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Thus, a network perspective can provide a number of novel ways that learning can be represented and addressed, guide efforts in evaluation, and aid in designing learning experiences and technologies that foster and support networked learning (Haythornthwaite, 2008, 2011; Daly, 2010). SNA has been used in learning research to depict teacher and learner communication patterns from LMS data (Dawson, Bakharia, & Heathcote, 2010), to identify collaborative work patterns across different media and channels among online learners (Haythornthwaite, 1999), to identify learners who are absent or peripheral to a course’s learning network in order to identify disengaged and at-risk students (Macfadyen & Dawson, 2010), and to explore how students from different cultures interact, develop friendships, and forge learning relationships within an interactional classroom (Rientes, Héliot, & JindalSnape, 2013; see also Haythornthwaite, de Laat, & Schreurs, 2016). The network approach focuses on how patterns of interaction afford an environment for exchange of resources (Wasserman & Faust, 1994). This perspective views learning as social relations in a network: transactions, exchanges, and shared experiences that emerge from interaction between individuals, and engagement across a larger group that forms a community of learning. The characteristics of community learning exemplify the principles of SNA derived from graph theory, which looks at patterns of relational connections between nodes in a graph: Actors are seen as nodes in the network connected by relations that form interpersonal ties. In formal educational settings, actors can be teachers, students, or administrators. In informal learning settings, actors may be interested learners, students, experts, organizations, institutions, researchers, practitioners, co-workers, or collaborators. Learning can occur through interaction with other people, through participation in events, or through experiences. Thus, learning networks may be multi-modal; actors in learning networks may be people, sources, or activities (Haythornthwaite & de Laat, 2010). The relations through which these actors interact and connect — exchanges of information, provisions of support and resources, collaborations and communication — define the kind of relationship between actors, from close personal friendships to professional acquaintances, to people who do not know each ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 52 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 other beyond interacting within the same network of actors (Gruzd & Haythornthwaite, 2013; Haythornthwaite, 2008). In closer relationships, more types of exchanges between people occur and more importance is placed on these exchanges as they often demonstrate a higher level of self-disclosure and intimacy (Granovetter, 1973). Such ties are referred to as strong ties, where paired actors engage in high levels of resource sharing, are often similar to each other, and tend to know and interact with similar sets of actors within a network. Trust and familiarity between close tie relationships foster environments in which learners feel comfortable asking questions and exchanging feedback. However, due to homophily in information sources and perspectives, reliance on only strong tie relationships can result in a filter bubble where new information and differing opinions are suppressed. In contrast, weak ties exhibit fewer exchanges, fewer different types of exchanges, and are less motivated to share resources. However, the “strength of weak ties” (Granovetter, 1973) is that they are dissimilar in terms of habits, circles of friends, etc., and thus offer greater access to different resources circulating in other domains. A learning network that provides a variety of ties across varying degrees of strength and closeness is optimal in that it provides a wealth of knowledge sources and perspectives, and a variety of interaction opportunities in which learners may engage. SNA depicts conditions that support learning in several ways. SNA can reveal how information flows through ties in a network, and how a network’s structure and configuration allows knowledge to be disseminated and created across actors (Haythornthwaite, 2011). The configuration of a network may affect learning by indicating which actors have access to information and resources, and which actors lack access. In high-density networks with many links between nodes, high degrees of sharing and access to information are more probable. Sparse networks often exhibit structural holes between clusters of highly connected nodes, where specific actors may serve as information brokers, required to bridge such gaps so that information can be shared between groups (Burt, 2004). By viewing a network from the perspective of an individual learner, one can understand what information sources that learner has been exposed to and with whom they may be learning, along with where conflicts in their understanding may come from (i.e., opposing viewpoints or contradictory information), and may also reveal conflicting or complementary demands on individuals, particularly for adults at work (Haythornthwaite & de Laat, 2010). Viewing a network as a whole allows one to see how learning may be occurring across an entire set of people, and provides a view on the norms and character of the larger network to which individuals belong. For example, is the network collaborative, highly active, helpful, and inclusive? Is the network clustered into cliques? How do clusters tend to form? A whole network perspective allows one to understand the social conditions and relations that underpin learning behaviours within that network, and what holds the network together (Haythornthwaite & de Laat, 2010). Table 2 presents some examples of current social network analysis tools and their key features. ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 53 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 Table 2. Examples of social network analysis tools and key features Key features A cloud-based text and social network analysis tool that allows users to capture and import online conversational data, and build and visualize communication networks. Netlytic can automatically build chain networks and personal name networks, based on who replies to whom and who mentioned whom. Netlytic also allows for comparison of networks across a number of centrality and other network measures. Gephi A network analysis and visualization package that allows for interaction and exploratory analysis of graph data that offers a number of different layouts based on force-based algorithms, and offers common SNA metrics. Gephi also allows for visualization over time so that one can see how a network evolves across a timeline. UCINet and NetDraw A comprehensive social network analysis and visualization tool. Allows users to include and add attribute data alongside relational data typically used in SNA. Supports matrix analysis routines and multivariate statistics. NodeXL A Microsoft Excel add-in and C#/.Net library for network analysis and visualization. Adds “directed graph” as a chart type to Excel spreadsheets, and offers a number of network metrics and visualization options. R (igraph, sna, and R contains several packages that can be used for social network analysis, network packages) including igraph, sna, and network. These represent a sample of a larger collection of network analysis and visualization packages available in R. Using R for social network analysis allows one to complement SNA work with other statistical analysis within the R environment. Tool name Netlytic 3 CASE STUDY 3.1 Dataset To provide further explanation and demonstration of our analytic strategy and framework, this section focuses on several analysis methods we rely upon and how they are used in combination to generate new insights about learning. For this case study, we use a sample of public tweets posted by participants in a 2011 cMOOC led by Stephen Downes and George Siemens, called Connectivism and Connective Knowledge 2011 (CCK11, http://cck11.mooc.ca/). CCK11 ran for 12 weeks, from January to April 2011, and addressed the topic of connectivist perspectives on networked, distributed learning and construction of knowledge. Discussions and learning processes in this course were supported through the following four tasks: 1) Aggregate: Participants were given access to a wide variety of resources to read, watch, or play with. 2) Remix: Participants were encouraged to keep track of and reflect on their in-class activities using blogs or other types of online posts. ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 54 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 3) Repurpose: Participants were asked not just to repeat what other people have said, but also to create their own content. 4) Feed Forward: Participants were encouraged to share their work with others in the course or outside the course to spread the networked knowledge. Course resources were distributed through a central course site, along with online seminars delivered using Elluminate. The course, however, was not restricted to a single platform or environment. Participants were free to use a variety of technologies for sharing and participating in the course, and hence the content was distributed across the web. To keep track of their learning and sharing content, participants were encouraged to create blogs using any blogging service (e.g., blogger.com or wordpress.com), use del.icio.us, discuss on Google groups forums, tweet about items on Twitter, or use any other platform such as Flickr, Second Life, Yahoo Groups, Facebook, or YouTube. To keep track of their content, participants were asked to use the #cck11 tag in whatever content they created and shared. This tag was used by aggregators to recognize content related to the courses. The aggregated content was then displayed in an online “newsletter” created every day to highlight new content posted by learners. To collect data for our study, we scraped the archives of the daily newsletters for each course and used automated extraction for Twitter messages, discussion threads, blog posts, and comments on blogs. The platform that generated the greatest number of posts was Twitter, followed by blogs. The sample used in the case study presented here is limited to tweets using the course hashtag #CCK11, posted between January 21 and March 10, 2011. This dataset consists of 1,617 Tweets, from 467 unique Twitter users. The methods detailed in this section are available in the cloud-based text and social network analysis tool suite called Netlytic. Along with a description of text, network analysis, and visualization techniques, this section offers potential insights and explorations facilitated by such analyses. 3.2 Text Analysis 3.2.1 Most frequently used words The first step in our case was to build concise summaries of the communal textual discourse present in the dataset by identifying frequently used words (mostly nouns). Figure 1 shows a word cloud visualization of the top 50 most frequently used words in the #CCK11 Twitter chat over the data collection period. The search keyword (#CCK11) and other common words (also known as “stop-words”) such as “of,” “will,” and “to” were automatically removed prior to building this visualization. The size of a word in the visualization is directly related to the number of times it appears in the dataset relative to the other words found in that same dataset. In Netlytic, this visualization allows users to click on any of the words in the cloud in order to explore the context(s) in which the word appears. ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 55 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 Figure 1. Top 50 most frequently used words in #CCK11 Twitter chat. By exploring the top 50 words, we can group words into four broad categories. The first category includes words relevant to the class but not necessarily unexpected, including “learning,” “education,” “social,” “teaching,” and “knowledge.” The most frequently mentioned word in this category (and in the whole dataset) is “connectivism” referring to the new learning theory at the core of this class (Siemens, 2005; Ravenscroft, 2011). While one would expect to see these words in this category, their presence is a helpful check confirming that class discussions were indeed focusing on the topics related to the class objectives. Such an observation would be useful for any instructor. The second group of frequently used words includes Twitter hashtags: #edchat, #eltchat, and #edtech. The first hashtag, #edchat, was used to organize a Twitter community and weekly chats by educators wishing to discuss current trends in educational technologies and policies (http://edchat.pbworks.com/). The second hashtag, #eltchat, is described as a social network for English Language Teaching (ELT) professionals (primarily English language teachers), which is also used to facilitate weekly chat and continuous education (http://eltchat.org/). The third hashtag, #edtech, is frequently used in conjunction with #edchat by educators, technology bloggers, developers, and organizations interested in sharing some of the latest news and technology trends in academia. Other hashtags such as #edtech20 and #lak11 were used to connect class participants to relevant conferences on online education and teaching technologies. All these hashtags are highly relevant to the CCK11 class, considering its focus on “understanding of educational systems of the future.” The prevalence of hashtags other than the one for the class #CCK11 suggests that class participants were actively connecting to other relevant communities and information on Twitter, discovering and sharing relevant resources outside the class. This exemplifies Twitter’s ability to connect to other relevant people and communities, and facilitate the formation of weak ties across different communities, thereby introducing members of those communities to potentially new and diverse sources of information. ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 56 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 The third category includes a set of Twitter users frequently mentioned in the dataset1 such as @profesortbaker, @downes, and @gsiemens. These were active participants and facilitators in the course. Active Twitter users will be discussed in the following section as part of the network analysis of the communication network. The fourth category of frequent words reveals what types of online content were found to be useful and shared within the class. For example, the presence of words like “presentation,” “post,” “live,” and “video” in the word cloud suggests that Twitter is in part being used to disseminate online presentations by instructors, students, and experts. In addition to the four broad categories found in the dataset, we also observed the frequent use of the symbol “RT,” added manually or automatically to tweets when they are “retweeted” by others. The use of RTs may indicate the extent to which class participants paid attention to what others post; the prominence here suggests frequent attention to classmates’ posts with retweeting content to their own followers fulfilling the “Feed Forward” action. It is important to note that there is no suggested “optimal” ratio of retweets or replies to original posts that one might want to see in successful class discussions on Twitter. It would largely depend on the primary reasons why the social media platform, in this case Twitter, is being used in the class, and to the pedagogical approach intended by the instructor. For example, if Twitter is used as a primary forum with an intent to foster dialogue among students, then one might want to see a higher ratio of interactive-type tweets such as replies. Whatever the use and intent, we recommend the instructor establish some baseline values of the ratios based on the first couple of weeks of the class (or data from the previous iteration of the same class) and then follow the changes in ratios over time to see whether there are any sudden changes and why. In our case, there were 444 messages with RTs (27% of the total number of messages), which is comparable to that found in other Twitter communities (Suh, Hong, Pirolli, & Chi, 2010; Zhou, Bandari, Kong, Qian, & Roychowdhury, 2010; Stieglitz & Dang-Xuan, 2012). 3.2.2 Following topics over time In addition to using computer-led, top-down text analysis, the instructor may explore how a particular topic was discussed over time. Examining the distribution of messages over time may help to confirm whether students understand a new terminology after it has been introduced in the course and whether they are incorporating this new terminology as part of their vocabulary. There are couple of ways of doing this. One way is to build a chart showing the number of tweets mentioning a particular topic over time to confirm whether it was discussed in accordance with the syllabus. For example, Figure 2 shows that the words “theory” or “theories” were only mentioned by 66 Twitter users (14% of the 467 who participated in the class discussions on Twitter). The messages about theory concentrated around the 1 IDs (Twitter usernames) and associated tweets are publicly available through the CCK11 newsletters and Twitter (e.g., see http://cck11.mooc.ca/archive/11/03_01_newsletter.htm, where it says, “If you use the CCK11 tag on Twitter, your Twitter posts will be collected and listed here”). ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 57 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 second week of February and at the end of the course. Knowing this, the instructor can consider whether this accords with intentions, and adjust the syllabus or time on discussion about the topic. Figure 2. The number of tweets mentioning “theory” or “theories” over time. Alternatively, the instructor may review frequently used words over time and compare them to the course outline. Figure 3 shows the patterns of frequently used terms over the span of the course. This allows instructors to see where discussion topics followed expected course topics (according to the course outline and scheduled readings for each week), and where discussion topics diverged from expected topics. For example, week 6 of the course focused on personal learning environments and networks, and yet these terms are largely absent from the dataset. Such an analysis could be used by instructors to review curriculum for that week to identify why discussion strayed far from the topic, and perhaps provide further scaffolding or engagement for student discussion to prompt further exploration of these concepts. Figure 3. The relative number of tweets mentioning the top 100 frequently used words over time. The visualization in Figure 3 potentially also allows instructors to discover patterns and relationships between concepts that emerge from learner discussions and that may influence future design of the course. For example, instructors may choose to re-sequence or potentially merge sections of the course based on how concepts and discussions co-occur or re-emerge in relation to the course design. ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 58 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 Overall, these simple forms of text analysis allow for the confirmation

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Function of Information Processing Questions

Thus, a network perspective can provide a number of novel ways that learning can be represented and addressed, guide efforts in evaluation, and aid in designing learning experiences and technologies that foster and support networked learning (Haythornthwaite, 2008, 2011; Daly, 2010). SNA has been used in learning research to depict teacher and learner communication patterns from LMS data (Dawson, Bakharia, & Heathcote, 2010), to identify collaborative work patterns across different media and channels among online learners (Haythornthwaite, 1999), to identify learners who are absent or peripheral to a course’s learning network in order to identify disengaged and at-risk students (Macfadyen & Dawson, 2010), and to explore how students from different cultures interact, develop friendships, and forge learning relationships within an interactional classroom (Rientes, Héliot, & JindalSnape, 2013; see also Haythornthwaite, de Laat, & Schreurs, 2016). The network approach focuses on how patterns of interaction afford an environment for exchange of resources (Wasserman & Faust, 1994). This perspective views learning as social relations in a network: transactions, exchanges, and shared experiences that emerge from interaction between individuals, and engagement across a larger group that forms a community of learning. The characteristics of community learning exemplify the principles of SNA derived from graph theory, which looks at patterns of relational connections between nodes in a graph: Actors are seen as nodes in the network connected by relations that form interpersonal ties. In formal educational settings, actors can be teachers, students, or administrators. In informal learning settings, actors may be interested learners, students, experts, organizations, institutions, researchers, practitioners, co-workers, or collaborators. Learning can occur through interaction with other people, through participation in events, or through experiences. Thus, learning networks may be multi-modal; actors in learning networks may be people, sources, or activities (Haythornthwaite & de Laat, 2010). The relations through which these actors interact and connect — exchanges of information, provisions of support and resources, collaborations and communication — define the kind of relationship between actors, from close personal friendships to professional acquaintances, to people who do not know each ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 52 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 other beyond interacting within the same network of actors (Gruzd & Haythornthwaite, 2013; Haythornthwaite, 2008). In closer relationships, more types of exchanges between people occur and more importance is placed on these exchanges as they often demonstrate a higher level of self-disclosure and intimacy (Granovetter, 1973). Such ties are referred to as strong ties, where paired actors engage in high levels of resource sharing, are often similar to each other, and tend to know and interact with similar sets of actors within a network. Trust and familiarity between close tie relationships foster environments in which learners feel comfortable asking questions and exchanging feedback. However, due to homophily in information sources and perspectives, reliance on only strong tie relationships can result in a filter bubble where new information and differing opinions are suppressed. In contrast, weak ties exhibit fewer exchanges, fewer different types of exchanges, and are less motivated to share resources. However, the “strength of weak ties” (Granovetter, 1973) is that they are dissimilar in terms of habits, circles of friends, etc., and thus offer greater access to different resources circulating in other domains. A learning network that provides a variety of ties across varying degrees of strength and closeness is optimal in that it provides a wealth of knowledge sources and perspectives, and a variety of interaction opportunities in which learners may engage. SNA depicts conditions that support learning in several ways. SNA can reveal how information flows through ties in a network, and how a network’s structure and configuration allows knowledge to be disseminated and created across actors (Haythornthwaite, 2011). The configuration of a network may affect learning by indicating which actors have access to information and resources, and which actors lack access. In high-density networks with many links between nodes, high degrees of sharing and access to information are more probable. Sparse networks often exhibit structural holes between clusters of highly connected nodes, where specific actors may serve as information brokers, required to bridge such gaps so that information can be shared between groups (Burt, 2004). By viewing a network from the perspective of an individual learner, one can understand what information sources that learner has been exposed to and with whom they may be learning, along with where conflicts in their understanding may come from (i.e., opposing viewpoints or contradictory information), and may also reveal conflicting or complementary demands on individuals, particularly for adults at work (Haythornthwaite & de Laat, 2010). Viewing a network as a whole allows one to see how learning may be occurring across an entire set of people, and provides a view on the norms and character of the larger network to which individuals belong. For example, is the network collaborative, highly active, helpful, and inclusive? Is the network clustered into cliques? How do clusters tend to form? A whole network perspective allows one to understand the social conditions and relations that underpin learning behaviours within that network, and what holds the network together (Haythornthwaite & de Laat, 2010). Table 2 presents some examples of current social network analysis tools and their key features. ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 53 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 Table 2. Examples of social network analysis tools and key features Key features A cloud-based text and social network analysis tool that allows users to capture and import online conversational data, and build and visualize communication networks. Netlytic can automatically build chain networks and personal name networks, based on who replies to whom and who mentioned whom. Netlytic also allows for comparison of networks across a number of centrality and other network measures. Gephi A network analysis and visualization package that allows for interaction and exploratory analysis of graph data that offers a number of different layouts based on force-based algorithms, and offers common SNA metrics. Gephi also allows for visualization over time so that one can see how a network evolves across a timeline. UCINet and NetDraw A comprehensive social network analysis and visualization tool. Allows users to include and add attribute data alongside relational data typically used in SNA. Supports matrix analysis routines and multivariate statistics. NodeXL A Microsoft Excel add-in and C#/.Net library for network analysis and visualization. Adds “directed graph” as a chart type to Excel spreadsheets, and offers a number of network metrics and visualization options. R (igraph, sna, and R contains several packages that can be used for social network analysis, network packages) including igraph, sna, and network. These represent a sample of a larger collection of network analysis and visualization packages available in R. Using R for social network analysis allows one to complement SNA work with other statistical analysis within the R environment. Tool name Netlytic 3 CASE STUDY 3.1 Dataset To provide further explanation and demonstration of our analytic strategy and framework, this section focuses on several analysis methods we rely upon and how they are used in combination to generate new insights about learning. For this case study, we use a sample of public tweets posted by participants in a 2011 cMOOC led by Stephen Downes and George Siemens, called Connectivism and Connective Knowledge 2011 (CCK11, http://cck11.mooc.ca/). CCK11 ran for 12 weeks, from January to April 2011, and addressed the topic of connectivist perspectives on networked, distributed learning and construction of knowledge. Discussions and learning processes in this course were supported through the following four tasks: 1) Aggregate: Participants were given access to a wide variety of resources to read, watch, or play with. 2) Remix: Participants were encouraged to keep track of and reflect on their in-class activities using blogs or other types of online posts. ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 54 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 3) Repurpose: Participants were asked not just to repeat what other people have said, but also to create their own content. 4) Feed Forward: Participants were encouraged to share their work with others in the course or outside the course to spread the networked knowledge. Course resources were distributed through a central course site, along with online seminars delivered using Elluminate. The course, however, was not restricted to a single platform or environment. Participants were free to use a variety of technologies for sharing and participating in the course, and hence the content was distributed across the web. To keep track of their learning and sharing content, participants were encouraged to create blogs using any blogging service (e.g., blogger.com or wordpress.com), use del.icio.us, discuss on Google groups forums, tweet about items on Twitter, or use any other platform such as Flickr, Second Life, Yahoo Groups, Facebook, or YouTube. To keep track of their content, participants were asked to use the #cck11 tag in whatever content they created and shared. This tag was used by aggregators to recognize content related to the courses. The aggregated content was then displayed in an online “newsletter” created every day to highlight new content posted by learners. To collect data for our study, we scraped the archives of the daily newsletters for each course and used automated extraction for Twitter messages, discussion threads, blog posts, and comments on blogs. The platform that generated the greatest number of posts was Twitter, followed by blogs. The sample used in the case study presented here is limited to tweets using the course hashtag #CCK11, posted between January 21 and March 10, 2011. This dataset consists of 1,617 Tweets, from 467 unique Twitter users. The methods detailed in this section are available in the cloud-based text and social network analysis tool suite called Netlytic. Along with a description of text, network analysis, and visualization techniques, this section offers potential insights and explorations facilitated by such analyses. 3.2 Text Analysis 3.2.1 Most frequently used words The first step in our case was to build concise summaries of the communal textual discourse present in the dataset by identifying frequently used words (mostly nouns). Figure 1 shows a word cloud visualization of the top 50 most frequently used words in the #CCK11 Twitter chat over the data collection period. The search keyword (#CCK11) and other common words (also known as “stop-words”) such as “of,” “will,” and “to” were automatically removed prior to building this visualization. The size of a word in the visualization is directly related to the number of times it appears in the dataset relative to the other words found in that same dataset. In Netlytic, this visualization allows users to click on any of the words in the cloud in order to explore the context(s) in which the word appears. ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 55 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 Figure 1. Top 50 most frequently used words in #CCK11 Twitter chat. By exploring the top 50 words, we can group words into four broad categories. The first category includes words relevant to the class but not necessarily unexpected, including “learning,” “education,” “social,” “teaching,” and “knowledge.” The most frequently mentioned word in this category (and in the whole dataset) is “connectivism” referring to the new learning theory at the core of this class (Siemens, 2005; Ravenscroft, 2011). While one would expect to see these words in this category, their presence is a helpful check confirming that class discussions were indeed focusing on the topics related to the class objectives. Such an observation would be useful for any instructor. The second group of frequently used words includes Twitter hashtags: #edchat, #eltchat, and #edtech. The first hashtag, #edchat, was used to organize a Twitter community and weekly chats by educators wishing to discuss current trends in educational technologies and policies (http://edchat.pbworks.com/). The second hashtag, #eltchat, is described as a social network for English Language Teaching (ELT) professionals (primarily English language teachers), which is also used to facilitate weekly chat and continuous education (http://eltchat.org/). The third hashtag, #edtech, is frequently used in conjunction with #edchat by educators, technology bloggers, developers, and organizations interested in sharing some of the latest news and technology trends in academia. Other hashtags such as #edtech20 and #lak11 were used to connect class participants to relevant conferences on online education and teaching technologies. All these hashtags are highly relevant to the CCK11 class, considering its focus on “understanding of educational systems of the future.” The prevalence of hashtags other than the one for the class #CCK11 suggests that class participants were actively connecting to other relevant communities and information on Twitter, discovering and sharing relevant resources outside the class. This exemplifies Twitter’s ability to connect to other relevant people and communities, and facilitate the formation of weak ties across different communities, thereby introducing members of those communities to potentially new and diverse sources of information. ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 56 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 The third category includes a set of Twitter users frequently mentioned in the dataset1 such as @profesortbaker, @downes, and @gsiemens. These were active participants and facilitators in the course. Active Twitter users will be discussed in the following section as part of the network analysis of the communication network. The fourth category of frequent words reveals what types of online content were found to be useful and shared within the class. For example, the presence of words like “presentation,” “post,” “live,” and “video” in the word cloud suggests that Twitter is in part being used to disseminate online presentations by instructors, students, and experts. In addition to the four broad categories found in the dataset, we also observed the frequent use of the symbol “RT,” added manually or automatically to tweets when they are “retweeted” by others. The use of RTs may indicate the extent to which class participants paid attention to what others post; the prominence here suggests frequent attention to classmates’ posts with retweeting content to their own followers fulfilling the “Feed Forward” action. It is important to note that there is no suggested “optimal” ratio of retweets or replies to original posts that one might want to see in successful class discussions on Twitter. It would largely depend on the primary reasons why the social media platform, in this case Twitter, is being used in the class, and to the pedagogical approach intended by the instructor. For example, if Twitter is used as a primary forum with an intent to foster dialogue among students, then one might want to see a higher ratio of interactive-type tweets such as replies. Whatever the use and intent, we recommend the instructor establish some baseline values of the ratios based on the first couple of weeks of the class (or data from the previous iteration of the same class) and then follow the changes in ratios over time to see whether there are any sudden changes and why. In our case, there were 444 messages with RTs (27% of the total number of messages), which is comparable to that found in other Twitter communities (Suh, Hong, Pirolli, & Chi, 2010; Zhou, Bandari, Kong, Qian, & Roychowdhury, 2010; Stieglitz & Dang-Xuan, 2012). 3.2.2 Following topics over time In addition to using computer-led, top-down text analysis, the instructor may explore how a particular topic was discussed over time. Examining the distribution of messages over time may help to confirm whether students understand a new terminology after it has been introduced in the course and whether they are incorporating this new terminology as part of their vocabulary. There are couple of ways of doing this. One way is to build a chart showing the number of tweets mentioning a particular topic over time to confirm whether it was discussed in accordance with the syllabus. For example, Figure 2 shows that the words “theory” or “theories” were only mentioned by 66 Twitter users (14% of the 467 who participated in the class discussions on Twitter). The messages about theory concentrated around the 1 IDs (Twitter usernames) and associated tweets are publicly available through the CCK11 newsletters and Twitter (e.g., see http://cck11.mooc.ca/archive/11/03_01_newsletter.htm, where it says, “If you use the CCK11 tag on Twitter, your Twitter posts will be collected and listed here”). ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 57 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 second week of February and at the end of the course. Knowing this, the instructor can consider whether this accords with intentions, and adjust the syllabus or time on discussion about the topic. Figure 2. The number of tweets mentioning “theory” or “theories” over time. Alternatively, the instructor may review frequently used words over time and compare them to the course outline. Figure 3 shows the patterns of frequently used terms over the span of the course. This allows instructors to see where discussion topics followed expected course topics (according to the course outline and scheduled readings for each week), and where discussion topics diverged from expected topics. For example, week 6 of the course focused on personal learning environments and networks, and yet these terms are largely absent from the dataset. Such an analysis could be used by instructors to review curriculum for that week to identify why discussion strayed far from the topic, and perhaps provide further scaffolding or engagement for student discussion to prompt further exploration of these concepts. Figure 3. The relative number of tweets mentioning the top 100 frequently used words over time. The visualization in Figure 3 potentially also allows instructors to discover patterns and relationships between concepts that emerge from learner discussions and that may influence future design of the course. For example, instructors may choose to re-sequence or potentially merge sections of the course based on how concepts and discussions co-occur or re-emerge in relation to the course design. ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution – NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) 58 (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. http://dx.doi.org/10.18608/jla.2016.33.4 Overall, these simple forms of text analysis allow for the confirmation

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