WO2021098187A1 - 一种学生学习兴趣画像生成系统、方法、装置及存储介质 - Google Patents

一种学生学习兴趣画像生成系统、方法、装置及存储介质 Download PDF

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WO2021098187A1
WO2021098187A1 PCT/CN2020/094635 CN2020094635W WO2021098187A1 WO 2021098187 A1 WO2021098187 A1 WO 2021098187A1 CN 2020094635 W CN2020094635 W CN 2020094635W WO 2021098187 A1 WO2021098187 A1 WO 2021098187A1
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data
student
interest
learning
portrait
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PCT/CN2020/094635
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English (en)
French (fr)
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郑倩如
邹才凤
林岳镔
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广东机电职业技术学院
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Publication of WO2021098187A1 publication Critical patent/WO2021098187A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/951—Indexing; Web crawling techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • 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
    • G06Q50/205—Education administration or guidance

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  • the invention relates to the technical field of data analysis, in particular to a system, method, device and storage medium for generating portraits of students' learning interests.
  • users' basic information such as age, occupation, income, education level, etc.
  • geographic location information such as zip code, postal address, etc.
  • behavior logs of users using the system are generally collected to generate user portraits.
  • schools often evaluate teachers’ teaching level, students’ academic performance, and school-related business conditions based on these user portraits.
  • this implementation method facilitates the unified management of the school to a certain extent, It cannot completely and truly reflect the comprehensive quality of students, nor can it help students to have effective self-awareness. It is also difficult for teachers to give targeted guidance from student user portraits. This method is not conducive to the training of students in schools. It is also difficult for students to have an accurate positioning of themselves.
  • inventions of the present invention provide a system, method, device, and storage medium for generating portraits of students’ learning interests, which can analyze and mine students’ learning interests and help students and teachers to more accurately and comprehensively understand where students’ learning interests are. field.
  • an embodiment of the present invention provides a student learning interest portrait generation system, including: a business framework and an execution unit;
  • the business framework includes a data source layer, a big data platform, and a display layer;
  • the execution unit includes:
  • a data collection module which is used to collect and obtain data of the business system in the campus network through the data source layer;
  • the data preprocessing module is used to clean and filter the data collected by the data collection module based on the preset data dimensions of the student's learning interest;
  • the portrait generation module is used to target a single student, analyze and process statistical data related to the student through the big data platform, and generate a portrait of the student's learning interest;
  • the portrait presentation module is used to present the portraits of students' learning interests to the corresponding students and/or teachers through the presentation layer.
  • the business system on the campus network includes a course selection system, a grade system, a book borrowing system, a network system, and a professional interest testing system.
  • the data of the business system in the campus network includes: elective data, grade data, book borrowing data, network browsing data, and career interest test result data;
  • the elective data includes the type and frequency of elective subjects and the attendance rate of elective courses; the score data includes the credit scores and rankings of compulsory and elective courses; the book borrowing data includes the number of borrowed books, the type of borrowed books, and the borrowing rate. Duration; the web browsing data includes website visit type and traffic data; the career interest test result data includes Holland career interest test result type.
  • the portrait generation module is used to import the data obtained from the course selection system, the grading system, the book borrowing system and the professional interest test system into the HDFS file system through the sqoop tool, perform offline calculation through the Hive cluster, and import the result into the Hbase database;
  • the portrait generation module is also used to analyze the data obtained from the network system through the Spark cluster, and import the result into the Hbase database.
  • an embodiment of the present invention provides a method for generating a portrait of a student's learning interest, which includes the following steps:
  • the business system on the campus network includes a course selection system, a grade system, a book borrowing system, a network system, and a professional interest testing system.
  • the elective data includes the type and frequency of elective subjects and the attendance rate of elective courses; the score data includes the credit scores and rankings of compulsory and elective courses; the book borrowing data includes the number of borrowed books, the type of borrowed books, and the borrowing rate. Duration; the web browsing data includes website visit type and traffic data; the career interest test result data includes Holland career interest test result type.
  • the step of analyzing the statistical data related to the student to generate a portrait of the student's learning interest specifically includes:
  • a portrait of student learning interest is generated.
  • an embodiment of the present invention provides a device for generating a portrait of a student's learning interest, including:
  • At least one processor At least one processor
  • At least one memory for storing at least one program
  • the at least one processor When the at least one program is executed by the at least one processor, the at least one processor is caused to implement the method for generating the student learning interest portrait.
  • an embodiment of the present invention provides a storage medium in which instructions executable by a processor are stored, and the instructions executable by the processor are used to perform the generation of the student learning interest portrait when executed by the processor. method.
  • the embodiment of the present invention can analyze the data of the relevant system in the campus network to dig out and present the learning interest of the students. On the one hand, it is convenient for the students to understand themselves more comprehensively, and to make good learning and career planning as soon as possible, which is helpful for the future development of the students. Analysis; On the other hand, it is also convenient for teachers and schools to give targeted suggestions and guidance to each student, so that students' learning potential can be fully and outstandingly used, which facilitates the efficient development of student work.
  • FIG. 1 is a schematic diagram of a business framework of a specific embodiment of a system for generating a portrait of student learning interest according to the present invention
  • FIG. 2 is a block diagram of the execution unit of a specific embodiment of a system for generating a portrait of student learning interest according to the present invention
  • FIG. 3 is a schematic flowchart of a specific embodiment of a method for generating a portrait of student learning interest according to the present invention
  • FIG. 4 is a schematic diagram of a specific embodiment of a portrait of a student's learning interest according to the present invention.
  • Fig. 5 is a structural block diagram of a specific embodiment of a device for generating a portrait of student learning interest according to the present invention.
  • the embodiment of the present invention provides a student learning interest portrait generation system, which is used to provide the function of digging the learning interest points of the student, can accurately identify and dig the learning interest of the student in the relevant field, and is helpful for analyzing the future development of the student.
  • the embodiment of the present invention can analyze the data of the relevant system in the campus network to dig out and present the learning interests of the students.
  • the system includes: a business framework 100 and an execution unit 200;
  • the business framework 100 includes a data source layer 101, a big data platform 102, and a display layer 103;
  • the execution unit 200 includes:
  • the data collection module 201 is configured to collect and obtain data of the business system in the campus network through the data source layer 101;
  • the data preprocessing module 202 is used to clean and filter the data collected by the data collection module 201 based on the preset data dimensions of the students' learning interests;
  • the portrait generation module 203 is configured to target a single student and analyze and process statistical data related to the student through the big data platform 102 to generate a portrait of the student's learning interest;
  • the portrait presentation module 204 is used to present the portraits of students' learning interests to the corresponding students and/or teachers through the display layer 103.
  • the business system on the campus network includes a course selection system, a grade system, a book lending system, a network system, and a professional interest testing system.
  • the data of the business system in the campus network includes: elective data, grade data, book borrowing data, network browsing data, and career interest test result data;
  • the elective data includes the type and frequency of elective subjects and the attendance rate of elective courses; the score data includes the credit scores and rankings of compulsory and elective courses; the book borrowing data includes the number of borrowed books, the type of borrowed books, and the borrowing rate. Duration; the web browsing data includes website visit type and traffic data; the career interest test result data includes Holland career interest test result type.
  • the portrait generation module 203 is used to import data obtained from the course selection system, grading system, book lending system and career interest test system into the HDFS file system through the sqoop tool, and perform offline calculations through the Hive cluster, and Import the results into the Hbase database;
  • the portrait generation module is also used to analyze the data obtained from the network system through the Spark cluster, and import the result into the Hbase database.
  • the service framework 100 in the embodiment of the present invention mainly includes:
  • the data source layer 101 is used to link other business systems in the campus network, and to obtain and store data related to the portraits of students’ learning interests.
  • the business systems include, but are not limited to, the course selection system, grading system, book borrowing system, network system, Vocational testing system.
  • the data that can be obtained include the types of subjects, the number of electives and the attendance rate, etc.; from the score system, the data that can be obtained include the grades and professional rankings of each elective and compulsory course of the student Information; from the book borrowing system, the data that can be obtained includes the number of times students have borrowed books, the type of books borrowed and the length of time they borrowed books; the data that can be obtained from the career test system includes the type of Holland career interest test results; From the network system, the data that can be obtained includes website access data, such as access status and traffic data of portal websites, entertainment websites, shopping websites, comprehensive information websites, etc. The traffic data can be subdivided into video traffic, live broadcast, etc. Traffic, download traffic, game traffic, etc.
  • the big data platform 102 can be used to process and analyze various types of data in the data source layer 101, and generate portraits of students' learning interests based on these data.
  • the data processing form of the big data platform 102 is specifically divided into offline calculation and online calculation.
  • the data obtained by the course selection system, grade system, book borrowing system and professional interest test system can be imported into the HDFS file system through the sqoop tool. This part of the data
  • the structure is relatively simple, so the relational database Hive can be used for storage. Through the offline calculation of these data through the Hive cluster, some portrait-related indicators can be obtained.
  • the data obtained from the network system can be collected using Flume log collection, Python crawler, etc., and then transmitted to the Spark cluster for processing and analysis. After all the predetermined indicators are calculated, the results of big data analysis are stored in the Hbase database system.
  • the Hbase database system has the characteristics of fast retrieval, which is very convenient for students to effectively display the portraits of learning interests.
  • the display layer 103 can display the results of the student's learning interest portraits to designated personnel.
  • the big data platform 102 can calculate the student's learning interest portraits in the form of web pages, APP applications, WeChat official accounts, etc., to be displayed to relevant personnel with permission to view them, which can be accessed using a PC or a mobile terminal.
  • it can be shown to teachers or students, which can help students and teachers to obtain information about students’ learning interests more accurately and comprehensively.
  • it can help students understand themselves more clearly and help students find work areas that match their interests and personality.
  • it can also facilitate teachers to guide students in a targeted manner, and the effect of carrying out student work is better.
  • the portraits of students' learning interests can also be shown to related cooperative enterprises to facilitate students' job search.
  • the portraits of students' learning interests contain personal private information, which should be generated and displayed with the permission of the students.
  • an embodiment of the present invention provides a method for generating a portrait of a student's learning interest, including the following steps:
  • S1 Obtain the access authority of the business system in the campus network, and collect the data in the business system;
  • S2 Purify and filter the data based on the preset data dimensions of the student's learning interest
  • S4 Present the student's learning interest portrait to the corresponding student and/or teacher.
  • the business system on the campus network includes a course selection system, a grade system, a book lending system, a network system, and a professional interest testing system.
  • the step of collecting data in the business system specifically includes: collecting elective data, grade data, book borrowing data, web browsing data, and professional interest test result data;
  • the elective data includes the type and frequency of elective subjects and the attendance rate of elective courses; the score data includes the credit scores and rankings of compulsory and elective courses; the book borrowing data includes the number of borrowed books, the type of borrowed books, and the borrowing rate. Duration; the web browsing data includes website visit type and traffic data; the career interest test result data includes Holland career interest test result type.
  • the step of analyzing statistical data related to the student to generate a portrait of the student's learning interest specifically includes:
  • S31 Use the sqoop tool to import the data obtained from the course selection system, grading system, book borrowing system and professional interest test system into the HDFS file system, perform offline calculations through the Hive cluster, and import the results into the Hbase database;
  • the embodiment of the present invention first, to collect data of related systems in the school, it is necessary to obtain the access authority of the business system in the campus network, and then access the corresponding business system to collect data in batches from the database therein. Since the collected data is probably not all necessary, these redundant data will reduce the efficiency of generating portraits in the embodiment of the present invention, so we can clean and filter these data according to some preset data dimensions, and clear them. Remove the illegal data.
  • a specific portrait generation goal that is, a single student: get the subject of their elective interest, elective course score ranking, compulsory course score ranking, elective/compulsory course attendance rate data; get the total number of borrowing books, borrowing books The most frequent category, the category with the longest time to borrow books, and other data; obtain the data information of their network access content preference; obtain the test results of their professional interests. Summarizing all or part of the above indicators becomes the result of the student's learning interest portrait of the student, and presents it to the student and/or his instructor.
  • a portrait of a student's learning interest obtained through an embodiment of the present invention can effectively reflect the student's learning interest in a certain field.
  • the specific presentation results include:
  • the degree of interest of the student in the major reflecting the indicators: the attendance rate of the professional course and the ranking of the professional course performance. If a student's professional course attendance rate is high, and the professional course scores are ranked high, it means that the student is very interested in the professional course.
  • the degree of interest of students in elective courses reflects the indicators: attendance rate of elective courses, ranking of elective courses, and statistics of elective courses. If a student's elective subjects are concentrated in a certain subject, the attendance rate is high, and the elective course scores are ranked high, indicating that the student is very interested in the subject knowledge.
  • the analysis of students’ interest in borrowing books reflects indicators: the number of borrowed books, statistics on the types of borrowed books, and the length of borrowing books. If a student often borrows a certain category of books, or borrows a certain category of books for a long time, it means that the student is very interested in the category of books.
  • the results of the student's career interest test reflecting the index: Holland's career interest test results.
  • the results of Holland's career interest test can be divided into social type, enterprise type, conventional type, practical type, research type and artistic type. Each type has its own personality characteristics, as well as the types of work that meet the characteristics of the personality. For example, if a student's test result is realistic, then there are technical occupations and skilled occupations suitable for this type of occupation.
  • Teachers and schools can have a more comprehensive understanding of students' comprehensive qualities by looking at the portraits of students' learning interests. It is helpful for teachers to give targeted advice and guidance based on their own social experience and teaching experience, aiming at students’ learning interests and professional interests, so as to improve the effectiveness of student work and benefit the training of students in the school.
  • an embodiment of the present invention provides an apparatus for generating a portrait of a student's learning interest, including:
  • At least one processor 301 At least one processor 301;
  • the at least one processor 301 When the at least one program is executed by the at least one processor 301, the at least one processor 301 is caused to implement the method for generating the student learning interest portrait.
  • the embodiment of the present invention also provides a storage medium in which instructions executable by the processor 301 are stored. When the instructions executable by the processor 301 are executed by the processor 301, they are used to execute the student learning interest portrait generation. method.
  • the functions/operations mentioned in the block diagram may occur out of the order mentioned in the operation diagram.
  • two blocks shown in succession may actually be executed substantially simultaneously or the blocks may sometimes be executed in the reverse order.
  • the embodiments presented and described in the flowchart of the present invention are provided by way of example, with the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logic flow presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of a larger operation are performed independently.
  • the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solution of the present invention essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present invention.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes. .
  • a "computer-readable medium” can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, device, or device or in combination with these instruction execution systems, devices, or devices.
  • computer-readable media include the following: electrical connections (electronic devices) with one or more wiring, portable computer disk cases (magnetic devices), random access memory (RAM), Read only memory (ROM), erasable and editable read only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program can be printed, because it can be used, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other suitable media if necessary. The program is processed in a way to obtain the program electronically and then stored in the computer memory.
  • each part of the present invention can be implemented by hardware, software, firmware or a combination thereof.
  • multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a suitable instruction execution system For example, if it is implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: Discrete logic circuits, application-specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

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Abstract

一种学生学习兴趣画像生成系统、方法、装置及存储介质，其中该系统包括业务框架和执行单元，其中，所述业务框架包括数据源层（101）、大数据平台（102）和展示层（103）；所述执行单元包括数据采集模块（201）、数据预处理模块（202）、画像生成模块（203）和画像呈现模块（204）。该方法通过所述系统可生成学生学习兴趣画像，并呈现给对应的学生和/或老师。通过使用该方法，可使学生更加深入了解自己所喜爱感兴趣的领域，有助于学生根据自己学习兴趣和职业兴趣来选择未来的就业方向，帮助学生进行准确的自我定位；还能使老师了解学生的学习兴趣和职业兴趣，给予针对性的建议和指导，从而提高学生工作的效果，有利于学校的学生培养。该方法及系统可广泛应用于数据分析技术领域内。

Description

一种学生学习兴趣画像生成系统、方法、装置及存储介质 技术领域
本发明涉及数据分析技术领域，尤其是一种学生学习兴趣画像生成系统、方法、装置及存储介质。
背景技术
随着校园网发展、数字校园的建设，校园里产生了各种各样的数据，包含校园管理、行政管理、教学教育、学生就业等各个方面。这些数据伴随各个系统的使用而产生，除了反映用户使用系统的行为，还可以进行进一步分析和挖掘。在这其中，用户画像是数据分析和数据挖掘的一种应用，其目的是通过分析用户产生的数据，挖掘其中有价值的信息，方便日后管理和运用。
目前在互联网领域，一般采集用户基本信息(如年龄、职业、收入、教育水平等)、地理位置信息、用户使用系统的行为日志等生成用户画像。在校园网数据的应用中，往往是学校基于这些用户画像对老师的教学水平、学生的学习成绩以及学校相关的业务状况进行评价，这种实施手段虽然在一定程度方便了学校的统一管理，但其并不能完整、真实地反映出学生的综合素质，也无法帮助学生进行有效的自我认知，老师也难以从学生的用户画像中作出针对性指导，这种方式并不利于学校的学生培养，学生也很难对自身有准确的定位。现有技术中存在的这些问题亟待解决。
发明内容
本发明的目的在于至少一定程度上解决现有技术中存在的技术问题之一。为此，本发明实施例提供一种学生学习兴趣画像生成系统、方法、装置及存储介质，能够对学生学习兴趣进行分析和挖掘，帮助学生和老师更准确、更全面地了解学生学习兴趣的所在领域。
本发明实施例所采取的技术方案是：
第一方面，本发明实施例提供一种学生学习兴趣画像生成系统，包括：业务框架和执行单元；
所述业务框架包括数据源层、大数据平台和展示层；
所述执行单元包括：
数据采集模块，用于通过所述数据源层采集获取校园网内业务系统的数据；
数据预处理模块，用于基于预设的学生学习兴趣的数据维度，对所述数据采集模块采集到的数据进行清洗和筛选；
画像生成模块，用于以单个学生为目标，通过所述大数据平台对与所述学生有关的统计 数据进行分析处理，生成学生学习兴趣画像；
画像呈现模块，用于通过所述展示层将学生学习兴趣画像呈现给对应的学生和/或老师。
进一步，所述校园网内业务系统包括选课系统、成绩系统、图书借阅系统、网络系统和职业兴趣测试系统。
进一步，所述校园网内业务系统的数据包括：选修数据、成绩数据、图书借阅数据、网络浏览数据和职业兴趣测试结果数据；
其中，所述选修数据包括选修科目的种类、次数和选修课出勤率；所述成绩数据包括必修课和选修课的学分成绩及排名；所述图书借阅数据包括借阅图书次数、借阅图书类别和借阅时长；所述网络浏览数据包括网站访问类型和流量数据；所述职业兴趣测试结果数据包括霍兰德职业兴趣测试结果类型。
进一步，所述画像生成模块用于通过sqoop工具将从选课系统、成绩系统、图书借阅系统和职业兴趣测试系统获取的数据导入HDFS文件系统，通过Hive集群进行离线计算，并将结果导入Hbase数据库；
所述画像生成模块还用于通过Spark集群对从网络系统获取的数据进行分析，并将结果导入Hbase数据库。
第二方面，本发明实施例提供一种学生学习兴趣画像生成方法，包括以下步骤：
获取校园网内业务系统的访问权限，采集所述业务系统中的数据；
基于预设的学生学习兴趣的数据维度，对所述数据进行清洗和筛选；
以单个学生为目标，对与所述学生有关的统计数据进行分析，生成学生学习兴趣画像；
将所述学生学习兴趣画像呈现给对应的学生和/或老师。
进一步，所述校园网内业务系统包括选课系统、成绩系统、图书借阅系统、网络系统和职业兴趣测试系统。
进一步，采集选修数据、成绩数据、图书借阅数据、网络浏览数据和职业兴趣测试结果数据；
其中，所述选修数据包括选修科目的种类、次数和选修课出勤率；所述成绩数据包括必修课和选修课的学分成绩及排名；所述图书借阅数据包括借阅图书次数、借阅图书类别和借阅时长；所述网络浏览数据包括网站访问类型和流量数据；所述职业兴趣测试结果数据包括霍兰德职业兴趣测试结果类型。
进一步，所述对与所述学生有关的统计数据进行分析，生成学生学习兴趣画像这一步骤，其具体包括：
通过sqoop工具将从选课系统、成绩系统、图书借阅系统和职业兴趣测试系统获取的数据导入HDFS文件系统，通过Hive集群进行离线计算，并将结果导入Hbase数据库；
通过Spark集群对从网络系统获取的数据进行分析，并将结果导入Hbase数据库；
基于所述Hbase数据库中的数据，生成学生学习兴趣画像。
第三方面，本发明实施例提供了一种学生学习兴趣画像生成装置，包括：
至少一个处理器；
至少一个存储器，用于存储至少一个程序；
当所述至少一个程序被所述至少一个处理器执行时，使得所述至少一个处理器实现所述的学生学习兴趣画像生成方法。
第四方面，本发明实施例提供了一种存储介质，其中存储有处理器可执行的指令，所述处理器可执行的指令在由处理器执行时用于执行所述的学生学习兴趣画像生成方法。
本发明的优点和有益效果将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本发明的实践了解到：
本发明实施例可通过对校园网内相关系统的数据进行分析，挖掘、呈现学生的学习兴趣，一方面能够方便学生更全面了解自己，尽早做好学习和职业规划，有助于学生的未来发展分析；另一方面，还可方便老师及学校对每个学生给予针对性的建议和指导，以使得学生的学习潜质得到充分、出色的发挥，方便了学生工作的高效开展。
附图说明
为了更清楚地说明本发明实施例或者现有技术中的技术方案，下面对本发明实施例或者现有技术中的相关技术方案附图作以下介绍，应当理解的是，下面介绍中的附图仅仅为了方便清晰表述本发明的技术方案中的部分实施例，对于本领域的技术人员来说，在无需付出创造性劳动的前提下，还可以根据这些附图获取到其他附图。
图1为本发明一种学生学习兴趣画像生成系统具体实施例的业务框架示意图；
图2为本发明一种学生学习兴趣画像生成系统具体实施例的执行单元模块图；
图3为本发明一种学生学习兴趣画像生成方法具体实施例的流程示意图；
图4为本发明一种学生学习兴趣画像具体实施例的示意图；
图5为本发明一种学生学习兴趣画像生成装置具体实施例的结构框图。
具体实施方式
下面详细描述本发明的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的 实施例是示例性的，仅用于解释本发明，而不能理解为对本发明的限制。对于以下实施例中的步骤编号，其仅为了便于阐述说明而设置，对步骤之间的顺序不做任何限定，实施例中的各步骤的执行顺序均可根据本领域技术人员的理解来进行适应性调整。
本发明实施例提供了一种学生学习兴趣画像生成系统，该系统用于提供挖掘学生学习兴趣点的功能，可准确识别和挖掘学生在相关领域的学习兴趣，有助于分析学生的未来发展。本发明实施例可通过对校园网内相关系统的数据进行分析，挖掘、呈现学生的学习兴趣，一方面能够方便学生更全面了解自己，尽早做好学习和职业规划；另一方面，还可方便老师及学校对每个学生给予针对性的建议和指导，以使得学生的学习潜质得到充分、出色的发挥。
参照图1、2，本发明实施例中，所述系统包括：业务框架100和执行单元200；
所述业务框架100包括数据源层101、大数据平台102和展示层103；
所述执行单元200包括：
数据采集模块201，用于通过所述数据源层101采集获取校园网内业务系统的数据；
数据预处理模块202，用于基于预设的学生学习兴趣的数据维度，对所述数据采集模块201采集到的数据进行清洗和筛选；
画像生成模块203，用于以单个学生为目标，通过所述大数据平台102对与所述学生有关的统计数据进行分析处理，生成学生学习兴趣画像；
画像呈现模块204，用于通过所述展示层103将学生学习兴趣画像呈现给对应的学生和/或老师。
进一步作为优选的实施方式，所述校园网内业务系统包括选课系统、成绩系统、图书借阅系统、网络系统和职业兴趣测试系统。
进一步作为优选的实施方式，所述校园网内业务系统的数据包括：选修数据、成绩数据、图书借阅数据、网络浏览数据和职业兴趣测试结果数据；
其中，所述选修数据包括选修科目的种类、次数和选修课出勤率；所述成绩数据包括必修课和选修课的学分成绩及排名；所述图书借阅数据包括借阅图书次数、借阅图书类别和借阅时长；所述网络浏览数据包括网站访问类型和流量数据；所述职业兴趣测试结果数据包括霍兰德职业兴趣测试结果类型。
进一步作为优选的实施方式，所述画像生成模块203用于通过sqoop工具将从选课系统、成绩系统、图书借阅系统和职业兴趣测试系统获取的数据导入HDFS文件系统，通过Hive集群进行离线计算，并将结果导入Hbase数据库；
所述画像生成模块还用于通过Spark集群对从网络系统获取的数据进行分析，并将结果 导入Hbase数据库。
下面结合附图1、2对本发明实施例中的所述系统作以下说明：
本发明实施例中所述业务框架100主要包括：
数据源层101，用于链接校园网内其它的业务系统，并获取存储学生学习兴趣画像相关的数据，其中所述的业务系统包括但不限于选课系统、成绩系统、图书借阅系统、网络系统、职业测试系统。从所述选课系统中，可获取的数据包括学生选修课的科目种类、选修次数和出勤率等；从所述成绩系统中，可获取的数据包括学生每门选修课和必修课成绩及专业排名信息；从所述图书借阅系统中，可获取的数据包括学生借阅图书次数，借阅图书类别和借阅图书时长；从所述职业测试系统中，可获取的数据包括霍兰德职业兴趣测试结果类型；从所述网络系统中，可获取的数据包括网站访问数据，例如门户网站，娱乐网站，购物网站，综合资讯网站等的访问情况和流量数据，所述流量数据又可细分为视频流量、直播流量、下载流量和游戏流量等。
大数据平台102，可用于处理、分析数据源层101中的各类数据，并基于这些数据生成学生学习兴趣画像。其中所述大数据平台102处理数据的形式具体分为离线计算和在线计算，由选课系统、成绩系统、图书借阅系统和职业兴趣测试系统获取的数据可以通过sqoop工具导入HDFS文件系统，这部分数据结构比较简单，因而采用关系数据库Hive存储即可。通过Hive集群对这些数据进行离线计算，可以得到部分画像相关指标。从网络系统获取的数据可以使用Flume日志采集、Python爬虫等方式进行采集，然后传输到Spark集群进行处理分析。当所有预定的指标计算完毕后，将大数据分析的结果存到Hbase数据库系统，Hbase数据库系统具有快速检索的特点，非常便于学生学习兴趣画像有效展示。
展示层103，能够把学生学习兴趣画像结果展示给指定人员。具体地，可以将大数据平台102统计得出学生学习兴趣画像以web网页、APP应用、微信公众号等形式展现给有权限查看的相关人员，可以使用PC端或者移动端访问。例如展示给教师或者学生，这可以帮助学生和老师更准确、更全面地获得学生学习兴趣信息，一方面可以帮助学生更清楚地了解自己，帮助学生发现与自己兴趣和性格相匹配的工作领域，另一方面也能够方便老师有针对性地指导学生学习，开展学生工作的效果更好。另外，还可以将学生学习兴趣画像展示给相关的合作企业，以方便学生的求职工作，当然，所述学生学习兴趣画像包含有个人隐私信息，应当在学生允许的情况下生成、展示。
参照图3，本发明实施例提供了一种学生学习兴趣画像生成方法，包括以下步骤：
S1：获取校园网内业务系统的访问权限，采集所述业务系统中的数据；
S2：基于预设的学生学习兴趣的数据维度，对所述数据进行清洗和筛选；
S3：以单个学生为目标，对与所述学生有关的统计数据进行分析，生成学生学习兴趣画像；
S4：将所述学生学习兴趣画像呈现给对应的学生和/或老师。
进一步作为优选的实施方式，所述校园网内业务系统包括选课系统、成绩系统、图书借阅系统、网络系统和职业兴趣测试系统。
进一步作为优选的实施方式，所述采集所述业务系统中的数据这一步骤，其具体包括：采集选修数据、成绩数据、图书借阅数据、网络浏览数据和职业兴趣测试结果数据；
其中，所述选修数据包括选修科目的种类、次数和选修课出勤率；所述成绩数据包括必修课和选修课的学分成绩及排名；所述图书借阅数据包括借阅图书次数、借阅图书类别和借阅时长；所述网络浏览数据包括网站访问类型和流量数据；所述职业兴趣测试结果数据包括霍兰德职业兴趣测试结果类型。
进一步作为优选的实施方式，所述对与所述学生有关的统计数据进行分析，生成学生学习兴趣画像这一步骤，其具体包括：
S31：通过sqoop工具将从选课系统、成绩系统、图书借阅系统和职业兴趣测试系统获取的数据导入HDFS文件系统，通过Hive集群进行离线计算，并将结果导入Hbase数据库；
S32：通过Spark集群对从网络系统获取的数据进行分析，并将结果导入Hbase数据库；
S33：基于所述Hbase数据库中的数据，生成学生学习兴趣画像。
本发明实施例中，首先，要收集学校内相关系统的数据，需要获取校园网内业务系统的访问权限，然后接入对应的业务系统，从其中的数据库中批量地采集数据。由于采集的数据很可能并不完全都是需要的，这些多余的数据会降低本发明实施例中画像的生成效率，所以我们可以根据预设的一些数据维度对这些数据进行清洗和筛选，并且清除掉其中非法的数据。然后，针对特定的画像生成目标，即单个的学生：获取其选修课兴趣所在的科目、选修课成绩排名、必修课成绩排名、选/必修课出勤率数据；获取其借阅图书总次数，借阅图书最频繁的类别，借阅图书时间最长的类别等数据；获取其网络访问内容偏好数据信息；获取其职业兴趣测试结果。汇总以上所述全部或者部分指标成为所述学生的学生学习兴趣画像结果，并呈现给该学生和/或其指导老师。
参照图4，通过本发明实施例获取的一种学生学习兴趣画像，该画像能够有效地反映出学生在某个领域的学习兴趣，具体的呈现结果包括：
学生所在专业的兴趣程度，反映指标：专业课程出勤率和专业课程成绩排名。如果一个 学生专业课程出勤率高、专业课程成绩排名高，说明该学生对所在专业课程非常感兴趣。
学生选修课的兴趣程度，反映指标：选修课程出勤率、选修课程成绩排名、选修课程科目统计。如果一个学生选修课科目集中在某个学科、出勤率高、选修课程成绩排名高，说明该学生对该学科知识非常感兴趣。
学生借阅图书的兴趣分析，反映指标：借阅图书次数、借阅图书类别统计、借阅图书时长。如果一个学生经常借阅某个类别的图书、或者借阅某个类别的图书时间很长，说明该学生对该类别图书非常感兴趣。
学生网络访问内容分析，反映指标：访问内容偏好统计、访问时长。如果一个学生经常访问某个类型的网站、或者访问时间比较长，说明该学生对这个类型的资讯非常感兴趣。
学生职业兴趣测试结果，反映指标：霍兰德职业兴趣测试结果。霍兰德职业兴趣测试结果可以分为社会型、企业型、常规型、实际型、调研型和艺术型。每一个类型都有其性格特点，以及符合该性格特点的工作类别。比如，如果一个学生的测试结果是现实型，那么适合该类型的职业有技术性职业、技能型职业。
本发明实施例的优点包括但不限于以下几点：
学生通过了解自身的学习兴趣画像，可以更加深入了解自己喜爱感兴趣的领域，有助于学生根据自己学习兴趣和职业兴趣来选择未来的就业方向，帮助学生进行准确的自我定位。
老师和学校通过查看学生学习兴趣画像，可以更全面了解学生的综合素质。有助于使得老师根据自身的社会经验和教学经验，针对学生学习兴趣和职业兴趣，给予针对性的建议和指导，从而提高学生工作的效果，有利于学校的学生培养。
参照图5，本发明实施例提供了一种学生学习兴趣画像生成装置，包括：
至少一个处理器301；
至少一个存储器302，用于存储至少一个程序；
当所述至少一个程序被所述至少一个处理器301执行时，使得所述至少一个处理器301实现所述的学生学习兴趣画像生成方法。
可见，上述方法实施例中的内容均适用于本装置实施例中，本装置实施例所具体实现的功能与上述方法实施例相同，并且达到的有益效果与上述方法实施例所达到的有益效果也相同。
本发明实施例还提供了一种存储介质，其中存储有处理器301可执行的指令，所述处理器301可执行的指令在由处理器301执行时用于执行所述的学生学习兴趣画像生成方法。
同理，上述方法实施例中的内容均适用于本存储介质实施例中，本存储介质实施例所具 体实现的功能与上述方法实施例相同，并且达到的有益效果与上述方法实施例所达到的有益效果也相同。
在一些可选择的实施例中，在方框图中提到的功能/操作可以不按照操作示图提到的顺序发生。例如，取决于所涉及的功能/操作，连续示出的两个方框实际上可以被大体上同时地执行或所述方框有时能以相反顺序被执行。此外，在本发明的流程图中所呈现和描述的实施例以示例的方式被提供，目的在于提供对技术更全面的理解。所公开的方法不限于本文所呈现的操作和逻辑流程。可选择的实施例是可预期的，其中各种操作的顺序被改变以及其中被描述为较大操作的一部分的子操作被独立地执行。
此外，虽然在功能性模块的背景下描述了本发明，但应当理解的是，除非另有相反说明，所述的功能和/或特征中的一个或多个可以被集成在单个物理装置和/或软件模块中，或者一个或多个功能和/或特征可以在单独的物理装置或软件模块中被实现。还可以理解的是，有关每个模块的实际实现的详细讨论对于理解本发明是不必要的。更确切地说，考虑到在本文中公开的装置中各种功能模块的属性、功能和内部关系的情况下，在工程师的常规技术内将会了解该模块的实际实现。因此，本领域技术人员运用普通技术就能够在无需过度试验的情况下实现在权利要求书中所阐明的本发明。还可以理解的是，所公开的特定概念仅仅是说明性的，并不意在限制本发明的范围，本发明的范围由所附权利要求书及其等同方案的全部范围来决定。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，“计算机可读介质”可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。
计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本发明的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。例如，如果用硬件来实现，和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
在本说明书的描述中，参考术语“一个实施方式”、“另一实施方式”或“某些实施方式”等的描述意指结合实施方式或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施方式或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施方式或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施方式或示例中以合适的方式结合。
尽管已经示出和描述了本发明的实施方式，本领域的普通技术人员可以理解：在不脱离本发明的原理和宗旨的情况下可以对这些实施方式进行多种变化、修改、替换和变型，本发明的范围由权利要求及其等同物限定。
以上是对本发明的较佳实施进行了具体说明，但本发明并不限于所述实施例，熟悉本领域的技术人员在不违背本发明精神的前提下还可做作出种种的等同变形或替换，这些等同的变形或替换均包含在本申请权利要求所限定的范围内。

Claims (10)

  1. 一种学生学习兴趣画像生成系统，其特征在于，包括：业务框架和执行单元；
    所述业务框架包括数据源层、大数据平台和展示层；
    所述执行单元包括：
    数据采集模块，用于通过所述数据源层采集获取校园网内业务系统的数据；
    数据预处理模块，用于基于预设的学生学习兴趣的数据维度，对所述数据采集模块采集到的数据进行清洗和筛选；
    画像生成模块，用于以单个学生为目标，通过所述大数据平台对与所述学生有关的统计数据进行分析处理，生成学生学习兴趣画像；
    画像呈现模块，用于通过所述展示层将学生学习兴趣画像呈现给对应的学生和/或老师。
  2. 根据权利要求1所述的一种学生学习兴趣画像生成系统，其特征在于：所述校园网内业务系统包括选课系统、成绩系统、图书借阅系统、网络系统和职业兴趣测试系统。
  3. 根据权利要求2所述的一种学生学习兴趣画像生成系统，其特征在于，所述校园网内业务系统的数据包括：选修数据、成绩数据、图书借阅数据、网络浏览数据和职业兴趣测试结果数据；
    其中，所述选修数据包括选修科目的种类、次数和选修课出勤率；所述成绩数据包括必修课和选修课的学分成绩及排名；所述图书借阅数据包括借阅图书次数、借阅图书类别和借阅时长；所述网络浏览数据包括网站访问类型和流量数据；所述职业兴趣测试结果数据包括霍兰德职业兴趣测试结果类型。
  4. 根据权利要求2-3中任一项所述的一种学生学习兴趣画像生成系统，其特征在于：
    所述画像生成模块用于通过sqoop工具将从选课系统、成绩系统、图书借阅系统和职业兴趣测试系统获取的数据导入HDFS文件系统，通过Hive集群进行离线计算，并将结果导入Hbase数据库；
    所述画像生成模块还用于通过Spark集群对从网络系统获取的数据进行分析，并将结果导入Hbase数据库。
  5. 一种学生学习兴趣画像生成方法，其特征在于，包括以下步骤：
    获取校园网内业务系统的访问权限，采集所述业务系统中的数据；
    基于预设的学生学习兴趣的数据维度，对所述数据进行清洗和筛选；
    以单个学生为目标，对与所述学生有关的统计数据进行分析，生成学生学习兴趣画像；
    将所述学生学习兴趣画像呈现给对应的学生和/或老师。
  6. 根据权利要求5所述的一种学生学习兴趣画像生成方法，其特征在于：所述校园网内业务 系统包括选课系统、成绩系统、图书借阅系统、网络系统和职业兴趣测试系统。
  7. 根据权利要求6所述的一种学生学习兴趣画像生成方法，其特征在于，所述采集所述业务系统中的数据这一步骤，其具体包括：采集选修数据、成绩数据、图书借阅数据、网络浏览数据和职业兴趣测试结果数据；
    其中，所述选修数据包括选修科目的种类、次数和选修课出勤率；所述成绩数据包括必修课和选修课的学分成绩及排名；所述图书借阅数据包括借阅图书次数、借阅图书类别和借阅时长；所述网络浏览数据包括网站访问类型和流量数据；所述职业兴趣测试结果数据包括霍兰德职业兴趣测试结果类型。
  8. 根据权利要求6-7中任一项所述的一种学生学习兴趣画像生成方法，其特征在于，所述对与所述学生有关的统计数据进行分析，生成学生学习兴趣画像这一步骤，其具体包括：
    通过sqoop工具将从选课系统、成绩系统、图书借阅系统和职业兴趣测试系统获取的数据导入HDFS文件系统，通过Hive集群进行离线计算，并将结果导入Hbase数据库；
    通过Spark集群对从网络系统获取的数据进行分析，并将结果导入Hbase数据库；
    基于所述Hbase数据库中的数据，生成学生学习兴趣画像。
  9. 一种学生学习兴趣画像生成装置，其特征在于，包括：
    至少一个处理器；
    至少一个存储器，用于存储至少一个程序；
    当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如权利要求5-8中任一项所述的学生学习兴趣画像生成方法。
  10. 一种存储介质，其中存储有处理器可执行的指令，其特征在于：所述处理器可执行的指令在由处理器执行时用于实现如权利要求5-8中任一项所述的学生学习兴趣画像生成方法。
PCT/CN2020/094635 2019-11-22 2020-06-05 一种学生学习兴趣画像生成系统、方法、装置及存储介质 WO2021098187A1 (zh)

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