Teaching effect instant feedback evaluation system based on automatic data verification

Abstract

The invention relates to the field of teaching evaluation, in particular to a teaching effect instant feedback evaluation system based on automatic data verification, which comprises a surface layer data acquisition module, a data representation mining module, an effect evaluation module, a data verification module and an instant feedback module. According to the method, key teaching effect influence factors are extracted from teaching result data in a concentrated manner and quantified, so that the evaluation data can more accurately reflect a real and complex teaching process and effect, after an evaluation result is obtained, the evaluation process and result are automatically verified through dual rules of consistency verification and process reproduction verification, and the evaluation efficiency is improved. The logic conflict between the data and the conclusion can be effectively identified, the stability and reproducibility of the evaluation process can be verified, and the accuracy, reliability and anti-interference capability of the final teaching evaluation result can be obviously improved.

Description

Technical Field

[0001] This invention relates to the field of teaching evaluation, specifically to a real-time feedback evaluation system for teaching effectiveness based on automatic data verification. Background Technology

[0002] With the widespread application of information technology in education, various teaching platforms have been able to collect and store a large amount of teaching-related data, such as student attendance records, homework submissions, test scores, etc. Some teaching evaluation systems also generate teaching effectiveness evaluation reports by directly analyzing the raw data through direct statistics or simple weighting. These systems have achieved automation of teaching evaluation to a certain extent.

[0003] Nevertheless, while it has initially alleviated the problems of high subjectivity and low efficiency in traditional manual assessment, it still cannot effectively identify and quantify the key influencing factors hidden in the teaching process. In actual teaching feedback, due to the lack of an automatic verification mechanism for the validity of the assessment process and results, the rationality of the assessment logic and the consistency between data and conclusions cannot be guaranteed. There may be erroneous assessments due to data anomalies or model biases, affecting its credibility and practicality, resulting in a deviation between the assessment results and the actual teaching effect, and failing to provide timely and effective decision support for teachers to adjust their teaching strategies.

[0004] To address the aforementioned technical problems, this invention proposes a solution. Summary of the Invention

[0005] This invention extracts and quantifies key factors influencing teaching effectiveness from teaching outcome datasets, enabling the evaluation data to more accurately reflect the real and complex teaching process and results. After obtaining the evaluation results, it also uses two rules—consistency testing and process reproducibility testing—to automatically verify the evaluation process and results, effectively identifying logical conflicts between data and conclusions, and verifying the stability and reproducibility of the evaluation process. This significantly improves the accuracy, reliability, and anti-interference ability of the final teaching evaluation results, thus addressing the technical deficiencies mentioned in the background technology. Therefore, it proposes a real-time feedback evaluation system for teaching effectiveness based on automatic data verification.

[0006] The objective of this invention can be achieved through the following technical solution: a teaching effectiveness real-time feedback evaluation system based on automatic data verification, including a surface data acquisition module, wherein the surface data acquisition module collects teaching outcome-related data through a teaching platform, and organizes the collected teaching outcome-related data to obtain a teaching outcome dataset;

[0007] The data representation and mining module performs in-depth mining and analysis on the teaching outcome dataset to obtain the teaching effectiveness influencing factors represented by the teaching outcome data, and quantifies each teaching effectiveness influencing factor to obtain evaluation data.

[0008] The effectiveness evaluation module combines and analyzes the evaluation data to generate teaching evaluation results.

[0009] The data verification module obtains the teaching outcome dataset through the surface data acquisition module, obtains the teaching evaluation results through the effect evaluation module, and automatically verifies the accuracy of the teaching evaluation results through preset verification rules.

[0010] The instant feedback module receives the verified teaching evaluation results through the data verification module, and backs up and outputs the teaching evaluation results to the network.

[0011] In a preferred embodiment of the present invention, the teaching outcome-related data acquired by the surface data acquisition module includes:

[0012] Student attendance records, frequency of classroom interaction, homework submission status, study time, and test scores;

[0013] After acquiring relevant data on teaching outcomes, the surface data acquisition module performs noise reduction, missing value interpolation and completion, outlier detection and removal, and format quantization on the raw data, and records the processed data as a teaching outcome dataset.

[0014] In a preferred embodiment of the present invention, the data characterization and mining module performs in-depth mining and analysis on the teaching outcome dataset to obtain the effective teaching time and teaching mastery.

[0015] The data representation and mining module obtains the effective teaching time in the following way:

[0016] The system integrates student attendance records, classroom interaction frequency, and learning duration to obtain the single learning duration corresponding to each attendance in the student attendance record. It then records the classroom interaction frequency within the single learning duration corresponding to each attendance, generates weights based on the classroom interaction frequency, corrects the single learning duration, and accumulates the corrected single learning durations to obtain the effective teaching duration.

[0017] The data representation and mining module obtains the teaching mastery level in the following way:

[0018] The submission status of assignments and test scores are quantified to obtain the assignment completion rate and test accuracy. The assignment completion rate and test accuracy are weighted to obtain the knowledge mastery progress. The knowledge mastery progress obtained from multiple acquisitions is estimated using a tracking model to obtain the teaching mastery rate.

[0019] The data characterization and mining module records effective teaching time and teaching mastery as evaluation data.

[0020] In a preferred embodiment of the present invention, the step of the effect evaluation module performing combined analysis of the evaluation data is as follows:

[0021] S1: Obtain the effective teaching time and compare it with the preset total course time reference value to calculate the class completion rate index;

[0022] S2: Obtain the teaching mastery level and compare it with the preset knowledge mastery target threshold to calculate the knowledge achievement rate index;

[0023] S3: Based on the preset teaching objective weighting table, the class completion rate and knowledge attainment rate indicators are weighted and integrated to generate a comprehensive teaching effectiveness score;

[0024] S4: Match the overall teaching effectiveness score with the predefined evaluation level range and output the final teaching evaluation result.

[0025] In a preferred embodiment of the present invention, the data verification module performs the following verification rules for the teaching evaluation results: consistency verification and process reproducibility verification.

[0026] Specifically, the consistency test is as follows: select at least one data point from the teaching outcome dataset, generate a possible range of evaluation results based on a single data point, and then compare the teaching evaluation results with the possible range of evaluation results to obtain the judgment result;

[0027] The process reproducibility test is as follows: Select all data in the teaching outcome dataset, evaluate the teaching outcome through a pre-set simplified process, obtain the evaluation reproducibility result, compare the evaluation reproducibility result with the teaching evaluation result, and obtain the judgment result.

[0028] In a preferred embodiment of the present invention, when performing consistency verification, the data verification module takes the selected data as key data items and generates the possible range of evaluation results corresponding to the key data items according to the pre-set mapping rules between data items and evaluation results.

[0029] The teaching evaluation results are compared with the possible range of evaluation results: if the teaching evaluation results are within the possible range, the consistency test is deemed to have passed; if the teaching evaluation results exceed the possible range, the consistency test is deemed to have failed.

[0030] In a preferred embodiment of the present invention, when performing process reproduction verification, the data verification module records the generated evaluation reproduction results as a comprehensive teaching effectiveness score, restores the teaching evaluation results to a comprehensive teaching effectiveness score, obtains the score difference by comparison, compares the score difference with a set threshold, and if the score difference exceeds the threshold, the verification is deemed to have failed; if the score difference does not exceed the threshold, the verification is deemed to have passed.

[0031] In a preferred embodiment of the present invention, the data verification module determines that the teaching evaluation result verification is passed when both the consistency test and the process reproduction test are passed. If any item fails the consistency test or the process reproduction test, the teaching evaluation result verification is determined to be abnormal, and an early warning is automatically issued.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] 1. This invention performs in-depth mining and analysis of teaching outcome datasets, and can extract and quantify key teaching effectiveness influencing factors such as effective teaching time and teaching mastery from the raw data. It overcomes the limitations of existing technologies that only perform superficial statistics, and makes the evaluation data more accurately reflect the real and complex teaching process and effectiveness.

[0034] 2. This invention also introduces a data verification module, which automatically verifies the evaluation process and results through two preset rules: consistency test and process reproducibility test. This effectively identifies logical conflicts between data and conclusions, verifies the stability and reproducibility of the evaluation process, and significantly improves the accuracy, reliability and anti-interference ability of the final teaching evaluation results.

[0035] 3. The present invention also uses an instant feedback module to back up the evaluation results in real time and push them to relevant network terminals after the data verification is passed, so as to realize closed-loop management and instant response of teaching effectiveness evaluation. This helps teachers and teaching administrators to obtain reliable evaluation conclusions as soon as possible, quickly identify problems, optimize teaching strategies, and greatly improve the practical value of teaching evaluation and the decision-making efficiency of teaching management. Attached Figure Description

[0036] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0037] Figure 1 This is a system block diagram of the present invention;

[0038] Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0039] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1: Please refer to Figure 1 - Figure 2 As shown, the teaching effectiveness real-time feedback evaluation system based on automatic data verification includes a surface data acquisition module, a data representation and mining module, an effectiveness evaluation module, a data verification module, and a real-time feedback module. The surface data acquisition module collects teaching outcome-related data through the teaching platform. The teaching outcome-related data obtained by the surface data acquisition module includes: student attendance records, classroom interaction frequency, homework submission status, learning duration, and test scores.

[0041] After acquiring teaching outcome-related data, the surface data acquisition module organizes the acquired data, performing noise reduction, missing value interpolation and completion, outlier detection and removal, and format quantization on the raw data, and records the processed data as a teaching outcome dataset.

[0042] The data representation and mining module performs in-depth mining and analysis on the teaching outcome dataset to obtain the teaching effectiveness influencing factors represented by the teaching outcome data. These factors include effective teaching time and teaching mastery. Each teaching effectiveness influencing factor is then quantified to obtain evaluation data.

[0043] Specifically, the data representation and mining module obtains effective teaching time in the following ways:

[0044] The system integrates student attendance records, classroom interaction frequency, and learning duration to obtain the single learning duration corresponding to each attendance in the student attendance record. It then records the classroom interaction frequency within the single learning duration corresponding to each attendance, generates weights based on the classroom interaction frequency, corrects the single learning duration, and accumulates the corrected single learning durations to obtain the effective teaching duration.

[0045] The data representation and mining module obtains the teaching mastery level in the following ways:

[0046] The homework submission status and test scores are quantified to obtain homework completion rate and test accuracy. The homework completion rate and test accuracy are weighted to obtain the knowledge mastery progress. The knowledge mastery progress is estimated using a Bayesian knowledge tracing model through multiple acquisitions to obtain the teaching mastery rate.

[0047] The data representation and mining module records effective teaching time and teaching mastery as evaluation data.

[0048] The effectiveness evaluation module performs combined analysis on the evaluation data to generate teaching evaluation results. The specific steps of the combined analysis are as follows:

[0049] S1: Obtain the effective teaching time and compare it with the preset total course time reference value to calculate the class completion rate index;

[0050] S2: Obtain the teaching mastery level and compare it with the preset knowledge mastery target threshold to calculate the knowledge achievement rate index;

[0051] S3: Based on the preset teaching objective weighting table, the class completion rate and knowledge attainment rate indicators are weighted and integrated to generate a comprehensive teaching effectiveness score;

[0052] S4: Match the overall teaching effectiveness score with the predefined evaluation level range and output the final teaching evaluation result. For example, create three level ranges: poor, satisfactory, and excellent. Compare the overall teaching effectiveness score with the score range corresponding to each range to obtain the range to which it belongs as the final teaching evaluation result.

[0053] This invention automates and automates the entire process of teaching evaluation, from data collection to result output. The system integrates multi-dimensional raw data such as student attendance, interaction, homework, learning time and test scores through the surface data collection module, and performs standardized cleaning and quantitative processing to form a high-quality teaching outcome dataset.

[0054] In the in-depth data analysis phase, the data representation and mining module introduces two key influencing factors: effective teaching time and teaching mastery. The former is adjusted for learning time by weighting the frequency of interaction, which more realistically reflects the quality of teaching participation; the latter combines homework and test data and uses a knowledge tracking model to dynamically assess the progress of knowledge mastery, effectively overcoming the limitations of shallow data utilization and single indicators in traditional assessments. The effectiveness assessment module integrates and analyzes the above factors according to preset rules, and outputs structured teaching assessment results, making the assessment process interpretable and consistent.

[0055] Example 2: Please refer to Figure 1 - Figure 2 As shown, the data verification module obtains the teaching outcome dataset through the surface data acquisition module, obtains the teaching evaluation results through the effect evaluation module, and automatically verifies the accuracy of the teaching evaluation results through preset verification rules. The verification rules for the teaching evaluation results by the data verification module include: consistency test and process reproducibility test.

[0056] Specifically, the consistency test is as follows: Select at least one data item from the teaching outcome dataset, use the selected data item as the key data item, generate the possible range of evaluation results corresponding to the key data item according to the pre-set mapping rules between data items and evaluation results, and then compare the teaching evaluation results with the possible range of evaluation results. If the teaching evaluation results are within the possible range, the consistency test is deemed to have passed; if the teaching evaluation results exceed the possible range, the consistency test is deemed to have failed.

[0057] The process reproducibility test is as follows: Select all data in the teaching outcome dataset, evaluate the teaching outcome through a pre-set simplified process, obtain the evaluation reproducibility results, record the generated evaluation reproducibility results in the form of a comprehensive teaching effectiveness score, restore the teaching evaluation results to the form of a comprehensive teaching effectiveness score, compare the evaluation reproducibility results with the teaching evaluation results, obtain the score difference, compare the score difference with a set threshold, if the score difference exceeds the threshold, the test is deemed to have failed, if the score difference does not exceed the threshold, the test is deemed to have passed.

[0058] The data verification module determines that the teaching evaluation results are verified if both the consistency test and the process reproduction test are passed. If either the consistency test or the process reproduction test fails, the teaching evaluation results are determined to be abnormal, and an early warning will be automatically issued.

[0059] The instant feedback module receives the verified teaching evaluation results through the data verification module, and backs up and outputs the teaching evaluation results to the network.

[0060] To ensure the reliability of the evaluation results, a dual automated verification mechanism was designed: consistency testing ensures the logical consistency between data and conclusions, while process reproducibility testing verifies the stability and repeatability of the evaluation process. Upon successful verification, the instant feedback module pushes the evaluation results to relevant terminals in real time, forming a complete closed loop of data collection, analysis, verification, and feedback. This not only improves the objectivity, accuracy, and timeliness of teaching evaluation but also provides reliable data support and decision-making basis for the continuous optimization of the teaching process.

[0061] Thresholds, preset values, preset ranges, etc. are set for result comparison and analysis to determine whether they are good or bad. The value of these thresholds is determined by a combination of large-scale model analysis of sample data and human experience. They can also be adjusted appropriately based on seasonal or common-sense influences.

[0062] Furthermore, the settings for weighting ratios, influence factors, etc., are based on the magnitude of each parameter's influence on the results. The specific values ​​are allocated to ultimately reflect the impact on the results. The settings for input and storage are also determined by a combination of large-scale model analysis of sample data and human experience. Appropriate adjustments can also be made based on seasonal or rational influence conditions.

[0063] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A teaching effectiveness real-time feedback evaluation system based on automatic data verification, characterized in that: It includes a surface data acquisition module, which collects teaching outcome-related data through the teaching platform, organizes the collected teaching outcome-related data, and obtains a teaching outcome dataset. The data representation and mining module performs in-depth mining and analysis on the teaching outcome dataset to obtain the teaching effectiveness influencing factors represented by the teaching outcome data, and quantifies each teaching effectiveness influencing factor to obtain evaluation data. The effectiveness evaluation module combines and analyzes the evaluation data to generate teaching evaluation results. The data verification module obtains the teaching outcome dataset through the surface data acquisition module, obtains the teaching evaluation results through the effect evaluation module, and automatically verifies the accuracy of the teaching evaluation results through preset verification rules. The instant feedback module receives the verified teaching evaluation results through the data verification module, and backs up and outputs the teaching evaluation results to the network.

2. The real-time feedback evaluation system for teaching effectiveness based on automatic data verification according to claim 1, characterized in that, The teaching outcome-related data acquired by the surface data acquisition module includes: Student attendance records, frequency of classroom interaction, homework submission status, study time, and test scores; After acquiring relevant data on teaching outcomes, the surface data acquisition module performs noise reduction, missing value interpolation and completion, outlier detection and removal, and format quantization on the raw data, and records the processed data as a teaching outcome dataset.

3. The teaching effectiveness real-time feedback evaluation system based on automatic data verification according to claim 3, characterized in that, The data representation and mining module obtains the effective teaching time and teaching mastery after conducting in-depth mining and analysis of the teaching outcome dataset. The data representation and mining module obtains the effective teaching time in the following way: The system integrates student attendance records, classroom interaction frequency, and learning duration to obtain the single learning duration corresponding to each attendance in the student attendance record. It then records the classroom interaction frequency within the single learning duration corresponding to each attendance, generates weights based on the classroom interaction frequency, corrects the single learning duration, and accumulates the corrected single learning durations to obtain the effective teaching duration. The data representation and mining module obtains the teaching mastery level in the following way: The submission status of assignments and test scores are quantified to obtain the assignment completion rate and test accuracy. The assignment completion rate and test accuracy are weighted to obtain the knowledge mastery progress. The knowledge mastery progress obtained from multiple acquisitions is estimated using a tracking model to obtain the teaching mastery rate. The data characterization and mining module records effective teaching time and teaching mastery as evaluation data.

4. The teaching effectiveness real-time feedback evaluation system based on automatic data verification according to claim 1, characterized in that, The steps for the effect evaluation module to perform combined analysis of the evaluation data are as follows: S1: Obtain the effective teaching time and compare it with the preset total course time reference value to calculate the class completion rate index; S2: Obtain the teaching mastery level and compare it with the preset knowledge mastery target threshold to calculate the knowledge achievement rate index; S3: Based on the preset teaching objective weighting table, the class completion rate and knowledge attainment rate indicators are weighted and integrated to generate a comprehensive teaching effectiveness score; S4: Match the overall teaching effectiveness score with the predefined evaluation level range and output the final teaching evaluation result.

5. The real-time feedback evaluation system for teaching effectiveness based on automatic data verification according to claim 1, characterized in that, The data verification module uses the following verification rules for the teaching evaluation results: consistency verification and process reproducibility verification. Specifically, the consistency test is as follows: select at least one data point from the teaching outcome dataset, generate a possible range of evaluation results based on a single data point, and then compare the teaching evaluation results with the possible range of evaluation results to obtain the judgment result; The process reproducibility test is as follows: Select all data in the teaching outcome dataset, evaluate the teaching outcome through a pre-set simplified process, obtain the evaluation reproducibility result, compare the evaluation reproducibility result with the teaching evaluation result, and obtain the judgment result.

6. The teaching effectiveness real-time feedback evaluation system based on automatic data verification according to claim 5, characterized in that, When performing consistency checks, the data verification module uses the selected data as key data items and generates possible ranges of evaluation results corresponding to the key data items according to the pre-set mapping rules between data items and evaluation results. The teaching evaluation results are compared with the possible range of evaluation results: if the teaching evaluation results are within the possible range, the consistency test is deemed to have passed; if the teaching evaluation results exceed the possible range, the consistency test is deemed to have failed.

7. The teaching effectiveness real-time feedback evaluation system based on automatic data verification according to claim 5, characterized in that, When performing process reproduction verification, the data verification module records the generated evaluation reproduction results as a comprehensive teaching effectiveness score, restores the teaching evaluation results to a comprehensive teaching effectiveness score, obtains the score difference by comparison, and compares the score difference with a set threshold. If the score difference exceeds the threshold, the verification is deemed to have failed; if the score difference does not exceed the threshold, the verification is deemed to have passed.

8. The teaching effectiveness real-time feedback evaluation system based on automatic data verification according to claim 1, characterized in that, The data verification module determines that the teaching evaluation result verification is passed when both the consistency test and the process reproduction test are passed. If any item fails either the consistency test or the process reproduction test, the teaching evaluation result verification is determined to be abnormal, and an early warning will be automatically issued.

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