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Article

Research on the Influence of Socially Regulated Learning on Online Collaborative Knowledge Building in the Post COVID-19 Period

by
Jia Lu
1,2,
Xiaohui Chen
1,
Xiaodan Wang
2,
Rong Zhong
1 and
Hanxi Wang
3,*
1
School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
2
School of Computer Engineering, Guilin University of Electronic Technology, Beihai 536000, China
3
School of Geographical Sciences, Harbin Normal University, Harbin 150025, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(22), 15345; https://doi.org/10.3390/su142215345
Submission received: 11 October 2022 / Revised: 14 November 2022 / Accepted: 15 November 2022 / Published: 18 November 2022
(This article belongs to the Section Sustainable Education and Approaches)
Browse Figures
Review Reports Versions Notes

Abstract

:
Online teaching has become an important initiative to maintain normal educational order in the post COVID-19 period. However, learners face multiple challenges in the online learning process, which cannot be successfully carried out without the support of socially regulated learning (SoRL). This study designed SoRL intervention strategies from the perspective of shared metacognitive scripts. A total of 77 undergraduate students participated in this study and were randomly assigned into experimental and control groups. The students in the experimental group received an SoRL intervention, and the students in the control group learned with the traditional online collaborative learning approach. The results showed that there was variability in the supply of SoRL intervention scripts and the actual selection status of the learners. The regulation foci activated in this study were time management, content monitoring, and atmosphere activation. Atmosphere activation drove collaborative learning activities to continue. Time management and content monitoring drove collaborative knowledge building (CKB) to a deeper level. This study is of great significance in revealing the impact mechanism of SoRL on CKB.

1. Introduction

The outbreak of COVID-19 made global online teaching develop rapidly in December 2019. Countries around the world have successively issued relevant guidance to promote the integrated application of information technology and teaching [1]. In July 2021, China released the 2020 National Research Report on minors’ Internet use, which showed that 93.6% of minors studied through online classes during the COVID-19 pandemic. Similarly, the proportion of the online completion of higher education in China was relatively large [2].
The control and prevention of the COVID-19 global pandemic situation showed normal characteristics in the post-COVID-19 period [3]. Affected by the intermittent outbreak of COVID-19, online teaching has become a normal backup means for countries around the world to maintain normal teaching and keep it in order [4]. In the spring semester of 2022, the COVID-19 affected 28 provincial-level regions in China. In some areas, all primary and secondary school courses have been transferred online, and some universities have also begun to implement online teaching. Online teaching has provided a guarantee to protect the safety of teachers and students, but it also has had a certain negative impact on them [5]. For example, one survey showed that it had been difficult for teachers to obtain a timely and omni-directional monitoring of students in the process of online teaching, which had led to an online teaching burnout. Meanwhile, students felt that online learning was increasingly impersonal, isolated, non-interactive, and boring [6,7]. In order to make up for the negative impact of the time and space separation between teachers and students in their teaching interaction, teachers tried to use online collaboration to carry out their daily teaching activities [8,9]. However, learners faced more complex and diversified difficulties and challenges in cognition, metacognition, motivation, and emotion in the online context [10]. How to carry out appropriate regulatory learning to deal with the above challenges effectively has become the key to improving the quality of online teaching.
In the context of socially regulated learning (SoRL), the role of students is to monitor task objectives, use tools and methods to strategically monitor the learning process as well as progress, optimize their task performance, and regulate the learning process constantly [11,12]. SoRL focuses on collaborative learning motivation, emotional, and collaborative learning strategies, such as task understanding and shared metacognitive strategies, which are the key elements affecting the performance of collaborative learning [13]. SoRL includes self-regulated learning (SRL), co-regulated learning, and socially shared regulated learning [11]. Some research had found that there was a positive correlation between SoRL and learning performance [14]. For example, SoRL can support learners to reflect on their own mental models, optimize problem-solving strategies, and improve the collaborative learning performance [15,16]. Students can establish a common understanding of the task, clarify their common ideas, and maintain the ongoing knowledge building with the support of relevant intervention techniques [17]. Mobile SRL systems can significantly improve the students’ learning performance and SRL skills [18].
Collaborative knowledge building (CKB) reflects the performance of online collaborative learning, which is based on the view of the learning knowledge creation metaphor [19]. The purpose is to develop public knowledge that is valuable to the community [20,21]. This type of knowledge is independent of tangible objects and the human thinking process, which exists in a way that philosopher Karl Popper described as “world 3” [22]. CKB emphasizes the importance of ideas and encourages students to think and explore problems like scientists [23]. Learners continue to promote knowledge creation through the generation and continuous improvement of valuable ideas to the community [24]. The cognitive mechanism of CKB is composed of twelve principles on ideas, communities, and means, among which the principles of ideas include real ideas, authentic problems, improvable ideas, idea diversity, and rising above; the principles of communities include epistemic agency, community knowledge, democratizing knowledge, symmetric knowledge advancement, and pervasive knowledge building; and the principles of means include constructive uses of authoritative sources, knowledge building discourse, and concurrent, embedded, and transformative assessment [25].
CKB emphasizes the idea-centered, and opposes the task-centered teaching method [23]. However, it is often difficult for learners to achieve a deep state of knowledge building in the absence of the effective management of tasks and collaborative processes [26]. For example, students discussed theoretical issues for the first three weeks and then designed curricula for the second four weeks in an online knowledge-building activity about “information technology in school curricula”. However, students did not achieve an in-depth online interaction which would be related to the 12 principles of the implementation of knowledge building [27]. Some researchers stressed that learners should strengthen the application of the twelve principles in order to improve the level of CKB [28].
Some studies proposed to explore the impact of collaboration scripts on the CKB results from the perspective of providing scaffolding [29,30,31]. The forms of scaffolding mainly included process evaluation scaffolding, structured scripts, and role assignment scaffolding. A formative evaluation of the embedded activities required that the evaluation was viewed as a part of knowledge building to identify problems in ongoing CKB activities [25]. Collaboration scripts provide the necessary scaffolding for the collaborative process [32]. However, structured scripts may reduce the emergence of diverse ideas. Therefore, learners should focus on the flexibility of scripts [33]. The role of members has impacted on the level of CKB in the knowledge building community. Role rotation promotes the continuous reconstruction of learners’ ideas [34]. A shared vision among team members can motivate more collaboration among members in the initial stages of collaboration. Combined with the requirement of knowledge building for problem authenticity, the vision should be grounded in the students’ zone of proximal development. Guided by a shared vision, learners came up with authentic ideas based on their own experiences and revised these ideas through continuous interactions [35].
To sum up, the above factors only reflect the impact of some elements of SoRL on the level of CKB, and lack of relevant research based on the need for integration. The objective of this research is further clarifying the mechanism of the influence of SoRL on CKB. This study puts forward and solves the following problems:
(1).
What are the characteristics of regulation focus under the influence of intervention scripts?
(2).
What are the levels and behavioral characteristics of CKB under the influence of intervention scripts?
(3).
What is the influence mechanism of SoRL on CKB?
This study is of great significance to explore the influence mechanism of SoRL on CKB and to further clarify the relationship between them.

2. Materials and Methods

The research adopted the experimental method. The author selected students from a comprehensive university to participate in the study. The author designed the experimental process. The experimental process is shown in Figure 1.

2.1. Participants

This study was conducted in the context of higher education, with participants from a comprehensive university in southwest China. A total of 77 students participated in the Digital Media Video Creation Course, and their average age was 20 years old (SD = 1.5). The power analyses for a priori sample sizes for t-tests and F-tests indicated the minimum sample size of n = 68 for an anticipated large effect size, a statistical power level of β = 0.90, and an α-level of 0.05 [36]. Therefore, the sample size of 77 participants was a statistically relatively fair size. All participants were randomly divided into 5 experimental groups and 5 control groups. Each group had 6–8 students. The experimental group and the control group have no significant differences in gender (X2 = 2.268, p = 0.132 > 0.05), the basic knowledge reserve of digital media video creation (t = 0.682, p = 0.498 > 0.05), and the socially regulated learning ability (t = 1.032, p = 0.307 > 0.05). Therefore, the two groups of students were statistically similar in gender, the basic knowledge reserve of digital media video creation, and the methods of the socially regulated learning ability. All the participants had the experience of collaborative learning, which mainly included three aspects. First, they skillfully used the online collaborative learning platform. Second, they had the intention to collaborate with their peers in online situations to solve problems together. Third, they actively adopted the intervention measures provided by teachers and reasonably responded to the challenges emerging in the online collaborative process. However, they had not collaborated before this experiment. Due to COVID-19, all students participated in the courses and discussion in a fully online format, and the course was guided by the same teacher.

2.2. Instruments

A pretest was designed in this study. The purpose of the pretest was to evaluate whether the learners in the experimental group and control group had the same level of SoRL and prior knowledge. The previous knowledge level test questions were developed and verified by two experienced teachers, including five single-choice questions, five right and wrong questions, and five short-answer questions, with a maximum score of 100 points. As an index of the test’s effectiveness, Cronbach’s alpha of this test was 0.82.
The questionnaire of the socially regulated learning ability was adapted from Olakanmi’s collaborative regulated learning questionnaire [37], including shared metacognition, shared motivation, shared emotion, the effort control strategy, and the social help strategy. It was in the form of Likert’s five-point scale. Cronbach’s alpha of the questionnaire was 0.950, ranging from 0.823 to 0.913 in each dimension. The above tests were completed at the initial stage of the collaborative activities.

2.3. Research Design

This study was conducted in a course entitled Digital Media Video Creation. Students were required to create digital media works based on their knowledge of photography, audio-visual language, and relevant video creation experience in the course. All participants conducted collaborative learning activities through the Chinese social media platform QQ, which was widely used for online learning in China. The functions of QQ included a file distribution, the automatic storage of communication records and texts, and video and audio communication, which all met the needs of online collaborative learning. A total of 10 QQ groups were established in this study. Figure 2 shows the interface of one QQ group.
The collaborative activity was divided into two parts: (1) construct a conceptual diagram of a micro-video production process and (2) create a micro-video guided by this diagram. The teacher was added to each collaboration group to provide technical assistance. The intervention of SoRL was only performed in the experimental group. All team members were required to communicate about questions related to the learning tasks using the QQ group. At the end of the collaborative learning activity, each group submitted digital artwork and the discourse of the whole process as the group’s work.

2.4. Learning Tasks and Collaborative Learning Design

Both the experimental group and the control group carried out online CKB activities. The difference was that the experimental group added an SoRL intervention, while the control group did not. SoRL interventions were mainly presented in scripted form. The script provided support for learners to realize the progress in the zone of proximal development [38]. It clarified all the links in the process of collaborative learning and gave hints related to the learning tasks, which reduced the risk of the collaborative learning failure of learners to a certain extent and further improved the motivation for collaborative learning [39]. From the perspective of the nature of regulation activities, SoRL were kinds of conscious and goal-oriented shared metacognitive activities. Shared metacognition included shared metacognitive knowledge and a shared metacognitive control [40]. Therefore, an SoRL intervention was carried out from two aspects: shared metacognitive knowledge and shared metacognitive control.

2.4.1. Shared Metacognitive Knowledge Prompt Script

Shared metacognitive knowledge included knowledge about learners, tasks and learning strategies [15]. The knowledge about learners was mainly related to the knowledge of learners’ abilities and task characteristics. The knowledge about learning strategies mainly involved sharing cognitive strategies and sharing the related knowledge of metacognitive strategies. Shared cognitive strategies mainly referred to knowledge building strategies in collaborative learning activities, which included sharing, argumentation, negotiation, creation, and other strategies related to the continuous improvement of ideas. Shared metacognitive strategy knowledge included planning, monitoring, regulating, reflection and evaluation, and other related strategy knowledge. By sharing a metacognitive knowledge prompt script, learners were inspired to reflect on the reserve of all kinds of knowledge mentioned above, and to gradually activate and extract them from the long-term memory to the working memory. This prepared us for subsequent task processing.

2.4.2. Shared Metacognitive Control Prompt Script

Shared metacognitive control mainly involved learners’ shared task understanding, planning, monitoring, evaluation, and reflection in the process of collaborative learning, which showed an obvious temporal characteristic [41]. When the necessary shared metacognitive control experience was lacked, the team tended to be oriented to the completion of collaborative tasks. However, the cooperation effect was poor. Therefore, it was necessary to provide support for the collaborative group process so that learners dynamically monitored the application of shared cognition and shared metacognitive strategies in the process of online collaboration. Learners reflected on their shortcomings in the application process and timely adjusted the application of follow-up strategies. The SoRL intervention script is shown in Table S1. The transcribed example of the regulation process of the experimental group is shown in Table S2.

2.5. Coding Scheme

The discourse data from students in the QQ group was collected, encoded, and analyzed to explore the regulation focus and the level of CKB. All these coding schemes were adapted from previous research, and modifications had to be made to better match this study. The first coding scheme was adapted from Su’s study and used to examine the students’ regulation focus, which included seven indicators such as task understanding, time management, content monitoring, positive emotion, negative emotion, atmosphere activation, and organizing [42]. Its inter-rater reliability coefficient was 0.68 (Cohen’s kappa coefficient). Table S3 shows the code, definition, and example of the regulation focus. The second set of coding schemes were adapted from the study of Stahl and used to examine the students’ level of CKB [43]. According to the purpose of a student interaction, CKB was divided into four levels: information sharing (CKB1), idea argumentation (CKB2), idea negotiation (CKB3), and artifacts creating (CKB4). Its inter-rater reliability coefficient was 0.70 (Cohen’s kappa coefficient). Table S4 shows the code, definition, and example of CKB.

2.6. Data Collection and Analysis

The conversation data of all the team members on the QQ platform were collected and analyzed by Excel. All the discourses were coded according to the coding schemes using content analysis. The basic unit of content analysis was based upon dialogue episodes. The behaviors of SoRL consisted of multiple conversation episodes among the group members. Table S5 shows the dialogue episodes and code of SoRL. The CKB behaviors were able to be characterized by a single conversation post from a group member. Table S6 shows the dialogue episodes and CKB code. To ensure the reliability of the encoding scheme, two experienced educational technology researchers performed back-to-back encoding on the utterances of the group regulation process. The kappa statistic was 0.822, which showed a high consistency.
The epistemic network analysis (ENA) was used to analyze the characteristics of CKB. ENA is a quantitative ethnography technique that models the internal epistemic network of group and learners through the associated structures between the data [44]. The ENA included two basic processes: section-based coding and dynamic network modeling. The section-based coding included segmentation and data coding, and the dynamic network modeling included six processes: the accumulation with the section as a unit, the creation of an adjacency matrix, an adjacency matrix in the accumulation unit, vector normalization, singular value dimension reduction decomposition, and final modeling [45]. ENA was a suitable method to analyze the level of CKB within the groups.
The lag sequential analysis (LSA) was used to analyze the behavioral characteristics of CKB. This method was mainly used to test the frequency of another behavior after the occurrence of one behavior and whether there was a statistically significant difference [46]. The level of CKB was characterized by specific behaviors when viewed from the perspective of process. LSA was used to analyze the behavior mode of CKB and explain the process mechanism of CKB from the perspective of behavior. This provided support for the analysis of the relationship between SoRL and CKB.

3. Results

The prior knowledge level and SoRL level of the experimental group and control group were tested, and there was no significant difference between the two groups in the prior knowledge level (p = 0.213 > 0.05) and SoRL level (p = 0.105 > 0.05). Therefore, the effects of the participants’ prior knowledge and SoRL level on the research results were excluded.

3.1. Features of SoRL Regulation Focus

Table 1 shows a general picture of the regulation focus between the experimental group and the control group. The Kolmogorov–Smirnov method was used to test the normality of the number of regulation focus between the experimental group and the control group. This did not accord with the normal distribution, so it was necessary to use the Mann–Whitney U test in the non-parametric rank sum test. There were significant differences in the time management, content monitoring, and atmosphere activation between the experimental group and the control group, but there were no significant differences in the task understanding, positive emotion, negative emotion, and organization. The SoRL intervention activated three regulation focus including time management, content monitoring, and atmosphere activation.

3.2. Analysis of CKB Level Differences

The knowledge building discourse coding results were introduced into the online ENA tool. The experimental group and control group were taken as analysis units to construct networks, respectively. The node represented the positions of the coding elements. The connection between nodes represented the associated relationship between these elements, and the thickness of the connection represented the co-occurrence intensity of the element at both ends of the connection.
Figure 3a shows that the experimental group established relatively strong connections in CKB1–CKB2 while relatively weak connections in CKB1–CKB4, CKB1–CKB3, and CKB2–CKB3. Figure 3b shows that the control group established a strong connection between CKB1 and CKB4, a relatively strong connection between CKB1 and CKB3, and a relatively weak connection between CKB1 and CKB4 as well as between CKB2 and CKB3.
To highlight the differences in CKB between the experimental group and the control group, the epistemic network overlay of CKB between the above groups was visualized. Figure 3c shows that the experimental group was stronger in the positive direction on the X axis, and its centroid position was close to CKB3 and CKB4. The control group was stronger in the negative direction on the X axis, and the centroid position was close to CKB1 and CKB2. The difference between the two groups was mainly reflected in CKB1 and CKB2 and followed by CKB1 and CKB3.
The Kolmogorov–Smirnov method was adopted to conduct the normal distribution test for all knowledge building discourses (p < 0.05). The results of the normality checks suggested that the distribution of the projected points within the ENA of the two groups was not normal, so it needed to be evaluated by the Mann–Whitney U test in a non-parametric test. In terms of the overall network of CKB, there was a significant difference between the experimental group and control group at the alpha = 0.05 level (U = 124.00, p = 0.00, and r = 0.55).
Comparison of the encoding node projections in the ENA space between the two groups was evaluated using the Mann–Whitney U test. Table 2 shows that there was significant difference in the alpha = 0.05 level on CKB1–CKB2 (U = 148.50, p = 0.01, r = −0.46), CKB1–CKB3 (U = 180.00, p = 0.03, r = −0.35) in X axis, while there was no significant difference in CKB1–CKB4, CKB2–CKB3, CKB2–CKB4, CKB3–CKB4 in X and Y axis.

3.3. CKB Behavior Sequence Analysis

GSEQ software (version 5.1) was used to analyze the CKB behavior of the experimental and the control group, and the adjusted residual Z value was obtained through the calculation. According to the lagging sequence analysis theory, if the Z-value was greater than 1.96, the behavior path was significant (p < 0.05). Table 3 shows the adjusted contingency table of the residual values, respectively, after regulating the CKB behavior sequence of the experimental and control group.
Figure 4a shows five pairs of CKB behavioral sequences with significant levels in the experimental group, which were CKB1–CKB1, CKB2–CKB2, CKB3–CKB3, CKB4–CKB4, and CKB2–CKB3. Figure 4b shows four pairs of CKB behavioral sequences with significant levels in the control group, which were CKB1–CKB1, CKB2–CKB2, CKB3–CKB3, and CKB4–CKB4.

4. Discussion

4.1. Analysis of the Activation of SoRL Intervention on Regulation Focus

With the support of intervention tools, the frequency of the SoRL behavior in the experimental group was higher than that in the control group in each regulation focus. This finding was consistent with the results of previous studies showing that the scaffolding mechanisms led to significant improvements in the learners’ socially shared metacognition [47].
However, there were only significant differences in time management, content monitoring, and atmosphere activation, which indicated that the intervention tools activated the regulatory behavior of the cooperative group in the above three aspects. The reason for this difference may be related to the influencing factors of the learners’ adoption of technology [48]. The SoRL intervention script provided in this study was a way of supplying technology. According to the Unified theory of acceptance and the use of technology (UTAUT) theory, the learners’ adoption of technology was affected by many factors, such as performance expectations, difficulty expectations, convenience conditions, and social influence, which was indirectly affected by gender, age, and voluntary use [49]. The intervention script provided in this study was a universal script. The researchers mainly considered performance expectations, difficulty expectations, and convenience factors, but it had not fully considered other relevant factors that affect technology adoption. These factors might be the reasons for the difference in the activation status. This difference also provided inspiration for the design and application of follow-up SoRL interventions. In addition, the students’ adoption of an SoRL intervention also was related to the stages of collaborative tasks and the characteristics of the tasks. A previous study found that task characteristics influenced how elementary school students activated their SRL [50]. Therefore, compared with the learning activities of acquiring metaphors, the CKB activities for creative metaphors advocated in this study stimulated the students’ regulatory learning behavior.

4.2. ENA of CKB

There were significant differences in the overall CKB network between the experimental group and the control group. The centroid position of the experimental group was close to CKB3 and CKB4, while the control group was close to CKB1 and CKB2, which indicated that the experimental group achieved a higher level of CKB than the control group. The differences between the two groups indicated that high-frequency regulatory behaviors drove the CKB behavior into deeper levels. However, the group lacking regulatory behavior could not produce sustainable knowledge building behavior. This was consistent with the results drawn from the study of Volet et al. [51], and the group with a higher frequency of regulation had more interactive CKB behaviors. Molenaar et al. found that metacognitive scaffolding enhanced the students’ understanding of tasks and improved the quality of their knowledge, but cannot affect the quantity of their knowledge [17]. Therefore, the scaffolding provided by this study played a positive role in improving the level of CKB.
The study also revealed the differences in the co-occurrence level of each dimension of CKB. The experimental group and control group only showed significant differences in CKB1–CKB2 and CKB1–CKB3. The CKB behavior of the experimental group was dominated by the levels of CKB1–CKB2 and CKB1–CKB3, and this had not yet reached a deeper level. The reasons for this outcome might be related to the nature of this collaborative learning task. This collaborative task required each team to construct a conceptual diagram of a micro-video production process. The teams generated a rich practical knowledge in the process of co-creating micro-videos, but team members did not characterize it in the form of a clear discourse on the QQ platform. This led to the lack of sufficient discourse types with which to characterize the building of deeper knowledge in the corpus analyzed by this research.

4.3. Lag Sequence Analysis of CKB

When comparing the behavioral sequences of the experimental and control group, the experimental group showed the CKB2–CKB3 behavioral sequence, but the control group did not see this sequence, which indicated that the experimental group would conduct an in-depth discussion and negotiation on their differences after discovering the similarities and differences in their ideas in order to achieve a consistent understanding, while the control group did not show such behavior. In general, the significant behavioral sequences formed in the control group were relatively loose and fragmented. The CKB behaviors formed by the experimental groups tended to be orderly and showed local holistic characteristics. The reason may be that under the influence of the intervention mechanism of SoRL, the CKB activities were effectively activated, and the ideas emerging in this process showed the periodic characteristics of life evolution, such as death, disappearance, transformation, and sublimation, which promoted the development of CKB to show continuous characteristics [52].
The unsystematic CKB behavior Indicated that the team members lacked the receptive response to the above views when they conducted different CKB behaviors. The theory of knowledge building held that the idea had the characteristic of “vitality”, which required learners to continue to improve their ideas to promote their evolution, and finally obtained knowledge innovation [23]. However, when a lack of correlation between ideas occurred, it was difficult for learners to achieve deep CKB. It should be emphasized that the Z-value in the LSA was different from the transformed kappa, Yule’s Q, and phi. The Z-score required the rejecting of the random zero hypothesis, but it did not indicate the degree of existence of the pattern [53]. Therefore, although the Z-value of the control group was greater than that of the experimental group in many aspects, it could not be used to characterize that the frequency of the related knowledge-building behaviors in the control group was higher than that of those in the experimental group.

4.4. The Mechanism of SoRL Promoting CKB

Time management, content monitoring, and atmosphere activation were the focus types of SoRL to promote the deep building of collaborative learning. Time management was the learners’ regulation to the time schedule of the collaborative learning tasks, which helped to activate the team members’ awareness of time and task management. On this basis, the team members clearly defined the learning task requirements of the different stages and adopted matching strategies to regulate the status of the learning input, and finally promoted the development of collaborative learning activities. This was consistent with the results of Trentepohl et al. [54]. The time management ability was an important prerequisite for students to learn efficiently. Through the time management process, learners actively managed when and how long they engaged in activities that were considered necessary to achieve their academic goals [55].
In terms of content monitoring, team members carried out a large amount of content monitoring activities in the process of collaboration, which was consistent with the results of a previous study [56]. The content monitoring strategies widely used by learners might be related to the nature of the task. This study adopted CKB activities based on the concept of creation, whose core goal was the “continuous improvement of valuable ideas to the community”. This type of learning was different from knowledge memory learning, which required learners to put forward valuable ideas to the community based on their own practical experience and, in the end, to generate conceptual artifacts at the collective level through the collision of ideas.
However, it was difficult to complete high-quality knowledge building only by individual learners. Therefore, team members promoted the collaborative process by synchronizing cognitive processing and content monitoring so as to generate valuable community knowledge. For instance, a team member pointed out: “In fact, I don’t think what we can do it at this time, there will be wide differences in the actual shooting”, and “Note that what we need to discuss is the production process of micro-video, so let’s not digress!”. This difference also was explained by the cognitive load theory. According to the cognitive load theory, meaning building and monitoring activities occurred in the working memory space synchronously, which was limited in capacity [53]. Learners formed a new working memory space at the group level. Some group members carried out meaning building, while other peers were responsible for content monitoring, and this avoided a cognitive overload at the individual level and promoted the development of knowledge building to a deeper level. Therefore, the content monitoring strategy had become the core strategy to promote the development of CKB at deeper levels.
The atmosphere activation was one of the important emotion regulation strategies, which promoted the development of collaborative learning activities. In this study, the groups collaborated through simultaneous text and emoticon communication on the social media platform QQ. The team members paid attention to the construction of the cooperative atmosphere, which resulted in a large amount of atmosphere activation emotional regulation behavior. Social and emotional challenges were the key factors affecting the success of collaborative learning [57]. Based on the qualitative analysis of discussion discourse, group members were very likely to have cognitive conflicts in the process of CKB2 and CKB3, which were accompanied by social and emotional challenges. The experimental group members tended to use emoticons to regulate the collaborative atmosphere, such as smiley faces, thumbs-up, and other regulation strategies which helped to create a safe collaborative learning atmosphere. Creating a positive team atmosphere was conducive to the implementation of positive feedback among learners, which played a significant role in improving the learning performance [58]. Online learning and interpersonal communication had become the students’ norm due to the continuous influence of COVID-19 on the offline normal teaching order [59]. Students had accumulated a certain number of emoticons, which had significant personalized features and had become important tools for students to express individual emotions and alleviate tensions within the group atmosphere. This was also the unique feature of social media in supporting the emotional expression, which was superior to other platforms.

5. Conclusions

The research conclusions mainly include the following contents:
(1) There was a difference between the supply of SoRL intervention scripts and the actual selection of the learners. The regulation focus activated in this study was time management, content monitoring, and atmosphere activation. (2) Atmosphere activation promoted the continuous implementation of collaborative learning activities. (3) Time management and content monitoring promoted the development of CKB to a deeper level.
The SoRL intervention scripts developed in this study did not activate the regulation focus of the collaborative group on a larger level. The data analyzed in this study only came from a collaborative discourse, which had some limitations in depicting the complete pattern of collaborative learning. In the future, researchers should combine UTAUT and other individual techniques to accept other relevant theories, design more appropriate intervention scripts, introduce multi-modal analysis methods, and introduce facial expression recognition, an electroencephalogram, and other technologies into the collaborative process analysis framework to form a more perfect framework to make up for the limitations of the discourse analysis.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su142215345/s1, Table S1: The script of collaborative learning; Table S2: The transcribed example of the experimental group’ regulation process. Table S3: Codes of regulation focus; Table S4: Codes of CKB level; Table S5: Codes of regulation dialogue; Table S6: Codes of CKB dialogue.

Author Contributions

Conceptualization, J.L., H.W. and X.C.; methodology, J.L., H.W. and X.C.; software, J.L., H.W. and R.Z.; validation, X.W., H.W. and X.C.; formal analysis, H.W. and X.C.; investigation, J.L. and X.W.; resources, J.L. and X.W.; data curation, R.Z., J.L. and X.W.; writing—original draft preparation, J.L.; writing—review and editing, J.L., H.W. and X.C.; visualization, J.L. and X.W.; supervision, H.W. and X.C.; project administration, X.C.; funding acquisition, X.C. and H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Project of Province-Ministry Co-constructing Teacher Education Collaborative Innovation Center of Northeast Normal University (No. CITE20200102), the Jilin Province Industrial Independent Innovation Ability Special Project (No. 2019C033), the 2022 Humanities and Social Science Research Planning Foundation of the Ministry of Education (No. 22YJA880007), and the High-level Talent Foundation Project of Harbin Normal University (No. 1305122210).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

A special thanks to the Network and Intelligent Education Engineering Laboratory of Jilin Province (Northeast Normal University) for the experimental platform. The authors are also grateful to the editor and the anonymous reviewers for their insightful comments and suggestions.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 15345 g001 550
Figure 1. Experimental process diagram.
Figure 1. Experimental process diagram.
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Figure 2. QQ group interaction interface.
Figure 2. QQ group interaction interface.
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Figure 3. Mean ENA network of the experimental group and control group and mean epistemic reduction network between the two groups. (a) Experimental group. (b) Control group. (c) Mean epistemic reduction network between the experimental and control group.
Figure 3. Mean ENA network of the experimental group and control group and mean epistemic reduction network between the two groups. (a) Experimental group. (b) Control group. (c) Mean epistemic reduction network between the experimental and control group.
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Figure 4. CKB Behavioral sequence between the experimental and control groups. (a) Experimental group. (b) Control group.
Figure 4. CKB Behavioral sequence between the experimental and control groups. (a) Experimental group. (b) Control group.
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Table
Table 1. Differences in the SoRL regulation focus between the experimental group and the control group. Note: * p < 0.05.
Table 1. Differences in the SoRL regulation focus between the experimental group and the control group. Note: * p < 0.05.
Regulation FocusGroupFrequencyM (n = 5)SDp
Task understandingExperimental group1002015.000.275
Control group701415.17
Time managementExperimental group1402812.550.011 *
Control group2044.18
Content monitoringExperimental group515103112.280.009 *
Control group60126.71
Positive emotionExperimental group1452917.100.114
Control group55114.18
Negative emotionExperimental group551111.400.126
Control group512.24
OrganizingExperimental group851721.100.054
Control group000.00
Atmosphere activationExperimental group820164117.550.009 *
Control group145298.94
Table
Table 2. Test of difference in CKB levels between the experimental and control group. Note: * p < 0.05.
Table 2. Test of difference in CKB levels between the experimental and control group. Note: * p < 0.05.
LevelXY
UprUpr
CKB1–CKB2148.500.01 *0.46237.500.410.14
CKB1–CKB3180.000.03 *0.35275.000.990.00
CKB1–CKB4194.500.070.30310.500.44−0.12
CKB2–CKB3221.000.170.20277.000.990.00
CKB2–CKB4233.000.200.16299.000.50−0.08
CKB3–CKB4252.500.310.09275.501.000.00
Table
Table 3. Residual table: CKB behavior of the experimental and control groups.
Table 3. Residual table: CKB behavior of the experimental and control groups.
LevelExperimental GroupControl Group
CKB1CKB2CKB3CKB4CKB1CKB2CKB3CKB4
CKB18.34−1.93−4.81−0.245.93−2.94−4.52−2.49
CKB2−3.143.914.641.66−4.225.751.79−1.13
CKB3−2.460.765.88−0.61−2.9−1.28.041.24
CKB4−0.510.531.32.03−1.07−1.06−0.555.69
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Lu, J.; Chen, X.; Wang, X.; Zhong, R.; Wang, H. Research on the Influence of Socially Regulated Learning on Online Collaborative Knowledge Building in the Post COVID-19 Period. Sustainability 2022, 14, 15345. https://doi.org/10.3390/su142215345

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Lu J, Chen X, Wang X, Zhong R, Wang H. Research on the Influence of Socially Regulated Learning on Online Collaborative Knowledge Building in the Post COVID-19 Period. Sustainability. 2022; 14(22):15345. https://doi.org/10.3390/su142215345

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Lu, Jia, Xiaohui Chen, Xiaodan Wang, Rong Zhong, and Hanxi Wang. 2022. "Research on the Influence of Socially Regulated Learning on Online Collaborative Knowledge Building in the Post COVID-19 Period" Sustainability 14, no. 22: 15345. https://doi.org/10.3390/su142215345

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