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Article

Implementing a Social Presence-Based Teaching Strategy in Online Lecture Learning

by
Liangliang Xia
1,
Lianghui Wang
2,* and
Changqin Huang
2
1
School of Education Technology, Beijing Normal University, Beijing 102206, China
2
Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China
*
Author to whom correspondence should be addressed.
Eur. J. Investig. Health Psychol. Educ. 2024, 14(9), 2580-2597; https://doi.org/10.3390/ejihpe14090170
Submission received: 10 June 2024 / Revised: 12 September 2024 / Accepted: 18 September 2024 / Published: 21 September 2024
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Abstract

:
Previous studies have focused on the design of video lectures to improve students’ social presence by enhancing instructor presence for learners in lecture-based online courses; however, there has been limited emphasis on the peer presence in which learning from video lectures takes place. This study’s first objective is to develop a social presence (SP)-based teaching strategy to design online learning activities aimed at improving students’ social presence by providing social clues about peer presence and encouraging peer communication. The second objective is to compare students’ social presence, social interaction, and academic performance from lecture-based online learning supported by either a conventional teaching strategy or an SP-based teaching strategy. Using a quasi-experiment, we selected 81 Chinese university students to participate in a ten-week online course. The participants were randomly assigned to either an experimental group (EG) (N = 43) or a control group (CG) (N = 38). This study revealed that the SP-based strategy enhanced EG members’ social presence in online learning and that EG members achieved better academic performance than CG members. A significant correlation was found between the EG members’ academic performance and their social presence. The researchers also identified more concentrated social network sociograms with more cohesive subgroups in the EG members’ online interactions. The results indicate the necessity of applying an SP-based teaching strategy in lecture-based online courses to promote students’ social presence, social interaction, and academic performance.

1. Introduction

Within higher education, online teaching with the support of information and communication technology has been largely recognized as an important method by which to implement instruction [1]. However, despite the great potential of technology in online education, not all students can achieve success in online education with the support of technology, given the technological gap and the limited technological proficiency among both teachers and students [2,3]. In the current online education environment, lecture-based teaching methods have become increasingly popular, especially in large-scale online open courses and during emergency situations such as that of COVID-19 [4,5,6,7,8]. However, this teaching practice may result in students’ online isolation and could be seen as lacking the ability to create learning-conducive interactions, which in turn could impede students from achieving their learning objectives through online classes [9,10,11,12].
Researchers have found that social presence, as a key construct in online learning, plays a critical role in student online course engagement, emotional experience, and online interaction [13,14,15,16,17,18]. In addition, social presence has positive effects on students’ learning outcomes [19,20,21]. The community of inquiry (COI) framework assumes that effective online learning occurs when students experience sufficient social presence, teaching presence, and cognitive presence while learning online [16]. The primary role of social presence in the COI framework is to create group cohesion and an open communication environment associated with higher-order learning [22]. Given the impact of social presence on students’ online learning, the practice of transferring lecture-based instruction online can be improved by enhancing students’ social presence.
Online educators have continued to explore various methods to establish and sustain social presence in online courses [23]. Previous research has focused primarily on using video design to enhance students’ social presence in lecture-based online courses, including factors such as the instructor’s eye gaze, the instructor’s image in accompanying lecture slides, the instructor’s gaze guidance, and interactive visualization tools [8,24,25,26]. However, while much attention has been given to the design of video lectures to provide more instructor presence for learners, there has been limited emphasis on the social environment in which learning from video lectures takes place [7]. Social psychology theory emphasizes the role of another’s presence in learning [27]. Another presence in lecture-based online learning includes not only the instructor presence but also the co-learner presence. For example, Brand et al. (2003) [28] discovered that, compared with learners who learned alone, those who learned in the presence of peers who were participating in the same activity were better at solving learning tasks. Therefore, in lecture-based learning, designing activities for students with peers can help improve students’ academic performance and social presence.
The present study aimed to explore whether a social presence (SP)-based teaching strategy can provide students with important social cues about the presence of peers in a lecture-based online learning community, thereby enhancing students’ social presence and resulting in meaningful learning. Previous research has not directly designed instructional strategies that address the presence of peers based on theoretical frameworks in the field of social presence. The theoretical framework of social presence describes the dimensions of social presence, and an in-depth understanding of social presence helps to scientifically design teaching strategies to enhance the presence of peers, thereby enhancing students’ perceived social presence. Therefore, based on Biocca et al.’s (2003) [29] social presence theory, this study proposed an SP-based teaching strategy to improve lecture-based online instruction by designing online activities that aimed to enhance students’ social presence. Additionally, the researchers implemented six online learning activities under the guidance of this SP-based teaching strategy as an intervention that focused on improving students’ social presence in an online course. To examine the effect of the SP-based teaching strategy, this study compared students’ social presence, social interaction, and academic performance from lecture-based online learning supported by either a conventional teaching strategy or an SP-based teaching strategy. The research questions were as follows:
(1)
Will the SP-based teaching strategy significantly enhance students’ social presence in online learning?
(2)
Will the SP-based teaching strategy enhance students’ online interaction?
(3)
Will the SP-based teaching strategy significantly improve students’ academic performance at the end of the online course?

2. Literature Review

2.1. Lecture-Based Instruction in Online Courses

Lecture-based instruction is the teaching method that most teachers use in their regular online teaching. Teachers usually deliver online lecture-based instruction through synchronous interaction (e.g., providing live online lectures via Zoom and Ding Talk) and asynchronous interaction (e.g., providing prerecorded lectures and discussion forums via Moodle) [30]. Synchronous instruction allows real-time communication between teachers and students despite their geographical distance [31,32]. Asynchronous instruction provides greater flexibility to suit students’ schedules and allows students to review video lectures repeatedly at their convenience [33]. Thus far, the findings regarding the effects of synchronous and asynchronous instruction have been mixed, as researchers have found that both synchronous and asynchronous instruction have advantages and limitations. In terms of students’ perceptions, some researchers have found that students prefer instruction in an asynchronous format [34,35]. In contrast, Moridani (2007) [36] discovered that students had lower satisfaction with asynchronous lectures and preferred live interactive sessions. For students’ learning performance, Kubey et al. (2010) [37] reported that students receiving synchronous online instruction had lower academic achievement than those receiving asynchronous online instruction. However, Somenarain et al.’s (2010) [38] research showed no significant difference between synchronous online instruction and asynchronous online instruction in terms of students’ course outcomes.
It has become widely accepted that online learning requires more interaction to maintain students’ course engagement and retention [39,40,41,42]. However, lecture-based online instruction, as a teacher-centered teaching approach, may cause several issues, such as a lack of social and emotional support during teacher–student and peer interactions, neglect of students’ diverse learning needs and preferences, and monotonous online learning activities [20,43,44,45,46,47]. The above issues hinder students from achieving their learning goals in online courses. Therefore, teachers who rely entirely on lecture-based instruction in their online courses need to apply effective teaching strategies to improve their students’ social presence, online interaction, and academic performance.

2.2. Social Presence

Short et al. (1976) [48] defined social presence as “the degree of salience of the other person in the interaction” in the field of social psychology and communication. Short’s team focused on the degree of technology users’ awareness of other people and conceptualized social presence as a quality of communication media. That is, some media were considered to have a greater capability to convey social presence (e.g., video-based conferencing), while others, such as text-based communication, had a weaker capability to convey social presence. Researchers subsequently questioned whether media quality determined social presence and reconceptualized social presence as a feeling of having someone socially present in one’s life even if they were not physically in the same space [49]. For instance, Rourke et al. (1999) [50] defined social presence as “the ability of learners to project themselves socially and affectively into a community of inquiry.” In 1999, Garrison et al. [51] introduced the CoI model, emphasizing the crucial role of social presence in supporting cognitive presence. They proposed that social presence indirectly facilitated the process of critical thinking carried out by the community of learners. In this study, we describe social presence as the ability to perceive others in an online environment.

2.2.1. Biocca et al.’s Social Presence Theory

The dimensions of social presence, including intimacy, immediacy [48], social respect (e.g., timely response), open mind (e.g., self-disclosure), social identity (e.g., learner’s characteristic), social sharing (e.g., sharing information) [52], interactivity, social context (e.g., social relationships), online communication [53], cohesion [51], and privacy [54], have been proposed over the past four decades. In this study, we chose Biocca et al.’s (2003) [29] dimensions of social presence because they were comprehensive and hierarchical and were thus more instructive for the present research.
Biocca et al. (2003) [29] proposed a robust theory that outlines three dimensions of social presence by reviewing existing definitions of social presence. These dimensions are co-presence, psychological involvement, and behavioral engagement. Co-presence “is grounded on the basic sensory awareness of other” [29] and “implies the reception of embodied messages” [55]. Together, these describe a feeling of being with others in the same space and with mutual awareness, even when these others are physically separate. Psychological involvement emphasizes the deeply immersed feeling of another person, such as people’s modeling of the intelligence of the other rather than just the co-presence of a body, and considers the actions of the body as cues for the activation of the body’s intellectual state [29]. Hwang and Park (2007) [56] referred to psychological involvement as emotional connectedness. In other words, psychological involvement involves people’s perception of others’ thoughts, cognition and emotions, and focuses on people’s expression of their emotions and opinions on issues and the exchange of opinions with each other through social interaction and communication. Biocca et al. (2001) [57] described behavioral engagement as “the degree to which the observer believes his or her actions are interdependent, connected to, or responsive to the other and the perceived responsiveness of the other to the observer’s action.” Specifically, behavioral engagement refers to behavioral interaction, mutual assistance, and dependent action [57].
Biocca et al.’s (2003) [29] social presence theory was mainly used to help people understand the concept of social presence [13,15,58,59]. In addition, it is an important theoretical framework for analyzing social presence and related studies. For instance, Kim et al. (2016) [13] investigated the predictive relationship between the two dimensions of social presence (i.e., co-presence and psychological involvement) and students’ online learning experiences. They found that co-presence and psychological involvement explained 50% of the total variance in teacher–student relationship satisfaction, 51% of the variance in class satisfaction, and 57% of the variance in perceived knowledge gain.

2.2.2. The Role of Social Presence in Online Learning

Previous research has shown that social presence can positively influence students’ learning experiences, such as course and instructor satisfaction [17,58,60], perceived learning [13,61], learning engagement [62], learning performance [19,21], and learning motivation [18,63]. For example, Cobb and Susan (2011) [64] have demonstrated that social presence explained 44% of the total variance in overall learner satisfaction and 36% of the total variance in student perceived learning. Furthermore, social presence can effectively predict students’ online course retention and enrollment intentions [65,66].
Several studies have investigated the relationships between students’ social presence and their academic performance. Hostetter and Busch (2013) [67] used content analysis to code students’ discussion postings and found that students who were engaged in more social presence indicators during online communication achieved better performance. Similarly, Yang et al. (2016) [20] reported that students’ perceived social presence accounted for 52.6% of the total variance in perceived learning outcomes and 15.8% of the total variance in actual course results. Moreover, Samad et al. (2019) [68] proposed a model to investigate the impact of social networking sites on students’ academic performance. They found a positive relationship between students’ social wellbeing and their academic performance, and students’ social presence could significantly influence their social wellbeing.
Finally, previous studies have shown a positive correlation between students’ social presence and online interaction [13,69,70,71]. However, the findings about the influential relationships between social presence and interactions are still mixed. For instance, Horzum (2017) [70] used structural equation modeling to investigate the relationships among interaction, structure, social presence, and satisfaction and concluded that students’ social presence was positively predicted by their online interaction. Additionally, through structural equation modeling, Wei et al. (2012) [15] showed that social presence had a significant impact on interaction.

2.2.3. Effective Teaching Strategies to Improve Students’ Social Presence

A number of researchers have found that the factors influencing students’ social presence in online courses include situational variables [14,72], teacher participation and student attitudes [73], cultural backgrounds [74,75], course duration [76,77], and computer-mediated communication tools [78].
In terms of improving students’ social presence, some researchers have used various computer-based emerging technological tools that facilitate interpersonal connections to establish students’ social presence, such as online forums [79], Wike [49], Twitter [80], and VoiceThread [81]. For instance, Akcaoglu and Lee (2018) [82] used Facebook groups as outside-class social spaces in two asynchronous online courses to supplement cognitive and affective learning. The researchers found that participants perceived more social presence after joining the class Facebook group. In addition, many researchers have adopted various teaching strategies to promote social presence, such as digital storytelling [83,84], the case method of instruction [85], and online protocols [86]. Lowenthal and Dunlap (2010) [83] have demonstrated that storytelling improved students’ social presence by disclosing their personal information and relating to each other’s common experiences. Moreover, Aragon (2003) [87] adopted a series of approaches to establishing social presence, including developing welcome messages, providing frequent feedback, sharing personal stories, and using humor and emoticons.
The above strategies to enhance students’ social presence were not under the guidance of specific and creditable theoretical frameworks in the field of social presence. Therefore, it is necessary to find a specific theory to guide improvements in students’ social presence. Biocca et al.’s (2003) [29] social presence theory might be a rational choice.

3. The Social Presence-Based Teaching Strategy

Based on Biocca et al.’s (2003) [29] social presence theory, the present study proposed an SP-based teaching strategy that focused on improving students’ social presence in terms of co-presence, psychological involvement, and behavioral engagement in an online course (Figure 1). Furthermore, the researchers designed six activities under the guidance of the SP-based teaching strategy to establish closer connections between students and improve their social presence (Table 1).
According to Biocca et al.’s (2003) [29] social presence theory, co-presence focuses on students’ awareness of their peers’ existence, which implies that students need to receive the embodied messages of their peers. In a mediated environment, the other was frequently embodied by an avatar, agent, or simpler representational device [88]. In this study, with the support of online course platforms, all students were embodied by their personal accounts. Therefore, to improve student co-presence, students are required to participate in learner identification activities by providing personal information on the course platform (e.g., name, grade, age, specialty, and hobby) to provide individual clues that might help them sense that they were learning in an online community.
Biocca et al. (2013) [29] considered the inert bodies in virtual environments as representations that were not “inhabited” by intelligence, human or artificial, and were without spirit or agency within them. Psychological involvement hinges more on students’ models of their peers’ intelligence rather than just the co-presence of a body (Biocca et al., 2003) [29]. Therefore, students need to display their intelligence in the online course. In the present study, to help students build intelligence models for others, the researchers required the students to (a) express their online learning needs and expectations, (b) sign learning contracts to ensure that they could follow the rules of rewards and punishments during online learning, and (c) provide emotional cues during online communication (e.g., humor and emoticons in public).
Behavioral engagement emphasizes the interdependence between students’ behavior and the exchange of behavior between the students, including the reaction to others’ behavior and the degree to which one’s behavior receives feedback from others [29]. To improve students’ behavioral engagement, the researchers in the present study encouraged the students to form study groups and create WeChat groups for online communication. Such practices would provide students with more opportunities for behavioral interaction. Additionally, after forming study groups, the students in each study group were required to develop a group contract to set shared learning goals and clarify rewards and sanctions, peer responsibilities, and obligations.

4. Experimental Design

4.1. Participants and Course Context

Eighty-one junior undergraduates taking an online course (digital learning resource design and development) at a Chinese university were chosen as samples for the experiment. These participants were from two disciplinary backgrounds (i.e., ideological and political education and international Chinese language education). These participants were already acquainted with each other beforehand and took other courses in the same semester; however, these courses were conducted online, and they did not meet face-to-face. The participants were randomly divided into two groups, with one labeled the control group (CG) (N = 43) and the other labeled the experimental group (EG) (N = 38). The teaching teams of three teachers in both the CG and EG were identical, with no prior exposure to online instruction. The participants ranged in age from 21 to 22 years; 10 were male, and 71 were female. All participants had prior online learning experience, successfully completed the course without any dropouts, and provided all necessary data.
The course was delivered completely online without face-to-face (F-T-F) contact. This course aimed to help students comprehend key concepts of digital learning and principles of digital learning resource development. When completing the course, the students were expected to be able to design and develop digital learning resources (e.g., PowerPoint slides and micro-lectures). The course lasted for ten weeks and included six learning modules. The course resources in the six modules included prerecorded online lectures and PowerPoints. All videos were recorded in advance, and the teacher appeared in the prerecorded online lectures. Moreover, each module contained one online forum, and the learning tasks included watching teachers’ podcasts and completing online test items. The students were encouraged to ask questions, express opinions and emotions, and exchange opinions with each other in the online forum activities of each module. The teacher closed the forum when the learning session of each module was finished. Finally, the students were required to submit their design work (i.e., PowerPoint slides and a micro-lecture) at the end of the online course.

4.2. Experimental Procedure

During the ten-week online course, the CG members received lecture-based instruction throughout the course. The EG attended online lectures, as did the CG for the first three weeks. From week 4 to week 9, the researchers complemented six activities (Table 1) that were designed under the guidance of the SP-based teaching strategy among the EG. The following paragraphs describe the process of the researchers’ implementation of the experiment. Table 2 shows an overview of the experimental procedure.
(1)
During the exposure phase (weeks 1–3), the DG and EG members received lecture-based instruction, including viewing online course resources, completing course-related tasks, and participating in discussion forums. The purpose of the exposure phase was to examine whether the two groups would have significant differences in their social presence and social network characteristics before the intervention.
(2)
In the pretest phase (week 3), the CG and EG members were required to complete a test to assess their foreknowledge about digital learning resource design and development, which was learned during the exposure phase. After the pretest, all participants completed an online questionnaire survey addressing their perceived social presence during the exposure phase.
(3)
During the treatment phase (weeks 4–9), the CG members continued receiving lecture-based instruction in the online course, while the EG members were provided with the intervention to promote their social presence. In week 4, the EG students were asked to participate in learner identification, learning expectations and learning contracts activities. They provided personal information, expressed learning expectations, and signed learning contracts on the course platform. Additionally, they completed study groups and group contracts this week. Specifically, they signed learning contracts, formed study groups, developed group contracts, and published the results on the platform. The formation of study groups was not assigned by the teacher but was determined by the students according to their hobbies, learning needs, course goals, etc. Furthermore, from weeks 4 to 9, teachers encouraged students to utilize emotional cues in their online interactions, such as incorporating humor and emojis in public communications.
(4)
During the posttest phase (week 10), the CG and EG members were requested to submit design works, including PowerPoint slides and a micro-lecture. In addition, both groups were invited to complete a questionnaire survey assessing their perceived social presence.

4.3. Instruments

To examine the effect of the SP-based teaching strategy on the participants’ social presence, a two-round questionnaire survey with a pretest and posttest and a coding schema on the participants’ social presence were conducted at week 3 and week 10, respectively. Because the course objectives focused on the development of students’ competencies in using information and communications technology in teaching, the participants’ academic performance was indicated by the scores of their design (i.e., PowerPoint slides and a micro-lecture). The following paragraphs provide detailed information about the instruments used to address these factors.
The instruments used to measure the participants’ social presence included the social presence scale [89] and the social presence coding schema [50]. The social presence scale indicated the participants’ perceived social presence with nine items in three dimensions, demonstrating a high reliability with a Cronbach’s Alpha of 0.923. This scale uses a five-point Likert scale (from 1 = strongly disagree to 5 = strongly agree) to rate the extent to which the participants agreed or disagreed with each statement, with reliability and validity tests performed by experts. The social presence coding schema instrument includes three categories and twelve indicators. The three categories were affective response, interactive response, and cohesive response. To ensure the reliability of the coding, two researchers independently screened the discussion postings and coded the data. Any disagreements were resolved through discussion.
Regarding the participants’ foreknowledge, the researchers administered a standardized test designed by the teaching staff. The test aimed to assess the participants’ knowledge of the principles of designing and developing digital learning resources. The participants’ academic performance was indicated by the results of their design work (i.e., a micro-lecture and PowerPoint slides) submitted at week 10. Two teachers independently marked the students’ final work at the end of the online course according to the grading criteria, including aesthetics, the correctness of content, and the rationality and richness of the technology used. Any disagreements were resolved through discussion.
Finally, the factor of the participants’ online interaction was addressed by the frequency of their participation in the online forums of all the learning modules.

4.4. Data Collection

The researchers obtained ethics approval from the bioethics committee of the university (Human Experiments of Zhejiang Normal University, the code is ZSRT2024201) and consent from the teaching staff and participants before conducting the research. A standard test was used to assess the participants’ foreknowledge in week 3. Both the EG and the CG students were required to complete the survey simultaneously on the platform, and the camera was turned on to supervise the process of the pretest. The participants’ academic performance at the end of the online course was collected from the results of their final studies (i.e., PowerPoint slides and micro-lectures) in week 10.
To collect the data on the participants’ perceived social presence, a pretest and a posttest of their social presence questionnaire were posted online, and the link of the questionnaire was sent to the EG and CG through WeChat groups. The EG and CG members were required to complete the questionnaire in week 3 and week 10, respectively.
In addition, to provide behavioral evidence, the researchers investigated the occurrence and frequency of social presence indicators from online discussion postings generated by the participants in six discussion forums.
Finally, we generated network sociograms of the EG and the CG during the exposure and treatment phases. We investigated the interaction data (e.g., questioning and responding between students) in six discussion forums. Specifically, the participants’ interaction data during the exposure phase were gathered from two online forums in learning modules 1–2. Their interaction data during the treatment phase were collected from four online forums in learning modules 3–6.

4.5. Data Analysis

A t test was conducted to examine the differences in students’ perceived social presence scores and foreknowledge between the EG and the CG. The level of significance was set to 95%. As the non-normal distribution of the data indicated the EG members’ academic performance, the Mann–Whitney U test was used to compare the difference in academic performance scores between the EG and the CG students.
The researchers coded students’ discussion postings in discussion forums according to the social presence coding schema developed by Rourke et al. (1999) [50] and used content analysis to examine the social presence participation of both the EG and the CG in three categories of social presence during online communication. The unit of analysis is a sentence or paragraph that conveys important thoughts or ideas. A total of 158 units were collected (see Table 3).
Additionally, Spearman’s correlation analysis was performed to examine the correlation between students’ academic performance and their perceived social presence scores.
Moreover, social network analysis could offer a better understanding of the relationships between participants and the characteristics of students’ online interactions. The interaction data were entered into UCINET6 for Windows [90], and we used social network analysis to analyze the network sociograms, social network characteristics, and cohesive subgroups of the EG and the CG. The sociograms showing the network structure are depicted with lines (representing relationships between people) and nodes (representing individual people) [91]. The variables in the social network analysis we examined included density, average distance, distance-based cohesion, and hybrid reciprocity. The density indicates the level of participants’ engagement in the network [92]. The cohesion measured through the geodesic distance between two participants reveals the degree of centralization of communication in the discussion forums [92]. Reciprocity indicates the pairwise relationship between two nodes (i.e., the ratio of mutual dyads to all possible dyads) [93]. Finally, researchers investigated the number of cohesive subgroups of the EG and CG through analysis of the cliques of the network. A cohesive subgroup is a “tightly knit” subset of actors in a social network [94]. A clique embodies a perfect cohesive subgroup where every two actors in the subgroup are directly connected to each other [95]. In this study, the maximum distance between two members in a subgroup was two, and the minimum number of members in a subgroup was four.

5. Results

5.1. Social Presence

The results of the t test showed that there was no statistically significant difference in students’ perceived social presence at the pretest. At the posttest, the EG members showed a significantly greater perceived social presence than the CG (Table 3).
Table 4 shows the behavioral evidence of participants’ online interactions in discussion forums, representing students’ contributions to the three categories of social presence from the exposure phase to the treatment phase. During the exposure phase, there was no significant difference in the total number of contributions to each category of social presence. The EG made two more analysis units than the CG, and the difference was mainly focused on the interactive response category. During the treatment phase, the gap in the total number of analysis units between the two groups became larger, with the EG having 45 more analysis units than the CG. Among the categories of increased social presence in the EG, affective response increased the most, followed by cohesive response and finally interactive response, whereas the CG increased only in the interactive response category.

5.2. Social Network Characteristics

Figure 2 and Figure 3 display the network sociograms of the EG and the CG during the exposure and treatment phases, respectively. The size of each node represents the degree of each member’s total interaction frequency with others. The arrows indicate the direction of the online interaction between the participants. The thickness of the lines indicates the total interaction frequency between two participants.
During the exposure phase, the researchers found twelve isolated nodes in the CG and seven isolated nodes in the EG. In addition, there were some connections, loose structures, and weak links in both the CG and the EG.
During the following phase of treatment, the isolated nodes in the CG and EG disappeared. The structure of the CG was still loose, while the structure of the EG was more concentrated. The number of connections between the group members in both the CG and EG increased, but there were more remarkable increases in connections between the group members in the EG compared to those in the CG. Regarding the strength of the links among the group members in the CG and EG, the EG demonstrated strengthened connections among its members, while there was no marked change in the CG from the exposure to the treatment phase.
The researchers identified more features of the network sociograms in Table 5. During the exposure phase, the density, average distance, and cohesion of the EG were slightly greater than those of the CG. Additionally, the reciprocity in both the EG and CG was zero.
During the treatment phase, the density and cohesion of the EG were greater than those of the CG, and the gap between the EG and CG became larger. In addition, despite the greater average distance traveled by the EG than by the CG in the exposure phase, the average distance traveled by the EG was lower than that traveled by the CG in the treatment phase. However, the reciprocity of the CG was slightly greater than that of the EG. Approximately 20% of the relationships unilaterally initiated by the EG and CG members were replied to by others and formed a dialog.
Finally, the researchers examined the number of cohesive subgroups of the EG and CG. There were no eligible subgroups in either the control or the experimental groups during the exposure phase.
During the treatment phase, the CG formed one subgroup. A total of 9.3% of the CG students appeared in the subgroups (Table 6). The EG members formed 11 subgroups, and 68.4% of all the EG students appeared in the subgroups (Table 6). Moreover, many members of the EG, such as members 9, 17, and 24, appeared in multiple subgroups at the same time, indicating that they actively interacted with others.

5.3. Academic Performance

The researchers found no significant difference between the two groups in terms of foreknowledge during the exposure phase (Table 7). Due to the non-normal distribution of the data indicating the EG members’ academic performance, we used the Mann–Whitney U test to investigate the effect of the SP-based teaching strategy on students’ academic performance at the end of the online course. As shown in Table 8, there was a significant difference between the two groups (U = 462.5, z = −3.366, p = 0.001), indicating that EG members were more capable of designing and developing digital learning resources than CG members were.
Pearson’s correlation analysis and Spearman’s correlation analysis were conducted to investigate the correlation between the participants’ academic performance and perceived social presence. The CG students’ academic performance was not significantly related to their perceived social presence. In contrast, there was a significant correlation between EG members’ academic performance and perceived social presence (Table 9).

6. Discussion and Implications

This study proposed an SP-based teaching strategy to support online teachers in designing learning activities from three dimensions of SP. Furthermore, the experimental results indicate that the intervention of the SP-based teaching strategy could improve students’ online learning in several aspects, such as perceiving significantly greater social presence, participating in more categories of social presence, achieving significantly better academic performance, and experiencing more frequent online interaction. This implies that teachers could enhance students’ social presence through modifications in their instructional design without necessarily relying on complex technological tools. The SP-based teaching strategy was shown to be effective in assisting online teachers in smoothly engaging in online teaching and enhancing students’ learning.
In terms of the students’ perceived social presence during the exposure phase, the researchers did not find any significant difference in the participants’ perceived social presence between the EG and CG. However, during the treatment phase, the EG members perceived greater social presence than those in the CG. This difference can be attributed to the SP-based teaching strategy intervention, which enhances students’ perceived social presence through online activities that promote co-presence, psychological involvement, and behavior engagement.
Additionally, before the intervention, the participants in the EG and CG mainly responded interactively to the course forums. Both the EG and CG members rarely posted affective and cohesive responses to the course forums. After receiving the intervention during the treatment phase, the EG members posted more interactive, affective, and cohesive responses to online discussion forums, while there were no such remarkable increases among the CG members. We designed emotional disclosure activities to promote students’ psychological involvement, which helps explain why the EG group exhibited more affective responses during the treatment phase. Moreover, emotional interaction has a positive effect on students’ learning engagement (e.g., interactive responses) [96,97]. On the other hand, we encouraged students to form study groups and use a mobile instant messaging-facilitated (MIM) app (i.e., WeChat) for online communication to enhance students’ behavioral engagement. Tang and Hew (2020) [98] examined to what extent social presence was presented in a MIM environment and compared it with that in a threaded discussion forum. They found that the MIM promoted greater online social presence among students than the asynchronous threaded forum. Therefore, the use of WeChat may encourage EG members to post more contributions to online study groups, and enriched online communication more extensively represents the three categories of social presence on platform forums.
Regarding the participants’ online interactions during the exposure phase, the social network analysis indicated that there was no significant difference in the network sociograms or social network characteristics between the EG and CG. However, during the treatment phase, compared with the CG, the EG displayed a more concentrated network sociogram, with more connections, stronger strength of links, greater density and cohesion, and lower average distance. Previous studies have also shown a positive relationship between students’ social presence and their online interactions [13,69,71]. As indicated by Tirado et al. (2015) [92], the cohesion index and the centralization index of the social network sociogram have a positive influence on students’ social presence. This finding implies that the intervention of the SP-based teaching strategy could promote students’ behavioral engagement, which might in turn improve their social presence. Importantly, this study revealed that the EG formed more subgroups than did the CG, and more EG members appeared in multiple subgroups at the same time during the treatment phase. This implies that the interaction among students in the EG became dense, which could facilitate students’ knowledge construction [99].
Regarding the students’ academic performance at the end of the online course, the EG members showed significantly better performance than the CG members, while there was no difference in students’ foreknowledge. Furthermore, the researchers found that the academic performance of the EG was significantly correlated with perceived social presence, while such a significant correlation was not identified for the CG. This finding indicates that the effect of social presence on EG students’ academic performance was stronger than that on CG students’ academic performance. Previous studies have identified a positive relationship between students’ social presence and academic performance [19,20,21]. This study further revealed that a positive relationship between students’ social presence and academic performance may exist only under high social presence conditions.
A few limitations were identified in the present study due to the restriction of sampling. First, the majority of the participants specialized in social and human science at a Chinese university. Therefore, the present findings cannot be generalized to a large student population in other subject areas. Future research should further examine the effect of the SP-based teaching strategy on students’ learning, with consideration of different demographic backgrounds (e.g., age, cultural background, and discipline background). Additionally, we did not further investigate how the three dimensions of social presence (i.e., co-presence, psychological involvement, and behavioral engagement) interact with each other during the intervention process to increase participants’ social presence and improve their academic performance. In the future, researchers can further investigate how each dimension of social presence could influence students’ positive online learning experiences and performance.

7. Conclusions

Improving students’ learning experience and performance has been identified as one of the most challenging tasks in online education. Due to insufficient support and training to prepare teachers for online teaching, online teachers rely heavily on lecture-based instruction, such as prerecorded lectures. They might consider lecture-based online instruction to be a practical teaching approach for delivering course content. However, they may overlook the important role of other factors (e.g., social and emotional support and peer interaction) on students’ learning experience and performance. Therefore, courses that are mainly delivered by lecture-based instruction and lack learning support are very likely to result in unsatisfactory learning experiences for the students. The contribution of the current study highlights the importance of social presence in online learning by proposing an SP-based teaching strategy and approving the effectiveness of this teaching strategy with empirical evidence. This study promotes the practical application of social presence in teaching.
Moreover, the results of this study further suggest that social presence can be improved even when complicated technological tools are not applied in online teaching. Specifically, instructors and course builders can develop some well-designed online activities with sound pedagogical consideration and can effectively improve students’ perceived social presence and participation in three categories of social presence. In addition, when designing training and professional development programs for online teaching, relevant institutions should not only help teachers master the necessary technological tools but also include a specific amount of content related to teaching theories and strategies. Ensuring a seamless integration of theory and technology is essential for effective online instruction.

Author Contributions

Methodology, L.W.; software, L.X.; resources, C.H.; data curation, L.X.; writing—original draft, L.X.; writing—review and editing, C.H.; supervision, L.W.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Leading Talent Project of Philosophy and Social Science Planning of Zhejiang Province, grant number 22YJRC02ZD-1YB.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Ethics Committee for Human Experiments of Zhejiang Normal University (protocol code ZSRT2024201).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

Social presenceSP
Experimental groupEG
Control groupCG
Face-to-faceF-T-F
Emergency remote teachingERT
Community of inquiryCOI
Mobile instant messaging-facilitatedMIM

References

  1. Sobaih, A.E.E.; Hasanein, A.M.; Abu Elnasr, A.E. Responses to COVID-19 in higher education: Social media usage for sustaining formal academic communication in developing countries. Sustainability 2020, 12, 6520. [Google Scholar] [CrossRef]
  2. Liu, X.; Gong, Z.; Miao, K.; Yang, P.; Liu, H.; Feng, Z.; Chen, Z. Attitude and Performance for Online Learning during COVID-19 Pandemic: A Meta-Analytic Evidence. Int. J. Environ. Res. Public Health 2022, 19, 12967. [Google Scholar] [CrossRef] [PubMed]
  3. Winter, E.; Costello, A.; O’brien, M.; Hickey, G. Teachers’ use of technology and the impact of COVID-19. Ir. Educ. Stud. 2021, 40, 235–246. [Google Scholar] [CrossRef]
  4. An, Y.; Kaplan-Rakowski, R.; Yang, J.; Conan, J.; Kinard, W.; Daughrity, L. Examining K-12 teachers’ feelings, experiences, and perspectives regarding online teaching during the early stage of the COVID-19 pandemic. Educ. Technol. Res. Dev. 2021, 69, 2589–2613. [Google Scholar] [CrossRef] [PubMed]
  5. Baran, E.; Correia, A.-P.; Thompson, A. Transforming online teaching practice: Critical analysis of the literature on the roles and competencies of online teachers. Distance Educ. 2011, 32, 421–439. [Google Scholar] [CrossRef]
  6. Hartshorne, R.; Baumgartner, E.; Kaplan-Rakowski, R.; Mouza, C.; Ferdig, R.E. Special issue editorial: Preservice and inservice professional development during the COVID-19 pandemic. J. Technol. Teach. Educ. 2020, 28, 137–147. [Google Scholar]
  7. Pi, Z.; Zhang, Y.; Yu, Q.; Yang, J. Difficulty level moderates the effects of another’s presence as spectator or co-actor on learning from video lectures. Educ. Technol. Res. Dev. 2023, 71, 1887–1915. [Google Scholar] [CrossRef]
  8. Stull, A.T.; Fiorella, L.; Mayer, R.E. An eye-tracking analysis of instructor presence in video lectures. Comput. Hum. Behav. 2018, 88, 263–272. [Google Scholar] [CrossRef]
  9. Barr, N.B.; Johnson, J.E. Trajectories in Turmoil: A Case Study of Engineering Students’ Reactions to Disruptions in Their Community of Practice. IEEE Trans. Dependable Secur. Comput. 2021, 64, 38–51. [Google Scholar] [CrossRef]
  10. Başal, A.; Eryılmaz, A. Engagement and affection of pre-service teachers in online learning in the context of COVID 19: Engagement-based instruction with web 2.0 technologies vs direct transmission instruction. J. Educ. Teach. 2021, 47, 131–133. [Google Scholar] [CrossRef]
  11. Kern, P.; Tague, D.B. Students’ Perception of Online Learning During COVID-19: A U.S.-Based Music Therapy Survey. J. Music Ther. 2022, 59, 127–155. [Google Scholar] [CrossRef] [PubMed]
  12. Walsh, S.; Weber, A.; Bell, A. Pedagogical foundations to online lectures in health professions education. Rural Remote Health 2020, 20, 6038. [Google Scholar] [CrossRef]
  13. Kim, J.; Song, H.; Luo, W. Broadening the understanding of social presence: Implications and contributions to the mediated communication and online education. Comput. Hum. Behav. 2016, 65, 672–679. [Google Scholar] [CrossRef]
  14. Lowenthal, P.R.; Dunlap, J.C. Social presence and online discussions: A mixed method investigation. Distance Educ. 2020, 41, 490–514. [Google Scholar] [CrossRef]
  15. Wei, C.-W.; Chen, N.-S.; Kinshuk. A model for social presence in online classrooms. Educ. Technol. Res. Dev. 2012, 60, 529–545. [Google Scholar] [CrossRef]
  16. Weidlich, J.; Bastiaens, T.J. Explaining social presence and the quality of online learning with the SIPS model. Comput. Hum. Behav. 2017, 72, 479–487. [Google Scholar] [CrossRef]
  17. Weidlich, J.; Bastiaens, T.J. Designing sociable online learning environments and enhancing social presence: An affordance enrichment approach. Comput. Educ. 2019, 142, 103622. [Google Scholar] [CrossRef]
  18. Zuo, M.; Hu, Y.; Luo, H.; Ouyang, H.; Zhang, Y. K-12 students’ online learning motivation in China: An integrated model based on community of inquiry and technology acceptance theory. Educ. Inf. Technol. 2021, 27, 4599–4620. [Google Scholar] [CrossRef]
  19. Qureshi, M.A.; Khaskheli, A.; Qureshi, J.A.; Raza, S.A.; Yousufi, S.Q. Factors affecting students’ learning performance through collaborative learning and engagement. Interact. Learn. Environ. 2021, 31, 2371–2391. [Google Scholar] [CrossRef]
  20. Yang, J.C.; Quadir, B.; Chen, N.-S.; Miao, Q. Effects of online presence on learning performance in a blog-based online course. Internet High. Educ. 2016, 30, 11–20. [Google Scholar] [CrossRef]
  21. Yin, B.; Yuan, C.-H. Blended learning performance influence mechanism based on community of inquiry. Asia Pac. J. Educ. 2022, 1–16. [Google Scholar] [CrossRef]
  22. Layne, M.; Ice, P. Merging the best of both worlds: Introducing coi-TLP model. In Teaching and Learning Online: New Models of Learning for a Connected World; Sutton, B., Basiel, A., Eds.; Routledge: New York, NY, USA, 2014; pp. 3–20. [Google Scholar]
  23. Lowenthal, P.R.; Dunlap, J.C. Investigating students’ perceptions of instructional strategies to establish social presence. Distance Educ. 2018, 39, 281–298. [Google Scholar] [CrossRef]
  24. Chen, Y.; Gao, Q. Visualizing timeline-anchored comments enhanced social presence and information searching in video-based learning. Comput. Appl. Eng. Educ. 2023, 31, 1306–1320. [Google Scholar] [CrossRef]
  25. Pi, Z.; Hong, J.; Yang, J. Does instructor’s image size in video lectures affect learning outcomes? J. Comput. Assist. Learn. 2017, 33, 347–354. [Google Scholar] [CrossRef]
  26. Wang, H.; Pi, Z.; Hu, W. The instructor’s gaze guidance in video lectures improves learning. J. Comput. Assist. Learn. 2019, 35, 42–50. [Google Scholar] [CrossRef]
  27. Zajonc, R.B. Social facilitation. Science 1965, 149, 269–274. [Google Scholar] [CrossRef]
  28. Brand, S.; Reimer, T.; Opwis, K. Effects of metacognitive thinking and knowledge acquisition in dyads on individual problem solving and transfer performance. Swiss J. Psychol. 2003, 62, 251–261. [Google Scholar] [CrossRef]
  29. Biocca, F.; Harms, C.; Burgoon, J.K. Toward a More Robust Theory and Measure of Social Presence: Review and Suggested Criteria. Presence Virtual Augment. Real. 2003, 12, 456–480. [Google Scholar] [CrossRef]
  30. Le, K. Pre-Recorded Lectures, Live Online Lectures, and Student Academic Achievement. Sustainability 2022, 14, 2910. [Google Scholar] [CrossRef]
  31. Katai, Z.; Iclanzan, D. Impact of instructor on-slide presence in synchronous e-learning. Educ. Inf. Technol. 2023, 28, 3089–3115. [Google Scholar] [CrossRef]
  32. Murphy, E.; Rodríguez-Manzanares, M.A.; Barbour, M. Asynchronous and synchronous online teaching: Perspectives of Canadian high school distance education teachers. Br. J. Educ. Technol. 2011, 42, 583–591. [Google Scholar] [CrossRef]
  33. Metz, C.J.; Metz, M.J. The benefits of incorporating active learning into online, asynchronous coursework in dental physiology. Adv. Physiol. Educ. 2022, 46, 11–20. [Google Scholar] [CrossRef] [PubMed]
  34. Buxton, E.C. Pharmacists’ Perception of Synchronous Versus Asynchronous Distance Learning for Continuing Education Programs. Am. J. Pharm. Educ. 2014, 78, 8. [Google Scholar] [CrossRef] [PubMed]
  35. Islam, M.; Kim, D.-A.; Kwon, M. A Comparison of Two Forms of Instruction: Pre-Recorded Video Lectures vs. Live ZOOM Lectures for Education in the Business Management Field. Sustainability 2020, 12, 8149. [Google Scholar] [CrossRef]
  36. Moridani, M. Asynchronous video streaming vs. synchronous videoconferencing for teaching a pharmacogenetic pharmacotherapy course. Am. J. Pharm. Educ. 2007, 71, 16. [Google Scholar] [CrossRef]
  37. Kubey, R.W.; Lavin, M.J.; Barrows, J.R. Internet use and collegiate academic performance decrements: Early findings. J. Commun. 2001, 51, 366–382. [Google Scholar] [CrossRef]
  38. Somenarain, L.; Akkaraju, S.; Gharbaran, R. Student perceptions and learning outcomes in asynchronous and synchronous online learning environments in a biology course. MERLOT J. Online Learn. Teach. 2010, 6, 353–356. [Google Scholar]
  39. Bolliger, D.U.; Inan, F.A. Development and validation of the Online Student Connectedness Survey (OSCS). Int. Rev. Res. Open Distrib. Learn. 2012, 13, 41–65. [Google Scholar] [CrossRef]
  40. Gherghel, C.; Yasuda, S.; Kita, Y. Interaction during online classes fosters engagement with learning and self-directed study both in the first and second years of the COVID-19 pandemic. Comput. Educ. 2023, 200, 104795. [Google Scholar] [CrossRef]
  41. Guo, L.; Du, J.; Zheng, Q. Understanding the evolution of cognitive engagement with interaction levels in online learning environments: Insights from learning analytics and epistemic network analysis. J. Comput. Assist. Learn. 2023, 39, 984–1001. [Google Scholar] [CrossRef]
  42. Yuan, J.; Kim, C. Guidelines for facilitating the development of learning communities in online courses. J. Comput. Assist. Learn. 2014, 30, 220–232. [Google Scholar] [CrossRef]
  43. Accettone, S.L.W. Student Perceptions of Remote Chemistry Lecture Delivery Methods. J. Chem. Educ. 2021, 98, 3667–3679. [Google Scholar] [CrossRef]
  44. Choe, R.C.; Scuric, Z.; Eshkol, E.; Cruser, S.; Arndt, A.; Cox, R.; Toma, S.P.; Shapiro, C.; Levis-Fitzgerald, M.; Barnes, G.; et al. Student Satisfaction and Learning Outcomes in Asynchronous Online Lecture Videos. CBE—Life Sci. Educ. 2020, 18, ar55. [Google Scholar] [CrossRef] [PubMed]
  45. Goob, J.; Erdelt, K.; Güth, J.-F.; Liebermann, A. Dental education during the pandemic: Cross-sectional evaluation of four different teaching concepts. J. Dent. Educ. 2021, 85, 1574–1587. [Google Scholar] [CrossRef] [PubMed]
  46. Kositanurit, W.; Vivatvakin, S.; Kaikaew, K.; Varachotisate, P.; Burana, C.; Chayanupatkul, M.; Thanprasertsuk, S.; Wangsaturaka, D.; Kulaputana, O. Asynchronous online lecture may not be an effective method in teaching cardiovascular physiology during the COVID-19 pandemic. BMC Med. Educ. 2022, 22, 162. [Google Scholar] [CrossRef] [PubMed]
  47. Tadesse, T.; Gillies, R.M.; Manathunga, C. Shifting the instructional paradigm in higher education classrooms in Ethiopia: What happens when we use cooperative learning pedagogies more seriously? Int. J. Educ. Res. 2020, 99, 101509. [Google Scholar] [CrossRef]
  48. Short, J.; Williams, E.; Christie, B. The Social Psychology of Telecommunications; John Wiley and Sons: London, UK, 1976. [Google Scholar]
  49. Ke, F. Examining online teaching, cognitive, and social presence for adult students. Comput. Educ. 2010, 55, 808–820. [Google Scholar] [CrossRef]
  50. Rourke, L.; Anderson, T.; Garrison, D.R.; Archer, W. Assessing social presence in asynchronous text-based computer conferencing. J. Distance Educ. 1999, 14, 51–70. [Google Scholar]
  51. Garrison, D.R.; Anderson, T.; Archer, W. Critical inquiry in a text-based environment: Computer conferencing in higher education. Internet High. Educ. 1999, 2, 87–105. [Google Scholar] [CrossRef]
  52. Sung, E.; Mayer, R.E. Five facets of social presence in online distance education. Comput. Hum. Behav. 2012, 28, 1738–1747. [Google Scholar] [CrossRef]
  53. Tu, C.-H. On-line learning migration: From social learning theory to social presence theory in a CMC environment. J. Netw. Comput. Appl. 2000, 23, 27–37. [Google Scholar] [CrossRef]
  54. Tu, C.-H. The relationship between social presence and online privacy. Internet High. Educ. 2002, 5, 293–318. [Google Scholar] [CrossRef]
  55. Goffman, E. The Presentation of Self in Everyday Life; Anchor: Garden City, NY, USA, 1959. [Google Scholar]
  56. Hwang, H.; Park, S. Being together: User’s subjective experience of social presence in CMC environments. In Proceedings of the Human-Computer Interaction: Interaction Design and Usability, Beijing, China, 22–27 July 2007. [Google Scholar]
  57. Biocca, F.; Harms, C.; Gregg, J. The networked minds measure of social presence: Pilot test of the factor structure and concurrent validity. In Proceedings of the 4th Annual International Workshop on Presence, Philadelphia, PA, USA, 21–23 May 2001. [Google Scholar]
  58. Richardson, J.C.; Maeda, Y.; Lv, J.; Caskurlu, S. Social presence in relation to students’ satisfaction and learning in the online environment: A meta-analysis. Comput. Hum. Behav. 2017, 71, 402–417. [Google Scholar] [CrossRef]
  59. Sivunen, A.; Nordbäck, E. Social Presence as a Multi-Dimensional Group Construct in 3D Virtual Environments. J. Comput. Commun. 2015, 20, 19–36. [Google Scholar] [CrossRef]
  60. Song, H.; Kim, J.; Park, N. I know my professor: Teacher self-disclosure in online education and a mediating role of social presence. Int. J. Human–Computer Interact. 2019, 35, 448–455. [Google Scholar] [CrossRef]
  61. Grech, J. Exploring nursing students’ need for social presence and its relevance to their learning preferences. Nurs. Open 2022, 9, 1643–1652. [Google Scholar] [CrossRef]
  62. Miao, J.; Ma, L. Students’ online interaction, self-regulation, and learning engagement in higher education: The importance of social presence to online learning. Front. Psychol. 2022, 13, 815220. [Google Scholar] [CrossRef]
  63. Lee, M.; Lim, J. Do online teaching and social presences contribute to motivational growth? Distance Educ. 2023, 44, 66–85. [Google Scholar] [CrossRef]
  64. Cobb, S.C. Social presence, satisfaction, and perceived learning of RN-to-BSN students in web-based nursing courses. Nurs. Educ. Perspect. 2011, 32, 115–119. [Google Scholar] [CrossRef]
  65. Liu, S.Y.; Gomez, J.; Yen, C.J. Community college online course retention and final grade: Predictability of social presence. J. Interact. Online Learn. 2009, 8, 165–182. [Google Scholar]
  66. Reio, T.G.; Crim, S.J. Social presence and student satisfaction as predictors of online enrollment intent. Am. J. Distance Educ. 2013, 27, 122–133. [Google Scholar] [CrossRef]
  67. Hostetter, C.; Busch, M. Community matters: Social presence and learning outcomes. J. Scholarsh. Teach. Learn. 2013, 13, 77–86. [Google Scholar]
  68. Samad, S.; Nilashi, M.; Ibrahim, O. The impact of social networking sites on students’ social wellbeing and academic performance. Educ. Inf. Technol. 2019, 24, 2081–2094. [Google Scholar] [CrossRef]
  69. Çakiroğlu, U.; Kahyar, S. Modelling online community constructs through interaction data: A learning analytics based Approach. Educ. Inf. Technol. 2022, 27, 8311–8328. [Google Scholar] [CrossRef] [PubMed]
  70. Horzum, M.B. Interaction, Structure, Social Presence, and Satisfaction in Online Learning. Eurasia J. Math. Sci. Technol. Educ. 2017, 11, 505–512. [Google Scholar] [CrossRef]
  71. Miao, J.; Chang, J.; Ma, L. Teacher–Student Interaction, Student–Student Interaction and Social Presence: Their Impacts on Learning Engagement in Online Learning Environments. J. Genet. Psychol. 2022, 183, 514–526. [Google Scholar] [CrossRef]
  72. Wang, M.-J.; Chen, H.C. Social presence for different tasks and perceived learning in online hospitality culture exchange. Australas. J. Educ. Technol. 2013, 29, 667–684. [Google Scholar] [CrossRef]
  73. Savvidou, C. ‘Thanks for sharing your story’: The role of the teacher in facilitating social presence in online discussion. Technol. Pedagog. Educ. 2013, 22, 193–211. [Google Scholar] [CrossRef]
  74. Hall, A.; Herrington, J. The development of social presence in online Arabic learning communities. Australas. J. Educ. Technol. 2010, 26, 1012–1027. [Google Scholar] [CrossRef]
  75. Yen, C.-J.; Tu, C.-H. A Multiple-Group Confirmatory Factor Analysis of the Scores for Online Social Presence: Do They Measure the Same Thing across Cultural Groups? J. Educ. Comput. Res. 2011, 44, 219–242. [Google Scholar] [CrossRef]
  76. Akyol, Z.; Vaughan, N.; Garrison, D.R. The impact of course duration on the development of a community of inquiry. Interact. Learn. Environ. 2011, 19, 231–246. [Google Scholar] [CrossRef]
  77. Hilliard, L.P.; Stewart, M.K. Time well spent: Creating a community of inquiry in blended first-year writing courses. Internet High. Educ. 2019, 41, 11–24. [Google Scholar] [CrossRef]
  78. Kuşcu, E.; Deryakulu, D. Effect of type of computer mediated communication tools on social and cognitive presence in online learning community. Hacet. Univ. Egit. Fak. Derg.-Hacet. Univ. J. Educ. 2011, 41, 349–359. [Google Scholar]
  79. Poquet, O.; Kovanović, V.; De Vries, P.; Hennis, T.; Joksimović, S.; Gašević, D.; Dawson, S. Social Presence in Massive Open Online Courses. Int. Rev. Res. Open Distrib. Learn. 2018, 19, 43–68. [Google Scholar] [CrossRef]
  80. Dunlap, J.C.; Lowenthal, P.R. Tweeting the night away: Using Twitter to enhance social presence. J. Inf. Syst. Educ. 2009, 20, 129–136. [Google Scholar]
  81. Guo, C.; Shea, P.; Chen, X. Investigation on Graduate Students’ Social Presence and Social Knowledge Construction in Two Discussion Settings. Educ. Inf. Technol. 2021, 27, 2751–2769. [Google Scholar] [CrossRef]
  82. Akcaoglu, M.; Lee, E. Using Facebook groups to support social presence in online learning. Distance Educ. 2018, 39, 334–352. [Google Scholar] [CrossRef]
  83. Lowenthal, P.R.; Dunlap, J.C. From pixel on a screen to real person in your students’ lives: Establishing social presence using digital storytelling. Internet High. Educ. 2010, 13, 70–72. [Google Scholar] [CrossRef]
  84. Nam, C.W. The effects of digital storytelling on student achievement, social presence, and attitude in online collaborative learning environments. Interact. Learn. Environ. 2017, 25, 412–427. [Google Scholar] [CrossRef]
  85. Sanders-Smith, S.C.; Smith-Bonahue, T.M.; Soutullo, O.R. Practicing teachers’ responses to case method of instruction in an online graduate course. Teach. Teach. Educ. 2016, 54, 1–11. [Google Scholar] [CrossRef]
  86. Zydney, J.M.; Denoyelles, A.; Seo, K.K.-J. Creating a community of inquiry in online environments: An exploratory study on the effect of a protocol on interactions within asynchronous discussions. Comput. Educ. 2012, 58, 77–87. [Google Scholar] [CrossRef]
  87. Aragon, S.R. Creating social presence in online environments. New Dir. Adult Contin. Educ. 2003, 2003, 57–68. [Google Scholar] [CrossRef]
  88. Cassell, J.; Sullivan, J.; Prevost, S.; Churchill, E. (Eds.) Embodied Conversational Agents; MIT Press: Cambridge, UK, 2000. [Google Scholar]
  89. Garrison, D.R.; Cleveland-Innes, M.; Fung, D.T. Student role adjustment in online communities of inquiry: Model and instrument validation. J. Asynchronous Learn. Netw. 2004, 8, 61–74. [Google Scholar] [CrossRef]
  90. Borgatti, S.P.; Everett, M.G.; Freeman, L.C. Ucinet for Windows: Software for Social Network Analysis; Analytic Technologies: Harvard, MA, USA, 2002. [Google Scholar]
  91. Kothari, A.; Hamel, N.; MacDonald, J.-A.; Meyer, M.; Cohen, B.; Bonnenfant, D. Exploring Community Collaborations: Social Network Analysis as a Reflective Tool for Public Health. Syst. Pract. Action Res. 2014, 27, 123–137. [Google Scholar] [CrossRef]
  92. Tirado, R.; Hernando, A.; Aguaded, J.I. The effect of centralization and cohesion on the social construction of knowledge in discussion forums. Interact. Learn. Environ. 2015, 23, 293–316. [Google Scholar] [CrossRef]
  93. Wu, J.-Y.; Nian, M.-W. The dynamics of an online learning community in a hybrid statistics classroom over time: Implications for the question-oriented problem-solving course design with the social network analysis approach. Comput. Educ. 2021, 166, 104120. [Google Scholar] [CrossRef]
  94. Luce, R.D.; Perry, A.D. A method of matrix analysis of group structure. Psychometrika 1949, 14, 95–116. [Google Scholar] [CrossRef]
  95. Ertem, Z.; Veremyev, A.; Butenko, S. Detecting large cohesive subgroups with high clustering coefficients in social networks. Soc. Netw. 2016, 46, 1–10. [Google Scholar] [CrossRef]
  96. Huang, C.; Yu, J.; Wu, F.; Wang, Y.; Chen, N. Uncovering emotion sequence patterns in different interaction groups using deep learning and sequential pattern mining. J. Comput. Assist. Learn. 2024, 40, 1777–1790. [Google Scholar] [CrossRef]
  97. Li, X.; Li, Y.; Wang, R.; Li, J. Exploring fluctuations in collaborative engagement: How do cognitive and socio-emotional interaction intertwine in online collaborative learning? Educ. Technol. Res. Dev. 2024, 1–25. [Google Scholar] [CrossRef]
  98. Tang, Y.; Hew, K.F. Does mobile instant messaging facilitate social presence in online communication? A two-stage study of higher education students. Int. J. Educ. Technol. High. Educ. 2020, 17, 15. [Google Scholar] [CrossRef]
  99. Lin, X.; Hu, X.; Hu, Q.; Liu, Z. A social network analysis of teaching and research collaboration in a teachers’ virtual learning community. Br. J. Educ. Technol. 2016, 47, 302–319. [Google Scholar] [CrossRef]
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Figure 1. The SP-based teaching strategy.
Figure 1. The SP-based teaching strategy.
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Figure 2. Network sociograms of the exposure phase.
Figure 2. Network sociograms of the exposure phase.
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Figure 3. Network sociograms of the treatment phase.
Figure 3. Network sociograms of the treatment phase.
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Table 1. Activities designed under the guidance of the SP-based teaching strategy.
Table 1. Activities designed under the guidance of the SP-based teaching strategy.
DimensionActivity NameDescription
Co-presenceLearner identificationFilling in personal information on the course platform (e.g., name, grade, age, specialty, and hobby).
Psychological involvementLearning expectationsExpressing their online learning needs and expectations.
Learning contractsFollowing the rules for rewards and punishments during online learning.
Emotional disclosureProviding emotional cues during online communication (e.g., humor and emoticons in public).
Behavioral engagementStudy groupsForming study groups and creating WeChat groups for online communication.
Group contractsSetting shared learning goals and clarifying reward and punishment, peer responsibilities, and obligations.
Table 2. Overview of the procedure of the experimental study.
Table 2. Overview of the procedure of the experimental study.
PhaseDuration
(1) Exposure phase
The EG and CG participated in the lecture-based online learning activities in learning modules 1–2.		Week 1–3
(2) Pretest phase
The EG and CG were assessed on their perceived social presence and foreknowledge.		40 min in Week 3
(3) Treatment phase
The CG would receive the lecture-based instruction in learning modules 3–6.
The EG would receive the lecture-based online learning activities and intervention in learning modules 3–6.		Week 4–9
(4) Posttest phase
The EG and CG were assessed on their perceived social presence and academic performance.		70 min in week 10
Total timeTen weeks
Table 3. A comparison of student’s perceived social presence between the control and experimental groups pre- and posttests.
Table 3. A comparison of student’s perceived social presence between the control and experimental groups pre- and posttests.
GroupNMeanSDtd
PretestCG4316.426.241.2020.27
EG3814.746.34
PosttestCG4322.215.10−7.486 ***1.68
EG3831.185.69
Note. CG = control group; EG = experimental group; N = number; SD = standard deviation; t = T-value; d = Cohen’s effect size; *** p < 0.001.
Table 4. Units of analysis.
Table 4. Units of analysis.
PhaseGroupCategory
Affective ResponseInteractive ResponseCohesive ResponseTotal
ExposureEG0819
CG1517
TreatmentEG25141554
CG1719
Note. CG = control group; EG = experimental group.
Table 5. Structure of network sociograms.
Table 5. Structure of network sociograms.
GroupPhaseDensity aAverage DistanceDistance-Based Cohesion bHybrid Reciprocity c
CGExposure0.01381.9400.0190.0000
Treatment0.04372.9570.0800.2222
EGExposure0.02352.5400.0360.0000
Treatment0.20202.6050.4440.2152
Note. CG = control group; EG = experimental group; a Range 0–1, higher values indicate a greater connection; b Range 0–1, higher values indicate greater cohesiveness; c Range 0–1, higher values indicate a greater proportion of reciprocity.
Table 6. Subgroups of the CG in the treatment phase.
Table 6. Subgroups of the CG in the treatment phase.
GroupMembers
CG14, 27, 41, 43
EG9, 10, 15, 25, 32, 38
9, 15, 24, 32, 38
9, 19, 24, 32, 38
6, 9, 19, 24
4, 20, 21, 31
7, 8, 17, 36
7, 17, 30, 36
7, 12, 17, 36
8, 17, 18, 33
8, 27, 33, 34
13, 22, 28, 30
Note. CG = control group; EG = experimental group.
Table 7. A comparison of student’s foreknowledge.
Table 7. A comparison of student’s foreknowledge.
GroupNMeanSDtd
CG4385.494.50−1.0880.24
EG3886.745.81
Note. CG = control group; EG = experimental group; N = number; SD = standard deviation; t = T-value; d = Cohen’s effect size.
Table 8. A comparison of students’ academic performance.
Table 8. A comparison of students’ academic performance.
GroupNMeanSDp
CG4388.292.830.001
EG3889.861.96
Note. CG = control group; EG = experimental group; N = number; SD = standard deviation; p = p-value.
Table 9. A bivariate correlation coefficient of students’ perceived social presence and academic performance.
Table 9. A bivariate correlation coefficient of students’ perceived social presence and academic performance.
SDCorrelation Coefficient
CGAcademic performance2.830.25
EGAcademic performance1.960.41 *
Note. CG = control group; EG = experimental group; SD = standard deviation; * Correlation is significant at the 0.05 level (2-tailed).
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Xia, L.; Wang, L.; Huang, C. Implementing a Social Presence-Based Teaching Strategy in Online Lecture Learning. Eur. J. Investig. Health Psychol. Educ. 2024, 14, 2580-2597. https://doi.org/10.3390/ejihpe14090170

AMA Style

Xia L, Wang L, Huang C. Implementing a Social Presence-Based Teaching Strategy in Online Lecture Learning. European Journal of Investigation in Health, Psychology and Education. 2024; 14(9):2580-2597. https://doi.org/10.3390/ejihpe14090170

Chicago/Turabian Style

Xia, Liangliang, Lianghui Wang, and Changqin Huang. 2024. "Implementing a Social Presence-Based Teaching Strategy in Online Lecture Learning" European Journal of Investigation in Health, Psychology and Education 14, no. 9: 2580-2597. https://doi.org/10.3390/ejihpe14090170

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