CN112016829B - Online course learning evaluation method based on eye movement information - Google Patents
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Abstract
The invention discloses an on-line course learning evaluation method based on eye movement information, which comprises the steps of firstly, sequentially dividing course contents into unit modules, knowledge points and related resources, secondly, calculating the attention degree of student unit modules, judging whether the attention degree of each learning unit is within a threshold range, then calculating the attention degree of students to the unit knowledge points, judging whether the attention degree of the students to the learning knowledge points is within the threshold range, analyzing the attention degree of the learning resources of the knowledge points of the students again, and judging whether the learning resources of the knowledge points are interested by the students; and finally, matching and comparing the examination scores with the attention of the unit modules, analyzing the online learning effect and quality of students, and further providing optimization suggestions for the design of the course unit modules. The invention expands the current online course learning evaluation method, takes eye movement information as a quantitative basis for evaluating the online learning quality of students, breaks through the mode of the traditional evaluation system, and guides the improvement of the online learning efficiency of the students.
Description
Technical Field
The invention belongs to the technical field of intelligent education service and visual cognition, and particularly relates to an on-line course learning evaluation method based on eye movement information.
Background
With the deep application and promotion of the internet plus education mode, the artificial intelligence technology will bring a series of innovations for the development of the field of online learning, and the society is entering a new era of intelligence plus. In 2017, 7, 8, the State Council of China promulgated 'Notification of New Generation Artificial Intelligence development planning' formally. The notification explicitly addresses the concept of intelligent education and indicates: the intelligent technology is utilized to accelerate and promote talent culture mode and teaching method innovation, and a novel education system comprising intelligent learning and interactive learning is constructed. In particular, the implementation of the education strategy of 'stopping classes and learning without stopping' in the period of the coronavirus epidemic situation enables various colleges and universities, middle and primary schools, education institutions and the like to develop online teaching in an online remote learning mode. Therefore, how to improve the learning quality and effect of students in the online learning process is very important.
The existing online teaching strategy mainly depends on a designed teaching flow and strategy, lacks evaluation on the learning effect of a student in the actual online learning process, and does not form an accurate online teaching process meeting the requirements of the student. The eye movement information is collected, and the attention of the students to unit modules, unit knowledge points and learning resources in the learning process is calculated, so that the online course learning evaluation method based on the eye movement information is used for evaluating the real state of online learning of the students. On the basis, pairwise matching comparison is carried out on whether the unit module examination is qualified and whether the unit module attention is normal, teaching design optimization is given according to specific comparison results, and the method has guidance value for on-line platform teaching and learning depth fusion.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide an online course learning evaluation method based on eye movement information, aiming at the defects of the prior art.
In order to achieve the technical purpose, the technical scheme adopted by the invention is as follows:
an on-line course learning evaluation method based on eye movement information, wherein: the method comprises the following steps:
step S1: according to the relation of online course knowledge granularity, dividing course contents into unit modules, knowledge points and related resources step by step from large to small;
step S2: the method comprises the steps of collecting eye movement data of students in an online learning process in real time, wherein the eye movement data comprise fixation point coordinates and fixation point time, calculating the attention degree of a student learning unit module according to effective fixation points and retention time, and judging whether the attention degree is in a threshold range;
step S3: if the attention degree of the student learning unit module is not in the threshold range, calculating the attention degree of the student to each knowledge point in the unit module, and judging whether the attention degree of the student learning knowledge point is in the threshold range;
step S4: if the attention degree of the knowledge points of the student learning unit is not in the threshold value range, analyzing the attention degree of learning resources of the knowledge points of the student, and judging the interest degree of the student in the learning resources of the knowledge points;
step S5: and (3) examining each unit module of the student, comparing whether the examination is qualified and whether the unit module is concerned normally in pairwise matching, analyzing the online learning effect and quality of the student, and further providing an optimization suggestion for the design of the course unit module.
In order to optimize the technical scheme, the specific measures adopted further comprise:
further, step S1 is specifically:
s11: a certain unit module of a course A is denoted as aiI e (1, n), all the module units of a course a are expressed as a ═ a1,a2,…ai,…,an]；
S12: a unit knowledge point in a course A is represented as bijI belongs to (1, n), j belongs to (1, m), all the unit knowledge points of course A are expressed as
S13: a certain knowledge point bijThe learning resources involved are represented as
Wherein the learning resources comprise PPT, lecture notes and videos;
s14: setting learning time of a certain knowledge point as tijI belongs to (1, n), j belongs to (1, m), and the planned learning time corresponding to all knowledge points of a course A is expressed as
Further, step S2 is specifically:
s21: eye movement data is collected and labeled fp＝{(xp,yp),zpJudging whether the fixation point coordinate is in the position range of effective information on the student mobile terminal, if so, judging whether the fixation point coordinate (x) is in the position range of the effective information on the student mobile terminalp,yp) Within the position range of effective information on the mobile terminal of the student, and the point of regard time zpIn the effective time range, judging the gaze point to be an effective gaze point, otherwise, judging the gaze point to have no learning behavior and belong to an ineffective gaze point;
wherein f ispIndicates the point of fixation, (x)p,yp) Indicating the point of fixation fpCorresponding coordinate, zpIndicating the point of fixation fpIn (x)p,yp) Time of gaze;
s22: the gazing time of the effective gazing point in the unit module is summed to obtain the effective learning time d of the student in the unit moduleiFinally, calculating the actual attention degree u of the student in the unit modulei＝di/si；
S23: setting unit module aiCorresponding attention threshold uiIf, if
Judging that the attention of the student unit learning is normal, wherein the learning effect of the unit module is good, otherwise, judging that the student is tired of learning or not interested;
wherein,
denoted as unit module aiThe corresponding highest threshold value of the degree of attention,
denoted as unit module aiThe corresponding attention minimum threshold.
Further, step S3 is specifically:
s31: if it is
Or
The gazing time of the effective gazing point of the module unit is summed to obtain the module a of the unitiSingle element knowledge point bijEffective learning time v ofijCalculating the actual attention degree w of the student in the unit knowledge pointij＝vij/tij；
S32: setting unit knowledge point bijIs most concerned with
And shut offMinimum threshold of degree of attention
If it is
And judging that the learning of the student unit knowledge points is normal, wherein the learning resources of the student unit knowledge points are reasonably designed, otherwise, judging that the students are tired of learning or are not interested.
Further, step S4 is specifically:
s41: if it is
Or
The fixation time z for the effective fixation point of the learning resource in the knowledge pointpSumming to obtain the unit knowledge point b of the studentijResource for middle school learning
Effective learning time of
Calculating student knowledge points b in unitsijMiddle pair learning resource
Degree of interest of
S42: setting learning resources
Threshold of interest of
If it is
And judging that the student is not interested in learning the learning resource, which indicates that the learning resource is unreasonable in design or the student is too familiar with the resource content to learn.
Further, step S5 is specifically:
s51: after each unit module course is finished, a unit test mode is carried out, and the matching comparison between the qualification of the test and the normal attention of the unit modules is carried out;
s52: if the examination is qualified and the attention of the unit module is normal, the teaching content of the unit module is designed better, so that the expected learning effect is achieved;
s53: if the examination is qualified and the attention of the unit module is abnormal, if the attention of the student is lower than the minimum threshold of the attention, the unit knowledge point is simple for the student, and the online learning process can be accelerated; if the attention of the student is higher than the highest attention threshold, the unit knowledge point is difficult for the student, and the high attention of the knowledge points can be analyzed specifically;
s54: if the examination is unqualified and the unit attention is normal, the students possibly have a state of being out of the mind, the interest degree of the learning resources of wrong answering knowledge points is analyzed, the showing form, the preference design and the like of the learning resources are adjusted, and the purpose of the students for studying with great concentration is achieved;
s55: if the examination is unqualified and the unit attention is abnormal, if the attention of the student is lower than the minimum threshold value of the attention, the student is indicated to be possibly in a state of boredom, and the student needs to be warned in a targeted manner; if the attention of the student is higher than the highest attention threshold, the student is possibly labored to learn, and special tutoring needs to be conducted on the student in a targeted mode.
The invention has the beneficial effects that:
1) the invention utilizes the eye movement information of on-line learning of students as the data basis of evaluation quantification, and breakthroughs and combines a novel physiological perception technology (eye movement tracking technology) into the on-line learning evaluation process, so that the course on-line learning evaluation method has higher reliability and effectiveness;
2) the invention refines the knowledge granularity of the course, gives a definite threshold range for the evaluation of the eye movement information of the knowledge under each layer of granularity, and directly and effectively judges whether the student is interested in the online learning knowledge;
3) the method has higher application value and has guiding significance and promoting effect on the on-line course learning quality evaluation of students.
Drawings
Fig. 1 is a schematic structural view of the present invention.
Detailed Description
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
As shown in fig. 1, the present invention is an online course learning evaluation method based on eye movement information, wherein: the method comprises the following steps:
step S1: according to the relation of online course knowledge granularity, dividing course contents into unit modules, knowledge points and related resources step by step from large to small;
step S1 specifically includes:
s11: a certain unit module of a course A is denoted as aiI e (1, n), all the module units of a course a are expressed as a ═ a1,a2,…ai,…,an]；
S12: a unit knowledge point in a course A is represented as bijI belongs to (1, n), j belongs to (1, m), all the unit knowledge points of course A are expressed as
S13: a certain knowledge point bijThe learning resources involved are represented as
Wherein the learning resources comprise PPT, lecture notes and videos;
s14: setting learning time of a certain knowledge point as tijI belongs to (1, n), j belongs to (1, m), and the planned learning time corresponding to all knowledge points of a course A is expressed as
Step S2: the method comprises the steps of collecting eye movement data of students in an online learning process in real time, wherein the eye movement data comprise fixation point coordinates and fixation point time, calculating the attention degree of a student learning unit module according to effective fixation points and retention time, and judging whether the attention degree is in a threshold range;
step S2 specifically includes:
s21: eye movement data is collected and labeled fp＝{(xp,yp),zpJudging whether the fixation point coordinate is in the position range of effective information on the student mobile terminal, if so, judging whether the fixation point coordinate (x) is in the position range of the effective information on the student mobile terminalp,yp) Within the position range of effective information on the mobile terminal of the student, and the point of regard time zpIn the effective time range, judging the gaze point to be an effective gaze point, otherwise, judging the gaze point to have no learning behavior and belong to an ineffective gaze point;
wherein f ispIndicates the point of fixation, (x)p,yp) Indicating the point of fixation fpCorresponding coordinate, zpIndicating the point of fixation fpIn (x)p,yp) Time of gaze;
s22: the gazing time of the effective gazing point in the unit module is summed to obtain the effective learning time d of the student in the unit moduleiFinally, calculating the actual attention degree u of the student in the unit modulei＝di/si；
S23: setting unit module aiCorresponding attention threshold uiIf, if
Judging that the attention of the student unit learning is normal, wherein the learning effect of the unit module is good, otherwise, judging that the student is tired of learning or not interested;
wherein,
denoted as unit module aiIs correspondingly provided withIs the highest threshold of the degree of attention,
denoted as unit module aiThe corresponding attention minimum threshold.
Step S3: if the attention degree of the student learning unit module is not in the threshold range, calculating the attention degree of the student to each knowledge point in the unit module, and judging whether the attention degree of the student learning knowledge point is in the threshold range;
step S3 specifically includes:
s31: if it is
Or
The gazing time of the effective gazing point of the module unit is summed to obtain the module a of the unitiSingle element knowledge point bijEffective learning time v ofijCalculating the actual attention degree w of the student in the unit knowledge pointij＝vij/tij；
S32: setting unit knowledge point bijIs most concerned with
And minimum threshold of attention
If it is
And judging that the learning of the student unit knowledge points is normal, wherein the learning resources of the student unit knowledge points are reasonably designed, otherwise, judging that the students are tired of learning or are not interested.
Step S4: if the attention degree of the knowledge points of the student learning unit is not in the threshold value range, analyzing the attention degree of learning resources of the knowledge points of the student, and judging the interest degree of the student in the learning resources of the knowledge points;
s41: if it is
Or
The fixation time z for the effective fixation point of the learning resource in the knowledge pointpSumming to obtain the unit knowledge point b of the studentijResource for middle school learning
Effective learning time of
Calculating student knowledge points b in unitsijMiddle pair learning resource
Degree of interest of
S42: setting learning resources
Threshold of interest of
If it is
And judging that the student is not interested in learning the learning resource, which indicates that the learning resource is unreasonable in design or the student is too familiar with the resource content to learn.
Step S5: and (3) examining each unit module of the student, comparing whether the examination is qualified and whether the unit module is concerned normally in pairwise matching, analyzing the online learning effect and quality of the student, and further providing an optimization suggestion for the design of the course unit module.
S51: after each unit module course is finished, a unit test mode is carried out, and the matching comparison between the qualification of the test and the normal attention of the unit modules is carried out;
s52: if the examination is qualified and the attention of the unit module is normal, the teaching content of the unit module is designed better, so that the expected learning effect is achieved;
s53: if the examination is qualified and the attention of the unit module is abnormal, if the attention of the student is lower than the minimum threshold of the attention, the unit knowledge point is simple for the student, and the online learning process can be accelerated; if the attention of the student is higher than the highest attention threshold, the unit knowledge point is difficult for the student, and the high attention of the knowledge points can be analyzed specifically;
s54: if the examination is unqualified and the unit attention is normal, the students possibly have a state of being out of the mind, the interest degree of the learning resources of wrong answering knowledge points is analyzed, the showing form, the preference design and the like of the learning resources are adjusted, and the purpose of the students for studying with great concentration is achieved;
s55: if the examination is unqualified and the unit attention is abnormal, if the attention of the student is lower than the minimum threshold value of the attention, the student is indicated to be possibly in a state of boredom, and the student needs to be warned in a targeted manner; if the attention of the student is higher than the highest attention threshold, the student is possibly labored to learn, and special tutoring needs to be conducted on the student in a targeted mode.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (4)
1. An on-line course learning evaluation method based on eye movement information is characterized by comprising the following steps:
step S1: according to the relation of online course knowledge granularity, dividing course contents into unit modules, knowledge points and related resources step by step from large to small;
step S2: the method comprises the steps of collecting eye movement data of students in an online learning process in real time, wherein the eye movement data comprise fixation point coordinates and fixation point time, calculating the attention degree of a student learning unit module according to effective fixation points and retention time, and judging whether the attention degree is in a threshold range;
step S3: if the attention degree of the student learning unit module is not in the threshold range, calculating the attention degree of the student to each knowledge point in the unit module, and judging whether the attention degree of the student learning knowledge point is in the threshold range;
step S4: if the attention degree of the knowledge points of the student learning unit is not in the threshold value range, analyzing the attention degree of learning resources of the knowledge points of the student, and judging the interest degree of the student in the learning resources of the knowledge points;
step S5: each unit module of the student is examined, whether the examination is qualified or not is compared with whether the unit module is normally concerned in pairwise matching, the online learning effect and quality of the student are analyzed, and further an optimization suggestion is provided for the design of the course unit module;
the step S1 specifically includes:
s11: a certain unit module of a course A is denoted as aiI e (1, n), all the module units of a course a are expressed as a ═ a1,a2,…ai,…,an]；
S12: a unit knowledge point in a course A is represented as bijI belongs to (1, n), j belongs to (1, m), all the unit knowledge points of course A are expressed as
S13: a certain knowledge point bijThe learning resources involved are represented as
Wherein the learning resources comprise PPT, lecture notes and videos;
s14: setting learning time of a certain knowledge point as tijI belongs to (1, n), j belongs to (1, m), and the planned learning time corresponding to all knowledge points of a course A is expressed as
The step S2 specifically includes:
s21: eye movement data is collected and labeled fp＝{(xp,yp),zpJudging whether the fixation point coordinate is in the position range of effective information on the student mobile terminal, if so, judging whether the fixation point coordinate (x) is in the position range of the effective information on the student mobile terminalp,yp) Within the position range of effective information on the mobile terminal of the student, and the point of regard time zpIn the effective time range, judging the gaze point to be an effective gaze point, otherwise, judging the gaze point to have no learning behavior and belong to an ineffective gaze point;
wherein f ispIndicates the point of fixation, (x)p,yp) Indicating the point of fixation fpCorresponding coordinate, zpIndicating the point of fixation fpIn (x)p,yp) Time of gaze;
s22: the gazing time of the effective gazing point in the unit module is summed to obtain the effective learning time d of the student in the unit moduleiFinally, calculating the actual attention degree u of the student in the unit modulei＝di/si；
S23: setting unit module aiCorresponding attention threshold uiIf, if
Judging that the attention of the student unit learning is normal, wherein the learning effect of the unit module is good, otherwise, judging that the student is tired of learning or not interested;
wherein,
denoted as unit module aiThe corresponding highest threshold value of the degree of attention,
denoted as unit module aiThe corresponding attention minimum threshold.
2. The on-line course learning evaluation method based on eye movement information as claimed in claim 1, wherein: the step S3 specifically includes:
s31: if it is
Or
The gazing time of the effective gazing point of the module unit is summed to obtain the module a of the unitiSingle element knowledge point bijEffective learning time v ofijCalculating the actual attention degree w of the student in the unit knowledge pointij＝vij/tij；
S32: setting unit knowledge point bijIs most concerned with
And minimum threshold of attention
If it is
And judging that the learning of the student unit knowledge points is normal, wherein the learning resources of the student unit knowledge points are reasonably designed, otherwise, judging that the students are tired of learning or are not interested.
3. The on-line course learning evaluation method based on eye movement information as claimed in claim 2, wherein: the step S4 specifically includes:
s41: if it is
Or
The fixation time z for the effective fixation point of the learning resource in the knowledge pointpSumming to obtain the unit knowledge point b of the studentijResource for middle school learning
Effective learning time of
Calculating student knowledge points b in unitsijMiddle pair learning resource
Degree of interest of
S42: setting learning resources
Threshold of interest of
If it is
And judging that the student is not interested in learning the learning resource, which indicates that the learning resource is unreasonable in design or the student is too familiar with the resource content to learn.
4. The on-line course learning evaluation method based on eye movement information as claimed in claim 3, wherein: the step S5 specifically includes:
s51: after each unit module course is finished, a unit test mode is carried out, and the matching comparison between the qualification of the test and the normal attention of the unit modules is carried out;
s52: if the examination is qualified and the attention of the unit module is normal, the teaching content of the unit module is designed better, so that the expected learning effect is achieved;
s53: if the examination is qualified and the attention of the unit module is abnormal, if the attention of the student is lower than the minimum threshold of the attention, the unit knowledge point is simple for the student, and the online learning process can be accelerated; if the attention of the student is higher than the highest attention threshold, the unit knowledge point is difficult for the student, and the high attention of the knowledge points can be analyzed specifically;
s54: if the examination is unqualified and the unit attention is normal, the students possibly have a state of being out of the mind, the interest degree of the learning resources of wrong answering knowledge points is analyzed, the showing form, the preference design and the like of the learning resources are adjusted, and the purpose of the students for studying with great concentration is achieved;
s55: if the examination is unqualified and the unit attention is abnormal, if the attention of the student is lower than the minimum threshold value of the attention, the student is indicated to be possibly in a state of boredom, and the student needs to be warned in a targeted manner; if the attention of the student is higher than the highest attention threshold, the student is possibly labored to learn, and special tutoring needs to be conducted on the student in a targeted mode.
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| CN103595753A (en) * | 2013-05-24 | 2014-02-19 | 漳州师范学院 | Remote learning monitoring system based on eye movement locus tracking, and monitoring method of remote learning monitoring system |
| US20180232567A1 (en) * | 2017-02-14 | 2018-08-16 | Find Solution Artificial Intelligence Limited | Interactive and adaptive training and learning management system using face tracking and emotion detection with associated methods |
| CN107463691A (en) * | 2017-08-11 | 2017-12-12 | 北京点易通科技有限公司 | A kind of learning state collects the method and system with identification |
| CN111325643A (en) * | 2020-02-17 | 2020-06-23 | 合肥工业大学 | Admire class teaching capability guiding method |
| CN111429316A (en) * | 2020-03-23 | 2020-07-17 | 宁波视科物电科技有限公司 | Online learning special attention detection system and method based on augmented reality glasses |
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