A study of fuzzy modeling analysis of factors influencing socially regulation of learning performance in an online environment

Abstract

Investigating the factors influencing the performance of social conditioning in the network environment is the core issue for improving academic performance. Through the search of existing literature, the paper analyzes the main factors that influence social conditioning learning in current research, and through the questionnaire survey and in-depth processing of the raw data, the advanced behavioral indicators related to learning are obtained and analyzed by Spearman correlation coefficient and fuzzy modeling in machine learning. The results showed that the twelve dimensions of motivation regulation, trust building, efficacy management, cognitive strategy, time management, goal setting, task strategy, peer support, team assessment, help seeking, environment construction, and team supervision were significantly related to group performance, with team supervision having a significant negative relationship with group performance. In addition, trust building, team supervision and environment construction were the main factors for online social learning, effectiveness management, task strategy, peer support and help-seeking were the secondary factors, while motivation regulation, cognitive strategies, goal setting and team assessment had little impact on the final performance. The findings have some implications for the optimization of social conditioning learning support services and the improvement of social conditioning learning performance.

Keywords

Learning analysis online collaborative learning socially modulated learning machine learning

1 Introduction

The computer-supported network learning environment breaks the boundaries of time and space, provides effective support for the cognitive, behavioral and emotional interaction of learners in different places, and effectively promotes online socially mediated learning among learners. Socially mediated learning in the online environment is one of the important organizational forms for the training of innovative talents in the “Internet+” era [1]. Socially Shared Regulation of Learning (SSRL), also known as Shared Regulation of Learning, is a further development of collaborative learning theories, from self-regulated learning in collaborative learning to the gradual integration of the effects of social interactions, which refers to the process of constructing knowledge through communication, discussion, and collaboration among group members [2, 3]. Socially mediated learning has been shown to be effective in facilitating collaborative learning in teaching [4] and improving the performance of collaborative team learning [5]. However, socially mediated learning does not happen easily; it is a complex social process in which learners need to conduct task analysis to develop appropriate plans or programs. Ask your peers for help when you encounter insurmountable difficulties in implementing a plan or program, and support your peers when they encounter difficulties. Of course, it is also important to maintain positive emotional interactions with peers and to monitor and reflect on the progress of the project. In addition, milestones should be evaluated at appropriate times to ensure that the final results are correct and complete. Therefore, in order to enhance the effectiveness of online collaborative learning, overcome the challenges of collaborative learning, and stimulate the occurrence of socially mediated learning in online environments, it is necessary to investigate how socially mediated learning occurs in online contexts. What factors influence its occurrence? How do these factors affect learner performance?

In recent years, with the widespread application of big data, fuzzy predictive model construction as the application and development of learning analysis in the field of education has received more and more attention and attention from researchers [6]. Learning analysis in education is the process of analyzing and interpreting data generated and collected from learners to assess their academic achievement, fuzzy predict their learning performance, and identify problems [7]. Therefore, it is effective to explore the factors affecting performance through fuzzy prediction.

Based on this, this study first combed the current research on social conditioning learning by reviewing the current research on social conditioning, taking into account the characteristics of social conditioning learning, and using small groups as the unit of analysis to sort out the influencing factors of social conditioning learning in a network environment. Then, the strength of each influencing factor was determined using Spearman correlation coefficient and fuzzy modeling analysis in machine learning.

2 Related work

At present, there are numerous studies on the influencing factors of socially mediated learning at home and abroad, and the analysis approaches and influencing factors adopted by scholars for socially mediated learning in different learning contexts vary to some extent. Therefore, this study reviews the existing literature in four aspects: researchers, research contexts, influencing factors/elements, and research methods (see Table 1).

Table 1
Current Status of Research on Socially Adjusted Learning

Researcher Research context Analysis dimensions/elements Research methods

Toni and Lisa [8] Face-to-face Planning, monitoring, behavioral engagement Video analysis

Serka and Mary [9] Face-to-face Emotions, motivation, metacognition, planning, monitoring, evaluation Video analysis

Anna et al. [10] Face-to-face Program and goal setting, content monitoring, task monitoring, task evaluation, content assessment Content analysis

Jonna et al. [11] Network environment Cognitive regulation, motivational regulation, time management, and environmental structure Content analysis, Survey Method

Sanna et al. [12] Network environment Mission understanding, monitoring, evaluation, motivation, strategy, planning Learning analysis

Jaana et al. [13] Face-to-face Planning, monitoring, adaptation or evaluation, cognition, motivation, and emotion Video analysis

Zheng [14] Face-to-face Goal setting, planning, strategy, monitoring, evaluation and reflection, metacognition Survey Method

Valeska et al. [15] Face-to-face Metacognitive regulation, planning, monitoring, supervision, and evaluation Video analysis

Muhterem et al. [16] Face-to-face Cognitive, behavioral, motivational, or emotional Video analysis

Chai Yangli et al. [17] Network environment Information sharing, consultation and discussion, comprehensive commentary, problem generation, problem discussion, problem summarization Content analysis, Survey Method

Juan et al. [18] Network environment Mission analysis, planning, refinement, monitoring Learning analysis

Researcher	Research context	Analysis dimensions/elements	Research methods
Toni and Lisa [8]	Face-to-face	Planning, monitoring, behavioral engagement	Video analysis
Serka and Mary [9]	Face-to-face	Emotions, motivation, metacognition, planning, monitoring, evaluation	Video analysis
Anna et al. [10]	Face-to-face	Program and goal setting, content monitoring, task monitoring, task evaluation, content assessment	Content analysis
Jonna et al. [11]	Network environment	Cognitive regulation, motivational regulation, time management, and environmental structure	Content analysis, Survey Method
Sanna et al. [12]	Network environment	Mission understanding, monitoring, evaluation, motivation, strategy, planning	Learning analysis
Jaana et al. [13]	Face-to-face	Planning, monitoring, adaptation or evaluation, cognition, motivation, and emotion	Video analysis
Zheng [14]	Face-to-face	Goal setting, planning, strategy, monitoring, evaluation and reflection, metacognition	Survey Method
Valeska et al. [15]	Face-to-face	Metacognitive regulation, planning, monitoring, supervision, and evaluation	Video analysis
Muhterem et al. [16]	Face-to-face	Cognitive, behavioral, motivational, or emotional	Video analysis
Chai Yangli et al. [17]	Network environment	Information sharing, consultation and discussion, comprehensive commentary, problem generation, problem discussion, problem summarization	Content analysis, Survey Method
Juan et al. [18]	Network environment	Mission analysis, planning, refinement, monitoring	Learning analysis

Table 1 shows that current scholars have mainly explored the influences of face-to-face collaborative learning or socially regulated learning in the network context through surveys, video analysis, content analysis, learning analysis, and other methods. Most of these studies focus on the influence of factors related to learners’ regulatory behaviors, while often neglecting the influence of learners’ internal psychological regulation. Socially regulated learning is the development of self-regulated learning based on social interactions; therefore, the factors influencing self-regulated learning should be related to socially regulated learning. Self-regulated learning is well established and is influenced by factors such as cognitive strategies, motivational beliefs, self-efficacy, environmental constructs, help-seeking, goal setting, time management, task strategies, and self-assessment. Cognitive regulation, motivational regulation, time management, environmental constructs, task strategies, and assessment are factors that have been shown to have a significant impact on socially regulated learning in online environments [19 –22]. However, whether factors such as efficacy, help-seeking, and goal-setting affect the performance of socially mediated learning in an online environment remains to be demonstrated. In addition, socially mediated learning is a complex social process that requires group members to construct knowledge through mutual negotiation, cooperation, and monitoring. The entire process is influenced by the co-construction and sharing of task understanding, goals, strategies, cognitive strategies, and trust among group members, as well as the monitoring and regulation of motivation, emotion, and behavior [23].

In summary, the current research on social conditioning learning in the network environment lacks systematic analysis methods, which makes it difficult to carry out a comprehensive analysis; on the other hand, when analyzing social conditioning learning in the network environment, more attention is paid to the mining and analysis of behavioral data, while neglecting the internal psychological regulation of learners. In order to improve the analysis dimension and method of social conditioning learning in network environment, this paper, based on the literature, sorts out the important factors that influence social conditioning learning, builds an analysis framework for social conditioning learning, and further determines the influence strength of each influencing factor through Spearman correlation coefficient and Ridge regression modeling analysis in machine learning.

3 Analysis framework

This study refers to the above-mentioned research findings on social conditioning in the literature, and after normalizing the basic raw data, and combining the factors affecting performance from the literature, the study selects twelve dimensions of motivation regulation, trust building, effectiveness management, cognitive strategies, goal setting, time management, task strategies, peer support, team monitoring, team evaluation, help-seeking, and environmental constructs. As an indicator for analyzing the academic performance of online socially regulated learning and based on the specific performance of socially regulated learning behaviors, a framework for analyzing socially regulated learning in the online environment was constructed (see Table 2). Each dimension is described as follows:

Table 2
Framework for analyzing social conditioning learning in a network environment

Code The concrete manifestation of social regulation learning behavior Collection Methods

MR Motive Regulation Discuss the value or use of the learning content Active collection

Break down the learning objectives and tasks assigned by the instructor according to their own circumstances Active collection

Make an effort to learn even if you are not interested in the content Active collection

TB Trust Building Share your new discoveries and learning experiences Active collection

Share individual learning plans and progress Active collection

Share your feelings and opinions among the group Active collection

EM Efficacy Management Encourage and support each other in the group Active collection

Compliment each other on what they do well Active collection

Adopt and support team members in making sound recommendations Active collection

CS Cognitive Strategies Repeated viewing of instructional materials and instructional videos Passive collection

Prepare questions in advance of joining chat rooms and discussions Active collection

Rehearsal during the group online presentation Active collection

Combining different sources of information (people, websites, books) when studying Active collection

Try to relate new knowledge to what you already know Active collection

GS Goal Setting Negotiating the group’s learning objectives Active collection

Shared Personal Learning Goals Active collection

Adapting learning objectives to your own circumstances and realities during the learning process Active collection

TM Time Management Develop a reasonable study plan based on the learning task Passive collection

Appropriate scheduling of task completion according to the study plan Passive collection

Allocation of study time to more specific learning levels of difficulty Passive collection

MS Mission Strategy Co-develop or adapt learning plans Active collection

Assign tasks based on team members’ interests and strengths Active collection

Study and complete tasks according to assignment and learning plan Active collection

Repeatedly revise and refine the group’s work to achieve a better grade Passive collection

Regularly discuss group learning tasks and share learning experiences Active collection

PA Peer Assistance Offering to help group members Passive collection

Reaching out to other members for help Passive collection

TS Team Supervision Proactively give prompts or warnings to members who are passive-aggressive. Passive collection

Proactively share progress on tasks to ensure that the learning program is carried out effectively Passive collection

Raise the need when a team member is unable to complete a task on time Passive collection

Monitor each other’s progress on learning tasks Passive collection

TA Team Assessment Evaluate each other’s learning task outcomes and suggest changes accordingly Active collection

Communicate with other groups to see how they are doing in this course Active collection

Comparing the learning outcomes of other groups Active collection

Evaluate your group’s learning and completion of tasks Active collection

HS Help Seeking Get help from an instructor or teaching assistant Passive collection

Seeking solutions to problems through Baidu, Zhihu, Sticky Bar, etc. Active collection

Discuss and share learning problems with other groups Passive collection

EC Environment Construction Choose an appropriate online communication and learning tool (QQ, Ding Talk, WeChat, etc.) Active collection

Choose a period of time when people’s attention is focused on learning to communicate Active collection

A quiet and stable online learning environment for communication Active collection

Code	The concrete manifestation of social regulation learning behavior	Collection Methods
MR	Motive Regulation	Discuss the value or use of the learning content	Active collection
		Break down the learning objectives and tasks assigned by the instructor according to their own circumstances	Active collection
		Make an effort to learn even if you are not interested in the content	Active collection
TB	Trust Building	Share your new discoveries and learning experiences	Active collection
		Share individual learning plans and progress	Active collection
		Share your feelings and opinions among the group	Active collection
EM	Efficacy Management	Encourage and support each other in the group	Active collection
		Compliment each other on what they do well	Active collection
		Adopt and support team members in making sound recommendations	Active collection
CS	Cognitive Strategies	Repeated viewing of instructional materials and instructional videos	Passive collection
		Prepare questions in advance of joining chat rooms and discussions	Active collection
		Rehearsal during the group online presentation	Active collection
		Combining different sources of information (people, websites, books) when studying	Active collection
		Try to relate new knowledge to what you already know	Active collection
GS	Goal Setting	Negotiating the group’s learning objectives	Active collection
		Shared Personal Learning Goals	Active collection
		Adapting learning objectives to your own circumstances and realities during the learning process	Active collection
TM	Time Management	Develop a reasonable study plan based on the learning task	Passive collection
		Appropriate scheduling of task completion according to the study plan	Passive collection
		Allocation of study time to more specific learning levels of difficulty	Passive collection
MS	Mission Strategy	Co-develop or adapt learning plans	Active collection
		Assign tasks based on team members’ interests and strengths	Active collection
		Study and complete tasks according to assignment and learning plan	Active collection
		Repeatedly revise and refine the group’s work to achieve a better grade	Passive collection
		Regularly discuss group learning tasks and share learning experiences	Active collection
PA	Peer Assistance	Offering to help group members	Passive collection
		Reaching out to other members for help	Passive collection
TS	Team Supervision	Proactively give prompts or warnings to members who are passive-aggressive.	Passive collection
		Proactively share progress on tasks to ensure that the learning program is carried out effectively	Passive collection
		Raise the need when a team member is unable to complete a task on time	Passive collection
		Monitor each other’s progress on learning tasks	Passive collection
TA	Team Assessment	Evaluate each other’s learning task outcomes and suggest changes accordingly	Active collection
		Communicate with other groups to see how they are doing in this course	Active collection
		Comparing the learning outcomes of other groups	Active collection
		Evaluate your group’s learning and completion of tasks	Active collection
HS	Help Seeking	Get help from an instructor or teaching assistant	Passive collection
		Seeking solutions to problems through Baidu, Zhihu, Sticky Bar, etc.	Active collection
		Discuss and share learning problems with other groups	Passive collection
EC	Environment Construction	Choose an appropriate online communication and learning tool (QQ, Ding Talk, WeChat, etc.)	Active collection
		Choose a period of time when people’s attention is focused on learning to communicate	Active collection
		A quiet and stable online learning environment for communication	Active collection

Motive Regulation: Motive regulation refers to activities in which individuals act purposefully to initiate, maintain, or supplement their willingness to start work, provide motivation for work, or accomplish a specific goal [24];

Trust Building: Trust is an important influence on individual cognition and behavior and is a prerequisite for cooperation. Mutual trust between group members facilitates communication during collaboration and effectively enhances collaborative learning;

Efficacy Management: Self-efficacy is the degree of confidence people have in their ability to use the skills they have to perform a task [25]. Efficacy management refers to the regulation of self-efficacy among group members through praise and the setting and achievement of milestones;

Cognitive Strategies: Cognitive strategies are skills that learners use to select and regulate their internal processes of attention, learning, memory, and thinking [26]. Learners differ in their learning experiences, physiological characteristics, and personality traits, which determine the application of cognitive strategies;

Goal Setting: Goal setting refers to learners setting or adjusting the group’s learning goals in accordance with their own ability levels and the external environment. Clear and appropriate learning goals can help learners improve their learning efficiency, and when learners reach their learning goals, they can gain the joy of success and increase their sense of self-efficacy;

Time Management: Time management refers to the planning and control activities that an individual undertakes in order to effectively use time resources [27];

Mission Strategy: Task strategies refer to the rules, methods, techniques, and controls that learners use to accomplish learning tasks. Examples include plan making, task analysis, task assignment, plan adjustment, and experience exchange;

Peer Assistance: Peer assistance is a strategy of facilitated learning that involves proactively helping and supporting other peers through peers of equal or matched status [28];

Team Supervision: Team supervision refers to a type of moderating behavior used by group members in the learning process to ensure the smooth implementation of plans and schedules. For example, sharing progress on tasks, managing time, and negotiating team members’ proposals or actions;

Team Assessment: Team assessment refers to team members’ reflection on the process and results of social regulation learning, and is divided into process evaluation and summative evaluation. The process evaluation refers to the team members’ assessment of the task progress and shared regulation process at different stages of collaborative learning; the summative evaluation refers to the team members’ need to reflect on whether their task outcomes have met their goals [29];

Help Seeking: Help seeking refers to the process of seeking academic assistance from teachers, other groups, or learning communities and solving problems and challenges encountered in the learning process by learning from the experiences or methods of others. Online socially mediated learning is a form of learning in distance education that is characterized by the separation of the act of teaching from the act of learning [30]. This characteristic leads to additional learning difficulties and challenges that learners inevitably face during the social conditioning learning process. It is therefore important to use learning platforms to seek academic help from teachers, other groups or learning communities, and to gain external support by learning from others’ experiences or approaches;

Environment Construction: Environment construction refers to the process by which learners adjust their learning environment and actively construct a stable, non-disturbing, and comfortable learning environment to enhance their attention and learning effectiveness. Constructing a learning environment is especially necessary in online socially mediated learning because the attention span of learners in an e-learning environment is poor, and the entertainment and communication software in the online environment can interfere with the learning process [31].

Social regulation learning is a complex learning process, and a single source of data cannot guarantee coverage of all influences. Therefore, in order to effectively avoid errors caused by cognitive biases, this study collects objective data on learners’ psychological, behavioral, and environmental social conditioning learning behaviors through a combination of active and passive collection, based on existing measurement methods and taking into account the characteristics of social conditioning learning in a network environment. In particular, active collection refers to the collection of behavioral data through reports completed by students after each week of study. For example, the number of times they shared their individual learning plans and progress during the week, the number of times they negotiated group learning goals, etc. Passive collection refers to the extraction of relevant behavioral data from the log files of the learning management system. For example, the number of times instructional materials and videos were viewed, the number of times group work was revised and improved, the number of times group members monitored each other’s progress on learning tasks, and other behavioral data reflecting socially regulated learning.

4 Data collection, processing, results and analysis

4.1 Data collection

The study was conducted over a 14-week period during the last academic year of 2020 in the context of University A’s “Computer Animation” course for 76 students in the second semester of their first year of university with some experience in online collaborative learning. These students were randomly divided by the teacher into 24 study groups. Throughout the learning period, learners conduct online self-study, live research, offline experiments and online tutorials in small groups through the learning management system (Er Ya platform + Ding Talk), which are the four main components of socially mediated learning. Online self-study means that the instructor sends messages through the learning management system about course announcements, learning objectives, group tasks, etc., and the group receives the messages and completes the tasks as required. Live research refers to learning activities such as teacher quizzes, problem-solving workshops, and work presentations through online meetings via the learning management system. Offline labs are self-assessments that are conducted by learning groups under the guidance of a “lab guide” and through a learning management system. Online tutoring means that teachers and teaching assistants (specially trained older students) are available online to answer questions and provide one-on-one support to the groups. The learning team utilized the learning management system for online social conditioning learning and communication with peers and teachers throughout the learning process, and filled out weekly reports at the end of each week to report on the group’s and individual’s learning for the week.

4.2 Data processing

At the end of the experiment, the study analyzed the behavioral data of 24 groups of students in the learning management system and related data in the weekly reports using the analysis framework and measurement tools constructed in this study. In order to eliminate the effects of magnitude and the magnitude of the variables’ own variation, we standardized the data via Min-max standardization and then explored the relationship between the various factors of online social conditioning learning and group performance via Spearman correlation coefficient analysis. However, correlation is not a proxy for causality, so we further determined the impact of each influencing factor on performance through Ridge regression modeling analysis.

4.2.1 Spearman correlation analysis

Correlation analysis is a measure of how closely two variable factors are related. In order to identify the influences that are closely related to learner performance we calculated the correlation between various types of SSRL regulation processes and performance through Spearman’s correlation analysis. The formula for calculating this is as follows. $ρ = \frac{\sum_{i = 1}^{N} (x_{i} - \bar{x}) (y_{i} - \bar{y})}{\sqrt{\sum_{i = 1}^{N} {(x_{i} - \bar{x})}^{2}} \sqrt{\sum_{i = 1}^{N} {(y_{i} - \bar{y})}^{2}}}$ (1) where, xi is the value of feature x of the i-th sample in the training set; $\hat{x}$ is the average of the feature over all samples; yi is the value of feature y of the i-th sample; $\hat{y}$ is its mean value, i = 1, 2,...., N, N is the total number of samples in the training set. In order to determine whether the factors associated with performance significantly affect their performance, a P-Value test is used to measure the significance of each factor’s correlation with performance. P indicates the likelihood that the correlation is due to chance, with P < = 0.05 for a correlation and P < = 0.01 for a significant correlation.

4.2.2 Ridge regression modeling analysis

Regression analysis is often used to judge the causal relationship between independent variables and dependent variables. In this study, ridge regression, which can reduce the influence of multiple collinearity, is used as our analysis method. Ridge regression is a modified least-squares estimation method. By abandoning the unbiased nature of the least-squares method, it is possible to obtain more realistic and reliable regression coefficients at the cost of losing part of the information and decreasing the accuracy, as well as reducing the variance of the fit and improving its future prediction [32]. The formula is as follows. $L (ω) = {∥ \hat{y} - y ∥}_{2}^{2} + λ {∥ ω ∥}_{2}^{2}$ (2) where ω is the regression coefficient and $\hat{y}$ is the predicted value.

The training set is used to train the model, while the test set is used to evaluate the model’s strengths and weaknesses, and the above 12 factors are used to construct the regression model.

4.3 Data results

4.3.1 Results of spearman correlation analysis

In order to identify which factors are significantly related to performance in the network environment, we calculated the correlation coefficients between each factor and performance through Spearman’s correlation analysis, and finally visualized the statistical results as a correlation coefficient matrix heat map (see Fig. 1). The positive and negative correlation coefficients in Fig. 1 correspond to the positive and negative correlation between the two factors. The larger the absolute value of the correlation coefficient, the darker the color of the corresponding cell and the stronger the correlation between the factors. Figure 1 shows that mission strategy has the highest correlation coefficient with effectiveness management (ρ= 0.88), while time management has the lowest correlation coefficient with goal setting (ρ= –0.002). In addition, motive regulation (ρ= 0.47), trust building (ρ= 0.64), efficacy management (ρ= 0.52), cognitive strategies (ρ= 0.53), goal setting (ρ= 0.37), time management (ρ= 0.49), mission strategy (ρ= 0.51), peer assistance (ρ= 0.43), team supervision (ρ= –0.67), team assessment (ρ= 0.34), help seeking (ρ= 0.51), and environmental construction (ρ= 0.61) were all related to performance, with absolute values of correlation coefficients > = 0.34. It is noteworthy that team supervision is negatively correlated with performance, while the rest of the factors are positively correlated with performance.

Fig. 1

Correlation Analysis of Factors and Performance Heat Map (MR: Motive Regulation; TB: Trust Building; EM: Efficacy Management; CS: Cognitive Strategies; GS: Goal Setting; TM: Time Management; MS: Mission Strategy; PA: Peer Assistance; TS: Team Supervision; TA: Team Assessment; HS: Help Seeking; EC: Environment Construction; AC: Achievement).

To further determine the significance of each factor’s correlation with performance, we calculated the P-Value, which represents the degree of correlation between each factor and performance (see Table 3 for the statistical results). Table 3 shows that trust building, efficacy management, cognitive strategies, team supervision, and environmental construction all had P-Values < = 0.01, while motive regulation, goal setting, time management, peer assistance, and help seeking all had P-Values < = 0.05.

Table 3

Significance of the correlation between each factor and performance

Dimensions	MR	TB	EM	CS	GS	TM	MS	PA	TS	TA	HS	EC
P	0.021	0.001	0.009	0.007	0.077	0.016	0.010	0.037	0.000	0.107	0.011	0.001
MR: Motive Regulation; TB: Trust Building; EM:Efficacy Management; CS: Cognitive Strategies; GS: Goal Setting; TM: Time Management; MS: Mission Strategy; PA: Peer Assistance; TS: Team Supervision; TA: Team Assessment; HS: Help Seeking; EC: Environment Construction

4.3.2 Ridge regression modeling analysis results

In order to further analyze the effect of each factor on performance, we construct a Ridge regression model by machine learning, which is a good fit and can predict the performance of social conditioning learning in a network environment (R²= 0.85, MAE = 1.14, MSE = 2.07, RMSE = 1.44). In order to obtain the degree of influence of each factor on performance, we calculated the regression coefficients for each factor, and the visualization results are shown in Fig. 2. Figure 2 shows that trust building (α= 1.66), team supervision (α= –2.75), and environmental construction (α= 2.80) had relatively large effects on performance; efficacy management (α= 1.02), mission strategy (α= 1.11), peer assistance (α= 1.01), and help seeking (α= 1.37) had the next largest effects on performance; motive regulation (α= 0.43), cognitive strategies (α= 0.52), goal setting (α= 0.97), and team assessment (α= 0.88) had the least effect on performance.

Fig. 2

Comparison of regression coefficients for each factor (MR: Motive Regulation; TB: Trust Building; EM: Efficacy Management; CS: Cognitive Strategies; GS: Goal Setting; TM: Time Management; MS: Mission Strategy; PA: Peer Assistance; TS: Team Supervision; TA: Team Assessment; HS: Help Seeking; EC: Environment Construction).

In order to better visualize the impact of each factor on performance, we plotted a linear regression model of each factor and academic performance (see Fig. 3). Figure 3 shows that the absolute values of the slopes of trust building, team supervision, and environmental construction are greater than 1.5, the absolute values of the slopes of efficacy management, mission strategies, peer assistance, and help seeking are between 1 and 1.5, and the absolute values of the slopes of motive regulation, cognitive strategies, goal setting, and team assessment are less than 1. It is noteworthy that as the frequency of team supervision increases it leads to a decrease in academic achievement in the online environment.

Fig. 3

Linear Regression Model of Factors and Performance (MR: Motive Regulation; TB: Trust Building; EM: Efficacy Management; CS: Cognitive Strategies; GS: Goal Setting; TM: Time Management; MS: Mission Strategy; PA: Peer Assistance; TS: Team Supervision; TA: Team Assessment; HS: Help Seeking; EC: Environment Construction).

4.4 Data analysis

The results of Spearman correlation analysis and significance analysis show that trust building, efficacy management, cognitive strategies and environment construction have a significant positive correlation with group achievement, which is consistent with the existing research results [33]. In addition, the data showed that five factors, motive regulation, goal setting, time management, peer assistance, and help seeking, were also significantly and positively related to group achievement. It is noteworthy that there is a significant negative relationship between team supervision and group achievement, which is somewhat different from the results of existing studies [34]. One-on-one interviews were conducted with the teams to explore the reasons for this discrepancy. From the interviews, we found that each team’s moderating behavior in terms of team supervision consisted of reminding the team members to submit the tasks on time. The lower-achieving team members’ lower self-control tended not to complete the tasks on time, leading to an increased probability of triggering the regulative behavior of team monitoring. The higher-performing teams are self-monitoring and self-regulating, each completing their assigned learning tasks successfully and on time, and thus requiring little supervision or intervention from others.

The results of the regression analysis showed that trust building, team supervision, and environmental construction were the main factors for online social regulation of learning, while efficacy management, mission strategies, peer assistance, and help seeking were secondary factors. As far as trust building is concerned, the findings indicate the important role of trust in team members’ collaboration, and the more trust-building regulatory behaviors among team members, the stronger the sense of trust among members, i.e., trust building is an effective guarantee of social regulation of learning [35]. In terms of team supervision, the findings suggest that self-regulation and self-control play an important role in online socially mediated learning, and that reducing unnecessary supervisory behaviors and enhancing learners’ self-control is the key to improving socially mediated learning performance. In terms of environment construction, the online socially mediated learning environment is a complex and changing environment that can be controlled by the learner. A comfortable, non-intrusive, and stable online social learning environment is an effective guarantee for the smooth implementation of social learning.

In summary, teachers should fully consider the twelve factors of motive regulation, trust building, efficacy management, cognitive strategies, time management, goal setting, mission strategies, peer assistance, team supervision, team assessment, help seeking, and environment construction when conducting social regulation learning in an online environment, and focus on the group’s performance in the three aspects of trust building, team supervision, and environment construction in the process of social regulation learning, and provide appropriate guidance or intervention to promote performance improvement according to the learners’ behaviors.

5 Summary

Learners exist in a complex social network, and their learning cannot be separated from social interaction. Therefore, clarifying the influencing factors of socially mediated learning is the key to ensuring the smooth implementation of mediation activities such as establishing common learning goals, reaching consensus on task understanding, and negotiating and implementing plans and strategies, as well as promoting discussion and cooperation among learners to achieve knowledge construction. In this study, twelve factors influencing social modulation learning in network environments were identified through a review of the relevant studies on social modulation learning. Then, based on the characteristics of the network environment and social conditioning learning, an analysis framework for socially shared conditioning learning in the network environment and corresponding measurement tools are proposed. Finally, the influence of each factor on performance is analyzed by Spearman correlation analysis and Ridge regression model.

There are two limitations to this study. First, although this study obtained and analyzed data from self-report and learning management system data to ensure richness and diversity of the data, the study was based on a specific course and the sample was relatively homogeneous, which may reduce the generalizability of the findings.

In spite of these limitations, this study is highly informative. The results of this study showed that eleven factors, including motive regulation, trust building, efficacy management, cognitive strategies, time management, goal setting, mission strategies, peer assistance, team assessment, help seeking, and environment building, were significantly and positively related to performance, while team supervision was significantly and negatively related to performance. In addition, trust building, team supervision, and environment construction are major factors in online socially mediated learning; efficacy management, mission strategies, peer assistance, and help seeking are minor factors, while motive regulation, cognitive strategies, goal setting, and team assessment have little impact on the final performance. Therefore, when conducting socially mediated learning in an online environment, teachers should fully consider the various influencing factors of socially mediated learning, and focus on learners’ guidance and interventions in trust building, team supervision, and environment construction to promote performance improvement. Specifically, the instructor should provide opportunities for communication between learning groups to promote mutual understanding and trust among the group members. Secondly, since online learning does not have the constraints of offline learning classroom management, many online learners unconsciously procrastinate in their courses. Although teams can monitor each other, their monitoring does not work. Therefore, enhancing learners’ motivation and awareness of self-monitoring and converting other discipline into self-discipline is the most important task in socially mediated learning in online environments. In addition, the complexity and variability of the online learning environment makes it difficult to keep the learning environment in the most suitable state for the learners at all times, so strengthening the learners’ awareness of the regulation of the online socially regulated learning environment to ensure the comfort and stability of the learning environment is also the focus of teachers’ attention.

In future studies, we will expand the sample size and increase the diversity of the sample to extend the generalizability of this study. At the same time, we will further research on the collection and analysis of social conditioning learning behavior data, in order to achieve real-time monitoring of the online social conditioning learning process and visualization of behavior data, and to provide a simpler, more intuitive and convenient analysis tool for teachers’ targeted instruction and intervention.

Footnotes

Acknowledgments

This work was supported by the National General Cultivation Project of China West Normal University (Project No. 19B032): “Research and Application Demonstration of the Core Key Technologies of Mixed Reality-Based General Technology Experimental Training System”.

The anonymous reviewers have also contributed considerably to the publication of this paper. I would like to thank the anonymous reviewers who helped to improve the paper.

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