Effect evaluation method and system for online learning

Abstract

The invention relates to the technical field of learning effect evaluation, in particular to an effect evaluation method and system for online learning. The method comprises the following steps: acquiring an online operation behavior data set, analyzing the online operation behavior data set, and determining a weak subjective dimension effect index set; acquiring a physiological data set, analyzing the physiological data set, and determining a strong objective dimension effect index set; based on the strong objective dimension effect index set, analyzing the weak subjective dimension effect index set, and determining a deep learning effect evaluation result; and acquiring a learning feedback data set, analyzing the learning feedback data set based on the deep learning effect index evaluation result, and determining and outputting an online learning effect report. According to the method, the deep learning effect of the student in the online learning process is scientifically and comprehensively reflected through a multi-dimensional analysis means, and scientific suggestions are provided for the improvement of the subsequent learning plan of the student through the online learning effect report, so that the student is guided to find an online learning method suitable for the student, and the learning quality of the student is improved.

Description

Technical Field

[0001] The present application relates to the technical field of learning effectiveness evaluation, and in particular to an online learning effectiveness evaluation method and system. Background Art

[0002] With the popularization of the Internet and the advancement of technology, online learning has become a common way of learning. This model is not only convenient and flexible, allowing learners to acquire knowledge at any time and any place, but also provides educational institutions with a wider range of teaching and learning opportunities. Online learning effectiveness evaluation is an important means to ensure the quality of online learning.

[0003] However, the existing online learning effectiveness evaluation technology is based on the analysis method of online learning representation data, which makes it difficult to conduct a comprehensive and accurate assessment of the deep learning effectiveness of students in the online learning process, resulting in difficulty in significantly improving the quality of students' online learning, which in turn limits the development of students' high-order thinking ability during the online learning process. Summary of the invention

[0004] The present application provides an online learning effectiveness evaluation method and system to solve the above technical problems.

[0005] In a first aspect, the present application provides a method for evaluating the effectiveness of online learning, the method comprising:

[0006] Acquire an online operation behavior data set, analyze the online operation behavior data set, and determine a weak subjective dimension effectiveness indicator set;

[0007] Acquiring a physiological data set, analyzing the physiological data set, and determining a set of strong objective dimension performance indicators;

[0008] Based on the strong objective dimension effectiveness indicator set, the weak subjective dimension effectiveness indicator set is analyzed to determine the deep learning effectiveness evaluation result;

[0009] Acquire a learning feedback data set, analyze the learning feedback data set based on the deep learning effectiveness indicator evaluation result, and determine and output an online learning effectiveness report.

[0010] Through this scheme, the learning outcomes of students are analyzed from weak subjective dimensions and strong objective dimensions based on their online operation behavior datasets and physiological datasets, and the corresponding indicators reflecting the learning outcomes of students are quantified to construct weak subjective dimension effectiveness indicator sets and strong objective dimension effectiveness indicator sets. On this basis, the deep learning effectiveness evaluation results that fully reflect the deep learning outcomes of students are integrated, and combined with the learning feedback dataset, the online learning effectiveness report is determined and output. Through multi-dimensional analysis methods, the deep learning outcomes of students in the online learning process are scientifically and comprehensively reflected, and the online learning effectiveness report provides scientific suggestions for the improvement of students' subsequent learning plans, so as to guide students to find online learning methods suitable for themselves and improve their learning quality.

[0011] Optionally, the online operation behavior data set includes learning time, interaction frequency, pause times, and fast-forward times, and the analyzing the online operation behavior data set to determine the weak subjective dimension effectiveness indicator set includes:

[0012] Obtaining course content segmentation information, and determining an ideal learning duration and a normal interaction frequency based on the course content segmentation information;

[0013] Analyze the learning time and the ideal learning time to determine a time impact index;

[0014] Analyze the interaction frequency and the normal interaction frequency to determine an interaction impact index;

[0015] Analyze the number of pauses and the number of fast-forwards to determine a pause impact index and a fast-forward impact index respectively;

[0016] Determining a weak subjective comprehensive evaluation index according to the duration impact index, the interaction impact index, the pause impact index, and the fast-forward impact index;

[0017] The weak subjective dimension effectiveness indicator set is constructed according to the duration impact index, the interaction impact index, the pause impact index, the fast-forward impact index and the weak subjective comprehensive evaluation index.

[0018] Through this plan, the four data dimensions of learning time, interaction frequency, pause times and fast-forward times in students' operation behaviors are analyzed respectively to influence the students' learning degree, and the time influence index, interaction influence index, pause influence index and fast-forward influence index are obtained. The influence indexes corresponding to the four data dimensions are integrated to obtain a weak subjective comprehensive evaluation index that can comprehensively reflect the students' learning effectiveness under the weak subjective dimension, and a weak subjective dimension effectiveness indicator set is constructed based on this, which significantly improves the scientificity and comprehensiveness of the students' learning effectiveness evaluation process under the weak subjective dimension. At the same time, by incorporating multidimensional influence indicators into the weak subjective dimension effectiveness set, the deficiencies in the students' learning methods can be more accurately located in the future.

[0019] Optionally, the weak subjective comprehensive evaluation index is determined according to the duration impact index, the interaction impact index, the pause impact index and the fast-forward impact index, specifically as the following formula:

[0020]

[0021] Wherein, E is the weak subjective comprehensive evaluation index, k1 is the preset time influence coefficient, T is the learning time, α1 is the preset time influence index, T d For the ideal study time, is the duration impact index, k2 is the preset interaction impact coefficient, I is the interaction frequency, α2 is the preset interaction impact index, I d is the normal interaction frequency, is the interaction influence index, k3 is the preset pause influence coefficient, P is the number of pauses, β is the preset pause influence index, k3·P β is the pause impact index, k4 is the preset fast-forward impact coefficient, α3 is the preset fast-forward impact index, F is the fast-forward times, is the fast-forward impact index.

[0022] Through this plan, mathematical analysis is used to clearly and accurately quantify the duration impact index, interaction impact index, pause impact index and fast-forward impact index according to the learning time, ideal learning time, interaction frequency, normal interaction frequency, number of pauses and number of fast-forwards through corresponding mathematical formulas. Based on this, several subjective comprehensive evaluation indexes are scientifically calculated to improve the accuracy and scientificity of learning effectiveness evaluation under the weak subjective dimension, and provide clear quantitative data support for subsequent improvement analysis of students' learning effectiveness.

[0023] Optionally, the physiological data set includes eye movement data, expression change data, heart rate change data, and skin conductance change data, and the analyzing the physiological data set to determine a strong objective dimension performance indicator set includes:

[0024] Performing focus change frequency analysis on the eye movement data to determine an attention index;

[0025] Performing emotion change analysis on the expression change data to determine an emotion index;

[0026] According to the attention index and the emotion index, determining an attention impact index and an emotion impact index respectively, and determining an index representing physiological effectiveness accordingly;

[0027] Determining an average heart rate and an average skin conductivity respectively according to the heart rate change data and the skin conductance change data;

[0028] According to the average heart rate and the average skin conductivity, a heart rate influence index and a skin conductance influence index are determined respectively, and a deep physiological effectiveness index is determined accordingly;

[0029] The strong objective dimension performance indicator set is constructed according to the attention influence index, the emotion influence index, the physiological performance index, the heart rate influence index, the skin conductance influence index and the deep physiological performance index.

[0030] Through this solution, the students' physiological data are divided into representational dimensions and deep dimensions. Starting from the eye movement data, expression change data, heart rate change data and skin conductance change data corresponding to the two dimensions, the attention impact indicators, emotion impact indicators, heart rate impact indicators and skin conductance impact indicators that will directly affect the learning outcomes are analyzed respectively. On this basis, the representational physiological effectiveness index and the deep physiological effectiveness index are determined respectively. Based on the above-mentioned several influencing indicators and indexes, a strong objective dimension effectiveness indicator set is constructed to improve the scientificity and comprehensiveness of the learning effectiveness evaluation under the strong objective dimension, while further improving the accuracy of the subsequent analysis of students' shortcomings.

[0031] Optionally, the attention impact index and the emotion impact index are determined respectively according to the attention index and the emotion index, and the physiological effectiveness index is determined accordingly, specifically as follows:

[0032]

[0033] Wherein, C is the index characterizing physiological efficacy, k a is the preset attention influence coefficient, γ is the preset attention adjustment index, A is the attention index, is the attention impact index, k e is the preset emotion influence coefficient, δ is the preset emotion suppression parameter, ψ is the preset emotion enhancement parameter, S is the emotion index, k e ·(-δS 2 +ψS) is the emotion impact index.

[0034] Through this plan, mathematical analysis methods are used to quantify the attention impact index and the emotion impact index according to the attention index and the emotion index respectively. On this basis, the physiological effectiveness index is calculated through a clear mathematical formula to achieve scientific quantification of the attention impact index, emotion impact index and physiological effectiveness index, and improve the accuracy of the analysis of learning effectiveness in the physiological dimension.

[0035] Optionally, the heart rate influence index and the skin conductance influence index are determined respectively according to the average heart rate and the average skin conductance, and the deep physiological effectiveness index is determined accordingly, which is specifically the following formula:

[0036]

[0037] Where D is the deep physiological efficacy index, k H is the preset heart rate influence coefficient, λ is the preset heart rate influence adjustment parameter, H is the average heart rate, H t is the reference value of resting heart rate, is the heart rate impact index, k G is the preset skin conductance influence coefficient, G is the average skin conductivity, k G G is the skin conductance impact index.

[0038] Through this scheme, mathematical analysis methods are used to quantify the heart rate influence index and the skin conductance influence index according to the average heart rate and the average skin conductivity, and on this basis, the deep physiological effectiveness index is accurately calculated through a clear mathematical formula to achieve scientific quantification of the heart rate influence index, skin conductance influence index and deep physiological effectiveness index, thereby improving the accuracy of the analysis of learning effectiveness in the deep physiological dimension.

[0039] Optionally, the step of analyzing the weak subjective dimension effectiveness indicator set based on the strong objective dimension effectiveness indicator set to determine the deep learning effectiveness evaluation result includes:

[0040] The strong objective dimension performance indicator set and the weak subjective dimension performance indicator set are combined to determine a deep learning performance evaluation indicator set corresponding to each student;

[0041] Obtain an indicator weight set, and based on the indicator weight set and the deep learning effectiveness evaluation indicator set corresponding to each student, construct a multi-user deep learning effectiveness scoring matrix;

[0042] Determining a deep learning effectiveness score for each student according to the multi-user deep learning effectiveness scoring matrix;

[0043] The multi-user deep learning effectiveness rating matrix and the deep learning effectiveness ratings of all trainees are used as the deep learning effectiveness evaluation results.

[0044] Through this solution, the strong objective dimension effectiveness indicator set and the weak subjective dimension effectiveness indicator set are merged to obtain the deep learning effectiveness evaluation indicator set. Combined with the indicator weight set, a scientific evaluation framework for the students' deep learning effectiveness is constructed in the form of a mathematical matrix. The multi-user deep learning effectiveness scoring matrix and the deep learning effectiveness scores of all students are used as the deep learning effectiveness evaluation results. While improving the accuracy of learning effectiveness evaluation, it provides an important data framework for subsequent analysis of the direction of improvement of students' learning effectiveness.

[0045] Optionally, based on the indicator weight set, according to the deep learning effectiveness evaluation indicator set corresponding to each student, a multi-user deep learning effectiveness scoring matrix is ​​constructed, which is specifically the following matrix expression:

[0046]

[0047] Where I is the multi-user deep learning performance scoring matrix, Y mn is the nth evaluation indicator in the deep learning effectiveness evaluation indicator set of the mth student, W n is the weight value corresponding to the nth evaluation indicator in the indicator weight set;

[0048] The deep learning effectiveness score of each student is determined according to the multi-user deep learning effectiveness score matrix, which is specifically the following formula:

[0049]

[0050] Among them, RI i is the score of the deep learning effectiveness of the i-th student, n is the total number of evaluation indicators, I j It is the effectiveness score of the jth evaluation indicator corresponding to the current student in the multi-user deep learning effectiveness scoring matrix.

[0051] Through this solution, mathematical analysis methods are used to clarify the multi-user deep learning effectiveness scoring matrix through mathematical expressions, and mathematical formulas are used to accurately calculate the deep learning effectiveness score of each student based on the multi-user deep learning effectiveness scoring matrix, further improving the scientificity and accuracy of the learning effectiveness evaluation process.

[0052] Optionally, the analyzing the learning feedback data set based on the deep learning effectiveness indicator evaluation result to determine and output an online learning effectiveness report includes:

[0053] Analyze the learning feedback data set to determine a multi-type feedback indicator information set corresponding to each student;

[0054] Extracting the corresponding deep learning effectiveness evaluation indicator set in the multi-user deep learning effectiveness scoring matrix according to the multi-type feedback indicator information set;

[0055] Based on the multi-type feedback indicator information set, analyzing the deep learning effectiveness evaluation indicator set, and determining a number of contradictory feedback indicators;

[0056] The online learning effectiveness report is constructed and outputted based on the deep learning effectiveness score of each student and a plurality of the conflicting feedback indicators.

[0057] Through this plan, on the basis of completing the evaluation of students' learning outcomes, we further combine the students' feedback data, extract several contradictory feedback indicators based on the multi-type feedback indicator information set and the deep learning effectiveness evaluation indicator set, and construct and output an online learning effectiveness report based on each student's deep learning effectiveness score and several contradictory feedback indicators, so as to clarify the areas that students need to focus on in order to improve their learning outcomes in the future, and provide a significant positive effect on improving the quality of learning for students.

[0058] In a second aspect, the present application provides an online learning effectiveness evaluation system, the system comprising:

[0059] A weak subjective analysis module, used to obtain an online operation behavior data set, analyze the online operation behavior data set, and determine a weak subjective dimension effectiveness indicator set;

[0060] A strong objective analysis module, used to obtain a physiological data set, analyze the physiological data set, and determine a strong objective dimension performance indicator set;

[0061] A deep analysis module, used to analyze the weak subjective dimension effectiveness indicator set based on the strong objective dimension effectiveness indicator set to determine the deep learning effectiveness evaluation result;

[0062] The output module is used to obtain a learning feedback data set, analyze the learning feedback data set based on the deep learning effectiveness indicator evaluation result, and determine and output an online learning effectiveness report. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0064] Figure 1A schematic diagram of an application scenario provided for an embodiment of the present application;

[0065] Figure 2 A flowchart of an online learning effectiveness evaluation method provided in one embodiment of the present application;

[0066] Figure 3 A schematic diagram of the structure of an online learning effectiveness evaluation system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0068] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0069] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0070] The existing online learning effectiveness evaluation technology focuses on the analysis method of online learning representation data, which makes it difficult to comprehensively and accurately evaluate the deep learning effectiveness of students in the online learning process, resulting in difficulty in significantly improving the quality of students' online learning, which in turn limits the development of students' high-order thinking ability during the online learning process.

[0071] Based on this, the present application provides an online learning effectiveness evaluation method and system. According to the online operation behavior data set and physiological data set of the students, the learning effectiveness of the students is analyzed from the weak subjective dimension and the strong objective dimension, and the corresponding indicators reflecting the learning effectiveness of the students are quantified to construct a weak subjective dimension effectiveness indicator set and a strong objective dimension effectiveness indicator set. On this basis, the deep learning effectiveness evaluation results that fully reflect the deep learning effectiveness of the students are integrated, and combined with the learning feedback data set, the online learning effectiveness report is determined and output. Through multi-dimensional analysis means, the deep learning effectiveness of the students in the online learning process is scientifically and comprehensively reflected, and the online learning effectiveness report provides scientific suggestions for the improvement of the students' subsequent learning plans, so as to guide the students to find online learning methods suitable for themselves and improve the learning quality of the students.

[0072] Figure 1 This is a schematic diagram of an application scenario provided by this application. In the process of evaluating the online learning effectiveness of students, the method provided by this application is applied to accurately evaluate the deep learning effectiveness of students, so as to improve the quality of students' online learning.

[0073] Specifically, the method of the present application is applied to any server, which communicates with the background log system and the physiological data collection device respectively, and obtains the online operation behavior data set provided by the background log system and the physiological data set provided by the physiological data collection device through the server, and analyzes the learning effectiveness of the students from the weak subjective dimension and the strong objective dimension according to the online operation behavior data set and the physiological data set of the students, and quantifies the corresponding indicators reflecting the learning effectiveness of the students, and constructs the weak subjective dimension effectiveness indicator set and the strong objective dimension effectiveness indicator set. On this basis, the deep learning effectiveness evaluation results that fully reflect the deep learning effectiveness of the students are integrated, and combined with the learning feedback data set provided by the students, the online learning effectiveness report is determined and output, and the deep learning effectiveness of the students in the online learning process is scientifically and comprehensively reflected through multi-dimensional analysis methods, and the online learning effectiveness report provides scientific suggestions for the improvement of the students' subsequent learning plans, so as to guide the students to find online learning methods suitable for themselves and improve the learning quality of the students. The specific implementation method can refer to the following embodiments.

[0074] Figure 2 This is a flow chart of an online learning effectiveness evaluation method provided by an embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes:

[0075] S201. Obtain an online operation behavior data set, analyze the online operation behavior data set, and determine a weak subjective dimension effectiveness indicator set.

[0076] The online operation behavior data set may be a data set corresponding to a series of operation behaviors performed by students in the online learning platform during the online learning process. The online operation behavior data set may be obtained through the background log system of the online learning platform.

[0077] The weak subjective dimension effectiveness indicator set may be a collection of a series of indicators that can reflect the student's learning effectiveness in online operation behaviors with a slight subjective color of the students.

[0078] Specifically, online learning is often in a relatively autonomous and flexible learning environment. Students choose when, where and how to learn. This flexibility makes it easier for students to adjust their learning methods according to their own needs. The series of operational behaviors generated by students in the process of learning using the learning platform can reveal their learning strategies. The operational behaviors performed by students on the learning platform are usually generated unconsciously based on their learning habits and learning status. Although they have a certain degree of subjective consciousness, this subjective consciousness has a clear weakening trend compared to the objective feedback brought by the operational behavior. Therefore, the students' online operational behavior can reflect their learning effectiveness from the weak subjective dimension. Through mathematical analysis, the students' online operational behavior data set is scientifically processed, and several indicators that can reflect the students' learning effectiveness in the weak subjective dimension are extracted and quantified to construct a weak subjective dimension effectiveness indicator set.

[0079] S202, obtaining a physiological data set, analyzing the physiological data set, and determining a strong objective dimension performance indicator set.

[0080] The physiological data set can be a collection of a series of physiological data of students during the learning process, such as heart rate, eye movement data, etc. The physiological data set can be obtained through different physiological data collection devices, such as wearable devices, eye trackers, etc. after obtaining the authorization and consent of the students.

[0081] The strong objective dimension effectiveness indicator set can be a collection of a series of indicators in the students' physiological data with guaranteed objectivity that can reflect the students' learning effectiveness.

[0082] Specifically, in the process of learning on online learning platforms, students are affected by a series of positive and negative influences such as attention, learning pressure, and learning achievement. Their physiological data, such as heart rate, eye movement data, skin conductivity, etc., will change significantly. The changes in physiological data can reflect the cognitive load and emotional state of students in the learning process, which in turn directly affects the students' learning outcomes. In addition, the students' physiological data cannot be controlled by the students' subjective behavior and are highly objective. Therefore, physiological data can reflect the students' learning outcomes from a strongly objective dimension. Through mathematical analysis, the students' physiological data sets are scientifically processed, and several indicators that can reflect the students' learning outcomes in a strongly objective dimension are extracted and quantified, so as to construct a set of strong subjective dimension effectiveness indicators.

[0083] S203. Based on the strong objective dimension effectiveness indicator set, analyze the weak subjective dimension effectiveness indicator set to determine the deep learning effectiveness evaluation results.

[0084] The results of deep learning effectiveness evaluation can be the evaluation information obtained by analyzing the combination of weak subjective dimension effectiveness indicator set and strong objective dimension effectiveness indicator set, which can objectively and comprehensively reflect the learning effectiveness of students.

[0085] Specifically, the strong objective dimension effectiveness indicator set and the weak subjective dimension effectiveness indicator set obtained in the above steps reflect the students' learning effectiveness from the strong objective dimension and the weak subjective dimension respectively. By combining the two, we can have a deeper insight into the students' learning outcomes and improve the comprehensiveness of the evaluation of students' learning effectiveness. By using mathematical analysis methods, the strong objective dimension effectiveness indicator set and the weak subjective dimension effectiveness indicator set are integrated to obtain deep learning effectiveness evaluation results, and the students' learning effectiveness is described comprehensively and accurately through scientific mathematical language.

[0086] S204: Obtain a learning feedback data set, analyze the learning feedback data set based on the deep learning effectiveness indicator evaluation results, and determine and output an online learning effectiveness report.

[0087] The learning feedback dataset can be a collection of learning experience information provided by learners using electronic feedback tools, such as online questionnaires.

[0088] The online learning effectiveness report may be report information that includes various effectiveness indicators of students and effectiveness indicators that need to be focused on, and is used to reflect the effectiveness of students' online learning.

[0089] Specifically, after obtaining the deep learning effectiveness indicator evaluation results that can objectively reflect the students' learning effectiveness, although the students' learning effectiveness can be accurately judged, it is difficult to clarify the direction in which the students need to improve in the future. The students' feedback information can reflect their subjective feelings in the learning process. These subjective feelings are based on the students' understanding of the learning content, the adaptability of the learning method, and the evaluation of the learning environment. These cognitive differences may lead to differences between their subjective evaluation of the learning effect and the actual learning results, and these differences are the directions that students need to improve in order to improve their learning effectiveness. Therefore, by analyzing the deep learning effectiveness indicator evaluation results and the learning feedback data set, we find and extract the indicators that are contradictory due to the cognitive differences of the students. We use these indicators as the directions that need to be focused on in the subsequent learning process of the students. In combination with the data that can reflect the current learning effectiveness of the students, we integrate and generate the corresponding online learning effectiveness report through data visualization technology, and provide the online learning effectiveness report to the students through human-computer interaction equipment, so as to provide scientific data reference for the students to improve their learning plans.

[0090] Through this scheme, the learning outcomes of students are analyzed from weak subjective dimensions and strong objective dimensions based on their online operation behavior datasets and physiological datasets, and the corresponding indicators reflecting the learning outcomes of students are quantified to construct weak subjective dimension effectiveness indicator sets and strong objective dimension effectiveness indicator sets. On this basis, the deep learning effectiveness evaluation results that fully reflect the deep learning outcomes of students are integrated, and combined with the learning feedback dataset, the online learning effectiveness report is determined and output. Through multi-dimensional analysis methods, the deep learning outcomes of students in the online learning process are scientifically and comprehensively reflected, and the online learning effectiveness report provides scientific suggestions for the improvement of students' subsequent learning plans, so as to guide students to find online learning methods suitable for themselves and improve their learning quality.

[0091] In some embodiments, course content segmentation information is obtained, and the ideal learning time and normal interaction frequency are determined based on the course content segmentation information; the learning time and the ideal learning time are analyzed to determine the time impact index; the interaction frequency and the normal interaction frequency are analyzed to determine the interaction impact index; the number of pauses and the number of fast-forwards are analyzed to determine the pause impact index and the fast-forward impact index, respectively; the weak subjective comprehensive evaluation index is determined based on the time impact index, the interaction impact index, the pause impact index, and the fast-forward impact index; a weak subjective dimension effectiveness indicator set is constructed based on the time impact index, the interaction impact index, the pause impact index, the fast-forward impact index, and the weak subjective comprehensive evaluation index.

[0092] The online operation behavior dataset includes learning time, interaction frequency, number of pauses, and number of fast-forwards.

[0093] The learning time can be the time that the students spend studying the current online course.

[0094] The interaction frequency can be the frequency with which students communicate and interact with teachers or other students while studying the current online course.

[0095] The number of pauses may be the number of pauses that students experience when watching the video material corresponding to the current online course.

[0096] The number of fast-forward times may be the number of fast-forward times that students have fast-forwarded while watching the corresponding video material of the current online course.

[0097] The course content segment information may be content segment information of the current online course studied by the student.

[0098] The ideal learning time can be the average time required for the current online course in the learning history of different students.

[0099] The normal interaction frequency may be the average interaction frequency of the current online course in the learning history of different students.

[0100] The duration impact index may be a quantitative value reflecting the impact of learning duration on learning outcomes.

[0101] The interaction impact index can be a quantitative value that reflects the impact of interaction frequency on learning outcomes.

[0102] The pause impact index may be a quantitative value reflecting the impact of the number of pauses on learning outcomes.

[0103] The fast-forward impact index may be a quantitative value reflecting the impact of the number of fast-forwards on learning outcomes.

[0104] The weak subjective comprehensive evaluation index can be a comprehensive evaluation value that reflects the learning outcomes of students under the weak subjective dimension.

[0105] Specifically, in the analysis of students' online operation behaviors, we mainly start from the learning time, interaction frequency, number of pauses and number of fast-forwards. Among them, the learning time is usually directly related to the depth and breadth of learning. A longer learning time means that students have invested more time in a certain topic, which enhances their mastery and understanding of knowledge. However, if the learning time is too long but there is no substantial learning progress (such as lack of concentration), it will lead to fatigue and decreased learning effectiveness; the interaction frequency reflects the degree of interaction between students and learning content, teachers and other students. A higher interaction frequency usually means that students actively participate in learning, which helps to better understand and master knowledge. However, if the interaction frequency is too high but lacks actual knowledge communication, it will This leads to a superficial understanding of the learning content, which has a negative impact on learning outcomes. The number of pauses can reflect the time and depth of students' thinking during the learning process. Too many pauses may indicate that students have great difficulties in certain knowledge points, which will affect the continuity of learning and lead to decreased learning outcomes. The number of fast-forwards usually indicates that students skip over certain content. Appropriate fast-forwarding can help students save time, quickly skip mastered or repeated content, and concentrate on more challenging learning materials. However, if the number of fast-forwards is too many, it means that students lack interest in the course content or think that the content is not profound enough, which will affect their overall learning motivation and knowledge mastery, and thus have a negative impact on their learning outcomes.

[0106] Therefore, by analyzing the segmented information of course content, the learning time and interaction frequency of each student in different course segments are extracted, and the ideal learning time and normal interaction frequency that can have a positive impact on learning outcomes are determined. On this basis, the time impact index is determined according to the students' learning time and ideal learning time through mathematical analysis methods. At the same time, the interaction impact index is determined according to the students' interaction frequency and normal interaction frequency. In addition, through mathematical analysis methods, the impact and mapping of pauses and fast-forwards on learning outcomes are quantified according to the number of pauses and fast-forwards, respectively, to obtain the pause impact index and fast-forward impact index. After combining the above four impact indices, further mathematical analysis is performed to determine the weak subjective comprehensive evaluation index that can comprehensively reflect the students' learning outcomes under the weak subjective dimension. After integrating the above four impact indices and the weak subjective comprehensive evaluation index, a weak subjective dimension effectiveness indicator set is constructed.

[0107] Through this plan, the four data dimensions of learning time, interaction frequency, pause times and fast-forward times in students' operation behaviors are analyzed respectively to influence the students' learning degree, and the time influence index, interaction influence index, pause influence index and fast-forward influence index are obtained. The influence indexes corresponding to the four data dimensions are integrated to obtain a weak subjective comprehensive evaluation index that can comprehensively reflect the students' learning effectiveness under the weak subjective dimension, and a weak subjective dimension effectiveness indicator set is constructed based on this, which significantly improves the scientificity and comprehensiveness of the students' learning effectiveness evaluation process under the weak subjective dimension. At the same time, by incorporating multidimensional influence indicators into the weak subjective dimension effectiveness set, the deficiencies in the students' learning methods can be more accurately located in the future.

[0108] In some embodiments, a weak subjective comprehensive evaluation index is determined according to the duration impact index, the interaction impact index, the pause impact index, and the fast-forward impact index, specifically the following formula (1):

[0109]

[0110] Among them, E is the weak subjective comprehensive evaluation index, k1 is the preset time influence coefficient, T is the learning time, α1 is the preset time influence index, T d For the ideal study time, is the duration impact index, k2 is the preset interaction impact coefficient, I is the interaction frequency, α2 is the preset interaction impact index, I d is the normal interaction frequency, is the interaction impact index, k3 is the preset pause impact coefficient, P is the number of pauses, β is the preset pause impact index, k3·P β is the pause impact index, k4 is the preset fast-forward impact coefficient, α3 is the preset fast-forward impact index, F is the number of fast-forward times, The fast forward impact index.

[0111] The preset duration impact coefficient may be a preset value used to reflect the linear impact of learning duration on learning outcomes. The preset duration impact coefficient may be obtained by performing linear fitting on historical learning data.

[0112] The preset duration impact index may be a preset value used to reflect the nonlinear impact of learning duration on learning outcomes. The preset duration impact index may be obtained by performing polynomial regression on historical learning data.

[0113] The preset interaction influence coefficient may be a preset value used to reflect the linear influence degree of the interaction frequency on the learning effect. The preset interaction influence coefficient may be obtained by linear fitting of the historical learning data.

[0114] The preset interaction impact index may be a preset value used to reflect the nonlinear impact of interaction frequency on learning outcomes. The preset interaction impact index may be obtained by performing polynomial regression on historical learning data.

[0115] The preset pause influence coefficient may be a preset value used to reflect the linear influence of the number of pauses on the learning effect, and the preset interaction influence coefficient may be obtained by linear fitting of historical learning data.

[0116] The preset pause impact index may be a preset value used to reflect the nonlinear impact of the number of pauses on learning outcomes. The preset pause impact index may be obtained by performing polynomial regression on historical learning data.

[0117] The preset fast-forward influence coefficient may be a preset value used to reflect the linear influence of the number of fast-forward times on the learning outcome. The preset fast-forward influence coefficient may be obtained by performing linear fitting on historical learning data.

[0118] The preset fast-forward impact index can be a preset value used to reflect the nonlinear impact of the number of fast-forwards on learning outcomes. The preset fast-forward impact index can be obtained by performing polynomial regression on historical learning data.

[0119] Specifically, through formula (1) It reflects the nonlinear relationship between the student's current learning time and his / her learning effect. As the learning time increases, the learning effect should gradually improve, but this improvement is not linear and will tend to stabilize after reaching a certain point. That is, when the learning time is close to the ideal learning time, the improvement of learning effect will gradually weaken. Similarly, through formula (1) Reflects the nonlinear relationship between the interaction frequency of students and their learning effect; through k3·P β Reflects the relationship between the number of pauses and learning outcomes. Since an increase in the number of pauses usually worsens learning outcomes, a power model is used here to describe the weakening effect of the number of pauses on learning outcomes. It reflects the relationship between the number of fast-forwards and learning outcomes. Too much fast-forwarding means that students lack interest in the learning content. An exponential function is used here to describe this relationship. Specifically, the shorter the number of fast-forwards, the less significant the improvement in learning outcomes.

[0120] Through this plan, mathematical analysis is used to clearly and accurately quantify the duration impact index, interaction impact index, pause impact index and fast-forward impact index according to the learning time, ideal learning time, interaction frequency, normal interaction frequency, number of pauses and number of fast-forwards through corresponding mathematical formulas. Based on this, several subjective comprehensive evaluation indexes are scientifically calculated to improve the accuracy and scientificity of learning effectiveness evaluation under the weak subjective dimension, and provide clear quantitative data support for subsequent improvement analysis of students' learning effectiveness.

[0121] In some embodiments, the focus change frequency analysis is performed on the eye movement data to determine the attention index; the emotion change analysis is performed on the expression change data to determine the emotion index; the attention influence index and the emotion influence index are determined according to the attention index and the emotion index, and the physiological effectiveness index is determined accordingly; the average heart rate and the average skin conductivity are determined according to the heart rate change data and the skin conductance change data; the heart rate influence index and the skin conductance influence index are determined according to the average heart rate and the average skin conductivity, and the deep physiological effectiveness index is determined accordingly; a strong objective dimension effectiveness index set is constructed according to the attention influence index, the emotion influence index, the physiological effectiveness index, the heart rate influence index, the skin conductance influence index and the deep physiological effectiveness index.

[0122] The physiological data set includes eye movement data, expression change data, heart rate change data, and skin conductance change data.

[0123] Eye movement data can be data on changes in a learner's eye gaze behavior during the learning process.

[0124] The expression change data may be the facial expression change data of the learner during the learning process.

[0125] The heart rate variation data may be the heart rate variation data of the trainee during the learning process.

[0126] The skin conductance change data may be skin conductance change data of trainees during the learning process.

[0127] The attention index may be a numerical value reflecting the degree of concentration of the students during the learning process.

[0128] The emotional index may be a value reflecting whether the student's emotional state during the learning process tends to be positive or negative.

[0129] The representative physiological effectiveness index may be a quantitative value obtained by analyzing the representative physiological data of the trainees and reflecting the degree of its influence on the learning effectiveness.

[0130] The average heart rate may be an average heart rate value of different students in the process of learning the current online course obtained from statistics of historical learning data.

[0131] The average skin conductivity may be the average skin conductivity of different students in the process of learning the current online course obtained from statistics of historical learning data.

[0132] The deep physiological effectiveness index may be a quantitative value obtained by analyzing the deep physiological data of the trainees and reflecting the degree of their influence on the learning effectiveness.

[0133] Specifically, changes in students' physiological data during the learning process will directly affect and map their learning outcomes. In the process of analyzing physiological data, the physiological data are divided into two dimensions. One is the representation dimension. The physiological data under this dimension reflects the students' direct and explicit physiological responses, such as changes in eye gaze area and facial expressions. The other is the deep dimension. The physiological data under this dimension covers the students' complex and non-explicit physiological mechanisms during the learning process, such as changes in heart rate and skin conductivity. Through analysis in two dimensions, the mapping relationship between changes in students' physiological data and learning outcomes can be more comprehensively reflected.

[0134] Among them, the students' eye movement data reflects the changes in their attention during the learning process. Frequent eye movements indicate that the students have difficulty concentrating or understanding the course, which will directly have a negative impact on the students' learning outcomes. Changes in panel expressions can reflect the students' emotional state during the learning process, such as joy, confusion, and frustration. The more positive the students' emotions are, the better their learning outcomes will be. Changes in heart rate reflect the students' psychological pressure and tension during the learning process. When students feel anxious, their information processing ability will be significantly reduced, which will have a negative impact on their learning outcomes. Skin conductivity can also reflect changes in students' psychological pressure. Therefore, the frequency of students' line of sight changes reflected in the statistical staff's eye movement data can be used to determine the students' attention index. The higher the frequency of line of sight changes, the lower the concentration and the lower the corresponding attention index. Conversely, the higher the attention index, the lower the concentration and the lower the corresponding attention index. High; through the emotion classification model, such as support vector machine or random forest, etc., the expression change data of the students are analyzed to determine the main emotion types of the students in the learning process. According to the emotion type, it is judged whether the students' emotions tend to be positive or negative, and the degree of tendency is determined. The higher the degree to which the students' emotions tend to be positive, the higher the corresponding emotion index, and vice versa, the lower the emotion index; according to the attention index and the emotion index, the attention influence index and the emotion influence index are quantified through mathematical analysis; at the same time, based on the average heart rate and the average skin conductivity, the heart rate influence index and the skin conductance influence index are calculated by mathematical analysis process, and the deep physiological effectiveness index is quantified; further, the attention influence index, emotion influence index, physiological effectiveness index, heart rate influence index, skin conductance influence index and deep physiological effectiveness index are included in the strong objective dimension effectiveness index set.

[0135] Through this solution, the students' physiological data are divided into representational dimensions and deep dimensions. Starting from the eye movement data, expression change data, heart rate change data and skin conductance change data corresponding to the two dimensions, the attention impact indicators, emotion impact indicators, heart rate impact indicators and skin conductance impact indicators that will directly affect the learning outcomes are analyzed respectively. On this basis, the representational physiological effectiveness index and the deep physiological effectiveness index are determined respectively. Based on the above-mentioned several influencing indicators and indexes, a strong objective dimension effectiveness indicator set is constructed to improve the scientificity and comprehensiveness of the learning effectiveness evaluation under the strong objective dimension, while further improving the accuracy of the subsequent analysis of students' shortcomings.

[0136] In some embodiments, according to the attention index and the emotion index, the attention impact index and the emotion impact index are determined respectively, and the physiological effectiveness index is determined accordingly, which is specifically the following formula (2):

[0137]

[0138] Among them, C is the index representing physiological effectiveness, k a is the preset attention influence coefficient, γ is the preset attention adjustment index, A is the attention index, is the attention impact indicator, k e is the preset emotion influence coefficient, δ is the preset emotion suppression parameter, ψ is the preset emotion enhancement parameter, S is the emotion index, k e ·(-δS 2 +ψS) is the emotional impact indicator.

[0139] The preset attention influence coefficient may be a numerical value reflecting the linear influence of the attention index on the learning outcome, and the preset attention influence coefficient may be obtained by linear fitting of historical learning data.

[0140] The preset attention adjustment index may be a value reflecting the nonlinear effect of the attention index on the learning outcome. The preset attention adjustment index may be obtained by polynomial regression fitting of historical learning data.

[0141] The preset emotion suppression parameter may be a numerical value reflecting the degree of influence of negative emotions on learning outcomes, and the preset emotion suppression parameter may be obtained by linear fitting of historical learning data.

[0142] The preset emotion enhancement parameter may be a numerical value reflecting the influence of positive emotions on learning outcomes, and the preset emotion enhancement parameter may be obtained by linear fitting of historical learning data.

[0143] Specifically, through formula (2) Describe the impact of students' attention changes on their learning outcomes, and use an exponential function to reflect the nonlinear effect of attention on physiological outcomes, that is, an increase in attention may lead to an exponential improvement in physiological outcomes; at the same time, through k e ·(-δS 2 +ψS) describes the impact of students’ emotional changes on their learning outcomes, where -δS 2 Reflecting the asymmetric impact of negative emotions on learning outcomes, the negative impact of emotions increases with the increase of emotion intensity. Therefore, a negative quadratic term is introduced, and the degree of this negative impact is controlled through the emotion suppression parameter. The linear term ψS reflects the reinforcing effect of positive emotions on learning outcomes. The higher the emotion index, the greater the positive impact. Through the coordination of the above items, the physiological effectiveness index can be accurately calculated.

[0144] Through this plan, mathematical analysis methods are used to quantify the attention impact index and the emotion impact index according to the attention index and the emotion index respectively. On this basis, the physiological effectiveness index is calculated through a clear mathematical formula to achieve scientific quantification of the attention impact index, emotion impact index and physiological effectiveness index, and improve the accuracy of the analysis of learning effectiveness in the physiological dimension.

[0145] In some embodiments, the heart rate impact index and the skin conductance impact index are determined according to the average heart rate and the average skin conductance, respectively, and the deep physiological effectiveness index is determined accordingly, specifically, the following formula (3):

[0146]

[0147] Among them, D is the deep physiological effectiveness index, k H is the preset heart rate influence coefficient, λ is the preset heart rate influence adjustment parameter, H is the average heart rate, H t is the reference value of resting heart rate, is the heart rate impact index, k G is the preset skin conductance influence coefficient, G is the average skin conductivity, k G G is the skin conductance impact index.

[0148] The preset heart rate influence coefficient may be a numerical value reflecting the linear influence of heart rate on learning outcomes. The preset heart rate influence coefficient may be obtained by linearly fitting historical learning data.

[0149] The preset heart rate impact adjustment parameter may be a numerical value reflecting the nonlinear impact of heart rate on learning outcomes. The preset heart rate impact adjustment parameter may be obtained by performing polynomial regression fitting on historical learning data.

[0150] The calm heart rate reference value may be a heart rate reference value under normal learning pressure, and the calm heart rate reference value may be obtained by statistically analyzing heart rate change data of different students.

[0151] The preset skin conductance influence coefficient may be a numerical value reflecting the linear influence of skin conductivity on learning outcomes. The preset skin conductance influence coefficient may be obtained by linearly fitting historical learning data.

[0152] Specifically, through formula (3) The deviation between the average heart rate of the students and the reference value of the calm heart rate is converted into an influence factor to indicate that when the average heart rate of the students is higher than the calm heart rate, the learning effect is inhibited; G G describes the relationship between skin conductivity and learning outcomes, and expresses the negative correlation between skin conductivity and learning outcomes. By combining the above items, the deep physiological effectiveness index is calculated using formula (3).

[0153] Through this scheme, mathematical analysis methods are used to quantify the heart rate influence index and the skin conductance influence index according to the average heart rate and the average skin conductivity, and on this basis, the deep physiological effectiveness index is accurately calculated through a clear mathematical formula to achieve scientific quantification of the heart rate influence index, skin conductance influence index and deep physiological effectiveness index, thereby improving the accuracy of the analysis of learning effectiveness in the deep physiological dimension.

[0154] In some embodiments, a strong objective dimension performance indicator set and a weak subjective dimension performance indicator set are merged to determine a deep learning effectiveness evaluation indicator set corresponding to each student; an indicator weight set is obtained, and based on the indicator weight set, a multi-user deep learning effectiveness scoring matrix is ​​constructed according to the deep learning effectiveness evaluation indicator set corresponding to each student; based on the multi-user deep learning effectiveness scoring matrix, a deep learning effectiveness score for each student is determined; and the multi-user deep learning effectiveness scoring matrix and the deep learning effectiveness scores of all students are used as deep learning effectiveness evaluation results.

[0155] The deep learning effectiveness evaluation indicator set may be a set of all indicators that reflect the deep learning effectiveness of students.

[0156] The indicator weight set may be a collection of weights of each evaluation indicator in the deep learning effectiveness evaluation indicator set in the process of measuring the deep learning effectiveness of students. The indicator weight set may be obtained through expert evaluation.

[0157] The multi-user deep learning effectiveness scoring matrix can be a mathematical matrix with a two-dimensional array structure, integrating all learning effectiveness evaluation indicators and corresponding weights corresponding to different students.

[0158] The deep learning effectiveness score may be a numerical score that reflects the comprehensive learning effectiveness of the students. The higher the deep learning effectiveness score, the better the learning effectiveness of the corresponding students.

[0159] Specifically, through set splicing, the strong objective dimension effectiveness indicator set and the weak subjective dimension effectiveness indicator set are merged, and the relative order of different indicators in the original sets is maintained during the merging process to obtain the deep learning effectiveness evaluation indicator set. Through mathematical analysis, according to the deep learning effectiveness evaluation indicator set and indicator weight set corresponding to each student, a multi-user deep learning effectiveness scoring matrix is ​​constructed. Based on the multi-user deep learning effectiveness scoring matrix, a one-step mathematical analysis process is performed to quantify the students' deep learning effectiveness scores, so as to obtain scientific and accurate deep learning effectiveness evaluation results for each student.

[0160] Through this solution, the strong objective dimension effectiveness indicator set and the weak subjective dimension effectiveness indicator set are merged to obtain the deep learning effectiveness evaluation indicator set. Combined with the indicator weight set, a scientific evaluation framework for the students' deep learning effectiveness is constructed in the form of a mathematical matrix. The multi-user deep learning effectiveness scoring matrix and the deep learning effectiveness scores of all students are used as the deep learning effectiveness evaluation results. While improving the accuracy of learning effectiveness evaluation, it provides an important data framework for subsequent analysis of the direction of improvement of students' learning effectiveness.

[0161] In some embodiments, based on the indicator weight set and according to the deep learning effectiveness evaluation indicator set corresponding to each student, a multi-user deep learning effectiveness scoring matrix is ​​constructed, which is specifically the following matrix expression (4):

[0162]

[0163] Among them, I is the multi-user deep learning effectiveness rating matrix, Y mn is the nth evaluation indicator in the deep learning effectiveness evaluation indicator set of the mth student, W n is the weight value corresponding to the nth evaluation indicator in the indicator weight set; according to the multi-user deep learning effectiveness scoring matrix, the deep learning effectiveness score of each student is determined, which is specifically the following formula (5):

[0164]

[0165] Among them, RI i is the deep learning effectiveness score of the i-th student, n is the total number of evaluation indicators, I j It is the effectiveness score of the jth evaluation indicator corresponding to the current student in the multi-user deep learning effectiveness scoring matrix.

[0166] Specifically, through the matrix structure described by matrix expression (4), the deep learning effectiveness evaluation indicator set and indicator weight set corresponding to each student are automatically integrated to construct a multi-user deep learning effectiveness scoring matrix, and through formula (5), the corresponding products of the evaluation indicators and weights corresponding to each student in the matrix are automatically summed to accurately obtain the deep learning effectiveness score corresponding to each student under the multi-user deep learning effectiveness scoring matrix.

[0167] Through this solution, mathematical analysis methods are used to clarify the multi-user deep learning effectiveness scoring matrix through mathematical expressions, and mathematical formulas are used to accurately calculate the deep learning effectiveness score of each student based on the multi-user deep learning effectiveness scoring matrix, further improving the scientificity and accuracy of the learning effectiveness evaluation process.

[0168] In some embodiments, a learning feedback data set is analyzed to determine a multi-type feedback indicator information set corresponding to each student; based on the multi-type feedback indicator information set, a corresponding deep learning effectiveness evaluation indicator set in a multi-user deep learning effectiveness scoring matrix is ​​extracted; based on the multi-type feedback indicator information set, the deep learning effectiveness evaluation indicator set is analyzed to determine a number of contradictory feedback indicators; based on each student's deep learning effectiveness score and a number of contradictory feedback indicators, an online learning effectiveness report is constructed and output.

[0169] The multi-type feedback indicator information set may be a set of feedback information given by trainees in response to different types of questionnaire questions.

[0170] Contradictory feedback indicators can be indicators in the feedback information given by students based on their self-cognition that contradict the objective evaluation indicators in the deep learning effectiveness evaluation indicator set.

[0171] Specifically, after completing the scientific evaluation of the students' learning outcomes, in order to further improve the students' online learning outcomes, the students can use electronic feedback tools, such as questionnaires, to provide feedback on their self-perceptions during the learning process. However, the students' self-perceptions are usually highly subjective and easily conflict with the objective effectiveness evaluation indicators in the collected deep learning effectiveness evaluation indicator set. This indicates that the students have self-cognitive biases in the learning process, and it is necessary to focus on the students' cognitive biases to improve their subsequent learning outcomes. In the process of determining several contradictory feedback indicators, data standardization processing techniques, such as Z-score standardization, are used to standardize the indicator information in the multi-type feedback indicator information set and the deep learning effectiveness evaluation indicator set, respectively, so that different indicators are comparable. Then, through correlation quantification algorithms, such as the Pearson correlation coefficient, the correlation between different indicators is quantified, and several feedback indicators with strong correlation and information contradictions are used as contradictory feedback indicators. Through data visualization technology, the deep learning effectiveness score of each student and several contradictory feedback indicators are integrated and visualized to construct and output an online learning effectiveness report.

[0172] Through this plan, on the basis of completing the evaluation of students' learning outcomes, we further combine the students' feedback data, extract several contradictory feedback indicators based on the multi-type feedback indicator information set and the deep learning effectiveness evaluation indicator set, and construct and output an online learning effectiveness report based on each student's deep learning effectiveness score and several contradictory feedback indicators, so as to clarify the areas that students need to focus on in order to improve their learning outcomes in the future, and provide a significant positive effect on improving the quality of learning for students.

[0173] Figure 3 A schematic diagram of the structure of an online learning effectiveness evaluation system provided in one embodiment of the present application is shown in FIG. Figure 3As shown, an online learning effectiveness evaluation system 300 of this embodiment includes: a weak subjective analysis module 301 , a strong objective analysis module 302 , a deep analysis module 303 and an output module 304 .

[0174] A weak subjective analysis module 301 is used to obtain an online operation behavior data set, analyze the online operation behavior data set, and determine a weak subjective dimension effectiveness indicator set;

[0175] A strong objective analysis module 302, used to obtain a physiological data set, analyze the physiological data set, and determine a strong objective dimension performance indicator set;

[0176] A deep analysis module 303 is used to analyze the weak subjective dimension effectiveness indicator set based on the strong objective dimension effectiveness indicator set to determine a deep learning effectiveness evaluation result;

[0177] The output module 304 is used to obtain a learning feedback data set, analyze the learning feedback data set based on the deep learning effectiveness indicator evaluation result, and determine and output an online learning effectiveness report.

[0178] Optionally, the weak subjective analysis module 301 is specifically used to:

[0179] Obtain course content segmentation information, and determine the ideal learning time and normal interaction frequency based on the course content segmentation information; analyze the learning time and the ideal learning time to determine the time impact index; analyze the interaction frequency and the normal interaction frequency to determine the interaction impact index; analyze the number of pauses and the number of fast-forwards to determine the pause impact index and the fast-forward impact index respectively; determine the weak subjective comprehensive evaluation index based on the time impact index, the interaction impact index, the pause impact index, and the fast-forward impact index; construct the weak subjective dimension effectiveness indicator set based on the time impact index, the interaction impact index, the pause impact index, the fast-forward impact index, and the weak subjective comprehensive evaluation index.

[0180] Optionally, when the weak subjective analysis module 301 determines the weak subjective comprehensive evaluation index according to the duration impact index, the interaction impact index, the pause impact index, and the fast-forward impact index, the specific formula is as follows:

[0181] Wherein, E is the weak subjective comprehensive evaluation index, k1 is the preset time influence coefficient, T is the learning time, α1 is the preset time influence index, T d For the ideal study time, is the duration impact index, k2 is the preset interaction impact coefficient, I is the interaction frequency, α2 is the preset interaction impact index, I d is the normal interaction frequency, is the interaction influence index, k3 is the preset pause influence coefficient, P is the number of pauses, β is the preset pause influence index, k3·P β is the pause impact index, k4 is the preset fast-forward impact coefficient, α3 is the preset fast-forward impact index, F is the fast-forward times, is the fast-forward impact index.

[0182] Optionally, the strong objective analysis module 302 is specifically used for:

[0183] Perform focus change frequency analysis on the eye movement data to determine the attention index; perform emotion change analysis on the expression change data to determine the emotion index; determine the attention influence index and the emotion influence index respectively according to the attention index and the emotion index, and determine the physiological effectiveness index accordingly; determine the average heart rate and the average skin conductivity respectively according to the heart rate change data and the skin conductance change data; determine the heart rate influence index and the skin conductance influence index respectively according to the average heart rate and the average skin conductivity, and determine the deep physiological effectiveness index accordingly; construct the strong objective dimension effectiveness index set according to the attention influence index, the emotion influence index, the physiological effectiveness index, the heart rate influence index, the skin conductance influence index and the deep physiological effectiveness index.

[0184] Optionally, the strong objective analysis module 302 determines the attention impact index and the emotion impact index respectively according to the attention index and the emotion index, and determines the physiological effectiveness index accordingly, specifically using the following formula:

[0185] Wherein, C is the index characterizing physiological efficacy, k a is the preset attention influence coefficient, γ is the preset attention adjustment index, A is the attention index, is the attention impact index, k e is the preset emotion influence coefficient, δ is the preset emotion suppression parameter, ψ is the preset emotion enhancement parameter, S is the emotion index, k e ·(-δS 2 +ψS) is the emotion impact index.

[0186] Optionally, the strong objective analysis module 302 determines the heart rate influence index and the skin conductance influence index according to the average heart rate and the average skin conductance, respectively, and determines the deep physiological effectiveness index accordingly, specifically using the following formula:

[0187] Where D is the deep physiological efficacy index, k His the preset heart rate influence coefficient, λ is the preset heart rate influence adjustment parameter, H is the average heart rate, H t is the reference value of resting heart rate, is the heart rate impact index, k G is the preset skin conductance influence coefficient, G is the average skin conductivity, k G G is the skin conductance impact index.

[0188] Optionally, the deep analysis module 303 is specifically used to:

[0189] The strong objective dimension performance indicator set and the weak subjective dimension performance indicator set are combined to determine the deep learning effectiveness evaluation indicator set corresponding to each student; an indicator weight set is obtained, and based on the indicator weight set and according to the deep learning effectiveness evaluation indicator set corresponding to each student, a multi-user deep learning effectiveness scoring matrix is ​​constructed; according to the multi-user deep learning effectiveness scoring matrix, a deep learning effectiveness score of each student is determined; and the multi-user deep learning effectiveness scoring matrix and the deep learning effectiveness scores of all students are used as the deep learning effectiveness evaluation results.

[0190] Optionally, when the deep analysis module 303 constructs a multi-user deep learning effectiveness scoring matrix based on the indicator weight set and the deep learning effectiveness evaluation indicator set corresponding to each student, the matrix expression is specifically the following:

[0191] Where I is the multi-user deep learning performance scoring matrix, Y mn is the nth evaluation indicator in the deep learning effectiveness evaluation indicator set of the mth student, W n is the weight value corresponding to the nth evaluation indicator in the indicator weight set; the deep learning effectiveness score of each student is determined according to the multi-user deep learning effectiveness score matrix, which is specifically the following formula:

[0192] Among them, RI i is the score of the deep learning effectiveness of the i-th student, n is the total number of evaluation indicators, I j It is the effectiveness score of the jth evaluation indicator corresponding to the current student in the multi-user deep learning effectiveness scoring matrix.

[0193] Optionally, the output module 304 is specifically used to:

[0194] Analyze the learning feedback data set to determine the multi-type feedback indicator information set corresponding to each student; extract the corresponding deep learning effectiveness evaluation indicator set in the multi-user deep learning effectiveness scoring matrix based on the multi-type feedback indicator information set; analyze the deep learning effectiveness evaluation indicator set based on the multi-type feedback indicator information set to determine a number of contradictory feedback indicators; construct and output the online learning effectiveness report based on the deep learning effectiveness score of each student and the several contradictory feedback indicators.

[0195] The system of this embodiment can be used to execute the method of any of the above embodiments. The implementation principles and technical effects are similar and will not be described in detail here.

Claims

1. A method for evaluating the effectiveness of online learning, characterized in that: include: Acquire an online operation behavior data set, analyze the online operation behavior data set, and determine a weak subjective dimension effectiveness indicator set; Acquiring a physiological data set, analyzing the physiological data set, and determining a set of strong objective dimension performance indicators; Based on the strong objective dimension effectiveness indicator set, the weak subjective dimension effectiveness indicator set is analyzed to determine the deep learning effectiveness evaluation result; Acquire a learning feedback data set, analyze the learning feedback data set based on the deep learning effectiveness indicator evaluation result, and determine and output an online learning effectiveness report.

2. The method according to claim 1, characterized in that: The online operation behavior data set includes learning time, interaction frequency, pause times, and fast-forward times. The analyzing the online operation behavior data set to determine the weak subjective dimension effectiveness indicator set includes: Obtaining course content segmentation information, and determining an ideal learning duration and a normal interaction frequency based on the course content segmentation information; Analyze the learning time and the ideal learning time to determine a time impact index; Analyze the interaction frequency and the normal interaction frequency to determine an interaction impact index; Analyze the number of pauses and the number of fast-forwards to determine a pause impact index and a fast-forward impact index respectively; Determining a weak subjective comprehensive evaluation index according to the duration impact index, the interaction impact index, the pause impact index, and the fast-forward impact index; The weak subjective dimension effectiveness indicator set is constructed according to the duration impact index, the interaction impact index, the pause impact index, the fast-forward impact index and the weak subjective comprehensive evaluation index.

3. The method according to claim 2, characterized in that The weak subjective comprehensive evaluation index is determined according to the duration impact index, the interaction impact index, the pause impact index and the fast-forward impact index, specifically as the following formula: Wherein, E is the weak subjective comprehensive evaluation index, k1 is the preset time influence coefficient, T is the learning time, α1 is the preset time influence index, T d For the ideal study time, is the duration impact index, k2 is the preset interaction impact coefficient, I is the interaction frequency, α2 is the preset interaction impact index, I d is the normal interaction frequency, is the interaction influence index, k3 is the preset pause influence coefficient, P is the number of pauses, β is the preset pause influence index, k3·P β is the pause impact index, k4 is the preset fast-forward impact coefficient, α3 is the preset fast-forward impact index, F is the fast-forward times, is the fast-forward impact index.

4. The method according to claim 2, characterized in that: The physiological data set includes eye movement data, expression change data, heart rate change data and skin conductance change data. The analysis of the physiological data set to determine a strong objective dimension performance indicator set includes: Performing focus change frequency analysis on the eye movement data to determine an attention index; Performing emotion change analysis on the expression change data to determine an emotion index; According to the attention index and the emotion index, determining an attention impact index and an emotion impact index respectively, and determining an index representing physiological effectiveness accordingly; Determining an average heart rate and an average skin conductivity respectively according to the heart rate change data and the skin conductance change data; According to the average heart rate and the average skin conductivity, a heart rate influence index and a skin conductance influence index are determined respectively, and a deep physiological effectiveness index is determined accordingly; The strong objective dimension performance indicator set is constructed according to the attention influence index, the emotion influence index, the physiological performance index, the heart rate influence index, the skin conductance influence index and the deep physiological performance index.

5. The method according to claim 4, characterized in that According to the attention index and the emotion index, an attention impact index and an emotion impact index are determined respectively, and the physiological effectiveness index is determined accordingly, which is specifically the following formula: C=k a ·(e γA -1)+k e ·(-δS 2 +ψS); Wherein, C is the index characterizing physiological efficacy, k a is the preset attention influence coefficient, γ is the preset attention adjustment index, A is the attention index, k a ·(e γA -1) is the attention influence index, k e is the preset emotion influence coefficient, δ is the preset emotion suppression parameter, ψ is the preset emotion enhancement parameter, S is the emotion index, k e ·(-δS 2 +ψS) is the emotion impact index.

6. The method according to claim 4, characterized in that According to the average heart rate and the average skin conductivity, the heart rate influence index and the skin conductance influence index are determined respectively, and the deep physiological effectiveness index is determined accordingly, which is specifically the following formula: Where D is the deep physiological efficacy index, k H is the preset heart rate influence coefficient, λ is the preset heart rate influence adjustment parameter, H is the average heart rate, H t is the reference value of resting heart rate, is the heart rate impact index, k G is the preset skin conductance influence coefficient, G is the average skin conductivity, k G G is the skin conductance impact index.

7. The method according to claim 6, characterized in that The step of analyzing the weak subjective dimension effectiveness indicator set based on the strong objective dimension effectiveness indicator set to determine the deep learning effectiveness evaluation result includes: The strong objective dimension performance indicator set and the weak subjective dimension performance indicator set are combined to determine a deep learning performance evaluation indicator set corresponding to each student; Obtain an indicator weight set, and based on the indicator weight set and the deep learning effectiveness evaluation indicator set corresponding to each student, construct a multi-user deep learning effectiveness scoring matrix; Determining a deep learning effectiveness score for each student according to the multi-user deep learning effectiveness scoring matrix; The multi-user deep learning effectiveness rating matrix and the deep learning effectiveness ratings of all trainees are used as the deep learning effectiveness evaluation results.

8. The method according to claim 7, characterized in that Based on the indicator weight set, according to the deep learning effectiveness evaluation indicator set corresponding to each student, a multi-user deep learning effectiveness scoring matrix is ​​constructed, which is specifically the following matrix expression: Where I is the multi-user deep learning performance scoring matrix, Y mn is the nth evaluation indicator in the deep learning effectiveness evaluation indicator set of the mth student, W n is the weight value corresponding to the nth evaluation indicator in the indicator weight set; The deep learning effectiveness score of each student is determined according to the multi-user deep learning effectiveness score matrix, which is specifically the following formula: Among them, RI i is the score of the deep learning effectiveness of the i-th student, n is the total number of evaluation indicators, I j It is the effectiveness score of the jth evaluation indicator corresponding to the current student in the multi-user deep learning effectiveness scoring matrix.

9. The method according to claim 8, characterized in that The step of analyzing the learning feedback data set based on the deep learning effectiveness indicator evaluation result, and determining and outputting an online learning effectiveness report includes: Analyze the learning feedback data set to determine a multi-type feedback indicator information set corresponding to each student; Extracting the corresponding deep learning effectiveness evaluation indicator set in the multi-user deep learning effectiveness scoring matrix according to the multi-type feedback indicator information set; Based on the multi-type feedback indicator information set, analyzing the deep learning effectiveness evaluation indicator set, and determining a number of contradictory feedback indicators; The online learning effectiveness report is constructed and outputted based on the deep learning effectiveness score of each student and a plurality of the conflicting feedback indicators.

10. An online learning effectiveness evaluation system, characterized in that: include: A weak subjective analysis module, used to obtain an online operation behavior data set, analyze the online operation behavior data set, and determine a weak subjective dimension effectiveness indicator set; A strong objective analysis module, used to obtain a physiological data set, analyze the physiological data set, and determine a strong objective dimension performance indicator set; A deep analysis module, used to analyze the weak subjective dimension effectiveness indicator set based on the strong objective dimension effectiveness indicator set to determine the deep learning effectiveness evaluation result; The output module is used to obtain a learning feedback data set, analyze the learning feedback data set based on the deep learning effectiveness indicator evaluation result, and determine and output an online learning effectiveness report.

Images