CN110782189A - Method and device for measuring teaching level based on cognitive load - Google Patents

Method and device for measuring teaching level based on cognitive load Download PDF

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CN110782189A
CN110782189A CN201911120825.6A CN201911120825A CN110782189A CN 110782189 A CN110782189 A CN 110782189A CN 201911120825 A CN201911120825 A CN 201911120825A CN 110782189 A CN110782189 A CN 110782189A
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张羽
吕勇强
刘惠琴
李曼丽
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Abstract

The application discloses a method and a device for measuring teaching level based on cognitive load. The method includes measuring a pre-school assessment score of a learner prior to teaching; collecting various neuro-physiological signals of a learner in a teaching process, respectively extracting neuro-physiological data characteristic values from the various neuro-physiological signals, and carrying out fusion processing on the various neuro-physiological characteristic values to obtain a cognitive load score of the learner; measuring the post-learning evaluation score of the learner after the teaching is finished; and carrying out modeling analysis according to the difference between the measured scores of the learners before and after the learner teaches and the cognitive load score of the learner to obtain the external cognitive load generated by the teaching content, and judging the teaching level according to the external cognitive load generated by the teaching content. The quantitative evaluation of teaching material design/teacher lecture level based on student learning efficiency is realized, the measurement result has transverse comparability, and the subjectivity and incomparable problems of expert evaluation are reduced to the greatest extent.

Description

Method and device for measuring teaching level based on cognitive load
Technical Field
The application relates to the field of computer cognitive psychology, in particular to a method and a device for measuring teaching level based on cognitive load.
Background
The classroom teaching of teachers is an important link for assisting students in learning. The lecture logic is clear, the expression is simple and smooth, the explanation is vivid, the learners can understand the learning content more easily, the learning difficulty is reduced, and the learning efficiency is improved. Teaching materials and courseware (including traditional paper teaching materials, slide show, on-line course and other electronic courseware) are important materials and bases for learning. The good teaching materials and courseware can make learners understand the learning content more easily, reduce the learning difficulty and improve the learning efficiency. However, the evaluation of the teaching level of teachers and teaching materials is lack of objective evaluation standards, and the subjective evaluation of experts, college teachers and students is mainly relied on.
The traditional method for quantitatively measuring the design level of the teaching materials mainly depends on experts to subjectively score the teaching materials according to certain dimensionality. The evaluation of the teaching level of the teaching of the teacher is more subjective, and the teaching level evaluation method is mainly characterized in that the expert and the peer teacher listen to the lessons and evaluate the lessons during the demonstration lessons, the lecture competition or the daily class teaching of the teacher. For example: the four dimensions of the educational department evaluation materials are: the method comprises the following steps of thought level, scientific level, teaching level and text and picture level, wherein specific indexes are described as follows:
Figure BDA0002275421980000011
Figure BDA0002275421980000021
as can be seen from the above table, although the items have a certain structure, in actual scoring, on the one hand, the items have subjectivity, and on the second hand, the learning efficiency of the students using the teaching material cannot be directly evaluated, so that a more objective and effective reference basis for selecting the teaching material cannot be provided for a large number of teachers and learners, and objective and accurate feedback and guidance opinions cannot be provided for the teachers to improve the teaching level. This is not favorable to teaching materials market and the more healthy development of courseware platform, also is not favorable to the promotion of teaching quality management.
Disclosure of Invention
The application provides a method for measuring teaching level based on cognitive load, which comprises the following steps:
measuring the pre-school evaluation score of the learner before teaching;
collecting various neuro-physiological signals of a learner in a teaching process, respectively extracting neuro-physiological data characteristic values from the various neuro-physiological signals, and carrying out fusion processing on the various neuro-physiological characteristic values to obtain a cognitive load score of the learner;
measuring the post-learning evaluation score of the learner after the teaching is finished;
and carrying out modeling analysis according to the difference between the measured scores of the learners before and after the learner teaches and the cognitive load score of the learner to obtain the external cognitive load generated by the teaching content, and judging the teaching level according to the external cognitive load generated by the teaching content.
The method for measuring teaching level based on cognitive load as described above further comprises grouping learners, specifically: selecting students of proper age corresponding to the teaching materials/teacher, learning in advance to confirm that the students do not have related knowledge to learn in advance, randomly dividing the students into a plurality of learning groups according to the pre-learning evaluation result of the teaching materials or the teaching contents used in the teaching, and enabling the pre-learning evaluation results of the learning groups to be not obviously different.
The method for measuring educational levels based on cognitive load as described above, wherein the neurophysiological signals include at least one of EEG different frequency band energy ratios, heart rate mean and standard deviation, picomean, and PPG morphometry feature values.
The method for measuring teaching level based on cognitive load, wherein modeling analysis is performed according to the assessment scores and the characteristic values of the neurophysiological signals before and after learner teaching, comprises the following steps:
reducing the multi-modal neuro-physiological data characteristic values of each omic trainee reading different teaching materials or listening to different teachers to give a cognitive load score through factor analysis;
taking the evaluation score difference of each omic trainee before and after teaching as internal load;
and carrying out hierarchical multiple regression model analysis on the cognitive load score and the internal load to determine the external cognitive load generated by the teaching content.
The method for measuring teaching level based on cognitive load comprises the following steps of:
load ij＝α 0+α 1ΔS ij+ε ij+μ j
wherein, load ijIs the total cognitive load, Δ S, generated by the learner i reading the textbook/listening teacher giving a lecture j ijIs the score difference (back measurement-front measurement) of reading the teaching material j ijIs the individual residual, μ jIs the residual error of each teaching material/teacher layer;
comparing epsilon corresponding to different teaching materials/teachers through t test and variance analysis ijAnd mu jWhether they are the same, epsilon ijAnd mu jThe larger the teaching material/teacher gives a lecture, the higher the external cognitive load generated by the teaching material/teacher gives a lecture, and the worse the teaching material design/teacher gives a lecture; epsilon ijAnd mu jThe smaller the instruction material/teacher lecture generates the lower the external cognitive load, the higher the instruction material design/teacher lecture level.
The application also provides a device based on cognitive load measures teaching level, includes:
the pre-and post-teaching score evaluation module is used for measuring the pre-learning evaluation score of the learner before teaching and measuring the post-learning evaluation score of the learner after the teaching is finished;
the neuro-physiological signal acquisition module is used for acquiring various neuro-physiological signals of the learner in the teaching process;
the cognitive load score processing module is used for respectively extracting the neurophysiological data characteristic values from the various neurophysiological signals and carrying out fusion processing on the various neurophysiological characteristic values to obtain the cognitive load score of the learner;
and the teaching level measuring module is used for carrying out modeling analysis according to the difference between the measured scores of the learners before and after teaching and the cognitive load score of the learners to obtain the external cognitive load generated by the teaching content and judging the teaching level according to the external cognitive load generated by the teaching content.
The apparatus for measuring teaching levels based on cognitive load as described above further comprises a learner selecting module for selecting the students of the proper age corresponding to the teaching materials/teacher's lecture contents to be tested, learning in advance to confirm that the students have not learned relevant knowledge in advance, and randomly dividing the students into a plurality of learning groups according to the pre-school evaluation results of the teaching materials or the lecture contents used in the teaching, wherein the pre-school evaluation results of the learning groups are not significantly different.
The device for measuring teaching level based on cognitive load as described above, wherein the teaching level measuring module is specifically configured to reduce the characteristic values of the multi-modal neuro-physiological data of each omic learner reading different teaching materials or listening to different teachers for teaching into cognitive load scores through factor analysis; taking the evaluation score difference of each omic trainee before and after teaching as internal load; and carrying out hierarchical multiple regression model analysis on the cognitive load score and the internal load to determine the external cognitive load generated by the teaching content.
The device for measuring teaching level based on cognitive load as described above, wherein the teaching level measuring module is specifically configured to obtain a calculated individual residual and a residual of each teaching material/teacher level through cognitive load scores and internal loads, and determine the teaching level according to the individual residual and the residual of the teaching material/teacher levels corresponding to different teaching materials/teachers through t-test and variance analysis.
The beneficial effect that this application realized is as follows: by adopting the method for measuring the teaching level based on the cognitive load, the quantitative evaluation of teaching material design/teacher lecture level based on the learning efficiency of students can be realized, the measurement result has transverse comparability, and the subjectivity and incomparable problem of expert evaluation are reduced to the greatest extent.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a flowchart of a method for measuring teaching levels based on cognitive load according to an embodiment of the present application;
fig. 2 is a schematic diagram of PPG waveform measurement feature values;
FIG. 3 is a flow chart of a specific method of modeling analysis.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
The embodiment of the application provides a method for measuring teaching level based on cognitive load, wherein the step of measuring the teaching level comprises measuring teaching materials or teacher teaching level, as shown in fig. 1, the method comprises the following steps:
step 110, measuring the pre-school evaluation score of the learner before teaching;
in order to accurately measure the teaching level generated by teaching materials or the teaching contents of teachers, the method selects students of the suitable age corresponding to the teaching materials/the teaching contents of the teachers to be measured, and knows and confirms that the students do not have relevant learning knowledge in advance, and then performs preschool evaluation on the teaching materials or the teaching contents used in the teaching;
in addition, in order to reduce the influence of preschool knowledge on the teaching level, the application randomly divides the tested learners into a plurality of learning groups (such as J teaching groups, J is 1, 2, … …, J) according to the preschool evaluation results of the tested learners, and the overall preschool evaluation results are not obviously different among the learning groups.
Step 120, collecting the neurophysiological signals of the learner in the teaching process, and extracting the neurophysiological data characteristic values from the neurophysiological signals;
learners of each learning group read the same measured teaching material courseware or listen to the classroom teaching of the same measured teacher, and acquire the neural physiological signals of the learners in the teaching process;
the neural physiological signals of the learner include at least one of energy ratios of different frequency bands of EEG (electroencephalogram), PPG (blood vessel volume pulse wave) morphology characteristic value, heart rate mean value and standard deviation and skin electricity mean value; preferably, different neuro-physiological information is selected to be acquired according to specific acquisition equipment and the age of the tested population, for example, at least one characteristic value of fusion EEG energy ratio in different frequency bands, heart rate mean value and standard deviation, skin electric mean value and PPG morphology is adopted.
(1) Collecting energy ratio of EEG (electroencephalogram) in alpha and theta frequency bands
The energy of the EEG signal is expressed in the time domain as the square of the amplitude:
Figure BDA0002275421980000051
the average power may be obtained by dividing the energy by the number of samples:
Figure BDA0002275421980000052
the time domain information cannot show the frequency domain information of the signal, and the time-frequency domain conversion is usually performed by using fourier transform (FFT); since the acquired EEG signal is a discrete sequence s (n), the Discrete Fourier Transform (DFT) is mostly used in practical operation:
Figure BDA0002275421980000053
where S (k) is the frequency domain information of the acquired EEG signal, s (n) represents a discrete sequence of acquired EEG signals, DFT [ s (n)]Denotes performing discrete Fourier transform on a discrete sequence s (N), where N is the number of samples, and k denotes the sample sequence number，
Figure BDA0002275421980000054
Is a transformation matrix;
the method comprises the following steps of decomposing an original EEG signal into five frequency bands by operations of filtering, denoising, frequency division and the like on collected data, wherein the frequency bands of the EEG signal are divided into the following types from low to high according to the frequency:
EEG frequency band Frequency of
Delta(δ) 1～4Hz
Theta(θ) 4～8Hz
Alpha(α) 8～12Hz
Beta(β) 13～30Hz
Gamma(γ) 31～45Hz
In the time domain, the energy ratio of different frequency bands is calculated by taking α waves as an example, and the calculation can be performed by the following formula:
Figure BDA0002275421980000055
wherein epsilon (E) f-α) Representing the ratio of α wave bands to total energyA value; e f-αRepresents the energy of the α wave band, SIGMA E fRepresenting the accumulated sum of the energies of all frequency bands; and respectively calculating the energy ratio of alpha and theta wave frequency bands, wherein the lower the alpha energy ratio, the higher the theta energy ratio, and the higher the cognitive load.
(2) Acquisition of PPG (vascular volume pulse wave) morphometric characteristic values
The PPG pulse waveform is acquired in a discrete form, so that the first-order and second-order dispersion differences of the PPG pulse waveform are respectively calculated to obtain a first-order derivative and a second-order derivative;
the first derivative is: y1(n) ═ x (n +1) -x (n)
The second derivative is: y2(n) ═ x (n +2) -2x (n +1) + x (n)
FIG. 2 is a schematic diagram of a PPG waveform curve and its first and second derivative waveform curves;
on the PPG waveform curve: x is the systolic peak, y is the diastolic peak, z is the dicrotic notch, tpi is the pulse interval, Δ T is the peak interval time between systole and diastole, w is half the systolic peak time, a1, a2 represents the corresponding marker region;
on the first derivative curve: a1 and b1 are the first maximum and minimum points, respectively; e1 and f1 are the first maximum and minimum points, respectively, after the dicrotic notch.
On the second derivative curve: a2 and b2 are the first maximum and minimum points, respectively; e2 and f2 are the first maximum and minimum points after b1, respectively;
performing fast Fourier transform on PPG pulse waveform to obtain 6 frequency domain characteristics, namely frequency f of basic component baseMagnitude of the fundamental component | s baseI, second harmonic frequency f 2ndMagnitude of second harmonic | s 2ndFrequency f of the third harmonic 3rdAnd magnitude of third harmonic | s 3rd|；
The following table is a morphometric signature of the PPG waveform shown in connection with fig. 2:
Figure BDA0002275421980000061
Figure BDA0002275421980000071
it should be noted that, some feature values have a monotonic correspondence with the cognitive load, which depends on the specific acquisition device and the age of the population to be tested.
(3) Collecting heart rate mean and standard deviation
The mean heart rate values collected were:
Figure BDA0002275421980000072
the standard deviation of the heart rate collected was:
Figure BDA0002275421980000073
wherein, α iFor the heart rate of the ith sample number acquired, represents the mean value of the heart rate over time t; σ represents heart rate standard deviation; n denotes the number of samples.
(4) Collecting characteristic value of galvanic skin decomposition
The time-series data of the skin conductance can be characterized by slowly varying Skin Conductance Levels (SCL) and rapidly varying phase activity, i.e. Skin Conductance Response (SCR), which can be abbreviated to the following form:
SC＝SC tonic+SC phasic
wherein SC is the skin electricity time sequence data, SC tonicTo the level of skin conductance, SC phasicIs a skin conductance response;
the skin electric mean value:
Figure BDA0002275421980000075
wherein N is fs.t
Wherein, γ iFor the acquired electrodermal value of the ith sample number,
Figure BDA0002275421980000076
representing the mean value of the skin over time t (seconds); fs represents the sampling rate; n represents the number of samples in time t. The higher the cognitive load, the higher the mean value of the skin conductance level will be.
Step 130, measuring the evaluation score of the learner after the teaching is finished;
140, carrying out modeling analysis according to the difference of the measured scores and the characteristic values of the neurophysiological signals before and after the learner teaches to obtain the external cognitive load generated by the teaching content;
as shown in fig. 3, the modeling analysis according to the evaluation score and the characteristic value of the neurophysiological signal before and after the learner's teaching specifically includes the following sub-steps:
step 310, reducing the characteristic values of the multi-modal neuro-physiological data of each omic trainee reading different teaching materials or listening to different teachers to give a cognitive load score through factor analysis;
in order to enable the measurement result to be more accurate, the method adopts a mode of fusing various neurophysiological data characteristic values as a basis of modeling, and performs factor analysis and dimension reduction on the various neurophysiological data characteristic values to obtain a total cognitive load score;
step 320, taking the evaluation score difference of each omic trainee before and after teaching as an internal load;
and calculating the difference value between the post-school evaluation score of the learner after the teaching is finished and the pre-school evaluation score before the teaching, and taking the difference value as the internal load.
Step 330, performing hierarchical multiple regression model analysis on the cognitive load scores and the internal loads, and determining external cognitive loads generated by the teaching contents;
in the embodiment of the present application, the analysis of the hierarchical multiple regression model specifically includes:
load ij＝α 0+α 1ΔS ij+ε ij+μ j
wherein, α 0And α 1For the parameter to be estimated, load ijIs the total cognitive load, Δ S, generated by the learner i reading the textbook/listening teacher giving a lecture j ijIs that the learner i reads the teaching material-Score difference (back test-front test) of front and back test of j for teachers teaching lessons, epsilon ijIs the individual residual, μ jIs the residual error of each teaching material/teacher layer;
specifically, the parameter to be estimated α 0And α 1The values of (A) are as follows:
Figure BDA0002275421980000081
Figure BDA0002275421980000082
wherein the content of the first and second substances,
Figure BDA0002275421980000083
Figure BDA0002275421980000084
denotes an occupant, Δ S' iThe score difference, y, measured before and after reading the same teaching material/listening to the same teacher for the learner i iIs load i，load iRepresenting the total cognitive load generated by the learner i reading the textbook/listening teacher.
ε ijAnd mu jThe composition is stripped of external load after students learn harvest, namely external cognitive load generated by teaching contents (reading different teaching material courseware or listening to different teachers for lectures).
And 150, judging the teaching level according to the external cognitive load generated by the teaching content.
Specifically, the teaching level was judged by t-test and analysis of variance:
Figure BDA0002275421980000091
Figure BDA0002275421980000092
in the above formula:
Figure BDA0002275421980000093
Figure BDA0002275421980000094
Figure BDA0002275421980000095
Figure BDA0002275421980000096
thereby comparing (epsilon) corresponding to different teaching materials/teachers ijAnd mu j) Whether or not they are the same, (epsilon) ijAnd mu j) The larger the teaching material/teacher gives a lecture, the higher the external cognitive load generated by the teaching material/teacher gives a lecture, and the worse the teaching material design/teacher gives a lecture; (ε ijAnd mu j) The smaller the instruction material/teacher lecture generates the lower the external cognitive load, the higher the instruction material design/teacher lecture level.
Example two
The embodiment of the application provides a device based on cognitive load measurement teaching level, includes:
the pre-and post-teaching score evaluation module is used for measuring the pre-learning evaluation score of the learner before teaching and measuring the post-learning evaluation score of the learner after the teaching is finished;
the neuro-physiological signal acquisition module is used for acquiring various neuro-physiological signals of the learner in the teaching process;
the cognitive load score processing module is used for respectively extracting the neurophysiological data characteristic values from the various neurophysiological signals and carrying out fusion processing on the various neurophysiological characteristic values to obtain the cognitive load score of the learner;
and the teaching level measuring module is used for carrying out modeling analysis according to the difference between the measured scores of the learners before and after teaching and the cognitive load score of the learners to obtain the external cognitive load generated by the teaching content and judging the teaching level according to the external cognitive load generated by the teaching content.
Further, the teaching level measurement module is specifically used for reducing the multi-modal neuro-physiological data characteristic values of the students reading different teaching materials or listening to different teachers to give lessons into cognitive load scores through factor analysis; taking the evaluation score difference of each omic trainee before and after teaching as internal load; performing hierarchical multiple regression model analysis on the cognitive load score and the internal load to determine an external cognitive load generated by the teaching content;
furthermore, the teaching level measurement module is specifically configured to obtain a calculated individual residual and residuals at each teaching material/teacher level through the cognitive load score and the internal load, and determine the teaching level according to the individual residual and the residuals at the teaching material/teacher level corresponding to different teaching materials/teachers through t-test and variance analysis.
The device also comprises a selected learner module which is used for selecting the students with the proper age corresponding to the content of the tested teaching materials/teachers, learning and confirming that the students do not have related knowledge in advance, and randomly dividing the students into a plurality of learning groups according to the pre-school evaluation results of the teaching materials or the content of the lectures used in the teaching, wherein the pre-school evaluation results among the learning groups have no significant difference.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims (9)

1. A method for measuring teaching levels based on cognitive load, comprising:
measuring the pre-school evaluation score of the learner before teaching;
collecting various neuro-physiological signals of a learner in a teaching process, respectively extracting neuro-physiological data characteristic values from the various neuro-physiological signals, and carrying out fusion processing on the various neuro-physiological characteristic values to obtain a cognitive load score of the learner;
measuring the post-learning evaluation score of the learner after the teaching is finished;
and carrying out modeling analysis according to the difference between the measured scores of the learners before and after the learner teaches and the cognitive load score of the learner to obtain the external cognitive load generated by the teaching content, and judging the teaching level according to the external cognitive load generated by the teaching content.
2. The method of claim 1, further comprising grouping learners by: selecting students of proper age corresponding to the teaching materials/teacher, learning in advance to confirm that the students do not have related knowledge to learn in advance, randomly dividing the students into a plurality of learning groups according to the pre-learning evaluation result of the teaching materials or the teaching contents used in the teaching, and enabling the pre-learning evaluation results of the learning groups to be not obviously different.
3. The cognitive load measurement instructional level-based method of claim 1 wherein the neurophysiological signals include at least one of EEG different frequency band energy ratios, heart rate mean and standard deviation, picomean, and PPG morphometry eigenvalues.
4. The method for measuring educational levels based on cognitive load as set forth in claim 1, wherein the modeling analysis is performed based on the evaluation scores and the characteristic values of the neurophysiological signals before and after the learner's education, and the method comprises the following sub-steps:
reducing the multi-modal neuro-physiological data characteristic values of each omic trainee reading different teaching materials or listening to different teachers to give a cognitive load score through factor analysis;
taking the evaluation score difference of each omic trainee before and after teaching as internal load;
and carrying out hierarchical multiple regression model analysis on the cognitive load score and the internal load to determine the external cognitive load generated by the teaching content.
5. The cognitive load measurement instructional level-based method of claim 4,
the analysis of the hierarchical multiple regression model for the cognitive load score and the internal load specifically comprises the following steps:
load ij＝α 0+α 1ΔS ij+ε ij+μ j
wherein, α 0And α 1For the parameter to be estimated, load ijIs the total cognitive load, Δ S, generated by the learner i reading the textbook/listening teacher giving a lecture j ijIs the score difference (back measurement-front measurement) of reading the teaching material j ijIs the individual residual, μ jIs the residual error of each teaching material/teacher layer;
comparing epsilon corresponding to different teaching materials/teachers through t test and variance analysis ijAnd mu jWhether they are the same, epsilon ijAnd mu jThe larger the teaching material/teacher gives a lecture, the higher the external cognitive load generated by the teaching material/teacher gives a lecture, and the worse the teaching material design/teacher gives a lecture; epsilon ijAnd mu jThe smaller the instruction material/teacher lecture generates the lower the external cognitive load, the higher the instruction material design/teacher lecture level.
6. An apparatus for measuring teaching levels based on cognitive load, comprising:
the pre-and post-teaching score evaluation module is used for measuring the pre-learning evaluation score of the learner before teaching and measuring the post-learning evaluation score of the learner after the teaching is finished;
the neuro-physiological signal acquisition module is used for acquiring various neuro-physiological signals of the learner in the teaching process;
the cognitive load score processing module is used for respectively extracting the neurophysiological data characteristic values from the various neurophysiological signals and carrying out fusion processing on the various neurophysiological characteristic values to obtain the cognitive load score of the learner;
and the teaching level measuring module is used for carrying out modeling analysis according to the difference between the measured scores of the learners before and after teaching and the cognitive load score of the learners to obtain the external cognitive load generated by the teaching content and judging the teaching level according to the external cognitive load generated by the teaching content.
7. The apparatus for measuring teaching levels based on cognitive load as set forth in claim 6, further comprising a learner selecting module for selecting students of suitable ages corresponding to the measured textbook/teacher's lecture contents and learning in advance to confirm that the students have not learned related knowledge in advance, wherein the students are randomly divided into a plurality of learning groups according to the pre-school assessment results on the textbook or the lecture contents used in the current teaching without significant difference in pre-school assessment results between the learning groups.
8. The device for measuring teaching levels based on cognitive load according to claim 6, wherein the teaching level measurement module is specifically configured to reduce the multi-modal neuro-physiological data characteristic values of each omic learner for reading different teaching materials or listening to different teachers for teaching into cognitive load scores through factor analysis; taking the evaluation score difference of each omic trainee before and after teaching as internal load; and carrying out hierarchical multiple regression model analysis on the cognitive load score and the internal load to determine the external cognitive load generated by the teaching content.
9. The apparatus of claim 6, wherein the teaching level measurement module is specifically configured to obtain the calculated personal residuals and the residuals at each teaching material/teacher level according to the cognitive load scores and the internal loads, and determine the teaching level according to the personal residuals corresponding to different teaching materials/teachers and the residuals at each teaching material/teacher level through t-test and variance analysis.
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* Cited by examiner, † Cited by third party
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CN111553618A (en) * 2020-05-15 2020-08-18 北京师范大学 Operation and control work efficiency analysis method, device and system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111553618A (en) * 2020-05-15 2020-08-18 北京师范大学 Operation and control work efficiency analysis method, device and system

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