Chung, K. S. K., & Paredes, W. C. (2015). Towards a Social Networks Model for Online Learning & Performance. Educational Technology & Society, 18 (3), 240–253.

240 ISSN 1436-4522 (online) and 1176-3647 (print). This article of the Journal of Educational Technology & Society is available under Creative Commons CC-BY-ND-NC 3.0 license (https://creativecommons.org/licenses/by-nc-nd/3.0/). For further queries, please contact Journal Editors at ets-editors@ifets.info.

Towards a Social Networks Model for Online Learning & Performance

Kon Shing Kenneth Chung*and Walter Christian Paredes Complex Systems Research Group, Project Management Program, The University of Sydney, Australia //

ken.chung@sydney.edu.au // walter.paredes.e@gmail.com *Corresponding author

(Submitted May 28, 2014; Revised December 16, 2014; Accepted December 22, 2014) ABSTRACT

In this study, we develop a theoretical model to investigate the association between social network properties, “content richness” (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we refer to its structural, position and relationship attributes. Analysis of data collected from an e-learning environment shows that rather than performance, social learning correlates with properties of social networks: (i) structure (density, inter-group and intra-network communication) and (ii) position (efficiency), and (iii) relationship (tie strength). In particular, individuals who communicate with internal group members rather than external members express higher tendencies of “content richness” in social learning. The contribution of this study is three-fold: (i) a theoretical development of a social network based model for understanding learning and performance, which addresses the lack of empirical validation of current models in social learning; (ii) the construction of a novel metric called “content richness” as a surrogate indicator of social learning; and (iii) demonstration of how the use of social network analysis and computational text-mining approaches can be used to operationalize the model for studying learning and performance. In conclusion, a useful implication of the study is that the model fosters understanding social factors that influence learning and performance in project management. The study concludes that associations between social network properties and the extent to which interactions are “content-rich” in eLearning domains cannot be discounted in the learning process and must therefore be accounted for in the organizational learning design.

Keywords Social networks, Structural holes, Strength of weak ties, Individual learning, Group learning, Social learning, Performance, Situated learning, Connectivism, e-Learning, Learning analytics

Introduction New ways of social interaction, learning and performance (in the form of learning outcomes) have been heavily influenced by the appearance of different Internet technologies and Massive Open Online Courses (MOOCs) such as Coursera and Khan Academy. These platforms provide stimulating and interactive channels of communication that foster the creation and exchange of user-generated content for learning. Learning is a social process of progressive knowledge acquisition that is shaped by individuals and their interaction with others who can contribute new ideas, opinions and experiences (Rosen, 2010). Thus, social networks within which people interact play an important role in the learning process by expanding the possibilities of learners to reach new sources of information, and by providing (existing and latent) channels for open collaboration among individuals (Haythornthwaite, 2002b; Greenhow, 2011). Despite the attractive advantages presented by scholars about the impact of technology in the learning process, there is still a lack of understanding of the dynamics of social interaction within learning communities. Therefore, the motivating questions that inspire this study are (i) Is there an interplay between social networks, learning and performance? (ii) If so, what is the role of social learning in the inherent relationship between properties of social networks and performance? (iii) How does one quantify and measure learning within a social context? (iv) How does one account for social network properties of structure, relations and position in modelling learning for the purpose of learning analytics? In this exploratory study, we develop a theoretical model based on social learning and social network theories to understand how knowledge professionals engage in learning and performance, both as individuals and as groups. The study also focuses on how an individual’s levels of participation and depth of engagement in the learning process are impacted by social interactions. The following section conducts a review of relevant learning theories and social network theories to arrive at a social networks model for understanding learning and performance. The model is then tested within an e-learning domain in a Group of Eight (Go8) university in Australia. The Go8 (group of Eight) comprises eight leading universities in Australia (www.go8.edu.au). The section on Results discusses the findings of

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the study, followed by a discussion of these results in light of theory. Implications of the study, limitations and avenue for future research are provided in the conclusion. Conceptual foundations There are many different theories describing the general aspects and perspectives of human learning. According to Lave and Wenger (1991), Situated Learning Theory (SLT) supports the notion that learning takes place in social situations where individuals develop skills by interacting with others who can provide them with insights about existing knowledge and previous personal experiences within a “community of practice.” They argue that knowledge is acquired in a context that normally involves the practical use of that knowledge, in what we commonly know as “learn by doing.” Thus, social interactions play a fundamental role in order to involve learners in a community that has a determined behavior and beliefs. In enabling situated learning, social technologies in particular have great potential and educational value due to their inherent capacity to increase learners’ motivation and engagement through participation and knowledge creation (Greenhow, 2011). Due to its notion of community, SLT presents an interesting perspective to analyze learning and performance in that it is closely aligned with the social networks perspective where a social phenomenon such as learning can be understood in terms of the interactions amongst the learners and their mentors.

Towards a model for learning and performance through social networks Social network analysis (SNA) enables the study of social systems from a structural perspective through the identification of behavioral patterns based on node and tie attributes (Freeman, 2006). One of the fundamental principles motivating studies regarding the relationship among learning, performance and social networks is that an individual’s social structure can influence an individual’s access to valuable resources (Leavitt, 1951; Brass, 1984; Coleman, 1988; Ibarra, 1993; Borgatti, 2005). Those resources that are rich in new information and knowledge, and depending on the level of engagement of the individual, can be conducive and translated into considerable improvements for performance and learning (Chung et al., 2009, 2010). Borgatti and Cross (2003) defined a formal model to explain social behavior during the learning process and the importance of relations to facilitate the search of information. In distributed settings, the density of interactions within social networks of learners facilitate rich information exchange and allows for developing a sense of belonging of the learners in a distributed setting (Haythornthwaite, 2002a). In the case of Yoon (2011), social network graphs (or sociograms) of student interactions were used in learning settings for students to make informed decisions about their choice of interactions with others, which in turn assisted in their understanding of complex socioscientific issues. Similarly, Tomsic and Suthers (2006) investigated the social network structure of Hawaiian police officers and how the introduction of an online discussion tool affected their learning their booking operation and increased collaboration across districts. They concluded that formation of new collaborative ties is more significant for learning rather than raw frequency of interaction. In a more recent study, Hommes et al. (2012) studied the influence of social networks, motivation, social integration and prior performance on the learning of 301 medical students. While they claimed to be the first to use social networks to study its effect on student learning, a number of key authors (Haythornthwaite, 2002a; Siemens et al., 2011; Paredes et al., 2012) in the domain of learning analytics researching in this area has been omitted. Furthermore, Hommes et al. (2012) utilised degree centrality only in their study to represent the social network construct and found it to be the key predictor for student learning. Thus, it is interesting to examine theories of network properties that explain how information is disseminated through ties within networks, and how network structures are conducive to the learning process and performance.

Strength of weak ties theory One of the most seminal works related to information diffusion and learning is documented by Granovetter (1973). He reasoned that as individuals connect with closely-knit groups bound together by strong ties, information diffuses rapidly and becomes redundant. So although dense networks indicate high degree of communication, new

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information come from weak ties, which act as bridges and connects one to new groups of people. In the context of learning, these weak ties are instrumental for new learning.

Krackhardt and Stern (1988) developed a measure to capture the extent to which ties are represented internally or externally within the groups. This is also referred to as the E-I index, which ranges from -1, meaning that ties are completely internal or intra-unit based, to 1, meaning that ties are completely external or inter-unit based. For instance, groups could be based on certain individual-based attributes such as group affiliation (e.g., office site location, or coursework or research student group, etc.) or roles (e.g., marketing, development, etc.). In organizational settings, groups that have ties that are externally based (i.e., high E-I indices) are more likely to implement large-scale organizational change and collaborate better (Nelson, 1989; McGrath et al., 2003), whereas groups with ties that are mostly internally based (i.e., low E-I indices) demonstrate higher resistance to external pressure (McGrath et al., 2003). It can thus be argued that when it comes to learning and performance, individuals who have E-I indices are those who demonstrate higher or richer levels of information exchange; such individuals will thus be those who are most likely to perform better.

Structural holes theory Digressing from ties and groups, there is also a great deal of literature documenting the positive association between network position and performance. Burt postulates that one can optimize one’s network by efficiently maintaining one’s ties in non-redundant contacts such that one is effectively connected to a diverse variety of groups of closely connected contacts (e.g., a clique), where the groups themselves are not connected. The bridging of such holes within the network structure is termed structural holes, which yields information and control benefits. Thus, two network design principles in play here are efficiency and effectiveness. Efficiency entails maximizing the number of non-redundant contacts to maximize the gains through structural holes. Effectiveness means the preservation of primary contacts in the network considering also the contact’s diversity principle. As documented in the section on Measures below, the measure of efficiency incorporates effectiveness as well.

Research model and hypotheses

Figure 1. Social networks model for understanding learning and performance

While most social network studies in the context of learning have generally been associated with individual and group performance or medium of communication, very few have incorporated learning as an important construct in

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their model (Haythornthwaite, 2001; Yang et al., 2003; Chiocchio, 2007). Furthermore, given the unprecedented advancement and adoption of social technologies, this study provides much needed evidence in the learning and knowledge analytics domain to help understand how networks interact with technology to foster learning and performance in an information technology-dominant era (Bennett et al., 2010). It is anticipated that the theoretical model depicted below based on network and social learning theories would address these research gaps. Therefore, based on the discussion above, the following hypotheses are proposed, as depicted diagrammatically in Figure 1: H1: Network structure: Density of an individual’s network is negatively associated with learning H2: Network position: Efficiency of an individual’s network is positively associated with learning H3: Network relation: H3a: The extent to which an individual engages in communication within the network (intra-network

communication) is positively associated with learning H3b: The extent to which an individual contributes internally and externally to his group (inter-group

communication) is positively associated with learning H3c: Weak ties within an individual’s network is positively associated with learning H4: Learning is positively associated with performance

Context and methodology The e-learning environment The domain for this study is an e-learning environment where an online project management course was delivered for the Project Management Graduate Program in a leading “Group of Eight (Go8)” university in Australia. Being a postgraduate program that is similar to an MBA, the course was undertaken by 36 full-time working industry professionals ranging from healthcare to banking, information technology and engineering. Students were based locally, nationally and globally. The online mode of study required a high level of engagement with the course activities and peers through the university’s e-Learning website. In addition, the e-learning platform provides a channel for synchronous communication via chat, and asynchronous communication via group discussion boards. The discussion board is further classified into varying forums including “Public” and “Group” forums (which are private to the group). Within each forum, students can post and reply to messages. Assessments included an individual assignment (15%), an online quiz (10%), a group assignment (25%), and a final exam (50%). For the purpose of this study, group assignment marks were left out of the analysis as the primary focus is on individual learning and performance. Group interactions, however, were considered given that group communications added further insight into an individual’s level of engagement. Groups ranged from two to four members, with 12 groups in total. As the entire learning took place online, analyzing communication structures of such ‘virtual’ groups is another boon given that coordinating and collaborating with team members located in different time zones is one of the challenges of virtual collaboration and one of the capabilities expected to be developed by individuals as part of learning. Data collection, storage and extraction The main collaborative tool provided for group coordination and collaboration was the two discussion forums. The public forum focused upon solving general questions about the course, assignments and so on. The private forums, or group forums were created exclusively for internal group coordination and collaboration. Every student had the chance to post messages to the lecturer, other students using the public forum or directly to a group member using the private forums. In total there were 845 messages in the public forum, and a total of 722 across the private forum. Given that the purpose of this study is to model learning and performance of students only, interactions to and from the lecturer were discarded.

The data collection, storage and extraction comprised of the following steps and can be depicted in Figure 2 below: (1) Database development (2) Development of a Java program for network data extraction and measures calculation (3) Visualization of sociograms using social network analysis tools (4) Message content classification (5) Collation of network and attribute data for statistical analysis

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Figure 2. Processes involved in data collection, storage and extraction

Database development The information of the messages was extracted directly from the online discussion forums. Due to the unstructured nature of the message logs, a preliminary process of data preparation was executed. As a first step, a database schema (Figure 3) was designed so that the data could be loaded into a MySQL database. This provided us with the required flexibility to extract a variety of relational and attribute data about the levels of interaction.

Figure 3. Database schema for storage of social network (relational) data

Development of a Java program for network data extraction and measures calculation Once the database was loaded, a small Java program was written to extract the information required to generate the node data and tie data required. This application provides some important functionality for this study that allows us to: • Generate the message frequency matrix • Calculate the contribution index (Gloor et al., 2003) • Calculate the External- Internal Index (Krackhardt et al., 1988) • Calculate the Content Richness Score • Calculate Average Tie Strength • Generate Node and Tie data

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Visualization of sociograms using social network analysis tools In addition, the Java program (Figure 4) facilitates the data extraction for the visualization of communication networks at various points in time (total of 6 periods) as shown in Figure 6.

Figure 4. Java program for extracting relational data and network-level measures

After extraction, visualization analysis was done using UCINet and Netdraw to generate the network sociograms and statistics (Borgatti et al., 2002). The sociograms below show progressive visualizations of individuals and groups interactions and its evolution over time (classified into 6 periods) shown in Figure 5 & 6.

Table 1. Number of new and total messages over 6 cumulative periods Period From To Cumulative no. of new messages Cumulative total no. of messages

1 27/07/2009 16/08/2009 97 97 2 27/07/2009 06/09/2009 88 185 3 27/07/2009 27/09/2009 155 340 4 27/07/2009 18/10/2009 206 546 5 27/07/2009 08/11/2009 288 834 6 27/07/2009 26/11/2009 11 845

Figure 5. Legend for groups (colour of node) and degree (shape of nodes)

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Period 1

Period 2

Period 3

Period 4

Period 5

Period 6 Figure 6. Visualization of sociograms over 6 cumulative periods

The UCINet program also calculated network-level measures such as density and structural holes (efficiency). Computation of other network-level measures (discussed in the following section) such as density, average tie strength, contribution index, external-internal index and content richness index were provided by the customised Java program. Message content classification As discussed before, given the lack of evidence of measures of social learning using a structural perspective, it is arguable that the main source of information about social learning in virtual teams resides in the richness of the dialogues between team members. A meaningful exchange of dialogue among team members and classmates is instrumental to enhancing their learning process. Thus, to develop a surrogate measure of social learning, for each message in the forums, we analyzed its content in order to identify behavioral patterns and sought to classify such patterns. The classification was based on past research that defined several methods to categorize different types of communication based on specific message features such as length (Licoppe et al., 2005), channel of dissemination (Peters, 1999; Licoppe et al., 2005), content (Pérez-Alcázar et al., 2003), and meaning (Gilbert et al., 2005), among others.

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In this study, we have defined a classification method based on message content and meaning in order to categorize each message sent in the public and private forums. We term such categories “content richness.” The five categories of content richness, with non-exhaustive descriptions, as listed in Table 2, are: • Empty Message: Inexistent content, file exchange without dialogue, greeting messages. • Team Building Message: Team member personal introductions and very basic coordination. Final group closing

activities, congratulations for group achievements and recognition for mutual cooperation. Team building messages support the creation of a sense of common goal and a shared set of beliefs and values.

• Dissemination Message: Information about group submissions and notifications about new document versions. Information dissemination is fundamental to keep all team members on track and aware of the status of project activities.

• Coordination Message: Team meeting coordination, a very important factor considering the time zone difference issue that some groups have to face.

• Collaboration Message: Messages that add value to the group work in terms of knowledge creation. Problem-solving dialogues, different insights about group work issues and project activities.

Thus, in terms of content richness, we consider the last category the most important; however, in terms of social learning all the other categories are also important as indicators of evidence of social learning. Every category was assigned a different weight that represents the content richness of the messages classified in each category (see Table 1). This parameter is considered later to calculate the content richness scores for an individual as explained further below.

Table 2. Content categories and their assigned weights, with examples Weight Content category Message example

0 Empty “Alright, see you later!” “Bye,” “Thanks.” 1 Team Building “Excellent work, team,” “The last task has really got me enjoying this group work.” 2 Dissemination “I submitted the last version of our report,” “The deadline has been extended.” 3 Coordination “Let’s meet tomorrow at 7pm,” “I can write this section of the report. John, can you

do the other part and Emily integrate it all?” 4 Collaboration “Dear Peter, I think your answer to the question is correct. However, I found this

article in which the authors analyse the issues from the different perspective. Please consider also…”

Collation of network and attribute data for statistical analysis Finally, after all network measures were computed for each individual, the measures were stored as an attribute data in a spreadsheet format for further statistical analysis in SPSS. This master spreadsheet contained all network and attribute-level data for each individual. Measures We adopted an egocentric approach to collect data from the e-learning course social network. Ego networks are formed by a focal node called “ego,” the nodes to which that ego node is directly connected (alters), and the connections or ties among alters (Scott, 2000). Density: Density D is described as a measure of network cohesiveness and is defined as the relation of the existing number of ties to the maximum number of ties possible in a directed graph of n nodes:

where xij is the value of the connection from i to j.

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Efficiency: Effective size is a measure of the number of alters minus the average degree of the alters within the ego network (Burt, 1992). The effective size of an ego’s network has been defined as:

where i is the ego, j is a primary contact, and q is also a primary contact who has strong ties with the ego i (represented by piq) and j (represented by mjq). Efficiency is measured then by dividing the effective size by the number of alters in the ego’s network. Contribution Index: Gloor et al. (2003) introduces the notion of Contribution Index (CI) to measure the level of participation in social learning settings. The contribution index is defined as:

where i is the contributor (ego), Σsi is the total of messages sent by i and Σri is the total of messages received by i. The contribution index value can be in a range from -1 to +1. If the learner mostly received messages, then his or her contribution index will be close to -1. On the other hand, if the learner mostly sent messages the contribution index will be close to +1. In terms of social learning we are looking for highly interactive dialogues. A contribution index near to 0 is indicative of a balanced dialogue of the learner with his or her team colleagues. External-Internal Index: Krackhardt and Stern (1988) described a measure of interaction for both intra and inter work teams. External-Internal (E-I) index is a measure that compares the number and average strength of external ties to internal ties within different sub-groups in a network. The E-I index has been defined as follows:

where q is the ego, Σeq represents the total number of external messages sent by q, and Σiq represents the total number of messages sent within the team. Similarly to the contribution index presented before, the E-I index ranges from -1 (only internal group communication) to +1 (indicating interaction only with external contacts rather than with members of the same team). Content Richness Score: Each message of the dataset was classified according to the level of richness of its content. Each class was assigned with a weight that reflects the meaningfulness of the message in the dialogue. This is a novel contribution of the study - the Content Richness score. Content Richness (CR) is a measure of learning engagement in a dialogic context where the meaningful information exchange among team members drives the individual and group learning process and is thus evidence of learning. The CR score is defined as:

where q represents the ego, mci represents the message content value of the message i, n is the total number of messages sent by q, and max(mc) is the maximum possible value of message content quality. The CR score range from 0 to 1. If a learner’s CR score is 0 that means that his or her level of contribution richness to the discussions was non-existent. On the other hand, a CR score of 1 means a highly meaningful participation and engagement in course and group discussions. The importance of the CR score for this study is that we hypothesized that the

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learner’s performance is directly related to his or her level of engagement in a social learning environment, and this metric can help us to determine if there is such relation between these two indicators. Average Tie Strength: In keeping with social network literature (Marsden et al., 1984), we used frequency of contact to calculate average tie strength. Therefore, for the purpose of this study, a learner’s tie strength has been measured as the average of all his or her tie strength (frequency of contact) to all other actors in the network. Performance Measures: As stated previously, during the semester the students were assessed on three individual components - individual assignment (15%), online quiz (10%), and final exam (50%). Results As per Table 3, the mean of the CI is well balanced (CI = .015), and tends towards zero, which indicates an equally distributed rate of messages sent and received. In terms of E-I index, the mean value (E-I = -.872) indicates that there was more internal communication rather than dialogues external to the groups, which was expected given the large number of messages of internal group communication in the dataset. The mean CR score is quite high (CR = .667) which is indicative of meaningful content exchange within the groups.

Table 3. Descriptive statistics of measures Min Max Mean SD Engagement

Contribution Index (CI) -1.000 1.000 0.011 0.334 External-Internal Index (E-I) -1.000 1.000 -0.872 0.359 Content Richness Score (CR) .000 .875 0.667 0.151

Network Position Efficiency .333 1.083 0.769 0.251

Network Structure Density .000 1.000 0.447 0.436

Relations (Ties) Average Tie Strength .000 16.667 4.895 4.252

Pearson’s correlation analysis was used to test the hypotheses (see Table 4 for a detailed description of the correlations as at end of period 6 (i.e., overall semester)).

Table 4. Correlation matrix

Individual assignment Quiz Exam CI E-I CR Efficiency Tie

strength Individual assignment

Quiz .281* Exam .379* .885** CI -.550** .045 -.109 E-I .035 .083 .171 -.464** CR .311* .341* .253 .344* -.354* Efficiency -.297* .049 -.095 .304* -.138 .394** Density .227 -.101 .069 -.318* .173 -.406** -.90** Tie strength .116 .44 .131 .72 -.271 .422** .066 .46 Note. **Correlation is significant at the 0.01 level (1-tailed). *Correlation is significant at the 0.05 level (1-tailed). In summary, the significant results are summarized below: • Network Structure: There is a significant negative relationship between network density and CR score, r = -.406,

p (one-tailed) < 0.01. In terms of social learning, this result makes sense because it is better to have a few meaningful dialogues, rather than many meaningless conversations. Given this result, we find support for H1.

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• Network Position: Network efficiency is significantly correlated with CR score, r = .394, p (one-tailed) < 0.01. Highly efficient learners are connected with contacts that are expected to provide good quality content (high CR), so they can fulfil the informational needs of the learner without having to look for other sources (Burt, 1992). This result provides us with evidence to support H2. Thus, when a learner has many sources of information, the cost of maintaining those connections increases as well as the level of information redundancy in the network. Consequently, his or her level of network efficiency decreases. Even though theoretically, a decrement in efficiency should affect learner’s performance, the results obtained do not allow us to conclude such phenomena (i.e., no statistically significant relationship between any of the performance variables and efficiency).

• Engagement: A significant positive correlation exists between CR and CI (r = .344, p < 0.05 (one-tailed)), thus

lending support to H3a. According to the Gloor’s definition of CI (Gloor et al., 2003), an optimal contributor would present a balanced rate of messages sent compared to the number of messages received. Therefore, the CI value should tend to be zero for optimal communication. The conclusion that we can make from this result is that learners with higher CR score send more messages than they receive, and as a consequence, there is no reciprocity in terms of meaningful content exchange for social learning.

• In addition, the CR score is significantly negatively correlated to E-I index, r = -.354, p < 0.05(one-tailed), thus

allowing us to reject H3b. In the case of E-I index we are also looking for a balanced rate of internal and external communication (Krackhardt et al., 1988). A high E-I index indicates a relatively higher communication by an individual to those outside his group relative to those internal to his group. This is beneficial for avoiding “group think.” Accordingly, those who communicate more frequently internally within groups relative to externally outside groups are also engaged in higher or richer levels of communication. This can be attributed to the fact that the large number of internal group messages in comparison to the external ones influences the E-I index. In fact, there were more interactions between group members, rather than among external contacts, which indicates that learning as evidenced by content richness took place within groups rather than outside of groups. This result is very likely due to the large number of meaningful internal dialogues.

• Network Ties: There is a significant positive relationship between the average strength of ties and CR score, r =

.422, p < 0.01(one-tailed). Therefore, there is sufficient evidence to reject H3c. The stronger the tie the more frequently the contacts occur. This implication means that contacts with high level of interaction tend to mutually exchange valuable information. These dialogues are rich in content and provide more in depth insights about the topics of learning. Although Granovetter’s theory may not hold true in this circumstance, other researchers have claimed that strong ties are symbol of closeness and trust, which are two determinant components for social learning (Lave et al., 1991; Krackhardt, 1992).

• Performance: There is a significant positive relationship between CR score and the individual assignment

marks, r = .311, as well as between CR score and the quiz mark, r = .341, both p < 0.05(one-tailed). However the exam result, which has the highest assessment weight in terms of learning outcome, does not seem to be significantly associated with CR score and for none of the engagement measures proposed in this study. Taken altogether, we consider that these results are somewhat indicative enough of how meaningful dialogic exchange among contacts can enhance learners’ performance. Therefore, we find partial support for H4.

The following table (Table 5) summarizes the results of the hypothesis testing:

Table 5. Summary of hypothesis testing results

Hypothesis number Brief description Support/No support H1 Network structure & Learning Support H2 Network position & Learning Support H3a CI index & Learning Support H3b E-I Index & Learning No support H3c Network relations & Learning No support H4 Performance and Learning Partial support

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Discussion According to the results obtained after the correlation analysis we can argue that rather than performance, social learning is influenced by social network properties such as structure, relations and network position. In summary, • denser networks are not conducive to learning; • those highly efficient or who hold key brokerage positions are able to learn better than those in less efficient

network positions. • strong ties matter more so than weak ties as far as learning is concerned. This can be attributed to the complexity

associated with learning and such findings are consistent with literature (Krackhardt, 1992). • In terms of engagement, those with higher contribution indices demonstrate higher levels of learning as measured

by content richness. Furthermore, those who communicate more frequently within their groups than to those outside the group demonstrated higher levels of learning.

The findings of this study provide further evidence of the importance of social networks in affecting learning. The results are aligned with the arguments presented by Lave and Wenger (Lave et al., 1991) in their situated learning theory, and with the principles that provide the basis of Siemens’ theory on connectivism (Siemens, 2004). Content Richness (CR) was shown to be a good predictor of social learning due to the interesting findings that connect the measure with most social network characteristics modelled here. It makes sense that an individual with a less dense but more efficient network shows higher levels of engagement when interacting with his or her contacts (Burt, 1992). The exchange of meaningful information involves trust and benevolence amongst actors in the network and this is directly related to the strength of ties. In terms of limitations, first of all, the size of the dataset raises a concern about the level of representation and generalization of the results. However, given the central limit theorem’s rule of thumb of a minimum of 30 samples and the fact that this study is an exploratory one, the results are indicative of the power of social networks in influencing learning and indirectly, performance, although not generalizable to the entire population. Secondly, the classification method for classifying content richness is in preliminary stages of maturity and questions may arise in terms of reliability and robustness. However, the development of a taxonomy or vocabulary in studies of linguistics and semantic data mining could allow the automatic identification of significant keywords in a message, which could be automatically classified in a predetermined set of categories. Content mining and information retrieval techniques can provide important benefits in this area of analysis. Another limitation of this study is that most of the dialogues analyzed took place within groups, and as a consequence there is no clear evidence of interaction beyond groups, including those with the teacher (note that the purpose of this study was to model social learning amongst students only). It would be interesting to study the relation between the meanings of internal and external dialogues. However, when group formation is compulsory, according to our results we can state that the level of quality of the dialogues is significant. One important factor that should not be discarded is that group discussion boards are just one channel through which learners interacted during the semester. The messages in the discussion forum may have been followed up through use of other types of communication channels such as social media, video conferences, emails, face-to-face meetings and phone calls. These interactions, although nice to have for the study, have not been accounted for. In future, a holistic approach that accounts for such interactions, including interactions with teachers and even faculties, can be considered for further research. Conclusion Although many theories advocate the importance of social interaction in influencing learning and performance, empirical evidence is relatively few. In this study, we presented the development of a theoretical model for understanding the impact of social networks in learning and performance. The novelty of this research is driven by the construction of content-based measure called “content richness” which provides a new approach for measuring the level of engagement of learners in a social learning environment by exchanging meaningful information. We analyzed the communicational patterns of students and groups located in different cities, countries, and time zones. They were part of an online course that relied heavily on using the discussion forums as an important communication

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tool. The results show that rather than performance, social learning is highly influenced by the learners’ network of contacts. The implications of this study can be understood from a theoretical, methodological and practical standpoint. Although exploratory, this study challenges the theoretical notion of how social networks directly impact performance and questions the existence of antecedents and other confounding variables in the networks-performance equation. Methodologically, as described above, this study offers a novel approach to quantify social learning. Practically, the results are significant for educational and curriculum designers to facilitate the development of informal networks for improved learning. For instance, the number of opportunities to collaborate on a frequent basis over a semester may be considered as opposed to changing groups for varying assessments. For future research, it would be useful to run regression models with a larger dataset to test whether networks and performance is significantly mediated by social learning. In an age of MOOCs and learning analytics, such a model would allow educators, professional development leaders and academics to enhance learning and make informed decisions about designing learning strategies for an Internet-enabled society of the 21st century.

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