CN116362524A - Collaborative learning group construction method and device based on role collaboration - Google Patents
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Abstract
The invention provides a collaborative learning group construction method and device based on role collaboration, wherein the method comprises the following steps: acquiring project characteristic data of at least one project, which comprises a plurality of project indexes, and acquiring student comprehensive data of each student, wherein the student comprehensive data at least comprises project capability data and personal characteristic data; acquiring an item matching degree matrix of students and items according to the item feature data and the item capability data, and acquiring a group leader identification result by applying a group leader allocation model based on role cooperation in combination with first constraint data; and acquiring a comprehensive compatibility matrix of the students and the panelists according to the personal characteristic data and the panelist identification result, and applying a panelist allocation model based on role cooperation by combining the second constraint data to allocate the panelists, wherein each panelist and the corresponding panelist form each cooperative learning panel. The invention can more accurately and objectively describe students, provides a more accurate grouping scheme, and improves the overall completion quality of all projects in project system courses and the overall learning effect of all collaborative learning groups.
Description
Technical Field
The invention belongs to the technical field of intelligent education, and particularly relates to a cooperative learning group construction method and device based on role cooperation.
Background
In learning of a conventional course, all students divide into units in administrative classes, systematically learn the necessary courses specified in talent training schemes, and students located in the same specialty or class want to learn the same course knowledge and complete the same learning task. Unlike the learning of the conventional courses, the project system courses are usually set in the form of a selection and repair type course, are professional practice courses set in the form of practical training or experiments, allow students from different professions and even different grades to perform free selection and repair, and the students are required to complete different project tasks through grouping cooperation in the project system courses, and evaluate the learning effect of the students according to the completion quality of the project tasks.
In recent years, students have studied a plurality of different collaborative learning group construction methods, but still have obvious problems and disadvantages, especially in the aspect of collaborative learning for project courses, and no targeted effective solution is available. How to comprehensively consider learning requirements and characteristics of project course, on the basis of objectively and comprehensively analyzing characteristics of students participating in project course learning, mathematical modeling of construction problems of a collaborative learning group is carried out in a standardized paradigm, and a high-efficiency and feasible solving method is provided, so that the method is an important problem to be solved in the construction research of the current collaborative learning group.
The existing collaborative learning group construction method usually uses a measurement mechanism based on a single value to characterize students, cannot accurately reflect uncertainty of actual characteristics of the students, can cause deviation of grouping results, and finally affects collaborative learning quality of a collaborative learning group. In a real-world scenario, a student's characteristic performance in a single course may be affected by aspects such as physical or mental motivation, current emotion, current knowledge reserve and technical maturity, and the like, and the student's characteristic ability exhibits significant uncertainty and volatility. In addition, the existing method cannot be well applied to scenes of project courses, and the overall completion quality of all projects as high as possible cannot be ensured while the learning effect of all students is improved.
Disclosure of Invention
The invention provides a cooperative learning group construction method and device based on role cooperation, which solve the problems that the conventional cooperative learning group construction cannot accurately reflect the uncertainty of the actual characteristics of students and influence the cooperative learning quality of the cooperative learning group.
Based on the above purpose, the invention provides a collaborative learning group construction method based on role collaboration, which comprises the following steps: acquiring project characteristic data of at least one project, wherein the project characteristic data comprises a plurality of project indexes, and acquiring student comprehensive data of each student, wherein the student comprehensive data comprises personal information, project capability data of a plurality of index capability characteristics corresponding to any project and personal characteristic data of at least one personal characteristic; acquiring an item matching degree matrix of each student for each item according to the item characteristic data and the item capacity data, and acquiring a group length identification result by combining first constraint data and applying a group length distribution model based on role cooperation, wherein the first constraint data is determined according to the item characteristic data and the student comprehensive data; and acquiring comprehensive compatibility matrixes of each student and each panelist according to the personal characteristic data and the panelist identification result, carrying out panelist allocation by combining second constraint data and applying a panelist allocation model based on role cooperation, wherein each panelist and the corresponding panelist form each cooperative learning group, and the second constraint data is determined according to the project characteristic data and the student comprehensive data.
Optionally, the acquiring the item matching degree matrix of the student and the item according to the item feature data and the item capability data includes: for any student, calculating the interval value of each index capability characteristic according to the project capability data; calculating the weight value of each item index in any item according to the item characteristic data; calculating the item matching degree of any student to each item according to the interval value of each index capability characteristic of any student, the weight value of each item index in each item and the item characteristic data, and combining the item matching degree of each student to each item to obtain an item matching degree matrix of the student and the item.
Optionally, the calculating the interval value of each index capability feature according to the project capability data includes: aiming at the index capability feature corresponding to any item index, if the number of the collected questions of the questionnaire or the test is less than the number threshold, determining the interval value of the index capability feature according to the maximum expression value and the minimum expression value of all the questions; if the number of the collected questionnaires or the test questions is not less than the number threshold, calculating numerical characteristics including expected, entropy and super entropy of the index capability characteristics by using an inverse cloud generator algorithm in a cloud model theory, and determining interval values of the index capability characteristics according to the numerical characteristics;
The calculating the weight value of each item index in any item according to the item characteristic data comprises the following steps: for any item, constructing a fuzzy judgment matrix according to the relative importance degree of each item index in the item characteristic data, wherein any element in the fuzzy judgment matrix
Represents +.>
Individual item index and->
The ratio of the importance of the individual project indicators, +.>
、/>
,/>
,/>
,/>
，/>
The number of the project indexes; calculating the sum of any row and any column in the fuzzy judgment matrix, and constructing a fuzzy consistency matrix according to the sum of all rows and the sum of all columns; calculating any row +.>
And is normalized to obtain +.>
And the weight value of each item index in any item.
Optionally, the calculating the item matching degree of any student to each item according to the interval value of each index capability feature of any student and the weight value of each item index in each item and the item feature data includes: for any item index of any item, calculating the probability that the interval value of the index capability feature of any student is not smaller than the expected interval value of the item to the item index according to the interval value of the index capability feature of any student corresponding to the item index and the expected interval value of the item index in the item feature data, so as to obtain the interval number probability of the index capability feature of any student; and calculating the sum of products of the weight value of each item index in the item and the corresponding interval number probability of any student aiming at any item to obtain the item matching degree of any student on the item.
Optionally, the step of obtaining the group leader identification result by combining the first constraint data and applying the group leader allocation model based on role coordination includes: constructing a first objective function according to the item matching degree matrix and the group length distribution matrix; and solving the group leader allocation matrix which meets the first constraint data and optimizes the first objective function by using a group leader allocation model based on role coordination to obtain a group leader identification result, wherein one item is allocated to one group leader.
Optionally, the obtaining the comprehensive compatibility matrix of the student and the group leader according to the personal characteristic data and the group leader identification result includes: calculating interval values and weight values of all the personal features according to the personal feature data; for any personal feature, calculating the compatibility degree of each student on the personal feature of any group of individuals according to the weight value of the personal feature and the interval value of each student and the personal feature of any group of individuals; for any student, carrying out weighted summation on the compatibility degree of the student and any group leader on each personal characteristic to obtain the comprehensive compatibility degree of the student and any group leader; and combining the comprehensive compatibility degree of each student and each panelist to obtain a comprehensive compatibility degree matrix of the students and the panelists.
Optionally, the assigning the panelist by applying a panelist assignment model based on role coordination in combination with the second constraint data includes: constructing a second objective function according to the comprehensive compatibility matrix and the panelist allocation matrix; and (3) applying a group member distribution model based on role coordination to obtain the group member distribution matrix which meets second constraint data and optimizes the second objective function.
Based on the same inventive concept, the invention also provides a cooperative learning group construction device based on role cooperation, which comprises: a data acquisition unit, configured to acquire item feature data including a plurality of item indexes of at least one item, and acquire student integrated data of each student, where the student integrated data includes personal information, item capability data including a plurality of index capability features corresponding to any one of the items, and personal feature data including at least one personal feature; the group length distribution unit is used for acquiring an item matching degree matrix of students and items according to the item characteristic data and the item capability data, and acquiring a group length identification result by combining a first constraint data application group length distribution model based on role cooperation, wherein the first constraint data is determined according to the item characteristic data and the student comprehensive data; and the panelist distribution unit is used for acquiring the comprehensive compatibility matrix of the students and the panelists according to the personal characteristic data and the panelist identification result, and carrying out panelist distribution by combining second constraint data and applying a panelist distribution model based on role cooperation, wherein each panelist and the corresponding panelist form each cooperative learning panel, and the second constraint data is determined according to the project characteristic data and the student comprehensive data.
Based on the same inventive concept, the invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the collaborative learning group construction method based on role collaboration when executing the program.
Based on the same inventive concept, the invention also provides a computer storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to execute the collaborative learning group construction method based on role collaboration.
From the above, the technical scheme provided by the invention has the beneficial effects that: the invention provides a collaborative learning group construction method and device based on role collaboration, wherein the method comprises the following steps: acquiring project characteristic data of at least one project, wherein the project characteristic data comprises a plurality of project indexes, and acquiring student comprehensive data of each student, wherein the student comprehensive data comprises personal information, project capability data of a plurality of index capability characteristics corresponding to any project and personal characteristic data of at least one personal characteristic; acquiring an item matching degree matrix of students and items according to the item feature data and the item capability data, and acquiring a group length identification result by combining first constraint data and applying a group length distribution model based on role cooperation, wherein the first constraint data is determined according to the item feature data and the student comprehensive data; and acquiring a comprehensive compatibility matrix of the students and the panelists according to the personal characteristic data and the panelist identification result, carrying out panelist allocation by applying a panelist allocation model based on role cooperation in combination with second constraint data, wherein each panelist and the corresponding panelist form each cooperative learning panel, and the second constraint data is determined according to the project characteristic data and the student comprehensive data, so that the students can be more accurately and objectively depicted, a more accurate grouping scheme can be provided, and the overall completion quality of all projects in a project course and the overall learning effect of all cooperative learning panels are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a collaborative learning group construction method based on role collaboration according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a collaborative learning group construction device based on role collaboration according to an embodiment of the present invention;
fig. 3 is a schematic hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same.
It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present invention should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present disclosure pertains. The terms "first," "second," and the like, as used in embodiments of the present invention, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
The embodiment of the invention implements a collaborative learning group construction method based on role collaboration, as shown in fig. 1, the collaborative learning group construction method based on role collaboration comprises the following steps:
step S11: item feature data of at least one item including a plurality of item indicators is acquired, and student integrated data of each student is acquired, the student integrated data including personal information, item capability data of a plurality of indicator capability features corresponding to any one of the items, and personal feature data of at least one personal feature.
In the embodiment of the invention, the project course is usually a selected course with limited number of students or a professional practice course which is simply set up in a practical training or experimental form, and the scale of the students is about 30. In the initial course, the teacher issues some practical innovative projects, which have obvious differences, and different projects require that the basic knowledge and skill reserves of the completers are not the same, and each student must and can only participate in one project. An example of a set of items is shown in table 1. Because of the differences among the projects, the team leader selection standards of the projects are different, so that the matching degree between student individuals and the projects is different.
Table 1 items and capability requirements
In step S11, item feature data of at least one item input by a teacher is received, including but not limited to: project name, number of project members, a plurality of project indexes, expected section values of the project indexes, and the like. The project index is the capability index that the project member needs to possess. I.e., various characteristic data of the item are input by the teacher at the beginning of the course of the project. The number of item members is an interval number, for example, [5, 6] means that the number of item members is a minimum of 5 and a maximum of 6; the expected interval value of project capability is used to screen students whose capability value does not reach the standard. If a student's ability value does not meet the preset requirements of an item, such student would not be suitable to attend the item, i.e., the student has a conflict with the item.
The student comprehensive data of each student comprises a plurality of comprehensive data such as personal characteristics, project capability, personal information and the like of the student. In step S11, personal information input by the students is also received, and personal characteristic data and project capability data of each student are collected by means of a related measurement questionnaire or quiz. Personal characteristics of students include cognitive ability, leadership ability, social ability, learning style, personality, and the like. The cognitive ability characteristics and the learning style characteristics can well reflect the learning ability of students; the leadership and social ability features can reflect the participation of the student in the group activity; the personality traits may provide decision support for student behavioral pre-decisions. The cognitive ability characteristic is analyzed and obtained to obtain a cognitive ability representation value of the student mainly through a mode of carrying out knowledge test on the student, so as to obtain cognitive ability characteristic data; the other four characteristics of the leadership capability, the social capability, the learning style, the individuality and the like are mainly obtained through data collection through a relevant measurement questionnaire, and the performance values of the students on the four characteristics are obtained through data analysis. The project capability data of students are mainly collected in a test mode, and the representation value of the index capability characteristic of each student corresponding to each project index is estimated according to the project indexes in the project characteristic data. Personal information of the student is input by the student, specifically including gender, rejection of the group intention (i.e., against who is in the same collaborative learning group as who is in the same collaborative learning group), priority of the group intention (i.e., who is desired to be in the same collaborative learning group as who is in the same collaborative learning group), and the like.
Step S12: and acquiring an item matching degree matrix of each student for each item according to the item characteristic data and the item capacity data, and acquiring a group length identification result by combining first constraint data and applying a group length distribution model based on role cooperation, wherein the first constraint data is determined according to the item characteristic data and the student comprehensive data.
In the embodiment of the invention, firstly, for any student, calculating the interval value of each index capability characteristic according to the project capability data; calculating the weight value of each item index in any item according to the item characteristic data; calculating the item matching degree of any student to each item according to the interval value of each index capability characteristic of any student, the weight value of each item index in each item and the item characteristic data, and combining the item matching degree of each student to each item to obtain an item matching degree matrix of the student and the item.
When calculating the interval value of each index capability feature, optionally, for the index capability feature corresponding to any item index, if the number of collected questions of a questionnaire or a test is less than the number threshold, determining the interval value of the index capability feature according to the maximum expression value and the minimum expression value of all the questions; and if the number of the collected questionnaires or the test questions is not less than the number threshold, calculating numerical characteristics including expected, entropy and super entropy of the index capability characteristics by using an inverse cloud generator algorithm in a cloud model theory, and determining interval values of the index capability characteristics according to the numerical characteristics. The number threshold is preferably 6, and may be set as needed, without being particularly limited thereto. By students 
For example, according to the number of questions of the index capability feature test, different methods are adopted to obtain the student +.>
Interval values of the indicator capability features of (a). When only one test question is tested, namely, student +.>
Has only one representation value +.>
Interval value of index capability feature
. When the test is less than 6 test questions, the interval value of the index capability feature is +.>
Wherein->
And->
Representing the minimum and maximum representation values, respectively. When more than 6 test questions exist in the test, the student's ++is calculated by applying the reverse cloud generator algorithm in the cloud model theory>
Three numerical features on the index capability feature, namely the desire +.>
Entropy->
And super entropy->
. The number of values represented determines the accuracy of the numerical feature. The calculation formula of the numerical characteristics of the cloud model is as follows:
wherein,,
number of questions representing the index capability feature test, < ->
Representing student->
In test question->
The above-mentioned performance value of the index capability feature. At this time, student->
The interval value of the index capability feature of (2) is +.>
The calculation formula is as follows:
wherein,,
interval value representing the characteristic of index ability +.>
Lower bound of->
Interval value representing index capability feature
Upper bound of->
Is->
Is a constant.
When calculating the weight value of each item index in any item, optionally, constructing a fuzzy judgment matrix according to the relative importance degree of each item index in the item characteristic data for any item, wherein any element in the fuzzy judgment matrix
Represents +.>
Individual item index and->
The ratio of the importance of the individual project indicators, +.>
、/>
,
,/>
,/>
，/>
Is the number of project indexes. When the teacher inputs project characteristic data, the teacher inputs the relative importance degree of project indexes and personal characteristics. Constructing a fuzzy judgment matrix of the relative importance degree of the project indexes>
Satisfies the following conditions
。/>
Determined by five levels of complementary scale, as shown in table 2.
Table 2 five-level complementary scale
Obtaining a fuzzy judgment matrix
Then, the sum of any row and any column in the fuzzy judgment matrix is calculated, and according to eachThe sum of the rows and the sum of the columns construct a fuzzy consistency matrix. Specifically, a fuzzy judgment matrix is calculated according to the following formula>
Sum of each row:
and then carrying out mathematical conversion according to the sum of each row and the application of each column and the following relational expression to construct a fuzzy consistency matrix:
wherein,,
and->
Respectively represent fuzzy judgment matrix->
Middle->
Row and->
And (3) row sum. According to all ∈ >
Constructing a fuzzy consistency matrix->
。
Recalculating the fuzzy consistency matrix
Any one line->
Sum and go through markNormalization to obtain->
Weight value of individual item indicator in any one of the items +.>
The calculation formula is as follows:
After the interval value of each index capability characteristic of all students and the weight value of each item index are obtained, calculating the item matching degree of any student to each item according to the interval value of each index capability characteristic of any student, the weight value of each item index in each item and the item characteristic data. Optionally, first, calculating, for any item index of any item, a probability that an interval value of the index capability feature of any student is not less than an expected interval value of the item to the item index according to an interval value of the index capability feature of any student corresponding to the item index and an expected interval value of the item index in the item feature data, so as to obtain an interval number probability of the index capability feature of any student; specifically, the method is calculated according to the following relation:
wherein the interval number likelihood
Interval value representing student index ability characteristic +.>
(i.e. student- >
In item index->
Upper interval value) is not less than the item +.>
Project index->
Is>
Probability of->
Representing interval value +.>
Upper bound of (2); />
Representation->
Lower bound of (2); />
Representing the desired interval value +.>
Upper bound of (2); />
Representing the desired interval value +.>
Is defined below.
The embodiment of the invention can also calculate the conflict relation matrix of students and projects through the interval number probability
Matrix element->
Wherein->
Representing student->
Meeting the project->
Is qualified as item +.>
Is a member of (2); />
Representative is student->
Is not sufficient to compete with the project +.>
I.e. cannot become an item +.>
Is a member of the group (a). The calculation formula is as follows:
After the interval number possibility of all index capability features of each student is obtained, calculating the item matching degree of any student to any item according to the interval number possibility and the weight value of the item index. Optionally, for any item, calculating the sum of products of the weight value of each item index in the item and the corresponding interval number probability of any student to obtain the item matching degree of any student on the item. The specific calculation formula is as follows:
Here the number of the elements is the number,
representing student->
For items->
Item matching degree of (2); />
Representing the number of project indexes;
representative item index->
In item->
Weight value of (a); />
Interval value representing the possibility of interval number and representing the ability of student to work>
(i.e. student->
In item index->
The interval value above) is not less than the interval number +.>
Is a probability of (2). And combining the item matching degree of each student on each item to obtain an item matching degree matrix of the student and the item.
And after the student and the project are matched with the project, a group leader allocation model based on role coordination is applied according to the project matching degree matrix and combined with the first constraint data to obtain a group leader identification result. The group leader allocation model based on Role coordination in the embodiment of the invention applies a basic model based on a Role-Base Collaboration (RBC) theory, namely an E-CARGO (Environments-Classes, agents, roles, groups, objects) model.
The E-CARGO model is a basic model based on role synergy (RBC) theory, and is used to formalize the synergy assignment problem. The E-CARGO model uses roles as the underlying mechanism, emphasizes the importance of roles, including components of environments, classes, agents, roles, groups, and objects, and can be formalized by mathematical symbolization. Roles are a kind of characterization that members of a group restrict their behavior according to certain specifications, in a group an individual should take on a specific role and play a specific role. The E-CARGO model focuses on complex relationships among roles, sets a group of roles according to requirements and meets certain constraint conditions, and interaction among the roles is achieved, so that the complex problems in reality are solved, and therefore the E-CARGO model is widely applied to the fields of industrial engineering, crowdsourcing distribution, social simulation and the like. The embodiment of the invention realizes collaborative learning team construction with multiple constraint conditions by applying an E-CARGO model. Firstly, constructing a first objective function according to the item matching degree matrix and the group leader allocation matrix; and then, solving the group leader allocation matrix which meets the first constraint data and optimizes the first objective function by using a group leader allocation model based on role coordination to obtain a group leader identification result, wherein one item is allocated to one group leader. Constructing a first objective function of the group leader allocation model according to the following relation:
Wherein,,
representing the number of students, < >>
Representing the number of items>
A matrix is assigned to the group leader. />
Representing student->
For items->
Item matching degree of (2); />
Representing student->
Whether or not to be selected as item +.>
Group length of (2), e.g.)>
Then indicate student ++>
Is item->
Is a small group of length. />
The first constraint data is satisfied, including the following constraints:
(a) Each student takes the most part of the group leader of any item;
(b) Each project has and has only one team leader;
(c) The finally selected group length meets the requirement of the expected interval value of each item index in the project.
The embodiment of the invention utilizes CPLEX optimization package to carry out optimization solution on the first objective function under constraint, and the solution process is as follows:
leading in CPLEX optimization package, and initializing group leader allocation model; a data format of the group leader allocation model is determined, and first objective function coefficients and first constraint data are input. By passing through
、/>
、/>
Define the linear programming problem in CPLEX, +.>
Is the target function coefficient>
Is a constraint coefficient, < >>
Is a variable, wherein->
，/>
、/>
The method comprises the steps of carrying out a first treatment on the surface of the Setting a first objective function and constraint expression, using a matrix +.>
And->
A first objective function of the group length identification problem is formed in a one-dimensional array form, and a constraint expression is added in an iteration mode; calling CPLEX optimization package cplex.sole () method according to first objective function and Constraint expression to calculate maximized +.>
Value, and corresponding
Values.
Assigning matrices based on group length
The team leader of each project is obtained, and the team leader of each project plays an important role in the completion process of the project, so that all projects are ensured to obtain the highest possible global completion quality.
Step S13: and acquiring comprehensive compatibility matrixes of each student and each panelist according to the personal characteristic data and the panelist identification result, carrying out panelist allocation by combining second constraint data and applying a panelist allocation model based on role cooperation, wherein each panelist and the corresponding panelist form each cooperative learning group, and the second constraint data is determined according to the project characteristic data and the student comprehensive data.
In the embodiment of the present invention, optionally, the interval value and the weight value of each personal feature are calculated according to the personal feature data. The calculation of the interval value of the personal feature is the same as the calculation method of the interval value of the index capability feature in the front, and the calculation of the weight value of the personal feature is the same as the calculation method of the weight value of the item index in the front, and the difference is only that a fuzzy judgment matrix is constructed according to the relative importance degree of the personal feature, so that the subsequent calculation is performed, and detailed description is omitted.
And then calculating the comprehensive compatibility matrix of the students and the group leader according to the interval value and the weight value of each personal characteristic of any student and the group leader identification result. Optionally, for any personal feature, calculating the compatibility degree of each student and any group of individuals on the personal feature according to the weight value of the personal feature and the interval value of each student and the personal feature of any group of individuals; for any student, carrying out weighted summation on the compatibility degree of the student and any group leader on each personal characteristic to obtain the comprehensive compatibility degree of the student and any group leader; and combining the comprehensive compatibility degree of any student and each panelist to obtain a comprehensive compatibility degree matrix of the student and the panelist.
Calculating the compatibility degree of other students and any group length on any personal characteristic by taking the group length as the center, and carrying out weighted summation on the compatibility degree of any student on each personal characteristic to obtain the comprehensive compatibility degree of any student on any group length
The calculation formula is as follows:
wherein,,
representing student->
And (2) with project->
The degree of comprehensive compatibility among the teams of the group length; />
Represent the first
Weights of individual features; / >
Representing the number of personal features; />
And->
Interval values representing the individual characteristics of the student, respectively +.>
(i.e. student->
In->
Interval value on individual personal characteristics); />
And->
Respectively represent the first
The team length of the individual items is +.>
Interval value on personal characteristics +.>
Upper and lower bounds of (2). Combining the comprehensive compatibility degree of each student and each panelist to obtain a comprehensive compatibility degree matrix of the students and the panelists>
。
Obtaining the comprehensive compatibility matrix of students and panelists
Thereafter, according to the integrated compatibility matrix +.>
And performing panelist allocation by applying a panelist allocation model based on role coordination in combination with the second constraint data. The group member distribution model based on role coordination in the embodiment of the invention is also applied to a basic model based on role coordination theory, namely an E-CARGO model.
In the embodiment of the invention, a second objective function is constructed according to the comprehensive compatibility matrix and the panelist allocation matrix; and then, a group member allocation model based on role coordination is applied to obtain the group member allocation matrix which satisfies second constraint data and optimizes the second objective function. Specifically, a second objective function of the panelist assignment model is constructed according to the following relation:
wherein,,
representing the number of students; / >
Representing the number of items, namely the number of collaborative learning groups; />
Assigning a matrix to a team member->
Representing student->
Whether or not to be allocated to an item->
I.e., collaborative learning team j. Such as
Then indicate student ++>
Assigned to item->
。/>
The second constraint data is satisfied, including the following constraints:
(a) Ensuring that a team leader for each item is not assigned to other items;
(b) Ensuring that each student can only be assigned to one item;
(c) Ensuring that the member number requirements of each project are met;
(d) Ensuring that each member of the finally selected project group meets the requirement of the expected interval value of each project index in the project;
(e) The conflict of rejection of the team forming will is avoided among the members of the same project;
(f) The sex restrictions for each group member in the cooperative learning group are ensured to be satisfied, and specifically, the sex restrictions are classified into the following three cases:
1) When the number of girls is less than the number of the cooperative learning groups, only allocating at most one girl to each cooperative learning group;
2) When the number of men is less than that of the cooperative learning subgroups, only allocating at most one men for each cooperative learning subgroup;
3) When the number of men and women is greater than the number of cooperative learning teams, then at least one men and one women are allocated to each cooperative learning team.
(g) On the premise of meeting the constraint, the requirement of meeting the priority team forming will of students is ensured.
The embodiment of the invention utilizes CPLEX optimization package to carry out optimization solution on the second objective function under constraint, the solution process is the same as the optimization solution method on the first objective function, and after the CPLEX optimization package cplex.wave () method is called, the maximized objective function is calculated according to the second objective function and constraint expression
Values, and corresponding team member allocation matrix +.>
. Assigning a matrix according to panellists>
Suitable panelists are identified for each item. Each team leader and the corresponding team members form each collaborative learning team and are responsible for the corresponding project, so that the collaborative learning team facing the project course is constructed.
According to the collaborative learning group construction method based on role collaboration, an interval number theory and a cloud model theory are introduced, a fuzzy evaluation method is designed to describe uncertainty of students on personal characteristics and index capability characteristics, then a fuzzy analytic hierarchy process is adopted to respectively determine weights of various project indexes and personal characteristics in an application scene, and the project matching degree and comprehensive compatibility degree of the students are weighted and summed, so that students can be accurately and objectively described, requirements of the application scene on the characteristics of the students are met, and the problem that the existing collaborative learning group construction method ignores possible uncertainty in the aspects of the personal characteristics and project capability of the students is solved. Compared with the prior art, the method and the device have the advantages that the personal characteristics and the index capability characteristics of students are measured by adopting a mechanism based on the interval number, the uncertainty and the fluctuation of the personal characteristics and the index capability characteristics of the students can be described, a more accurate grouping scheme is further provided, and the overall completion quality of all projects in the project course and the overall learning effect of all collaborative learning groups are improved.
In order to accurately construct a collaborative learning group oriented to project courses, the embodiment of the invention starts from two targets of project courses, namely, the overall completion quality of all projects and the overall collaborative learning effect of all collaborative learning groups as much as possible, applies an E-CARGO model to formally model the collaborative learning group construction problem as a collaborative optimization problem, ensures that all projects obtain the overall completion quality as much as possible by identifying a group leader with high project matching degree, and improves the collaborative learning effect of the collaborative learning group by selecting a group leader with high comprehensive compatibility degree. Compared with the prior art, the method and the device not only accord with the learning requirements and characteristics of project courses, can pertinently construct a collaborative learning group according to two targets of the project courses, but also can efficiently solve the construction problem of the collaborative learning group under the limitation of a plurality of constraint conditions, thereby better providing a grouping scheme for teachers and students and improving the learning effect of the students.
In summary, according to the collaborative learning group construction method based on role collaboration in the embodiment of the present invention, by acquiring item feature data including a plurality of item indexes of at least one item, and acquiring student comprehensive data of each student, the student comprehensive data includes personal information, item capability data including a plurality of index capability features corresponding to any one of the items, and personal feature data including at least one personal feature; acquiring an item matching degree matrix of students and items according to the item feature data and the item capability data, and acquiring a group length identification result by combining first constraint data and applying a group length distribution model based on role cooperation, wherein the first constraint data is determined according to the item feature data and the student comprehensive data; and acquiring a comprehensive compatibility matrix of the students and the panelists according to the personal characteristic data and the panelist identification result, carrying out panelist allocation by applying a panelist allocation model based on role cooperation in combination with second constraint data, wherein each panelist and the corresponding panelist form each cooperative learning panel, the second constraint data is determined according to the project characteristic data and the student comprehensive data, and the students are more accurately and objectively depicted, so that a more accurate grouping scheme can be provided, and the overall completion quality of all projects in a project course and the overall learning effect of all cooperative learning panels are improved.
The foregoing describes certain embodiments of the present invention. In some cases, the acts or steps recited in the embodiments of the present invention may be performed in a different order than in the embodiments 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 are also possible or may be advantageous.
The embodiment of the invention also provides a collaborative learning group construction device based on role coordination, as shown in fig. 2, the collaborative learning group construction device based on role coordination comprises: a data acquisition unit, a group leader allocation unit and a group member allocation unit. Wherein,,
a data acquisition unit, configured to acquire item feature data including a plurality of item indexes of at least one item, and acquire student integrated data of each student, where the student integrated data includes personal information, item capability data including a plurality of index capability features corresponding to any one of the items, and personal feature data including at least one personal feature;
the group length distribution unit is used for acquiring an item matching degree matrix of students and items according to the item characteristic data and the item capability data, and acquiring a group length identification result by combining a first constraint data application group length distribution model based on role cooperation, wherein the first constraint data is determined according to the item characteristic data and the student comprehensive data;
And the panelist distribution unit is used for acquiring the comprehensive compatibility matrix of the students and the panelists according to the personal characteristic data and the panelist identification result, and carrying out panelist distribution by combining second constraint data and applying a panelist distribution model based on role cooperation, wherein each panelist and the corresponding panelist form each cooperative learning panel, and the second constraint data is determined according to the project characteristic data and the student comprehensive data.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each unit may be implemented in the same piece or pieces of software and/or hardware when implementing the embodiments of the present invention.
The device of the above embodiment is applied to the corresponding method of the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein.
Based on the same inventive concept, the embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the method according to any one of the embodiments above.
Embodiments of the present invention provide a non-transitory computer storage medium storing at least one executable instruction for performing a method as described in any of the embodiments above.
Fig. 3 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 301, a memory 302, an input/output interface 303, a communication interface 304 and a bus 305. Wherein the processor 301, the memory 302, the input/output interface 303 and the communication interface 304 are communicatively coupled to each other within the device via a bus 305.
The processor 301 may be implemented by a general-purpose CPU (Central Processing Unit ), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided by the method embodiments of the present invention.
The Memory 302 may be implemented in the form of ROM (Read Only Memory), RAM (Random AccessMemory ), static storage device, dynamic storage device, or the like. Memory 302 may store an operating system and other application programs, and when implementing the technical solutions provided by the method embodiments of the present invention by software or firmware, the relevant program codes are stored in memory 302 and invoked by processor 301 for execution.
The input/output interface 303 is used to connect with an input/output module to realize information input and output. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
The communication interface 304 is used to connect a communication module (not shown in the figure) to enable the present device to interact with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
It should be noted that, although the above device only shows the processor 301, the memory 302, the input/output interface 303, the communication interface 304, and the bus 305, in the implementation, the device may further include other components necessary for achieving normal operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary for implementing the embodiments of the present invention, and not all the components shown in the drawings.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the disclosure, including the claims, is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined under the idea of the present disclosure, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present invention as described above, which are not provided in details for the sake of brevity.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalents, improvements, and the like, which are within the spirit and principles of the embodiments of the invention, are intended to be included within the scope of the present disclosure.
Claims (10)
1. The collaborative learning group construction method based on role collaboration is characterized by comprising the following steps:
acquiring project characteristic data of at least one project, wherein the project characteristic data comprises a plurality of project indexes, and acquiring student comprehensive data of each student, wherein the student comprehensive data comprises personal information, project capability data of a plurality of index capability characteristics corresponding to any project and personal characteristic data of at least one personal characteristic;
Acquiring an item matching degree matrix of students and items according to the item feature data and the item capability data, and acquiring a group length identification result by combining first constraint data and applying a group length distribution model based on role cooperation, wherein the first constraint data is determined according to the item feature data and the student comprehensive data;
and acquiring a comprehensive compatibility matrix of the students and the panelists according to the personal characteristic data and the panelist identification result, carrying out panelist allocation by applying a panelist allocation model based on role cooperation in combination with second constraint data, wherein each panelist and the corresponding panelist form each cooperative learning panel, and the second constraint data is determined according to the project characteristic data and the student comprehensive data.
2. The method for constructing a collaborative learning team based on role coordination according to claim 1, wherein the obtaining an item matching degree matrix of students and items according to the item feature data and the item capability data comprises:
for any student, calculating the interval value of each index capability characteristic according to the project capability data;
calculating the weight value of each item index in any item according to the item characteristic data;
Calculating the item matching degree of any student to each item according to the interval value of each index capability characteristic of any student, the weight value of each item index in each item and the item characteristic data, and combining the item matching degree of each student to each item to obtain an item matching degree matrix of the student and the item.
3. The method for constructing a collaborative learning group based on role collaboration according to claim 2, wherein calculating the interval value of each index capability feature according to the project capability data comprises: for the index capability feature corresponding to any one of the project indices,
if the number of the collected questions of the questionnaire or the test is less than the number threshold, determining the interval value of the index capability characteristic according to the maximum expression value and the minimum expression value of all the questions;
if the number of the collected questionnaires or the test questions is not less than the number threshold, calculating numerical characteristics including expected, entropy and super entropy of the index capability characteristics by using an inverse cloud generator algorithm in a cloud model theory, and determining interval values of the index capability characteristics according to the numerical characteristics;
the calculating the weight value of each item index in any item according to the item characteristic data comprises the following steps:
For any item, constructing a fuzzy judgment matrix according to the relative importance degree of each item index in the item characteristic data, wherein any element in the fuzzy judgment matrix
Represents +.>
Individual item index and->
The ratio of the importance of the individual project indicators, +.>
、/>
,/>
,/>
，/>
，/>
The number of the project indexes;
calculating the sum of any row and any column in the fuzzy judgment matrix, and constructing a fuzzy consistency matrix according to the sum of all rows and the sum of all columns;
calculating the sum of any row i in the fuzzy consistency matrix and performing standardization to obtain a first row
The individual item index is in any itemWeight value of (a) in (b).
4. The method for constructing a collaborative learning group based on role coordination according to claim 2, wherein the calculating the item matching degree of any student to each item according to the interval value of each index capability feature of any student and the weight value of each item index in each item and the item feature data comprises:
for any item index of any item, calculating the probability that the interval value of the index capability feature of any student is not smaller than the expected interval value of the item to the item index according to the interval value of the index capability feature of any student corresponding to the item index and the expected interval value of the item index in the item feature data, so as to obtain the interval number probability of the index capability feature of any student;
And calculating the sum of products of the weight value of each item index in the item and the corresponding interval number probability of any student aiming at any item to obtain the item matching degree of any student on the item.
5. The method for constructing a collaborative learning group based on role coordination according to claim 1, wherein the step of obtaining a group leader identification result by applying a group leader allocation model based on role coordination in combination with first constraint data comprises:
constructing a first objective function according to the item matching degree matrix and the group length distribution matrix;
and solving the group leader allocation matrix which meets the first constraint data and optimizes the first objective function by using a group leader allocation model based on role coordination to obtain a group leader identification result, wherein one item is allocated to one group leader.
6. The method for constructing a collaborative learning team based on role coordination according to claim 1, wherein the step of obtaining a comprehensive compatibility matrix of students and team members based on the personal characteristic data and the team member identification result comprises:
calculating interval values and weight values of all the personal features according to the personal feature data;
for any personal feature, calculating the compatibility degree of each student on the personal feature of any group of individuals according to the weight value of the personal feature and the interval value of each student and the personal feature of any group of individuals;
For any student, carrying out weighted summation on the compatibility degree of the student and any group leader on each personal characteristic to obtain the comprehensive compatibility degree of the student and any group leader;
and combining the comprehensive compatibility degree of each student and each panelist to obtain a comprehensive compatibility degree matrix of the students and the panelists.
7. The method for constructing a collaborative learning group based on role coordination according to claim 1, wherein the applying a group member allocation model based on role coordination in combination with second constraint data to perform group member allocation includes:
constructing a second objective function according to the comprehensive compatibility matrix and the panelist allocation matrix;
and (3) applying a group member distribution model based on role coordination to obtain the group member distribution matrix which meets second constraint data and optimizes the second objective function.
8. A cooperative learning group construction device based on role cooperation, characterized in that the cooperative learning group construction device based on role cooperation comprises:
a data acquisition unit, configured to acquire item feature data including a plurality of item indexes of at least one item, and acquire student integrated data of each student, where the student integrated data includes personal information, item capability data including a plurality of index capability features corresponding to any one of the items, and personal feature data including at least one personal feature;
The group length distribution unit is used for acquiring an item matching degree matrix of students and items according to the item characteristic data and the item capability data, and acquiring a group length identification result by combining a first constraint data application group length distribution model based on role cooperation, wherein the first constraint data is determined according to the item characteristic data and the student comprehensive data;
and the panelist distribution unit is used for acquiring the comprehensive compatibility matrix of the students and the panelists according to the personal characteristic data and the panelist identification result, and carrying out panelist distribution by combining second constraint data and applying a panelist distribution model based on role cooperation, wherein each panelist and the corresponding panelist form each cooperative learning panel, and the second constraint data is determined according to the project characteristic data and the student comprehensive data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the collaborative learning group construction method based on role coordination of any of claims 1-7 when the computer program is executed.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the role-synergy-based collaborative learning team construction method of any one of claims 1 to 7.
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