Teaching competency hierarchical evaluation method and application thereof

Abstract

The invention relates to the technical field of computers, in particular to a hierarchical evaluation method for teaching competency and application thereof. Based on the teaching performance behavior sequence data, mining a support degree of teaching performance behaviors and a conversion relation between the behaviors, and constructing a first feature vector representing a teaching competency second-level index level; a Euclidean distance calculation method is adopted to obtain the characteristic distance of the secondary index level of the teacher; based on the characteristic distance of the second-level index level, obtaining a first characteristic distance of the first-level index level of the teaching competency through weighted summation, and carrying out level evaluation on the first-level index level; and finally, based on the second-level index level feature distance and the first-level index level feature distance, realizing hierarchical evaluation of the teaching competency index of the target teacher. The objective of the invention is to solve the problem of how to quantitatively evaluate the teaching competency of teachers.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a hierarchical assessment method for teaching competence and its application. Background Technology

[0002] Teachers' teaching competence is the core of their professional ability and a key factor influencing teaching quality. Because teaching competence encompasses a complex set of qualities that enable teachers to effectively achieve teaching objectives, including knowledge, teaching skills, professional ethics, and personal traits, traditional methods of assessing teaching competence often rely on questionnaires and interviews. Assessment results obtained through these self-reported methods are highly subjective.

[0003] In view of this, this application proposes a hierarchical assessment method for teaching competence, aiming to achieve a quantitative assessment of teachers' teaching competence. Summary of the Invention

[0004] The main purpose of this application is to provide a hierarchical assessment method for teaching competence, aiming to solve the problem of how to quantitatively assess teachers' teaching competence.

[0005] To achieve the above objectives, this application provides a hierarchical assessment method for teaching competence, the method comprising: S10, construct a sequence of teaching performance behaviors from the collected teaching data of the target teachers; S20, calculate the support and residual values ​​corresponding to the teaching performance behavior sequence, and determine the first feature vector corresponding to each secondary indicator of teaching competence based on the support and residual values; S30, calculate the Euclidean distance between the first feature vector and the reference feature vector, determine the weight value corresponding to each Euclidean distance under the first-level indicator of teaching competence, and weight each first feature vector with its corresponding weight value to obtain the second feature vector corresponding to the first-level indicator of teaching competence. S40, the weighted sum of each primary indicator of teaching competence in the second feature vector is used as the distance value of the second feature vector; S50, based on the sub-interval in which the Euclidean distance is located within the secondary indicator interval, determine the secondary indicator evaluation result of the target teacher's teaching competence, and based on the sub-interval in which the distance value is located within the primary indicator interval, determine the primary indicator evaluation result of the target teacher's teaching competence.

[0006] Optionally, S20 includes: S21, Calculate the support level corresponding to each teaching performance behavior in the teaching performance behavior sequence: In the formula, For support, number(X) is the number of instructional performance behavior sequence data in itemset X that contain instructional performance behaviors, and number(AllSamples) is the total number of instructional performance behavior sequence data. S22, using lag sequence analysis, to obtain the adjusted residual values ​​of each teaching performance behavior sequence before and after transformation under each preset secondary indicator; S23, normalize and weight the support and the residual value to obtain the first feature vector.

[0007] Optionally, S23 includes: S231, normalize each of the support values ​​to obtain the normalized support values. ; S232, the support The support feature vector of the corresponding secondary indicator is obtained by weighting the feature vector with the influence weight of the corresponding secondary indicator level. : In the formula, Secondary indicator Support for the j-th instructional performance behavior The result of normalization, For the j-th teaching performance behavior, the secondary indicator is... The influence weight of the level, j∈[1,n], where n is the secondary indicator. The number of performance behaviors in teaching. It is a 1×n vector; S233, Obtain residual values; set residual values ​​less than or equal to a preset threshold to 0; normalize or assign 1 to residual values ​​greater than the preset threshold to obtain the processed target residual value set rZ. bl-bk ; The target residual value set The influence weights of the significant behavioral sequences among various teaching performance behaviors on the corresponding secondary indicator levels are weighted to obtain the feature vector sequence of the corresponding secondary indicator behavior transformation relationship. : In the formula, The residual value of the transformation from the l-th line to the k-th line. The result of normalization, The significance of the transition between behaviors bl and bk for secondary indicators The influence weights of ability level, l∈[1,n], k∈[1,n], where n is a secondary indicator. The number of performance behaviors in teaching. For a 1×n 2 ; S234, the support partial feature vector With the feature vector The first feature vector is obtained by concatenating the features. .

[0008] Optionally, in step S30, the expression for the second feature vector is: In the formula: This is the second feature vector. The primary indicator P q The i-th secondary indicator The feature distance, The primary indicator P q The i-th secondary indicator Feature distance For the primary indicator P q The weight values ​​for the level, i∈[1,m], where m is the first-level index P q The number of sub-secondary indicators.

[0009] Optionally, in S50, the lower limit of the secondary indicator interval is the minimum Euclidean distance, the upper limit is the maximum Euclidean distance, and the sub-intervals of the secondary indicator interval are divided by a multiple of the first preset increment. The expression for the first preset increment is: rangeS i =(Max_TDS i -Min_TDS i ) / n In the formula, rangeS i Max_TDS is the first preset increment. i Min_TDS is the maximum Euclidean distance. i is the minimum Euclidean distance, and n is the number of subintervals of the secondary index interval.

[0010] Optionally, in S50, the lower limit of the first-level indicator interval is the minimum distance value, the upper limit is the maximum distance value, and the sub-intervals of the first-level indicator interval are divided by a multiple of the second preset increment. The expression for the second preset increment is: rangeP q =(Max_TDP q -Min_TDP q ) / k In the formula, rangeP qMax_TDP is the preset increment. q Min_TDP is the maximum distance value. q is the minimum distance value, and k is the number of sub-intervals of the first-level indicator interval.

[0011] In addition, to achieve the above objectives, this application also provides a hierarchical assessment method for teaching competence as described in any of the preceding claims, and its application in the assessment of teachers' teaching competence.

[0012] Furthermore, to achieve the above objectives, this application also provides a teaching competence assessment model, which includes: The teaching performance behavior sequence construction module is used to construct teaching performance behavior sequences from the collected teaching data of the target teachers. The teaching competence secondary indicator quantification module is used to calculate the support and residual values ​​corresponding to the teaching performance behavior sequence, and to determine the first feature vector corresponding to each teaching competence secondary indicator based on the support and residual values. The teaching competence level one indicator quantification module is used to calculate the Euclidean distance between the first feature vector and the reference feature vector, determine the weight value corresponding to each Euclidean distance under the teaching competence level one indicator, weight each first feature vector with its corresponding weight value to obtain the second feature vector corresponding to the teaching competence level one indicator; and use the weighted sum of each teaching competence level one indicator in the second feature vector as the distance value of the second feature vector. The teaching competence grading and assessment module is used to determine the secondary indicator assessment result of the target teacher's teaching competence based on the sub-interval in which the Euclidean distance is located within the secondary indicator interval, and to determine the primary indicator assessment result of the target teacher's teaching competence based on the sub-interval in which the distance value is located within the primary indicator interval.

[0013] In addition, to achieve the above objectives, this application also provides a computer system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the hierarchical assessment method for teaching competence as described in any of the preceding claims.

[0014] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the hierarchical assessment method for teaching competence as described in any of the preceding claims.

[0015] This application has at least the following beneficial effects: 1. To achieve quantitative assessment of teachers' teaching competence indicators at all levels; 2. Compared with traditional teaching competence assessment methods, it reduces the subjectivity of assessment results; 3. Compared with existing teacher competence assessment methods based on teaching performance behavior sequence data and lag sequence analysis, this method can obtain accurate assessment results of teachers' teaching competence and the level of each indicator in the group, helping teachers understand their own teaching competence position in the group. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the hierarchical assessment method for teaching competence involved in the embodiments of this application; Figure 2 This is a schematic diagram illustrating the specific operation process of the hierarchical assessment method for teaching competence involved in the embodiments of this application; Figure 3 This is a schematic diagram of the express classroom teaching competency index system involved in the embodiments of this application; Figure 4 This is a schematic diagram of the assessment results of the delivery classroom teaching competency levels involved in the embodiments of this application; Figure 5 This is a schematic diagram of the architecture of the teaching competency assessment model involved in the embodiments of this application; Figure 6 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0017] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0019] First Embodiment Reference Figure 1 This embodiment provides a hierarchical assessment method for teaching competence, the method comprising the following steps: S10, construct a sequence of teaching performance behaviors from the collected teaching data of the target teachers; In this embodiment, a single teacher is used as the target unit. Image recognition and text classification technologies are used to identify the teaching performance behaviors of the selected target teacher's teaching video recordings and teacher's post-class teaching reflection texts. The identified teaching performance behavior sequence data is stored row by row, with the teaching video recordings or teacher's post-class teaching reflection texts as units.

[0020] In some alternative implementations, the teaching competency level assessment is set within the teacher group. T ={ T i , T 2 ,..., T f In the process of} f For the teaching community T The number of people. Primary indicator. P Set representation is P ={ P 1 , P 2 ,..., P g}, g This refers to the number of primary indicators of teaching competence. P q ( q ∈[1, g The set of secondary indicators under ]) is S ={ S 1 , S 2 ,..., S m}, m The number of secondary indicators of teaching competence, among which, S i ( i ∈[1, m Below is n Individual teaching performance behaviors, categorized as B ={ b 1 , b 2 ,..., b n}

[0021] S20, calculate the support and residual values ​​corresponding to the teaching performance behavior sequence, and determine the first feature vector corresponding to each secondary indicator of teaching competence based on the support and residual values; In this embodiment, association rule mining is performed on the sequence data of teachers' teaching performance behaviors to obtain the support level of the teaching performance behaviors, and adjusted residual values ​​of the transitions between teaching performance behaviors under each secondary indicator are obtained through lag sequence analysis. The vector composed of the quantitative values ​​of the secondary indicators of teaching competence, i.e., the first feature vector, is calculated from the support level and the residual values.

[0022] Further and optionally, S20 specifically includes: S21, Calculate the support level corresponding to each teaching performance behavior in the teaching performance behavior sequence: In the formula, For support, number(X) is the number of instructional performance behavior sequence data in itemset X that contain instructional performance behaviors, and number(AllSamples) is the total number of instructional performance behavior sequence data. S22, using lag sequence analysis, to obtain the adjusted residual values ​​of each teaching performance behavior sequence before and after transformation under each preset secondary indicator; S23, normalize and weight the support and the residual value to obtain the first feature vector.

[0023] S30, calculate the Euclidean distance between the first feature vector and the reference feature vector, determine the weight value corresponding to each Euclidean distance under the first-level indicator of teaching competence, and weight each first feature vector with its corresponding weight value to obtain the second feature vector corresponding to the first-level indicator of teaching competence. In this embodiment, after obtaining the first feature vector, a second feature vector is determined based on the quantitative values ​​representing the primary indicators of teaching competence. The reference feature vector refers to the optimal first feature vector constructed from the optimal values ​​of each dimension of the feature vectors representing the secondary indicators in the teacher group.

[0024] For example, the Euclidean distance between the first feature vector and the reference feature vector is calculated as follows: In the formula, ES i Teacher secondary indicator S i The first feature vector at the horizontal level, bestTES i For the secondary indicator S of the teacher group i Reference feature vector, ES ia bestTES ia For the feature vector ES i With bestTES i The corresponding feature dimension, a∈[1,n+n]2 ].

[0025] Teacher group T in primary indicator P q Secondary indicator S i The set of Euclidean distances is TDS i ={DS i 1 , DS i 2 ,...,DS i f}

[0026] In this embodiment, after obtaining the Euclidean distance, the Euclidean distance associated with each first feature vector is associated with a weight value of a certain indicator in the first-level teaching competence index. In the process of quantifying the first-level teaching competence index, the weight value corresponding to the Euclidean distance under the first-level teaching competence index is determined by looking up a table or key-value matching. Multiplying the weight value by the Euclidean distance yields the score value of the target teacher under the first-level teaching competence index. The scores are then concatenated in order to obtain the second feature vector.

[0027] Specifically, the primary indicator for teachers, P q The second eigenvector is: In the formula, This is the second feature vector. The primary indicator P q The i-th secondary indicator The feature distance, The primary indicator P q The i-th secondary indicator Feature distance For the primary indicator P q The weight values ​​for the level, i∈[1,m], where m is the first-level index P q The number of sub-secondary indicators.

[0028] S40, the weighted sum of each primary indicator of teaching competence in the second feature vector is used as the distance value of the second feature vector; In this embodiment, after obtaining the second feature vector, unlike the first feature vector which uses Euclidean distance as the distance value, the weighted sum of each primary indicator of teaching competence in the second feature vector is used as the feature distance of the second feature vector, i.e., the distance value. For example, the set of distance values ​​TDP q The mathematical expression is: TDP q ={DP q1, DPq2 ,...,DP qf} In the formula, Let f be the feature distance of each primary indicator of teaching competence in the second feature vector, where c = 1, 2, ..., f.

[0029] S50, based on the sub-interval in which the Euclidean distance is located within the secondary indicator interval, determine the secondary indicator evaluation result of the target teacher's teaching competence, and based on the sub-interval in which the distance value is located within the primary indicator interval, determine the primary indicator evaluation result of the target teacher's teaching competence.

[0030] In this embodiment, after calculating the Euclidean distance corresponding to the secondary indicators of teaching competence and the distance value corresponding to the primary indicators of teaching competence, the two different distance values ​​are used for their respective indicator assessments. This completes the tiered assessment of the target teacher's teaching competence indicators.

[0031] In the technical solution provided in this embodiment, based on the teaching performance behavior sequence data, the support level of teaching performance behaviors and the conversion relationship between behaviors are mined to construct a first feature vector representing the level of secondary indicators of teaching competence; the feature distance of the teacher's secondary indicator level is obtained by using the Euclidean distance calculation method; then, based on the feature distance of the secondary indicator level, the first feature distance of the primary indicator level of teaching competence is obtained by weighted summation, and the primary indicator level is evaluated hierarchically; finally, based on the feature distance of the secondary indicator level and the feature distance of the primary indicator level, the hierarchical evaluation of the teaching competence indicators of the target teacher is realized.

[0032] Second Embodiment Based on the first embodiment, this embodiment provides a specific method for constructing a first feature vector reflecting the level of secondary indicators of teaching competence based on support and residual values, including: S231, normalize each of the support values ​​to obtain the normalized support values. ; S232, the support The support feature vector of the corresponding secondary indicator is obtained by weighting the feature vector with the influence weight of the corresponding secondary indicator level. : In the formula, Secondary indicator Support for the j-th instructional performance behavior The result of normalization, For the j-th teaching performance behavior, the secondary indicator is... The influence weight of the level, j∈[1,n], where n is the secondary indicator. The number of performance behaviors in teaching. It is a 1×n vector; S233, Obtain residual values; set residual values ​​less than or equal to a preset threshold to 0; normalize or assign 1 to residual values ​​greater than the preset threshold to obtain the processed target residual value set rZ. bl-bk ; The target residual value set The influence weights of the significant behavioral sequences among various teaching performance behaviors on the corresponding secondary indicator levels are weighted to obtain the feature vector sequence of the corresponding secondary indicator behavior transformation relationship. : In the formula, The residual value of the transformation from the l-th line to the k-th line. The result of normalization, The significance of the transition between behaviors bl and bk for secondary indicators The influence weights of ability level, l∈[1,n], k∈[1,n], where n is a secondary indicator. The number of performance behaviors in teaching. For a 1×n 2 ; In some alternative implementations, the preset threshold is 1.96.

[0033] It should be noted that replacing residual values ​​less than or equal to the preset threshold with 0 indicates that the conversion between behaviors is a non-significant behavior sequence, while normalizing or assigning a value of 1 to residual values ​​greater than the preset threshold indicates that the conversion between behaviors is a significant behavior sequence.

[0034] S234, the support partial feature vector With the feature vector The first feature vector is obtained by concatenating the features. .

[0035] Third Embodiment Based on any of the above embodiments, this embodiment provides a method for constructing a secondary indicator interval, specifically including: The lower limit of the secondary indicator range is the minimum Euclidean distance, and the upper limit is the maximum Euclidean distance.

[0036] For example, the mathematical expression for the secondary indicator interval is: [Max_TDS i -Min_TDS i ] In addition, the sub-intervals of the secondary indicator interval are divided by multiples of a first preset increment, the expression of which is: rangeS i =(Max_TDS i -Min_TDS i ) / n In the formula, rangeS i Max_TDS is the first preset increment. i Min_TDS is the maximum Euclidean distance. i is the minimum Euclidean distance, and n is the number of subintervals of the secondary index interval.

[0037] For example, suppose a teacher's individual DS i Located in the interval [Min_TDS i Min_TDS i +rangeS i ], Evaluation of secondary indicator S i The horizontal hierarchy is n; when the individual teacher's DS i Within the interval (Min_TDS) i +rangeSi, Min_TDS i When +2rangeSi] is in the range, the secondary index S is evaluated. i The horizontal hierarchy is as follows, and so on.

[0038] Fourth embodiment Based on any of the above embodiments, this embodiment provides a method for constructing a primary indicator range, specifically including: The lower limit of the primary indicator range is the minimum distance value, and the upper limit is the maximum distance value.

[0039] For example, the mathematical expression for the secondary indicator interval is: [Max_TDP q Min_TDP q ] In addition, the sub-intervals of the primary indicator interval are divided by multiples of the second preset increment; The expression for the second preset increment is: rangeP q =(Max_TDP q -Min_TDP q ) / k In the formula, rangeP q Max_TDP is the preset increment. q Min_TDP is the maximum distance value. q is the minimum distance value, and k is the number of sub-intervals of the first-level indicator interval; When the teacher's individual DP q Located in the interval [Min_TDP] q Min_TDP q +rangeP q In the process of evaluation, the primary indicator P q The horizontal level is k; when the teacher's individual DP q Within the interval (Min_TDP) q +rangeP q Min_TDP q +2rangeP q In the process of evaluation, the primary indicator P q The horizontal level is k-1, and so on.

[0040] Furthermore, as an implementation scheme, this embodiment also provides a hierarchical assessment method for teaching competence based on the methods involved in the first to fourth embodiments, referring to... Figure 2 The steps in this embodiment include: S10, construct a sequence of teaching performance behaviors from the collected teaching data of the target teachers; S20, calculate the support and residual values ​​corresponding to the teaching performance behavior sequence, and determine the first feature vector corresponding to each secondary indicator of teaching competence based on the support and residual values; Wherein, S20 includes: S21, Calculate the support level corresponding to each teaching performance behavior in the teaching performance behavior sequence: In the formula, For support, number(X) is the number of instructional performance behavior sequence data in itemset X that contain instructional performance behaviors, and number(AllSamples) is the total number of instructional performance behavior sequence data. S22, using lag sequence analysis, to obtain the adjusted residual values ​​of each teaching performance behavior sequence before and after transformation under each preset secondary indicator; S23, normalize and weight the support and the residual value to obtain the first feature vector.

[0041] Wherein, S23 includes: S231, normalize each of the support values ​​to obtain the normalized support values. ; S232, the support The support feature vector of the corresponding secondary indicator is obtained by weighting the feature vector with the influence weight of the corresponding secondary indicator level. : In the formula, Secondary indicator Support for the j-th instructional performance behavior The result of normalization, For the j-th teaching performance behavior, the secondary indicator is... The influence weight of the level, j∈[1,n], where n is the secondary indicator. The number of performance behaviors in teaching. It is a 1×n vector; S233, Obtain residual values; set residual values ​​less than or equal to a preset threshold to 0; normalize or assign 1 to residual values ​​greater than the preset threshold to obtain the processed target residual value set rZ. bl-bk ; The target residual value set The influence weights of the significant behavioral sequences among various teaching performance behaviors on the corresponding secondary indicator levels are weighted to obtain the feature vector sequence of the corresponding secondary indicator behavior transformation relationship. : In the formula, The residual value of the transformation from the l-th line to the k-th line. The result of normalization, The significance of the transition between behaviors bl and bk for secondary indicators The influence weights of ability level, l∈[1,n], k∈[1,n], where n is a secondary indicator. The number of performance behaviors in teaching. For a 1×n 2 ; S234, the support partial feature vector With the feature vector The first feature vector is obtained by concatenating the features. .

[0042] S30, calculate the Euclidean distance between the first feature vector and the reference feature vector, determine the weight value corresponding to each Euclidean distance under the first-level indicator of teaching competence, and weight each first feature vector with its corresponding weight value to obtain the second feature vector corresponding to the first-level indicator of teaching competence. In step S30, the expression for the second feature vector is: In the formula: This is the second feature vector. The primary indicator Pq The i-th secondary indicator The feature distance, The primary indicator P q The i-th secondary indicator Feature distance For the primary indicator P q The weight values ​​for the level, i∈[1,m], where m is the first-level index P q The number of sub-secondary indicators.

[0043] S40, the weighted sum of each primary indicator of teaching competence in the second feature vector is used as the distance value of the second feature vector; S50, based on the sub-interval in which the Euclidean distance is located within the secondary indicator interval, determine the secondary indicator evaluation result of the target teacher's teaching competence, and based on the sub-interval in which the distance value is located within the primary indicator interval, determine the primary indicator evaluation result of the target teacher's teaching competence.

[0044] In S50, the lower limit of the secondary indicator interval is the minimum Euclidean distance, and the upper limit is the maximum Euclidean distance. The sub-intervals of the secondary indicator interval are divided by a multiple of the first preset increment. The expression for the first preset increment is: rangeS i =(Max_TDS i -Min_TDS i ) / n In the formula, rangeS i Max_TDS is the first preset increment. i Min_TDS is the maximum Euclidean distance. i is the minimum Euclidean distance, and n is the number of subintervals of the secondary index interval.

[0045] In S50, the lower limit of the first-level indicator interval is the minimum distance value, the upper limit is the maximum distance value, and the sub-intervals of the first-level indicator interval are divided by a multiple of the second preset increment. The expression for the second preset increment is: rangeP q =(Max_TDP q -Min_TDP q ) / k In the formula, rangeP q Max_TDP is the preset increment. q Min_TDP is the maximum distance value. q is the minimum distance value, and k is the number of sub-intervals of the first-level indicator interval.

[0046] In addition, as an implementation scheme, this embodiment also provides any of the above-mentioned hierarchical assessment methods for teaching competence, and its application in the assessment of teachers' teaching competence.

[0047] As an example, taking the rural teacher's practice of the dedicated classroom teaching model as an example, the teaching competency indicators required of rural teachers are as follows: Figure 3 It includes 7 primary indicators and 23 secondary indicators, covering knowledge, instructional design, instructional implementation, instructional design, personal traits, affective attitudes and values, and achievement motivation. The weight of each indicator in the previous level is known.

[0048] The hierarchical assessment method for teaching competence in this embodiment requires a group of teachers as the assessment sample. For example, using 110 rural teachers as the assessment sample, and setting each level of the delivery classroom teaching competence assessment to five levels, each rural teacher can obtain any delivery classroom teaching competence assessment result, as shown below. Figure 4 .

[0049] In addition, as an implementation scheme, refer to Figure 5 This embodiment also provides a teaching competency assessment model, which includes: The teaching performance behavior sequence construction module 100 is used to construct teaching performance behavior sequences from the collected teaching data of the target teachers. The teaching competence secondary indicator quantification module 200 is used to calculate the support and residual values ​​corresponding to the teaching performance behavior sequence, and to determine the first feature vector corresponding to each teaching competence secondary indicator based on the support and residual values. The teaching competency level one indicator quantification module 300 is used to calculate the Euclidean distance between the first feature vector and the reference feature vector, determine the weight value corresponding to each Euclidean distance under the teaching competency level one indicator, weight each first feature vector with its corresponding weight value to obtain the second feature vector corresponding to the teaching competency level one indicator; and to use the weighted sum of each teaching competency level one indicator in the second feature vector as the distance value of the second feature vector. The teaching competence grading assessment module 400 is used to determine the secondary indicator assessment result of the target teacher's teaching competence based on the sub-interval in which the Euclidean distance is located within the secondary indicator interval, and to determine the primary indicator assessment result of the target teacher's teaching competence based on the sub-interval in which the distance value is located within the primary indicator interval.

[0050] As one implementation scheme, Figure 6 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0051] like Figure 6 As shown, the computer system may include: a processor 1001, such as a CPU; a memory 1005; a user interface 1003; a network interface 1004; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0052] Those skilled in the art will understand that Figure 6 The computer system architecture shown does not constitute a limitation on the computer system and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0053] like Figure 6 As shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and computer programs. The operating system is a program that manages and controls the hardware and software resources of the computer system, as well as the operation of the computer programs and other software or programs.

[0054] exist Figure 6 In the computer system shown, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal; the network interface 1004 is mainly used to communicate with the backend server; and the processor 1001 can be used to call the computer program stored in the memory 1005.

[0055] In this embodiment, the computer system includes: a memory 1005, a processor 1001, and a computer program stored in the memory and executable on the processor, wherein: When processor 1001 calls a computer program stored in memory 1005, it performs the following operations: S10, construct a sequence of teaching performance behaviors from the collected teaching data of the target teachers; S20, calculate the support and residual values ​​corresponding to the teaching performance behavior sequence, and determine the first feature vector corresponding to each secondary indicator of teaching competence based on the support and residual values; S30, calculate the Euclidean distance between the first feature vector and the reference feature vector, determine the weight value corresponding to each Euclidean distance under the first-level indicator of teaching competence, and weight each first feature vector with its corresponding weight value to obtain the second feature vector corresponding to the first-level indicator of teaching competence. S40, the weighted sum of each primary indicator of teaching competence in the second feature vector is used as the distance value of the second feature vector; S50, based on the sub-interval in which the Euclidean distance is located within the secondary indicator interval, determine the secondary indicator evaluation result of the target teacher's teaching competence, and based on the sub-interval in which the distance value is located within the primary indicator interval, determine the primary indicator evaluation result of the target teacher's teaching competence.

[0056] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations: S21, Calculate the support level corresponding to each teaching performance behavior in the teaching performance behavior sequence: In the formula, For support, number(X) is the number of instructional performance behavior sequence data in itemset X that contain instructional performance behaviors, and number(AllSamples) is the total number of instructional performance behavior sequence data. S22, using lag sequence analysis, to obtain the adjusted residual values ​​of each teaching performance behavior sequence before and after transformation under each preset secondary indicator; S23, normalize and weight the support and the residual value to obtain the first feature vector.

[0057] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations: S231, normalize each of the support values ​​to obtain the normalized support values. ; S232, the support The support feature vector of the corresponding secondary indicator is obtained by weighting the feature vector with the influence weight of the corresponding secondary indicator level. : In the formula, Secondary indicator Support for the j-th instructional performance behavior The result of normalization, For the j-th teaching performance behavior, the secondary indicator is... The influence weight of the level, j∈[1,n], where n is the secondary indicator. The number of performance behaviors in teaching. It is a 1×n vector; S233, Obtain residual values; set residual values ​​less than or equal to a preset threshold to 0; normalize or assign 1 to residual values ​​greater than the preset threshold to obtain the processed target residual value set rZ. bl-bk ; The target residual value set The influence weights of the significant behavioral sequences among various teaching performance behaviors on the corresponding secondary indicator levels are weighted to obtain the feature vector sequence of the corresponding secondary indicator behavior transformation relationship. : In the formula, The residual value of the transformation from the l-th line to the k-th line. The result of normalization, The significance of the transition between behaviors bl and bk for secondary indicators The influence weights of ability level, l∈[1,n], k∈[1,n], where n is a secondary indicator. The number of performance behaviors in teaching. For a 1×n 2 ; S234, the support partial feature vector With the feature vector The first feature vector is obtained by concatenating the features. .

[0058] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations: The lower limit of the secondary indicator interval is the minimum Euclidean distance, and the upper limit is the maximum Euclidean distance. The sub-intervals of the secondary indicator interval are divided by a multiple of the first preset increment. The expression for the first preset increment is: rangeS i =(Max_TDS i -Min_TDS i ) / n In the formula, rangeS i Max_TDS is the first preset increment. i Min_TDS is the maximum Euclidean distance. i is the minimum Euclidean distance, and n is the number of subintervals of the secondary index interval.

[0059] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations: The lower limit of the primary indicator interval is the minimum distance value, and the upper limit is the maximum distance value. The sub-intervals of the primary indicator interval are divided by multiples of the second preset increment. The expression for the second preset increment is: rangeP q =(Max_TDP q -Min_TDP q ) / k In the formula, rangeP q Max_TDP is the preset increment. q Min_TDP is the maximum distance value. q is the minimum distance value, and k is the number of sub-intervals of the first-level indicator interval.

[0060] Furthermore, those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in a computer system to implement the process steps of the embodiments of the above methods.

[0061] Therefore, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the various steps of the hierarchical assessment method for teaching competence as described in the above embodiments.

[0062] The computer-readable storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0063] It should be noted that, since the storage medium provided in the embodiments of this application is the storage medium used to implement the methods of the embodiments of this application, those skilled in the art can understand the specific structure and variations of the storage medium based on the methods described in the embodiments of this application, and therefore will not be repeated here. All storage media used in the methods of the embodiments of this application fall within the scope of protection of this application.

[0064] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can 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, etc.) containing computer-usable program code.

[0065] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0068] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. This application can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0069] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0070] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A hierarchical assessment method for teaching competence, characterized in that, The method includes the following steps: S10, construct a sequence of teaching performance behaviors from the collected teaching data of the target teachers; S20, calculate the support and residual values ​​corresponding to the teaching performance behavior sequence, and determine the first feature vector corresponding to each secondary indicator of teaching competence based on the support and residual values; S30, calculate the Euclidean distance between the first feature vector and the reference feature vector, determine the weight value corresponding to each Euclidean distance under the first-level indicator of teaching competence, and weight each first feature vector with its corresponding weight value to obtain the second feature vector corresponding to the first-level indicator of teaching competence. S40, the weighted sum of each primary indicator of teaching competence in the second feature vector is used as the distance value of the second feature vector; S50, based on the sub-interval in which the Euclidean distance is located within the secondary indicator interval, determine the secondary indicator evaluation result of the target teacher's teaching competence, and based on the sub-interval in which the distance value is located within the primary indicator interval, determine the primary indicator evaluation result of the target teacher's teaching competence.

2. The hierarchical assessment method for teaching competence as described in claim 1, characterized in that, S20 includes: S21, Calculate the support level corresponding to each teaching performance behavior in the teaching performance behavior sequence: ； In the formula, For support, number(X) is the number of instructional performance behavior sequence data in itemset X that contain instructional performance behaviors, and number(AllSamples) is the total number of instructional performance behavior sequence data. S22, using lag sequence analysis, to obtain the adjusted residual values ​​of each teaching performance behavior sequence before and after transformation under each preset secondary indicator; S23, normalize and weight the support and the residual value to obtain the first feature vector.

3. The hierarchical assessment method for teaching competence as described in claim 2, characterized in that, S23 includes: S231, normalize each of the support values ​​to obtain the normalized support values. ; S232, the support The support feature vector of the corresponding secondary indicator is obtained by weighting the feature vector with the influence weight of the corresponding secondary indicator level. : ； In the formula, Secondary indicator Support for the j-th instructional performance behavior The result of normalization, For the j-th teaching performance behavior, the secondary indicator is... The influence weight of the level, j∈[1,n], where n is the secondary indicator. The number of performance behaviors in teaching. It is a 1×n vector; S233, Obtain residual values; set residual values ​​less than or equal to a preset threshold to 0; normalize or assign 1 to residual values ​​greater than the preset threshold to obtain the processed target residual value set rZ. bl-bk ; The target residual value set The eigenvectors of the eigenvector sequence of the transformation relationship between the behaviors of each teaching performance behavior are weighted by the influence weights of the significant behavioral sequences on the corresponding secondary indicator levels, thus obtaining the eigenvectors of the eigenvector sequence ... : ； In the formula, The residual value of the transformation from the l-th line to the k-th line. The result of normalization, The significance of the transition between behaviors bl and bk for secondary indicators The influence weights of ability level, l∈[1,n], k∈[1,n], where n is a secondary indicator. The number of performance behaviors in teaching. For a 1×n 2 ; S234, the support partial feature vector With the feature vector The first feature vector is obtained by concatenating the features. .

4. The hierarchical assessment method for teaching competence as described in claim 1, characterized in that, In step S30, the expression for the second feature vector is: ； In the formula: This is the second feature vector. The primary indicator P q The i-th secondary indicator The feature distance, The primary indicator P q The i-th secondary indicator Feature distance For the primary indicator P q The weight values ​​for the level, i∈[1,m], where m is the first-level index P q The number of sub-secondary indicators.

5. The hierarchical assessment method for teaching competence as described in claim 1, characterized in that, In S50, the lower limit of the secondary indicator interval is the minimum Euclidean distance, and the upper limit is the maximum Euclidean distance. The sub-intervals of the secondary indicator interval are divided by a multiple of the first preset increment. The expression for the first preset increment is: rangeS i =(Max_TDS i -Min_TDS i ) / n; In the formula, rangeS i Max_TDS is the first preset increment. i Min_TDS is the maximum Euclidean distance. i is the minimum Euclidean distance, and n is the number of subintervals of the secondary index interval.

6. The hierarchical assessment method for teaching competence as described in claim 1 or 5, characterized in that, In S50, the lower limit of the first-level indicator interval is the minimum distance value, and the upper limit is the maximum distance value. The sub-intervals of the first-level indicator interval are divided by multiples of the second preset increment. The expression for the second preset increment is: rangeP q =(Max_TDP q -Min_TDP q ) / k; In the formula, rangeP q Max_TDP is the preset increment. q Min_TDP is the maximum distance value. q is the minimum distance value, and k is the number of sub-intervals of the first-level indicator interval.

7. The application of a hierarchical assessment method for teaching competence as described in any one of claims 1 to 6 in the assessment of teachers' teaching competence.

8. A teaching competence assessment model, characterized in that, The teaching competency assessment model includes: The teaching performance behavior sequence construction module is used to construct teaching performance behavior sequences from the collected teaching data of the target teachers. The teaching competence secondary indicator quantification module is used to calculate the support and residual values ​​corresponding to the teaching performance behavior sequence, and to determine the first feature vector corresponding to each teaching competence secondary indicator based on the support and residual values. The teaching competence level one indicator quantification module is used to calculate the Euclidean distance between the first feature vector and the reference feature vector, determine the weight value corresponding to each Euclidean distance under the teaching competence level one indicator, weight each first feature vector with its corresponding weight value to obtain the second feature vector corresponding to the teaching competence level one indicator; and use the weighted sum of each teaching competence level one indicator in the second feature vector as the distance value of the second feature vector. The teaching competence grading and assessment module is used to determine the secondary indicator assessment result of the target teacher's teaching competence based on the sub-interval in which the Euclidean distance is located within the secondary indicator interval, and to determine the primary indicator assessment result of the target teacher's teaching competence based on the sub-interval in which the distance value is located within the primary indicator interval.

9. A computer system, characterized in that, The computer system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it implements the steps of the hierarchical assessment method for teaching competence as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the hierarchical assessment method for teaching competence as described in any one of claims 1 to 6.

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