An online teaching method based on big data

Abstract

The present invention relates to the field of online teaching technology, and discloses an online teaching method based on big data, comprising the following steps: S1: collecting learning data D of students on an online learning platform; S2: real-time processing and analysis of the learning data D to generate a learning status evaluation S of the students. By collecting and analyzing the learning data of students on the online learning platform, real-time evaluation and dynamic adjustment of the students' learning status are achieved. The method can timely identify problems and difficulties encountered by students in the learning process, and then provide personalized intervention measures, effectively improving teaching effectiveness and students' learning experience. First, through steps S1 and S2, multi-dimensional learning data of students are collected and analyzed, covering multiple factors such as learning progress, participation, feedback, homework submission, test scores, course viewing time, and class participation. By using these data, students' learning behavior and status can be fully understood, laying the foundation for personalized teaching.

Description

Technical Field

[0001] The present invention relates to the field of online teaching technology, and in particular to an online teaching method based on big data. Background Art

[0002] With the rapid development of information technology, online education has gradually become an integral part of modern education. However, traditional online teaching methods often lack personalized learning support for individual students. Traditional online teaching relies primarily on standardized course content and a fixed teaching pace, failing to respond to students' learning needs and feedback in real time. This approach can cause some students to fall behind due to inability to keep up with the course progress. It can also lead to a waste of learning resources, as not all students require the same learning content and teaching methods. Furthermore, teachers struggle to accurately grasp each student's learning status and level of understanding, hindering their ability to provide timely and effective help and guidance.

[0003] Existing online teaching systems have the following main problems: lack of real-time response capabilities and inability to quickly adjust teaching strategies; insufficient personalization, only simple content adjustments, and inability to deeply understand students' learning behaviors; limited predictive capabilities, making it difficult to accurately identify and provide early warnings of difficulties students may encounter; in addition, these systems increase the burden on teachers, requiring manual analysis of students' learning situations and adjustment of teaching strategies, which affects teaching efficiency and effectiveness. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an online teaching method based on big data, which solves the problems of poor real-time response, insufficient personalization, limited predictive ability and increased burden on teachers in the existing online teaching system.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an online teaching method based on big data, comprising the following steps:

[0006] S1: Collect students’ learning data D on the online learning platform;

[0007] S2: Process and analyze the learning data D in real time to generate a student's learning status evaluation S;

[0008] S3: Adjust teaching strategy T based on learning status assessment S;

[0009] S4: Build a learning behavior prediction model to predict students’ future learning performance

[0010] S5: Based on the prediction results Generate early warning signal W;

[0011] S6: Provide personalized interventions, including customized learning paths and real-time feedback;

[0012] S7: Use supervised learning algorithms to train learning behavior prediction models and optimize model parameters.

[0013] Preferably, the S1 includes:

[0014] Collect students’ learning data D={d1,d2,......,d n}, including learning progress, engagement, feedback, assignment submissions, quiz grades, course viewing time, and class participation.

[0015] Preferably, the S2 includes:

[0016] The learning data D is processed in real time to generate the student's learning status evaluation S, which is expressed by the following formula:

[0017]

[0018] Among them, S i is the learning status evaluation of the i-th student, wik is the weight of data type k, dik is the k-th data indicator of the i-th student, and m is the number of data types;

[0019] The learning data D is standardized and the standardization formula is:

[0020]

[0021] Among them, d' ik is the standardized data, μk is the mean of data type k, and σk is the standard deviation of data type k.

[0022] Preferably, the S3 includes:

[0023] Adjust the teaching strategy T based on the learning status evaluation S. The adjustment formula is:

[0024] T=f(S)

[0025] Where f is the teaching strategy adjustment function, which dynamically adjusts the teaching content and rhythm according to S;

[0026] Dynamically adjust the difficulty of course content C based on learning status evaluation S d , the adjustment formula is:

[0027] C d =α1S i +α2

[0028] Among them, α1 and α2 are adjustment coefficients;

[0029] Adjust interaction frequency I f , through the formula:

[0030] I f =γ1S i +γ2

[0031] Where γ1 and γ2 are the interaction frequency adjustment coefficients.

[0032] Preferably, the S4 includes:

[0033] Construct a learning behavior prediction model P to predict students' future learning performance The prediction model formula is:

[0034]

[0035] in is the predicted learning performance, β0 is the model constant term, β j For learning status assessment, S j The coefficient of , P is the number of evaluation variables;

[0036] When the predicted value When it is lower than the predetermined threshold θ, an early warning signal W is generated, where W-1 indicates that the early warning is triggered, and W=0 indicates that the early warning is not triggered.

[0037] Preferably, the S5 includes:

[0038] Provide customized learning path L, based on learning status assessment S and predicted results The path selection formula is:

[0039]

[0040] Where ι is the set of all optional learning paths, and u is the path utility function;

[0041] Provide real-time feedback Where h is the feedback generation function, which generates feedback information based on the learning state and prediction results.

[0042] Preferably, the S7 includes:

[0043] Use supervised learning algorithms to train historical learning data and optimize model parameters by minimizing the loss function. The loss function is:

[0044]

[0045] Among them, N is the number of samples, yk is the actual learning result, Predict the results for the model.

[0046] Beneficial effects

[0047] The present invention provides an online teaching method based on big data. Compared with the existing technology, it has the following advantages:

[0048] In the present invention, by collecting and analyzing students' learning data on the online learning platform, real-time evaluation and dynamic adjustment of students' learning status are achieved. This method can timely identify students' problems and difficulties in the learning process, and then provide personalized intervention measures to effectively improve teaching effects and students' learning experience. First, through steps S1 and S2, students' multi-dimensional learning data are collected and analyzed, covering various factors such as learning progress, participation, feedback, homework submission, test scores, course viewing time, and class participation. Using these data, students' learning behavior and status can be fully understood, laying the foundation for personalized teaching. Secondly, in step S3, the teaching strategy is adjusted based on the results of the learning status assessment, so that the teaching content and rhythm are more in line with the actual needs of students. This dynamic adjustment The teaching method can avoid a one-size-fits-all teaching model and ensure that each student can learn at the pace and difficulty that best suits them, which helps improve learning outcomes. Thirdly, the learning behavior prediction model constructed through steps S4 and S5 can foresee students' future learning performance and generate early warning signals. This function enables teachers to take intervention measures in advance to prevent students from falling behind in their studies. Through real-time prediction and early warning, preventive intervention can be carried out for potential learning difficulties, thereby improving the foresight and effectiveness of teaching. Finally, the personalized learning path and real-time feedback provided by steps S6 and S7, combined with the learning behavior prediction model optimized by the supervised learning algorithm, enable the system to provide customized learning plans and instant feedback based on students' personalized needs, further enhancing students' learning experience and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flowchart of an online teaching method based on big data proposed by the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] See also Figure 1 The present invention provides two technical solutions, specifically including the following embodiments:

[0052] Example:

[0053] An online teaching method based on big data includes the following steps:

[0054] S1: Collect students' learning data D on the online learning platform. This data is automatically collected through the online learning platform. This data reflects students' learning behavior from multiple dimensions and provides a basis for subsequent analysis and processing.

[0055] S2: Real-time processing and analysis of learning data D to generate a student's learning status assessment S. Data analysis algorithms are used to process the collected learning data in real time, generating a comprehensive learning status assessment through methods such as weighted summation. Data standardization eliminates scale differences between different data types.

[0056] S3: Adjust the teaching strategy T based on the learning status assessment S. Based on the learning status assessment results, the adjustment function is used to dynamically adjust the teaching content and pace. Difficulty adjustment and interaction frequency adjustment ensure that the teaching strategy can adapt to the student's actual learning situation.

[0057] S4: Build a learning behavior prediction model to predict students’ future learning performance Build a learning behavior prediction model to predict students' future learning performance through linear regression or other machine learning algorithms. If the predicted result falls below a predetermined threshold, the system generates an early warning signal to alert the teacher.

[0058] S5: Based on the prediction results Generates an early warning signal W. The system provides personalized learning paths and real-time feedback based on the prediction results and learning status assessment. The path selection formula and feedback generation formula ensure that each student receives support that meets their learning needs.

[0059] S6: Provide personalized interventions, including customized learning paths and real-time feedback;

[0060] S7: Use supervised learning algorithms to train learning behavior prediction models and optimize model parameters.

[0061] S1 includes:

[0062] Collect students’ learning data D={d1,d2,......,d n}, including learning progress, participation, feedback, homework submission, test scores, course viewing time and class participation. The specific details of learning progress, participation, feedback, homework submission, test scores, course viewing time and class participation are:

[0063] Learning progress: the course chapters or modules completed by the student;

[0064] Participation: students’ participation in discussion forums and interactive sessions;

[0065] Feedback: student evaluation of course content or teaching methods;

[0066] Assignment submission status: submission time, completion quality, etc.;

[0067] Test scores: scores of regular tests and their changing trends;

[0068] Course viewing time: the time and duration of video viewing;

[0069] Class participation: the level of activity in the online class, including the number of questions asked and the frequency of interaction;

[0070] S2 includes:

[0071] The learning data D is processed in real time to generate the student's learning status evaluation S, which is expressed by the following formula:

[0072]

[0073] Among them, S i is the learning status evaluation of the i-th student, wik is the weight of data type k, dik is the k-th data indicator of the i-th student, and m is the number of data types;

[0074] The learning data D is standardized and the standardization formula is:

[0075]

[0076] Among them, d' ik is the standardized data, μk is the mean of data type k, and σk is the standard deviation of data type k.

[0077] S3 includes:

[0078] Adjust the teaching strategy T based on the learning status evaluation S. The adjustment formula is:

[0079] T=f(S)

[0080] Where f is the teaching strategy adjustment function, which dynamically adjusts the teaching content and rhythm according to S;

[0081] Dynamically adjust the difficulty of course content C based on learning status evaluation S d , the adjustment formula is:

[0082] C d =α1S i +α2

[0083] Among them, α1 and α2 are adjustment coefficients;

[0084] Adjust interaction frequency I f , through the formula:

[0085] If =γ1S i +γ2

[0086] Where γ1 and γ2 are the interaction frequency adjustment coefficients.

[0087] S4 includes:

[0088] Construct a learning behavior prediction model P to predict students' future learning performance The prediction model formula is:

[0089]

[0090] in is the predicted learning performance, β0 is the model constant term, β j For learning status assessment, S j The coefficient of , P is the number of evaluation variables;

[0091] When the predicted value When it is lower than the predetermined threshold θ, an early warning signal W is generated, where W-1 indicates that the early warning is triggered, and W=0 indicates that the early warning is not triggered.

[0092] S5 includes:

[0093] Provide customized learning path L, based on learning status assessment S and predicted results The path selection formula is:

[0094]

[0095] Where ι is the set of all optional learning paths, and u is the path utility function;

[0096] Provide real-time feedback Where h is the feedback generation function, which generates feedback information based on the learning state and prediction results.

[0097] S7 includes:

[0098] Use supervised learning algorithms to train historical learning data and optimize model parameters by minimizing the loss function. The loss function is:

[0099]

[0100] Among them, N is the number of samples, yk is the actual learning result, Predict the results for the model.

[0101] When working, it mainly evaluates students' learning status in real time by collecting and analyzing students' behavioral data on the online learning platform, and adjusts teaching strategies accordingly to provide personalized learning experience. The working principle of this method mainly includes five core steps: data collection, data processing, teaching strategy adjustment, learning behavior prediction and personalized intervention. First, in the data collection stage (S1), the system automatically collects students' learning data through the online learning platform. These data include but are not limited to learning progress, participation, feedback, homework submission, test scores, course viewing time and class participation. These data reflect students' learning behavior from multiple dimensions and provide a basis for subsequent analysis and processing. Next, in the data processing and learning status evaluation stage (S2), the system uses data analysis algorithms to process the collected learning data in real time. Different types of data are combined through methods such as weighted summation to generate a comprehensive learning status evaluation S. The formula is Among them, S i is the learning status evaluation of the i-th student, wik is the weight of data type k, dik is the score of the i-th student, and m is the number of data types. Data standardization is performed through the formula In order to eliminate the scale differences between different data types, in the teaching strategy adjustment stage (S3), the system uses the adjustment function T = f (S) to dynamically adjust the teaching content and rhythm based on the learning status evaluation result S. For difficulty adjustment, the system uses formula C d =α1S i +α2, calculate the difficulty of the course content, and adjust the interaction frequency through formula I f =γ1S i +γ2, these adjustments ensure that the teaching strategy can adapt to the students' actual learning situation and provide personalized teaching content. In the learning behavior prediction stage (S4), the system builds a learning behavior prediction model P to predict students' future learning performance. By using linear regression or other machine learning algorithms, the model calculates the predicted value in is the predicted learning performance, β0 is the model constant term, β j For learning status assessment, S j The coefficient of P is the number of evaluation variables. If the prediction result When the learning rate falls below the predetermined threshold θ, the system generates an early warning signal W to alert the teacher. Finally, in the personalized intervention stage (S5 and S6), the system provides personalized learning paths and real-time feedback based on the prediction results and learning status assessment. The path selection formula is: The real-time feedback generation formula is: This ensures that every student receives support tailored to their learning needs. By optimizing model parameters through supervised learning algorithms, the accuracy and reliability of the prediction model are further improved, thereby continuously improving personalized teaching results. In short, this invention integrates big data analysis, machine learning, and educational technology to build an intelligent online teaching system, enabling personalized teaching management and real-time learning status monitoring, providing comprehensive technical support for online education.

[0102] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements and improvements made within the spirit and principles of the application should be included in the scope of protection of the present application.

Claims

1. An online teaching method based on big data, characterized by: The following steps are involved: S1: Collect students’ learning data D on the online learning platform; S2: Process and analyze the learning data D in real time to generate a student's learning status evaluation S; S3: Adjust teaching strategy T based on learning status assessment S; S4: Build a learning behavior prediction model to predict students’ future learning performance S5: Based on the prediction results Generate early warning signal W; S6: Provide personalized interventions, including customized learning paths and real-time feedback; S7: Use supervised learning algorithms to train learning behavior prediction models and optimize model parameters; The S4 includes: Construct a learning behavior prediction model P to predict students' future learning performance The prediction model formula is: in is the predicted learning performance, β0 is the model constant term, β j For learning status assessment, S j The coefficient of , P is the number of evaluation variables; When the predicted value When it is lower than the predetermined threshold θ, a warning signal W is generated, where W-1 indicates that the warning is triggered, and W=0 indicates that the warning is not triggered; The S5 includes: Provide customized learning path L, based on learning status assessment S and predicted results The path selection formula is: Where ι is the set of all optional learning paths, and u is the path utility function; Provide real-time feedback Where h is the feedback generation function, which generates feedback information based on the learning state and prediction results.

2. The online teaching method based on big data according to claim 1, characterized in that: Said S1 comprises: Collect students’ learning data D={d1,d2,......,d n }, including learning progress, engagement, feedback, assignment submissions, quiz grades, course viewing time, and class participation.

3. The online teaching method based on big data according to claim 1, characterized in that: The S2 includes: The learning data D is processed in real time to generate the student's learning status evaluation S, which is expressed by the following formula: Among them, S i is the learning status evaluation of the i-th student, wik is the weight of data type k, dik is the k-th data indicator of the i-th student, and m is the number of data types; The learning data D is standardized and the standardization formula is: Among them, d' ik is the standardized data, μk is the mean of data type k, and σk is the standard deviation of data type k.

4. The online teaching method based on big data according to claim 1, characterized in that: The S3 includes: Adjust the teaching strategy T based on the learning status evaluation S. The adjustment formula is: T=f(S) Where f is the teaching strategy adjustment function, which dynamically adjusts the teaching content and rhythm according to S; Dynamically adjust the difficulty of course content C based on learning status evaluation S d , the adjustment formula is: C d =α1S i +a2 Among them, α1 and α2 are adjustment coefficients; Adjust interaction frequency I f , through the formula: I f =γ1S i +γ2 Where γ1 and γ2 are the interaction frequency adjustment coefficients.

5. The online teaching method based on big data according to claim 1, characterized in that: The S7 includes: Use supervised learning algorithms to train historical learning data and optimize model parameters by minimizing the loss function. The loss function is: Among them, N is the number of samples, yk is the actual learning result, Predict the results for the model.

Images