WO2017152532A1 - Procédé et dispositif de formation à la pensée informatique sur la base d'un modèle cognitif - Google Patents

Procédé et dispositif de formation à la pensée informatique sur la base d'un modèle cognitif Download PDF

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WO2017152532A1
WO2017152532A1 PCT/CN2016/085666 CN2016085666W WO2017152532A1 WO 2017152532 A1 WO2017152532 A1 WO 2017152532A1 CN 2016085666 W CN2016085666 W CN 2016085666W WO 2017152532 A1 WO2017152532 A1 WO 2017152532A1
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thinking
evaluation
training
computational
user
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PCT/CN2016/085666
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Chinese (zh)
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刘刚
Nanshan District Shenzhen Guangdong 518060 Nanhai Ave 3688
张兆芹
杨烜
贾维辰
陈守芳
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深圳大学
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    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B19/00—Teaching not covered by other main groups of this subclass

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  • the invention belongs to the technical field of computers and internet, and in particular relates to a computational thinking training method and device based on a cognitive model.
  • Computational thinking is a cognitive approach based on computation and with the core of thinking science. Computational thinking trains people how to solve the problem in the basic way of solving problems in order to construct corresponding algorithms and basic programs.
  • the current vocational education focuses on skills training, lacking the ability to solve problems, and the way of thinking through computer solutions to industry problems is the key to training.
  • the training of thinking thinking ability is aimed at training problem solving ability.
  • Moodle Modular Object-Oriented Dynamic Learning Environment
  • Moodle is an object-oriented dynamic learning environment. At present, many organizations and individuals use Moodle. People pay attention to how to use Moodle to manage online courses, especially in distance learning. Among them, the module is used to do the curriculum design, and the focus is mainly on how to localize and redevelop it.
  • the invention provides a computational thinking training method and device based on a cognitive model.
  • a computational thinking cognitive model By setting a computational thinking cognitive model, a training platform for training computational thinking is constructed, and the user's computational thinking is trained through the training platform, aiming at solving vocational education.
  • the invention provides a cognitive thinking training method based on a cognitive model, comprising:
  • the training platform Constructing a training platform for training computational thinking according to a computational thinking cognitive model set by the user; and according to the request of the user, the training platform displays training data corresponding to the computational thinking cognitive model, and acquiring the user Training feedback data for the training data, and, Calculating a level of computational thinking of the user according to the training feedback data and a preset rating criterion, and the training data corresponding to the computational thinking cognitive model includes: training data for calculating thinking consciousness, training data for calculating thinking method, and calculating Training data of thinking use ability; performing multi-level fuzzy evaluation on the user's computational thinking according to the evaluation level of the user's computational thinking.
  • the invention provides a cognitive thinking training device based on a cognitive model, comprising:
  • a building module configured to build a training platform for training computational thinking according to a computational thinking cognitive model set by a user
  • a display module configured to display and the computational thinking cognitive model according to the request of the user
  • the training data corresponding to the computational thinking cognitive model includes: training data for calculating thinking consciousness, training data for calculating thinking method, and training data for calculating thinking operation ability; and acquiring module for acquiring the user pair a training feedback data of the training data; a ranking evaluation module, configured to evaluate a level of computational thinking of the user according to the training feedback data and a preset rating criterion; and an evaluation module, configured to calculate a thinking according to the user
  • the evaluation level is a multi-layer fuzzy evaluation of the user's computational thinking.
  • an echo cognitive model is proposed, and based on the cognitive model, learning and ability training are performed from three dimensions: computational thinking, computational thinking, and computational thinking ability training; From the "consciousness dimension”, “method dimension” and “capability dimension”, the computational thinking ability is graded and evaluated, and nineteen ability indicators are proposed.
  • the multi-level fuzzy evaluation model is used to evaluate the computational thinking ability of vocational education learners. . Therefore, combining the computational thinking ability training for vocational education with online learning, under the guidance of the cognitive model, it is effective to train and evaluate the abstract thinking ability of learners through the unique training methods and evaluation system in the platform. Change the way of thinking of learners in vocational education, and complete the transformation of technically-operated talents into information processing talents.
  • FIG. 1 is a schematic flowchart showing an implementation process of a cognitive thinking training method based on a cognitive model according to a first embodiment of the present invention
  • FIG. 2 is a schematic diagram of a cognitive model in an embodiment of the present invention.
  • FIG. 3 is a schematic structural diagram of a cognitive model-based computational thinking training apparatus according to a second embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of a cognitive model-based computational thinking training apparatus according to a third embodiment of the present invention.
  • FIG. 1 is a schematic flowchart of an implementation process of a cognitive thinking training method based on a cognitive model according to a first embodiment of the present invention.
  • the cognitive model-based computational thinking training method provided by this embodiment mainly includes the following steps S101 to S103:
  • the user is a learner who performs computational thinking training.
  • the training platform is an online large-scale learning platform, which is set in the server and can provide various data for the user to carry out computational thinking training, and obtain training feedback data of the user to evaluate the user's computational thinking.
  • the computational thinking cognitive model includes: a cognitive problem sub-model, a problem solving sub-model, an interpretation problem sub-model, and a selection method sub-model.
  • the computational thinking cognitive model is a computational thinking echo cognitive model, which includes the following contents:
  • H The learner processes the problem according to the previous interpretation of the problem and the corresponding computational thinking method of choice;
  • Figure 2 is a schematic diagram of the echo cognitive model.
  • the cognitive thinking stage is divided into four cognitive processes: consciousness problem, problem processing, problem interpretation, and method selection.
  • people receive information flow from the environment, they form a flow of thought through the human brain, and deal with the problem according to the mental model of the person.
  • the computational thinking is based on heuristic reasoning, systematic thinking, and thinking thinking. The separation of concerns and other aspects to decompose the problem, after the problem is effectively decomposed and interpreted, further need to choose how to deal with the problem.
  • the training platform displays training data corresponding to the computational thinking cognitive model according to the request of the user, and obtains training feedback data of the training data by the user, and according to the training feedback data and preset rating standards. Evaluate the level of computational thinking for the user;
  • the training data corresponding to the computational thinking cognitive model includes: training data for calculating thinking consciousness, training data for calculating thinking method, and training data for calculating thinking ability.
  • the step S102 may specifically include: according to the training request of the user to calculate the thinking cognition, the training platform displays the training data of the cognitive thinking in a video form, and according to the query request of the user, the training platform displays the constructed webpage in the form of a webpage.
  • the computational thinking network corresponding to the training data of computational thinking cognition.
  • the training platform displays test data corresponding to the training data of the computational thinking cognition, and obtains test feedback data corresponding to the test data, and according to a preset cognitive ability rating standard, The rating level of each capability indicator of the user's computational thinking.
  • the basic knowledge and concepts of computational thinking can be divided into eight basic concepts: computer development history, computer hardware composition, computer software system, Windows operating system use, common software use, computer network.
  • Basic concepts basic concepts of databases, basic concepts of high-performance computers.
  • the training platform When the training platform receives the training request of the user's computational thinking cognition, according to the request, the training platform explains the basic concept in the form of video and text, and constructs according to the setting information of the computing thinking network Wiki (wiki).
  • Computational thinking wiki. Wiki is a multi-person collaborative writing tool. Wiki sites can be maintained by multiple people. People can express their opinions or expand or explore common themes.
  • the training platform displays the computational thinking wiki corresponding to the training data of the computational thinking cognition that has been constructed in the form of a webpage. Through the computational thinking wiki, the learner can consult the textual explanation of the relevant concept to deepen the concept. Learn more.
  • the training platform builds its learning trajectory for each user, records the content that the user has learned and mastered, and recommends relevant learning content according to the learning trajectory.
  • a test question bank for providing a basic concept of computational thinking is provided, and a test paper randomly formed by the test question bank is provided for the user.
  • the training platform displays training data related to the computational thinking cognition.
  • Corresponding test data obtaining test feedback data corresponding to the test data, and rating the ability of the user's computational thinking cognition according to a preset cognitive ability rating standard, that is, providing a test paper for the user, and collecting the user's test paper
  • the answer data based on the answer data of the test paper and the cognitive ability rating standard, evaluates and ranks the learning effects of the basic concepts of each user's computational thinking.
  • Step S102 may specifically include:
  • the training platform displays the training selected by the user.
  • Cases, training cases are used to train the basic concepts and practical applications of computational thinking methods.
  • the classification of computational thinking methods is to divide the computational thinking method into the following categories: regular discovery, problem description, problem reduction, Problem optimization, program evaluation, efficiency improvement, and system protection;
  • the algorithms, system design, and human behavior understanding in computer thinking can be explicitly displayed through video, animation, etc., so that learners can understand computational thinking more easily.
  • a relevant training case is designed, and the method of training the computational thinking is applied in solving the practical problem.
  • the training case involves functions such as concept explanation, promotion and system help, so that learners can actively participate in the interaction by learning while playing, and further enhance the initiative and interest of computational thinking learning.
  • the training feedback data of the user is obtained, and the level of the computing thinking method capability of the user is evaluated according to a preset method capability rating standard.
  • the level of the evaluation corresponds to the category after the division of the computational thinking method, that is, the above seven categories respectively have corresponding evaluation levels.
  • Step S102 may specifically include:
  • the training platform displays a pre-designed form of an industry case for calculating a mind map, which is used to embody the application of computational thinking in specific industry problems, and obtain the user solution.
  • the design industry case reflects the application of computational thinking in specific industry issues, and trains learners' computational thinking application capabilities by solving industry-specific problems.
  • the thinking process is presented in the form of a map, and the learner guides the correct thinking process through the mind map.
  • the training of the present invention will enable the learner to experience the learning process of "from scratch” and “from vagueness to clarity” to guide the learner to further application in other industries.
  • S103 Perform multi-level fuzzy evaluation on the user's computational thinking according to the evaluation level of the user's computational thinking.
  • the computational thinking is divided into multi-level evaluation indicators.
  • the first-level evaluation indicators are divided into computational thinking consciousness dimensions, computational thinking method dimensions and computational thinking application ability dimensions;
  • the computational thinking consciousness dimension includes the following second-level evaluation index: computational thinking cognition;
  • the computational thinking method dimension includes the following seven second-level evaluation indicators: regular discovery, problem description, problem reduction, problem optimization, program evaluation, Efficiency improvement, system protection;
  • the computational thinking ability dimension includes the following second-level evaluation index: the use of computational thinking;
  • the computational thinking cognition includes the following third-level evaluation indicator: cognition;
  • the law discovery includes the following third-level evaluation indicator: learning;
  • the problem description includes the following six third-level evaluation indicators: abstraction, recursion, statute, Decomposition, transformation and embedding;
  • the problem optimization includes the following four third-level evaluation indicators: planning, compromise, optimization and heuristic;
  • the program evaluation includes one of the following third-level evaluation indicators: simulation;
  • the efficiency improvement includes the following two The third level of evaluation indicators: parallel and scheduling;
  • the system protection includes the following three third-level evaluation indicators: redundancy protection, fault-tolerant error correction and system recovery;
  • the computational thinking application includes the following third-level evaluation indicators: capabilities.
  • the learner's computational thinking ability is evaluated, and the computational thinking ability is evaluated from the dimensions of computational thinking consciousness, computational thinking method and computational thinking ability.
  • the ability characteristics using multi-layer fuzzy evaluation model to construct vocational education learners' computational thinking ability evaluation system.
  • the evaluation factor of computational thinking cognition since the evaluation factor of computational thinking cognition only corresponds to one second-level evaluation index, it only corresponds to one third-level evaluation index, and the evaluation level of the third-level evaluation index is used as the evaluation result of the computational thinking consciousness dimension. That is, the ability evaluation level of the user's computational thinking cognition is taken as the evaluation result C 1 of the computational thinking consciousness dimension:
  • V ⁇ V 1 , . . . , V n ⁇ represents a set of evaluation levels of the user's computational thinking, in which there are a total of n evaluation levels of computational thinking;
  • all seventeen third-level evaluation indicators of the computational thinking method dimension constitute a set U of evaluation factors, and the set U of the evaluation factors is divided into disjoint corresponding to the second-level evaluation indicators in the computational thinking method.
  • the number of evaluation factors of each U i may vary, here represented by K i, and for each of the evaluation are given a weighting factor, is constructed of U i
  • the weight vector A i [a i1 ,...,a iKi ];
  • the fuzzy mapping relationship vector of each evaluation factor at different evaluation levels is obtained, and all the fuzzy mapping relationship vectors of the evaluation factors are combined to obtain a fuzzy evaluation decision matrix R i of K i rows and n columns. ;
  • the fuzzy evaluation synthesis matrix is applied to the fuzzy evaluation decision matrix R i , and a fuzzy subset on the domain V is obtained as the fuzzy evaluation result of the second-level rating index of the computational thinking method at different evaluation levels;
  • B i is the fuzzy evaluation result of the i-th second-level rating index of the computational thinking method at different evaluation levels.
  • the second-level evaluation index has a total of seven;
  • the decision matrix R c is constructed according to the fuzzy evaluation results of the second-level rating indicators of the computational thinking method at different evaluation levels;
  • the weighting vector A [a 1 ,...,a 7 ] is further assigned to each of the second-level indicators in the method dimension, and the evaluation result C 2 of the calculation method dimension is obtained. ;
  • the evaluation level of the user's computing thinking ability is used as the evaluation result of the computing thinking ability dimension C 3 ;
  • a weight vector F [f 1 , f 2 , f 3 ] is assigned to the constructed decision matrix R B , and the evaluation result D of the user's computational thinking is obtained:
  • the multi-level fuzzy comprehensive evaluation model can not only reflect the different levels of the evaluation factors, but also solve the difficulty of assigning weights due to too many factors.
  • the cognitive thinking-based computational thinking training method proposes an echo cognitive model, and based on the cognitive model, three dimensions from the cognitive thinking, the computational thinking method, and the computational thinking ability training Conducting learning and ability training; at the same time, grading and evaluating computational thinking ability from the "consciousness dimension”, “method dimension” and “capability dimension”, and proposing 19 kinds of ability indicators, using multi-level fuzzy evaluation model to study vocational education
  • the computational thinking ability of the person is evaluated. Therefore, combining the computational thinking ability training for vocational education with online learning, under the guidance of the cognitive model, it is effective to train and evaluate the abstract thinking ability of learners through the unique training methods and evaluation system in the platform. Change the way of thinking of learners in vocational education, and complete the transformation of technically-operated talents into information processing talents.
  • FIG. 3 is a schematic structural diagram of a cognitive model-based computational thinking training apparatus according to a second embodiment of the present invention. For convenience of description, only parts related to the embodiment of the present invention are shown.
  • the cognitive model-based computational thinking training device illustrated in FIG. 3 may be an execution subject of the cognitive model-based computational thinking training method provided by the foregoing embodiment, which may be a functional module of a server or a server.
  • the device includes a construction module 301, a presentation module 302, an acquisition module 303, a rating evaluation module 304, and an evaluation module 305.
  • Each function module is described in detail as follows:
  • the building module 301 is configured to construct a training platform for training computational thinking according to a computational thinking cognitive model set by a user;
  • the computational thinking cognitive model includes: a cognitive problem sub-model, a problem solving sub-model, an interpretation problem sub-model, and a selection method sub-model.
  • the display module 302 is configured to display training data corresponding to the computing thinking cognitive model according to the user's request, and the training data corresponding to the computing thinking cognitive model comprises: training data for calculating thinking consciousness, and calculating thinking method Training data and training data for calculating the ability to use thinking;
  • An obtaining module 303 configured to acquire training feedback data of the training data by the user
  • a rating evaluation module 304 configured to evaluate a level of computational thinking of the user according to the training feedback data and a preset rating criterion
  • the evaluation module 305 is configured to perform multi-level fuzzy evaluation on the computing thinking of the user according to the evaluation level of the computing thinking of the user.
  • the embodiment of the present invention proposes an echo cognitive model, and based on the cognitive model, learns and competes in three dimensions: computational thinking, computational thinking, and computational thinking ability training;
  • the dimension of consciousness, the dimension of method and the dimension of ability classify and evaluate the ability of computational thinking, and put forward 19 kinds of ability indicators.
  • the multi-level fuzzy evaluation model is used to evaluate the computational thinking ability of vocational education learners. Therefore, combining the computational thinking ability training for vocational education with online learning, under the guidance of the cognitive model, it is effective to train and evaluate the abstract thinking ability of learners through the unique training methods and evaluation system in the platform. Change the way of thinking of learners in vocational education, and complete the transformation of technically-operated talents into information processing talents.
  • FIG. 4 is a schematic structural diagram of a cognitive model-based computational thinking training apparatus according to a third embodiment of the present invention. For convenience of description, only parts related to the embodiment of the present invention are shown.
  • the cognitive model-based computational thinking training device illustrated in FIG. 4 may be an execution subject of the cognitive model-based computational thinking training method provided by the foregoing embodiment, which may be a server or one of the functional modules.
  • the difference from the cognitive model-based computational thinking training device illustrated in FIG. 3 is that:
  • the display module 302 is further configured to: according to the training request of the user to calculate the thinking cognition, the training platform displays the training data of the cognition thinking in a video form, and, according to the query request of the user, the training platform uses the webpage The form shows the computational thinking network corresponding to the training data of the computational thinking cognition that has been constructed.
  • the display module 302 is further configured to: when receiving the test request of the user, the training platform displays test data corresponding to the training data of the computational thinking cognition.
  • the display module 302 is further configured to display, according to the user's selection instruction, the training platform selected by the user.
  • the display module 302 is further configured to perform an industry training request according to the user's computing thinking ability, and the training platform displays a pre-designed form of an industry case for calculating a mind map, which is used to embody computational thinking in a specific industry problem. Applications.
  • the obtaining module 303 is configured to obtain test feedback data corresponding to the test data.
  • the obtaining module 303 is further configured to: according to the training request of the user computing thinking method, obtain a preset training case corresponding to the classified method according to the classification of the computing thinking method in advance, and the classification of the computing thinking method includes: Computational thinking methods are divided into the following categories: Law discovery, problem description, problem reduction, problem optimization, program evaluation, efficiency improvement, and system protection. This training case is used to train the basic concepts and practical applications of computational thinking methods.
  • the obtaining module 303 is further configured to obtain training feedback data of the selected training case by the user.
  • the obtaining module 303 is further configured to obtain a level for the internship enterprise to evaluate the computing thinking ability of the user according to the preset operating capability rating standard.
  • the rating module 304 is configured to evaluate the level of ability of the user's computational thinking based on the preset cognitive ability rating criteria.
  • the rating evaluation module 304 is further configured to evaluate the level of the computing thinking method capability of the user according to the preset method capability rating standard, wherein the rating of the evaluation corresponds to the classified category of the computational thinking method.
  • the rating evaluation module 304 is further configured to evaluate the level of the computing thinking ability of the user according to the preset operating capability rating standard.
  • the evaluation module 305 further includes: a dividing module 3051, an evaluation submodule 3052
  • a dividing module 3051 configured to divide the computing thinking into multiple levels of evaluation indicators
  • the first-level evaluation indicators are divided into computational thinking consciousness dimensions, computational thinking method dimensions and computational thinking application ability dimensions;
  • the computational thinking consciousness dimension includes the following second-level evaluation index: computational thinking cognition, which includes the following seven second-level evaluation indicators: regular discovery, problem description, problem reduction, problem optimization, program evaluation, Efficiency improvement, system protection, the computational thinking ability dimension includes the following second-level evaluation index: the use of computational thinking;
  • the computational thinking cognition includes the following third-level evaluation indicator: cognition;
  • the law discovery includes the following third-level evaluation indicator: learning;
  • the problem description includes the following six third-level evaluation indicators: abstraction, recursion, statute, Decomposition, transformation and embedding;
  • the problem optimization includes the following four third-level evaluation indicators: planning, compromise, optimization and heuristic;
  • the program evaluation includes one of the following third-level evaluation indicators: simulation;
  • the efficiency improvement includes the following two The third level of evaluation indicators: parallel and scheduling;
  • the system protection includes the following three third-level evaluation indicators: redundancy protection, fault-tolerant error correction and system recovery;
  • the computational thinking application includes the following third-level evaluation indicators: capability;
  • the evaluation sub-module 3052 is used to evaluate the level of ability of the user's computational thinking cognition as the evaluation result C 1 of the computational thinking consciousness dimension:
  • the evaluation sub-module 3052 is further configured to: according to the evaluation level corresponding to each of the seven second-level evaluation indicators in the user's computational thinking method, evaluate the ability of the evaluation indicator dimension to evaluate the indicator;
  • V ⁇ V 1 , . . . , V n ⁇ represents a set of evaluation levels of the user's computational thinking, in which there are a total of n evaluation levels of computational thinking;
  • all the third-level evaluation indicators of the computational thinking method dimension constitute a set U of evaluation factors, and the set U of the evaluation factors is divided into disjoint children corresponding to the second-level evaluation indicators in the computational thinking method.
  • the weight vector A i of the evaluation factors of U i is constructed [i i1 , . . . , a iKi ];
  • the fuzzy mapping relationship vector of each evaluation factor at different evaluation levels is obtained, and all the fuzzy mapping relationship vectors of the evaluation factors are combined to obtain a fuzzy evaluation decision matrix R i of K i rows and n columns. ;
  • the fuzzy evaluation synthesis matrix is applied to the fuzzy evaluation decision matrix R i , and a fuzzy subset on the domain V is obtained as the fuzzy evaluation result of the second-level rating index of the computational thinking method at different evaluation levels;
  • B i is the fuzzy evaluation result of the i-th second-level rating index of the computational thinking method at different evaluation levels;
  • the decision matrix R c is constructed according to the fuzzy evaluation results of the second-level rating indicators of the computational thinking method at different evaluation levels;
  • the weighting vector A [a 1 ,...,a 7 ] is further assigned to each of the second-level indicators in the method dimension, and the evaluation result C 2 of the calculation method dimension is obtained. ;
  • the evaluation sub-module 3052 is further configured to use the evaluation level of the computing thinking ability of the user as the evaluation result C 3 of the computing thinking ability dimension;
  • Building matrix module 3053 used to calculate the dimensions of consciousness thought evaluation results C 1, the dimensions of the calculation method of the evaluation results C 2, thinking ability calculating the dimension C 3 Construction assessment decision matrix R B;
  • the embodiment of the present invention proposes an echo cognitive model, and based on the cognitive model, learns and competes in three dimensions: computational thinking, computational thinking, and computational thinking ability training;
  • the dimension of consciousness, the dimension of method and the dimension of ability classify and evaluate the ability of computational thinking, and put forward 19 kinds of ability indicators.
  • the multi-level fuzzy evaluation model is used to evaluate the computational thinking ability of vocational education learners. Therefore, combining the computational thinking ability training for vocational education with online learning, under the guidance of the cognitive model, it is effective to train and evaluate the abstract thinking ability of learners through the unique training methods and evaluation system in the platform. Change the way of thinking of learners in vocational education, and complete the transformation of technically-operated talents into information processing talents.
  • the disclosed apparatus and method may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the modules is only a logical function division.
  • there may be another division manner for example, multiple modules or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or module, and may be electrical, mechanical or otherwise.
  • the modules described as separate components may or may not be physically separated.
  • the components displayed as modules may or may not be physical modules, that is, may be located in one place, or may be distributed to multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.
  • the above integrated modules can be implemented in the form of hardware or in the form of software functional modules.
  • the integrated modules if implemented in the form of software functional modules and sold or used as separate products, may be stored in a computer readable storage medium.
  • the technical solution of the present invention which is essential or contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product stored in a storage medium.
  • a number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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Abstract

L'invention concerne un procédé et un dispositif de formation à la pensée informatique sur la base d'un modèle cognitif. Le procédé comprend les étapes suivantes : construction d'une plateforme de formation pour la formation à la pensée informatique selon un modèle cognitif de pensée informatique défini par un utilisateur (S101) ; affichage par la plateforme de formation de données de formation correspondant au modèle cognitif de pensée informatique, acquisition de données de commentaire de formation de l'utilisateur pour les données de formation, et exécution d'un degré d'évaluation pour la pensée informatique de l'utilisateur selon les données de commentaire de formation et une norme de notation prédéfinie (S102) ; et réalisation d'une évaluation floue à niveaux multiples sur la pensée informatique de l'utilisateur selon le degré d'évaluation (S103). La formation et l'évaluation de la capacité de pensée abstraite sont effectuées sur un étudiant par le biais d'une procédure de formation complète et d'un système d'évaluation dans la plateforme de formation, et la manière de pensée de l'étudiant pendant la formation professionnelle peut être modifiée efficacement afin de parvenir à la transformation du talent d'un type opération technique à un type traitement d'informations.
PCT/CN2016/085666 2016-03-09 2016-06-14 Procédé et dispositif de formation à la pensée informatique sur la base d'un modèle cognitif WO2017152532A1 (fr)

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