Social Media Use in Higher Education · PDF file31.08.2015 · SOCIAL MEDIA IN...

SOCIAL MEDIA IN HIGHER EDUCATION

Social Media Use in Higher Education

By Kelli Buckreus

August 2015

Running head: SOCIAL MEDIA IN HIGHER EDUCATION

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Introduction

Advancing cultures of change and innovation is a key long-term (five+ years) trend identified in

the New Media Consortium’s 2015 Horizon Report for Higher Education (Johnson, Adams, Becker,

Estrada, & Freeman, 2015). Since 2004 the New Media Consortium (NMC)1 has produced its annual

Horizon Report analysing emerging technological trends and corresponding pedagogical initiatives and

challenges in higher education. The 2015 report (Johnson, et al., 2015) paints a picture of learners

connected via diverse information and communication technology (ICT) tools, including mobile and

wearable hardware devices and cloud computing apps, with a focus on greater autonomy, learner-

centredness and personalization of learning within networked and collaborative learning environments.

Key trends highlighted are the blending of formal and informal learning contexts; redesigned, flexible,

opportunistic and adaptable learning environments (e.g. flipped classrooms, bring-your-own-device,

makerspaces); and inter-institutional collaboration. All are components of open, interactive and

experiential learning, with social media positioned as key facilitators (Johnson, et al., 2015).

TheFacebook (www.thefacebook.com), later changing to Facebook (www.facebook.com), was

launched by a group of students2 at Harvard University in February 2004 (Facebook, 2015), and has since

become the most ubiquitous social media application worldwide with more than two billion registered

users and 1.49 billion active users (as of June 2015) (List of virtual communities with more than 100

million active users, 2015). Facebook is one of a suite of social media applications that have proliferated

since the release of Web 2.0, sharing common characteristics such as the replication of natural social

processes within an online digital environment to provide novel means for people to connect across vastly

expanded environments mediating geographic and other barriers (Conole & Alevizou, 2010; Conole &

McAndrew, 2010; Dron, 2006; Dron, 2007). Conole and McAndrew (2010), described in Conole &

Alevizou (2010), point to an “alignment between the affordances of digital networked media (the focus on

user-generated content, the emphasis on communication and collective collaboration) and the

fundamentals of what is perceived to be good pedagogy (socio-constructivist approaches, personalised

and experiential learning)” (Conole & Alevizou, 2010, pg 10).

1 International in scope, comprising >250 higher learning institutions, libraries, museums and stakeholder corporations (New Media Consortium, 2014). 2 Mark Zuckerburg, founder (Facebook, 2015).


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Emerging learning theory proposed by education theorist George Siemens (2005) in his seminal

article “Connectivism: A Learning Theory for the Digital Age” identifies diverse meta-skills needed to

respond to the affordance of social media and digital networked environments, including the ability to

delineate patterns and connections, and the ability to evaluate the value of content and whether it is worth

learning (Ralph-Campbell, 2014; Siemens, 2005). Jenkins, Clinton, Purushotma, Robison and Weigel

(2006) identify similar meta-skills essential for engaging in the participatory culture that social media

affords, including performance (taking on different roles), collective intelligence (integrating knowledge

with others towards shared goals) and distributed cognition (user-tool interaction that expands mental

capacities) (Conole & Alevizou, 2010; Jenkins, et al., 2006).

This paper presents a review of research literature on social media use in higher education. Three

overarching thematic categories – emergent, process and administrative – are proposed to explain the

dimensions of social media use is diverse learning contexts, and some basic best practices for social

media integration are inferred. Additionally, drawing on actor-network theory (Latour, 1996), connectivism

(Siemens, 2005), the theory of transactional distance (Moore, 1989; Moore, 1990), the Community of

Inquiry framework (Anderson & Garrison, 1998; Garrison & Cleveland-Innes, 2005), self-determination

theory (Deci, et al., 1991; Ryan & Deci, 2000; Ryan & Deci, 2002), the theory of distributed cognition

(Hutchins, 2000), Dron and Anderson’s group/net/set/collective user aggregation construct (Dron, 2007;

Dron, 2013; Dron & Anderson, 2007; Dron & Anderson, 2014), and Jenkins et al. (2006) (described

above), a conceptual model is proposed positioning ICT tools as primary actors relative to (human) users

and content, and identifying three roles – learner, facilitator and contributor – that users occupy fluidly and

often simultaneously in relation to each other within formal and informal learning contexts. This model re-

envisions the mutually shaping relationships amongst these three primary actors, contextually embedded

within digitally connected environments.

Background

“Humans create their cognitive powers in part by creating the environments in which they exercise

those powers.” (Hutchins, 2000, pg. 9)

This quote from cognitive scientist Edwin Hutchins’ paper describing the theory of distributed

cognition highlights the concepts of social embeddedness and interaction that are prominent in


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contemporary learning theories, frameworks and research, especially those relating to technology-

mediated learning. Learning environments are conceptualized as being both autonomous and

collaborative, socially constructed, with self-organizing dimensions across distributed, digitally connected

networks comprising human and non-human actors (Burnham & Walden, 1997; Dron & Anderson, 2014;

Gibson, 1998; Hutchins, 2000; Latour, 1996; Siemens, 2005).

Social/environment embeddedness is a concept that cuts across disciplines. Education scholar

Chère Gibson (1998) (in Woods & Baker, 2004), for instance, drew on ecology in defining the concept of

learner-context interaction (Woods & Baker, 2004). Conservation ecologist Tim Ingold (1993) similarly

positions the individual at the centre of his spherical model of the environment, to explain the emergence

of a conservation/stewardship ethic in Aboriginal epistemology and culture. Canadian Jurist Michael Asch

(1997) posits that boundaries of environments are culturally-constructed and defined as being that with

which we interact, and he links this to the British common law concept of enclosure relating to resource

commons and the duality of individual and collective access and ownership.

Interaction, as a fundamental component of learning, is not a new idea, and similarly cuts across

disciplines. Educational technology scholar Andrew Ravenscroft (2011) points out that dialectic interaction

was central to Socrates’ method. Education theorist Jon Dron (2006) makes the important inference that

social media embody processes that parallel natural social processes, including inscribing the

environment with meaning through the creation of digital artefacts3. Biologist E.O Wilson (2012) situates

sociality within human DNA (Dron & Anderson, 2014). Anthropologist David H. Turner (1985; 1997; 1999)

observed amongst Australian Aborigines a duality of identity premised on the reciprocal/renunciative

individual-environment relationship, wherein parts coalesce but still maintain their autonomy (‘part-of-one-

in-the-other’). Turner (1985, 1997, 1999) describes that a complex federated sociocultural system

emerged from this epistemology, premised on local totemic relationships that mediate both geographic

and transactional distance.

Information and communications technology (ICT) affordance extend/expand learning

environments and the scale of potential interaction opportunities exponentially, infusing efficiency and

immediacy (e.g. compared to earlier forms of distance learning, such as correspondence), enhancing the

3 Stigmergy (Heylighen, 2006).


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fluidity of environment boundaries, and situating greater control within the hands of learners (Dron, 2007;

Siemens, 2005; Woods 7 Baker, 2004). The infusion of ICT transforms learning content and learner-

content relationships (Ralph-Campbell, 2014; Siemens, 2005), and the affordances that arise facilitate the

deep learning that is particularly appropriate for higher education context (Woods & Baker, 2004), and

support the development of meta-skills that Siemens (2005) describes as necessary for lifelong learning

in the digital age, namely, the ability to recognise, make and leverage connections. Education technology

researcher Ruben R. Puentedura (2014) proposed the SAMR scale for evaluating the transformative

effect of ICT integration in learning activities:

Figure 1. Puentedura’s (2014) SAMR Model

Systems Models of Social Learning

Michael Moore’s (1989; 1990) theory of transactional distance (described in Shearer, 2010)

considered the qualitative characteristic of psychological ‘closeness’ within three technology-mediated

relationships – learner-content, learner-instructor and learner-leaner – and the role of dialogue

(interaction) and structure (design). In general, Moore posited that degree of structure inversely correlates

to degree of dialogue, and that dialogue inversely correlates to transactional distance; in other words,

increased structure effects decreased dialogue and increased transactional distance (Moore, 1989;

Moore, 1990; Shearer, 2010). However, with respect to social media, Dron (2006) and Ravenscroft

(2011) describe dialogue and structure being mutually constitutive, with structure contributing process

momentum to dialogue. This concept has implications for pedagogical structure in technology-mediated

learning contexts.

Drawing on the Community of Inquiry (CoI) framework (Anderson & Garrison, 1998), Garrison and

Cleveland-Innes (2005) delineate surface versus deep learning to explain achievement-oriented (e.g.


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rewards, grades) versus mastery-oriented learning (triggering event, exploration, integration, resolution),

respectively, describing that reaching deeper learning relies on teaching presence (see below) that

shapes the learning environment by infusing appropriate pedagogical structure in terms of design,

facilitation and direction. This is within the context of community interaction, but is also relevant for

individual learner-teacher interaction, and goes beyond the simple dyadic communication that Woods and

Baker (2004) define as transaction.

The CoI framework represents a refinement of transactional distance theory, to which Anderson

and Garrison (1998) contributed teacher-teacher (e.g. professional development), teacher-content (e.g.

presenting information in new ways) and content-content (e.g. hyperlinks) interaction types and added

context for these interactions via three qualitative dimensions – social, cognitive and teaching presence –

that explain how community emerges and fosters progress towards deeper learning (Anderson, 2003;

Anderson & Garrison, 1998; Brown, 2011; Campos, Laferriere & Lapoint, 2005; Garrison, Anderson &

Archer, 2010; Garrison & Arbaugh, 2007; Garrison, Cleveland-Innes, 2005; Swan, 2003; Woods & Baker,

2004).

Social presence refers to the ‘self’ that individuals project that influences transactional distance

(Anderson, 2003; Anderson & Garrison, 1998; Brown, 2011; Dron, Seidel & Litten, 2004; Garrison, et al.,

2010; Garrison & Arbaugh, 2007; Garrison, Cleveland-Innes, 2005; Kanuka & Garrison, 2004; Swan,

2003). Cognitive presence aligns to the surface versus deeper learning constructs described above.

Teaching presence is also described above. The CoI framework has been validated through a number of

qualitative and quantitative research studies (Arbaugh, 2007; Arbaugh & Hwang, 2006; Campos, et al.,

2005; Garrison & Arbaugh, 2007; Garrison & Cleveland-Innes, 2005; Garrison, Cleveland-Innes & Fung,

2004).

Drawing on Mehrabian (1967) and Thweatt and McCroskey, (1996), Woods and Baker (2004)

describe immediacy as communicative behaviours that contribute to reduced transactional distance, and

that qualitative aspects (rather than increased frequency of interaction) translates to increased immediacy

(Arbaugh, 2000; Clow, 1999; Fulford & Zhang, 1993; Hacker & Wignall, 1997; LaRose & Whitten, 2000;

Phillips & Peters, 1999; Roblyer, 1999; Rafaeli, 1988). Design, in particular, influences perceived quality

of interactions, and thereby influences immediacy (LaRose & Whitten, 2000; Woods & Baker, 2004).


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Laurel (1991) similarly found that perceptions regarding the value/quality of interaction was

enhanced through frequency, range and significance of opportunities to participate. Use of social media

within learning contexts may contribute to immediacy by increasing access and perceived access, adding

qualitatively to learner-teacher and learner-learner interaction (and teacher-teacher) in particular (Laurel,

1991; Garrison & Cleveland-Innes, 2005; Woods & Baker, 2004).

Ryan and Deci (2000; 2002) include relatedness (relationships with others) as an innate

psychological need, the satisfaction of which contributes to experiences of competency in learning and

learner autonomy (their second and third innate psychological needs), and note that subjectiveness and

different experiential and behavioural aspects of interaction shape perceptions and influence self-

determined (volitional) versus (externally) controlled behaviour. Self-determination theory posits that of

the three innate psychological needs, autonomy arising from the learning context/environment enhances

motivation, persistence, deeper learning and improved outcomes (Deci, et al., 1991; Ryan & Deci, 2000;

Ryan & Deci, 2002). Opportunities for contributing content, collaborative engagement, discourse and

reflection, for instance, can provide experiences that satisfy these needs (Deci, et al., 1991; Ryan & Deci,

2000; Ryan & Deci, 2002).

Specifically considering social media, Dron (2007; 2013) and Dron and Anderson (2007; 2014)

provide a model that complements the CoI framework but more directly accommodates technological

integration and describes how individuals learn from each other in different technology-mediated

aggregations. Drawing on actor-network theory (Latour, 1996), action theory (Sociologyguide.com, 2015a;

2015b; 2015c) and distributed cognition (Hutchins, 2000; Hollan & Hutchins, 2000), Dron (2007; 2013)

and Dron and Anderson (2007; 2014) describe four aggregate contexts for interaction influenced by

different dimensions of technology and social system relationships:

1. Groups comprise associations with formalized processes, structures and goals (such as a

course);

2. Nets (networks) comprise informal associations of people we know;


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3. Sets comprise informal associations of people we do not know but with whom we share some

commonality (such as interests)4;

4. Collectives comprise aggregations with a degree of self-organizing structure mediated/connected

via algorithms.

(Dron, 2015, personal communication; Dron, 2013; Dron & Anderson, 2014)

As in distributed cognition theory (Hutchins, 2000; Hollan & Hutchins, 2000), these categories

may overlap, and individuals and aggregates can be part of multiple associations simultaneously (Dron,

2007; Dron, 2013; Dron & Anderson, 2007; Dron & Anderson, 2014). Knowledge is cumulative as less

complex aggregates coalesce and contribute to the collective knowledge of more complex aggregates

involving processes that parallel cognitive processes made possible through this coalescence (Dron,

2013; Dron & Anderson, 2014; Hutchins, 2000; Hollan & Hutchins, 2000).

The technical affordances of specific social media may be matched to the characteristics of each

of these categories to support interaction and aggregation processes and to address structural and other

weaknesses, thereby responding to different psychological and learning needs (including competence,

relatedness and autonomy) (Dron, 2006; Dron, 2007; Dron, 2013; Dron & Anderson, 2007; Dron &

Anderson, 2014; Deci, et al., 1991; Ryan & Deci, 2000; Ryan & Deci, 2002). Within this context, and

drawing on management scientist Peter Checkland’s soft systems methodology (Checkland, 2000;

Checkland & Poulter, 2010), Dron (2013) and Dron and Anderson (2014) delineate hard technologies,

which embed much of the orchestration (the tool makes the choice for the users) and soft technologies,

which place more responsibility for orchestration in the users’ hands. They describe social media as

inherently soft, but that hardness can be introduced through pedagogical structure and instructional

design (Dron, 2013; Dron & Anderson, 2014; Ravenscroft, 2011).

Informed by the background literature summarized above, this report synthesizes the author’s

review of research relating to social media use in higher education contexts, with the objective of

explaining and mapping the dimensions of social media use and inferring basic best practice guidelines

for social media integration.

4 Tönnies delineates gemeinschaft - collectives sustained by mutual bonds – and gesellschaft – collectives sustained through common individual aims/goals (Sociologyguide.com, 2015c).


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Methodology

The process for this review followed general methodology for systematic reviews and meta-

analyses. After background reading and defining the research objectives, the next step was identifying,

vetting and selecting sources for consideration.

A Boolean search of the EBSCO database (Education Research Complete) was initially

undertaken (yield = 1,501 sources with full abstracts available to the author) followed by a supplemental

search of Google Scholar (yield = 656 sources with full abstracts available to the author), utilizing the

search terms “social media” and “higher education” and iterations and synonyms of these (e.g.

universities & colleges AND online social network, internet in higher education AND collaborative

learning). Article titles were preliminarily vetted for relevance and inclusion of sources in in-application

libraries (for instance, My Library in Google Scholar). All retained sources were then uploaded to

RefWorks directly from the in-application libraries.

Duplicates were identified and discarded using RefWorks filters; in all cases, the more complete

instance of a duplicate source was kept, and the less complete instance was discarded (e.g. if one

instance contained a full abstract, but duplicate instance did not, only the instance containing the full

abstract was retained). This yielded 2,019 sources. Abstracts of these were then reviewed for relevance,

and to exclude articles that were mistakenly included, were not research studies, were off-topic or

inappropriate for the scope of the review, or were duplicates not identified previously via RefWorks filters.

For instance, some retrieved sources dealt with K-12, not higher education, and were deleted. Sources

dealing with social media use for general institutional administration (e.g. operational purposes),

institutional marketing/branding and recruiting, crisis communications, cyberbullying, plagiarism, and

student political mobilization (e.g. political protests of government) were excluded as these topics

peripherally relate to social media use in higher education learning contexts. Additionally, sources dealing

with social media use in academic library contexts were excluded, as this is an expansive topic that is

beyond the scope of this paper. This left 954 sources for consideration.

Given the limited timeline for completing this project, it was not possible to include all 954 sources

in the detailed analysis. Therefore, a manageable subset of 150 sources, a convenience sample, was

selected. In selecting these 150, one additional exclusion criteria was applied; namely, anecdotal


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accounts, including those dealing only with general surveys of user preferences or organic use of social

media, were not included in this subset5.

These 150 sources were then reviewed and coded (in Microsoft Excel) for primary descriptive

theme, learning environment (whether online, face-to-face / f2f, or blended/hybrid), formal or informal

learning context, identification of user types, social media tools, net/set/group/collective aggregation,

study design, and outcomes. Table 1, Figure 2 and Figure 3 provide some basic descriptive statistics.

Based on the relatedness of primary descriptive theme, sources were then categorized into

secondary thematic categories. These were then grouped into broader tertiary thematic categories based

on the nature of the social media integration and the outcomes, and linking dimensions of social media

use at a conceptual level. A construct depicting dimensional relationships was produced from this

thematic analysis.

An assessment of the collective findings drawn from the sources followed, with the aim of

developing a set of best practices drawn from the review. Assessment considered study design; however,

departing from general methodology for systematic reviews and meta-analyses, criteria for rigour study

design were not applied. Few sources included measured learning outcomes (such as GPA), for instance,

and even fewer contained experimental design. Additionally, many studies incorporated subjective

measures, such as self-perceived progress and personal perceptions of utility, ease-of-use and sense of

community.

Results and Analysis

More than half the sources reviewed incorporated quantitative methodology, with

survey/questionnaire most common. More than half involved formal learning contexts and f2f

environments, with social media used in relation to pedagogically structured tasks. Most dealt with

contexts of group or set aggregations (Dron, 2007, 2013; Dron & Anderson, 2007, 2014); however, the

author identified some that she believed comprised collective aggregations, such as two studies reviewed

that looked at ResearchGate for dissemination of scholarly work (Ovadia, 2014; Thelwall, & Kousha,

2015), and another that looked at institutional web-presence based on markers of integration amongst

5 Through the initial review of abstracts, the author observed discord between anecdotal accounts of user preferences and organic use versus actual tests of acceptance of social media adoption in learning contexts, even when dealing with social media tools that users were familiar with prior to adoption within the learning context.


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network participants comprising the European Higher Education Area (EHEA) (Ortego, Aquilo, Cothey &

Scharnhorst, 2008).

Table 1, Figure 2 and Figure 2 provide some descriptive statistics. These initial findings reveal

several gaps in the research literature that could be objectives for future research, most notably a need

for more research involving experimental design.

Even with the incorporation of Web 2.0 tools, few studies comprised flipped classrooms, a format

that the NMC’s 2015 Horizon Report indicates is or should be a short-term trend in higher education

(Johnson, et al., 2015). This suggests the transition to flipped classrooms may be more of a challenge

than the NMC predicts.

Table 1. Descriptive Statistics (N = 150 subset of sources)

Factor

Factor Descriptor

Frequency (% of studies)

Primary Environment

F2F

Online

Blended

65%

27%

9%

Context

Formal

Informal

Both

57%

39%

4%

Primary User Aggregation

Group(s)

Set(s)

Collective(s)

62%

30%

8%

Figure 2. Frequency (%) of study design type represented

*frequency of integration of individual study design types depicted elsewhere in graph


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Figure 3. Types and frequency (%) of social media represented


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Emerging Themes and the Dimensions of Social Media Use

Thematic analysis resulted in the emergence of three tertiary thematic categories – named

emergent, process, and administrative by the author – which cut across formal and informal learning

contexts, including faculty professional development. These tertiary categories are discrete, but may not

be limited to these three; additional categories may have emerged if, for instance, excluded topics such

as social media use in academic libraries, student recruitment and student mobilization had been included

in the analysis.

Broadly, learning contexts comprised student academic learning, “student life” (transition, social

support, accessing practical information, et cetera), professional development and job skills (including

faculty professional development), community of practice, and inter-program/institutional collaboration and

networking, all bridging formal and informal learning contexts. An interesting dynamic that emerged was

that tertiary classification appeared to cluster around the user aggregations described by Dron (2013) and

Dron and Anderson (2014), primarily groups, sets and collectives, and particularly in contexts where there

was potential for collectives to coalesce from sets and/or groups. Also interesting, each tertiary category

appears to embody the three types of learning – cognitive (learning through understanding), associative

(learning as activity through structured tasks), situative (learning as social practice) – identified by Mayes

and de Freitas (2004), as described in Conole et al (2010).

Emergent

The emergent category represents the most transformative (Puentedura, 2014) use of social

media observed in the research literature. Although in most instances initial technological and

pedagogical structure may have been provided for users, the ability for users to take control and shape

tool use, content, learning trajectory and collective learning represents the self-organizing, bottom-up

emergent structure Dron (2006) describes, and substitutes user-infused hardness rather than hardness

imposed by structure (Dron, 2013; Dron & Anderson, 2014)6. Common dimensions characterizing the

6 Dron (2007) provides the following as defining criteria for social media: “Social [media] is organic and self-organizing, underpinned by dynamics that parallel natural processes. It is

evolutionary, replicating the successful and diminishing or killing the unsuccessful. It is stigmergic: signs left in the environment communicate with others who leave further signs in the environment. It has an emergent structure, formed from bottom-up control rather than top-down design.” (pg. 62)


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diverse learning contexts comprising the emergent category include democratic participation; reciprocity,

teacher presence and immediacy; co-creation of learning content / collective knowledge; optional

participation; tool flexibility, shaped by users; self-organizing / self-sustaining; complimentary, dialogic and

user-shaped collaboration; fluidity in the roles occupied by users; stigmergic, and extending beyond

specific learning contexts; and pedagogically unstructured, opportunistic use of social media. The

emergent, self-organizing structure of these learning contexts facilitated enhanced dialogue momentum,

consistent with Dron (2007) and Ravenscroft (2011), which in turn influenced and shaped community

aggregation. Activities provided learners with opportunities for experiencing competency, relatedness and

autonomy, the innate psychological needs that Deci et al. (1991) and Ryan and Deci (2000, 2002),

describe as underlying self-determination and motivation.

Pollard (2014) describes implementing Twitter backchannelling in the context of a large f2f lecture

class, with the objective of enhancing interactivity between students and herself (the lecturer) in and

outside class, engaging students more deeply in content learning, and fostering collective learning.

Participation via Twitter was voluntary; verbal invitation was repeated regularly in class throughout the

semester. Questions posted to Twitter during and outside of class were encouraged, with Pollard

incorporating both into the lectures (Pollard, 2014). Pollard retweeted and responded directly to tweets

outside of class time, and additionally posted links to supplemental learning materials such as YouTube

videos and TED Talks which served as jumping-off points for discussions during lectures (Pollard, 2014).

This format approaches that of the flipped classroom described in the NMC’s 2015 Horizon Report for

Higher Education (Johnson, et al., 2015). A high degree of teacher presence and immediacy in student-

lecturer interaction was fostered, as well as enabling students to co-create some of the collective learning

content. Although learning trajectory generally followed the course syllabus, feedback provided to the

Pollard via Tweet Cloud – a visual representation depicting the degree of attention specific course themes

received via Twitter posts – which she shared with the class enabled collective content review and

collective influence on lecture focus (such as remedial review of themes that were de-emphasized on

Twitter) (Pollard, 2014).

Pollard reports that 40% of the class subscribed to the Twitter feed and participated (posted at

least once) over the course of the semester, and that tweet frequency was highest on lecture days and


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next highest the day before and the day after the lecture, suggesting questions were being posted in

preparation for lecture discussion as well as in response to reflection taking place afterwards (Pollard,

2014). A voluntary survey at the end of the semester indicated relatively high student acceptance of

Twitter (64% of respondents), including engagement with the supplemental materials accessed via the

links Pollard posted (Pollard, 2014). Ease of use was an important factor, and the lecturer’s consistent

activity responding to tweeted questions and incorporating Twitter content in lectures fostered student

motivation, investment and self-direction, creating a highly learner-centred context.

Pohl and Gehlen-Baum (2012) similarly found that implementing the Backstage digital

backchanneling environment in large f2f lecture classes corresponded to an increased frequency of

student questions (with the option of anonymity), fostered greater student-student communication, and

facilitated student influence on learning trajectory and the co-creation of collective learning content with

student microblogs linked to the lecturer’s presentation slides (Gehlen-Baum, Pohl, Weinberger, & Bry,

2012; Pohl & Gehlen-Baum, 2012).

These examples demonstrate formal learning contexts incorporating redesigned learning spaces

wherein social media parallels natural dialogic processes that are critical to the deeper learning

appropriate in higher education (Dron, 2007; Woods & Baker, 2004). These redesigned learning spaces

enabled tasks, activities and outcomes not previously possible in large lecture classes (Puentedura,

2014), such as direct dialogue in- and outside f2f lectures with collective participation; co-creation of

collective learning content; fluidity of user roles, and extending the learning environment beyond the

specific class context with the potential for sustained participation (including future class cohorts as well

as participants not related to the class); as well as development of a freely accessible digital archive of

learning artefacts/objects relating the subject matter more broadly and linking with other formal and

informal communities of practice. Teacher-content interaction is also changed, as social media integration

enabled learning content to be developed and presented in new ways, such as Pollard’s use of Tweet

Cloud (Anderson & Garrison, 1998; Garrison & Cleveland-Innes, 2005; Pollard, 2014).

Similar dynamics and outcomes were observed by Li and Greenhow (2015) regarding Twitter

backchannelling supplementing professional development activities for educational scholars at recent

conferences hosted by the American Educational Research Association (AERA), indicating symmetry with


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respect to affordances in informal learning contexts. In this example, conference participants actively co-

created collective learning content via Twitter, shaping immediate learning trajectory as well as future

conference programming and social media incorporation (Li & Greenhow, 2015).

Gold & Otte (2011) explored the CUNY (City University of New York) Academic Commons, a

closed-source network linking faculty and scholars across a multi-campus university community. Although

closed-source, the authors describe users having control of the design, presentation and content of their

web-based work, demonstrating an example of centralized institutional management of network

infrastructure but distributed and individualized user control of content (Gold & Otte, 2011). Capitalizing on

the network’s interactive affordances, and through the development of individualized content including the

incorporation of shared links, self-organizing communities of practice have emerged cutting across

campuses, programs and disciplines, which Gold & Otte (2011) describe as having the effect of breaking

down silos and encouraging collaboration.

Ford, Veletsianos and Resta (2014) describe #phdchat as an emergent online community

originating from a group of doctoral students who, outside of a specific class context, began using Twitter

for informal study and discussion groups. As the network has grown, a core group of sustaining users

developed, around which the online community is organized (Ford, Veletsianos & Resta, 2014). Ford et

al (2014) describe the #phdchat network as being in “a continuous state of emergence and change” and

that “disparate users can come together with little central authority in order to create their own communal

space”. The self-sustaining and self-organizing nature of #phdchat, along with democratic participation

and the fluidity of roles within the community, are defining characteristics, aligning with Dron’s criteria for

social media (2006), Jenkins’ et al. (2006) description of performance skill (Conole & Alevizou, 2010), and

the psychological needs underpinning self-determination and motivation described by Deci et al. (1991)

and Ryan and Deci (2000, 2002). The #phdchat community primarily shares resources and facilitates

peer support through open dialogue, providing accessible resources and connections within a broader

social network system (Ford, et al., 2014). For instance, Ford et al. (2014) note the core #phdchat users

participate in other social media groups, connected through shared links and user profiles, and cutting

across social media platforms such as Twitter and Facebook, characteristics common to the networked

learning that is central to connectivism (Ford et al, 2014; Siemens, 2005).


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Administrative

The administrative category comprises learning contexts relating to accessing information and

support services for learning and/or professional development, such as practical skills, social, emotional

and academic adjustments to college life, or course administration such as where to access course

materials and assignment deadlines. This category represents the least transformative use of social

media in learning, with uses tending to represent instances of substitution of augmentation per

Puentedura’s (2014) SAMR model. Utilizing social media, dialogic interactions within the administrative

category are, however, stigmeric with co-created learning content, such as questions and answers posted

to a peer support Facebook page, for instance, becoming an information resource for future users.

Additionally, the clustering of question and answer topics provide a self-organizing structure for the

presentation and retrieval of information, and can also shape resource and service availability (whether

offered institutionally, or available though informal networks) by providing indicators of demand,

substituting for structure in adding hardness to the context. In this way, the administrative category

embodies self-direction within a learner-centred context.

Knight and Rochon (2012) describe a case study wherein a pre-sessional initiative called

Startonline was delivered to incoming students via the Ning platform. Startonline provided access to

generic academic activities and resources (not subject-specific), social networking tools, and practical

information such as institutional policies. Knight and Rochon (2012) report that Startonline was most

commonly accessed for social networking purposes, indicating this to be an appropriate area for

institutional facilitation. Accessing practical information was also common.

This example is particularly interesting because while the authors observed little interest amongst

students with respect to generic academic activities (such as writing skills resources), high interest in

accessing subject-specific information was observed, correlating to the degree of direct involvement of

subject-specific faculty and staff. In other words, interest and demand was influenced by teaching

presence and immediacy. Additionally, this example demonstrates users shaping the Startonline tool to

address their needs, and emerging subject-based communities developing via these preliminary

connections. Not only did user-demand shape tool use and content, but also the type of service being

provided, enabled by the unique affordances of social media. This demonstrates the bottom-up structure


17

and the self-limiting effect that Dron (2006) includes amongst his criteria defining social media, and also

demonstrates dialogue and structure being mutually constitutive via social media (Dron, 2007;

Ravenscroft, 2011), departing from the inverse relationship construct between dialogue and structure in

Moore’s transactional distance theory (1989, 1990).

Process

The process category is conceptually the most complex of the three tertiary categories. Much of

the instances observed in the research literature incorporated problem- or project-based learning for skills

mastery through densely structured individual or collaborative work utilizing social media. Increased

pedagogical structure relating to social media incorporation generally appeared to correlate to increased

dialog across all interactions: learner-content, learner-tool, learner-learner and learner-teacher. This is

also consistent with Dron’s (2006) and Ravenscroft’s (2011) description of dialogue and structure being

mutually constitutive in social media use, with structure contributing process momentum to dialogue.

Degree of hardness added through structure influenced the interactive relationships afforded and

subsequently the attainment of pedagogical outcomes (Dron, 2007; Ravenscroft, 2011). Two kinds of

structure tended to be important and served to harden social media in these learning contexts: Technical

structure regarding tool use and pedagogical structure, including guidance provided to learners regarding

the relevance of tools and tasks to learning and specific pedagogical outcomes. Where these two

structures were lacking, negative user acceptance, motivation and engagement was consistently

observed, including cases wherein learners had prior experience with the social media tool that was used.

Similarly, there was weak concordance in the research literature between anecdotal accounts of user

preferences and organic use compared to user acceptability in relation to the same social media tools

used in specific learning contexts – (for this reasons, anecdotal accounts were excluded, as noted above).

Structure relating to accountability for one’s own created content, including self-reflection and

feedback provided to others, additionally contributed hardness and fostered self-limiting effect (Dron,

2007). Social media use ranged from substitution to modification per Puentedura’s SAMR model, making

the process category more transformative compared to the administrative category, but generally not

approaching the characteristics of redefinition observed in the research literature for the emergent

category.


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The process category generally incorporates formal learning contexts in which learners are

required to carry out specific tasks aimed at specific measurable learning outcomes. Of the three tertiary

categories, the process category best characterizes instances wherein learner-content interaction (e.g.

self-reflection) has the greatest impact of learning outcomes, shaped by the learner-tool relationship, and

supplemented by learner-facilitator and/or learner-learner interaction. Additionally, in this category, user

roles are the least fluid, with the least potential for learners controlling learning trajectory.

Dayter (2012) describes using Twitter for microblogging activity to support students in improving

content knowledge and reading skills. Students were required to post comments in response to course

readings through the semester, and to reflect on the content of their own posts with respect to the reading

strategies demonstrated in their posts. The anticipated learning outcome was improving individual reading

skill through this reflection process, from low- to high-level self-explanation strategies demonstrated in the

posts (Dayter, 2012). Learner-content interaction was critical, supplemented by learner-teacher interaction

for feedback, guidance and scaffolding. Learner-content interaction was shaped by learner-tool and tool-

content interaction, in that Twitter’s 140-character limit for posts influenced reflection on content and

supported the development of curatorial skills. Additionally, the self-reflection mediated by these learner-

content-tool interactions provided opportunities for experiencing competency and mastery, as well as

autonomous learning. While learner-learner interaction was not emphasized, the potential for peer

feedback, in addition to the expectation of teacher feedback and the incorporation of select tweets in class

discussions, similarly contributed a self-limiting dimension to these curatorial skills, as well as providing

for experiences of relatedness and competency. Dayter (2012) reports that students demonstrated a high

commitment to reading activities, and the use of Twitter contributed to the development of a sense of

community amongst actors.

Nerantxi (2012) describes an example of collaborative problem-based learning integrating

academic programs, involving scholars from diverse disciplines as well as professionals outside the

institution contributing to a post-graduate certificate program in teaching and learning. Collaborative

problem-based learning tasks utilized assessment and feedback tools via social media (Nerantxi, 2012).

The objective was to provide opportunities for learners to connect, communicate and collaborate with

academics, professionals and peers, with the larger objective of bridging academic and virtual silos,


19

fostering multidisciplinary collaboration, developing a sustainable community of practice outside the

certification program context (Nerantxi, 2012).

Junco, Elavsky & Heiberger (2013) similarly looked at microblogging in group work with the

objective of supporting learners in developing collaborative skills. Using a quasi-experimental design,

students were assigned to either a first group that was required to post on Twitter or Ning as a

supplemental learning activity, with regular learner-instructor interaction; or to a second group for which

posting as an option, with sporadic learner-instructor interaction. Improved learning outcomes (grades,

incorporating assessments of the group work) were reported only for the first group (Junco, Elavsky &

Heiberger, 2013).

Freeman & Brett (2012) describe the use of blogging to help learners develop skills for vetting and

linking information across online resources, clustering around common ideas and themes that foster the

emergence of community. These meta-skills are consistent with those identified by Siemens (2009) as

relevant to networked learning environments, namely way-finding, filtering, aggregating and curating; and

by Jenkins et al (2006) for participatory engagement in networked environments, including distributed

cognition and collective intelligence, networking, transmedia navigation, judgment, appropriation, and

negotiation (described in Conole et al, 2010). In this example, students were required to create and

maintain a blog wherein they were to reflect on academic, professional and personal topics. The goal was

to develop blogging skills as personal habit, a skill relevant for contexts outside of he specific class

context (Freeman & Brett, 2012).

These examples demonstrate pedagogical structure providing hardness to social media leading

to increased dialogue/interaction, and activities providing learners with opportunities for experiencing

competency, relatedness and autonomy (Deci, et al., 1991; Dron, 2007; Ravenscroft, 2011; Ryan & Deci,

2000; Ryan & Deci, 2002). Importantly, the process category in general, encompass learning contexts

involving skills mastery, and these examples demonstrate use of social media to support the development

of the meta-skills that the theory of connectivism holds are critical for engaging in lifelong learning in the

digital age (Siemens, 2005; Siemens, 2009).

Dimensional Concordance

Drawing on the above descriptions of the emergent, administrative and process categories, an


20

inverse relationship is discernable wherein learner autonomy and role fluidity increases as pedagogical

structure decreases and is replaced by emerging self-organizing structure and learner-control substituting

hardness into the learning contexts as more responsibility for orchestration moves into the hands of users

(softness) (Dron, 2006; Dron, 2007; Dron, 2013; Dron & Anderson, 2013; Dron & Anderson, 2014;

Ravenscroft, 2011). While dialogue is integrated in each of the categories, the open dialogue observed

within the emergent category corresponds to the greatest degree of learner autonomy and self-direction /

learner-control, and appears to foster the greatest degree of immediacy. Figure 4 provides a basic visual

representation depicting these relationships, wherein the technical affordances of social media are

matched to the characteristics of user aggregation processes to address their structural and other

weaknesses (Dron, 2006; Dron, 2007; Dron, 2013; Dron & Anderson, 2007; Dron & Anderson, 2014).

Directional flow in this figure does not suggest hierarchy, but rather depicts change.

Figure 4. Dimensional Concordance of Social Media Use in Higher Education Contexts

A Re-imagined Model

This review provided an impetus for a new model repositioning tools as primary actors relative to

users (learners, students, teachers/facilitators, peers, mentors, et cetera) and content, and re-envisioning

the mutually shaping interactions amongst these actors. This new model integrates observations from the

research literature with concepts from actor-network theory (Latour, 1996), the theory of transactional

distance (Moore, 1989; Moore, 1990), the Community of Inquiry framework (Anderson & Garrison, 1998;


21

Garrison & Cleveland-Innes, 2005), self-determination theory (Deci, et al., 1991; Ryan & Deci, 2000; Ryan

& Deci, 2002), Dron and Anderson’s group/net/set/collective user aggregation construct (Dron, 2007;

Dron, 2013; Dron & Anderson, 2007; Dron & Anderson, 2014), the three types of learning described by

Jenkins et al. (2006), and connectivism including the concept of machine learning (Siemens, 2005). To

clarify, the word “tools” used here encompasses hardware (including mobile devices) and software

(including apps and cloud computing), their symbiotic relationship, as well as network infrastructure.

While carrying out the thematic analysis, an impression of tool flexibility emerged, with tool uses

and affordances defined relative to user-user, user-content and content-content relationships and

influencing these in turn. (One might also include here the micro-relationship between hardware and

software, including operating systems, and the additional influence these might have on these

relationships. For instance, software and app functionality can vary immensely between devices and

operating systems, and a user may use the same application on different systems or devices with

somewhat different effect and user experience.)

While Moore’s theory of transactional distance identifies learner-content, learner-instructor and

learner-leaner interactions, and Garrison and Anderson’s CoI framework adds teacher-teacher, teacher-

content, and content-content, the new model proposed in Figure 5 additionally incorporates user-tool,

content-tool, tool-tool interactions. The impact of Twitter’s 140-character limit for posts described in Dayter

(2012) exemplifies mutually shaping user<->tool<->content interactions.

The autonomous user is positioned at the centre of their digitally connected environment(s),

engaging in interactive relationships with three primary sets of actors – content, tools and other users

(individuals and groups) – while occupying three fluid and often simultaneous roles – learner, facilitator

and contributor.

This new model may be relevant for other technology-based learning contexts besides those

incorporating social media.

Figure 5.

Embedded position, user<->tool<->content interactions, and learner/facilitator/contributor roles


22

The addition of the learner/facilitator/contributor roles provides a qualitative contextualization for

these interactions, and synthesizes insights drawn from the research and background literature: That

users may occupy multiple roles in relation to each other within given learning contexts; that these roles

are fluid; they shape and are shaped by other user<->content<->tool interactions; and they may cut

across environment boundaries. For instance, an individual may be a learner within a large lecture class,

but take on the role of facilitator when creating collective learning content by submitting Twitter posts

through a class backchannel, and the role of contributor as the content becomes a digital artefact within a

more complex user aggregation (such as a collective) extending beyond the specific learning context (e.g.

distributed cognition).

Looking at an asynchronous online learning context, Xie, Yu and Bradshaw (2014) observed that

the assignment of moderator role to students corresponded to significant increases in their participation

quantity, diversity and interaction attractiveness (to other students). Moving between roles extends

opportunities for experiencing personal competency vis-à-vis changed user-user interactions (learner-

learner interactions, in particular) (Deci, et al., 1991; Ryan & Deci, 2000; Ryan & Deci, 2002) and can add

structure negotiated by the learner (Dron, 2006; Dron, 2007; Dron, 2013; Dron & Anderson, 2007; Dron &

Anderson, 2014; Ravenscroft, 2011), contributing towards learner autonomy, motivation and self-

determination.


23

Discussion

Consistent with Dron (2013) and Dron and Anderson (2014), the findings of this review suggest a

mutually shaping relationship amongst the technical affordances of social media tools, pedagogical

structure and instructional design, and the types of learning contexts and user aggregates

(groups/nets/sets/collectives) supported. The typical softness of social media tools, similar to what the

author describes as flexibility above, enables specific tools to be used in different ways for different

purposes in different contexts, defined by interactive user<->content<->tool relationships (Dron, 2006;

Dron, 2007; Dron, 2013; Dron & Anderson, 2007; Dron & Anderson, 2014). Appropriately matching tool

characteristics to learning contexts and user aggregation processes can bridge structural and other

weaknesses, and provide opportunities for learning experiences that address the innate psychological

needs for competency, relatedness and autonomy that are critical for self-determination and motivation

(Dron, 2006; Dron, 2007; Dron, 2013; Dron & Anderson, 2007; Dron & Anderson, 2014; Ravenscroft,

2011; Deci, et al., 1991; Ryan & Deci, 2000; Ryan & Deci, 2002).

Appropriately matched incorporation of social media in higher education contexts, both formal and

informal, supports learners in developing the meta-skills (described above) essential for the higher order

learning and participatory engagement required for advancing cultures of change and innovation (Conole

& Alevizou, 2010; Jenkins, et al., 2006; Johnson, et al., 2015; Siemens, 2005; Siemens, 2009).

Some basic best practices for social media integration to support transformative learning can be

inferred from this review and the background literature summarized:

• Tool selection should be appropriately matched to learning context, desired outcomes and

anticipated user aggregations.

• Tool selection should consider how technical affordances can provide opportunities for learners to

experience personal competency, relatedness and autonomy appropriate to the learning.

• For learning outcomes relating to skills mastery and more formalized user aggregations, social

media incorporation should include increased pedagogical structure and more defined user roles

(adding hardness), which will contribute toward learner-centredness.

• For outcomes aiming towards collective learning, collective knowledge creation and self-

organizing user aggregations, social media integration should include decreased pedagogical


24

structure that facilitates self-organizing, bottom-up structure, more open dialogue, fluid user roles

(substituting user-infused hardness in place of hardness imposed through pedagogical structure).

• Social media use for practical purposes and more informal user aggregations, such as information

retrieval, should embody flexible structure that self-shapes to user demands.

• Providing technical guidance and communicating pedagogical relevance of tasks and tools to

learners will enhance user experience and acceptance. It should not be assumed that familiarity

with social media tools in other contexts will translate to learning contexts.

The three thematic categories emerging from this review, along with the dimensional aspects

depicted in Figure 4, provide a starting point for identifying the contextual characteristics for aligning social

media integration per these basic best practices.

Limitations

The scope of this review was limited by the timeframe in which the author had to complete it as

part of a single graduate course. In particular, quantitative analysis was truncated in order to enable this

review to be completed on time. As a result, Table 1 providing descriptive statistics for the subset of 150

sources may not be representative of all 954 unique sources retrieved, or generalizable. Since full review

of the 954 sources was not possible, a proper evaluation of evidence linked to assessment of study

design could not be undertaken to inform the best practices proposed above.

The author experienced some technical difficulties using Google Scholar that were never

resolved. Specifically, an internal Google Scholar error prevented the author from accessing about half

the sources originally saved in My Library. To resolve this issue, the author repeated the Google Scholar

search using a different Google account login, but notes that approximately 300 fewer sources were

yielded this second time (though this may be due to the inaccuracy of Google Scholar’s estimation of

yields).

Thematic analysis might have been enriched by including other topics, such as those that were

excluded from this review (e.g. social media use by academic libraries in higher education contexts). As

noted, the timeframe for completing this project was the primary factor in limiting topic inclusion.

Therefore, a more inclusive thematic analysis may be an objective for a future review.

Conclusion


25

Drawing on learning theory that has emerged in response to distance learning and increasing

technological integration, and synthesizing findings from research literature on the use of social media in

higher education contexts, this paper presents descriptive statistics and maps the dimensions of social

media use; proposes a conceptual model that repositions tools as actors relative to users and content,

and delineates three roles users occupy fluidly in learning contexts mediated by social media; and

suggests some basic best practices for social media integration. Findings from this review are consistent

with transactional distance theory, the CoI framework, self-determination theory, and theory regarding the

relationship between user aggregations and tool hardness/softness (Dron, 2013; Dron and Anderson,

2014). Insights regarding mutually shaping user<->tool<->content<-> interactions, as well as the influence

of learning context and user aggregations in defining social media, apply concepts from social

construction of technology (SCOT) theory (Pinch & Bijker, 1984).

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