of distractors, shortening the stimulus presentation time, increasing the tempo, increasing the complexity of the task rules, expanding the length of the memory sequence, increasing the number of targets, decreasing the gap between targets and distractors, or reducing the contrast between targets and distractors; or, when the real-time training performance level is worse than expected, performing a difficulty decrease operation, which includes at least reducing the number of distractors, extending the stimulus presentation time, decreasing the tempo, simplifying the task rules, shortening the length of the memory sequence, reducing the number of targets, increasing the gap between targets and distractors, or increasing the contrast between targets and distractors; or, when the real-time training performance level is in line with expectations, maintaining the difficulty parameters of the current training task unchanged.
[0050] It should be noted that this application introduces an adaptive difficulty matching mechanism. The system does not use fixed difficulty training, but automatically adjusts task parameters based on the child's real-time performance during training to maintain the training at a moderately challenging level.
[0051] For example, when a child performs well, the system can automatically increase the training difficulty, such as increasing the number of distractors, shortening the stimulus presentation time, increasing the complexity of rules, increasing the task load, or expanding the memory span; when a child's performance declines, the system can appropriately reduce the difficulty to avoid excessive frustration. Through this mechanism, training can continuously adapt to different ability levels and training stages, improving training efficiency and persistence.
[0052] In some embodiments of this application, after dynamically adjusting the difficulty parameters of the training task, it is also necessary to collect and record the full-process behavioral data of the child to be trained during the execution of the training task; wherein, the full-process behavioral data includes at least reaction time, accuracy rate, completion status of each training level, difficulty change trajectory, dimension progress curve, and stage training performance; based on the full-process behavioral data, the training effect of the child to be trained in the weak dimension is quantitatively evaluated in stages to obtain the stage quantitative evaluation results; based on the stage quantitative evaluation results, a training effect report containing the progress curve of the child to be trained in at least one cognitive dimension is created.
[0053] It should be noted that this application generates interim training reports based on the aforementioned data, compares the trend of ability changes in the same dimension before and after training, and identifies which dimensions have improved significantly and which dimensions still need to be strengthened. When necessary, the system can also call the screening and evaluation module for re-evaluation to verify the training effect and regenerate the cognitive profile, thereby forming a closed-loop optimization mechanism of evaluation-training-re-evaluation.
[0054] In this embodiment, on the one hand, the adaptive difficulty adjustment mechanism dynamically adjusts the parameters of the training task by collecting children's real-time performance data, thereby achieving dynamic closed-loop optimization of the training process. This ensures that the difficulty of the training task always matches the child's immediate ability level, maintaining a moderately challenging state, guaranteeing the effectiveness of training, enhancing its adaptability and individuality, and effectively improving training efficiency. On the other hand, when generating training tasks targeting specific weak dimensions, training objectives are combined with gamification elements, transforming core cognitive training into an attractive interactive game. This approach not only reduces children's resistance to training but also combines extrinsic and intrinsic motivation, significantly increasing children's enthusiasm, initiative, and the likelihood of long-term adherence to training. Furthermore, by systematically collecting behavioral data throughout the training process and conducting phased quantitative assessments, a visualized training effect report is generated. This approach not only clearly demonstrates children's progress in specific cognitive dimensions but also enables continuous tracking and evaluation of training effects.
[0055] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.
[0056] Please see Figure 8This illustration shows a schematic diagram of a children's reading ability improvement training system based on multidimensional assessment of basic cognitive functions of the brain, provided in an exemplary embodiment of this application. This children's reading ability improvement training system based on multidimensional assessment of basic cognitive functions of the brain can be implemented as all or part of an electronic device through software, hardware, or a combination of both. System 1 includes a multidimensional brain function profile data acquisition module 10, a weak dimension and its deviation degree identification module 20, an individualized training program construction module 30, and a difficulty parameter dynamic adjustment module 40.
[0057] The multidimensional brain function profile data acquisition module 10 is used to acquire multidimensional brain function profile data of the child to be trained in multiple preset cognitive dimensions; wherein, the multiple preset cognitive dimensions include at least perceptual organization, rhythm perception, memory span, visual crowding, visual search, and attention allocation, and the multidimensional brain function profile data is used to characterize the performance information of the child to be trained in each preset cognitive dimension. The weak dimension and deviation degree identification module 20 is used to identify the weak dimensions and deviation degree of the child to be trained in multiple preset cognitive dimensions based on multidimensional brain function profile data. The individualized training program construction module 30 is used to construct and execute an individualized training program for the child to be trained based on each weak dimension and its degree of deviation. The individualized training program includes training tasks corresponding to each weak dimension. The difficulty parameter dynamic adjustment module 40 is used to dynamically adjust the difficulty parameters of the training task based on the real-time performance data of the child to be trained during the execution of the training task.
[0058] For example Figure 9 As shown, the device 1 also includes a subject child information input module 60 and an assessment result report output module 70.
[0059] The whole process behavior data recording module 50 is used to collect and record the whole process behavior data of the children to be trained during the execution of training tasks; the whole process behavior data includes at least reaction time, accuracy rate, completion status of each training level, difficulty change trajectory, dimension progress curve, and stage training performance. The phased quantitative assessment module 60 is used to conduct phased quantitative assessments of the training effect on the weak dimensions of the children to be trained based on the behavioral data of the whole process, and obtain the phased quantitative assessment results. The training effectiveness report creation module 70 is used to create a training effectiveness report that includes the progress curve of the child to be trained in at least one cognitive dimension, based on the results of the phased quantitative assessment.
[0060] It should be noted that the above-described children's reading ability enhancement training system based on multidimensional assessment of basic cognitive functions, when implementing the children's reading ability enhancement training method based on multidimensional assessment of basic cognitive functions, is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the electronic device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the children's reading ability enhancement training system based on multidimensional assessment of basic cognitive functions and the children's reading ability enhancement training method embodiment based on multidimensional assessment of basic cognitive functions provided in the above embodiments belong to the same concept, and their implementation process is detailed in the method embodiment, which will not be repeated here.
[0061] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0062] In this embodiment, on the one hand, the adaptive difficulty adjustment mechanism dynamically adjusts the parameters of the training task by collecting children's real-time performance data, thereby achieving dynamic closed-loop optimization of the training process. This ensures that the difficulty of the training task always matches the child's immediate ability level, maintaining a moderately challenging state, guaranteeing the effectiveness of training, enhancing its adaptability and individuality, and effectively improving training efficiency. On the other hand, when generating training tasks targeting specific weak dimensions, training objectives are combined with gamification elements, transforming core cognitive training into an attractive interactive game. This approach not only reduces children's resistance to training but also combines extrinsic and intrinsic motivation, significantly increasing children's enthusiasm, initiative, and the likelihood of long-term adherence to training. Furthermore, by systematically collecting behavioral data throughout the training process and conducting phased quantitative assessments, a visualized training effect report is generated. This approach not only clearly demonstrates children's progress in specific cognitive dimensions but also enables continuous tracking and evaluation of training effects.
[0063] This application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implement the training method for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain provided in the above-described method embodiments.
[0064] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to execute the training method for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain, as described in the above-described method embodiments.
[0065] Please see Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 10As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.
[0066] The communication bus 1002 is used to realize the connection and communication between these components.
[0067] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.
[0068] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).
[0069] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts within the electronic device 1000 using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 1001 may integrate one or more of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip, without being integrated into the processor 1001.
[0070] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage system located remotely from the aforementioned processor 1001. Figure 10 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a children's reading ability improvement training application based on a multi-dimensional assessment of basic cognitive functions of the brain.
[0071] exist Figure 10 In the illustrated electronic device 1000, the user interface 1003 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 1001 can be used to call the children's reading ability improvement training application based on multidimensional assessment of basic cognitive functions of the brain stored in the memory 1005, and specifically perform the following operations: Acquire multidimensional brain function profiles of children to be trained across multiple preset cognitive dimensions; Based on multidimensional brain function profiling data, the weak dimensions and their degree of deviation in multiple preset cognitive dimensions of the children to be trained were identified. Based on each weak dimension and its degree of deviation, an individualized training plan is constructed and implemented for the child to be trained; wherein, the individualized training plan includes training tasks corresponding to each weak dimension; During the training process, the difficulty parameters of the training task are dynamically adjusted based on the real-time performance data of the children being trained.
[0072] In one embodiment, when the processor 1001 constructs and executes an individualized training program for the child to be trained based on each weak dimension and its degree of deviation, it specifically performs the following operations: Based on each weak dimension and its degree of deviation, an individualized training plan is constructed for the child to be trained; Schedule training tasks within an individualized training program; The training tasks are presented to the children in a gamified, interactive format so that they can perform the training tasks; among them, Gamified interaction forms include at least story scenarios, level-based mechanisms, reward feedback, character growth, point accumulation, badge incentives, or stage unlocking.
[0073] In one embodiment, when the processor 1001 constructs an individualized training plan for the child to be trained based on each weak dimension and its degree of deviation, it specifically performs the following operations: Based on each weak dimension and its degree of deviation, determine the quantitative deviation value of each weak dimension; Based on the magnitude of the quantization deviation value of each weak dimension, all identified weak dimensions are sorted to generate a training priority sequence; among them, the weak dimensions with larger quantization deviation values have higher training priority. Based on the training priority sequence and the preset dimension-task mapping relationship library, at least one preset training task is mapped and configured for each weak dimension; wherein, the preset dimension-task mapping relationship library defines one or more training task types corresponding to each cognitive weak dimension; Based on the training priority sequence and the training tasks configured by the mapping, an individualized training plan is generated for the child to be trained; the individualized training plan includes the combination of training dimensions, the sequence of training tasks, and the initial training parameters.
[0074] In one embodiment, when the processor 1001 dynamically adjusts the difficulty parameters of the training task based on the real-time performance data of the child to be trained, it specifically performs the following operations: Based on real-time performance data, determine the core performance indicators of the children to be trained under the current training task within the preset assessment window; The core performance indicators are compared with the preset target performance range to determine the real-time training performance level of the child to be trained; the real-time training performance level includes at least performance better than expected, performance in line with expectations, and performance lower than expected. The difficulty parameters of the training task are dynamically adjusted based on the real-time training performance level.
[0075] In one embodiment, when the processor 1001 dynamically adjusts the difficulty parameters of the training task based on the real-time training performance level, it specifically performs the following operations: When the real-time training performance level is "better than expected," a difficulty-increasing operation is performed. This operation includes at least: increasing the number of distractors, shortening stimulus presentation time, increasing beat speed, increasing task rule complexity, expanding the length of the memory sequence, increasing the number of targets, decreasing the distance between targets and distractors, or reducing the contrast between targets and distractors; or... When the real-time training performance level is below expectations, a difficulty reduction operation is performed. This operation includes at least reducing the number of distractors, extending stimulus presentation time, decreasing tempo, simplifying task rules, shortening the length of the memory sequence, reducing the number of targets, increasing the spacing between targets and distractors, or increasing the contrast between targets and distractors; or... When the real-time training performance level is "performance meets expectations", the difficulty parameter of the current training task remains unchanged.
[0076] In one embodiment, after the processor 1001 performs the dynamic adjustment of the difficulty parameters of the training task, it also performs the following operations: Collect and record the entire process of behavioral data of the children to be trained during the execution of training tasks; the entire process behavioral data includes at least reaction time, accuracy rate, completion status of each training level, trajectory of difficulty change, dimensional progress curve, and stage training performance; Based on the behavioral data throughout the entire process, the training effect on the weak dimensions of the children to be trained is quantitatively evaluated in stages, and the results of the quantitative evaluation in stages are obtained. Based on the phased quantitative assessment results, a training effectiveness report is created, which includes the progress curves of the children to be trained in at least one cognitive dimension.
[0077] In one embodiment, when the processor 1001 identifies the weak dimensions and their degree of deviation among multiple preset cognitive dimensions of a child to be trained based on multidimensional brain functional profiling data, it specifically performs the following operations: Analyze multidimensional brain function profile data to obtain quantitative assessment values for each cognitive dimension of the child to be trained in multiple preset cognitive dimensions; where the quantitative assessment values are percentiles. The quantitative assessment value of each preset cognitive dimension is compared with the preset dimension ability threshold; Cognitive dimensions whose quantitative assessment values are lower than their corresponding preset dimensional ability thresholds are identified as the weak dimensions of children to be trained; whereby the dimensional ability thresholds are preset based on norm data or clinical experience and are used to distinguish between normal and weak abilities. For the weak dimensions of the children to be trained, calculate the difference between the quantitative assessment value of the weak dimension and the corresponding dimension ability threshold; The difference is converted into a scale value, which represents the degree of deviation of the weak dimension.
[0078] In this embodiment, on the one hand, the adaptive difficulty adjustment mechanism dynamically adjusts the parameters of the training task by collecting children's real-time performance data, thereby achieving dynamic closed-loop optimization of the training process. This ensures that the difficulty of the training task always matches the child's immediate ability level, maintaining a moderately challenging state, guaranteeing the effectiveness of training, enhancing its adaptability and individuality, and effectively improving training efficiency. On the other hand, when generating training tasks targeting specific weak dimensions, training objectives are combined with gamification elements, transforming core cognitive training into an attractive interactive game. This approach not only reduces children's resistance to training but also combines extrinsic and intrinsic motivation, significantly increasing children's enthusiasm, initiative, and the likelihood of long-term adherence to training. Furthermore, by systematically collecting behavioral data throughout the training process and conducting phased quantitative assessments, a visualized training effect report is generated. This approach not only clearly demonstrates children's progress in specific cognitive dimensions but also enables continuous tracking and evaluation of training effects.
[0079] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium for the program for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain can be a magnetic disk, optical disk, read-only memory, or random access memory, etc.
[0080] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.
Claims
1. A training method for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain, characterized in that, The method includes: Acquire multidimensional brain function profiles of children to be trained across multiple preset cognitive dimensions; Based on the multidimensional brain function profile data, the weak dimensions and their degree of deviation in the multiple preset cognitive dimensions of the child to be trained are identified; Based on each weak dimension and its degree of deviation, an individualized training plan is constructed and executed for the child to be trained; wherein, the individualized training plan includes training tasks corresponding to each weak dimension; During the execution of the training task, the difficulty parameters of the training task are dynamically adjusted based on the real-time performance data of the child to be trained.
2. The method according to claim 1, characterized in that, The multiple preset cognitive dimensions include at least perceptual organization, rhythm perception, memory span, visual crowding, visual search, and attention allocation; among them... The multidimensional brain function profile data is used to characterize the specific performance information of the child to be trained in each preset cognitive dimension.
3. The method according to claim 1, characterized in that, The process of constructing and implementing an individualized training program for the child to be trained based on each weak dimension and its degree of deviation includes: Based on each weak dimension and its degree of deviation, an individualized training plan is constructed for the child to be trained; Schedule the training tasks in the individualized training scheme; The training task is presented to the child to be trained in a gamified interactive format to perform the training task; wherein, The gamified interaction forms include at least story scenarios, level-based mechanisms, reward feedback, character growth, point accumulation, badge incentives, or stage unlocking.
4. The method according to claim 3, characterized in that, The process of constructing an individualized training plan for the child to be trained based on each weak dimension and its degree of deviation includes: Based on each weak dimension and its degree of deviation, determine the quantized deviation value of each weak dimension; Based on the magnitude of the quantization deviation value of each weak dimension, all identified weak dimensions are sorted to generate a training priority sequence; wherein, the weak dimension with the larger the quantization deviation value has a higher training priority. Based on the training priority sequence and the preset dimension-task mapping relationship library, at least one preset training task is mapped and configured for each weak dimension; wherein, the preset dimension-task mapping relationship library defines one or more training task types corresponding to each cognitive weak dimension; Based on the training priority sequence and the training tasks configured by the mapping, an individualized training plan is generated for the child to be trained; wherein, the individualized training plan includes a combination of training dimensions, a training task sequence, and initial training parameters.
5. The method according to claim 1, characterized in that, The step of dynamically adjusting the difficulty parameters of the training task based on the real-time performance data of the child to be trained includes: Based on the real-time performance data, the core performance indicators of the child to be trained under the current training task within the preset evaluation window are determined; The core performance indicators are compared with a preset target performance range to determine the real-time training performance level of the child to be trained; wherein the real-time training performance level includes at least performance better than expected, performance in line with expectations, and performance lower than expected. The difficulty parameters of the training task are dynamically adjusted based on the real-time training performance level.
6. The method according to claim 5, characterized in that, The core performance indicators include at least the accuracy rate, reaction time, number of consecutive correct answers, number of consecutive incorrect answers, recent training trend, completion time, interruption rate, or fatigue performance of the child to be trained when performing the task.
7. The method according to claim 5, characterized in that, The step of dynamically adjusting the difficulty parameter of the training task based on the real-time training performance level includes: When the real-time training performance level is better than expected, a difficulty-increasing operation is performed. This difficulty-increasing operation includes at least increasing the number of distractors, shortening stimulus presentation time, increasing beat speed, increasing task rule complexity, expanding the length of the memory sequence, increasing the number of targets, decreasing the distance between targets and distractors, or reducing the contrast between targets and distractors; or... When the real-time training performance level is below expectations, a difficulty reduction operation is performed. This difficulty reduction operation includes at least reducing the number of distractors, extending stimulus presentation time, reducing tempo, simplifying task rules, shortening the memory sequence length, reducing the number of targets, increasing the spacing between targets and distractors, or increasing the contrast between targets and distractors; or... When the real-time training performance level is in line with expectations, the difficulty parameter of the current training task remains unchanged.
8. The method according to claim 1, characterized in that, After dynamically adjusting the difficulty parameters of the training task, the method further includes: Collect and record the entire process of the child to be trained's behavior during the execution of the training task; wherein, the entire process of the behavior data includes at least reaction time, accuracy rate, completion status of each training level, difficulty change trajectory, dimensional progress curve, and stage training performance; Based on the behavioral data throughout the entire process, the training effect of the children to be trained on the weak dimension is quantitatively evaluated in stages, and the results of the quantitative evaluation in stages are obtained. Based on the phased quantitative assessment results, a training effect report is created, which includes the progress curves of the children to be trained in at least one cognitive dimension.
9. The method according to claim 1, characterized in that, The step of identifying the weak dimensions and their degree of deviation among the multiple preset cognitive dimensions of the child to be trained based on the multidimensional brain function profile data includes: The multidimensional brain function profile data is analyzed to obtain a quantitative assessment value for each cognitive dimension of the child to be trained in the multiple preset cognitive dimensions; wherein, the quantitative assessment value is a percentile. The quantitative evaluation value of each preset cognitive dimension is compared with the preset dimension ability threshold; Cognitive dimensions whose quantitative assessment values are lower than their corresponding preset dimensional ability thresholds are identified as the weak dimensions of the children to be trained; wherein, the dimensional ability thresholds are preset based on norm data or clinical experience and are used to distinguish between normal and weak abilities. For the weak dimensions of the child to be trained, calculate the difference between the quantitative assessment value of the weak dimension and the corresponding ability threshold of the dimension; The difference is converted into a scale value, which represents the degree of deviation of the weak dimension.
10. A training system for improving children's reading ability based on multidimensional assessment of basic cognitive functions of the brain, characterized in that, The system includes: The multidimensional brain function profile data acquisition module is used to acquire multidimensional brain function profile data of the child to be trained in multiple preset cognitive dimensions; wherein, the multiple preset cognitive dimensions include at least perceptual organization, rhythm perception, memory span, visual crowding, visual search, and attention allocation, and the multidimensional brain function profile data is used to characterize the performance information of the child to be trained in each preset cognitive dimension. The weak dimension and deviation degree identification module is used to identify the weak dimensions and deviation degree of the child to be trained in the multiple preset cognitive dimensions based on the multidimensional brain function profile data. An individualized training program construction module is used to construct and execute an individualized training program for the child to be trained based on each weak dimension and its degree of deviation. The individualized training program includes training tasks corresponding to each weak dimension. The difficulty parameter dynamic adjustment module is used to dynamically adjust the difficulty parameter of the training task based on the real-time performance data of the child to be trained during the execution of the training task.