A computer-aided teaching system based on big data

Abstract

The present invention provides a computer-aided teaching system based on big data, comprising: a student analysis unit, a course analysis unit, a teaching plan auxiliary arrangement unit, a teaching process monitoring unit, and a teaching evaluation unit; wherein, the student analysis unit determines the current learning situation; the course analysis unit analyzes the course content to determine the mastery level of each knowledge point in the course content; the teaching plan auxiliary arrangement unit assists in arranging the teaching plan; the teaching process monitoring unit obtains the operation records of the teacher terminal and the student terminal as well as the video records in the classroom during the teaching process; the teaching evaluation unit conducts teaching evaluation based on the monitoring data of the teaching process monitoring unit. The computer-aided teaching system based on big data of the present invention evaluates the abilities of students, analyzes the courses to reasonably plan the course time of each knowledge point, supervises the courses and conducts post-event evaluation, realizing the supervision and assistance of the whole process of computer teaching and achieving scientific computer teaching.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and particularly relates to a computer-aided teaching system based on big data. Background Art

[0002] With the rapid development of modern science and technology, emerging technologies represented by computers have been popularized and widely applied throughout society. The demand for computer professionals in society and enterprises is increasing, and higher requirements are also put forward for the comprehensive level of computer professionals. This has brought pressure and challenges to the teaching of computer majors in colleges and universities. It is necessary to continuously strengthen the teaching reform of computer major courses, clarify the problems existing in the current teaching mode, and then optimize the teaching mode, enrich the teaching means, and improve the overall teaching level and quality of computer majors in colleges and universities according to the actual social development and the characteristics of computer majors.

[0003] At present, the teaching mode of many computer majors in colleges and universities still adopts the traditional teaching mode. Teachers only focus on whether their own teaching tasks are completed, and rarely pay attention to the reactions of students during the whole teaching process. They just blindly instill theoretical knowledge, lack interaction with students, affect the whole computer teaching atmosphere, reduce students' interest in computer learning, and also affect the improvement of students' information literacy. At the same time, in terms of teaching methods, college teachers all adopt a single teaching method in the teaching process. Since they only rely on a single multimedia courseware to grasp and describe the whole course content, there is a lack of effective communication between teachers and students, and students have few opportunities to participate in classroom teaching, which is not conducive to the improvement of students' self-learning awareness and innovation awareness, and thus affects the teaching efficiency of computer majors. Therefore, how to achieve scientific computer teaching has always been a concern for college teachers. Summary of the Invention

[0004] One of the purposes of the present invention is to provide a computer-aided teaching system based on big data, which can evaluate students' abilities, analyze courses, reasonably plan the course time of each knowledge point, supervise the courses and conduct post-event evaluation, realizing the supervision and assistance of the whole process of computer teaching and achieving scientific computer teaching.

[0005] A computer-aided teaching system based on big data provided by an embodiment of the present invention includes: a student analysis unit, a course analysis unit, a teaching plan auxiliary arrangement unit, a teaching process monitoring unit, and a teaching evaluation unit;

[0006] Among them, the student analysis unit analyzes the computer historical learning data and / or computer application operation evaluation data of students by means of big data analysis to determine the current learning situation; the course analysis unit analyzes the course content based on the current learning situation of students to determine the mastery degree of each knowledge point in the course content; the teaching plan auxiliary arrangement unit obtains the corresponding teaching plan content from the big data platform according to the mastery degree of each knowledge point in the course content and assists in arranging the teaching plan; the teaching process monitoring unit obtains the operation records of the teacher side and the student side and the video records in the classroom during the teaching process to monitor the teaching process; the teaching evaluation unit conducts teaching evaluation based on the monitoring data of the teaching process monitoring unit.

[0007] Preferably, the student analysis unit includes: a record retrieval subunit, an application operation evaluation subunit, and an analysis and construction subunit;

[0008] Among them, the analysis and construction subunit analyzes the computer historical learning data retrieved by the record retrieval subunit and / or the computer application operation evaluation data generated by the application operation evaluation subunit to determine the current learning situation.

[0009] Preferably, when receiving a request for active evaluation from a student, the application operation evaluation subunit retrieves the corresponding operation questions from a pre-configured operation question bank according to the student's ability information and outputs them; when the application operation evaluation subunit fails to retrieve the corresponding operation questions from the operation question bank based on the ability information, it retrieves a pre-configured set of operation questions.

[0010] Preferably, the analysis and construction subunit determining the current learning situation includes:

[0011] According to the knowledge points and corresponding instance operation situations and / or test paper answering situations in the computer historical learning data, data filling is performed on a pre-configured knowledge point details table to obtain a knowledge point mastery table representing the current learning situation.

[0012] Preferably, the analysis and construction subunit determining the current learning situation includes:

[0013] According to the knowledge points corresponding to each test question and the test paper answering situations in the computer application operation evaluation data, data filling is performed on a pre-configured knowledge point details table to obtain a knowledge point mastery table representing the current learning situation.

[0014] Preferably, the analysis and construction subunit also supplements the mastery situation of the prerequisite knowledge points associated with each knowledge point in the knowledge point mastery table according to the mastery situation of each knowledge point.

[0015] Preferably, when the record retrieval subunit fails to retrieve the computer historical data and the student refuses to perform the application operation assessment, the analysis and construction subunit also determines the student's current learning situation according to the student's ability information.

[0016] Preferably, the analysis and construction subunit determining the current learning situation further includes:

[0017] Generating a student identification parameter set based on the ability information;

[0018] Matching the student identification parameter set with the student identification parameter sets of other students on the big data platform;

[0019] Retrieving the knowledge point mastery table corresponding to the matched other students;

[0020] Converting the knowledge point mastery table into a description set, statistically analyzing the description set, positioning the description set according to the statistical results, and converting it into a knowledge point mastery table as the current learning situation.

[0021] Preferably, the teaching plan auxiliary arrangement unit includes: a time allocation subunit, a teaching plan content search subunit, and an arrangement subunit;

[0022] Among them, the time allocation subunit allocates the teaching time of each knowledge point according to the mastery degree of each knowledge point in the course content; the teaching plan content search subunit searches for the corresponding teaching plan content from the big data platform according to the knowledge points and the allocated teaching time; the arrangement subunit arranges the searched teaching plan content according to the teaching template configured for the course.

[0023] Preferably, the teaching process monitoring unit includes: a remote screen monitoring subunit, a network monitoring subunit, a remote resource monitoring subunit, a remote disk monitoring subunit, and a remote information management subunit.

[0024] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification and the drawings.

[0025] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0026] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0027] Figure 1 It is a schematic diagram of a computer-aided teaching system based on big data in an embodiment of the present invention;

[0028] Figure 2 Schematic diagram for the analysis and construction subunit to determine the current learning situation in the embodiment of the present invention;

[0029] Figure 3 Schematic diagram of the teaching process monitoring unit in the embodiment of the present invention. Detailed implementation manners

[0030] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0031] The embodiment of the present invention provides a computer-aided teaching system based on big data, as Figure 1 shown, including: a student analysis unit 1, a course analysis unit 2, a teaching plan auxiliary arrangement unit 3, a teaching process monitoring unit 4, and a teaching evaluation unit 5;

[0032] Among them, the student analysis unit analyzes the computer historical learning data and / or computer application operation evaluation data of students by using big data analysis to determine the current learning situation; the course analysis unit analyzes the course content based on the current learning situation of students to determine the mastery degree of each knowledge point in the course content; the teaching plan auxiliary arrangement unit obtains the corresponding teaching plan content from the big data platform according to the mastery degree of each knowledge point in the course content and assists in arranging the teaching plan; the teaching process monitoring unit obtains the operation records of the teacher side and the student side and the video records in the classroom during the teaching process to monitor the teaching process; the teaching evaluation unit conducts teaching evaluation based on the monitoring data of the teaching process monitoring unit.

[0033] The working principle and beneficial effects of the above technical solution are as follows:

[0034] The computer-aided teaching system based on big data of the present invention first analyzes the computer historical learning data and / or computer application operation evaluation data of students to understand the computer level of students who will participate in the course, and then analyzes the course on this basis to determine the mastery degree of each knowledge point in the course content by students, and rationally allocates the teaching time of each knowledge point in the course content, thereby assisting teachers in arranging teaching plans, monitoring the teaching process of the course, and evaluating the teaching effect afterwards, realizing the supervision and assistance of the whole process of computer teaching, and realizing scientific computer teaching. Among them, using big data in the analysis process of students can improve the accuracy of the analysis results; when assisting in arranging teaching plans, a large amount of data can be screened from the big data platform to ensure that appropriate teaching plan content is selected, ensuring the reliability of the arrangement of teaching plans.

[0035] In one embodiment, the student analysis unit includes: a record retrieval subunit, an application operation evaluation subunit, and an analysis and construction subunit;

[0036] Among them, the record retrieval subunit retrieves the computer historical learning data of students stored in the local database and / or the big data platform; it can be retrieved after searching and querying using identity identification information such as the student's name and student number;

[0037] The application operation evaluation subunit is used to enter the application evaluation mode when receiving a student's application operation evaluation request, and obtain the operation data of the student in the application evaluation mode as computer application operation evaluation data; that is, when receiving a request for a student's active evaluation, the application operation evaluation subunit retrieves the corresponding operation questions from the pre-configured operation question bank according to the student's ability information and outputs them; in addition, when the application operation evaluation subunit fails to retrieve the corresponding operation questions from the operation question bank based on the ability information, it retrieves the pre-configured operation question group; among them, the ability information includes: age, major, assessment scores of each course, names and numbers of the courses already studied, honorary awards, etc.; retrieving the corresponding operation questions from the pre-configured operation question bank according to the ability information and outputting them specifically means: through the pre-configured parameter extraction and quantification library, screening and extracting each ability item in the ability information, and quantifying the information of the extracted ability items to obtain ability parameter values, and then arranging the ability parameters in sequence to the corresponding positions in the ability item extraction template to generate a retrieval parameter set, and matching the retrieval parameter set with the question parameter sets corresponding to each operation question in the operation question bank, and retrieving the corresponding operation questions according to the matching result and outputting them. The operation question bank is pre-configured, and each operation question configured by professionals is stored in the operation question bank, and a question parameter set adapted to the retrieval parameter set is constructed based on the ability parameters corresponding to the ability item extraction template of the parameter extraction and quantification library; the operation question group is also pre-configured, and is composed of various operation questions arranged in sequence from simple to complex for each knowledge point of the computer, and is output in sequence according to the arrangement order of the questions when retrieving the operation question group.

[0038] The analysis and construction subunit analyzes the computer historical learning data retrieved by the record retrieval subunit and / or the computer application operation evaluation data generated by the application operation evaluation subunit to determine the current learning situation.

[0039] Among them, the analysis and construction subunit determining the current learning situation includes:

[0040] Based on the knowledge points in the computer history learning data, the corresponding instance operation situations and / or test paper answering situations, fill in the pre-configured knowledge point details table with data to obtain a knowledge point mastery table representing the current learning situation. Among them, the instance operation situation and / or test paper answering situation can be represented by specific score values. For example: below 60 points means not mastered, 60 - 70 means average mastery, 70 - 80 means good mastery, 80 - 90 means excellent mastery, 90 - 100 means perfect mastery, etc. When a knowledge point requires both practical operation and test paper answering, the comprehensive score can be calculated through a pre-configured weighted sum formula as the representation of the mastery situation; when filling, when there are multiple records of the same knowledge point in the calculation set of historical learning records, fill with the record that is the most recent and / or in the best situation; preferably, when the mastery situation increases successively, fill with the record that is the most recent and in the best situation; when the mastery situation fluctuates, use the records of the most recent N (any value from 2 to 10) times, construct a mastery situation evaluation vector in chronological order, and then through a pre-configured mastery situation analysis library, retrieve the mastery situation associated with the standard vector matching the mastery situation evaluation vector in the mastery situation analysis library as the filling basis;

[0041] In order to analyze the mastery situation of students after evaluation through active operation evaluation, the analysis and construction subunit determines the current learning situation including:

[0042] Based on the knowledge points corresponding to each test question and the test paper answering situation in the computer application operation evaluation data, fill in the pre-configured knowledge point details table with data to obtain a knowledge point mastery table representing the current learning situation.

[0043] In addition, to ensure the integrity of the knowledge point mastery table, effective filling can be carried out through the association relationship of knowledge points, that is, the analysis and construction subunit also supplements the mastery situation of the pre-requisite knowledge points associated with each knowledge point in the knowledge point mastery table according to the mastery situation of each knowledge point. The specific supplement can be through a supplement table associated with this knowledge point. According to the mastery situation, look up the mastery situation of the associated pre-requisite knowledge points in the table and then fill in; for example: when the mastery situation of this knowledge point in the supplement table is lower than a pre-configured first threshold (any value from 20 to 60), use the value corresponding to the mastery situation of this knowledge point as the mastery situation of the pre-requisite knowledge point.

[0044] In order to handle the evaluation of a student when the student is not stored in both the local database and the big data platform and the student refuses to perform the operation evaluation, that is, when the record retrieval subunit fails to retrieve the computer historical data and the student refuses to perform the application operation evaluation, the analysis and construction subunit also determines the current learning situation of the student according to the student's ability information.

[0045] Among them, asFigure 2 As shown in Figure 2 , the analysis and construction subunit's determination of the current learning situation further includes:

[0046] Step S1: Generate a student identification parameter set based on the ability information;

[0047] Step S2: Match the student identification parameter set with the student identification parameter sets of other students on the big data platform; the cosine similarity calculation method can be used to calculate the similarity, and a similarity greater than a preset similarity threshold (for example, any value between 0.8 and 0.95) is used as the basis for the matching judgment;

[0048] Step S3: Retrieve the knowledge point mastery table corresponding to the matched other students;

[0049] Step S4: Convert the knowledge point mastery table into a description set, perform statistical analysis on the description set, locate the description set according to the statistical results and convert it into a knowledge point mastery table as the current learning situation. One data in the description set represents the mastery situation of one knowledge point in a knowledge point mastery table.

[0050] Since the data on the big data platform is massive, in order to control the amount of data extracted, therefore, the generated student identification parameter set should be more comprehensive than the retrieval parameter set constructed based on the same ability information. That is, after quantifying each ability item in the ability information, fill it into the corresponding student identification parameter set template. In addition, data such as the student's age and the geographical location at each learning stage can be quantified and filled into the template to obtain a comprehensive student identification parameter set.

[0051] The specific statistical analysis of the description set is as follows: Calculate the similarity between the description sets. When the similarity is greater than the preset similarity threshold, it is recorded that the corresponding description set appears once, and locate the description set with the most occurrences.

[0052] In addition, when the total number of knowledge point mastery tables retrieved in step S3 is less than the preset quantity (any one between 100 and 1000), the knowledge point mastery tables at the historical moments of other students in the big data platform can be used to match and supplement the student identification parameter set data. The supplement order during supplementation is determined by the priority value, and the supplementation is carried out in the order from large to small according to the priority value. The analysis and determination steps of the priority value are as follows: According to the time difference between the historical moment and the current moment, query the pre-configured first numerical table to determine the first numerical value; according to the similarity of the student identification parameter set, query the pre-configured second numerical table to determine the second numerical value; the sum of the first numerical value and the second numerical value is used as the priority value; sorting and supplementing the supplementary data according to the priority value ensures the effectiveness of determining the current learning situation.

[0053] In one embodiment, the teaching plan auxiliary arrangement unit includes: a time allocation subunit, a teaching plan content search subunit, and an arrangement subunit;

[0054] Among them, the time allocation subunit allocates the teaching time of each knowledge point according to the mastery degree of each knowledge point in the course content; the teaching plan content search subunit searches for the corresponding teaching plan content from the big data platform according to the knowledge point and the allocated teaching time; the sorting subunit sorts the searched teaching plan content according to the teaching template configured for the course.

[0055] When the time allocation subunit allocates the teaching time, to ensure that each knowledge point of the course can be covered, it is necessary to first ensure the minimum time value configured for each knowledge point, and then allocate the remaining time. The allocation rule is to allocate proportionally according to the allocation coefficient of each knowledge point. The steps for determining the allocation coefficient are as follows: extract the characteristic parameters of the students' mastery degree, construct a quantization parameter set based on the extracted characteristic values, and then determine the quantization value corresponding to the quantization parameter set according to the preset quantization analysis library; take the product of the quantization value and the weight coefficient corresponding to the knowledge point as the allocation coefficient. Among them, the quantization parameter set includes parameter data representing the maximum value, minimum value, average value, variance, and total of the mastery degree; the quantization analysis library is pre-analyzed and constructed by professionals, and the quantization parameter set in the library is associated with the quantization value; the weight coefficient is pre-configured according to the difficulty level of the knowledge point. The more difficult the knowledge point, the greater the weight coefficient.

[0056] The teaching plan content search subunit searches for the corresponding teaching plan content from the big data platform according to the knowledge point and the allocated teaching time, including: constructing a first search parameter set and a second search parameter set respectively according to the knowledge point code corresponding to each knowledge point and the allocated teaching time; first searching for the overall teaching plan according to the first search parameter set; then performing a secondary search based on the search result of the second search parameter set to obtain the target teaching plan; when the second parameter set corresponding to the target teaching plan is the same as the second search parameter set, take the target teaching plan as the final result of the search; when the second parameter set corresponding to the target teaching plan is not completely the same as the second search parameter set, determine the difference data and the knowledge point code corresponding to the difference data; search for the teaching plan data corresponding to a single knowledge point on the big data platform based on the knowledge point code and the difference data to obtain multiple search data; use the search data and the data of this knowledge point in the teaching plan as the data group to be selected; obtain the uploader and upload terminal of the data to be selected; determine the relevance between each data to be selected in the data group to be selected and the target teaching plan based on the distance between the uploader and the upload terminal, determine the first competition value based on the relevance, and query the preset second competition value table based on the difference between the teaching time corresponding to each data to be selected and the allocated teaching time to determine the second competition value; replace the teaching plan data corresponding to the knowledge point in the target teaching plan with the data to be selected with the largest sum of the first competition value and the second competition value to generate the final result of the search.

[0057] Among them, the knowledge point code is a unique identification code pre-configured for each knowledge point. Based on the distance between the uploader and the upload terminal, the relevance between each piece of data to be selected in the data group to be selected and the target teaching plan is determined, and the first competition value is determined based on the relevance, including:

[0058] Feature extraction is performed on the personal information of the uploader and the distance between the upload terminals. The extracted feature values include: the feature code representing the name of the unit to which the person belongs, the feature code representing which preset interval range the distance value belongs to, etc.;

[0059] Then, based on the extracted feature values, the pre-configured first competition value table is queried to extract the first competition value.

[0060] In order to implement the monitoring of the teaching process, in one embodiment, as Figure 3 shown, the teaching process monitoring unit includes: a remote screen monitoring subunit, a network monitoring subunit, a remote resource monitoring subunit, a remote disk monitoring subunit, and a remote information management subunit. The teaching process monitoring unit performs screenshot monitoring on the screens of the teacher terminal or the student terminal through the remote screen monitoring subunit; the network monitoring subunit monitors the network transmission data of the teacher terminal or the student terminal; the remote resource monitoring subunit, the remote disk monitoring subunit, and the remote information management respectively monitor resources, disks, and information.

[0061] In one embodiment, the teaching evaluation unit conducts teaching evaluation based on the monitoring data of the teaching process monitoring unit; the teaching evaluation can start from two aspects: classroom performance and practical operation (examination). The classroom performance can analyze the classroom attention situation through the video in the monitoring data and the operation data of the operations performed on the computer, and then conduct a scoring evaluation.

[0062] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A computer teaching assistance system based on big data, characterized in that: include: Student analysis unit, course analysis unit, teaching plan auxiliary arrangement unit, teaching process monitoring unit and teaching evaluation unit; Among them, the student analysis unit uses big data analysis to analyze students' computer history learning data and / or computer application operation evaluation data to determine the current learning situation; the course analysis unit analyzes the course content based on the students' current learning situation to determine the degree of mastery of each knowledge point in the course content; the teaching plan auxiliary arrangement unit obtains the corresponding teaching plan content from the big data platform according to the degree of mastery of each knowledge point in the course content and assists in the arrangement of teaching plans; the teaching process monitoring unit obtains the operation records of the teacher and student ends and the video records in the teacher during the teaching process to monitor the teaching process; the teaching evaluation unit conducts teaching evaluation based on the monitoring data of the teaching process monitoring unit; Among them, the teaching plan auxiliary arrangement unit includes: time allocation subunit, teaching plan content search subunit and arrangement subunit; Among them, the time allocation subunit allocates the teaching time for each knowledge point according to the degree of mastery of each knowledge point in the course content; the teaching plan content search subunit searches for the corresponding teaching plan content from the big data platform according to the knowledge point and the allocated teaching time; the sorting subunit sorts the searched teaching plan content according to the teaching template configured for the course; When allocating teaching time, the time allocation subunit ensures that each knowledge point has a minimum time value, and then allocates the remaining time. The allocation rule is to allocate in equal proportion according to the allocation coefficient of each knowledge point. The steps for determining the allocation coefficient are as follows: extract characteristic parameters of the students' mastery level, build a quantitative parameter set based on the extracted characteristic values, and then determine the quantitative value corresponding to the quantitative parameter set based on the preset quantitative analysis library; the product of the quantitative value and the weight coefficient corresponding to the knowledge point is used as the allocation coefficient; wherein the quantitative parameter set includes: parameter data representing the maximum, minimum, average, variance and sum of the mastery level; the quantitative analysis library is constructed by professionals in advance, and the quantitative parameter set in the library is associated with the quantitative value; the weight coefficient is configured in advance; The teaching plan content search subunit searches for the corresponding teaching plan content from the big data platform based on the knowledge points and the allocated teaching time, including: constructing a first search parameter set and a second search parameter set respectively according to the knowledge point codes corresponding to each knowledge point and the allocated teaching time; firstly searching the teaching plan as a whole according to the first search parameter set; then performing a secondary search according to the search results of the second search parameter set to obtain the target teaching plan; when the second parameter set corresponding to the target teaching plan is the same as the second search parameter set, taking the target teaching plan as the final result of the search; when the second parameter set corresponding to the target teaching plan is not completely the same as the second search parameter set, determining the difference data and the knowledge point codes corresponding to the difference data; based on the knowledge point codes and the difference data The teaching plan data corresponding to a single knowledge point on the big data platform is searched to obtain a plurality of search data; the search data and the data of the knowledge point in the teaching plan are used as a data group to be selected; the uploader and the upload terminal of the data to be selected are obtained; based on the distance between the uploader and the upload terminal, the correlation between each data to be selected in the data group to be selected and the target teaching plan is determined, and a first competitive value is determined based on the correlation, and based on the difference between the teaching time corresponding to each data to be selected and the allocated teaching time, a preset second competitive value table is queried to determine the second competitive value; the teaching plan data corresponding to the knowledge point in the target teaching plan is replaced with the data to be selected with the maximum sum of the first competitive value and the second competitive value, so as to generate a final result of the search.

2. The computer-aided teaching system based on big data according to claim 1 is characterized in that: The student analysis unit includes: record retrieval subunit, application operation evaluation subunit, and analysis construction subunit; The analysis and construction subunit analyzes the computer history learning data retrieved by the record and retrieval subunit and / or the computer application operation evaluation data generated by the application operation evaluation subunit to determine the current learning situation.

3. The computer-aided teaching system based on big data according to claim 2 is characterized in that: When receiving a student's active assessment request, the application operation assessment sub-unit retrieves and outputs the corresponding operation test questions from the pre-configured operation question bank based on the student's ability information; when the application operation assessment sub-unit fails to retrieve the corresponding operation test questions from the operation question bank based on the ability information, it retrieves the pre-configured operation test question group.

4. The computer-aided teaching system based on big data according to claim 2 is characterized in that: Analyze the subunits to determine the current learning situation including: According to the key knowledge points in the computer history learning data and the corresponding instance operation conditions and / or test answer conditions, the pre-configured knowledge point details table is filled with data to obtain a knowledge point mastery table representing the current learning situation.

5. The computer-aided teaching system based on big data according to claim 2, characterized in that: Analyze the subunits to determine the current learning situation including: According to the knowledge points and test answer status corresponding to each test question in the computer application operation assessment data, the pre-configured knowledge point details table is filled with data to obtain a knowledge point mastery table representing the current learning situation.

6. The computer-aided teaching system based on big data according to claim 4 or 5, characterized in that: The analysis and construction sub-unit also supplements the mastery of the associated prerequisite knowledge points based on the mastery of each knowledge point in the knowledge point mastery table.

7. The computer-aided teaching system based on big data according to claim 2, characterized in that: When the record retrieval subunit fails to retrieve computer history data and the student refuses to conduct application operation assessment, the analysis and construction subunit also determines the student's current learning situation based on the student's ability information.

8. The computer-aided teaching system based on big data according to claim 7, characterized in that: Analyzing the construction subunits to determine the current learning situation also includes: Generate a set of student identification parameters based on the ability information; matching the student identification parameter set with the student identification parameter sets of various other students on the big data platform; Retrieve the knowledge points mastery table corresponding to other matching students; The knowledge point mastery table is converted into a description set, and the description set is statistically analyzed. According to the statistical results, the description set is located and converted into a knowledge point mastery table as the current learning situation.

9. The computer-aided teaching system based on big data according to claim 1, characterized in that: The teaching process monitoring unit includes: a remote screen monitoring subunit, a network monitoring subunit, a remote resource monitoring subunit, a remote disk monitoring subunit and a remote information management subunit.

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