Mechanisms of the Learning Impact of Teacher-Organized Online Schoolwork Sharing Among Primary School Students

Abstract

Despite the increasing amount of literature on the educational potential of social media for learning, little is understood about how different functions of social media might affect learning in the K-12 context, the primary school education context in particular. This study examined the effect on learning of a key function of social media—online sharing. It examined how teacher-organized online schoolwork sharing on a social media-based platform, Seesaw, influenced a group of primary school students’ learning. Survey responses from 337 primary school students revealed that students had positive perceptions of the impact of schoolwork sharing on learning. Structural equation modeling analysis of the survey responses revealed that online schoolwork sharing influenced individual student learning primarily through enhanced perceptions of the value of online sharing for learning from peer review, which influenced learners’ efforts in schoolwork. Efforts in schoolwork then positively influenced students’ self-efficacy in learning. The findings suggest that primary school teachers should actively utilize the sharing function of social media to facilitate student learning. The findings also underscore the importance of enhancing peer review and students’ perceptions of its value in order to maximize the learning potential of online schoolwork sharing for primary school students.

Keywords

social media primary school education social learning knowledge sharing

Introduction

Social media are online tools that “allow users to create and participate in various communities through functions such as communicating, sharing, collaborating, publishing, managing and interacting” (Mao, 2014, p. 213). Educators are paying increasing attention to how the affordances of social media can be utilized to enhance learning (Doleck & Lajoie, 2018; Greenhow & Askari, 2017). Sharing has been consistently identified as an important constitutive function of social media in the various conceptualizations of the key functions of social media (Ahmed, Ahmad, Ahmad, & Zakaria, 2019; Kietzmann, Hermkens, McCarthy, & Silvestre, 2011; Lu, Luo, Liang, & Jing, 2018; Ouirdi, El Ouirdi, Segers, & Henderickx, 2014). Research has shown that social sharing is one of the driving forces for adolescents’ engagement in some social media behaviors (Dhir & Torsheim, 2016), and that the sharing of administrative messages, snapshots of class materials and notes, solved homework solutions, and so on constitute the primary form of knowledge sharing via social media by K-12 students (Asterhan & Bouton, 2017; Greenhow & Askari, 2017; Greenhow & Robelia, 2009). The sharing function of social media affords potential for social learning in both formal and informal learning contexts (Henderson, Snyder & Beale, 2013). How social publishing and sharing might influence learning is an important but underexplored issue (Asterhan & Bouton, 2017). An understanding of the mechanism underlying such influence may provide important insights into how the educational potential of sharing could be leveraged and facilitated. The aim of this study is to provide insights into this issue by examining how a group of primary school students respond to a social media-based online schoolwork sharing platform in an instructional context.

By examining the mechanism of social media-based sharing for learning, this study is expected to fill two research gaps. First, the study adds to the emerging understanding of the impact of social media use on learning (Askari, Brandon, Galvin, & Greenhow, 2018; Doleck & Lajoie, 2018). Specifically, the study aims at revealing the mechanisms underlying the learning impact of a key function of social media—online sharing. In focusing specifically on the impact of individual key functions of social media, the study may provide some nuanced insights into the learning effects of social media. Such nuanced understanding is crucial for the informed use of individual functions of social media for different instructional design and pedagogical purposes. Second, the study adds to the rather limited research on the impact of social media in the K-12 context, the primary school education context in particular (Askari et al., 2018; Greenhow & Askari, 2017; Mao, 2014). Given that age is an important factor influencing how learners perceive and use social media (Dhir, Pallesen, Torsheim & Andreassen, 2016; Dhir & Torsheim, 2016; Lu, Hao, & Jing, 2016), it is essential to shed light on primary school students’ responses to, and use of, social media. This study adds to this body of literature by examining pupils’ perceptions of their schoolwork sharing experiences via Seesaw, an online portfolio-based social media platform.

Research Backgrounds

Online Knowledge Sharing

The social aspects of social media make knowledge and information sharing one of its essential functions. Knowledge sharing is defined as “the process when an individual disseminates his knowledge (i.e., know-what, know-how, and know-why) to others” (Arief, Wahyuni, & Sensuse, 2018, p. 2). In knowledge sharing, individual disseminate “their internal stored knowledge or external knowledge sources they have at their disposal by making it accessible to other” (Asterhan & Bouton, 2017, p. 50). Knowledge sharing, by nature, entails social interaction (Bock & Kim, 2002). However, different from peer collaborative knowledge construction and creation, knowledge sharing merely involves an individual’s action of making his or her existent know-how and information sources accessible to others (Asterhan & Bouton, 2017; van Aalst, 2009; Wang & Noe, 2010).

Research studies have shown that adolescents and young adults actively engage in a variety of self-initiated online sharing activities via social media for both entertainment- and social-oriented purposes, such as photo tagging and selfie posting, and study-related purposes such as snapshots of learning materials, class notes, and so forth (Asterhan & Bouton, 2017; Dhir et al., 2016; Dhir & Torsheim, 2016). These studies have further revealed the driving forces behind such sharing behaviors. For instance, Dhir and Torsheim (2016) found that seeking likes and comments, entertainment, and social sharing gratification played a greater role in driving adolescents’ engagement in photo-tagging compared with young adults. Focusing specifically on adolescents’ self-initiated knowledge sharing experience via social media outside the school, Asterhan and Bouton (2017) reported that the most frequent sharing behaviors that secondary school students engaged in was offering assistance to peer-raised content-related questions. This was followed by sharing administrative messages such as assignments and sharing snapshots of learning materials. Finally, sharing class notes and reading summaries were the least frequent behavior. These self-initiated online knowledge sharing behaviors were driven more by cognitive motives (e.g., heightening awareness of one’s knowledge gap when trying to answer others’ questions) and affective motives (e.g., positive self-concept) than by social motives (e.g., boosting social status). Furthermore, these adolescents perceived online knowledge sharing to have potential for learning.

Although knowledge sharing has been widely acknowledged for its organizational benefits such as boosting team and organization performance, reducing production costs, enhancing work efficiency, enhancing innovation capacities, and so on (Charband & Jafari Navimipour, 2018; Wang & Noe, 2010), there is limited understanding of whether or how knowledge sharing affects individual learning (Asterhan & Bouton, 2017; Endres, Endres, Chowdhury, & Alam, 2007). On the one hand, knowledge sharing might lead to the conversion of social knowledge into individual knowledge, which may subsequently improve individual knowledge (Asterhan & Bouton, 2017; John, 2013). On the other hand, knowledge sharing may not involve the interpretation and evaluation of the information shared and may not be reflective either (van Alast, 2009). Thus, it may not induce in-depth internal or external transactive dialogues, which are essential to individual learning gains, and hence may not contribute effectively to individual learning (Asterhan & Schwarz, 2016; Resnick, Asterhan, & Clarke, 2015). Thus, whether knowledge sharing can benefit individual learning is still open to question, and understanding whether or how knowledge sharing might affect individual learning is much needed in the education context (Asterhan & Bouton, 2017).

To fill this research gap, this study aimed at answering the following research question: How do primary school students perceive the impact on learning of teacher-organized schoolwork sharing online? As online knowledge sharing is a social phenomenon involving individuals not only sharing their own work with others but also viewing others’ work, the perceived effect of this online behavior would be closely related to peer influence. Thus, this study focused specifically on how peer influence in online sharing sites might induce mechanisms beneficial to learning.

Theoretical Framework

Individuals’ behavior in any context with the presence of peers is subject to peer influence, and individual learning can take place through individuals observing the behavior of others and reproducing their actions (Harris, 2010). Cooper and Rege (2011) theorized two broad categories of peer influence on learning: (a) the social learning effect and (b) the social interaction effect. These two mechanisms work together in shaping peer influence (Burztyn et al., 2015).

The social learning effect refers to changes in perceptions and behavior that are induced by the internalized value of the behavior either from information and advice from peers or from observing the choices of peers (Bougheas, Nieboer, & Sefton, 2015; Burztyn et al., 2014). Research has shown that both attention from others and seeing others’ comments lead to a heightened contribution of content on social media (Huberman, Romero, & Wu, 2008, 2009; Joyce & Kraut, 2006). Studies have shown that students tend to attribute the benefits of online social networking-based educational activities to peer comments and exchanges (Hamid, Waycott, Kurnia, & Chang, 2015; Tower, Latimer, & Hewitt, 2014). Online schoolwork sharing makes it easy for learners to receive acknowledgment and encouragement from peers with respect to their schoolwork, and peer encouragement and exchanges may make students think positively about the value of online sharing as a learning activity. Peers’ presence on the platform may also reinforce students’ positive perceptions of the value of this behavior for learning. However, this social learning effect is contingent upon whether learners perceive the platform as a sharing platform since different individuals may perceive the same technology differently, which may subsequently shape the way they interact with the technology. Thus, the extent to which students perceive the platform as affording easy online sharing may determine the likelihood of their perceiving the platform as a valuable tool for learning by making peer review available.

Hypothesis 1 (H1): Perceived sharing affordance (PSA) may positively influence perceived value for learning from peer review (PVLPR).

Researchers have found that peer interaction on social media helps boost students’ general academic self-efficacy and self-efficacy in performing specific academic tasks (DeAndrea, Ellison, LaRose, Steinfield, & Fiore, 2012; Tower, Latimer, & Hewitt, 2014). This has also been found to be true in the case of elementary students. For instance, in Nicolaidou’s (2013) study, the fourth graders reported that peer feedback on their web-based portfolios was a major source of information on their improvement in writing performance, and peer feedback was found to have a strong positive correlation with their writing self-efficacy overtime. This suggests that the more students believe in the value of online sharing for learning via peer review, the more likely they are to perceive that the online sharing activity boosts their self-efficacy in learning (SE). Furthermore, peer feedback has been found to induce greater engagement in coursework (Kitchakarn, 2013; Xie, 2013). Viewing feedback from peers also enhances a sense of audience, which is likely to lead to greater investment in generating and sharing quality work and engagement in associated learning behaviors (Barrot, 2016; Kitsis, 2008; Rifkin, Longnecker, Leach, Davis, & Ortia, 2009). Elementary school students have also been found to exhibit greater engagement and to produce more sophisticated work as a result of peer review (Liu, Lu, Wu, & Tsai, 2016; Nicolaidou, 2013). Thus, students’ perceptions of the value of online sharing for learning via peer review might be positively associated with the efforts they invest in schoolwork.

Hypothesis 2 (H2): Perceived value for learning from peer review (PVLPR) may positively predict self-efficacy in learning (SE).

Hypothesis 3 (H3): Perceived value for learning from peer review (PVLPR) may positively influence efforts in schoolwork (ES).

The social interaction effect refers to changes in perceptions and behavior induced by strategic complementarity in efforts that are activated by imitation (imitation of not only the exact behavior but also the behaviors of related attributes), competition, or conformity to social norms (Brechwald & Prinstein, 2011; Cooper & Rege, 2011; Falk & Ichino, 2006; Lahno & Serra-Garcia, 2015). Peer modeling and conformity have been found to exert a profound influence on elementary and secondary school students’ initiation of, and self-efficacy in, physical activities and their academic and extracurricular engagement (Efrat, 2009; Juvonen, Espinoza, & Knifsend, 2012). Access to peer performance induces social comparison that is often found to be associated with greater academic engagement and task perseverance among K-12 students (Dijkstra, Kuyper, van der Werf, Buunk, & van der Zee, 2008). In other words, the stronger the students perceive the platform as an online sharing platform, the more likely they are to imitate their peers’ sharing behaviors or conform to this social norm. This tendency may influence their level of investment in online schoolwork sharing and associated learning activities that might contribute to the sharing. This was shown in Hamid et al.’s (2015) study, in which students reported that increased attention to the quality of the content they shared and enhanced self-directed learning during online social networking-based educational activities. The greater efforts exerted by students in producing quality work and in learning in general are likely to reinforce students’ confidence in learning. Thus, we hypothesized:

Hypothesis 4 (H4): Perceived sharing affordance (PSA) may positively influence efforts in schoolwork (ES).

Hypothesis 5 (H5): Efforts in schoolwork (ES) may positively predict self-efficacy in learning (SE).

Self-efficacy affects human development through activating positive expectancies and goal setting, fostering positive cognitive motivation, and regulating anxiety arousal and stressors as well as behavior choices (Bandura, 1994). The greater the self-efficacy that the individuals possess, the more likely they are to overcome challenges and combat negative experience and emotions, and, consequently, the more likelihood there is of learning taking place. Meta-analyses of the relationships between self-efficacy beliefs and academic outcomes have attested to the positive influence of self-efficacy on academic performance and persistence (Multon, Brown, & Lent, 1991; Valentine, DuBios, & Cooper, 2004). When students hold positive self-efficacy beliefs about learning from sharing, they are more likely to perceive the positive impact of sharing on learning. Thus, we hypothesized:

Hypothesis 6 (H6): Self-efficacy in learning (SE) may positively influence perceived impact on learning (PIL).

Conceptual Model

Previous studies have suggested some variables that might explain how the presence of peers in online schoolwork sharing could induce students’ positive perceptions of the impact of online sharing on learning. However, none of these studies have tested how these variables might work together to influence students’ experience and perceptions of online schoolwork sharing. The aim of this study was to construct a conceptual model, based on the existing literature, and test the model to determine how the presence of peers in online schoolwork sharing might influence students’ perceptions of its impact. Figure 1 presents the conceptual model to be tested in this study. The conceptual model delineates the factors, and the interactions thereof, that might mediate the effects of schoolwork sharing via social media on students’ academic learning outcomes.

Figure 1.

The conceptual model.

Research Method

Research Context and Participants

The study was conducted at a primary school in Hong Kong. The school was a private school with students from families of high socioeconomic status. All the students had access to computers at home. The school was a one-on-one IPad school, and the computer-student ratio in each classroom was 1:2. The Chinese language department at the school had recently started implementing Seesaw (https://web.seesaw.me/), a social media-based portfolio platform (Exley, Willis, & McCosker, 2017), from Grade 3 to Grade 6 at the beginning of the 2017 to 2018 school year. On Seesaw, students could post their learning artifacts in written, spoken, and multimedia formats. The school set up Seesaw as public accounts whereby students uploaded learning artifacts for the whole class and their own parents to view and comment on, with some teachers choosing to disable the parent comment function. The study was conducted in the second semester of the school year. The students and teachers had already accumulated half a year of experience, and thus their use was relatively stable by the time of data collection. Collecting data at this stage could also help avoid the research findings from being biased by novelty effects. Sixteen Chinese language teachers from Grades 3 to 6 with their classes were recruited for this study.

A total of 374 students from the classes were invited to participate in the survey, and 337 students returned valid complete survey responses. The participants ranged from 7 to 11 years old, with an average age of 9. The participants consisted of 72 third graders, 96 fourth graders, 93 fifth graders, and 76 sixth graders. Of the participants, 51% were female and 49% were male. The participants expressed positive interest in Chinese language learning and had positive perceptions of their Chinese language skills. They also showed a positive attitude toward sharing.

Data Sources and Research Instruments

The whole study lasted for one school semester, a period of 4 months. The primary data sources were student questionnaire responses. The questionnaire contained 20 questions that elicited students’ perceptions of the impact of online schoolwork sharing along five dimensions.

Perceived sharing affordance

Students’ perceptions of the affordance of Seesaw for easy sharing was measured by two items (α = .80), using statements such as “Seesaw allowed me to share my work with others more easily.”

Efforts in schoolwork

This construct measured students’ perceptions of their investment in enhancing the quality of their Chinese language work and in Chinese language learning in general. Three items were used to measure this construct (α = .87). Items included, “I found myself spending more time studying Chinese since we started using Seesaw”; and “Use of Seesaw made me pay more attention to the quality of my Chinese works.”

Perceived value for learning from peer review

This construct measured students’ perceptions of the value of Seesaw for learning as a result of peer review. Two items (α = .76) were used to measure this construct, using statements like “Seeing other classmates’ comments on my Chinese works on Seesaw helped improve my Chinese.”

Self-efficacy in learning

This construct measured students’ perceptions of increased confidence in Chinese language learning as a result of online sharing. Two items were used to measure this construct (α = .77) using statement such as “Use of Seesaw increased my confidence in Chinese learning.”

Perceived impact on learning

This construct measured students’ perceptions of the impact of online sharing on their Chinese language learning. Four items were used to measure this construct (α = .88) using statement such as “Seesaw made me better at Chinese reading.”

All the questions were on a Likert-type scale point of 1 to 5, with 1 being strongly disagree and 5 being strongly agree. Some background information was also collected in the questionnaire including students’ perceptions of Chinese language learning and their attitudes toward schoolwork sharing. The questionnaire was written in both English and Chinese, the medium of instruction at this bilingual school. Considering that the respondents were children, simple words and the literal meanings of the words were used in writing up the questionnaire items, negatively worded questions were avoided, and the questionnaire was kept short (Horton, 2013). The back translation method was adopted by two bilingual speakers to ensure that the Chinese and English truthfully reflected each other. The questionnaires were checked by a few classroom teachers at different grade levels to make sure the language was appropriate and understandable to their students. The survey was field tested with a few students from different grade levels on the clarity of the items, and wording issues detected were corrected. Moreover, the questionnaires were administered in class with class teachers present to ensure help was available if students encountered problems understanding any of the questions.

In addition, teacher responses were elicited on their use of Seesaw during the semester and their observation of the impact of Seesaw activities on students. These responses were collected through two open-ended questions via Google form and through informal chats with some teachers. Teachers’ perceptions were collected for triangulation with the student perceptions of the impact of Seesaw activities on learning. Some teachers also shared a few saved screen captures of student work on Seesaw, and these screen captures provided some additional insights into the nature of the activities students engaged in and their interaction around peer work (see Figure 2 for an example).

Figure 2.

Screenshot of student work and peer interaction on Seesaw.

Instrument Validation and Data Analysis

Confirmatory factor analysis (CFA) was conducted to validate the constructs and assess the quality of structural reliabilities. It tested the model fit between the proposed construct and the data set. Maximum likelihood estimation of the covariance matrix was used to assess the validity of the scale. Various model fit indices were employed to assess model fit including the χ²-based goodness-of-fit indices (χ² statistic and χ²/df), the comparative fit index (CFI), the incremental fit index (TLI), and the parsimonious absolute fit index (RMSEA). The recommended threshold for χ²/df is less than 2.0, the cut-off point for RMSEA is .05, and the values for the comparative and incremental fit indices larger than 0.95 indicate good fit (Tabachnick & Fidell, 2013). The convergent validity—measured by the standardized factor loading of each item on the underlying construct (factor loading >0.6) and the average variance extracted (AVE > 0.5)—and the discriminant validity (squared root for AVE for a given construct > the correlations of that construct with all other constructs) were also tested. The internal reliability (α > .7) and composite reliability (CR > 0.7) of the items were also checked (Hair, Black, Babin, & Anderson, 2014).

Structural equation modeling (SEM) was then conducted on the validation of the measurement model. SEM was used to evaluate the conceptual model, whereby the perceived learning was the endogenous variable and the four impact constructs were the exogenous variables (Kline, 2005). The same set of model fit indices was used. The data were analyzed in Amos 23.0 using maximum likelihood estimation to fit the model and estimate parameters.

Results

There was a wide range of ways of using Seesaw at the school as revealed by the teacher responses. Some teachers incorporated Seesaw into in-class activities (e.g., “I designed some listening, speaking, reading and writing activities and embedded these activities in Seesaw”). They used Seesaw to upload a variety of activities such as eliciting students’ responses to picture prompts, inviting students to publish their commentaries on news and review each other’s commentaries, inviting students to publish different written outputs, asking students to upload individual recordings of their read-aloud of the texts, and so on. Some teachers reported using Seesaw for homework only. Other teachers used Seesaw for both in-class work, homework, and extension activity sharing, and for the sharing of reflections on class activities or field trips. Students’ responses were open to the whole class to access. Interaction with teachers and analyses of student feedback on Seesaw posts in the screen captures showed that students’ comments on classmates’ posts were mostly content-related (e.g., “It looks like you had a lot of fun! I wish I could go there next Christmas”; “You must be good friends”; “I like the card you made”). In a lot of cases, their feedback was in the form of likes.

Descriptive Statistics

The descriptive analysis of the five constructs showed that the students had slightly positive perceptions of the impact of online schoolwork sharing (Table 1), and there was large variation in their perceptions.

Table 1.

Descriptive Statistics.

	Mean	Standard Deviation
Perceived impact on learning	3.04	0.97
Perceived sharing affordance	3.68	0.77
Perceived value for learning from peer review	3.17	1.00
Effort in schoolwork	3.05	0.97
Self-efficacy in learning	3.30	1.03

The students agreed that the use of Seesaw helped them in developing various Chinese language skills (mean [M] = 3.04, standard deviation [SD] = 0.97). The students felt that the use of Seesaw boosted their confidence in Chinese language learning (M = 3.30, SD = 1.03), which was also reflected in some teachers’ observations. One teacher commented: “Students were more motivated when using Seesaw, and their confidence in learning Chinese increased over time,” and another recounted: “Students seemed to be more confident in writing and sharing their writing with others.” The students showed an appreciation of the positive effect of peer comments on their Chinese language learning (M = 3.17, SD = 1.00), a learning effect that was also commented on by teachers. Some teachers pointed out that Seesaw benefited students’ Chinese language learning by providing enhanced opportunities to access feedback: “Students got immediate feedback.” The students also agreed that their ES and learning increased with the introduction of Seesaw (M = 3.05, SD = 0.97). Teachers’ observations also confirmed this positive perception. Teachers reported observing greater student engagement in class. One teacher remarked: “Students seemed to be more engaged and attentive in class when using Seesaw.” This observation was echoed by another teacher who commented: “Students seemed to invest more in practising Chinese and take classroom activities more seriously.”

Thus, students’ questionnaire responses indicated that students did perceive a positive impact of Seesaw on learning. Teachers’ observations also confirmed these dimensions of the positive impact of Seesaw on student learning. The SEM analysis results were then examined to understand the mechanisms behind the positive perceptions of the impact of Seesaw on learning. SEM analysis consists of two sequential steps: First testing the measurement model to examine the relationship between the latent variables and their indicators, and then testing the structural model to reveal the relationship between the latent variables.

The Measurement Model

The results of CFA confirmed the fitness of the structure of the measurement model: χ² = 1123.213, df = 386, p < .0001, χ²/df =1.60, CFI = 0.99, TLI = 0.98, RMSEA = .04 (.02, .06). The CFA results suggest that the study’s measurement model had a fair factor structure. The standardized factor loading of all items on their respective underlying constructs were all above 0.60, and the AVE for all constructs were larger than 0.50, which suggests that all the constructs had satisfactory convergent validity. Furthermore, the squared root of AVE for all constructs were larger than the intercorrelations between the constructs, which suggests that all the constructs had satisfactory discriminatory validity. Cronbach’s α of all constructs were higher than .70, which suggests good internal reliability, and the CR of all the measurement items were also larger than 0.70 (see Table 2 and Appendix).

Table 2.

Correlation Matrix and Discriminant Validity.

Construct	PIL	PSA	PVLPR	ES	SE
Perceived impact on learning (PIL)	(.81)
Perceived sharing affordance (PSA)	.10	(.81)
Perceived value for learning frompeer review (PVLPR)	.66	.03	(.78)
Efforts in schoolwork (ES)	.76	.13	.76	(.83)
Self-efficacy in learning (SE)	.75	.13	.70	.76	(.78)

Note. Diagonal in parentheses: square root of AVE from observed variables (items); off-diagonal: correlations between constructs. The discriminant validity, indicated by greater values of the square roots of the AVEs than those of the off-diagonal elements in the correlation matrix, appeared to be satisfactory at the construct level for all constructs. AVE = average variance extracted.

The Structural Model

The conceptual model was then tested against the dataset and yielded the following model fit indices: χ²/df =1.56, CFI = 0.99, TLI = 0.99, RMSEA = .04 (.02, .06). The indices all met the recommended guidelines (Tabachnick & Fidell, 2013), which suggests that the conceptual model fit the survey data well. The model could explain 85% of the variation in students’ perceived Chinese language development over the period (see Figure 3).

Figure 3.

The structural model.Note: The bold paths were paths that were statistically significant. ***p < .001; **p < .01.

The structural model revealed that PSA of the platform had a significant positive influence on the students’ perceptions of the value of the platform for learning from peer review (β = 0.66, p < .001). PVLPR also positively influenced students’ ES (β = 0.86, p < .001). ES positively influenced SE (β = 0.74, p < .001), which in turn positively influenced perceived impact of Seesaw on learning (β = 0.91, p < .001).

Contrary to the hypotheses, PSA of the platform did not influence students’ ES directly (β = 0.07, p > .05), but rather influenced it indirectly via PVLPR (β = 0.56, p < .05). Moreover, PVLPR did not directly predict student efficacy in learning either (β = 0.22, p > .05) but rather influenced it indirectly via ES (β = 0.64, p < .05). Table 3 summarizes the hypothesis testing results of the separate paths in the conceptual model.

Table 3.

Hypothesis Testing Results.

Hypotheses	β	Sig.	Result
H1: Perceived sharing affordance → Perceived value for learning of peer review	0.66	.000	Supported
H2: Perceived value for learning from peer review → Self-efficacy in learning	0.22	.13	Not supported
H3: Perceived value for learning from peer review → Efforts in schoolwork	0.86	.000	Supported
H4: Perceived sharing affordance → Efforts in schoolwork	0.07	.32	Not supported
H5: Efforts in schoolwork → Self-efficacy in learning	0.74	.000	Supported
H6: Self-efficacy in learning → Perceived impact on learning	0.91	.000	Supported

Thus, students did perceive that the sharing function of Seesaw positively affected their Chinese language learning. This perceived positive impact primarily came from the value of viewing peer comments. The more the students perceived Seesaw as facilitating sharing, the more positively they perceived the value of Seesaw for learning through making peer review available. The boosted perception of peer review was associated with enhanced ES. Students’ elevated ES then positively influenced their self-efficacy beliefs in learning as a result of Seesaw activities, which consequently shaped their perception of the impact of Seesaw activities on the development of their Chinese language skills.

Discussion

This study explored students’ perceptions of the impact of online schoolwork sharing on learning in a less-explored educational context—the primary school education context (Greenhow & Askari, 2017; Henderson et al., 2013; Mao, 2014). The study revealed that the primary school students expressed positive perceptions of the impact of online schoolwork sharing, and these positive perceptions came from students’ positive perceptions of its value in learning from accessing peer review, which boosted students’ ES and confidence in learning. The finding suggests that although knowledge sharing is regarded as a less sophisticated form of interaction during collaborative learning and may not generate the in-depth internal or external transactive dialogues that are deemed critical to learning (Asterhan & Schwarz, 2016; Resnick et al., 2015; van Alast, 2009), it may still stimulate some perceptional and behavioral changes that could potentially benefit learning. Thus, the sharing function of social media should be actively leveraged, as a starting point, to facilitate and enhance primary school students’ learning (Asterhan & Bouton, 2017; Asterhan & Schwarz, 2016). The study focused on the sharing of schoolwork per se. Primary school students have been found to be more attentive to schoolwork, while students’ motivation for schoolwork has been found to decrease in the higher grades (Núñez, Suárez, Rosário, Vallejo, Valle, & Epstein, 2015). It is possible that the observed learning impact of schoolwork sharing might be weaker for older students since previous research has found that, among the various types of study-related knowledge that peers voluntarily share online, shared homework solutions are perceived by adolescents to be of relatively low value (Asterhan & Bouton, 2017). It may be that older children are more attuned to the other forms of school-related knowledge sharing. Thus, the relative efficacies of different types of shared knowledge across age levels deserve more attention.

This study specifically examined the peer influence mechanisms behind the impact of online schoolwork sharing on student learning. Following the current literature (Burztyn et al., 2014; Hamid et al., 2015), the study hypothesized the simultaneous activation of both social interaction (i.e., strategic complementarity in efforts) and social learning (i.e., perceived values of the behavior) influence from peers in online schoolwork sharing. However, the findings suggested a rather sequential progression from social learning to social interaction. Namely, the students’ perceptions of the sharing affordance of the platform first influenced their perceptions of the value of online sharing for learning due to peer feedback, which then influenced their ES. Therefore, viewing peers’ participatory behaviors and their shared work per se were not sufficient to enhance primary school students’ learning efforts. Only when the primary school students internalized the value of online schoolwork sharing for making peer review available did they invest greater efforts in quality sharing and associated learning behaviors.

The insignificant effect on imitative or conforming behaviors observed in the online sharing context might have something to do with the target student population in this study—elementary school students. Elementary school students may be less attuned to peers’ academic behaviors and less likely to engage in academic social comparison with peers for self-evaluation and behavioral changes than adolescents (Dweck, 2002; Keil, McClintock, Kramer, & Platow, 1990; Wang, Kiuru, Degol, & Salmela-Aro, 2018). Moreover, unlike adolescents, elementary school students’ academic efforts are influenced primarily by the behaviors of their intimate friends and not so much by group-level behaviors (Molloy et al., 2011). Thus, the expected imitative effect of boosting academic engagement from peer modeling might be significant and much stronger if adolescents are examined, since adolescents are found to engage frequently in social comparison with peers who perform better than them, an upward comparison that is more likely to induce greater academic engagement (Dijkstra et al., 2008; Dumas et al., 2005).

The findings showed that perceiving the accessibility to and the value of peer feedback in online schoolwork sharing did not significantly influence SE. This might have had something to do with the nature of the peer feedback provided by this group of primary school students as the feedback consisted primarily of likes and comments on the content of the shared work rather than on the use of language devices. Such feedback might have served more as an emotional boost that might fuel more associated behaviors, and less of a cognitive device exerting a strong influence on learners’ SE (Wang & Wu, 2008). Wang and Wu (2008) found that elaborated feedback online that provided information to help in developing higher level thinking contributed more to self-efficacy. Therefore, it is worth exploring what the model would look like in the case of different types of peer feedback received in online schoolwork sharing.

Findings from this study highlight that online schoolwork sharing, despite being simple and lacking the attributes of knowledge co-construction that is considered pivotal to learning, is an important form of collaborative learning that deserves attention, at least in the case of elementary school students (Asterhan & Schwarz, 2016). Moreover, the learning impact of online schoolwork sharing lies in enhanced behavioral engagement as a result of peer comments. The findings give rise to the following suggestions for teachers: First, online schoolwork sharing may induce greater academic engagement both inside and outside the classroom among learners, so it would be worthwhile implementing pedagogical interventions at least at the elementary education context. Second, given that the presence of peer comments and the perceived value of peer review mediated the degree to which online schoolwork sharing promoted greater effort in learning, special attention should be paid to increasing online peer comments and enhancing students’ perceptions of the values of peer review so as to increase the likelihood of online schoolwork sharing inducing greater learner investment in quality online sharing and learning. In addition, because the extent to which students perceived the sharing affordance of the platform was found to shape the degree of peer influence on academic engagement derived from the platform, teachers need to promote students’ positive perceptions of the platform and increase the likelihood of their perceiving the sharing affordance of the platform. The findings also suggest that online knowledge sharing deserves the attention of researchers, and more research efforts are needed to gain a nuanced understanding of the learning impact of online knowledge sharing (Asterhan & Bouton, 2017). Online knowledge sharing may take many forms and take place in different contexts, and this study only explored the sharing of schoolwork artifacts in the instructional context. Future research may explore the use of other forms of online knowledge sharing such as offering help, sharing class notes, and learning resources, among primary school students as well as across different age groups. The learning mechanisms of online knowledge sharing in self-initiated informal learning contexts also need exploration. Moreover, the revealed working mechanisms of peer influence in the online sharing context revealed in this study need to be examined across different student populations, given that learners of different age levels are found to approach and utilize social media differently (Dhir & Torsheim, 2016; Dhir et al., 2016; Dhir & Tsai, 2017).

Limitations and Future Research

This study revealed the nature of peer influence mechanisms behind the impact of social media-mediated schoolwork sharing on learning. It showed that online schoolwork sharing may benefit individual learning, and that it is an area that deserves further research attention. As this study focused on learner perceptions of online schoolwork sharing, it relied primarily on the survey responses from a group of upper grade primary school students, the validity and reliability of which might be questionable. To minimize the problems, the survey was constructed and administered in ways that enhanced the validity and reliability of the responses (Horton, 2013) and teachers’ voices were collected to confirm students’ responses. The study provided some initial understanding of the mechanisms underlying peer influence in online schoolwork sharing. Future researchers may wish to supplement self-report responses with detailed analysis of students’ behaviors on the platform, and how they make use of the shared materials to generate a deeper understanding, in order to explore further why different peer influence mechanisms work in different ways in influencing learning. The teachers in this study exhibited differences in their use of Seesaw in their classes. It is possible that different ways of using Seesaw might induce different types of peer influence on learning. Future research may also tap deeper into how the nature of teacher use of such social media-based portfolio platforms might determine the impact of online knowledge sharing on student learning. Moreover, the study examined the impact of online schoolwork sharing on language learning, whereby the schoolwork of the students serves as additional language exposure that would benefit language learning. This might explain the high explanatory power of online schoolwork sharing (85%) in predicting language learning in this study. The explanatory power might be different for the learning of other disciplines.

Conclusion

This study examined the impact on learning of teacher-organized online schoolwork sharing on a social media-based platform in the primary school context. Elementary school students had positive perceptions of its learning impact, and this positive impact primarily came from the enhanced academic engagement resulting from peer comments. The findings add to the debate on whether online knowledge sharing alone can contribute to learning and showed that this form of collaborative learning, despite lacking sophisticated knowledge co-construction, did have its value in enhancing primary school students’ behavioral engagement. Thus, the findings suggest that the sharing function of social media deserves to be capitalized on to benefit primary school students’ learning.

Research on social media has suggested that it has educational potential in both formal and informal contexts (Greenhow & Askari, 2017). However, social media is a composite of different functions, and understanding how these individual functions of social media contribute to learning provides important information to inform the effective use of different social media features in instructional design and teaching for different pedagogical purposes. This study adds to this body of knowledge by revealing the working mechanisms behind the learning impact of the sharing function of social media in the instructional context. Its findings suggest the educational potential of the sharing function of social media in general, and call for more research to contribute to a fuller understanding of the learning potential of social media-supported knowledge sharing in different forms and contexts, and shed nuanced insights into the nexus of learning and individual functions of social media other than sharing.

Compliance with Ethical Standards

Research Involving Human Participants

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee (HREC, EA1711001) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants involved in the study.

Appendix: Main Survey Instrument

		R ²	Standardized factor loading	AVE	Mean	SD	α
Perceived impact on learning	Seesaw makes me better at Chinese reading	0.70	0.84	0.66	3.04	0.97	.88
	Seesaw makes me better at Chinese writing	0.67	0.82
	Seesaw makes me better at Chinese speaking	0.61	0.78
	Seesaw makes me better at Chinese listening	0.64	0.80
Perceived sharing affordance	Seesaw allows me to share my work with others more easily	0.73	0.85	0.66	3.68	0.77	.80
Perceived sharing affordance	Seesaw allows me to see my classmates’ work more easily	0.59	0.77	0.66	3.68	0.77	.80
Perceived value for learning from peer review	Seeing other classmates’ comments on my Chinese works on Seesaw helped improve my Chinese	0.60	0.78	0.62	3.17	1.00	.76
Perceived value for learning from peer review	Seeing others’ comments on my Chinese works on Seesaw increased my interest in learning Chinese	0.62	0.79	0.62	3.17	1.00	.76
Efforts in schoolwork	Use of Seesaw made me pay more attention to the quality of my Chinese works	0.73	0.85	0.69	3.05	0.97	.87
	I found myself more attentive in Chinese class since we started using Seesaw	0.71	0.84
	I found myself spend more time studying Chinese since we started using Seesaw	0.64	0.80
Self-efficacy in learning	Use of Seesaw increased my confidence in Chinese learning	0.68	0.82	0.62	3.30	1.03	.77
Self-efficacy in learning	I feel proud of my Chinese after seeing my Chinese works on Seesaw	0.56	0.75	0.62	3.30	1.03	.77

Note. Impact on learning and the predictor variables were rated on a 5-point Likert-type scale, with 1 being strongly disagree and 5 being strongly agree. Convergent validity for all the measurement items and constructs indicated by 0.50 and above AVE values and 0.50 and above R² values were adequate in this study. AVE is computed by adding the squared factor loadings divided by the number of factors of the underlying construct. AVE >.50 (Fornell & Larcker, 1981) is the acceptable level of validity. SD = standard deviation; AVE = average variance extracted.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Chun Lai is an associate professor at the Academic Unit of Teacher Education and Learning Leadership, Faculty of Education, the University of Hong Kong, Hong Kong. Her research work focuses on technology-enhanced language learning in the formal and informal learning contexts.

Yun Wen is an assistant professor at the Department of Learning Sciences and Technology, National Institute of Education, Nanyang Technological University, Singapore. Her research interest includes computer-supported collaborative learning and learning design. Her research focuses on understanding how people learn through interaction and conversations in multimodal environments.

Tian Gao is a Chinese language teacher at the Chinese International School, Hong Kong. He is an active teacher researcher and is interested in understanding how technology could be used to enhance language teaching and learning.

Chin-Hsi Lin is an associate professor at the Academic Unit of Teacher Education and Learning Leadership, Faculty of Education, the University of Hong Kong, Hong Kong. His research interest includes online learning and self-regulated learning.

ORCID iDs

Chun Lai

Chin-Hsi Lin

References

Ahmed

Y. A.

Ahmad

M. N.

Ahmad

Zakaria

N. H.

(2019). Social media for knowledge-sharing: A systematic literature review. Telematics and informatics, 37, 72–112.

Arief

Wahyuni

Sensuse

D. I.

(2018). Critical success factor of knowledge sharing in online community. PACIS 2018 Proceedings, 76, 76–88.

Askari

Brandon

Galvin

Greenhow

(2018). Youth, learning and social media in K-12 education: The state of the field. In Kay

Luckin

(Eds.), Rethinking learning in the digital age: Making the learning sciences count, 13th International conference of the learning sciences (ICLS) 2018 ( Vol. 1). London, England: International Society of the Learning Sciences, pp. 344–351.

Asterhan

C. S.

Bouton

(2017). Teenage peer-to-peer knowledge sharing through social network sites in secondary schools. Computers & Education, 110, 16–34.

Asterhan

C. S.

Schwarz

B. B.

(2016). Argumentation for learning: Well-trodden paths and unexplored territories. Educational Psychologist, 51(2), 164–187.

Bandura

(1994). Self-efficacy. In Ramachaudran

V. S.

(Ed.), Encyclopaedia of human behaviour ( Vol. 4, pp. 71–81). New York, NY: Academic Press.

Barrot

J. S.

(2016). Using facebook-based e-portfolio in ESL writing classrooms: Impact and challenges. Language, Culture and Curriculum, 29(3), 286–301.

Bock

G. W.

Kim

Y. G.

(2002). Breaking the myths of rewards: An exploratory study of attitudes about knowledge sharing. Information Resources Management Journal, 15(2), 14–21.

Bougheas

Nieboer

Sefton

(2015). Risk taking and information aggregation in groups. Journal of Economic Psychology, 51, 34–47.

10.

Brechwald

W. A.

Prinstein

M. J.

(2011). Beyond homophily: A decade of advances in understanding peer influence processes. Journal of Research on Adolescence, 21(1), 166–179.

11.

Charband

Jafari Navimipour

(2018). Knowledge sharing mechanisms in the education: A systematic review of the state of the art literature and recommendations for future research. Kybernetes, 47, 7, 1456–1490.

12.

Cooper

D. J.

Rege

(2011). Misery loves company: Social regret and social interaction effects in choices under risk and uncertainty. Games and Economic Behaviour, 73(1), 91–110.

13.

DeAndrea

D. C.

Ellison

N. B.

LaRose

Steinfield

Fiore

(2012). Serious social media: On the use of social media for improving students’ adjustment to college. The Internet and Higher Education, 15(1), 15–23.

14.

Bursztyn

Jensen

(2015). How does peer pressure affect educational investments?. The quarterly journal of economics, 130(3), 1329–1367.

15.

Dhir

Pallesen

Torsheim

Andreassen

C. S.

(2016). Do age and gender differences exist in selfie-related behaviours?. Computers in Human Behavior, 63, 549–555.

16.

Dhir

Torsheim

(2016). Age and gender differences in photo tagging gratifications. Computers in Human Behavior, 63, 630–638.

17.

Dhir

Tsai

C. C.

(2017). Understanding the relationship between intensity and gratifications of Facebook use among adolescents and young adults. Telematics and Informatics, 34(4), 350–364.

18.

Dijkstra

Kuyper

Van der Werf

Buunk

A. P.

van der Zee

Y. G.

(2008). Social comparison in the classroom: A review. Review of educational research, 78(4), 828–879.

19.

Dumas

Huguet

Monteil

J. M.

Rastoul

Nezlek

J. B.

(2005). Social comparison in the classroom: Is there a tendency to compare upward in elementary school. Current Research in Social Psychology, 10(12), 166–187.

20.

Dweck

C. S.

(2002). The development of ability conceptions. In Wigfield

Eccles

J. S.

(Eds.), Development of achievement motivation (pp. 15–31). San Diego, CA: Academic Press.

21.

Doleck

Lajoie

(2018). Social networking and academic performance: A review. Education and Information Technologies, 23(1), 435–465.

22.

Efrat

M. W.

(2009). The Relationship between Peer and/or Friends' Influence and Physical Activity among Elementary School Children: A Review. Californian Journal of Health Promotion, 7, 48–61.

23.

Endres

M. L.

Endres

S. P.

Chowdhury

S. K.

Alam

(2007). Tacit knowledge sharing, self-efficacy theory, and application to the open source community. Journal of Knowledge Management, 11(3), 92–103.

24.

Exley

Willis

L. D.

McCosker

(2017). Children as advocates: The potential of using social media in the early and primary years. Practical Literacy: The Early and Primary Years, 22(2), 9–12.

25.

Falk

Ichino

(2006). Clean evidence on peer effects. Journal of Labor Economics, 24(1), 39–57.

26.

Fornell

Larcker

D. F.

(1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388.

27.

Greenhow

Askari

(2017). Learning and teaching with social network sites: A decade of research in K-12 related education. Education and Information Technologies, 22(2), 623–645.

28.

Greenhow

Robelia

(2009). Informal learning and identity formation in online social networks. Learning, Media and Technology, 34(2), 119–140.

29.

Hair

J. F.

Black

W. C.

Babin

B. J.

Anderson

R. E.

(2014). Multivariate data analysis (7th ed.). London, England: Pearson New International Edition.

30.

Hamid

Waycott

Kurnia

Chang

(2015). Understanding students’ perceptions of the benefits of online social networking use for teaching and learning. The Internet and Higher Education, 26(7), 1–9.

31.

Harris

D. N.

(2010). How do school peers influence student educational outcomes? Theory and evidence from economics and other social sciences. Teachers College Record, 112(4), 1163–1197.

32.

Henderson

Snyder

Beale

(2013). Social media for collaborative learning: A review of school literature. Australian Educational Computing, 28(2), 1–15.

33.

Horton

(2013). Improving validity and reliability in children’s self reports of technology use (doctoral thesis). Lancashire, England: University of Central Lancashire.

34.

Hsu

Y. C.

Ching

Y. H.

(2012). Mobile microblogging: Using Twitter and mobile devices in an online course to promote learning in authentic contexts. The International Review of Research in Open and Distributed Learning, 13(4), 211–227.

35.

Huberman

B. A.

Romero

D. M.

(2008). Social networks that matter: Twitter under the microscope. arXiv:0812.1045. Retrieve online on Dec. 16, 2019 from https://arxiv.org/pdf/0812.1045.pdf

36.

Huberman

B. A.

Romero

D. M.

(2009). Crowdsourcing, attention and productivity. Journal of Information Science, 35(6), 758–765.

37.

John

N. A.

(2013). The social logics of sharing. The Communication Review, 16(3), 113–131.

38.

Joyce

Kraut

R. E.

(2006). Predicting continued participation in newsgroups. Journal of Computer-Mediated Communication, 11(3), 723–747.

39.

Juvonen

Espinoza

Knifsend

(2012). The role of peer relationships in student academic and extracurricular engagement. In Handbook of research on student engagement (pp. 387-401). Springer, Boston, MA.

40.

Keil

L. J.

McClintock

C. G.

Kramer

Platow

M. J.

(1990). Children's use of social comparison standards in judging performance and their effects on self-evaluation. Contemporary Educational Psychology, 15(1), 75–91.

41.

Kietzmann

J. H.

Hermkens

McCarthy

I. P.

Silvestre

B. S.

(2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241–251.

42.

Kitchakarn

(2013). Peer feedback through blogs: An effective tool for improving students' writing abilities. Turkish Online Journal of Distance Education, 14(3), 152–164.

43.

Kitsis

S. M.

(2008). The Facebook generation: Homework as social networking. English Journal, 98(2), 30–36.

44.

Kline

R. B.

(2005). Principles and practice of structural equation modelling: Methodology in the social sciences (2nd ed.). New York, NY: Guilford Press.

45.

Lahno

A. M.

Serra-Garcia

(2015). Peer effects in risk taking: Envy or conformity? Journal of Risk and Uncertainty, 50(1), 73–95.

46.

Liu

C. C.

K. H.

L. Y.

Tsai

C. C.

(2016). The impact of peer review on creative self-efficacy and learning performance in web 2.0 learning activities. Journal of Educational Technology & Society, 19(2), 286–297.

47.

Hao

Jing

(2016). Consuming, sharing, and creating content: How young students use new social media in and outside school. Computers in Human Behavior, 64, 55–64.

48.

Luo

Liang

Jing

(2018). Measuring adolescents’ social media behavior outside and inside of school: Development and validation of two scales. Journal of Educational Computing Research, 57(5), 1108–1130.

49.

Mao

(2014). Social media for learning: A mixed methods study on high school students’ technology affordances and perspectives. Computers in Human Behaviour, 33, 213–223.

50.

Molloy

L. E.

Gest

S. D.

Rulison

K. L.

(2011). Peer influences on academic motivation: Exploring multiple methods of assessing youths' most “influential” peer relationships. The Journal of Early Adolescence, 31(1), 13–40.

51.

Nicolaidou

(2013). E-portfolios supporting primary students' writing performance and peer feedback. Computers & Education, 68, 404–415.

52.

Núñez

J. C.

Suárez

Rosário

Vallejo

Valle

Epstein

J. L.

(2015). Relationships between perceived parental involvement in homework, student homework behaviors, and academic achievement: differences among elementary, junior high, and high school students. Metacognition and learning, 10(3), 375–406.

53.

Multon

K. D.

Brown

S. D.

Lent

R. W.

(1991). Relation of self-efficacy beliefs to academic outcomes: A meta-analytic investigation. Journal of Counselling Psychology, 38(1), 30–38.

54.

Ouirdi

M. E.

El Ouirdi

Segers

Henderickx

(2014). Social media conceptualization and taxonomy: A Lasswellian framework. Journal of Creative Communications, 9(2), 107–126.

55.

Resnick

L. B.

Asterhan

C. S. C.

Clarke

S. N.

(Eds.) (2015). Socializing intelligence through academic talk and dialogue. Washington, DC: AERA.

56.

Rifkin

Longnecker

Leach

Davis

Ortia

(2009). Motivate students by having them publish in new media: An invitation to science lecturers to share and test. Paper presented at the Motivating Science Undergraduates: Ideas and Interventions, UniServe Science Proceedings.

57.

Tabachnick

B. G.

Fidell

L. S.

(2013). Using multivariate statistics (6th ed.). Boston, MA: Allyn and Bacon.

58.

Tower

Latimer

Hewitt

(2014). Social networking as a learning tool: Nursing students' perception of efficacy. Nurse Education Today, 34(6), 1012–1017.

59.

Valentine

J. C.

DuBois

D. L.

Cooper

(2004). The relation between self-beliefs and academic achievement: A meta-analytic review. Educational Psychologist, 39(2), 111–133.

60.

van Aalst

(2009). Distinguishing knowledge-sharing, knowledge-construction, and knowledge-creation discourses. International Journal of Computer-Supported Collaborative Learning, 4(3), 259–287.

61.

Wang

Noe

R. A.

(2010). Knowledge sharing: A review and directions for future research. Human Resource Management Review, 20(2), 115–131.

62.

Wang

S. L.

P. Y.

(2008). The role of feedback and self-efficacy on web-based learning: The social cognitive perspective. Computers & Education, 51(4), 1589–1598.

63.

Wang

M. T.

Kiuru

Degol

J. L.

Salmela-Aro

(2018). Friends, academic achievement, and school engagement during adolescence: A social network approach to peer influence and selection effects. Learning and Instruction, 58, 148–160.

64.

Xie

(2013). What do the numbers say? The influence of motivation and peer feedback on students’ behaviour in online discussions. British Journal of Educational Technology, 44(2), 288–301.