CN107193725B - Online student evaluation and education pseudo-identification method based on user mouse behavior identification - Google Patents
Online student evaluation and education pseudo-identification method based on user mouse behavior identification Download PDFInfo
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Abstract
An online student evaluation and education fake identification method based on user mouse behavior identification. According to the method, a JavaScript code is inserted into a student online evaluation page, mouse moving track and time information of a user are captured, three characteristic values such as mouse movement times, mouse pause times and mouse average pause time are extracted, the three characteristic values are combined into one characteristic value by a weighted average method, and whether evaluation data given by the user is false data or not is automatically identified according to the value of the characteristic value. The method is simple to realize, high in identification accuracy, free of influence on the evaluation and education experience of the user, and capable of being combined with the identification method through evaluation and education data analysis. The identification interval of the method is related to the evaluation and education page, and before the method is normally used, a certain scale of test needs to be carried out to obtain the corresponding identification interval.
Description
Technical Field
The invention relates to a method for automatically identifying the validity of student online evaluation and education data, in particular to a method for automatically identifying the validity of student online evaluation and education data by identifying the behavior characteristics of a user mouse, namely, the method is used for identifying whether the attitude of a student is seriously strict during evaluation and education by acquiring the behavior characteristics of the student mouse during online evaluation and education, further eliminating 'pseudo data' caused by laying and random evaluation and education and improving the validity of an evaluation and education data set.
Background
As one of important means of teaching management, the students evaluate teaching according to a certain standard system, and the qualitative and quantitative value judgment of the students on the teaching work of any teacher, such as teaching process, teaching achievement, teaching attitude, level and the like, has an important status in the evaluation and teaching of schools, and is an important reference basis for many schools to evaluate the teaching quality of the teacher, promote the job title and the job title of the teacher, promote the allowance of the teacher and select excellent teachers. With the application and popularization of network technology, many students adopt an online evaluation and education mode. Then, some students adopt an attitude of spreading and deriving rather than serious understanding when evaluating and teaching the teachers, so that the obtained evaluation and teaching data has high distortion, actual conditions cannot be well reflected, and the evaluation and teaching effect can be seriously influenced. Some studies or methods are dedicated to the statistical analysis of the original assessment and education data, and it is desirable to obtain valuable assessment and education results from the assessment and education data with certain distortion as much as possible. If the pseudo data can be effectively identified, the original reliability and effectiveness of evaluation and education are improved, and the accuracy and reference value of evaluation and education evaluation can be better improved.
When the user carries out online evaluation and education on the evaluation and education page, the user can leave the operation tracks of the mouse or the cursor, and according to the tracks, the characteristic values of the movement of the mouse, the pause times of the mouse and the average pause time of the mouse can be extracted. Through observation, if the user adopts dressing and random attitudes during evaluation and education, the characteristic values are less; and when the user reviews the knowledge seriously, the characteristic values are larger. Therefore, by identifying the mouse operation behavior of the user, whether the attitude of the user is arbitrarily derived or seriously considered when evaluating education can be identified to a certain extent, and then 'pseudo data' caused by deriving the evaluation education attitude can be eliminated or the user is required to submit the evaluation education data again.
Disclosure of Invention
The invention provides an online student evaluation and education identification method based on user mouse behavior identification, which mainly aims at the problem that the effectiveness of evaluation and education data is influenced by a certain number of dressing and diffraction and random behaviors when students evaluate and educate. The following aspects are mainly involved:
(1) and obtaining three characteristic values of mouse motion, mouse pause times and mouse average pause time of the mouse behavior of the user. The mouse motion is identified by identifying whether the mouse moving track is mutated or not, and the identification process is described in the attached figure 3; the number of times of mouse pauses is identified by identifying the interval between successive mouse actions again, the identification process of which is illustrated in fig. 4; the average mouse pause time can be obtained by recording the pause time of each mouse and taking the average value.
(2) And combining the characteristic values by adopting a weighted average method. The weighted average method comprises the following steps: x is w1 x1+ w2 x2+ w3 x3, wherein x1, x2 and x3 are three characteristic values of mouse movement, mouse pause times and mouse average pause time respectively, w1, w2 and w3 are weights corresponding to the three characteristic values, and the values of the weights simultaneously satisfy: w1+ w2+ w3 is 1,
wherein, ∑ x1, ∑ x1 and ∑ x1 respectively represent the sum of the three characteristic values of the number of sub-movements, the pause times and the average pause time in the data set obtained by the test.
(3) And obtaining the identification interval through testing. During testing, each user randomly confirms the testing attitude (seriously or externally), and carries out the claiming test according to the self-confirmed testing attitude. The data from the test are divided into two groups: t (data obtained by adopting the serious attitude) and F (data obtained by adopting the derived attitude), the number of elements of each set is required to reach a certain number, otherwise, test data is required to be supplemented. Let TminIs the minimum value of T, FmaxIs the maximum value in F. Thereby obtaining the identification intervals (-infinity, a), [ a, b)](b, + ∞), where a ═ min (T)min，Fmax)，b＝max(Tmin，Fmax)。
(4) And identifying the user commenting and teaching behaviors on line. And when the user evaluates and teaches online, capturing the mouse behavior of the user, extracting corresponding characteristic values, carrying out weighted average, and setting as y. When y < a, the user attitude can be considered as arbitrary; when y > b, the user can be recognized as serious; when a < y < b, the recognition can be performed as the case may be, or in combination with other methods.
The invention has the advantages that:
(1) corresponding JavaScript codes only need to be inserted into the original evaluation system, so that the influence on the original evaluation system is small, and the implementation is convenient.
(2) The identification interval can be extracted through a data set obtained through testing, can be adaptive to the page, and is good in universality and small in interference of human factors.
(3) An effective weight value determination method during weighted average is designed, and the method does not need to depend on empirical data.
Drawings
FIG. 1 is a flow of acquiring an identification interval.
FIG. 2 is a flow of online assessment and education identification.
FIG. 3 is a flow chart of acquiring cursor movements.
Fig. 4 is a flow chart of obtaining the average dwell time of the cursor.
Fig. 5 is a flow chart of obtaining the number of times of cursor pause.
Detailed Description
The invention relates to an online student evaluation and education fake identification method based on user mouse behavior identification. The method comprises the following specific steps:
(1) and capturing the mouse moving track of the user. Corresponding JavaScript codes are inserted into an evaluation and teaching page which needs to be accessed by a student, the time and the position of a user mouse staying on the page are captured periodically, and a series of data pairs (x, y, t) are formed, wherein x and y correspond to the mouse position, and t corresponds to the time.
(2) And extracting the characteristic value of the mouse operation. Through the series of data obtained in the first step, the number x1 of sub-movements of mouse operation (the obtaining process is shown in fig. 3), the number x2 of pause times (the obtaining process is shown in fig. 4), and the average pause time x3 (the obtaining process is shown in fig. 5) are respectively calculated, and the three characteristic values are combined into one characteristic value by adopting a weighted average method. The weighted average method comprises the following steps: x is w1 x1+ w2 x2+ w3 x3, wherein w1, w2 and w3 should satisfy: w1+ w2+ w3 is 1,
wherein, ∑ x1, ∑ x1 and ∑ x1 respectively represent the sum of the three characteristic values of the number of sub-movements, the pause times and the average pause time in the data set obtained by the test.
(3) And acquiring an identification interval. A certain number of users are invited to perform student assessment and education tests. During testing, each user randomly confirms the testing attitude (seriously or divergently), carries out teaching test according to the testing attitude confirmed by the user, and obtains the mouse behavior characteristic values of the users through the operations of the steps (1) and (2). The data from the test are divided into two groups: t (data obtained with a careful attitude) and F (data obtained with a derived attitude), let T beminIs the minimum value of T, FmaxIs in FIs measured. Thereby obtaining the identification intervals (-infinity, a), [ a, b)](b, + ∞), where a ═ min (T)min，Fmax)，b＝max(Tmin，Fmax)。
(4) And identifying the user commenting and teaching behaviors. And (4) setting the identification interval obtained in the step (3) in the evaluation and education page. And (3) when the user reviews the education, obtaining the mouse operation characteristic value of the user during the education review through the steps (1) and (2). If the characteristic value is less than a, prompting the user that the teaching evaluation attitude is not serious and requiring re-evaluation; if the characteristic value is greater than b, the data is considered to be effective evaluation data and is stored; if the characteristic value is between a and b, it can be identified by itself or in combination with other methods.
In the above steps, (1), (3), and (4) are used to obtain the identification interval, and the flow is shown in fig. 1; (1) the processes of (2), (4) and (2) are shown in fig. 2.
Claims (1)
1. An online student evaluation and education pseudo-identification method based on user mouse behavior identification is characterized by comprising the following steps: the method comprises the steps of inserting JavaScript codes into an online evaluation page of a student, capturing mouse moving track and time information of the user, extracting three characteristic values of mouse movement number, mouse pause times and mouse average pause time in the mouse moving track, combining the three characteristic values into one characteristic value by adopting a weighted average method, and identifying whether evaluation data given by the user is false data or not according to the value of the characteristic value, wherein the specific steps are as follows:
(1) capturing a mouse moving track of a user; corresponding JavaScript codes are inserted into an evaluation and teaching page which needs to be accessed by a student, and the time and the position of a user mouse staying on the page are captured periodically to form a series of data pairs (x, y, t), wherein x and y correspond to the mouse position and t corresponds to the time;
(2) extracting a characteristic value of mouse operation; respectively calculating the number x1 of mouse movements, the number x2 of pause times and the average pause time x3 in the movement track of the mouse operation through the series of data obtained in the first step, and combining the three characteristic values into one characteristic value by adopting a weighted average method; judging whether the mouse moving track is protruded or not by the movement of the mouseChange to identify; the weighted average method comprises the following steps: x is w1 x1+ w2 x2+ w3 x3, wherein w1, w2 and w3 should satisfy:
∑ x1, ∑ x2 and ∑ x3 respectively represent the sum of the motion number, the pause times and the average pause time of the mouse in the motion track of the data set obtained by testing;
(3) acquiring an identification interval; inviting a certain number of users to carry out student evaluation and education tests, wherein during the tests, each user randomly selects a serious or derived test attitude, carries out evaluation and education tests according to the test attitude confirmed by the user, and obtains mouse behavior characteristic values of the users through the operations of the steps (1) and (2); the data from the test are divided into two groups: using the data set T obtained with the carefully chosen attitude and the data set F obtained with the derived attitude, let TminIs the minimum value of T, FmaxIs the maximum value in F, thereby obtaining the identification interval (- ∞, a), [ a, b [ ]](b, + ∞), where a ═ min (T)min，Fmax)，b＝max(Tmin，Fmax)；
(4) Identifying the user commenting and teaching behaviors; setting the identification interval obtained in the step (3) in the evaluation and education page; when the user reviews the education, the mouse operation characteristic value of the user during the review is obtained in the steps (1) and (2); if the characteristic value is less than a, prompting the user that the teaching evaluation attitude is not serious and requiring re-evaluation; if the characteristic value is greater than b, the data is considered to be effective evaluation data and is stored; if the characteristic value is between a and b, it can be identified by itself or in combination with other methods.
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Citations (2)
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| CN101388103A (en) * | 2008-10-28 | 2009-03-18 | 上海拓迪信息技术有限公司 | Information investigating system and investigating method |
| CN101833619A (en) * | 2010-04-29 | 2010-09-15 | 西安交通大学 | Method for judging identity based on keyboard-mouse crossed certification |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101388103A (en) * | 2008-10-28 | 2009-03-18 | 上海拓迪信息技术有限公司 | Information investigating system and investigating method |
| CN101833619A (en) * | 2010-04-29 | 2010-09-15 | 西安交通大学 | Method for judging identity based on keyboard-mouse crossed certification |
Non-Patent Citations (1)
| Title |
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| 大学生网上评教现状调查;赵秋利 等;《中国高等医学教育》;20101231(第11期);第3-5页 * |
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Application publication date: 20170922 Assignee: GUANGXI CUIFA TECHNOLOGY CO.,LTD. Assignor: GUILIN University OF TECHNOLOGY Contract record no.: X2022450000050 Denomination of invention: A method of online students' teaching evaluation and false identification based on user's mouse behavior identification Granted publication date: 20200731 License type: Common License Record date: 20221118 |
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Application publication date: 20170922 Assignee: Guangxi Great Wall Broadband Network Service Co.,Ltd. Assignor: GUILIN University OF TECHNOLOGY Contract record no.: X2022450000522 Denomination of invention: A method of online students' teaching evaluation and false identification based on user's mouse behavior identification Granted publication date: 20200731 License type: Common License Record date: 20221229 |