# Willingness and Evaluation Model of College Students’ Online Learning Behavior Based on Distributed Cognition

Authors: Lingjing Chen; Shuying Huang
Journal: Scientific Programming
 (2021)

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## Abstract

Guided by distributed cognition theory, we analyze the influential elements of content, tools, and contextual interactions in the online learning process through research and case studies to explore the characteristics and evaluation of college students’ willingness to engage in online learning behavior under distributed cognition and provide guidance for the experience design of online education platforms. Based on distributed cognition, this paper designs a convolutional neural network model based on InceptionNet, which uses a global average pooling layer instead of a fully connected layer to reduce the number of parameters, and InceptionNet increases the depth and width of the network by branching to improve the performance of the network and avoid overfitting. Distributed cognitive theory emphasizes the distributed nature of cognition, and the intrinsic variables that influence the willingness to participate in online learning communities from a systemic viewpoint are mainly attitudes, subjective norms, expected emotions, competence, sense of relatedness, desire, and perceived behavioral control. In addition, perceived behavioral control has a direct positive effect on the willingness to participate in online learning communities.


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## Body

## 1. Introduction

Online education platforms have become an important tool to assist learning in recent years, and the number of online users and the variety and number of online education platforms continue to grow. The online education business is gradually developing in the direction of multidimensionality, depth, personalization, and segmentation, but the learning experience still suffers from the problems of designing a large number of cognitive activities around the individual cognitive ability of learners and resulting in low learning autonomy, lack of classroom participation, and little collaboration and communication among learners [1, 2].The rapid development and widespread use of Internet technology have led to dramatic changes in the way people live, work, learn, and communicate. More and more employees of enterprises, government officials, school teachers, and social workers are adopting online learning to realize lifelong learning [3]. The informal online learning communities built by the Internet are favored by adult learners and play an indispensable role in their learning process because they are not constrained by time and place [4]. For example, it can solve the problems of traditional learning communities, such as time and space constraints, difficulties in accessing resources, and lack of long-distance interaction; it can increase information exchange, mutual support, team commitment, cooperation, and satisfaction with the learning experience; it enables members to find like-minded and progressive learning partners on the Internet, and a common learning vision can stimulate their learning initiative and creativity and enhance their sense of belonging. It is conducive to constructing knowledge and exchanging emotions and paves the way for members’ common growth and the reduction of the knowledge gap [5]. With the deep integration of “Internet + education,” the significance and value of adult online learning communities have been highlighted, and their practical applications in different professional fields are becoming more and more widespread [6, 7].However, in practice, problems such as insufficient willingness of adults to participate in online learning communities, insufficient acceptance and use of mobile learning, and insufficient interaction among members still abound. What factors affect the willingness to participate in online learning communities? What is the relationship between them? The results of Bette’s study of Alberta Community Adult Learning Council coordinators’ participation in online learning communities show that their motivations for participation include learning new skills and gaining access to work practices, strengthening ties with colleagues and careers, and reducing isolation due to job functions and geographic location [8, 9]. Results of Karunanayaka’s case study of teachers suggest that interactive and collaborative course design, the role of online tutors, and the various strategies used by online learners have an impact on the learning outcomes of online learning communities. The authors of [10, 11] argued that learners’ conceptualization of prior knowledge and experience, focus on computer-mediated communication, and participation in courses and projects can enhance their perception of and social engagement in online learning communities. The study in [12] showed that whether adults choose to join online communities of practice is related to intrinsic factors such as stress relief, increased social interaction, community service, and improved work performance. The study in [13] argued that the role of the facilitator is important in virtual learning community learning activities, both to help form effective communication among participants and to play an active role in effectively managing the process of the activity. The study in [14] noted that, in informal online learning communities, social presence influences participants’ satisfaction and sustained attention.Although there is no shortage of studies that explore the behavioral decision-making process of adult participation in online learning communities from motivational factors and identify the key factors that influence adult learning behaviors, the psychological mechanisms of adult participation in online learning communities from the perspective of motivation are rarely examined. Given that adults’ behavioral decisions to participate in online learning communities are mostly accompanied by clear goals and that adult learning is characterized by distinctive self-direction, this study will introduce goal-oriented behavior theory and self-determination theory to clarify the drivers of adult participation in online learning communities and their underlying mechanisms from a microscopic perspective and provide theoretical support for enhancing the effectiveness of participation.The contributions of this paper are as follows:(1)
This paper designs a convolution neural network model based on InceptionNet, which uses the global average pooling layer instead of the full connection layer to reduce the number of parameters. At the same time, InceptionNet increases the depth and width of the network by branching, which can improve the performance of the network and avoid overfitting.(2)
Based on distributed cognitive theory, this paper analyzes the internal variables affecting the willingness to participate in online learning community from the perspective of system view. It also explores the relationship between these internal variables and students’ behavior intention.(3)
This paper makes a large number of experimental arguments and finds that the sense of relevance and perceived behavior control have a significant positive impact on the desire to participate in the online learning community, and desire further positively affects the generation of willingness to participate in behavior.


## 2. Overview of Distributed Cognitive Theory

The American psychologist Hutchens first introduced the concept of distributed cognition; that is, cognition is distributed in nature and cognitive activity depends on the joint functioning of all elements in the whole context [15]. Scholar Salomon proposed that individual cognition is the core of distributed cognition and that the interaction of elements in distributed cognitive activities resulted in the spiral development of individual cognition [16]. The theory gives designers a systematic perspective on research and aims to shift the researcher’s unit of analysis from the individual involved in cognition in the mind to consider the relationship between the individuals involved in cognitive activities in the external environment and the factors that together participate in cognitive activities [17].Some scholars have long applied distributed cognition theory to educational practice; the study in [18] introduced distributed cognition theory to the analysis and design of offline classroom learning activities, arguing that it is important to focus not only on the design of learning content and teaching tools but also on the collaborative communication activities among learners in the learning environment, which provides a new research perspective on the design of course learning. The study in [19] applied distributed cognition theory to the practice of online learning, using the individual, media, environmental, cultural, and social elements of distributed cognition to establish mapping relationships between learners, instructional organizers, learning resources, collaborative tools, and learning contexts in distributed learning environments as the entry point, as shown in Figure 1. The activities of the e-learning platform are classified into content interaction, tool interaction, and contextual interaction in three dimensions: interactive content, interactive behavior, and interactive environment, and strategies to promote collaborative learning in distributed e-learning environments are proposed [20]. However, none of the above studies have developed a detailed description of how to optimize the experience of e-learning practices through distributed cognitive theory from a design perspective.Figure 1
Model of collaborative learning interaction process in a distributed learning environment.


## 3. Case Study of Online Education Application Design

In order to ensure that the influential elements of online learning interactions are evidenced in real design practice, we use the Coursera platform [4, 21], a typical overseas online education platform, as a case study to explore the application of distributed cognitive theory in the design of online education platforms. Coursera is one of the largest online education platforms in the world, which supports students to obtain the certificate of completion of corresponding subjects through independent study. Currently, the platform has more than 30 million registered users and offers more than 2000 courses.


### 3.1. Distributed Online Education

Under the distributed cognition perspective, learner-learning content interaction is influenced by the content quality of learning resources, social interaction of online videos, knowledge structure relationship of content, and interface design of learning resources. Individual cognition is the core of distributed cognition, so learning resources designed based on learners’ purposes and needs are the first step in the design of online education platforms [22]. The Coursera platform provides a large number of excellent course resources in popular fields such as computer science, data science, information technology, mathematics, language learning, art and the humanities, and business. Learning materials mainly include videos, reading materials, graded tests, practice tests, and other forms, as shown in Figure 2. The instructor will provide learners with expandable learning materials including people, books, articles, and website links, based on their true level, to promote the construction of personal knowledge. Course materials set up systematic knowledge into staged course, and learners can discover the relevance of knowledge and form a systematic knowledge framework through different forms of learning materials according to the course they want to study. In terms of interface design, Coursera’s visual elements are consistent, with titles and navigation, content information, and so forth clearly visible and overall easy for learners to use, without excessive visual interference, in line with learners’ cognitive needs.Figure 2
Coursera platform home page and course selection page.The Coursera platform is based on a humanistic and emotional need for instructor-led course content design, where the online videos show the instructor interacting with the online learners, rather than just a one-way transfer of knowledge. The lecturer’s teaching style, language, and teaching behavior will enhance learners’ interest in learning. Learning materials of online classes in many domestic online education platforms are purely recorded screens, where only knowledge points appear without a lecturer, lacking classroom interactivity and making it difficult to establish learners’ trust and thus reducing learning initiative [8]. Some videos are recorded offline lectures where the lecturer appears on the screen. However, traditional offline lectures face offline students, while online lectures face learners beyond the screen, so they are not designed for the characteristics and needs of the online learning population. However, courses on Coursera platform are clearly optimized for online teaching, such as well-designed slides, short videos for mobile learning, live demonstrations of course recordings, and quizzes that pause in the middle of video playback.


### 3.2. Experience Design Strategies for Online Education Platforms from a Distributed Cognitive Perspective

Through the above analysis of content interaction, tool interaction, and contextual interaction, and combined with the case study of Coursera platform, the experience design strategy of online education platform from the perspective of distributed cognition was finally refined by substituting into specific contexts, as shown in Figure3.Figure 3
Experience design models of online education platform from the perspective of distributed cognition.


## 3.1. Distributed Online Education

Under the distributed cognition perspective, learner-learning content interaction is influenced by the content quality of learning resources, social interaction of online videos, knowledge structure relationship of content, and interface design of learning resources. Individual cognition is the core of distributed cognition, so learning resources designed based on learners’ purposes and needs are the first step in the design of online education platforms [22]. The Coursera platform provides a large number of excellent course resources in popular fields such as computer science, data science, information technology, mathematics, language learning, art and the humanities, and business. Learning materials mainly include videos, reading materials, graded tests, practice tests, and other forms, as shown in Figure 2. The instructor will provide learners with expandable learning materials including people, books, articles, and website links, based on their true level, to promote the construction of personal knowledge. Course materials set up systematic knowledge into staged course, and learners can discover the relevance of knowledge and form a systematic knowledge framework through different forms of learning materials according to the course they want to study. In terms of interface design, Coursera’s visual elements are consistent, with titles and navigation, content information, and so forth clearly visible and overall easy for learners to use, without excessive visual interference, in line with learners’ cognitive needs.Figure 2
Coursera platform home page and course selection page.The Coursera platform is based on a humanistic and emotional need for instructor-led course content design, where the online videos show the instructor interacting with the online learners, rather than just a one-way transfer of knowledge. The lecturer’s teaching style, language, and teaching behavior will enhance learners’ interest in learning. Learning materials of online classes in many domestic online education platforms are purely recorded screens, where only knowledge points appear without a lecturer, lacking classroom interactivity and making it difficult to establish learners’ trust and thus reducing learning initiative [8]. Some videos are recorded offline lectures where the lecturer appears on the screen. However, traditional offline lectures face offline students, while online lectures face learners beyond the screen, so they are not designed for the characteristics and needs of the online learning population. However, courses on Coursera platform are clearly optimized for online teaching, such as well-designed slides, short videos for mobile learning, live demonstrations of course recordings, and quizzes that pause in the middle of video playback.


## 3.2. Experience Design Strategies for Online Education Platforms from a Distributed Cognitive Perspective

Through the above analysis of content interaction, tool interaction, and contextual interaction, and combined with the case study of Coursera platform, the experience design strategy of online education platform from the perspective of distributed cognition was finally refined by substituting into specific contexts, as shown in Figure3.Figure 3
Experience design models of online education platform from the perspective of distributed cognition.


## 4. Online Learning Algorithms

### 4.1. Preference Function and Objective Function

For a pair of candidate jobsXi and Xj, the preference function between them is represented by (1) and results in a real number between [0, 1]; that is,(1)F:Xid×Xjd⟶0,1.A return value close to 1 indicates that the first candidate job has a higher preference for the second, and whenk different candidate jobs are given, the pairwise preference function can be applied to each possible combination of candidate job pairs to determine the best job. Using (1), the sum of the differences of the preferences of candidate jobs Xi in the set of candidate jobs under state θ is found, vXi,Jθ. The job with the largest value Xi is the best job in the current candidate job set Jθ, so for a given state θ the assignment policy π in the candidate job set Jθ is shown in(2)vXi|Jθ=∑Xi,Xj∈JθFXi,Xj−FXj,Xi,πJθ=argmaxXi∈θvXi|Jθ.The two main objectives to be achieved in the experiment are minimizing the average job time of QC and the idle distance of learning; we should measure the merit of the intentional decision in this regard and chooseTn and Dn minimum as the evaluation index to establish the objective function fn. Assuming that n learner jobs have been processed using this intentional strategy, the objective function (10) is used to measure the performance of this intentional decision.(3)fn=ωT×Tn+ωD×Dn,where Tn denotes the average job time of QC processing n learners, Dn denotes the average idle distance of learning to transport n learners, and ωT and ωD are the respective weights. Here Tn and Dn are calculated as follows:(4)Tn=Qn×tn−s,Dn=1n∑q∈Q∑J∈Fq,neJ,where Q is the set of QCs, tn is the job time of the QC when the nth job is completed, s is the start time Fq of all jobs, n is the set of jobs completed by the QC up to time tn, and eJ is the idle distance of learning for job J. ωT and ωD can change depending on the relative importance of the two objectives.


### 4.2. Training and Evaluation Function of the Sample

After each assignment of learner assignments to learn using the above preference function-based intentional policy, the preference function is updated for the next round of learner assignments. That is, each time an intention decision is completed, a short over-the-top simulation is performed to evaluate each candidate job, assuming that the assignment is made only amongk candidate jobs, treating each job in k as the best job once, and then performing a simulation in which the assignment rule ω remains unchanged, and after over-the-top simulation of k candidate jobs, the following objective function is used to evaluate the candidate jobs and obtain an evaluation matrix to find the initial job with the smallest evaluation function in the evaluation matrix [23]. In the first learning assignment, since there is no sample for learning, we use a random strategy for training sample acquisition.The evaluation function in the over-the-top simulation process is as follows:(5)fn+k=ωT×Tn+k+ωD×Dn+k,where Tn+k and Dn+k are calculated as follows:(6)Tn+k=tn+k−sminq∈QFq,n+k,Dn+k=1n+k∑q∈Q∑J∈Fq,n+keJ,where Tn+k is calculated in the same way as Dn, but Tn+k is obtained by calculating the average assignment time of the QC that handles the least number of learner assignments, because if equation (5) is used to calculate it in a short-term over-the-top simulation, it will make the QC go first to those learners with shorter assignments to get a smaller average QC assignment time, but this bias towards learners with shorter assignments bias would cause learner assignments with longer processing times to not be selected until the end, which would eventually lead to delays in assigning QC for these assignments. So, using equation (6) for evaluation would drive a relatively average job schedule for all QCs.The initial jobJ∗ with the smallest evaluation function in the evaluation matrix is found, and then the best job J∗ is paired two-by-two with other candidate jobs in k to generate (k − 1) training samples. Since the total number of (k − 1) training samples generated is usually not enough for the model to reliably learn the entire new function, the online preference learning algorithm maintains a set of recent sample pools T, and these new training samples have been accumulated in the sample pool T [24]. All samples in this pool T then learn new preferences and thus update the preference function for the next job assignment.


## 4.1. Preference Function and Objective Function

For a pair of candidate jobsXi and Xj, the preference function between them is represented by (1) and results in a real number between [0, 1]; that is,(1)F:Xid×Xjd⟶0,1.A return value close to 1 indicates that the first candidate job has a higher preference for the second, and whenk different candidate jobs are given, the pairwise preference function can be applied to each possible combination of candidate job pairs to determine the best job. Using (1), the sum of the differences of the preferences of candidate jobs Xi in the set of candidate jobs under state θ is found, vXi,Jθ. The job with the largest value Xi is the best job in the current candidate job set Jθ, so for a given state θ the assignment policy π in the candidate job set Jθ is shown in(2)vXi|Jθ=∑Xi,Xj∈JθFXi,Xj−FXj,Xi,πJθ=argmaxXi∈θvXi|Jθ.The two main objectives to be achieved in the experiment are minimizing the average job time of QC and the idle distance of learning; we should measure the merit of the intentional decision in this regard and chooseTn and Dn minimum as the evaluation index to establish the objective function fn. Assuming that n learner jobs have been processed using this intentional strategy, the objective function (10) is used to measure the performance of this intentional decision.(3)fn=ωT×Tn+ωD×Dn,where Tn denotes the average job time of QC processing n learners, Dn denotes the average idle distance of learning to transport n learners, and ωT and ωD are the respective weights. Here Tn and Dn are calculated as follows:(4)Tn=Qn×tn−s,Dn=1n∑q∈Q∑J∈Fq,neJ,where Q is the set of QCs, tn is the job time of the QC when the nth job is completed, s is the start time Fq of all jobs, n is the set of jobs completed by the QC up to time tn, and eJ is the idle distance of learning for job J. ωT and ωD can change depending on the relative importance of the two objectives.


## 4.2. Training and Evaluation Function of the Sample

After each assignment of learner assignments to learn using the above preference function-based intentional policy, the preference function is updated for the next round of learner assignments. That is, each time an intention decision is completed, a short over-the-top simulation is performed to evaluate each candidate job, assuming that the assignment is made only amongk candidate jobs, treating each job in k as the best job once, and then performing a simulation in which the assignment rule ω remains unchanged, and after over-the-top simulation of k candidate jobs, the following objective function is used to evaluate the candidate jobs and obtain an evaluation matrix to find the initial job with the smallest evaluation function in the evaluation matrix [23]. In the first learning assignment, since there is no sample for learning, we use a random strategy for training sample acquisition.The evaluation function in the over-the-top simulation process is as follows:(5)fn+k=ωT×Tn+k+ωD×Dn+k,where Tn+k and Dn+k are calculated as follows:(6)Tn+k=tn+k−sminq∈QFq,n+k,Dn+k=1n+k∑q∈Q∑J∈Fq,n+keJ,where Tn+k is calculated in the same way as Dn, but Tn+k is obtained by calculating the average assignment time of the QC that handles the least number of learner assignments, because if equation (5) is used to calculate it in a short-term over-the-top simulation, it will make the QC go first to those learners with shorter assignments to get a smaller average QC assignment time, but this bias towards learners with shorter assignments bias would cause learner assignments with longer processing times to not be selected until the end, which would eventually lead to delays in assigning QC for these assignments. So, using equation (6) for evaluation would drive a relatively average job schedule for all QCs.The initial jobJ∗ with the smallest evaluation function in the evaluation matrix is found, and then the best job J∗ is paired two-by-two with other candidate jobs in k to generate (k − 1) training samples. Since the total number of (k − 1) training samples generated is usually not enough for the model to reliably learn the entire new function, the online preference learning algorithm maintains a set of recent sample pools T, and these new training samples have been accumulated in the sample pool T [24]. All samples in this pool T then learn new preferences and thus update the preference function for the next job assignment.


## 5. Online Learning Based on Improved Convolutional Neural Networks

The preference selection between candidate jobs can be attributed to a multiclassification problem, and the preference function is learned by an improved convolutional neural network using an incremental learning strategy [25]. The improved convolutional neural network model is shown in Figure 4, which combines InceptionNet and traditional convolutional neural network to construct a new classification model in terms of network structure and parameter optimization. First, each job is compared two by two into a 15 × 7 matrix input to obtain the first layer, and three 15 × 16 convolutions are obtained by feature transformation through three different branches of 1 × 3 convolution, 1 × 5 convolution, and 1 × 7 convolution, followed by an aggregation operation to merge the results of the three branches to obtain the final filter as the input of the next convolutional layer [26]. According to the same logic as the previous layer, three 1 × 3 convolutions, 1 × 5 convolutions, and 1 × 7 convolutions are transformed, respectively, and three 8 × 32 convolutions are obtained through the global average pooling layer, and, finally, the results of the three branches are combined on the output channel through dimensional aggregation to output a vector of length 6, because there are six operations for classification at the same time, and the activation function is adopted as the Softmax function is used. Compared with the traditional convolutional neural network, the modified convolutional neural network based on InceptionNet [27] increases the number of network layers by adding three 1 × 3 convolutions, 1 × 5 convolutions, and 1 × 7 convolutions in front of each convolutional layer but removes the final fully connected layer and uses the global average pooling layer instead of the fully connected layer, which in turn reduces the number of parameters, increases the operation speed, and reduces overfitting. The size of the convolution kernels in the network varies, which increases the adaptability of the neural network to different scales.Figure 4
Network structure of the improved convolutional neural network.


## 6. Research Hypothesis and Model Construction

According to the goal-directed behavior model, four variables, namely, attitude, subjective norm, expected emotion, and perceived behavioral control, were identified as the main factors influencing desire as an intermediate variable; based on self-determination theory, feelings of competence and relatedness were identified as the other two main factors influencing desire [15, 25, 28]. The following research hypotheses were proposed in this study:H1: attitudes have a positive impact on the desire to participate in adult online learning communitiesH2: subjective norms have a positive impact on the desire to participate in adult online learning communitiesH3: positive anticipatory emotions have a positive impact on the desire to participate in adult online learning communitiesH4: competence has a positive impact on the desire to participate in adult online learning communitiesH5: a sense of connectedness has a positive impact on the desire to participate in adult online learning communitiesH6: perceived behavioral control has a positive effect on the desire to participate in adult online learning communitiesH7: perceived behavioral control has a positive effect on adults’ willingness to participate in online learning communitiesH8: desire has a positive impact on willingness to participate in adult online learning communitiesBased on the above hypothetical relationships, I constructed a theoretical model of the mechanisms underlying adults’ willingness to participate in online learning communities as shown in Figure5.Figure 5
Research conceptual model.


## 7. Experimental Scenarios and Analysis of Results

### 7.1. Experimental Scenes

The experiment is implemented in Python language, and the computer parameters for the experiment are Intel(R) Core(TM) i7-7700HQ CPU @ 2.80 GHz, 16 G RAM, Windows 10.Suppose that there are 6 QCs, the number of learner yards is 14, there are 900 learner jobs in total, learning times simulated experimentally are 12, 18, and 24, the speedv = 4 m/s, ωT: ωD = 1 : 1, and the number of yards for the purpose of learner jobs handled by QCs obeys a normal distribution N(μi, σi2). If μ4  = 9, it means that the learners handled by QC4 are stored near yard B9, and as σ4 increases or decreases, the learners are stored in a larger or smaller range of B9, so different experimental scenarios can be simulated by changing the values of μi and σi [29].Two experimental scenarios were designed, each containing 900 learner assignments. Scenario 1 was set up as the learners handled by each QC were mostly distributed around the nearest yard, and the learning load distances were relatively uniform and short, at which time the learner distribution parameters were set to (μ1, μ2, μ3, μ4, μ5, μ6) = (2, 4, 6, 9, 11, 13), σi = 1. Scenario 2 was set up as the learners were widely distributed in each yard, and the learning load distances were uneven, at which time the learner distribution parameters were set to (μ1, μ2, μ3, μ4, μ5, μ6) = (4, 5, 6, 7, 8, 9), σi = 5; scenario 2 is shown in Figure 6.Figure 6
Automated learner dock layout.


## 7.1. Experimental Scenes

The experiment is implemented in Python language, and the computer parameters for the experiment are Intel(R) Core(TM) i7-7700HQ CPU @ 2.80 GHz, 16 G RAM, Windows 10.Suppose that there are 6 QCs, the number of learner yards is 14, there are 900 learner jobs in total, learning times simulated experimentally are 12, 18, and 24, the speedv = 4 m/s, ωT: ωD = 1 : 1, and the number of yards for the purpose of learner jobs handled by QCs obeys a normal distribution N(μi, σi2). If μ4  = 9, it means that the learners handled by QC4 are stored near yard B9, and as σ4 increases or decreases, the learners are stored in a larger or smaller range of B9, so different experimental scenarios can be simulated by changing the values of μi and σi [29].Two experimental scenarios were designed, each containing 900 learner assignments. Scenario 1 was set up as the learners handled by each QC were mostly distributed around the nearest yard, and the learning load distances were relatively uniform and short, at which time the learner distribution parameters were set to (μ1, μ2, μ3, μ4, μ5, μ6) = (2, 4, 6, 9, 11, 13), σi = 1. Scenario 2 was set up as the learners were widely distributed in each yard, and the learning load distances were uneven, at which time the learner distribution parameters were set to (μ1, μ2, μ3, μ4, μ5, μ6) = (4, 5, 6, 7, 8, 9), σi = 5; scenario 2 is shown in Figure 6.Figure 6
Automated learner dock layout.


## 8. Analysis of Experimental Results

### 8.1. Analysis of the Results of Experiment 1

#### 8.1.1. Model Goodness-of-Fit Test

After completing validation of each variable, AMOS 24.0 was used to test the fit of the hypothesized model to the measured data. The fit indices of this model were as follows: CMIN/DF = 4.040, GFI = 0.727, AGFI = 0.671, RMSEA = 0.100, TLI = 0.839, and CFI = 0.847 (see Table1). Among them, CFI and TLI are very close to the critical value of 0.9, and RMSEA is greater than the critical value of 0.08, indicating that there is some room for improvement of the coefficients of the fitness indicators of this model. In this study, the model fit indices were corrected by the Bollen-Stine Bootstrap formula, and the corrected fit indices were as follows: CMIN/DF = 1.318, GFI = 0.937, AGFI = 0.921, RMSEA = 0.032, TLI = 0.987, and CFI = 0.984 [30].Table 1
Model fit test indicators.
Allocation indexCMIN/DFGFIAGFIRMSEATLICFIStandard value<5>0.9>0.8<0.08>0.9>0.9Index coefficient of this paper4.0400.7270.6710.1000.8390.847Corrected index coefficient0.3180.9370.9210.0320.9870.984


#### 8.1.2. Hypothesis Testing

The hypotheses were tested by measuring the standardized path coefficients among the variables. Table2 shows that the P values of H1, H2, H3, and H4 are not less than 0.001, indicating that the hypotheses do not pass the test; the P values of H5, H6, H7, and H8 are less than 0.001, indicating that the hypotheses pass the test. In addition, according to the magnitude of β values of the path coefficients of each hypothesis of the structural equation model, it can be seen that the effects of each variable on desire are, in descending order, relatedness, perceived behavioral control, competence, attitude, expected emotion, and subjective norm, while the effects on behavioral intention are, in descending order, perceived behavioral control and desire.Table 2
Structural equation model hypothesis testing results.
HypothesisPath relationshipPPath coefficientβConclusionH1Attitude ⟶ desire0.0240.124No supportH2Subjective norm ⟶ desire0.371-0.048No supportH3Expected emotion ⟶ desire0.2660.062No supportH4Competence ⟶ desire0.0150.131No supportH5Relevance ⟶ desire∗∗∗0.544SupportH6Perceived behavior control ⟶ desire∗∗∗0.417SupportH7Perceived behavioral control ⟶ behavioral intention∗∗∗0.492SupportH8Desire ⟶ willingness to act∗∗∗0.425Support


#### 8.1.3. Mediation Effect Test

The mediation effect was examined using the nonparametric percentile Bootstrap method with the PROCESS 2.16 plug-in installed in SPSS 24.0 software. The results showed that desire mediated the relationship between perceived behavioral control, sense of relatedness, and willingness to act; desire partially mediated the relationship between perceived behavioral control and willingness to act and fully mediated the relationship between sense of relatedness and willingness to act (see Table3).Table 3
Results of the mediation effect test.
Effect coefficientStandardization coefficientTPIntermediate effect confidence interval (estimated upper limit)Intermediate effect confidence interval (estimated lower bound)Perceived behavior controlTotal effect0.6630.05811.367∗∗∗0.5480.777Direct effect0.5130.0598.678∗∗∗0.3970.629Indirect effect0.1500.0393.846∗∗∗0.0830.234Sense of relevanceTotal effect0.1970.0513.861∗∗∗0.0970.298Direct effect0.0420.0530.7870.432-0.0630.147Indirect effect0.1550.0344.559∗∗∗0.0990.233


### 8.2. Analysis of the Results of Experiment 2

The intention results of learning under scenario 1 and scenario 2 for 12 to 24 units of different sizes are compared.The experimental results are shown in Table4, where the rate of decrease of Tn and Dn gradually slows down when the learning times gradually increase from 12 to 24, because the same may be accompanied by conflicts between learning as the learning times increase.(1)
The sums under scenario 2 are higher than those under scenario 1, which is because the learners in scenario 2 are scattered among the heap fields. The dynamic degree is higher, and the learning load distance is uneven.(2)
When the numbers of learner assignments are the same, the one under scenario 1 with 18 learning runs is close to the one under scenario 2 with 24 learning runs. Therefore, the assignment strategy can be adjusted according to the information collection of QC and learning and the preference of learner assignments, thus reducing the number of learning inputs.Table 4
Intention to learn in different scenarios.
Number of AGVSScenario 112166.74310.6348161.95295.3224159.13279.48Scenario 112217.35452.4818184.57429.5624164.03421.37As can be seen from Figure7, as the number of iterations of this paper’s model increases, the students’ learning intentions increase in different distributed scenarios, and the highest learning intention satisfaction can reach 138 GI. In different student interest context, this paper’s model can make students interested in online learning, which is our improvement of the model, digging deeper into the data features. It is also known that, according to each hypothesis of the knot model, it can be seen that the effect of each variable on desire from high to low is the sense of association, perceived behavioral control, sense of competence, attitude, expected emotion, and subjective norm, while the effect of the effect of willingness to act from high to low is the perceived behavioral control and desire.Figure 7
Student learning intention.


### 8.3. Analysis of the Results of Experiment 3

The intentional results of using a multifeature-based online learning assignment strategy with a shortest drive assignment strategy by varying the number of learner assignments are compared.As can be seen in Figure8, the total completion time with the multifeature online learning assignment strategy is approximately 13% greater than that with the shortest drive assignment strategyDn , but the difference between the multifeature online learning assignment strategy and the shortest drive assignment strategy remains approximately the same as the number of learners handled by the QC increases,Dn. The total completion time increases with the number of learners for both assignment strategies, and the total completion time with the multifeature online learning assignment strategy is about 12% shorter than the total completion time with the shortest drive assignment strategy on average, and the total completion time with the multifeature online learning assignment strategy saves about 21% when each QC handles about 225 TEUs on average.Figure 8
Intent results under different assignment rules.
(a)(b)As shown in Table5, the Tn differences of QC under the two assignment strategies are 3.9 s, 26.2 s, 33.8 s, 56.4 s, and so forth, and the Tn differences between the online learning assignment strategy and the shortest drive assignment strategy keep increasing as the number of learners increases, indicating that the online learning assignment strategy based on multiple features can effectively reduce the average assignment time and minimize the completion time of QC.Table 5
Average QC completion time under different assignment rules.
Number of containers handled by each QC100 (s)125 (s)150 (s)175 (s)200 (s)225 (s)Online learning assignment179.8174.2177.8172.2170.9169.3Shortest distance assignment183.7200.4211.6228.6248.4273.2From Figure9, we can know that the method in this paper helps students’ intentional interest in online learning the most, and the blue intentional interest points have been distributed above the other two schemes from the beginning of the iteration. It shows that our scheme is effective. Traditional CNN is simple, but it can also dig deeper into the data, so it also has some effect.Figure 9
Comparison of learning intentions of different programs.Using structural equation modeling, this study examined the pathway relationships among adults’ attitudes, subjective norms, expected emotions, competence, relatedness, perceived behavioral control, desires, and behavioral intentions to participate in online learning communities and analyzed the mechanisms that generate behavioral intentions to participate in online learning communities, focusing on the mechanisms that influence behavioral intentions. Four main conclusions were drawn from this study.First, attitudes, subjective norms, expected emotions, and competence did not play a significant antecedent role in the process of adults’ desire to participate in the online learning community. In particular, subjective norms had little effect on desire (β = −0.048), suggesting that the factors influencing adults’ participation in online learning communities were more focused on the community organizational environment and self-control.Second, a sense of connectedness has a positive impact on the desire to participate in an adult online learning community. A sense of connectedness can be viewed as an internal psychological experience for individuals, the key to which is to feel cared for and supported by other members of the organization. The acquisition of a sense of connectedness not only enhances identity and mutual understanding between individuals but also provides a sense of pleasure and satisfaction. Conversely, it can lead to feelings of isolation.Third, perceived behavioral control affects adults’ behavioral intentions not only directly but also indirectly through desires. The total effectβ of perceived behavioral control on behavioral intentions reached 0.663, indicating that perceived behavioral control is an important variable influencing the generation of behavioral intentions in adults.Fourth, adults’ desire to learn has a significantly positive effect on behavioral intentions.


## 8.1. Analysis of the Results of Experiment 1

### 8.1.1. Model Goodness-of-Fit Test

After completing validation of each variable, AMOS 24.0 was used to test the fit of the hypothesized model to the measured data. The fit indices of this model were as follows: CMIN/DF = 4.040, GFI = 0.727, AGFI = 0.671, RMSEA = 0.100, TLI = 0.839, and CFI = 0.847 (see Table1). Among them, CFI and TLI are very close to the critical value of 0.9, and RMSEA is greater than the critical value of 0.08, indicating that there is some room for improvement of the coefficients of the fitness indicators of this model. In this study, the model fit indices were corrected by the Bollen-Stine Bootstrap formula, and the corrected fit indices were as follows: CMIN/DF = 1.318, GFI = 0.937, AGFI = 0.921, RMSEA = 0.032, TLI = 0.987, and CFI = 0.984 [30].Table 1
Model fit test indicators.
Allocation indexCMIN/DFGFIAGFIRMSEATLICFIStandard value<5>0.9>0.8<0.08>0.9>0.9Index coefficient of this paper4.0400.7270.6710.1000.8390.847Corrected index coefficient0.3180.9370.9210.0320.9870.984


### 8.1.2. Hypothesis Testing

The hypotheses were tested by measuring the standardized path coefficients among the variables. Table2 shows that the P values of H1, H2, H3, and H4 are not less than 0.001, indicating that the hypotheses do not pass the test; the P values of H5, H6, H7, and H8 are less than 0.001, indicating that the hypotheses pass the test. In addition, according to the magnitude of β values of the path coefficients of each hypothesis of the structural equation model, it can be seen that the effects of each variable on desire are, in descending order, relatedness, perceived behavioral control, competence, attitude, expected emotion, and subjective norm, while the effects on behavioral intention are, in descending order, perceived behavioral control and desire.Table 2
Structural equation model hypothesis testing results.
HypothesisPath relationshipPPath coefficientβConclusionH1Attitude ⟶ desire0.0240.124No supportH2Subjective norm ⟶ desire0.371-0.048No supportH3Expected emotion ⟶ desire0.2660.062No supportH4Competence ⟶ desire0.0150.131No supportH5Relevance ⟶ desire∗∗∗0.544SupportH6Perceived behavior control ⟶ desire∗∗∗0.417SupportH7Perceived behavioral control ⟶ behavioral intention∗∗∗0.492SupportH8Desire ⟶ willingness to act∗∗∗0.425Support


### 8.1.3. Mediation Effect Test

The mediation effect was examined using the nonparametric percentile Bootstrap method with the PROCESS 2.16 plug-in installed in SPSS 24.0 software. The results showed that desire mediated the relationship between perceived behavioral control, sense of relatedness, and willingness to act; desire partially mediated the relationship between perceived behavioral control and willingness to act and fully mediated the relationship between sense of relatedness and willingness to act (see Table3).Table 3
Results of the mediation effect test.
Effect coefficientStandardization coefficientTPIntermediate effect confidence interval (estimated upper limit)Intermediate effect confidence interval (estimated lower bound)Perceived behavior controlTotal effect0.6630.05811.367∗∗∗0.5480.777Direct effect0.5130.0598.678∗∗∗0.3970.629Indirect effect0.1500.0393.846∗∗∗0.0830.234Sense of relevanceTotal effect0.1970.0513.861∗∗∗0.0970.298Direct effect0.0420.0530.7870.432-0.0630.147Indirect effect0.1550.0344.559∗∗∗0.0990.233


## 8.1.1. Model Goodness-of-Fit Test

After completing validation of each variable, AMOS 24.0 was used to test the fit of the hypothesized model to the measured data. The fit indices of this model were as follows: CMIN/DF = 4.040, GFI = 0.727, AGFI = 0.671, RMSEA = 0.100, TLI = 0.839, and CFI = 0.847 (see Table1). Among them, CFI and TLI are very close to the critical value of 0.9, and RMSEA is greater than the critical value of 0.08, indicating that there is some room for improvement of the coefficients of the fitness indicators of this model. In this study, the model fit indices were corrected by the Bollen-Stine Bootstrap formula, and the corrected fit indices were as follows: CMIN/DF = 1.318, GFI = 0.937, AGFI = 0.921, RMSEA = 0.032, TLI = 0.987, and CFI = 0.984 [30].Table 1
Model fit test indicators.
Allocation indexCMIN/DFGFIAGFIRMSEATLICFIStandard value<5>0.9>0.8<0.08>0.9>0.9Index coefficient of this paper4.0400.7270.6710.1000.8390.847Corrected index coefficient0.3180.9370.9210.0320.9870.984


## 8.1.2. Hypothesis Testing

The hypotheses were tested by measuring the standardized path coefficients among the variables. Table2 shows that the P values of H1, H2, H3, and H4 are not less than 0.001, indicating that the hypotheses do not pass the test; the P values of H5, H6, H7, and H8 are less than 0.001, indicating that the hypotheses pass the test. In addition, according to the magnitude of β values of the path coefficients of each hypothesis of the structural equation model, it can be seen that the effects of each variable on desire are, in descending order, relatedness, perceived behavioral control, competence, attitude, expected emotion, and subjective norm, while the effects on behavioral intention are, in descending order, perceived behavioral control and desire.Table 2
Structural equation model hypothesis testing results.
HypothesisPath relationshipPPath coefficientβConclusionH1Attitude ⟶ desire0.0240.124No supportH2Subjective norm ⟶ desire0.371-0.048No supportH3Expected emotion ⟶ desire0.2660.062No supportH4Competence ⟶ desire0.0150.131No supportH5Relevance ⟶ desire∗∗∗0.544SupportH6Perceived behavior control ⟶ desire∗∗∗0.417SupportH7Perceived behavioral control ⟶ behavioral intention∗∗∗0.492SupportH8Desire ⟶ willingness to act∗∗∗0.425Support


## 8.1.3. Mediation Effect Test

The mediation effect was examined using the nonparametric percentile Bootstrap method with the PROCESS 2.16 plug-in installed in SPSS 24.0 software. The results showed that desire mediated the relationship between perceived behavioral control, sense of relatedness, and willingness to act; desire partially mediated the relationship between perceived behavioral control and willingness to act and fully mediated the relationship between sense of relatedness and willingness to act (see Table3).Table 3
Results of the mediation effect test.
Effect coefficientStandardization coefficientTPIntermediate effect confidence interval (estimated upper limit)Intermediate effect confidence interval (estimated lower bound)Perceived behavior controlTotal effect0.6630.05811.367∗∗∗0.5480.777Direct effect0.5130.0598.678∗∗∗0.3970.629Indirect effect0.1500.0393.846∗∗∗0.0830.234Sense of relevanceTotal effect0.1970.0513.861∗∗∗0.0970.298Direct effect0.0420.0530.7870.432-0.0630.147Indirect effect0.1550.0344.559∗∗∗0.0990.233


## 8.2. Analysis of the Results of Experiment 2

The intention results of learning under scenario 1 and scenario 2 for 12 to 24 units of different sizes are compared.The experimental results are shown in Table4, where the rate of decrease of Tn and Dn gradually slows down when the learning times gradually increase from 12 to 24, because the same may be accompanied by conflicts between learning as the learning times increase.(1)
The sums under scenario 2 are higher than those under scenario 1, which is because the learners in scenario 2 are scattered among the heap fields. The dynamic degree is higher, and the learning load distance is uneven.(2)
When the numbers of learner assignments are the same, the one under scenario 1 with 18 learning runs is close to the one under scenario 2 with 24 learning runs. Therefore, the assignment strategy can be adjusted according to the information collection of QC and learning and the preference of learner assignments, thus reducing the number of learning inputs.Table 4
Intention to learn in different scenarios.
Number of AGVSScenario 112166.74310.6348161.95295.3224159.13279.48Scenario 112217.35452.4818184.57429.5624164.03421.37As can be seen from Figure7, as the number of iterations of this paper’s model increases, the students’ learning intentions increase in different distributed scenarios, and the highest learning intention satisfaction can reach 138 GI. In different student interest context, this paper’s model can make students interested in online learning, which is our improvement of the model, digging deeper into the data features. It is also known that, according to each hypothesis of the knot model, it can be seen that the effect of each variable on desire from high to low is the sense of association, perceived behavioral control, sense of competence, attitude, expected emotion, and subjective norm, while the effect of the effect of willingness to act from high to low is the perceived behavioral control and desire.Figure 7
Student learning intention.


## 8.3. Analysis of the Results of Experiment 3

The intentional results of using a multifeature-based online learning assignment strategy with a shortest drive assignment strategy by varying the number of learner assignments are compared.As can be seen in Figure8, the total completion time with the multifeature online learning assignment strategy is approximately 13% greater than that with the shortest drive assignment strategyDn , but the difference between the multifeature online learning assignment strategy and the shortest drive assignment strategy remains approximately the same as the number of learners handled by the QC increases,Dn. The total completion time increases with the number of learners for both assignment strategies, and the total completion time with the multifeature online learning assignment strategy is about 12% shorter than the total completion time with the shortest drive assignment strategy on average, and the total completion time with the multifeature online learning assignment strategy saves about 21% when each QC handles about 225 TEUs on average.Figure 8
Intent results under different assignment rules.
(a)(b)As shown in Table5, the Tn differences of QC under the two assignment strategies are 3.9 s, 26.2 s, 33.8 s, 56.4 s, and so forth, and the Tn differences between the online learning assignment strategy and the shortest drive assignment strategy keep increasing as the number of learners increases, indicating that the online learning assignment strategy based on multiple features can effectively reduce the average assignment time and minimize the completion time of QC.Table 5
Average QC completion time under different assignment rules.
Number of containers handled by each QC100 (s)125 (s)150 (s)175 (s)200 (s)225 (s)Online learning assignment179.8174.2177.8172.2170.9169.3Shortest distance assignment183.7200.4211.6228.6248.4273.2From Figure9, we can know that the method in this paper helps students’ intentional interest in online learning the most, and the blue intentional interest points have been distributed above the other two schemes from the beginning of the iteration. It shows that our scheme is effective. Traditional CNN is simple, but it can also dig deeper into the data, so it also has some effect.Figure 9
Comparison of learning intentions of different programs.Using structural equation modeling, this study examined the pathway relationships among adults’ attitudes, subjective norms, expected emotions, competence, relatedness, perceived behavioral control, desires, and behavioral intentions to participate in online learning communities and analyzed the mechanisms that generate behavioral intentions to participate in online learning communities, focusing on the mechanisms that influence behavioral intentions. Four main conclusions were drawn from this study.First, attitudes, subjective norms, expected emotions, and competence did not play a significant antecedent role in the process of adults’ desire to participate in the online learning community. In particular, subjective norms had little effect on desire (β = −0.048), suggesting that the factors influencing adults’ participation in online learning communities were more focused on the community organizational environment and self-control.Second, a sense of connectedness has a positive impact on the desire to participate in an adult online learning community. A sense of connectedness can be viewed as an internal psychological experience for individuals, the key to which is to feel cared for and supported by other members of the organization. The acquisition of a sense of connectedness not only enhances identity and mutual understanding between individuals but also provides a sense of pleasure and satisfaction. Conversely, it can lead to feelings of isolation.Third, perceived behavioral control affects adults’ behavioral intentions not only directly but also indirectly through desires. The total effectβ of perceived behavioral control on behavioral intentions reached 0.663, indicating that perceived behavioral control is an important variable influencing the generation of behavioral intentions in adults.Fourth, adults’ desire to learn has a significantly positive effect on behavioral intentions.


## 9. Conclusions

Given that behavioral decisions to participate in learning are mostly accompanied by clear goals and students’ learning has distinctive self-directed characteristics, it is a new perspective and an important way to advance research in this field to investigate the intrinsic influencing mechanisms of adults’ behavioral willingness to participate in online learning communities with the guidance of goal-oriented behavior theory and self-determination theory. Based on distributed cognition, this paper designs a convolutional neural network model based on InceptionNet to analyze the intrinsic variables influencing willingness to participate in online learning communities from a systems view perspective, mainly attitudes, subjective norms, expected emotions, competence, sense of relatedness, desire, and perceived behavioral control. The relationship between these intrinsic variables and behavioral intentions was further explored through model hypotheses, scale development, reliability analysis, and testing of research hypotheses using structural equation modeling [31].



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