WO2019033425A1 - Procédé d'attribution de tâches d'apprentissage, appareil, serveur d'enseignement et support d'informations - Google Patents

Procédé d'attribution de tâches d'apprentissage, appareil, serveur d'enseignement et support d'informations Download PDF

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Publication number
WO2019033425A1
WO2019033425A1 PCT/CN2017/098145 CN2017098145W WO2019033425A1 WO 2019033425 A1 WO2019033425 A1 WO 2019033425A1 CN 2017098145 W CN2017098145 W CN 2017098145W WO 2019033425 A1 WO2019033425 A1 WO 2019033425A1
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WIPO (PCT)
Prior art keywords
learning
task
user
tasks
learning task
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PCT/CN2017/098145
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English (en)
Chinese (zh)
Inventor
韩荣华
王雁
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深圳市华第时代科技有限公司
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Priority to PCT/CN2017/098145 priority Critical patent/WO2019033425A1/fr
Publication of WO2019033425A1 publication Critical patent/WO2019033425A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education

Definitions

  • the invention belongs to the field of online teaching technology, and in particular relates to a learning task allocation method, device, teaching server and storage medium.
  • the education field has also been increasingly influenced by online and distance education, and teaching has moved from offline classrooms to online.
  • the teacher can arrange assignments online and directly annotate the assignments handed over by the instructed students. Teachers and students can also communicate in real time through the system through short messages. In addition, the teacher's ratings and comments, students can also directly view, easy to urge students to better correct the deficiencies in the next stage, improve the writing level of the paper, thus breaking the time and space barriers of educational activities.
  • the existing online teaching system is simply to publish the assignments or experiments specified by the teacher when the operation or experiment is arranged. It is difficult to achieve targeted arrangement, and the role of the online teaching system cannot be effectively utilized, and the availability of the online teaching system is reduced.
  • the object of the present invention is to provide a learning task allocation method, device, teaching server and storage medium, aiming at solving the problem that the existing online teaching system cannot provide an effective task allocation method, resulting in poor availability of the existing online teaching system. .
  • the present invention provides a learning task allocation method, the method comprising the steps of:
  • the learning task flow being composed of a plurality of associated learning tasks
  • the present invention provides a learning task assigning apparatus, the apparatus comprising:
  • a task flow obtaining unit configured to: when receiving a job assignment request from the first learning user, acquire a preset learning task flow, where the learning task flow is composed of a plurality of associated learning tasks;
  • a task assignment unit configured to acquire a learning task assignment history record of the first learning user, and obtain, according to the allocation history record, a learning task assigned to the first learning user from the plurality of associated learning tasks, The acquired learning task is sent to the first learning user.
  • the present invention also provides a teaching server including a memory, a processor, and a computer program stored in the memory and operable on the processor, the processor implementing the computer program The steps of the method as described above.
  • the present invention also provides a computer readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as previously described.
  • the present invention When receiving the job assignment request from the learning user, the present invention acquires a preset learning task flow, which is composed of a plurality of associated learning tasks, and acquires a learning task allocation history record of the learning user, according to the allocation history record, The learning task assigned to the learning user is obtained from the plurality of related learning tasks, and the acquired learning task is sent to the learning user, so that the learning task is assigned to the learning user according to the task allocation history record of the learning user, and the difference of the learning task is realized. Distribution increases the availability of online teaching systems.
  • FIG. 1 is a flowchart of an implementation of a learning task allocation method according to Embodiment 1 of the present invention
  • FIG. 2 is a flowchart of implementing a learning task allocation method according to Embodiment 2 of the present invention
  • FIG. 3 is a flowchart of implementing a learning task allocation method according to Embodiment 3 of the present invention.
  • FIG. 4 is a schematic diagram of a learning task flow according to Embodiment 3 of the present invention.
  • FIG. 5 is a schematic structural diagram of a learning task allocation apparatus according to Embodiment 4 of the present invention.
  • FIG. 6 is a schematic structural diagram of a learning task allocation apparatus according to Embodiment 4 of the present invention.
  • FIG. 7 is a schematic structural diagram of a learning task allocation apparatus according to Embodiment 4 of the present invention.
  • FIG. 8 is a schematic structural diagram of a teaching server according to Embodiment 5 of the present invention.
  • Embodiment 1 is a diagrammatic representation of Embodiment 1:
  • FIG. 1 is a flowchart showing an implementation process of a learning task allocation method according to Embodiment 1 of the present invention. For convenience of description, only parts related to the embodiment of the present invention are shown, which are as follows:
  • step S101 when receiving a job assignment request from the first learning user, a preset learning task flow is acquired.
  • the embodiment of the present invention is applicable to an online teaching system, and is specifically applicable to a teaching server in an online teaching system.
  • the online teaching system may include a user terminal, a teacher terminal, and a teaching server.
  • the user terminal is used by a student user to acquire a learning task and Auxiliary student users complete the learning task
  • the teacher terminal is used by the teacher, is used for the arrangement of the teacher learning task and assists in processing the learning completion result submitted by the student
  • the teaching server is used for the learning task auxiliary management, for example, storing the learning task completion information submitted by the student,
  • the homework provided by the teacher The learning task flow is composed of a plurality of associated learning tasks, wherein one or more learning tasks may be pre-tasks of another task, and one or more of the preceding learning tasks must be completed before the latter task can be executed.
  • the learning task may be various jobs arranged by the teacher.
  • the task placement request When generating the task flow, first receiving a task placement request input by the teacher user through the teacher terminal, the task placement request includes a learning task to be arranged, and then dividing the placement learning task, for example, according to the order of each step in the learning task to be arranged And the association is divided, so that the learning task flow composed of the associated learning tasks is obtained, and the automatic generation or setting of the learning task flow is realized.
  • the teacher terminal may provide a graphical user interface to the teacher, receive the number of learning tasks in the task flow input by the teacher, and corresponding learning tasks, and then establish an association relationship between the learning tasks, for example, , parallel, serial and other relationships.
  • step S102 the learning task assignment history record of the first learning user is acquired, and the learning task assigned to the first learning user is obtained from the plurality of associated learning tasks according to the allocation history record, and the acquired learning task is sent to the first learning. user.
  • the learning task assignment history record reflects the learning task that the first learning user has practiced or executed. Therefore, in order to improve the learning efficiency of the first learning user and improve the pertinence of the learning task, the first step is obtained here.
  • the learning task assignment history record of the first learning user, the learning task assigned to the first learning user is obtained from the plurality of associated learning tasks according to the allocation history record, and the obtained learning task is sent to the first learning user, and the user terminal is The first learning user outputs the assigned learning tasks, thereby realizing the automatic and differentiated allocation of the learning tasks in the online teaching system, and improving the usability of the learning tasks and the online teaching system.
  • Embodiment 2 is a diagrammatic representation of Embodiment 1:
  • FIG. 2 is a flowchart showing an implementation process of a learning task allocation method according to Embodiment 2 of the present invention. For convenience of description, only parts related to the embodiment of the present invention are shown, which are as follows:
  • step S201 when receiving a job assignment request from the first learning user, a preset learning task flow is acquired.
  • the embodiment of the present invention is applicable to an online teaching system, and is specifically applicable to a teaching server in an online teaching system.
  • the online teaching system may include a user terminal, a teacher terminal, and a teaching server.
  • the user terminal is used by a student user to acquire a learning task and Auxiliary student users complete the learning task
  • the teacher terminal is used by the teacher, is used for the arrangement of the teacher learning task and assists in processing the learning completion result submitted by the student
  • the teaching server is used for the learning task auxiliary management, for example, storing the learning task completion information submitted by the student,
  • the homework provided by the teacher The learning task flow is composed of a plurality of associated learning tasks, wherein one or more learning tasks may be pre-tasks of another task, and one or more of the preceding learning tasks must be completed before the latter task can be executed.
  • the learning task may be various jobs arranged by the teacher.
  • the task placement request When generating the task flow, first receiving a task placement request input by the teacher user through the teacher terminal, the task placement request includes a learning task to be arranged, and then dividing the placement learning task, for example, according to the order of each step in the learning task to be arranged And the association is divided, so that the learning task flow composed of the associated learning tasks is obtained, and the automatic generation or setting of the learning task flow is realized.
  • the teacher terminal may provide a graphical user interface to the teacher, receive the number of learning tasks in the task flow input by the teacher, and corresponding learning tasks, and then establish an association relationship between the learning tasks, for example, , parallel, serial and other relationships.
  • step S202 the task type of each learning task in the learning task flow is acquired to obtain all task types associated with the learning task flow.
  • each learning task belongs to a specific task type.
  • the task type of the learning task may include a programming class, a program testing class, and a report writing.
  • Class the member responsible for the programming class learning task is responsible for programming
  • the member who undertakes the program test class learning task is responsible for testing the program
  • the member who undertakes the report writing class learning task is responsible for writing the final report.
  • step S203 all task types are matched in the learning task assignment history.
  • step S204 it is determined whether there is a mismatched task type among all the task types, if yes, step S205 is performed, otherwise step S206 is performed.
  • step S205 when there is a mismatched task type in all the task types, the learning task corresponding to the unmatched task type is obtained in the plurality of associated learning tasks, and is allocated to the first learning user.
  • the learning task corresponding to the unmatched task type is obtained in the plurality of associated learning tasks to be allocated, and is allocated to the first learning user, thereby expanding the task type assigned to the user, and realizing the automation and difference of the learning task in the online teaching system. Distribution, improving the availability of learning tasks and online teaching systems.
  • step S206 when all the task types are matched in the learning task assignment history, the number of completed learning tasks under each of the task types in all the task types is acquired in the learning task assignment history.
  • step S207 the task type corresponding to the minimum number of completed learning tasks is acquired, and is recorded as the assigned task type.
  • step S208 a learning task belonging to the assigned task type in the learning task flow is acquired and set as a learning task assigned to the first learning user.
  • the task if all the task types associated with the learning task flow match in the allocation history to the corresponding task type, it indicates that the first learning user has previously practiced or executed all the learning in the learning task flow.
  • the task obtains the number of completed learning tasks under each task type in all the task types in the learning task allocation history, and then obtains the task type corresponding to the minimum number of completed learning tasks, and records it as the assigned task type, and finally Obtaining a learning task belonging to the assigned task type in the learning task flow and setting it as a learning task assigned to the first learning user, so that even if all the task types involved in the learning task in the learning task flow are practiced, the user can practice less
  • the learning tasks are assigned to the user to practice and improve the pertinence of the assigned tasks, thereby realizing the automatic and differentiated allocation of learning tasks in the online teaching system, and improving the usability of the learning tasks and the online teaching system.
  • Embodiment 3 is a diagrammatic representation of Embodiment 3
  • FIG. 3 is a flowchart showing an implementation process of a learning task allocation method according to Embodiment 3 of the present invention. For convenience of description, only parts related to the embodiment of the present invention are shown, which are as follows:
  • step S301 when receiving a job assignment request from the first learning user, a pre-set learning task flow is acquired.
  • the embodiment of the present invention is applicable to an online teaching system, and is specifically applicable to a teaching server in an online teaching system.
  • the online teaching system may include a user terminal, a teacher terminal, and a teaching server.
  • the user terminal is used by a student user to acquire a learning task and Auxiliary student users complete the learning task
  • the teacher terminal is used by the teacher, is used for the arrangement of the teacher learning task and assists in processing the learning completion result submitted by the student
  • the teaching server is used for the learning task auxiliary management, for example, storing the learning task completion information submitted by the student,
  • the homework provided by the teacher The learning task flow is composed of a plurality of associated learning tasks, wherein one or more learning tasks may be pre-tasks of another task, and one or more of the preceding learning tasks must be completed before the latter task can be executed.
  • the learning task may be various jobs arranged by the teacher.
  • the task placement request When generating the task flow, first receiving a task placement request input by the teacher user through the teacher terminal, the task placement request includes a learning task to be arranged, and then dividing the placement learning task, for example, according to the order of each step in the learning task to be arranged And the association is divided, so that the learning task flow composed of the associated learning tasks is obtained, and the automatic generation or setting of the learning task flow is realized.
  • the teacher terminal may provide a graphical user interface to the teacher, receive the number of learning tasks in the task flow input by the teacher, and corresponding learning tasks, and then establish an association relationship between the learning tasks, for example, , parallel, serial and other relationships.
  • step S302 the learning task allocation history record of the first learning user is acquired, and the number of times that the first learning user has an association relationship with the learning task of each third-party learning user is obtained from the allocation history record.
  • step S303 the third-party learning user corresponding to the minimum number of associations is obtained, and the third-party learning user is recorded as the second learning user.
  • step S304 the learning task assigned to the second learning user is acquired from the plurality of associated learning tasks, and the learning task is selected from the unallocated learning tasks associated with the learning task assigned to the second learning user, and the selected learning is selected.
  • the task is assigned to the first learning user.
  • the association history between the learning tasks assigned to each learning user and each of the assigned learning tasks is recorded in the allocation history record, as shown in FIG. 4, and an allocation history is shown in the figure.
  • the assigned learning task flow includes learning tasks A, B, C, D, E, F, and G, wherein the learning task A is associated with B, the learning tasks B, C are associated with D, and the learning task D Associated with E and F, learning tasks E and F are associated with G.
  • Arrows between learning tasks can represent dependencies between tasks. For example, learning task B depends on A, and can only be executed after learning task A is completed. The result of task B, A may be the input of task B.
  • the third-party learning user corresponding to the least number of relationship times records the third-party learning user as the second learning user, and obtains the learning task assigned to the second learning user from the plurality of associated learning tasks, and assigns to the second learning
  • the learning task is selected from the unallocated learning tasks associated with the learning task of the user, and the selected learning task is assigned to the first learning user.
  • the task assignment is improved to improve the ability of the learning user to complete the task, and the interaction between the users is enhanced, and the degree of interaction between the users in the subsequent execution of the task process is improved, thereby improving the fun of the task, the learning task, and the online teaching system. Availability.
  • Embodiment 4 is a diagrammatic representation of Embodiment 4:
  • FIG. 5 is a diagram showing the structure of a learning task assigning apparatus according to Embodiment 4 of the present invention. For the convenience of description, only parts related to the embodiment of the present invention are shown, including:
  • the task flow obtaining unit 51 is configured to: when receiving the job assignment request from the first learning user, acquire a preset learning task flow, where the learning task flow is composed of a plurality of associated learning tasks;
  • the task assignment unit 52 is configured to acquire a learning task assignment history record of the first learning user, obtain a learning task assigned to the first learning user from the plurality of associated learning tasks according to the allocation history record, and send the acquired learning task to the first A learning user.
  • the task allocation unit includes 52:
  • a first type acquiring unit 521 configured to acquire a task type of each learning task in the learning task stream, to obtain all task types associated with the learning task stream;
  • a type matching unit 522 configured to match all task types in the learning task allocation history
  • the first allocation sub-unit 523 is configured to obtain a learning task corresponding to the unmatched task type among the plurality of associated learning tasks, and allocate the learning task corresponding to the unmatched task type to the first learning user.
  • the task assignment unit 52 further includes:
  • the quantity obtaining unit 524 is configured to acquire the number of completed learning tasks under each task type in all the task types in the learning task allocation history when the task types are matched in the learning task allocation history;
  • a second type acquiring unit 525 configured to acquire a task type corresponding to the minimum number of completed learning tasks, and record the task type as the task type;
  • the second task setting unit 526 is configured to acquire a learning task belonging to the assigned task type in the learning task flow and set as a learning task assigned to the first learning user.
  • the task assignment unit 52 includes:
  • the association number obtaining unit 527 is configured to acquire, from the allocation history record, the number of times that the first learning user has an association relationship with the learning task of each third-party learning user;
  • the user acquiring unit 528 is configured to acquire a third-party learning user corresponding to the least number of times of association, and record the third-party learning user as the second learning user;
  • a second allocation sub-unit 529 configured to acquire a learning task that has been assigned to the second learning user from the plurality of associated learning tasks, and select a learning task from the unallocated learning tasks associated with the learning task assigned to the second learning user, The selected learning task is assigned to the first learning user.
  • each unit of the learning task allocation device may be implemented by a corresponding hardware or software unit, and each unit may be an independent software and hardware unit, or may be integrated into a soft and hardware unit, and there is no need to limit the present.
  • each unit may be implemented by a corresponding hardware or software unit, and each unit may be an independent software and hardware unit, or may be integrated into a soft and hardware unit, and there is no need to limit the present.
  • each unit may be implemented by a corresponding hardware or software unit, and each unit may be an independent software and hardware unit, or may be integrated into a soft and hardware unit, and there is no need to limit the present.
  • each unit may be implemented by a corresponding hardware or software unit, and each unit may be an independent software and hardware unit, or may be integrated into a soft and hardware unit, and there is no need to limit the present.
  • each unit may be implemented by a corresponding hardware or software unit, and each unit may be an independent software and hardware unit, or may be integrated into a soft and hardware unit, and there is no need
  • Embodiment 5 is a diagrammatic representation of Embodiment 5:
  • FIG. 8 shows the structure of a teaching server according to Embodiment 5 of the present invention. For the convenience of description, only parts related to the embodiment of the present invention are shown.
  • the teaching server 8 of an embodiment of the present invention includes a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and executable on the processor 80.
  • the processor 80 executes the computer program 82, the steps in the above-described embodiment of the learning task assignment method are implemented, such as steps S101 to S102 shown in FIG. 1 or steps S201 to S208 shown in FIG.
  • processor 80 when executing computer program 82, implements the functions of the various units of the various apparatus embodiments described above, such as the functions of units 51 through 52 shown in FIG. 5, FIG. 6, or FIG.
  • the processor 80 in the embodiment of the present invention executes the computer program 82, when receiving the job assignment request from the learning user, the preset learning task stream is acquired, and the learning task stream is composed of a plurality of associated learning tasks, and the learning user is acquired.
  • the learning task assignment history record acquires the learning task assigned to the learning user from the plurality of associated learning tasks according to the allocation history record, and sends the acquired learning task to the learning user, thereby assigning the history record to the learning user for the learning user Targeted assignment of learning tasks, the realization of the differential assignment of learning tasks, and improved the availability of online teaching systems.
  • a computer readable storage medium storing a computer program, which when executed by a processor, implements the steps in the foregoing method for assigning a task assignment, for example, Steps S101 to S102 shown in Fig. 1 or steps S201 to S208 shown in Fig. 2 are shown.
  • the computer program when executed by the processor, implements the functions of the various units in the various apparatus embodiments described above, such as the functions of units 51 through 52 shown in FIG. 5, FIG. 6, or FIG.
  • the learning task flow is composed of a plurality of associated learning tasks, and acquiring learning
  • the learning task assignment history record of the user obtains the learning task assigned to the learning user from the plurality of associated learning tasks according to the allocation history record, and sends the acquired learning task to the learning user, thereby learning the history assignment history of the learning user
  • the user assigns learning tasks in a targeted manner, realizes the differential allocation of learning tasks, and improves the usability of the online teaching system.
  • the computer readable storage medium of the embodiments of the present invention may include any entity or device capable of carrying computer program code, a recording medium such as a ROM/RAM, a magnetic disk, an optical disk, a flash memory, or the like.

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Abstract

La présente invention est applicable au domaine technique de l'enseignement en ligne, et concerne un procédé d'attribution de tâches d'apprentissage, un appareil, un serveur d'enseignement et un support d'informations, le procédé comprenant les étapes consistant : à obtenir un flux de tâches d'apprentissage prédéfini à réception d'une demande d'attribution de tâches provenant d'un premier utilisateur en apprentissage, le flux de tâches d'apprentissage étant constitué d'une pluralité de tâches d'apprentissage connexes; à obtenir un enregistrement historique de distribution de tâches d'apprentissage du premier utilisateur en apprentissage; à obtenir une tâche d'apprentissage distribuée au premier utilisateur en apprentissage à partir de la pluralité de tâches d'apprentissage connexes selon l'enregistrement historique de distribution; et à envoyer la tâche d'apprentissage obtenue au premier utilisateur en apprentissage. Ainsi, des tâches d'apprentissage sont distribuées aux utilisateurs en apprentissage d'une manière ciblée selon les enregistrements historiques de distribution de tâches des utilisateurs en apprentissage, de sorte qu'une distribution différenciée de tâches d'apprentissage soit mise en œuvre et que la facilité d'utilisation du système d'enseignement en ligne soit améliorée.
PCT/CN2017/098145 2017-08-18 2017-08-18 Procédé d'attribution de tâches d'apprentissage, appareil, serveur d'enseignement et support d'informations WO2019033425A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120077158A1 (en) * 2010-09-28 2012-03-29 Government Of The United States, As Represented By The Secretary Of The Air Force Predictive Performance Optimizer
CN105512980A (zh) * 2016-01-20 2016-04-20 北京民安信科技发展有限公司 信息处理方法及装置
CN106157717A (zh) * 2015-04-24 2016-11-23 韦乃荣 一种小学生作业交互系统
CN106846191A (zh) * 2016-11-25 2017-06-13 北京粉笔蓝天科技有限公司 一种课程列表的编排方法、系统及服务器

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120077158A1 (en) * 2010-09-28 2012-03-29 Government Of The United States, As Represented By The Secretary Of The Air Force Predictive Performance Optimizer
CN106157717A (zh) * 2015-04-24 2016-11-23 韦乃荣 一种小学生作业交互系统
CN105512980A (zh) * 2016-01-20 2016-04-20 北京民安信科技发展有限公司 信息处理方法及装置
CN106846191A (zh) * 2016-11-25 2017-06-13 北京粉笔蓝天科技有限公司 一种课程列表的编排方法、系统及服务器

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