CN110059232B - Data visualization method based on user experience measurement - Google Patents
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Abstract
The invention discloses a data visualization method based on user experience measurement. The invention builds a metric model of data visualization narrative experience. A research method combining the qualitative and quantitative methods based on an eye movement research method and an electrocardio research method and a parallel nested research method is provided. The eye movement measurement and the electrocardio measurement are combined in the experimental process of the data visualization narrative example according to the proposed research method, the quantitative value of the experience index is extracted, and the experimental analysis result is applied to the optimization and improvement of the data visualization narrative method. The invention can creatively introduce an electrocardio measuring method and an eye movement measuring method into the field of data visualization narrative research, and avoids the common subjective error in the traditional measuring method by combining qualitative and quantitative methods. A space domain division method based on exploratory factor analysis is provided in the eye tracking data analysis, and a new method with higher accuracy is provided for the interest region division of the eye tracking data.
Description
Technical Field
The research relates to a data visualization method based on user experience measurement, belongs to the field of information visualization, and can provide theoretical and technical support for interdisciplinary research in various fields such as data information propagation, user cognitive behavior research and the like.
Background
The data set is converted into the image with easy understanding, namely data visualization is an important means for human to understand the content of the data and understand the data implication rule, and the data set with redundancy can be converted into the image with easy understanding. Data visualization comprehensively utilizes technologies such as computer graphics, image processing, man-machine interaction and the like, data are converted into recognizable objects, valuable information is presented to users, knowledge and intelligence are further acquired, and important influence is brought to data application development of the information era.
The invention aims to enable a data visualization product to better accord with audience habits based on a core thought of user experience and based on ergonomics and usability principles from a user. Although the research of user experience starts earlier and has a lot of mature research results, the data visualization interaction interface problem of visual representation and interaction technology symbiosis is solved without being well fused with the field of data visualization. At present, in the field of visual representation, research work is carried out on visual perception and cognition of users in a multi-attention mode to form certain authoritative design principles, different interaction technologies in a data visualization system are lacked, and particularly a system guidance method and a design principle for improving user experience satisfaction through interactive design are lacked. Therefore, according to the characteristics of the data visualization system, optimization and improvement on a user research method are performed, an interaction design theory in the data visualization system is perfected, and the key problems of how data are transmitted to audiences and perception and understanding of people in the interaction process are particularly important to solve. Meanwhile, the organic combination of data visualization and user experience research can help solve the problems caused by the fact that the user requirements are not met in the current data visualization field, provide a valuable guidance scheme for the data visualization system design approaching the psychological expectation of the user, and have important significance for the more comprehensive development of future data visualization and the application in different fields.
Disclosure of Invention
The research relates to a data visualization research method for objectively quantifying user experience based on physiological signal data, theories and models in the field of man-machine interaction are introduced, subjective self-evaluation is combined with an objective quantification method based on eye tracking data and physiological signal data, measurement research of data visualization narrative user experience is carried out, research such as user interaction technology, user demand externalization construction, usability test and iterative design in a data visualization system is carried out, and therefore the data visualization narrative model is optimized. To explore and facilitate the user's data visualization system experience is constantly approaching the user's psychological expectations. The technical scheme adopted by the invention for solving the technical problem specifically comprises the following steps:
step 1, designing a measurement model of data visualization narrative experience;
step 2, eye movement experiment design;
step 3, designing an electrocardiogram experiment;
step 4, collecting data based on a parallel nested hybrid method;
step 5, analyzing data based on a statistical method and a visualization means;
and 6, deriving an example process optimization model.
In step 1, due to the lack of quantitative data for objective evaluation of data visualization narrative experience metrics at home and abroad at the present stage, it is necessary to design a metric model for data visualization narrative experience that can combine objective quantitative research data with traditional subjective quantitative research data. According to the method, on the basis of analyzing the interrelation and the attribute among the user experience influence factors, a data visualization narrative experience measurement model is constructed according to the basic principle of hierarchical structures such as a target layer, a scheme layer, a criterion layer and the like. The model is described in detail as follows:
1. and establishing a target layer of the model, wherein the target is to comprehensively reflect the advantages and disadvantages of the visual narrative experience of the data in different layers according to the evaluation index to obtain a design method of the visual narrative application of the data with better user experience quality.
2. And establishing a standard layer of the model, wherein the standard layer corresponds to a sub-standard layer of the model aiming at user cognition and user experience, and the user experience comprises sensory experience, emotional experience, content experience and interactive experience, and a user experience measurement index corresponds to the sub-standard layer to form an index layer.
3. And establishing a scheme layer of the model, wherein the scheme layer is a corresponding method for carrying out user experience measurement.
And 2, setting a test task for a user in an experiment.
And setting a test based on free browsing of the user aiming at the target of the data visualization narrative experience measurement, and only setting a fuzzy task target for the user. And the user can freely browse in a specific page area.
Experimental studies must set a free-browsing time limit for the user based on the complexity of visualizing the narrative object's own information in the data under study. The method is to compare the experimental duration of several groups of testees through a pre-experiment to determine the task duration when the experiment is executed.
After the task of free browsing is finished, the user is asked to take vocal thinking to review the whole browsing process of the user, interview the user is carried out, an auxiliary survey table is filled in, the cognition condition of the user in the task process is assisted and known from the qualitative perspective, and eye movement tracking data is supplemented in the data analysis stage. The eye movement experiment comprises the following specific procedures:
2-1, determining a research object, and setting the research object to be specific sections in the specific data visualization narrative according to the complexity of the data visualization narrative;
2-2, an experimental hypothesis is proposed for a research object, for example, the invention integrates the user experience quantitative research of data visualization narrative with the common method of qualitative research of the data visualization narrative experience at present by adopting a parallel nesting method under a corresponding user experience metric model, and the following hypothesis is proposed based on the data visualization narrative example:
from the perspective of the sensory experience:
h1: the influence of the text and visualization layout on the user behavior reflects difference;
h2: a plurality of visualization views;
h3: legends affect the behavior of users learning understanding visualizations;
h4: the control distribution and the metaphor thereof have difference on the influence of the user behavior.
From the perspective of the interactive experience:
h5: the test person can quickly learn the product structure and find the relevant functions to visualize the narrative.
From the perspective of emotional experience:
h6: the data visualization narrative may cause the mood of the subject to be evoked.
And 2-3, dividing the eye movement space domain based on exploratory factor analysis, and facilitating the later extraction of effective data characteristics. The subjects were pre-divided into several areas of Interest (AOI) according to a hotspot Map (Heat Map) of the eye movement data. In the experiment, 9 AOI areas can be divided according to the hotspot graph. And obtaining the measured values of 10 eye movement indexes such as the fixation point duration, the fixation time sum, the fixation point number and the like in the AOI through the eye movement experiment in the eye movement experiment.
The eye movement index has different measurement units, so that the eye movement index needs to be standardized and listed as a region-of-interest detection matrix.
The test factor analysis is sufficient by the KMO test, which generally requires the test value KMO > 0.5. And a Bartlett test is carried out, and whether the overall correlation coefficient matrix is a unit matrix is checked so as to judge whether the group of data is suitable for factor analysis.
Bartlett test statistic-corrected likelihood ratio:
where n is the number of data records, p is the number of variables factored, and R is the sample correlation matrix.
The common factors are identified by using a principal component analysis method, 4 common factors are shared by data visualization narrative technology classification methods, m factors are taken according to different research objects, and the principal component of the factor analysis is decomposed into:
wherein the principal component factor analysis of the sample correlation matrix R is based on eigenvalue-eigenvector pairs (λ)i,ei) I 1, p and λ1≤λ2≤…≤λpThe designation is made to the user,
in order to estimate the matrix of the factor loadings,
for the special factor variance, S is the covariance matrix,
the factor load on the jth factor for the ith variable.
In some cases, the number of common factors set for the experimental cases is not clear, and the following can be solved based on the fitting model results of different m:
the maximum result is the optimal factor number.
Classifying the selected AOI according to the result of the common factor analysis;
the maximum quartic method orthogonal rotation method (Quartmax) is selected, and the method can keep the override factors of each variable with high load on the factors, so that the factors required for explaining each variable are the minimum, and the experimental target can be met through inspection. The rotated factors still keep independence, and the rotation is iterated to convergence to obtain a rotation component matrix, wherein several items with high components are selected as final AOI, and the specific implementation is as follows:
note the book
Loading the matrix for the rotated factor, and
obtaining:
wherein, wherein
For the factor load of the ith variable over the jth factor,
for the commonality variance, the required rotational component matrix can be obtained when V is maximized.
And 2-4, determining the number of the tested persons and characteristic information, such as sex, age, education degree and the like. According to the relevant theory of user research, before an experiment, the selected target population must be ensured to be suitable for the experiment. Due to the limitation of the eye tracking equipment, whether the tested physiological condition meets the experimental requirements needs to be examined, and the testee requires normal vision of both eyes without astigmatism.
And 2-5, selecting appropriate eye movement measurement indexes, such as eye movement tracks, the number of fixation points, fixation time and the like, wherein the user experience is manifold, and each experiment should select the eye movement measurement indexes capable of representing the user experience indexes needing measurement.
And 2-6, performing eye movement test, wherein the test is divided into a pre-test stage and an execution test stage, and the pre-test stage is a formal execution test stage to solve the problem of influencing the test.
And 2-7, preprocessing the acquired eye movement measurement index data. During the data pre-processing stage, noise is typically filtered using sliding mean filtering. The sliding mean filtering is based on a neighborhood averaging method, the noise is eliminated by continuously taking an average value in an interval to replace an original sequence value, the filtering effect of the sliding mean filtering is influenced by the size of a window taken by the sliding mean filtering, and the size of the window is controlled to be about 3 in the noise reduction of general eye movement data. Besides noise, data loss can occur, and a relatively common interpolation method is adopted for data compensation.
And 3, according to research and display of electrocardiosignals, the duration of short-term psychological signal detection needs more than five minutes, so that the user experiment condition needs to be known through pre-experiments aiming at the electrocardio experiment of data visualization narrative, and the experiment duration is controlled through screening and processing of experimental objects.
The experiment design based on the electrocardio comprises the following specific steps:
3-1, after sitting still for 5 minutes, recording the electrocardiosignals of the testee in the time period, and using the data as a later data processing reference standard;
and 3-2, formally starting the experiment, and recording the electrocardiosignal data by taking time as a sequence.
And 3-3, taking every 5 minutes after the experiment is finished as an electrocardiosignal data segment, and obtaining a group of experimental data by data processing of each data segment and comparing the experimental data with a reference standard.
3-4, preprocessing the acquired electrocardiosignal data by using wavelet denoising, wherein the wavelet denoising can effectively remove white noise in the electrocardiosignal and has good effect on extracting an effective part in the electrocardiosignal.
The wavelet denoising method mainly comprises the following steps:
performing wavelet transformation on original data;
rejecting noise by performing threshold processing;
and thirdly, performing wavelet inverse transformation to obtain the required de-noising data.
And 3-5, respectively calculating time domain, frequency domain and nonlinear indexes of the preprocessed electrocardiosignal data.
And 4, applying a parallel nesting method to experience research of data visualization narratives, namely embedding one data into a frame and a structure of another data by using a research data source in one normal form and using another data in another normal form in order to increase the interpretation degree of the data source, wherein the parallel nesting method is embodied as a quantitative eye tracking and electrocardiosignal research method and a qualitative user subjective evaluation method. The invention provides a user experience measurement method model of a data visualization narrative system based on parallel nesting by combining research steps of a parallel nesting research method and eye movement and electrocardiosignal experimental characteristics, which comprises the following specific steps:
and 4-1, removing abnormal values in the quantitative acquisition data by qualitative research, such as the missing of eye tracking data of a certain area. By studying the reasons of the abnormal values, whether the user subjectively intends not to browse the area or the omission caused by insufficient narrative is visualized, whether the data is eliminated or the data is included in the statistical analysis can be determined.
And 4-2, screening the quantitatively collected samples by the aid of qualitative research. For example, for a group of HRV indexes with significant changes before and after the experiment, compared with the emotion scale acquisition result of the user, samples meeting the purpose of exploring the positive emotion of the user through the experiment are selected.
4-3. improving the accuracy of the analysis results of quantitative studies through qualitative studies, such as eye tracking data analysis where the same result may be perceived by different users, long-term fixations may be due to the attractiveness of data visualization narratives, or may be due to the difficulty of extracting information by users, which aids in more accurate analysis of user cognition.
And 4, improving the precision of the analysis result of the quantitative research through qualitative research. For example, HRV quantification data for a set of analysis results with positive mood arousal, but HRV measurement metrics are typically processed over 5 minutes. The more precise time of the user's emotional peak in the narrative can be further explored by interviews with the user and vocal thinking review.
And 5, analyzing data based on a statistical method and a visualization means. Verifying the provided hypothesis, and analyzing the experimental data to obtain an analysis result;
and 6, demonstrating experimental assumptions according to the analysis results, providing a corresponding data visualization narrative optimization scheme, and establishing a process optimization model.
The invention has the following effective effects:
according to the invention, an experience measurement model of data visualization narrative is established, eye movement and electrocardio signals are adopted to carry out quantitative research on user behaviors, and an exploratory factor analysis-based spatial domain division method is innovatively provided, so that the eye movement information of a user can be effectively extracted; a user experience measurement method based on a parallel nested data visualization narrative system is provided, and collected quantitative data and materialized data are efficiently integrated; data analysis is carried out by adopting a statistical method and a visualization method to obtain a plurality of innovative results and the innovative results are applied to the improvement of the related data visualization narrative method; and providing a visual data story development design flow optimization model based on user feedback, and providing corresponding guidance for the development of the visual data story.
Drawings
FIG. 1. a metric model of data visualization narrative experience, consisting of a target layer, a criteria layer, an index layer and a schema layer, the connection between the upper and lower levels in the model being represented by action lines;
FIG. 2. eye tracking based study method;
FIG. 3 is a study method based on electrocardiosignals;
FIG. 4. building a user experience metric method model based on parallel nested hybrid, wherein QUAN is a quantitative study and QUAL is a qualitative study;
FIG. 5 is a schematic view of a data visualization narrative flow optimization model;
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
1. As shown in fig. 1, on the basis of analyzing the interrelation and attributes among user experience influence factors, a data visualization narrative experience measurement model is constructed according to the basic principle of hierarchical structures such as a target layer, a scheme layer, a criterion layer and the like;
2. as shown in fig. 2, an eye tracking data metric user experience study method is designed;
3. as shown in fig. 3, designing a user experience research method for measuring electrocardiosignal data;
4. as shown in fig. 4, building a user experience measurement method model based on parallel nested hybrid;
5. as shown in FIG. 5, a development design model of a data visualization narrative product is built.
Step 1, designing a measurement model of data visualization narrative experience;
step 2, eye movement experiment design;
step 3, designing an electrocardiogram experiment;
step 4, collecting data based on a parallel nested hybrid method;
step 5, analyzing data based on a statistical method and a visualization means;
and 6, deriving an example process optimization model.
In step 1, due to the lack of quantitative data for objective evaluation of data visualization narrative experience metrics at home and abroad at the present stage, it is necessary to design a metric model for data visualization narrative experience that can combine objective quantitative research data with traditional subjective quantitative research data. According to the method, on the basis of analyzing the interrelation and the attribute among the user experience influence factors, a data visualization narrative experience measurement model is constructed according to the basic principle of hierarchical structures such as a target layer, a scheme layer, a criterion layer and the like. The model is described in detail as follows:
1. and establishing a target layer of the model, wherein the target is to comprehensively reflect the advantages and disadvantages of the visual narrative experience of the data in different layers according to the evaluation index to obtain a design method of the visual narrative application of the data with better user experience quality.
2. And establishing a standard layer of the model, wherein the standard layer corresponds to a sub-standard layer of the model aiming at user cognition and user experience, and the user experience comprises sensory experience, emotional experience, content experience and interactive experience, and a user experience measurement index corresponds to the sub-standard layer to form an index layer.
3. And establishing a scheme layer of the model, wherein the scheme layer is a corresponding method for carrying out user experience measurement.
And 2, setting a test task for a user in an experiment.
And setting a test based on free browsing of the user aiming at the target of the data visualization narrative experience measurement, and only setting a fuzzy task target for the user. And the user can freely browse in a specific page area.
Experimental studies must set a free-browsing time limit for the user based on the complexity of visualizing the narrative object's own information in the data under study. The method is to compare the experimental duration of several groups of testees through a pre-experiment to determine the task duration when the experiment is executed.
After the task of free browsing is finished, the user is asked to take vocal thinking to review the whole browsing process of the user, interview the user is carried out, an auxiliary survey table is filled in, the cognition condition of the user in the task process is assisted and known from the qualitative perspective, and eye movement tracking data is supplemented in the data analysis stage. The eye movement experiment comprises the following specific procedures:
2-1, determining a research object, and setting the research object to be specific sections in the specific data visualization narrative according to the complexity of the data visualization narrative; the object of The experiment is a data visualization narrative website The Stories while a Line;
2-2, an experimental hypothesis is proposed for a research object, for example, the invention integrates the user experience quantitative research of data visualization narrative with the common method of qualitative research of the data visualization narrative experience at present by adopting a parallel nesting method under a corresponding user experience metric model, and the following hypothesis is proposed based on the data visualization narrative example:
from the perspective of the sensory experience:
h1: the influence of the text and visualization layout on the user behavior reflects difference;
h2: a plurality of visualization views;
h3: legends affect the behavior of users learning understanding visualizations;
h4: the control distribution and the metaphor thereof have difference on the influence of the user behavior.
From the perspective of the interactive experience:
h5: the test person can quickly learn the product structure and find the relevant functions to visualize the narrative.
From the perspective of emotional experience:
h6: the data visualization narrative may cause the mood of the subject to be evoked.
And 2-3, dividing the eye movement space domain based on exploratory factor analysis, and facilitating the later extraction of effective data characteristics. The subjects were pre-divided into several areas of Interest (AOI) according to a hotspot Map (Heat Map) of the eye movement data. In the experiment, 9 AOI areas can be divided according to the hotspot graph. And obtaining the measured values of 10 eye movement indexes such as the fixation point duration, the fixation time sum, the fixation point number and the like in the AOI through the eye movement experiment in the eye movement experiment.
The eye movement index has different measurement units, so that the eye movement index needs to be standardized and listed as a region-of-interest detection matrix.
The test factor analysis is sufficient by the KMO test, which generally requires the test value KMO > 0.5. And a Bartlett test is carried out, and whether the overall correlation coefficient matrix is a unit matrix is checked so as to judge whether the group of data is suitable for factor analysis.
Bartlett test statistic-corrected likelihood ratio:
where n is the number of data records, p is the number of variables factored, and R is the sample correlation matrix.
The test results are shown in the following table 4.1, the KMO value is between 0.6 and 0.7, factor analysis can be carried out, and Bartlett test statistic is remarkable, which indicates that the factor analysis method can be effectively used in the process.
The common factors are identified by using a principal component analysis method, 4 common factors are shared by data visualization narrative technology classification methods, m factors are taken according to different research objects, and the principal component of the factor analysis is decomposed into:
wherein the principal component factor analysis of the sample correlation matrix R is based on eigenvalue-eigenvector pairs (λ)i,ei) I 1, p and λ1≤λ2≤…≤λpSpecifying
In order to estimate the matrix of the factor loadings,
for the special factor variance, S is the covariance matrix,
the factor load on the jth factor for the ith variable.
In some cases, the number of common factors set for the experimental cases is not clear, and the following can be solved based on the fitting model results of different m:
the maximum result is the optimal factor number.
Classifying the selected AOI according to the result of the common factor analysis;
the maximum quartic method orthogonal rotation method (Quartmax) is selected, and the method can keep the override factors of each variable with high load on the factors, so that the factors required for explaining each variable are the minimum, and the experimental target can be met through inspection. The rotated factors still keep independence, and the rotation is iterated to convergence to obtain a rotation component matrix, wherein several items with high components are selected as final AOI, and the specific implementation is as follows:
note the book
Loading the matrix for the rotated factor, and
obtaining:
wherein, wherein
For the factor load of the ith variable over the jth factor,
for the commonality variance, the required rotational component matrix can be obtained when V is maximized.
Note: AOI labeling selected in final experiment
And 2-4, determining the number of the tested persons and characteristic information, such as sex, age, education degree and the like. According to the relevant theory of user research, before an experiment, the selected target population must be ensured to be suitable for the experiment. Due to the limitation of the eye tracking equipment, whether the tested physiological condition meets the experimental requirements needs to be examined, and the testee requires normal vision of both eyes without astigmatism.
And 2-5, selecting appropriate eye movement measurement indexes, such as eye movement tracks, the number of fixation points, fixation time and the like, wherein the user experience is manifold, and each experiment should select the eye movement measurement indexes capable of representing the user experience indexes needing measurement.
And 2-6, performing eye movement test, wherein the test is divided into a pre-test stage and an execution test stage, and the pre-test stage is a formal execution test stage to solve the problem of influencing the test.
And 2-7, preprocessing the acquired eye movement measurement index data. During the data pre-processing stage, noise is typically filtered using sliding mean filtering. The sliding mean filtering is based on a neighborhood averaging method, the noise is eliminated by continuously taking an average value in an interval to replace an original sequence value, the filtering effect of the sliding mean filtering is influenced by the size of a window taken by the sliding mean filtering, and the size of the window is controlled to be about 3 in the noise reduction of general eye movement data. Besides noise, data loss can occur, and a relatively common interpolation method is adopted for data compensation.
And 3, according to research and display of electrocardiosignals, the duration of short-term psychological signal detection needs more than five minutes, so that the user experiment condition needs to be known through pre-experiments aiming at the electrocardio experiment of data visualization narrative, and the experiment duration is controlled through screening and processing of experimental objects.
The experiment design based on the electrocardio comprises the following specific steps:
3-1, after sitting still for 5 minutes, recording the electrocardiosignals of the testee in the time period, and using the data as a later data processing reference standard;
and 3-2, formally starting the experiment, and recording the electrocardiosignal data by taking time as a sequence.
And 3-3, taking every 5 minutes after the experiment is finished as an electrocardiosignal data segment, and obtaining a group of experimental data by data processing of each data segment and comparing the experimental data with a reference standard.
3-4, preprocessing the acquired electrocardiosignal data by using wavelet denoising, wherein the wavelet denoising can effectively remove white noise in the electrocardiosignal and has good effect on extracting an effective part in the electrocardiosignal.
The wavelet denoising method mainly comprises the following steps:
performing wavelet transformation on original data;
rejecting noise by performing threshold processing;
and thirdly, performing wavelet inverse transformation to obtain the required de-noising data.
And 3-5, respectively calculating time domain, frequency domain and nonlinear indexes of the preprocessed electrocardiosignal data.
The invention adopts SDNN to evaluate the overall degree of damage and recovery of an autonomic nervous system, LF and LFnorm are sympathetic nerve activity indexes, HF and HFnorm are parasympathetic nerve activity indexes, and the complexity of C0 of an R-R interval sequence and an R wave peak value sequence reflects the degree of mutual regulation of a heart sympathetic nerve and a vagus nerve. Therefore, the selected electrocardio indexes are evaluated for user emotion arousal, variance test is carried out at the 5% significance level, and the analysis results are shown in the following table:
the results show that the frequency domain indexes related to the LF, LFnorm and LF/HF have obvious changes, and the changes before and after the SDNN experiment are also obvious. According to the theoretical basis of electrocardiosignal generation, the following can be obtained by analysis:
the sympathetic nerve activity indexes LF and LFnorm
The activity of sympathetic nerves represented by the LF indexes is obviously changed in the browsing process of a data visualization narrative system, and the LFnorm indexes are obviously increased. According to the group data, the situation that the user has emotion arousal in the reading process can be judged, and the specific reason for generating emotion change needs to be determined through interview with the user.
② parasympathetic activity indicators HF and HFnorm
The result of the fact that the HFnorm of the user rises but does not change significantly before and after the experiment, and the HFnorm index is influenced by the LF index and falls due to the rising, can also be used for supporting the last conclusion.
Third index of sympathetic/parasympathetic balance LF/HF
The trend of the number of the group is obvious in the experiment due to the increase of sympathetic nerve activity.
And 4, applying a parallel nesting method to experience research of data visualization narratives, namely embedding one data into a frame and a structure of another data by using a research data source in one normal form and using another data in another normal form in order to increase the interpretation degree of the data source, wherein the parallel nesting method is embodied as a quantitative eye tracking and electrocardiosignal research method and a qualitative user subjective evaluation method. Quantitative data collection is carried out by adopting a parallel nested hybrid method, a user experience measurement method model established according to the method is shown in figure 4, for carrying out quantitative data collection in a data collection stage, eye tracking data, electrocardio HRV data, interaction statistics and the like exist, qualitative data are collected by methods such as document reading, user questionnaire interview, occurrence thinking and the like, and the specific implementation steps are as follows:
and 4-1, removing abnormal values in the quantitative acquisition data by qualitative research, such as the missing of eye tracking data of a certain area. By studying the reasons of the abnormal values, whether the user subjectively intends not to browse the area or the omission caused by insufficient narrative is visualized, whether the data is eliminated or the data is included in the statistical analysis can be determined.
And 4-2, screening the quantitatively collected samples by the aid of qualitative research. For example, for a group of HRV indexes with significant changes before and after the experiment, compared with the emotion scale acquisition result of the user, samples meeting the purpose of exploring the positive emotion of the user through the experiment are selected.
4-3. improving the accuracy of the analysis results of quantitative studies through qualitative studies, such as eye tracking data analysis where the same result may be perceived by different users, long-term fixations may be due to the attractiveness of data visualization narratives, or may be due to the difficulty of extracting information by users, which aids in more accurate analysis of user cognition.
And 4, improving the precision of the analysis result of the quantitative research through qualitative research. For example, HRV quantification data for a set of analysis results with positive mood arousal, but HRV measurement metrics are typically processed over 5 minutes. The more precise time of the user's emotional peak in the narrative can be further explored by interviews with the user and vocal thinking review.
And 5, analyzing data based on a statistical method and a visualization means. Verifying the provided hypothesis, and analyzing the experimental data to obtain an analysis result; respectively analyzing the following groups of experimental data, and proposing a corresponding optimization scheme:
1) analysis of influence of text and visualization on user behavior
2) Analysis of influence of different visualization modes on user behavior
3) Correlation analysis of legends with visual ease of learning
4) Analysis of influence of control distribution and metaphors thereof on user behavior
5) System for analyzing emotion arousal of user
6) Analysis of influence of system usability on user behavior
And 6, demonstrating experimental assumptions according to the analysis results, providing a corresponding data visualization narrative optimization scheme, and establishing a process optimization model. A schematic diagram of a specific optimization model is shown in fig. 5.
In conclusion, the experience measurement model of the data visualization narrative is established, the eye movement and the electrocardiosignal are adopted to carry out quantitative research on the user behavior, the spatial domain division method based on the exploratory factor analysis is innovatively provided, and the eye movement information of the user can be effectively extracted; a user experience measurement method based on a parallel nested data visualization narrative system is provided, and collected quantitative data and materialized data are efficiently integrated; data analysis is carried out by adopting a statistical method and a visualization method to obtain a plurality of innovative results and the innovative results are applied to the improvement of the related data visualization narrative method; and providing a visual data story development design flow optimization model based on user feedback, and providing corresponding guidance for the development of the visual data story.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The foregoing has described the general principles and features of the present invention, as well as its advantages. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (4)
1. A data visualization method based on user experience measurement is characterized by comprising the following steps:
step 1, designing a measurement model of data visualization narrative experience;
step 2, eye movement experiment design;
step 3, designing an electrocardiogram experiment;
step 4, collecting data based on a parallel nested hybrid method;
step 5, analyzing data based on a statistical method and a visualization means; verifying the provided hypothesis, and analyzing the experimental data to obtain an analysis result;
step 6, deducing an example process optimization model, demonstrating experimental assumptions according to analysis results, providing a corresponding data visualization narrative optimization scheme, and establishing a process optimization model;
the step 1 is specifically realized as follows:
on the basis of analyzing the interrelation and the attribute among the user experience influence factors, a data visualization narrative experience measurement model is constructed according to the basic principle of hierarchical structures of a target layer, a scheme layer and a criterion layer, and the specific description of the model is as follows:
firstly, establishing a target layer of a model: the method aims at comprehensively reflecting the advantages and disadvantages of different levels of data visualization narrative experience according to evaluation indexes to obtain a design method of data visualization narrative application with better user experience quality;
establishing a criterion layer of the model: the criterion layer aims at user cognition and user experience, wherein the user experience comprises sensory experience, emotional experience, content experience and interactive experience, corresponds to the sub-criterion layer, and a user experience measurement index corresponds to the sub-criterion layer to form an index layer;
establishing a scheme layer of the model: the solution layer is a corresponding method of performing user experience metrics.
2. The method for visualizing data based on user experience metrics as claimed in claim 1, wherein in the experiment of step 2, a test task is first set for the user, specifically implemented as follows:
setting a user-based free-browsing test for a target of a data visualization narrative experience metric; comparing the experiment duration of several groups of testees through a pre-experiment to determine the task duration when the experiment is executed; after the freely browsed task is finished, a user carries out vocal thinking to review the whole browsing process, carries out interviewing of the user and fills in an auxiliary survey table, helps to know the cognitive condition of the user in the task process from a qualitative angle, and supplements eye movement tracking data in a data analysis stage;
the eye movement experiment comprises the following specific procedures:
2-1. determination of study subject: setting the study objects to specific ones of the sections of the specific data visualization narrative based on the complexity of the data visualization narrative;
2-2, the experimental hypothesis is put forward to the research object:
under a corresponding user experience measurement model, integrating a user experience quantitative research of data visualization narrative with a common method of qualitative research of the data visualization narrative experience at present by adopting a parallel nesting method, and proposing the following assumptions based on a data visualization narrative example:
from the perspective of the sensory experience:
h1: the influence of the text and visualization layout on the user behavior reflects difference;
h2: a plurality of visualization views;
h3: legends affect the behavior of users learning understanding visualizations;
h4: the control distribution and the metaphor thereof reflect difference on the influence of user behaviors;
from the perspective of the interactive experience:
h5: the testee can quickly know the product structure and find out the related functions of the visual narrative;
from the perspective of emotional experience:
h6: the data visualization narrative may cause the emotion of the subject to be evoked;
2-3, dividing the eye movement space domain based on exploratory factor analysis:
dividing an experimental object into a plurality of interest areas AOI in advance according to a hot area graph of eye movement data; the measured values of the eye movement indexes of the AOI are obtained in an eye movement experiment, wherein the measured values comprise the duration of the fixation point, the sum of fixation duration and the number of the fixation points; because the eye movement indexes have different measurement units, the eye movement indexes need to be standardized firstly and then listed as interest area detection matrixes;
the KMO is used for detecting whether the factor analysis is sufficient or not, and the required detection value KMO is greater than 0.5;
the Bartlett test is carried out, whether the overall correlation coefficient matrix is a unit matrix is tested to judge whether the group of data is suitable for factor analysis, and the likelihood ratio of the Bartlett test statistic correction is as follows:
wherein n is the number of data records, p is the number of variables for factor analysis, and R is the sample correlation matrix;
the common factors are identified by using a main component analysis method, 4 common factors are shared by data visualization narrative technology classification methods, m factors are taken according to different research objects, and the factor analysis is decomposed into:
wherein the principal component factor analysis of the sample correlation matrix R is based on eigenvalue-eigenvector pairs (λ)i,ei) I 1, p and λ1≤λ2≤…≤λpSpecify, specify
In order to estimate the matrix of the factor loadings,
for the special factor variance, S is the covariance matrix,
factor load of the ith variable of the matrix for estimating the factor load on the jth factor;
solving the fitting model result based on different m:
the result reaches the maximum value and is the optimal factor number;
classifying the selected AOI according to the result of the common factor analysis;
fifthly, obtaining the rotation component matrix by using a maximum four-time method orthogonal rotation method, wherein several items with high components are selected as the final AOI, and the specific implementation is as follows:
note the book
Loading the matrix for the rotated factor, and
obtaining:
wherein, therein
Is the intermediate variable(s) of the variable,
the factor load of the ith variable of the rotated factor load matrix on the jth factor,
the required rotation component matrix can be obtained when V reaches the maximum value as the common variance;
2-4, determining the number and characteristic information of the testee, wherein the characteristic information comprises sex, age and education degree, and simultaneously requiring the physiological condition of the testee to meet the requirements, namely normal vision of both eyes and no astigmatism;
2-5, selecting eye movement measurement indexes, wherein the eye movement measurement indexes comprise eye movement tracks, the number of fixation points and fixation duration; selecting an eye movement measurement index capable of representing a user experience index needing to be measured according to the requirement of each experiment;
2-6, performing eye movement test, wherein the test is divided into two stages of pretesting and performing test, and the pretesting is formal performing test to solve the problem of influencing the test;
and 2-7, preprocessing the acquired eye movement measurement index data, filtering noise by using sliding mean filtering, and performing data compensation by adopting an interpolation method.
3. The data visualization method based on the user experience metric as claimed in claim 2, wherein the experiment design based on the electrocardiogram in step 3 specifically comprises the following steps:
3-1, after sitting still for 5 minutes, recording the electrocardiosignals of the testee in the time period, and using the electrocardiosignal data as a later data processing reference standard;
3-2, formally starting the experiment, and recording the electrocardiosignal data by taking time as a sequence;
3-3, taking every 5 minutes after the experiment is finished as a section of electrocardiosignal data, and obtaining a group of experimental data by data processing of each data section and comparing the experimental data with a reference standard;
3-4, preprocessing the acquired electrocardiosignal data by using wavelet denoising;
and 3-5, respectively calculating time domain, frequency domain and nonlinear indexes of the preprocessed electrocardiosignal data.
4. The method for visualizing data based on user experience metrics as recited in claim 3, wherein step 4 is implemented as follows:
a user experience measurement method model of a data visualization narrative system based on parallel nesting is provided by combining research steps of a parallel nesting research method and experimental characteristics of eye movement and electrocardiosignal, and the method specifically comprises the following steps:
4-1, removing abnormal values in the quantitative collected data by qualitative research, and determining whether to remove the data or bring the data into statistical analysis by researching reasons of the abnormal values;
4-2, screening of quantitatively collected samples by qualitative research assistance;
4-3, the accuracy of the analysis result of the quantitative research is improved through qualitative research, the same result in the analysis of the eye tracking data can be caused by cognition of different users, the long-time fixation can be caused by the attraction of data visualization narrative, and the qualitative research can assist in more accurately analyzing the cognition of the users due to the fact that the users are difficult to extract information;
and 4, improving the precision of the analysis result of the quantitative research through qualitative research.
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| CN112932487B (en) * | 2021-01-22 | 2021-11-23 | 南京航空航天大学 | Man-machine experiment data analysis method based on eye movement, heart rate variability and behaviors |
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