CN112446523A - Learning path planning method, electronic device and computer storage medium - Google Patents

Learning path planning method, electronic device and computer storage medium Download PDF

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CN112446523A
CN112446523A CN201910831758.2A CN201910831758A CN112446523A CN 112446523 A CN112446523 A CN 112446523A CN 201910831758 A CN201910831758 A CN 201910831758A CN 112446523 A CN112446523 A CN 112446523A
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何明
杨亚洲
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Beijing Xintang Sichuang Educational Technology Co Ltd
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Abstract

The embodiment of the application provides a learning path planning method, electronic equipment and a storage medium, wherein the learning path planning method comprises the following steps: acquiring a first learning achievement of at least one student; determining the learning level of each student in at least one student according to the first learning achievement; determining a knowledge point association matrix corresponding to each learning level according to the learning level of each student; and planning a knowledge point learning path for each student according to the knowledge point association matrix of each learning level, wherein each knowledge point association matrix comprises at least one association parameter, and each association parameter is used for indicating the association between two knowledge points. Knowledge point learning paths are determined for students in each learning level according to the knowledge point association matrix of the learning level, the current learning condition of each student in each learning level is adapted, and the learning efficiency is improved.

Description

Learning path planning method, electronic device and computer storage medium
Technical Field
The embodiment of the application relates to the technical field of electronic information, in particular to a learning path planning method, electronic equipment and a computer storage medium.
Background
Along with the development of electronic information technology, education and teaching work is more and more intelligent, and the mode of education and teaching is also more meticulous. For example, computer software or an artificial intelligence neural network can count the knowledge points to be learned by the student in one period, and plan the learning of the knowledge points, so that the student can learn the knowledge points one by one in a certain sequence in one period, the learning of the student is facilitated, the learning content is split relatively small, and the difficulty in the learning process is found relatively easily. However, the knowledge points learned by each student are different, the learning situation is also different, and the learning of the same knowledge points by each student is also different, which results in that the planning of knowledge point learning cannot adapt to different students, and the learning efficiency is reduced.
Disclosure of Invention
In view of the above, an objective of the present invention is to provide a learned route planning method, an electronic device and a computer storage medium, which overcome the above-mentioned shortcomings in the prior art.
In a first aspect, an embodiment of the present application provides a learned path planning method, which includes: acquiring a first learning achievement of at least one student; determining the learning level of each student in at least one student according to the first learning achievement; determining a knowledge point association matrix corresponding to each learning level according to the learning level of each student; and planning a knowledge point learning path for each student according to the knowledge point association matrix of each learning level, wherein each knowledge point association matrix comprises at least one association parameter, and each association parameter is used for indicating the association between two knowledge points.
Optionally, in an embodiment of the present application, the planning a knowledge point learning path for each learner according to the knowledge point association matrix of each learning level respectively includes:
determining a 1 st knowledge point to be learned of an s-th student in an ith learning level, wherein i is an integer in [1, m ], and m is the number of layers of the learning level; s is an integer within [1, n ], and n is the total number of students in the ith learning level; determining the knowledge points to be learned of the s-th student in the knowledge point association matrix of the i-th learning level according to the 1 st knowledge points to be learned of the s-th student and the random probability, and obtaining a learning path of the knowledge points of the s-th student, wherein the random probability is greater than 0 and smaller than 1, and the random probability is reduced along with the increase of the learning level.
Optionally, in an embodiment of the present application, determining the knowledge point to be learned of the s-th student in the knowledge point association matrix of the i-th learning level according to the 1 st knowledge point to be learned of the s-th student and the random probability includes:
for the kth knowledge point to be learned of the s-th student, in the knowledge point association matrix of the i-th learning level, the knowledge point with the highest association with the kth knowledge point to be learned is determined as the (k + 1) -th knowledge point to be learned of the s-th student, or one knowledge point is randomly determined as the (k + 1) -th knowledge point from other knowledge points except the kth knowledge point to be learned.
Optionally, in an embodiment of the present application, determining the 1 st knowledge point to be learned of the s th learner in the ith learning level includes: and taking the knowledge point with the lowest score of the s-th student as the 1 st knowledge point to be learned.
Optionally, in an embodiment of the present application, the method further includes: acquiring a second learning achievement of at least one student; and updating the knowledge point association matrix of each learning level according to the second learning achievement of at least one student.
Optionally, in an embodiment of the present application, the updating the knowledge point association matrix of each learning level according to the second learning achievement of the at least one learner includes: calculating the second learning score of each student minus the first learning score to obtain the score difference of each student; and when the achievement difference of the target student is larger than 0, increasing the association parameters corresponding to every two adjacent knowledge points on the knowledge point learning path of the target student by a first preset step length in the knowledge point association matrix corresponding to the target student.
Optionally, in an embodiment of the present application, the updating the knowledge point association matrix of each learning level according to the second learning achievement of the at least one learner includes: calculating the second learning score of each student minus the first learning score to obtain the score difference of each student; and when the achievement difference of the target student is less than 0, reducing the association parameters corresponding to every two adjacent knowledge points on the knowledge point learning path of the target student by a second preset step length in the knowledge point association matrix corresponding to the target student.
Optionally, in an embodiment of the present application, the method further includes: re-determining the learning level of each student in the at least one student according to the second learning achievement of the at least one student; and for the students with the newly determined learning levels, planning a new knowledge point learning path for each student according to the knowledge point association matrix of each learning level.
In a second aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory, the memory having stored thereon a computer program; the processor is adapted to execute a computer program stored in the memory to implement the method as described in the first aspect or any one of the embodiments of the first aspect.
In a third aspect, the present application provides a computer storage medium storing a computer program, which when executed by a processor implements the method as described in the second aspect or any one of the embodiments of the second aspect.
In the embodiment of the application, the learning level of each student in at least one student is determined, each learning level corresponds to one knowledge point association matrix, the association between knowledge points of the student at the level can be reflected, the learning path of the knowledge points is determined for each learning level student according to the knowledge point association matrix at the level, the current learning condition of each student in each learning level is adapted, and the learning efficiency is improved.
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Some specific embodiments of the present application will be described in detail hereinafter by way of illustration and not limitation with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
fig. 1 is a flowchart of a learning path planning method according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a knowledge point association matrix according to an embodiment of the present application;
fig. 3 is a flowchart of a learning path planning method according to a second embodiment of the present application;
fig. 4 is a flowchart of a method for updating a knowledge point association matrix according to a third embodiment of the present application;
fig. 5 is a flowchart of a learning path planning method according to a fourth embodiment of the present application;
fig. 6 is a structural diagram of an electronic device according to a fifth embodiment of the present application.
Detailed Description
The following further describes specific implementations of embodiments of the present application with reference to the drawings of the embodiments of the present application.
Example one
An embodiment of the present application provides a learned route planning method, as shown in fig. 1, where fig. 1 is a flowchart of the learned route planning method provided in the embodiment of the present application. The learning path planning method comprises the following steps:
step 101, acquiring a first learning achievement of at least one student.
For example, if there are n trainees, n is an integer greater than 0, the first learning score of the n trainees can be represented as (g)11，g12，……，g1n)，g1nIndicating the first achievement of the nth student.
And step 102, determining the learning level of each student in at least one student according to the first learning achievement.
For example, m learning levels are total, the learning level of each student is determined, that is, according to the first learning score of n students, the n students are divided into corresponding learning levels by combining the score range corresponding to each learning level, m is an integer greater than 0, for example, the first learning score is 100 points and m may be equal to 3, students with the first learning score less than 60 points are divided into students with the 1 st learning level, students with the first learning score greater than or equal to 60 points and less than 80 points are divided into students with the 2 nd learning level, and students with the first learning score greater than or equal to 80 points are divided into students with the 3 rd learning level.
Of course, this is merely an exemplary illustration, and may also be divided into more levels, for example, the first achievement total is 100 points, m may be equal to 10, and each 10 points is divided into one learning level, or for example, the first achievement total is 100 points, m may be equal to 100, and each 1 point is divided into one learning level, which is not limited in this application.
And 103, determining a knowledge point association matrix corresponding to each learning level according to the learning level of each student.
The knowledge point incidence matrixes corresponding to different learning levels are different.
And step 104, planning a knowledge point learning path for each student according to the knowledge point association matrix of each learning level.
Each knowledge point association matrix comprises at least one association parameter, each association parameter is used for indicating the association between two knowledge points, the value of the association parameter can be [0,1], and the larger the association parameter is, the more compact the association between two knowledge points is. Specifically, the association parameters may have directionality, and one association parameter may represent an association from one knowledge point to another knowledge point. For example, for knowledge point x and knowledge point y, the association parameters of x to y are different from the association parameters of y to x. Of course, the value of the associated parameter may also be an integer between [1 and 10], or an integer between [1 and 100], and the present application only uses the value size to represent the association between one knowledge point and another knowledge point, and does not limit the value range of the associated parameter.
Fig. 2 is a schematic diagram of a knowledge point association matrix provided in an embodiment of the present application, where 10 knowledge points are shown in fig. 2, but this is merely an exemplary illustration, and in practical applications, the number of knowledge points may be many, for example, there may be 100 knowledge points, 200 knowledge points, etc., which is not limited in the present application, 10 knowledge points are taken as an example in fig. 2, and in the knowledge point association matrix shown in fig. 2, the association parameter P in the x-th row and the y-th column is the association parameter P in the x-th row(x,y)Representing the association between the x-th knowledge point to the y-th knowledge point, P(x,y)The larger the value, the more closely the relation between the x-th knowledge point and the y-th knowledge point is, and P(y,x)Representing the association between the y knowledge point to the x knowledge point. When x is y, the relation between the x-th knowledge point and the x-th knowledge point is represented, and P can be used in practical application(x,x)Is set to 0, P (x,x)0 means that in the actual learned path planning, the learning of the xth knowledge point is not continued after the learning of the xth knowledge point. Based on fig. 2, in an alternative implementation, P(x,y)Indicating the possibility of planning learning the y-th knowledge point after the x-th knowledge point. Of course, this is merely an example and does not represent a limitation of the present application.
It should be noted that the learning path of the knowledge point of each learner in the learners of the same learning level may be the same or different, and the present application is not limited thereto.
Optionally, in an embodiment of the present application, after step 104, the method further includes: re-determining the learning level of each student in the at least one student according to the second learning achievement of the at least one student; and for the students with the newly determined learning levels, planning a new knowledge point learning path for each student according to the knowledge point association matrix of each learning level.
In the embodiment of the application, the learning level of each student in at least one student is determined, each learning level corresponds to one knowledge point association matrix, the association between knowledge points of the student at the learning level can be reflected, the learning path of the knowledge points is determined for the student at each learning level according to the knowledge point association matrix at the learning level, the current learning condition of each student at each learning level is adapted, and the learning efficiency is improved.
Example II,
Based on the learned path planning method described in the first embodiment, a second embodiment of the present application provides a learned path planning method, and a specific manner of planning a learned path for each trainee according to the knowledge point association matrix of each layer of trainee in the step 104 of the first embodiment is described, which is, of course, only exemplary and not limiting to the present application, as shown in fig. 3, fig. 3 is a flowchart of the learned path planning method provided in the second embodiment of the present application, and the method includes the following steps:
and 301, determining a 1 st knowledge point to be learned of the s-th student in the ith learning level.
i is an integer in [1, m ], m is the number of levels of the learning level, m is an integer greater than 0, s is an integer in [1, n ], and n is the total number of trainees in the ith learning level.
Optionally, in an embodiment of the present application, determining the 1 st knowledge point to be learned of the s th learner in the ith learning level includes: and taking the knowledge point with the lowest score of the s-th student as the 1 st knowledge point to be learned.
The knowledge point with the score of the s-th student being worse than the worst can be determined as the knowledge point with the score of the s-th student being lowest. For example, taking the k-th knowledge point as an example, the score T of the k-th knowledge point in the total score is countedk(for example, the total score may be 100, the k-th knowledge point's score TkMay be 10 points), the score S obtained by the S-th student on the k-th knowledge point in the first learning score is countedk，(Tk-Sk)/TkThe largest knowledge point is the knowledge point with the lowest score of the s-th student.
Step 302, determining the knowledge points to be learned of the s-th student in the knowledge point association matrix of the i-th learning level according to the 1 st knowledge points to be learned of the s-th student and the random probability, and obtaining the knowledge point learning path of the s-th student.
The random probability is greater than 0 and less than 1, and the random probability decreases as the learning level increases.
Optionally, in an embodiment of the present application, determining the knowledge point to be learned of the s-th student in the knowledge point association matrix of the i-th learning level according to the 1 st knowledge point to be learned of the s-th student and the random probability includes: for the kth knowledge point to be learned of the s-th student, in the knowledge point association matrix of the i-th learning level, the knowledge point with the highest association with the kth knowledge point to be learned is determined as the (k + 1) -th knowledge point to be learned of the s-th student, or one knowledge point is randomly determined as the (k + 1) -th knowledge point from other knowledge points except the kth knowledge point to be learned.
Further optionally, in an embodiment of the present application, determining the knowledge point to be learned of the s-th student in the knowledge point association matrix of the i-th learning level according to the 1 st knowledge point to be learned of the s-th student and the random probability includes: for the kth knowledge point to be learned of the s student, in the knowledge point association matrix of the ith learning level, determining the knowledge point with the highest association with the kth knowledge point to be learned as the kth +1 knowledge point to be learned of the s student according to the probability of (1-epsilon), and randomly determining one knowledge point as the kth +1 knowledge point to be learned in other knowledge points except the kth knowledge point to be learned according to the probability of epsilon, wherein epsilon represents random probability.
It should be noted that, in the knowledge point association matrix, each association parameter is greater than 0 and less than 1, and the random probability is also greater than 0 and less than 1, taking m ═ 3 as an example, a random probability epsilon is set for the knowledge point association matrix of each learning level student, and the random probability represents the randomness for exploring a new learning path in the knowledge point learning path planning process, for example, for the knowledge point association matrix of the 1 st learning level, the knowledge point association matrix shown in fig. 2 is taken as the knowledge point association matrix of the 1 st learning level, and the random probability epsilon is set to 0.3, which represents the randomness for exploring a new learning path in the knowledge point learning path planning of each student in the 1 st learning level to be 0.3. The random probabilities corresponding to the knowledge point association matrices of different learning levels may be different. Taking the 1 st student in the 1 st learning level as an example, after determining the 1 st knowledge point to be learned of the 1 st student, generating a random number ω between (0,1), if ω is less than or equal to 0.3, randomly selecting a knowledge point as the 2 nd knowledge point to be learned from other knowledge points except the 1 st knowledge point, and if ω >0.3, taking the knowledge point with the highest association with the 1 st knowledge point as the 2 nd knowledge point to be learned in the knowledge point association matrix of the 1 st learning level. Since the random number is randomly generated within the (0,1) interval, there is a probability random number of 0.3 less than or equal to 0.3, at this time, the 2 nd knowledge point to be learned is randomly selected regardless of the association parameters in the knowledge point association matrix, and there is a probability random number of 0.7 greater than 0.3, at this time, the knowledge point having the highest association with the 1 st knowledge point to be learned in the knowledge point association matrix is determined as the 2 nd knowledge point to be learned. Therefore, each time the knowledge point to be learned is selected, a new learning path is explored with a probability of 0.3, and the knowledge point with high relevance is selected with a probability of 0.7, so that the learning paths of the knowledge points of each student are different, and the individuation of the learning path of each student is maintained. Meanwhile, the knowledge point learning path adapts to the current learning condition of each student, and a new learning path is continuously explored, so that the planning of the learning path can be continuously updated.
It should be noted that the value range of the random number ω may be (0,10) or (0,100), as long as the random probability ∈ is set between (0,10) or (0,100) correspondingly, and the principle is the same as that of the random number ω being set between (0,10), which is not limited in this application.
Of course, the learning paths of knowledge points of the trainees at the same learning level may be the same, and the present application is not limited thereto. For example, the value T of the k-th knowledge point in the total score is countedk(for example, the total score of the first learning score may be 100, the value of the k-th knowledge point TkMay be 10 points), the average value S of the scores obtained by the trainee of the ith learning level in the first learning score with respect to the k knowledge point is countedkWill (T)k-Sk)/TkThe knowledge point with the maximum value is used as the 1 st to-be-learned knowledge point of the student of the ith learning level, a random number omega is generated, when omega is not more than epsilon, one knowledge point is randomly determined from other knowledge points except the 1 st to-be-learned knowledge point to be used as the 2 nd to-be-learned knowledge point, and omega is not more than epsilon>When epsilon is generated, the knowledge point with the highest relevance with the 1 st knowledge point to be learned is determined as the 2 nd knowledge point of the student of the ith learning level, and by analogy, the knowledge point learning path of the student of the ith learning level is determined.
In the present application, the knowledge point with the highest relevance to the kth knowledge point to be learned refers to the highest relevance parameter from the kth knowledge point to be learned to other knowledge points.
Example III,
Based on the learning path planning method described in the first embodiment, the third embodiment of the present application provides a method for updating a knowledge point association matrix, and referring to fig. 4, fig. 4 is a flowchart of a method for updating a knowledge point association matrix provided in the third embodiment of the present application, where the method includes the following steps:
and step 401, acquiring a second learning achievement of at least one student.
For example, if there are n trainees, n is an integer greater than 0, the second learning score of the n trainees can be represented as (g)21，g22，……，g2n)，g2nIndicating the second achievement of the nth student.
And step 402, updating the knowledge point association matrix of each learning level according to the second learning achievement of at least one student.
Here, two examples are given to explain how to update the knowledge point association matrix of each learning level according to the second learning achievement of at least one trainee, and of course, this is only an example explanation:
optionally, in the first example, the updating the knowledge point association matrix of each learning level according to the second learning achievement of the at least one student includes: calculating the second learning score of each student minus the first learning score to obtain the score difference of each student; and when the achievement difference of the target student is larger than 0, increasing the association parameters corresponding to every two adjacent knowledge points on the knowledge point learning path of the target student by a first preset step length in the knowledge point association matrix corresponding to the target student.
Based on the first example, before the learning path planning is performed for the first time, each associated parameter in the initial knowledge point association matrix may be initialized to 0, in the subsequent update iteration process, for a knowledge point learning path with an improved learning score, the associated parameters corresponding to every two adjacent knowledge points on the knowledge point learning path are increased by a first preset step length, and for a knowledge point learning path with a reduced learning score, the associated parameters of the knowledge points on the learning path may be kept unchanged. For example, if the learning path of the knowledge point of the target student includes the knowledge point x and the knowledge point y, when the target student improves the learning performance, the knowledge point association matrix corresponding to the target student is obtained, the first preset step length is added to the association parameters from the knowledge point x to the knowledge point y in the knowledge point association matrix, and the first preset step length is added to the association parameters corresponding to every two adjacent knowledge points on the learning path of the target student in the knowledge point association matrix.
Optionally, in a second example, the updating the knowledge point association matrix of each learning level according to the second learning achievement of the at least one student includes: calculating the second learning score of each student minus the first learning score to obtain the score difference of each student; and when the achievement difference of the target student is less than 0, reducing the association parameters corresponding to every two adjacent knowledge points on the knowledge point learning path of the target student by a second preset step length in the knowledge point association matrix corresponding to the target student.
Based on the second example, before the learning path planning is performed for the first time, each association parameter in the initial knowledge point association matrix may be initialized to 1, and in the subsequent update iteration process, for the learning path of the knowledge point with the improved learning score, the association parameter of the knowledge point on the learning path of the knowledge point is unchanged, and for the learning path of the knowledge point with the reduced learning score, the association parameter corresponding to every two adjacent knowledge points on the learning path of the knowledge point may be reduced by a second preset step length. For example, if the knowledge point learning path of the target student includes the knowledge point x and the knowledge point y, the second preset step size is decreased for the associated parameters from the knowledge point x to the knowledge point y, and the second preset step size is decreased for the associated parameters corresponding to every two adjacent knowledge points on the knowledge point learning path.
It should be noted that, if the score difference of the target student is equal to 0, it indicates that the score of the target student is not improved or reduced, at this time, the associated parameters corresponding to the knowledge points on the knowledge point learning path of the target student are not changed, and of course, the associated parameters corresponding to every two adjacent knowledge points on the knowledge point learning path of the target student may be increased by a first preset step length, or the associated parameters corresponding to every two adjacent knowledge points on the knowledge point learning path of the target student are decreased by a second preset step length, which is not limited in this application.
Of course, the first example and the second example can also be combined, that is, the second learning achievement of each student is calculated to subtract the first learning achievement to obtain the achievement difference of each student; when the score difference of the target student is less than 0, reducing the association parameters corresponding to every two adjacent knowledge points on the knowledge point learning path of the target student by a second preset step length in the knowledge point association matrix corresponding to the target student; and when the achievement difference of the target student is larger than 0, increasing the association parameters corresponding to every two adjacent knowledge points on the knowledge point learning path of the target student by a first preset step length in the knowledge point association matrix corresponding to the target student. In another embodiment, before the learning path planning is performed for the first time, each associated parameter in the initial knowledge point association matrix may be initialized to 0.5, because there is no knowledge point learning path as a priori condition, all associated parameters are initialized to 0.5, the learning time and iteration number of the new knowledge point association matrix may be greatly reduced, and the personalized learning path may be more effectively planned for the current student. The more the iteration times, the more accurately the association matrix of the knowledge points can reflect the association between the knowledge points.
Example four,
Based on the methods described in the first embodiment, the second embodiment and the third embodiment, a fourth embodiment of the present application provides a learned route planning method, referring to fig. 5, where fig. 5 is a flowchart of the learned route planning method provided in the fourth embodiment of the present application, and the method includes the following steps:
and step 501, acquiring first learning scores of n students.
Wherein the first learning score of n trainees can be expressed as (g)11，g12，……，g1n)，g1nIndicating the first achievement of the nth student.
Step 502, determining the learning level of each student according to the first learning achievement of the n students.
Each learning level corresponds to a knowledge point association matrix, and each learning level corresponds to a random probability epsilon. For example, m is 3, there are three learning levels, the student at the first learning level is the student with the first learning level between [0,60 ], the student at the second learning level is the student with the first learning level between [60,80 ], the student at the third learning level is the student with the first learning level between [80,100], for the student at the first learning level, the reason of losing scores may be various, for the reason of learning carelessness, for some reason of improper methods, for some reason of different habits, the random probability epsilon of the student at the first learning level may be set to be larger, which is helpful for exploring the personalized learning path of the student, for example, the random probability of the student at the first learning level is 0.3, for the student with better learning level, the personalized learning path difference between each other may be appropriately reduced, since learners who have learned are not very different in the grasp of knowledge points, and learning habits and the like are relatively small, for example, the random probability of the learner at the second learning level may be set to 0.2, and the random probability of the learner at the third learning level may be set to 0.1.
Step 503, planning a knowledge point learning path for each student of the ith learning level according to the knowledge point association matrix of the ith learning level.
Taking the knowledge point with the lowest score of the s-th student in the ith learning level as the 1 st knowledge point to be learned to generate a random number omega,when omega is less than or equal to epsilon, randomly determining a knowledge point as a 2 nd knowledge point to be learned from other knowledge points except the 1 st knowledge point to be learned at omega>And e, determining the knowledge point with the highest relevance with the 1 st knowledge point to be learned as the 2 nd knowledge point of the student of the ith learning level, and so on to determine the learning path of the knowledge point of the s-th student, and according to the mode, determining the learning path of the knowledge point of each student of the n students. The learning path of knowledge points of n trainees can be expressed as (l)11，l12，……，l1n) Wherein l is1nAnd the knowledge point learning path of the nth student is shown.
And step 504, acquiring second learning achievements of the n students.
After the n students learn for a period of time according to the respective learning paths of the knowledge points, the n students are tested again to obtain second learning scores of the n students. The second learning score of the n trainees can be expressed as (g)21，g22，……，g2n)，g2nIndicating the second achievement of the nth student.
And step 505, calculating the second learning result of each student minus the first learning result to obtain the result difference of each student.
Let r denote the difference in performance of each student, then r1＝g11-g21，r2＝g12-g22，……rn＝g1n-g2n，rnThe result difference obtained by subtracting the first learning result from the second learning result of the nth student is shown.
If a certain element r in rnIf the number is more than 0, the learning path l of the knowledge point generated by the nth student is illustrated1nThe method is effective, the larger the value is, the better the planned knowledge point learning path is, and the information should be kept as much as possible in the next planning; on the contrary, if a certain element r in rnLess than 0, the learning path l of the knowledge point generated for the nth student is illustrated1nThe values are invalid, and the smaller the values are, the worse the learning path of the planned knowledge point is, and the important point needs to be adjusted in the next replanning.
And step 506, updating the knowledge point association matrix of each learning level according to the achievement difference of each student.
For example, r1When the value is more than 0, the knowledge points are associated with the knowledge point learning path l in the matrix11The corresponding associated parameters of every two adjacent knowledge points are increased by 0.1, if the associated parameters are 1, the associated parameters are kept unchanged, and r is1When the value is less than 0, the knowledge points are associated with the knowledge point learning path l in the matrix11The associated parameters corresponding to every two adjacent knowledge points are reduced by 0.1, and if the associated parameters are 0, the associated parameters are kept unchanged, so that the value range of the associated parameters is ensured to be [0,1]]In the meantime.
And 507, re-determining the learning level of each student according to the second learning achievement of the n students.
And step 508, planning a new knowledge point learning path for each student to the students with the newly determined learning levels according to the updated knowledge point association matrix of each learning level.
The method for planning the learning path of the knowledge point in step 508 is the same as that in step 503, and according to this method, the learning path of the knowledge point of each student in the n newly layered students can be determined. The learning path of the knowledge points of the n newly layered learners can be represented as (l)21，l22，……，l2n) Wherein l is2nAnd the knowledge point learning path of the nth student is shown.
The loop execution step 504-508 may continuously update the knowledge point association matrix to make it more suitable for the actual learning situation of the learner, and may also set the loop times, for example, set the loop times to be 4 times, or loop times to be 5 times, or more, and when the loop times of the knowledge point association matrix reaches the preset times, the knowledge point association matrix is not updated, or may set a preset ratio, and when the ratio of the performance-improved learner is greater than the preset ratio, the knowledge point association matrix is not updated. If the knowledge point association matrix is not updated any more, then after step 504, step 505 and step 506 are not executed, and step 507 and step 508 are directly executed. Of course, the application does not limit the method, and the more the number of update iterations of the knowledge point association matrix is, the more the method can adapt to the real learning situation of the learner.
Example V,
Based on the learned route planning method described in the foregoing embodiment, an embodiment of the present application provides an electronic device, configured to execute the learned route planning method described in the foregoing embodiment, as shown in fig. 6, where the electronic device 60 includes: at least one processor (processor)602, memory (memory)604, bus 606, and communication Interface (communication Interface) 608.
Wherein:
the processor 602, communication interface 608, and memory 604 communicate with one another via a communication bus 606.
A communication interface 608 for communicating with other devices.
The processor 602 is configured to execute the program 610, and may specifically perform relevant steps in the methods described in the first to fourth embodiments.
In particular, program 610 may include program code comprising computer operating instructions.
The processor 602 may be a central processing unit CPU or an ASIC specific integrated circuit
(Application Specific Integrated Circuit) or one or more Integrated circuits configured to implement embodiments of the invention. The electronic device comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 604 for storing a program 610. Memory 604 may comprise high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
Example six,
The embodiment of the present application provides a storage medium, which stores a computer program, and when a processor executes the computer program, the method described in the first to fourth embodiments is implemented.
The electronic device of the embodiments of the present application exists in various forms, including but not limited to:
(1) a mobile communication device: such devices are characterized by mobile communications capabilities and are primarily targeted at providing voice, data communications. Such terminals include: smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.
(2) Ultra-mobile personal computer device: the device belongs to the category of personal computers, has calculation and processing functions, and generally has the characteristic of mobile internet access. Such terminals include: PDA, MID, and UMPC devices, etc., such as ipads.
(3) A portable entertainment device: such devices can display and play multimedia content. This type of device includes: audio, video players (e.g., ipods), handheld game consoles, electronic books, and smart toys and portable car navigation devices.
(4) And other electronic devices with data interaction functions.
Thus, particular embodiments of the present subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular transactions or implement particular abstract data types. The application may also be practiced in distributed computing environments where transactions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (10)

1. A method of learning path planning, comprising:
acquiring a first learning achievement of at least one student;
determining a learning level of each student of the at least one student according to the first learning achievement;
determining a knowledge point association matrix corresponding to each learning level according to the learning level of each student;
and planning a knowledge point learning path for each student according to the knowledge point association matrix of each learning level, wherein each knowledge point association matrix comprises at least one association parameter, and each association parameter is used for indicating the association between two knowledge points.
2. The method of claim 1, wherein planning a knowledge point learning path for each student according to the knowledge point association matrix of each learning level comprises:
determining a 1 st knowledge point to be learned of an s-th student in an ith learning level, wherein i is an integer in [1, m ], and m is the number of layers of the learning level; s is an integer within [1, n ], and n is the total number of students in the ith learning level;
determining the knowledge points to be learned of the s-th student in the knowledge point association matrix of the i-th learning level according to the 1 st knowledge points to be learned of the s-th student and the random probability, and obtaining a learning path of the knowledge points of the s-th student, wherein the random probability is greater than 0 and smaller than 1, and the random probability is reduced along with the increase of the learning level.
3. The method of claim 2, wherein determining the knowledge points to be learned of the s-th student in the knowledge point association matrix of the i-th learning level according to the 1 st knowledge point to be learned of the s-th student and the random probability comprises:
for the kth knowledge point to be learned of the s-th student, in the knowledge point association matrix of the i-th learning level, determining a knowledge point with the highest association with the kth knowledge point to be learned as the (k + 1) -th knowledge point to be learned of the s-th student, or randomly determining one knowledge point as the (k + 1) -th knowledge point from other knowledge points except the kth knowledge point to be learned.
4. The method of claim 2, wherein determining the 1 st knowledge point to be learned of the s-th learner in the i-th learning level comprises:
and taking the knowledge point with the lowest score of the s-th student as the 1 st knowledge point to be learned.
5. The method of claim 1, further comprising:
acquiring a second learning achievement of the at least one student;
and updating the knowledge point association matrix of each learning level according to the second learning achievement of the at least one student.
6. The method of claim 5, wherein updating the knowledge point association matrix for each learning level according to the second learning achievement of the at least one student comprises:
subtracting the first learning score from the second learning score of each student to obtain a score difference of each student;
and when the achievement difference of the target student is larger than 0, increasing the association parameters corresponding to every two adjacent knowledge points on the knowledge point learning path of the target student by a first preset step length in the knowledge point association matrix corresponding to the target student.
7. The method of claim 5, wherein updating the knowledge point association matrix for each learning level based on the second learning achievement of the at least one student comprises:
calculating the second learning score of each student minus the first learning score to obtain the score difference of each student;
and when the achievement difference of the target student is less than 0, reducing the association parameters corresponding to every two adjacent knowledge points on the knowledge point learning path of the target student by a second preset step length in the knowledge point association matrix corresponding to the target student.
8. The method according to any one of claims 5-7, further comprising:
re-determining the learning level of each of the at least one student according to the second learning achievement of the at least one student;
and for the students with the newly determined learning levels, planning a new knowledge point learning path for each student according to the knowledge point association matrix of each learning level.
9. An electronic device, comprising: a processor and a memory, the memory having a computer program stored thereon; the processor is configured to execute the computer program stored in the memory to implement the method of any one of claims 1-8.
10. A computer storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the method according to any one of claims 1-8.
CN201910831758.2A 2019-09-04 2019-09-04 Learning path planning method, electronic device and computer storage medium Pending CN112446523A (en)

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