TW202518376A - Device and method for generating learning suggestion based on learning status and learning goal - Google Patents

Abstract

A device and a method for generating a learning suggestion based on a learning status and a learning goal are provided. By analyzing learning status data of a student to determining a question difficulty, determining a question type according to a learning objective of the student and a number of times that the student have been tested, selecting test questions corresponding to knowledge points based on the question difficulty and the question type, generating mastery level of each of the knowledge point according to a correct rate of the test questions, response time of each of the test question and the question difficulty, and generating learning suggestion based on the mastery level of each of the knowledge point, the system and the method can determine mastery level of each knowledge point accurately, and can achieve the effect of providing learning suggestion according to student's mastery level of each knowledge point.

Description

Device and method for generating learning suggestions based on learning status and learning goals

A learning suggestion generating system and method thereof, in particular, refers to a device and method for generating learning suggestions based on learning status and learning goals.

In order to verify the effectiveness of learning, students usually take paper-and-pencil tests in school. The test setter sets the test questions in advance and prints out the test papers. Students use the test papers to take the test at the same time. After the test, the test setter or the examiner marks the test papers and calculates the test scores.

If teachers or students want to analyze or review the test results, they usually compare the difference between the student's previous score and the current score, or decide the scope of strengthening learning based on the wrong answers in this test. However, as students' learning conditions are different, the parts of the test that need to be strengthened also vary from person to person. Generally, only the wrong questions can be analyzed or reviewed. In addition, since the scope of each test may be quite wide, students can usually only know the units that need to be strengthened through the wrong questions, but this learning method is often inefficient and cannot show the effect of learning.

With the popularization of computer information technology, there are now electronic question banks for students to use computers to connect to electronic question banks for testing. However, the current way to understand learning effectiveness through online tests is mostly to ask questions and explain the knowledge points in the test, and improve the learning status of the knowledge points through repeated answers. Therefore, just like the results of traditional manual review, it can only provide explanations for incorrectly answered questions, and cannot provide effective learning suggestions based on students' learning status.

In summary, it can be seen that the prior art has long had the problem of only being able to provide learning instructions for specific knowledge points for individual students based on the test results for all students. Therefore, it is necessary to propose improved technical means to solve this problem.

In view of the problem that the prior art only provides students with learning instructions on specific knowledge points based on test results for all students, the present invention discloses a device and method for generating learning suggestions based on learning conditions and learning goals, wherein:

The device disclosed in the present invention for generating learning suggestions based on learning status and learning objectives at least comprises: a data collection module for collecting learning status data of students on learning scope, wherein the learning scope includes multiple knowledge points, and the learning status data includes learning time, learning progress, learning process, and learning results; a data analysis module for analyzing the learning status data using an analysis model to generate analysis results; a test question selection module for determining the difficulty of the test questions based on the analysis results, and for determining the test question type based on the learning objectives and the number of times the students have taken the test on the learning scope, and continuously selecting the test questions based on the difficulty of the test questions and the test results. The question type selects test questions corresponding to the knowledge points, and the knowledge points at least correspond to the test questions; the answer judgment module is used to judge the answer results of the test questions; the result calculation module is used to calculate the answer reaction time of the test questions, and to calculate the answer accuracy rate based on the answer results of the test questions; the learning evaluation module is used to generate an evaluation report based on the answer accuracy rate and the answer reaction time of the test questions and the difficulty of the questions using the item response theory model, and the evaluation report includes the mastery data of each knowledge point; the learning suggestion module is used to generate corresponding learning suggestions based on the mastery data of each knowledge point.

The method disclosed in the present invention for generating learning suggestions based on learning status and learning objectives comprises at least the following steps: collecting learning status data of students on learning scope, wherein the learning scope includes a plurality of knowledge points, and the learning status data includes learning time, learning progress, learning process, and learning results; using an analysis model to analyze the learning status data to generate analysis results; determining the difficulty of test questions based on the analysis results; determining the test question type based on the learning objectives and the number of times the students have taken tests on the learning scope; Continuously select test questions corresponding to knowledge points based on the difficulty of the test questions and the test question types, and the knowledge points at least correspond to the test questions; calculate the response time of the test questions; judge the answer results of the test questions and calculate the correct answer rate; generate an evaluation report based on the correct answer rate and the response time of the test questions and the difficulty of the test questions using the item response theory model, and the evaluation report includes the mastery data of each knowledge point; generate corresponding learning suggestions based on the mastery data of each knowledge point.

The device and method disclosed in the present invention are as described above. The difference between the present invention and the prior art is that the present invention determines the difficulty of test questions by analyzing the students' learning status data and determines the test question type according to the learning goals and the number of times the students have taken tests on the learning scope. Then, the test questions corresponding to the knowledge points are selected according to the test question difficulty and the test question type, and the mastery data of each knowledge point in the learning scope is generated according to the correct answer rate, answer response time and test question difficulty of the test questions, and learning suggestions are generated according to the mastery data of each knowledge point, so as to solve the problems existing in the prior art and achieve the technical effect of giving learning suggestions according to the students' mastery of each knowledge point.

The following will be used in conjunction with drawings and embodiments to explain in detail the features and implementation methods of the present invention. The content is sufficient to enable anyone familiar with the relevant technology to easily and fully understand the technical means used by the present invention to solve the technical problems and implement them accordingly, thereby achieving the effects that can be achieved by the present invention.

The present invention can evaluate students' mastery of knowledge points within the scope of learning through test questions, so as to generate appropriate learning suggestions for students. The scope of learning mentioned in the present invention includes multiple knowledge points. For example, the scope of learning can be the content of one or more chapters of a specific subject, and the knowledge points can be the key points of each chapter, etc.; the learning suggestions can be learning methods of knowledge points, background knowledge related to knowledge points, advanced content of knowledge points, and/or learning resources related to knowledge points, etc., but the present invention is not limited to this.

The device for implementing the present invention may be a computing device. The computing device mentioned in the present invention includes but is not limited to one or more processing modules, one or more memory modules, and hardware components such as a bus connecting different hardware components (including memory modules and processing modules). Through the multiple hardware components included, the computing device can load and execute an operating system so that the operating system runs on the computing device, and can also execute software or programs. The computing device also includes a housing, and the above-mentioned hardware components are arranged in the housing.

The bus of the computing device mentioned in the present invention may include one or more types, such as a data bus, an address bus, a control bus, an expansion bus, and/or a local bus. The buses of computing devices include but are not limited to the Industry Standard Architecture (ISA) bus, the Peripheral Component Interconnect (PCI) bus, the Video Electronics Standards Association (VESA) local bus, the Universal Serial Bus (USB) bus, the PCI Express (PCI-E/PCIe) bus, etc.

The processing module of the computing device of the present invention is coupled to a bus. The processing module includes a register group or a register space, which can be completely set on the processing chip of the processing module, or completely or partially set outside the processing chip and coupled to the processing chip via a dedicated electrical connection and/or via a bus. The processing module can be a central processing unit, a microprocessor, or any suitable processing element. If the computing device is a multi-processor device, that is, the computing device includes multiple processing modules, the processing modules included in the computing device are the same or similar, and are coupled and communicated through a bus. In some embodiments, the processing module can interpret a computer instruction or a series of multiple computer instructions to perform specific calculations or operations, such as mathematical operations, logical operations, data comparison, copying/moving data, etc., so as to drive other hardware components in the computing device or run an operating system or execute various programs and/or modules. The computer instruction can be an assembly language instruction, an instruction set architecture instruction, a machine instruction, a machine-related instruction, a microinstruction, a firmware instruction, or a source code or object code written in any combination of one or more programming languages, and the computer instruction can be completely executed on a single computing device, partially executed on a single computing device, partially executed on one computing device and partially executed on another connected computing device. The above-mentioned programming languages include object-oriented programming languages, such as Common Lisp, Python, C++, Objective-C, Smalltalk, Delphi, Java, Swift, C#, Perl, Ruby, etc., and conventional procedural programming languages, such as C language or other similar programming languages.

Computing devices usually also include one or more chipsets. The processing module of the computing device can be coupled to the chipset or electrically connected to the chipset through a bus. The chipset is composed of one or more integrated circuits (ICs), including a memory controller and a peripheral input/output (I/O) controller, etc. That is, the memory controller and the peripheral input/output (I/O) controller can be included in one IC, or can be implemented using two or more ICs. The chipset usually provides input/output and memory management functions, as well as multiple general and/or dedicated registers, timers, etc., wherein the above-mentioned general and/or dedicated registers and timers can be accessed or used by one or more processing modules coupled or electrically connected to the chipset. In some embodiments, the chipset may also be part of the processing module.

The processing module of the computing device can also access the data in the memory module and the mass storage area installed on the computing device through the memory controller. The above-mentioned memory module includes any type of volatile memory and/or non-volatile memory (NVRAM) memory, such as static random access memory (SRAM), dynamic random access memory (DRAM), read-only memory (ROM), flash memory, etc. The mass storage area mentioned above may include any type of storage device or storage media, such as a hard drive, optical disc, flash drive, memory card, solid state disk (SSD), or any other storage device. In other words, the memory controller can access data in static random access memory, dynamic random access memory, flash memory, hard drive, and solid state disk.

The processing module of the computing device can also be connected and communicated with peripheral devices or interfaces such as peripheral output devices, peripheral input devices, communication interfaces, various data or signal receiving devices, etc. through the peripheral input/output controller via the peripheral input/output bus. The peripheral input device can be any type of input device, such as a keyboard, a mouse, a trackball, a touchpad, a joystick, etc. The peripheral output device can be any type of output device, such as a display, a printer, etc. The peripheral input device and the peripheral output device can also be the same device, such as a touch screen, etc. The communication interface may include a wireless communication interface and/or a wired communication interface. The wireless communication interface may include an interface supporting wireless local area networks (such as Wi-Fi, Zigbee, etc.), Bluetooth, infrared, near-field communication (NFC), 3G/4G/5G and other mobile communication networks (cellular networks) or other wireless data transmission protocols. The wired communication interface may be an Ethernet device, a DSL modem, a cable modem, an asynchronous transfer mode (ATM) device, or an optical fiber communication interface and/or component. The data or signal receiving device may include a GPS receiver or a physiological signal receiver. The physiological signals received by the physiological signal receiver include but are not limited to heartbeat, blood oxygen, etc. The processing module can periodically poll various peripheral devices and interfaces, so that the computing device can input and output data through various peripheral devices and interfaces, and can also communicate with another computing device having the hardware components described above.

The following first uses the schematic diagram of the components of the device for generating learning suggestions based on learning conditions and learning goals proposed in the present invention, "Figure 1", to explain the device for implementing the present invention. As shown in "Figure 1", the device 100 of the present invention includes a memory module 110, an input module 120, a communication interface 130, a storage medium 140, a processing module 150, a display module 170, and a bus 190. Among them, the memory module 110, the input module 120, the communication interface 130, the storage medium 140, and the display module 170 are connected to each other through the bus 190 and the processing module 150.

The memory module 110 can store one or more sets of computer instructions and can also provide the processing module with data storage.

The input module 120 can provide the answer content of the test question. It should be noted that the input module 120 can provide one or more ways to provide input, for example, it can provide the answer content in the form of text input through physical keys or by displaying virtual keys, and it can also provide the answer content in the form of voice input through a microphone, and the present invention has no special limitation.

The communication interface 130 can be connected to an external network storage device or server and request and download data from the connected network device, for example, download test questions. The communication interface 130 can also upload data to the connected network device, for example, upload the answer results of the test questions, etc. However, the data sent and received by the communication interface 130 is not limited to the above.

The communication interface 130 can also provide a client connection device 100, and can transmit the data or signals generated by the processing module 150 to the client for display, and can receive data or signals transmitted by the client, for example, transmitting test questions to the client and receiving the answers sent back by the client, etc., but the data or signals transmitted between the communication interface 130 and the client are not limited to the above.

The storage medium 140 can store multiple test questions for one or more learning scopes. Generally speaking, the test questions stored in the storage medium 140 correspond to one or more knowledge points in the same learning scope and include multiple test question difficulties, wherein each test question difficulty has multiple test question types, such as multiple-choice questions, fill-in-the-blank questions, essay questions, situational questions, etc., but the present invention is not limited thereto. The storage medium 140 can also store the number of times one or more students have taken tests on each learning scope. The storage medium 140 can also store relevant data of each knowledge point included in the learning scope.

The processing module 150 may be as shown in FIG. 2 , which is a system architecture diagram for generating learning suggestions based on learning status and learning objectives as proposed in the present invention, including a data collection module 210, a data analysis module 220, a test question selection module 230, a question answer judgment module 250, a result calculation module 260, a learning evaluation module 270, a learning suggestion module 280, and an attachable report presentation module 290 and other modules. In some embodiments, the processing module 150 can execute computer instructions stored in the memory module 110, and can generate each module in "Figure 2" after executing the computer instructions; in another part of the embodiments, each module in "Figure 2" can be generated by one or more circuits and/or complete or partial chips and other hardware components, that is, the processing module 150 includes hardware components that constitute each module in "Figure 2", that is, each module included in the processing module 150 can be a software module or a hardware module, and the present invention has no special limitations.

The display module 170 can display the data generated by the processing module 150, for example, displaying the answer results of the test questions, the correct answer rate, and the evaluation report.

The data collection module 210 is responsible for collecting the learning status data of students in one or more learning areas. The learning status data mentioned in the present invention includes the learning time spent by students in the learning area, the current learning progress, the learning process, and the learning results, wherein the learning time can include the date and duration (or the time to start learning and the time to end learning), the learning process includes but is not limited to the learning content and notes, etc., and the learning results include but are not limited to the homework performance and grades, etc.

The data collection module 210 can obtain the learning status data of the students in the learning scope through the learning management system or learning platform provided by the device 100 or the external device. For example, the data collection module 210 can obtain all or part of the data of the students using the learning management system/learning platform through the application programming interface (API) provided by the learning management system/learning platform, and can generate learning status data based on the obtained data, for example, based on the learning data of the students using each learning scope. The learning time is generated based on the date and time of the learning, the learning progress is judged based on the order and completion status of the learning materials in each learning scope used by the students, the learning process is generated based on the learning materials browsed, the notes collected, the tags created, etc. by the students on the learning management system/learning platform, the learning habits are judged based on the frequency and duration of students browsing the learning materials and the content of the discussions in which the students participate, and the learning performance is generated based on the homework correction content and test scores of the students using learning analysis tools. Among them, learning materials include materials provided for students to learn, including teaching materials, handouts, notes, exercises, etc., but the present invention is not limited to this.

The data collection module 210 can also collect students' personal interests. For example, the data collection module 210 can use learning tracking technology to determine students' personal interests based on the order in which students use learning data in various learning areas, or can provide students with personal interests input through the input module 120 of the device 100, or can receive personal interests input by students on the client used by the device 100 through the communication interface 130 of the device 100. However, the way in which the data collection module 210 collects students' personal interests is not limited to the above.

The data analysis module 220 is responsible for using the analysis model to analyze the learning status data obtained by the data collection module 210 to generate analysis results. For example, the data analysis module 220 can use the analysis model of the machine learning algorithm such as decision tree and random forests to analyze the student's learning status data, and can obtain the analysis results representing the student's ability level generated after the analysis model classifies the learning status data.

The test question selection module 230 is responsible for determining the difficulty of the test questions based on the analysis results generated by the data analysis module 220. For example, if the analysis results indicate the ability level of the students, the test question selection module 230 can obtain the test question difficulty corresponding to the ability level indicated by the analysis results from a pre-established ability difficulty table, but the present invention is not limited thereto. The ability difficulty table can be a table of correspondence between ability levels and test question difficulties, that is, the ability difficulty table can record multiple ability levels and corresponding test question difficulties.

The test question selection module 230 can also obtain the student's learning goal through the input module 120 or the communication interface 130, for example, the input module 120 provides the user of the device 100 (usually the student, the student's parent, or the teacher) with the student's learning goal, or the communication interface 130 receives the student's learning goal sent by the student, the student's parent, or the teacher operating the client, and the present invention has no special limitation. Among them, the learning goal includes but is not limited to learning basic knowledge, mastering knowledge points, and acquiring problem-solving skills.

The test question selection module 230 is also responsible for determining the test question type based on the achieved learning objectives and/or the number of times the student has taken tests on the learning area. For example, if the learning goal is to learn basic knowledge, the test question selection module 230 can determine the test question type to be multiple-choice questions or fill-in-the-blank questions; if the learning goal is to master knowledge points or acquire problem-solving skills, the test question selection module 230 can determine the test question type to be open-ended questions or situational simulation questions, etc.; in addition, if the number of times the student has been tested on the learning scope does not reach the number threshold, the test question selection module 230 can determine the test question type to be multiple-choice questions or fill-in-the-blank questions, and when the number of times the student has been tested on the learning scope reaches the number threshold, the test question selection module 230 can decide the test question type to be open-ended questions or situational simulation questions. It should be noted that if the test question type determined by the test question selection module 230 according to the learning goal and the number of times tested is different, then the test question selection module 230 can further determine the test question type according to the ratio of the number of times tested to the number of times threshold (and the weight value of the learning goal and the number of times tested). For example, when the ratio of the number of times tested to the number of times threshold (or the product of the ratio and the weight value of the number of times tested) is less than a specific value, the test question type is determined to be a multiple-choice question or a fill-in-the-blank question. Otherwise, Determine whether the test question type is an open-ended question or a situational simulation question; or, the test question selection module 230 may also determine the proportion of the test question type based on the ratio of the number of times tested to the number of times threshold (or the product of the ratio and the weight value of the number of times tested), for example, the proportion of the test question type of multiple-choice questions or fill-in-the-blank questions is determined as the ratio of the number of times tested to the number of times threshold (or the product of the ratio and the weight value of the number of times tested), and the remaining test question types are open-ended questions or situational simulation questions, etc. However, the way in which the test question selection module 230 determines the test question type is not limited to the above.

The test question selection module 230 is also responsible for selecting a plurality of test questions according to the determined test question difficulty and test question type. Each knowledge point within the learning scope corresponds to at least one selected test question. In other words, all test questions selected by the test question selection module 230 can cover all knowledge points within the learning scope and the difficulty and question type are consistent with the test question difficulty and test question type. The test question selection module 230 can read the test questions from the storage medium 140 of the device 100, or can connect to a test question server or storage device outside the device 100 through the communication interface 130 of the device 100 to obtain test questions that are consistent with the test question difficulty and test question type. The present invention has no particular limitation. Generally speaking, when the test question selection module 230 obtains the test question, it can also obtain the answer to the test question.

The test question selection module 230 can also adjust the previously determined test question difficulty according to the correct answer rate of all currently selected test questions calculated by the result calculation module 260. For example, when the correct answer rate is higher or lower than the correct threshold value and continues for a certain number of questions (such as when the correct answer rate remains higher or lower than the correct threshold value after 5 consecutive test questions), the test question difficulty can be increased or decreased, and test questions can be selected based on the adjusted test question difficulty and the previously determined test question type. In other words, if the student's correct answer rate is high, the test question selection module 230 can select a test question with a higher difficulty; conversely, if the student's correct answer rate is low, the test question selection module 230 can select a test question with a lower difficulty instead.

The test question selection module 230 may also stop selecting test questions corresponding only to the same specific knowledge point when the correct answer rate of the specific knowledge point calculated by the result calculation module 260 reaches an exclusion threshold, that is, when the student has mastered the same specific knowledge point; the test question selection module 230 may also increase the probability or number of selecting test questions corresponding to the same specific knowledge point when the correct answer rate of the specific knowledge point is lower than an additional threshold, that is, when the student needs to further learn the same specific knowledge point.

The answer determination module 250 can obtain the test answers made by the students to the test questions selected by the test selection module 230. Generally speaking, the answer determination module 250 can provide the students with test answers through the input module 120 of the device 100, and can also receive the test answers input by the students from the client through the communication interface 130 of the device 100.

The answer determination module 250 is responsible for determining the answer result of the test question selected by the test question selection module 230. More specifically, the answer determination module 250 can first determine the test question type determined by the test question selection module 230. When the test question type is a multiple-choice question or a fill-in-the-blank question, the answer determination module 250 can compare whether the student's test answer obtained is the same as the answer to the test question obtained by the test question selection module 230. If so, the answer determination module 250 can generate an answer result indicating that the answer is correct. Otherwise, the answer determination module 250 can generate an answer result indicating that the answer is incorrect. When the answer determination module 250 determines that the test question type is a composition question or a situational simulation question, the test answer obtained by the answer determination module 250 is a voice signal. The answer determination module 250 can convert the obtained voice signal into a text message, and can analyze the correlation between the text message and the answer to the test question obtained by the test selection module 230 through natural language processing technology to generate the answer result of the test question.

The result calculation module 260 is responsible for calculating the response time of each test question selected by the test question selection module 230. Generally speaking, the result calculation module 260 can calculate the time from the test question selection module 230 selecting the test question to the test answer determination module 250 obtaining the test answer as the response time, but the calculation start time and end time of the response time are not limited to the above. For example, the result calculation module 260 can also use the time when the display module 170 of the device 100 displays the test question or the time when the communication interface 130 of the device 100 transmits the test question as the start time of calculating the response time.

The result calculation module 260 is also responsible for calculating the correct answer rate of the test questions selected by the test question selection module 230 according to the answer result determined by the answer determination module 250, that is, the answer result generated by the answer determination module 250 represents the number of correct answers divided by the number of test questions selected by the test question selection module 230. In the present invention, the result calculation module 260 can recalculate (or update) the correct answer rate of all test questions selected by the current test question selection module 230 when the answer determination module 250 determines the answer result of a test question selected by the test question selection module 230.

The learning assessment module 270 is responsible for generating an assessment report using the Item Response Theory (IRT) model based on the correct answer rate, the response time of each test question, and the difficulty of each test question calculated by the result calculation module 260. The assessment report generated by the learning assessment module 270 may include the mastery data of each knowledge point within the learning scope. More specifically, the learning assessment module 270 can provide the correct answer rate of all test questions, the response time of each test question, and the difficulty of each test question to the item response theory model, so that the item response theory model outputs the student's ability level in the learning scope; the learning assessment module 270 can also provide the correct answer rate of test questions of each knowledge point, the response time of each test question, and the difficulty of each test question to the item response theory model. The time and the difficulty of the test questions of each knowledge point are provided to the item response theory model so that the item response theory model outputs the students' ability level at each knowledge point. The learning assessment module 270 can use the ability level of each knowledge point output by the item response theory model as the mastery data of each knowledge point, but the way in which the learning assessment module 270 generates the mastery data of each knowledge point is not limited to the above.

The learning assessment module 270 can also judge the strengths and weaknesses of students based on the mastery data of each knowledge point and/or the correct answer rate. For example, when the mastery data of a knowledge point or the correct answer rate reaches the advantage threshold, the learning assessment module 270 can judge the knowledge point as a strength, and when the mastery data of a knowledge point or the correct answer rate is lower than the weakness threshold (the advantage threshold is higher than the weakness threshold), the learning assessment module 270 can judge the knowledge point as a weakness. The learning assessment module 270 can also add the judged strengths and weaknesses to the generated assessment report.

The learning suggestion module 280 is responsible for generating corresponding learning suggestions based on the mastery data of each knowledge point determined by the learning assessment module 270. For example, when a knowledge point is a weakness or the mastery data of the knowledge point is lower than the weakness threshold, the learning suggestion module 280 can provide corresponding solutions, such as the learning method of the knowledge point, the background knowledge related to the knowledge point, etc.; when a knowledge point is a strength or the mastery data of the knowledge point is higher than the advantage threshold, the learning suggestion module 280 can In order to skip the prompt content related to the knowledge point or provide the advanced content of the knowledge point, for example, the content of a specific field or the content of subsequent related courses, etc.; and when the knowledge point is neither a strength nor a weakness, or the mastery data of the knowledge point is between the strength threshold value and the weakness threshold value, the learning suggestion module 280 can provide learning resources related to the knowledge point. Generally speaking, the learning suggestion module 280 can read the data provided to the student from the storage module 140 of the device 100, such as the advanced content of the knowledge point, learning resources, learning methods, background knowledge, etc.

The learning suggestion module 280 may also determine the mastery change of the same knowledge point based on the mastery data of each knowledge point and the mastery data of the same knowledge point in the historical test records, and generate learning suggestions based on the determined mastery change. For example, when it is determined that the mastery of the same knowledge point continues to increase, the learning suggestion module 280 can provide relevant learning resources and learning activities for the knowledge point based on the learning objectives obtained by the test selection module 230 and the student's personal interests obtained by the data collection module 210, thereby helping students to further consolidate and develop the knowledge points they have mastered, and promote students' in-depth learning and development on the knowledge points, helping students to better understand and apply the knowledge they have learned.

The report presentation module 290 may present the assessment report generated by the learning assessment module 270 in a visual manner.

Next, an embodiment is used to explain the operating system and method of the present invention, and please refer to the flowchart of the method for generating learning suggestions based on learning status and learning goals in "Figure 3A" of the present invention. In this embodiment, it is assumed that the device 100 is a server, and students can operate a client such as a mobile phone or a computer to connect to the device 100 to use the present invention. The processing module 150 of the device 100 can execute computer instructions to generate the modules shown in "Figure 2", or can include hardware components that form the modules shown in "Figure 2".

First, the data collection module 210 can collect learning status data of the learning scope including knowledge points (step 310). In this embodiment, it is assumed that the data collection module 210 can use the communication interface 130 of the device 100 to connect to the learning management system or learning platform according to the API provided by the learning management system or learning platform used by the student, so that the learning management system or learning platform can obtain the student's data in the learning management system or learning platform, and can generate learning status data based on the obtained data combined with learning tracking technology and learning analysis tools.

After the data collection module 210 collects the learning status data of the students in the learning scope (step 310), the data analysis module 220 can use the analysis model to analyze the learning status data collected by the data collection module 210 to generate corresponding analysis results after analysis (step 320). In this embodiment, it is assumed that the analysis results generated by the data analysis module 220 can represent the ability level of the students in the learning scope.

After the data analysis module 220 generates the analysis results, the test question selection module 230 can determine the difficulty of the test questions according to the analysis results generated by the data analysis module 220 (step 331). In addition, the test question selection module 230 can also determine the test question type according to the student's learning goal and the number of times the student has taken tests in the learning scope (step 335). In this embodiment, it is assumed that the test question selection module 230 can provide students with the ability to select learning objectives through the communication interface 130 of the device 100, and can read the number of times the student has taken tests in the learning scope from the historical test records stored in the storage medium 140 of the device 100, and can determine the test question type as multiple-choice questions, fill-in-the-blank questions, essay questions, or situational questions, etc. based on the learning objectives obtained through the communication interface 130 and the number of times the student has taken tests read from the storage medium 140.

After the question selection module 230 determines the question difficulty and the test question type, the question selection module 230 can continue to select test questions corresponding to the knowledge points based on the determined question difficulty and test question type (step 350), the answer determination module 250 can determine the answer results of the test questions selected by the question selection module 230 (step 360), the result calculation module 260 can calculate the student's answer reaction time for each test question, and can calculate the answer accuracy rate of the test questions selected by the question selection module 230 based on the answer results of each test question determined by the answer determination module 250 (step 370). In this embodiment, it is assumed that as shown in the process of "FIG. 3B", after the test question selection module 230 selects a test question whose difficulty and question type are consistent with the determined test question difficulty and test question type and corresponds to at least one knowledge point (step 351), the test question selection module 230 can transmit the selected test question to the client operated by the student through the communication interface 130 of the device 100 for display, and the answer judgment module 250 can receive the test answer input by the student operating the client through the communication interface 130, and can judge the selected test question by the test question selection module 230 according to the received test answer. The result calculation module 260 can calculate the answer results of the test questions determined by the answer judgment module 250 (step 360), and the result calculation module 260 can calculate the answer accuracy of all the test questions selected by the test selection module 230 according to the answer results of the test questions determined by the answer judgment module 250 (step 371), and can calculate the student's answer reaction time to the test questions (step 373), for example, the time when the test selection module 230 transmits the test questions is used as the starting time and the time when the answer judgment module 250 receives the test answers is used as the ending time, and the difference between the ending time and the starting time is calculated as the answer reaction time. After the result calculation module 260 calculates the answer accuracy and the answer response time, the test question selection module 230 can determine whether all the knowledge points in the learning scope have corresponding test questions (step 353), that is, determine whether the selected test questions have been matched to all the knowledge points in the learning scope. If not, the test question selection module 230 can adjust the determined test question difficulty according to the answer accuracy calculated by the result calculation module 260, and select test questions again according to the adjusted test question difficulty and the determined test question type (step 351). The question determination module 250 can again determine the answer results of the test questions selected by the question selection module 230 (step 360), and the result calculation module 260 can again calculate the answer accuracy of all the test questions selected by the question selection module 230 based on the answer results determined by the answer determination module 250 (step 371), and can calculate the answer response time of the test questions (step 373), and this is repeated until the question selection module 230 determines that all knowledge points within the learning scope have corresponding test questions (step 353). When the answer determination module 250 determines the answer result of the test question, if the data type of the test answer received is not text, such as a voice signal, the answer determination module 250 can convert the received voice signal into a text message (steps 363, 365) as shown in the process of "Figure 3C" to generate a test answer with the data type of text. At the same time, the answer determination module 250 can select the test question determined by the test selection module 230. The answer determination module 250 determines the answer result according to the test question type. When the test question type is a multiple-choice question or a fill-in-the-blank question with a standard answer, the answer determination module 250 can generate a corresponding answer result based on whether the received test answer is the same as the answer to the corresponding test question. When the test question type is a question-and-answer question or a situational question without a standard answer, the answer determination module 250 can analyze the test answer through natural language processing technology to determine the answer result of the test question (step 367).

When the question selection module 230 determines that all knowledge points within the learning scope have corresponding test questions (step 353), that is, the result calculation module 260 calculates the student's response time to each test question selected by the question selection module 230, and calculates the correct answer rate of the test questions selected by the question selection module 230 based on the answer results of each test question determined by the answer determination module 250 (step 370), the learning evaluation module 270 can generate an evaluation report based on the correct answer rate calculated by the result calculation module 260 and the response time and difficulty of each test question using the item theory response model (step 380). In this embodiment, it is assumed that the learning assessment module 270 can provide the correct answer rate, the response time and the difficulty of each test question selected by the test question selection module 230 to the project theory response model, and can also provide the correct answer rate, the response time and the difficulty of the test questions corresponding to each knowledge point to the project theory response model in batches, and can generate an assessment report including the mastery data of the overall learning scope and each knowledge point in the learning scope based on the results generated by all test questions and the results generated by the test questions corresponding to each knowledge point according to the project theory response model.

After the learning assessment module 270 generates the assessment report, the learning suggestion module 280 can generate corresponding learning suggestions based on the mastery data of each knowledge point in the assessment report generated by the learning assessment module 270 (step 390). The learning suggestion module 280 can also obtain the mastery data of each knowledge point from the historical test record of the device 100 as shown in the process of "Figure 3D", and compare the mastery data of the same knowledge point in the historical test record with the mastery data in the assessment report to determine the mastery change of the knowledge point (step 393), and can generate corresponding learning suggestions based on the determined mastery change (step 397).

Thus, through the present invention, test questions can be generated according to the students' learning status, and the students' mastery of knowledge points can be evaluated according to the correct answer rate, answer response time and test question difficulty of the test questions, so as to generate appropriate learning suggestions for the students.

In summary, the difference between the present invention and the prior art is that the present invention analyzes the students' learning status data to determine the difficulty of the test questions and determines the test question type according to the learning objectives and the number of times the students have taken the test on the learning scope. Then, the test questions corresponding to the knowledge points are selected according to the test question difficulty and the test question type, and the correct answer rate, answer response time and test question difficulty of the test questions are used to generate the test questions. The present invention provides a technical means for obtaining the mastery data of each knowledge point in the learning scope of students and generating learning suggestions based on the mastery data of each knowledge point. This technical means can solve the problem that the previous technology only provides students with learning instructions on specific knowledge points based on the test results of all students, and thus achieve the technical effect of providing learning suggestions based on the students' mastery of each knowledge point.

Furthermore, the method of generating learning suggestions based on learning status and learning goals of the present invention can be implemented in hardware, software, or a combination of hardware and software, and can also be implemented in a centralized manner in a computer system or in a distributed manner with different components distributed in several interconnected computer systems.

Although the implementation methods disclosed in the present invention are as above, the contents described are not intended to directly limit the scope of patent protection of the present invention. Any person with common knowledge in the technical field to which the present invention belongs, without departing from the spirit and scope disclosed by the present invention, makes slight changes and modifications to the implementation of the present invention in form and details, which are all within the scope of patent protection of the present invention. The scope of patent protection of the present invention shall still be based on the scope defined in the attached patent application.

100: device 110: memory module 120: input module 130: communication interface 140: storage medium 150: processing module 170: display module 190: bus 210: data collection module 220: data analysis module 230: test question selection module 250: answer judgment module 260: result calculation module 270: learning evaluation module 280: learning suggestion module 290: report presentation module Step 310: collect learning status data of learning scope, and learning scope includes knowledge points Step 320: Analyze the learning status data using the analysis model to generate analysis results Step 331: Determine the difficulty of the test questions based on the analysis results Step 335: Determine the test question type based on the number of times the learning objectives and learning scope have been tested Step 350: Continue to select test questions corresponding to all knowledge points based on the difficulty of the test questions and the test question type Step 351: Select test questions corresponding to the knowledge points based on the difficulty of the test questions and the test question type Step 353: Determine whether all knowledge points have corresponding test questions Step 355: Adjust the difficulty of the test questions based on the current correct answer rate Step 360: Determine the answer results of the test questions Step 363: Receive the voice signal of the student answering the test question Step 365: Convert the voice signal into a text message Step 367: When the test question type is determined to be a question-and-answer question or a situational simulation question, analyze the text message through natural language processing technology to determine the answer result of the test question Step 370: Calculate the answer reaction time of the test question and calculate the answer accuracy rate based on the answer result Step 371: Calculate the answer accuracy rate based on the answer result of the selected test question Step 373: Calculate the answer reaction time of the test question Step 380: Generate an evaluation report using the item theory response model based on the answer accuracy rate, the answer reaction time of the test question and the difficulty of the test question Step 390: Generate corresponding learning suggestions based on the mastery of the knowledge points in the evaluation report Step 393: Determine the mastery change of the knowledge points based on the mastery data of each knowledge point and the mastery data of the same knowledge point in the historical test records Step 397: Generate learning suggestions based on the mastery change

FIG. 1 is a schematic diagram of the components of the device for generating learning suggestions based on learning status and learning goals proposed in the present invention. FIG. 2 is a system architecture diagram for generating learning suggestions based on learning status and learning goals proposed in the present invention. FIG. 3A is a flow chart of the method for generating learning suggestions based on learning status and learning goals proposed in the present invention. FIG. 3B is a flow chart of the method for selecting test questions based on the difficulty of the test questions and the test question type proposed in the present invention. FIG. 3C is a flow chart of the method for judging the answer results of test questions proposed in the present invention. FIG. 3D is a flow chart of the method for generating learning suggestions based on the changes in the mastery of knowledge points proposed in the present invention.

Step 310: Collect learning status data of the learning scope, where the learning scope includes knowledge points

Step 320: Use the analysis model to analyze the learning status data to generate analysis results

Step 331: Determine the difficulty of the test questions based on the analysis results

Step 335: Determine the test question type based on the number of times the learning objectives and learning scope have been tested.

Step 350: Continue to select test questions corresponding to all knowledge points based on the difficulty of the test questions and the test question types.

Step 360: Determine the answer results of the test questions

Step 370: Calculate the response time for the test questions and calculate the correct answer rate based on the answer results

Step 380: Generate an evaluation report using the item theory response model based on the correct answer rate, test question response time and test question difficulty

Step 390: Generate corresponding learning suggestions based on the mastery of knowledge points in the assessment report

Claims (10)

A method for generating learning suggestions based on learning status and learning objectives is applied to a device, and the method comprises at least the following steps: Collecting a student's learning status data on a learning scope, the learning scope comprising a plurality of knowledge points, and the learning status data comprising learning time, learning progress, learning process, and learning results; Using an analysis model to analyze the learning status data to generate an analysis result; Determining a test question difficulty based on the analysis result; Determining a test question type based on a learning objective and the number of times the student has taken a test on the learning scope; Continuously selecting test questions corresponding to the knowledge point according to the difficulty of the test question and the test question type, each knowledge point corresponds to at least one test question; Calculating the response time of each test question; Determining the answer result of each test question and calculating a correct answer rate; Generating an evaluation report using an item response theory model according to the correct answer rate and the response time of each test question and the difficulty of the test question, the evaluation report includes the mastery data of each knowledge point; and Generating a corresponding learning suggestion according to the mastery data of each knowledge point. The method of generating learning suggestions based on learning status and learning objectives as described in claim 1, wherein the step of generating the corresponding learning suggestions based on the mastery data of each knowledge point further includes the step of determining a mastery change of the same knowledge point based on the mastery data of each knowledge point and the mastery data corresponding to the same knowledge point in the historical test records, and generating the learning suggestions based on the mastery change. A method for generating learning suggestions based on learning status and learning objectives as described in claim 1, wherein the step of determining the answer result of each test question further includes the steps of receiving a voice signal of the student answering the test question when determining that the test question type is a question-and-answer question or a situational simulation question, converting the voice signal into a text message, and analyzing the text message through natural language processing technology to determine the answer result of the test question. A method for generating learning recommendations based on learning status and learning objectives as described in claim 1, wherein the method further comprises, after the step of generating the evaluation report, the step of presenting the evaluation report in a visual manner. A method for generating learning suggestions based on learning status and learning objectives as described in claim 1, wherein the step of continuously selecting a test question corresponding to the knowledge point based on the difficulty of the test question and the test question type further includes the step of adjusting the difficulty of the test question based on the correct answer rate of all currently selected test questions. A device for generating learning suggestions based on learning status and learning goals, the device at least comprising: A data collection module for collecting a student's learning status data for a learning scope, the learning scope comprising a plurality of knowledge points, the learning status data comprising learning time, learning progress, learning process, and learning results; A data analysis module for analyzing the learning status data using an analysis model to generate an analysis result; A test question selection module is used to determine the difficulty of a test question based on the analysis result, and to determine a test question type based on a learning goal and the number of times the student has taken a test on the learning scope, and to continuously select a test question corresponding to a knowledge point based on the test question difficulty and the test question type, and each knowledge point corresponds to at least one test question; A question answering judgment module is used to judge the answer result of each test question; A result calculation module is used to calculate the answering reaction time of each test question, and to calculate an answering accuracy rate based on the answer result of each test question; A learning assessment module, for generating an assessment report based on the correct answer rate, the response time of each test question and the difficulty of the test question using an item response theory model, wherein the assessment report includes the mastery data of each knowledge point; and A learning suggestion module, for generating a corresponding learning suggestion based on the mastery data of each knowledge point. A device for generating learning suggestions based on learning status and learning goals as described in claim 6, wherein the learning suggestion module is further used to determine a mastery change of the same knowledge point based on the mastery data of each knowledge point and the mastery data corresponding to the same knowledge point in the historical test record, and generate the learning suggestion based on the mastery change. A device for generating learning suggestions based on learning status and learning objectives as described in claim 6, wherein the answer judgment module receives a voice signal of the student answering a test question when judging that the test question type is a question-and-answer question or a situational simulation question, converts the voice signal into a text message, and analyzes the text message through natural language processing technology to judge the answer result of the test question. A device for generating learning suggestions based on learning status and learning objectives as described in claim 6, wherein the device further includes a report presentation module for presenting the assessment report in a visual manner. A device for generating learning suggestions based on learning status and learning goals as described in claim 6, wherein the test question selection module is further used to adjust the test question difficulty based on the correct answer rate of all currently selected test questions, so as to select a test question corresponding to the knowledge point based on the adjusted test question difficulty and the test question type.

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