# Evaluation of Improvement of Information Teaching Ability Based on Complex Online Network

Authors: Li Feng
Journal: Discrete Dynamics in Nature and Society
 (2021)

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## Abstract

The evaluation of the improvement of information teaching ability (ITA) helps teachers to proactively learn advanced information teaching techniques and to realize innovative information teaching. The relevant studies mainly focus on the theoretical level and rarely deal with the growth of teachers through learning. Therefore, this paper presents an evaluation method for ITA improvement based on complex online network. Firstly, an analysis framework was established for the activity of the ITA improvement network (ITAIN) and used to measure the ITAIN activity under the learning condition of complex online network. Following the evaluation strategy of preference ranking organization method for enrichment evaluation (PROMETHEE), the author constructed an evaluation model for the improvement of ITA and an evaluation index system (EIS) of five primary indices. The proposed model was proved effective through experiments.


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## Body

## 1. Introduction

In the age of Internet and information, there is a growing demand for talents with comprehensive quality, which changes the direction of talent training [1, 2]. To keep abreast with the times and improve their ability of professional teaching, modern teachers need to actively integrate information teaching technology into daily teaching [3–7]. Information teaching requires a complete structure of professional knowledge, an advanced teaching concept, and mature practical skills. All these are the abilities to diversify classroom teaching. If a teacher wants to improve his/her information teaching ability (ITA), he/she can learn various resources online [8–13]. The evaluation of the ITA improvement helps teachers to proactively learn advanced information teaching techniques and to realize innovative information teaching.Gong [14] found that the teachers in rural schools are neither enthusiastic about nor capable of information teaching because the equipment is rarely used and difficult to repair and explored the factors affecting the ITA of these teachers from three aspects, namely, students, school, and government. Considering the need of information teaching training for college teachers, Claro et al. [15] designed and implemented an information training strategy, which includes five phases such as incubation, alliance, action, commemoration, and dispersion, and summarized the change law of ITA of the trained teachers.The knowledge framework of technique-driven subject teaching provides a brand-new cultivation path for improving the ITA of teachers [16, 17]. Liu [18] summarized the problems of new teachers of specialized courses as poor knowledge integration, course integration, and content rationality and renovated multiple aspects of the ITA of these teachers, namely, training scheme preparation, course teaching planning, and appraisal method.The innovative integration of information technology in course teaching is an important attribute of modern teaching in colleges [19, 20]. The existing studies mainly focus on the connotations, framework, and development trend of ITA. Romero-Rodriguez et al. [21] creatively took the innovative ITA as the object, analyzed the factors affecting the ability and their action mechanism, and constructed a structural equation model (SEM) for evaluating the ability based on Amos. Dan [22] discussed the correlations between the four influencing factors for the innovative ITA of college teachers and sorted out their direct promoting and inhibiting effects. The four factors are the application ability of information technology, the knowledge of information teaching, the awareness of information teaching, and teachers’ satisfaction of information construction.The previous studies on ITA improvement have laid a basis for the training model and evaluation of this ability. But most of them emphasize on value creation, quality evaluation, existing problems, and solutions. The relevant studies mainly focus on the theoretical level and rarely deal with the growth of teachers through learning.Drawing on the previous research of complex networks, this paper proposes an evaluation method for ITA improvement, which carries the features of the times. The main contents cover the following aspects: (1) building an analysis framework for the activity of the ITA improvement network (ITAIN) and analyzing the activity of ITAIN under the learning condition of complex online network; (2) setting up an evaluation model for ITA improvement under the evaluation strategy of preference ranking organization method for enrichment evaluation (PROMETHEE); (3) constructing five evaluation networks based on the proposed evaluation index system (EIS), namely, professional foundation of information teaching, design ability of information teaching, implementation and monitoring ability of information teaching, evaluation ability of information teaching, and research ability of information teaching; and (4) proving the effectiveness of the proposed model through experiments.


## 2. ITAIN Activity

This paper measures the improvement of ITA with the node connectivity, closeness, and mean path length of online network, as well as two centrality indices: betweenness and clustering coefficient. Figure1 shows the analysis framework of ITAIN activity.Figure 1
Analysis framework of ITAIN activity.In the complex online education network, there are lots of intricately connected platforms and resources for the learning and ITA training of teachers. Among the platform and resource clusters, each instance that promotes the ITA improvement was viewed as a node in the network. Then, the promoting and inhibiting relationships between the instances were treated as the edges between nodes. Each edge was weighed according to the importance of the relationship between the instances. On this basis, the activity of ITAIN was evaluated under the learning condition of complex online network. The above indices were quantified in the following manner.LetN be the number of nodes in the network and sab be the distance from node a to node b. Then, the node connectivity of the online network that characterizes the correlation between instances can be calculated by(1)Ga=∑b=1Nsab.Formula (1) shows that, in the complex online network, the node connectivity between nodes a and b is 1, if the two nodes are directly connected; the connectivity is 0, otherwise. In this paper, the ITA improvement of teachers is measured from two aspects: the instances of learning activities and the instances of improvement support. Among the former instances, the more the training platforms and effective training resources, the greater the improvement of the ITA. The rising ITA gives teachers more rights and chances to acquire more training opportunities and resources. As a result, the ITA of the teachers will improve continuously.LetN be the number of nodes in the online network and sab be the shortest distance from node a to node b. Then, the closeness GM(a) of node a, which measures how convenient it is for the node to communicate with other nodes in the network, can be calculated by(2)GMa=1∑b=1Nsab.Formula (2) shows that, unlike the node connectivity G(a) reflecting the basic connection between instances, the closeness GM(a) of network nodes reveals the number of intermediate nodes on the path between a node and another node. To a certain extent, this index shows the independence of the promoting and inhibiting effects between instances. The closeness characterizes the difficulty/easiness of implementing ITA improvement activities in the network. Its value mirrors the size of teachers’ ITA.In the online network, the transmission speed of various resources (e.g., knowledge and research results) depends on the mean path lengthK of the network:(3)K=11/2NN+1∑a≥bsab.Formula (3) shows that the K value reflects the number of intermediate nodes that a node needs to pass through to share information/knowledge effectively or connect with another node in the network. The minimum K value is the distance from the node to itself: 0. In this paper, the path length of ITA improvement is measured based on the ability improvement cost and speed, the teaching hours of training platforms during skill spillover, and the number of visits to resources.LetNbc(a) be the number of shortest paths in the network of node a and Nbc be the number of shortest paths linking up node b and node c in the network. Then, the betweenness IP(a), which reflects the importance of a resource to ITA improvement, can be calculated by(4)IPa=∑b<cNbcaNbc.Formula (4) shows that IP(a) describes the number of times a node is traversed during the ITA improvement activities, reflecting the influence of the node on ITA improvement among the shortest paths. If the IP(a) is high, the instance must boast enough necessary elements for ITA improvement activities. Here, the betweenness of the complex online network for ITA improvement is measured in terms of platforms, resource conditions, and policy environment. The instances of the complex online network can fully play their roles, if they are authentic training platforms and core teaching resources, supported by the policy environment.In the complex online network, it is assumed that nodeb is connected with node a and node u, respectively. Then, it is very likely that node a is connected with node u. Suppose the subnetwork of node a contains Na nodes and Fa edges, and the maximum number of possible edges is GNNa2, that is, Na(Na − 1)/2. Then, the node clustering effect in the network can be characterized by the clustering coefficient:(5)ICa=FaGNNa2=2FaNaNa−1.In the complex online network of ITA training,IC(a) reflects the cohesion between network nodes and the tightness of the promoting and inhibiting relationships between instances in the cluster. This paper measures the clustering ability of instances by platform scale, resource scope, education strategies, and teaching methods.


## 3. Model Construction

### 3.1. PROMETHEE Evaluation Strategy

The traditional evaluation method does not fully consider the fuzziness and stochasticity of the evaluation process. The complex structure of online education network cannot be directly described by simple linear relationships. To solve the problem, this paper introduces the cloud model theory to the traditional multi-objective decision making and evaluation, aiming to evaluate the ITA improvement of teachers. Comparative calculation and analysis were performed to evaluate the ITAIN activity more rigorously and scientifically.LetIC1, IC2, … , ICN be N clusters of instances that promote ITA improvement; GI1, GI2, … , GIM be the M indices of each instance cluster; AVijl be attribute l of index j in instance cluster i; and θ=(θ1, θ2, … , θj) be the weight of an index in its instance cluster. Then, the ITA improvement can be evaluated in the following steps based on complex online network.Step 1. Convert each attribute of instance cluster indices to the cloud.The expectation EXP of each attribute of indexj in instance cluster i can be calculated by(6)EXPij=∑l=1KAVijlK.The entropy ENT can be calculated by(7)ENTij=1K∑l=1K|AVijl−Eij.The hyperentropy HENT can be calculated by(8)HENT=D2=KK−1∑l=1KGIijl−EXPij2.Step 2. Define the comparison rules for each attribute of indices of instance clusters.LetGIj(EXPi, ENTi, HENTi) be the cloud attribute of instance cluster ICi under index GIj; sj(ICi, ICg) = sj(EXPig, ENTig, HENTig) be the cloud attribute difference between two instance clusters under index GIj; and GIj(EXPg, ENTg, HENTg) be the cloud attribute of instance cluster ICg (i ≠ g). Then, the expectation EXP, entropy ENT, and hyperentropy HENT between the two instance clusters can be compared by the following rules:(1)
For EXP, when indexGIj needs to be maximized, if EXPi > EXPg, then sj(ICi, ICg) > 0; if EXPi ≤ EXPg, then sj(ICi, ICg) = 0; when index GIj needs to be minimized, if EXPi < EXPg, then sj(ICi, ICg) > 0; if EXPi ≥ EXPg, then sj(ICi, ICg) = 0.(2)
For ENT, if ENTi ≥ ENTg, then sj(ICi, ICg) = 0; if ENTi < ENTg, then sj(ICi, ICg) > 0.(3)
For HENT, if HENTi ≥ HENTg, then sj(ICi, ICg) = 0; if HENTi < HENTg, then sj(ICi, ICg) > 0.Step 3. Convert the preference function to the cloud.Unlike other preference functions, Gaussian preference function changes nonlinearly and better suits the actual decision-making environment. Therefore, this paper chooses Gaussian preference function to transform the attribute cloud of the evaluation indices for each instance cluster.LetWj(ICi, ICg) = Wj(EXPig, ENTig, HENTig) be the preference index indicating that the cloud attribute GIj(EXPi, ENTi, HENTi) of instance cluster ICi is better than that of GIj(EXPg, ENTg, HENTg) of instance cluster ICg (i ≠ g) under index GIj and σ be the threshold of the Gaussian function. Then,(9)sj=EXPig,ENTig,HENTig=GIjEXPi,ENTi,HENTi−GIjEXPg,ENTg,HENTg=EXPi−EXPg,ENTi2+ENTg2,HENTi2+HENTg2.After comprehensive evaluation of all ITA improvement indices, the degree of cloud preferenceICg indicating that instance cluster ICi is better than instance cluster ICg, i.e., the cloud preference index Ω(ICi, ICg), can be calculated by(10)ΩICi,ICg=∑g=1NθjWjICi,ICg∑g=1Nθj=θ1+θ2+⋯+θjT⋅EXP1igENT1igHENT1ig⋮⋮⋮EXPjigENTjigHENTjig=θ1EXP1ig+θ2EXP2ig+⋯+θjEXPjigθ1ENT1ig2+θ2ENT2ig2+⋯+θjENTjig2θ1HENT1ig2+θ2HENT2ig2+⋯+θjHENTjig2T=EXPπig,ENTπig,HENTπig.Step 4. Compute cloud outflow, cloud inflow, and cloud net flow.The superiority of instance clusterICi over other instance clusters in learning activity can be measured by cloud outflow ΨP(ICi):(11)ΨPICi=1N−1∑g=1NΩICi,ICh=1N−1EXPΩi1+EXPΩ2i+⋯+EXPΩiNENTΩi12+ENTΩi22+⋯+ENTΩiN2HENTΩi12+HENTΩi22+⋯+HENTΩiN2T=EXPΩi1+EXPΩ2i+⋯+EXPΩiNN−1ENTΩi12+ENTΩi22+⋯+ENTΩiN2N−1HENTΩi12+HENTΩ2i2+⋯+HENTΩiN2N−1T=EXPΩP,EnΩP,HeΩP.The inferiority of instance clusterICi over other instance clusters in learning activity can be measured by cloud inflow ΨI(ICi):(12)ΨIICi=1N−1∑g=1NΩICg,ICi=1N−1EXPΩi1+EXPΩ2i+⋯+EXPΩNiENTΩ1i2+ENTΩ2i2+⋯+ENTΩNi2HENTΩ1i2+HENTΩ2i2+⋯+HENTΩNi2T=EXPΩi1+EXPΩ2i+⋯+EXPΩNiN−1ENTΩ1i2+ENTΩ2i2+⋯+ENTΩNi2N−1HENTΩi12+HENTΩ2i2+⋯+HENTΩNi2N−1T=EXPΩI,ENTΩI,HENTΩI.The cloud net flow Ψ(ICi) of instance cluster ICi can be calculated by(13)ΨICi=ΨPICi−ΨIICi=EXPΩ+−EXPΩ−,ENTΩ+2+ENTΩ−2,HENTΩ+2+HENTΩ−2=EXPΩi1−EXPΩ1i+EXPΩi2−EXPΩ2i+⋯+EXPΩiN−EXPΩNiN−1ENTΩi12−ENTΩ1i2+ENTΩi22−ENTΩ2i2+⋯+ENTΩiN2−ENTΩNi2N−1HENTΩi12−HENTΩ1i2+HENTΩi22−HENTΩ2i2+⋯+HENTΩiN2−HENTΩNi2N−1T.The above algorithm can output the cloud net flow of each instance cluster, making it possible to understand how much each platform or instance cluster improves the ITA activity.


### 3.2. Index Weighting

Primary indices:IT = {IT1, IT2, IT3, IT4, IT5} = {professional foundation of information teaching, design ability of information teaching, implementation and monitoring ability of information teaching, evaluation ability of information teaching, research ability of information teaching}.Secondary indices:IT1 = {IT11, IT12, IT13} = {awareness and attitude, basic knowledge, basic skills}.IT2 = {IT21, IT22, IT23, IT24} = {teaching objectives and planning ability, student condition analysis ability, teaching strategy design ability, learning situation design ability}.IT3 = {IT31, IT32, IT33} = {collaboration ability, organization and management ability, control and regulation ability}; IT4 = {IT41, IT42, IT43, IT44} = {ability to set appraisal rules, ability to utilize various means of information teaching, ability to provide real-time feedbacks with information technology, ability to visualize evaluation results}.IT5 = {IT51, IT52, IT53} = {teaching reflection ability, sustainable development ability of information teaching, innovation ability of information teaching}.Figures2 and 3 present the evaluation networks of professional foundation of information teaching and design ability of information teaching, respectively. The two networks have 3 and 5 connected components and 26 and 29 evaluation indices, respectively. It can be intuitively inferred that the ITA improvement promotes both professional foundation of information teaching and design ability of information teaching, and the relevant indices are closely correlated, indicating a certain promoting effect.Figure 2
Evaluation network of professional foundation of information teaching.Figure 3
Evaluation network of design ability of information teaching.Figures4–6 present the evaluation networks of implementation and monitoring ability of information teaching, evaluation ability of information teaching, and research ability of information teaching, respectively. The three networks have 3, 4, and 4 connected components and 27, 24, and 22 evaluation indices, respectively. Similarly, the ITA improvement promotes implementation and monitoring ability of information teaching, evaluation ability of information teaching, and research ability of information teaching.Figure 4
Evaluation network of implementation and monitoring ability of information teaching.Figure 5
Evaluation network of evaluation ability of information teaching.Figure 6
Evaluation network of research ability of information teaching.In the ITA evaluation system, the indices were treated as the edges between the nodes of the complex online network, and the connected instances were linked up as network nodes, forming reasonable ITA improvement paths. Based on the importance of instance nodes in the complex online network, the authors calculated and normalized the node degree, closeness centrality, and node centrality of each index, derived the net flows of all indices according to the normalized results, and further obtained the weight of each index. The specific steps are as follows:Step 1. Normalize index attributes of complex online network.The node degreeDEa of an N-node complex online network is smaller than N−1. The node degree can be normalized as(14)Gsua=DEaN−1.In the N-node complex online network, the sum of the shortest paths between any node and other nodes is∑j=1Nsij, which is greater than N−1. Then, the closeness centrality can be normalized as(15)NEeua=N−1∑b=1Nsab.The betweenness of nodeua can be described by IPua=∑b<chbci/hbc and normalized as(16)IPzua=2IPuaN−1N−2.Step 2. Select the preference function.LetLM be the network attributes of M indices GI = {GI1, GI2, …, GIM} and L1, L2, and L3 be the node degree, betweenness, and closeness centrality, respectively. The index attribute GIiM of GIi under LM can be obtained by evaluating ITA improvement against LM. Suppose (GIi, GIj) is the Gaussian preference function of index GIi relative to index GIj under LM, and GIiM−GIjM = ρM(GIi, GIj), which is denoted as ρ. Then,(17)Uρ=0,ρ≤0,1−e−ρ2/2σ2,ρ>0.Step 3. Calculate the multi-index preference order index of evaluation indices.The superiority of indexGIi over index GIj can be characterized by the preference order index:(18)∏GIi,GIj=∑M=13UMGIi,GIj.Step 4. Calculate outflow, inflow, and net flow of evaluation indices.The superiority of indexGIi over other indices can be characterized by the outflow:(19)ΓPGIi=∑j=1M∏GIi,GIj.The inferiority of indexGIi over other indices can be characterized by the inflow:(20)ΓIGIi=∑j=1M∏GIj,GIi.To a certain extent, the preference order of indexGIi can be reflected by the net flow:(21)ΓGIi=ΓPGIi−ΓIGIi.Step 5. Calculate the weights of evaluation indices.The indices can be ranked by the net flow obtained by formula (21). Then, the relative flow Δti of each index, i.e., the difference between net flow of each index and minimum net flow, can be calculated by(22)Δti=ΓGIi−minΓGIi.The weight of each index can be obtained by normalizing Δti:(23)θi=Ui∑i=1MUi.


## 3.1. PROMETHEE Evaluation Strategy

The traditional evaluation method does not fully consider the fuzziness and stochasticity of the evaluation process. The complex structure of online education network cannot be directly described by simple linear relationships. To solve the problem, this paper introduces the cloud model theory to the traditional multi-objective decision making and evaluation, aiming to evaluate the ITA improvement of teachers. Comparative calculation and analysis were performed to evaluate the ITAIN activity more rigorously and scientifically.LetIC1, IC2, … , ICN be N clusters of instances that promote ITA improvement; GI1, GI2, … , GIM be the M indices of each instance cluster; AVijl be attribute l of index j in instance cluster i; and θ=(θ1, θ2, … , θj) be the weight of an index in its instance cluster. Then, the ITA improvement can be evaluated in the following steps based on complex online network.Step 1. Convert each attribute of instance cluster indices to the cloud.The expectation EXP of each attribute of indexj in instance cluster i can be calculated by(6)EXPij=∑l=1KAVijlK.The entropy ENT can be calculated by(7)ENTij=1K∑l=1K|AVijl−Eij.The hyperentropy HENT can be calculated by(8)HENT=D2=KK−1∑l=1KGIijl−EXPij2.Step 2. Define the comparison rules for each attribute of indices of instance clusters.LetGIj(EXPi, ENTi, HENTi) be the cloud attribute of instance cluster ICi under index GIj; sj(ICi, ICg) = sj(EXPig, ENTig, HENTig) be the cloud attribute difference between two instance clusters under index GIj; and GIj(EXPg, ENTg, HENTg) be the cloud attribute of instance cluster ICg (i ≠ g). Then, the expectation EXP, entropy ENT, and hyperentropy HENT between the two instance clusters can be compared by the following rules:(1)
For EXP, when indexGIj needs to be maximized, if EXPi > EXPg, then sj(ICi, ICg) > 0; if EXPi ≤ EXPg, then sj(ICi, ICg) = 0; when index GIj needs to be minimized, if EXPi < EXPg, then sj(ICi, ICg) > 0; if EXPi ≥ EXPg, then sj(ICi, ICg) = 0.(2)
For ENT, if ENTi ≥ ENTg, then sj(ICi, ICg) = 0; if ENTi < ENTg, then sj(ICi, ICg) > 0.(3)
For HENT, if HENTi ≥ HENTg, then sj(ICi, ICg) = 0; if HENTi < HENTg, then sj(ICi, ICg) > 0.Step 3. Convert the preference function to the cloud.Unlike other preference functions, Gaussian preference function changes nonlinearly and better suits the actual decision-making environment. Therefore, this paper chooses Gaussian preference function to transform the attribute cloud of the evaluation indices for each instance cluster.LetWj(ICi, ICg) = Wj(EXPig, ENTig, HENTig) be the preference index indicating that the cloud attribute GIj(EXPi, ENTi, HENTi) of instance cluster ICi is better than that of GIj(EXPg, ENTg, HENTg) of instance cluster ICg (i ≠ g) under index GIj and σ be the threshold of the Gaussian function. Then,(9)sj=EXPig,ENTig,HENTig=GIjEXPi,ENTi,HENTi−GIjEXPg,ENTg,HENTg=EXPi−EXPg,ENTi2+ENTg2,HENTi2+HENTg2.After comprehensive evaluation of all ITA improvement indices, the degree of cloud preferenceICg indicating that instance cluster ICi is better than instance cluster ICg, i.e., the cloud preference index Ω(ICi, ICg), can be calculated by(10)ΩICi,ICg=∑g=1NθjWjICi,ICg∑g=1Nθj=θ1+θ2+⋯+θjT⋅EXP1igENT1igHENT1ig⋮⋮⋮EXPjigENTjigHENTjig=θ1EXP1ig+θ2EXP2ig+⋯+θjEXPjigθ1ENT1ig2+θ2ENT2ig2+⋯+θjENTjig2θ1HENT1ig2+θ2HENT2ig2+⋯+θjHENTjig2T=EXPπig,ENTπig,HENTπig.Step 4. Compute cloud outflow, cloud inflow, and cloud net flow.The superiority of instance clusterICi over other instance clusters in learning activity can be measured by cloud outflow ΨP(ICi):(11)ΨPICi=1N−1∑g=1NΩICi,ICh=1N−1EXPΩi1+EXPΩ2i+⋯+EXPΩiNENTΩi12+ENTΩi22+⋯+ENTΩiN2HENTΩi12+HENTΩi22+⋯+HENTΩiN2T=EXPΩi1+EXPΩ2i+⋯+EXPΩiNN−1ENTΩi12+ENTΩi22+⋯+ENTΩiN2N−1HENTΩi12+HENTΩ2i2+⋯+HENTΩiN2N−1T=EXPΩP,EnΩP,HeΩP.The inferiority of instance clusterICi over other instance clusters in learning activity can be measured by cloud inflow ΨI(ICi):(12)ΨIICi=1N−1∑g=1NΩICg,ICi=1N−1EXPΩi1+EXPΩ2i+⋯+EXPΩNiENTΩ1i2+ENTΩ2i2+⋯+ENTΩNi2HENTΩ1i2+HENTΩ2i2+⋯+HENTΩNi2T=EXPΩi1+EXPΩ2i+⋯+EXPΩNiN−1ENTΩ1i2+ENTΩ2i2+⋯+ENTΩNi2N−1HENTΩi12+HENTΩ2i2+⋯+HENTΩNi2N−1T=EXPΩI,ENTΩI,HENTΩI.The cloud net flow Ψ(ICi) of instance cluster ICi can be calculated by(13)ΨICi=ΨPICi−ΨIICi=EXPΩ+−EXPΩ−,ENTΩ+2+ENTΩ−2,HENTΩ+2+HENTΩ−2=EXPΩi1−EXPΩ1i+EXPΩi2−EXPΩ2i+⋯+EXPΩiN−EXPΩNiN−1ENTΩi12−ENTΩ1i2+ENTΩi22−ENTΩ2i2+⋯+ENTΩiN2−ENTΩNi2N−1HENTΩi12−HENTΩ1i2+HENTΩi22−HENTΩ2i2+⋯+HENTΩiN2−HENTΩNi2N−1T.The above algorithm can output the cloud net flow of each instance cluster, making it possible to understand how much each platform or instance cluster improves the ITA activity.


## 3.2. Index Weighting

Primary indices:IT = {IT1, IT2, IT3, IT4, IT5} = {professional foundation of information teaching, design ability of information teaching, implementation and monitoring ability of information teaching, evaluation ability of information teaching, research ability of information teaching}.Secondary indices:IT1 = {IT11, IT12, IT13} = {awareness and attitude, basic knowledge, basic skills}.IT2 = {IT21, IT22, IT23, IT24} = {teaching objectives and planning ability, student condition analysis ability, teaching strategy design ability, learning situation design ability}.IT3 = {IT31, IT32, IT33} = {collaboration ability, organization and management ability, control and regulation ability}; IT4 = {IT41, IT42, IT43, IT44} = {ability to set appraisal rules, ability to utilize various means of information teaching, ability to provide real-time feedbacks with information technology, ability to visualize evaluation results}.IT5 = {IT51, IT52, IT53} = {teaching reflection ability, sustainable development ability of information teaching, innovation ability of information teaching}.Figures2 and 3 present the evaluation networks of professional foundation of information teaching and design ability of information teaching, respectively. The two networks have 3 and 5 connected components and 26 and 29 evaluation indices, respectively. It can be intuitively inferred that the ITA improvement promotes both professional foundation of information teaching and design ability of information teaching, and the relevant indices are closely correlated, indicating a certain promoting effect.Figure 2
Evaluation network of professional foundation of information teaching.Figure 3
Evaluation network of design ability of information teaching.Figures4–6 present the evaluation networks of implementation and monitoring ability of information teaching, evaluation ability of information teaching, and research ability of information teaching, respectively. The three networks have 3, 4, and 4 connected components and 27, 24, and 22 evaluation indices, respectively. Similarly, the ITA improvement promotes implementation and monitoring ability of information teaching, evaluation ability of information teaching, and research ability of information teaching.Figure 4
Evaluation network of implementation and monitoring ability of information teaching.Figure 5
Evaluation network of evaluation ability of information teaching.Figure 6
Evaluation network of research ability of information teaching.In the ITA evaluation system, the indices were treated as the edges between the nodes of the complex online network, and the connected instances were linked up as network nodes, forming reasonable ITA improvement paths. Based on the importance of instance nodes in the complex online network, the authors calculated and normalized the node degree, closeness centrality, and node centrality of each index, derived the net flows of all indices according to the normalized results, and further obtained the weight of each index. The specific steps are as follows:Step 1. Normalize index attributes of complex online network.The node degreeDEa of an N-node complex online network is smaller than N−1. The node degree can be normalized as(14)Gsua=DEaN−1.In the N-node complex online network, the sum of the shortest paths between any node and other nodes is∑j=1Nsij, which is greater than N−1. Then, the closeness centrality can be normalized as(15)NEeua=N−1∑b=1Nsab.The betweenness of nodeua can be described by IPua=∑b<chbci/hbc and normalized as(16)IPzua=2IPuaN−1N−2.Step 2. Select the preference function.LetLM be the network attributes of M indices GI = {GI1, GI2, …, GIM} and L1, L2, and L3 be the node degree, betweenness, and closeness centrality, respectively. The index attribute GIiM of GIi under LM can be obtained by evaluating ITA improvement against LM. Suppose (GIi, GIj) is the Gaussian preference function of index GIi relative to index GIj under LM, and GIiM−GIjM = ρM(GIi, GIj), which is denoted as ρ. Then,(17)Uρ=0,ρ≤0,1−e−ρ2/2σ2,ρ>0.Step 3. Calculate the multi-index preference order index of evaluation indices.The superiority of indexGIi over index GIj can be characterized by the preference order index:(18)∏GIi,GIj=∑M=13UMGIi,GIj.Step 4. Calculate outflow, inflow, and net flow of evaluation indices.The superiority of indexGIi over other indices can be characterized by the outflow:(19)ΓPGIi=∑j=1M∏GIi,GIj.The inferiority of indexGIi over other indices can be characterized by the inflow:(20)ΓIGIi=∑j=1M∏GIj,GIi.To a certain extent, the preference order of indexGIi can be reflected by the net flow:(21)ΓGIi=ΓPGIi−ΓIGIi.Step 5. Calculate the weights of evaluation indices.The indices can be ranked by the net flow obtained by formula (21). Then, the relative flow Δti of each index, i.e., the difference between net flow of each index and minimum net flow, can be calculated by(22)Δti=ΓGIi−minΓGIi.The weight of each index can be obtained by normalizing Δti:(23)θi=Ui∑i=1MUi.


## 4. Experiments and Result Analysis

The distribution of node connectivity is an important global index in the complex network measuring system for ITA improvement. Figure7 presents the node connectivity distributions of three instance clusters: platform instance cluster, resource instance cluster, and hybrid instance cluster. The former two are clusters composed of a single type of simply connected instances, while the latter is a complex network consisting of multiple instances. In the platform instance cluster, each instance only shares information/knowledge with an average of two instances. The node connectivity calculation shows that the instances on the platform share information or knowledge with an average of two nearby instances, while the instances in the cluster of network resources share information or knowledge with an average of 14 nearby instances. Therefore, quite a lot of network resources can share information or knowledge via potential paths.Figure 7
Node connectivity distributions of different instance clusters.Figure8 compares the network closeness distributions of different instance clusters. The instances are sparsely connected in the single-instance simple network, while the instances are closely connected in the multi-instance complex network.Figure 8
Network closeness distributions of different instance clusters. (a) Single-instance simple network. (b) Multi-instance complex network.
(a)(b)To confirm the reliability of the index weights, this paper summarizes and analyzes the reliability and validity of ITA improvement indices. Firstly, the factor loadings and reliability coefficients of the primary and secondary indices were computed on SPSS and Amos. The results in Table1 show that Cronbach’s alpha of the entire index system was 0.911 and that of each dimension under each primary index was greater than 0.9. Taking IT1 for example, for all its secondary indices, Cronbach’s alphas were greater than 0.7, the mean variances were greater than 0.7, and the composite reliabilities were greater than 0.7. This means that the proposed evaluation indices are highly reliable and valid.Table 1
Reliability and validity of ITA improvement indices.
Primary indicesSecondary indicesFactor loadingReliability coefficientMeasuring errorCronbach’s alphaComposite reliabilityMean varianceIT1IT110.7150.5420.4510.8630.8590.562IT120.7620.5390.432IT130.7930.6310.379IT2IT210.8490.7360.2940.7620.7650.534IT220.5370.2790.726IT230.7240.6520.378IT240.6240.5540.518IT3IT310.9310.8120.1890.7640.7670.567IT320.5560.3090.632IT330.7460.5370.456IT4IT410.8510.7720.2180.8110.7470.521IT420.5170.2350.753IT430.7480.6280.311IT440.6180.5480.599IT5IT510.9170.8470.1870.7230.7760.579IT520.7290.5180.422IT530.7510.7110.549Then, the node degree, betweenness, and closeness centrality of each primary index were calculated on MATLAB (Table2). Taking the results as evaluation criteria, the network attributes of secondary indices were compared to obtain the net flow of each secondary index (Table 3).Table 2
Network attributes of primary indices.
Evaluation indicesNode degreeBetweennessCloseness centralityIT10.560.110.72IT20.270.060.79IT30.230.010.52IT40.230.010.56IT50.250.010.59Table 3
Net flows of secondary indices.
Evaluation indicesIT11IT12IT13IT21IT22IT23IT24Net flow10.2519.32−9.21−15.72−12.38−16.21−9.25Relative net flow25.3131.855.920.532.765.3223.75Evaluation indicesIT31IT32IT33IT41IT42IT43IT44Net flow−14.372.85−8.75−9.328.71−1.4516.48Relative net flow016.275.181.9721.8212.3929.75Evaluation indicesIT51IT52IT53Net flow2.6812.3123.75Relative net flow16.35268235.17As shown in Table3, collaboration ability IT31 and ability to set appraisal rules IT41 had relatively low net flows, indicating the low priority of the two indices in the ITA index system. That is, IT31 and IT41 in the EIS are not top considerations of the teachers during the improvement of their ITA. After that, the net flows of other indices relative to IT31 and IT41 were computed and normalized and used to derive the weights of secondary indices (Table 3).


## 5. Conclusions

This paper presents a novel evaluation method for ITA improvement based on complex online network. After setting up the analysis framework, ITAIN activity was analyzed from the perspectives of node connectivity, clustering coefficient, mean path length, and betweenness of the online network. Under the evaluation strategy of PROMETHEE, the author established an evaluation model for ITA improvement and built five primary index evaluation networks: professional foundation of information teaching, design ability of information teaching, implementation and monitoring ability of information teaching, evaluation ability of information teaching, and research ability of information teaching. Through experiments, the distribution of node connectivity and closeness in networks of different instance clusters were obtained, which reflects that the instances in multi-instance complex network have closer connections than those in single-instance simple network. Further, the indices of teachers’ ITA were subject to reliability and validity test, and the network attributes of primary indices and the net flows of secondary indices were calculated. The relevant results demonstrate the high reliability and good validity of the proposed indices.



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*Source: 8339160-2021-10-21.xml*