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The relationship between social network sites and perceived learning and satisfaction: A systematic review and meta-analysis.

September 2021

In book: Social Media: Influences on Education. (pp.231-262)
Chapter: 8
Publisher: Information Age Publishing.

Authors:

Daniela Castellanos-Reyes

North Carolina State University

Yukiko Maeda

Purdue University West Lafayette

Jennifer C Richardson

Purdue University West Lafayette

This chapter presents a systematic review of 31 studies that focused on the ef- fect of using social network sites (SNSs) for educational purposes on students’ perceived learning and satisfaction. It was found that SNSs support these out- comes when guidance and rules are clearly specified to learners. A subset of the studies was also used for a meta-analysis. Small positive relations between SNSs and student satisfaction (r = .17) and SNSs and perceived learning (r = .23) were found. 𝐼2 statistics (95% for satisfaction and 84% for perceived learning) indicated that effect sizes across studies were heterogeneous. Face- book was found to be a cost-effective alternative to learning management sys- tems. Research with additional constructs like motivation is recommended.

PRISMA flow diagram for study selection. Note: Studies included in the quantitative summaries are also part of the systematic review of SNSs used for learning purposes and learning outcomes. PL k: Studies from which a perceived learning effect size was extracted. SS k: Studies from which a student satisfaction effect size was extracted.

…

Forest plot of relationship between SNS use for academic purposes and student satisfaction.

…

Forest plot of the relationship between SNSs use for academic purposes and perceived learning.

…

Funnel plot for correlations of academic SNS use and students' satisfaction and perceived learning.

…

Summary of Studies on the Relation Between Social Network Sites Use for Academic Purposes and Affective Outcomes (continued)

…

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Content may be subject to copyright.

Social Media, pages 231–263

All rights of reproduction in any form reserved. 231

CHAPTER 8

THE RELATIONSHIP BETWEEN

SOCIAL NETWORK SITES

AND PERCEIVED LEARNING

AND SATISFACTION FOR

EDUCATIONAL PURPOSES

A Systematic Review and Meta-Analysis

Daniela Castellanos-Reyes

Purdue University

Yukiko Maeda

Purdue University

Jennifer C. Richardson

Purdue University

ABSTRACT

This chapter presents a systematic review of 31 studies that focused on the ef-

fect of using social network sites (SNSs) for educational purposes on students’

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232  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

perceived learning and satisfaction. It was found that SNSs support these out-

comes when guidance and rules are clearly speciﬁed to learners. A subset of

the studies was also used for a meta-analysis. Small positive relations between

SNSs and student satisfaction (r = .17) and SNSs and perceived learning

(r = .23) were found. 𝐼² statistics (95% for satisfaction and 84% for perceived

learning) indicated that eect sizes across studies were heterogeneous. Face-

book was found to be a cost-eective alternative to learning management sys-

tems. Research with additional constructs like motivation is recommended.

Social network sites (SNSs) are Internet-based communication platforms

that Ellison and boyd (2013) characterized with three main features: (a)

uniquely identiﬁable proﬁles for platform members that display data con-

tributed by users and the system, (b) semi-public connections that can

be visited and accessed by others, and (c) user-generated content made

by members of their network. Gramlich (2019) reported that seven in 10

adults use Facebook in the United States, making it the most widely used

SNS across all ages, and three-quarters of American Facebook users log in

daily to their accounts (Pew Research Center, 2019). The Pew Research

Center (2019) reported that 90% of Americans between the ages of 18–29

use at least one SNS. Snapchat and Instagram are signiﬁcantly popular

among young adults, which are used by 75% and 73% of 18–24 year-olds,

respectively (Perrin & Anderson, 2019). These values are similar to those

reported by Gramlich in teenage populations who reported using YouTube

(85%) and Instagram (72%) more than Facebook (51%).

Much research has focused on common uses of SNSs with fewer stud-

ies focused on the relationship between these sites and outcome variables

such as student learning in K–12 contexts (Greenhow & Askari, 2017).

However, Lim and Richardson (2016) found that college learners use SNSs

to gather and share information/resources and concluded that SNSs are

practical tools for educational purposes. They reported that SNSs are pre-

dominant components of learners’ lives based on the frequency of use;

of 89 online students in their study, 87.9% used SNSs for the past four

years, and 64% reported daily use. Lim and Richardson concluded that

students are already familiar with the interface and functionalities of SNSs

for social connections. Therefore, learners could escalate their SNS use to

include educational contexts without diculty (Lim & Richardson, 2016).

Xue and Churchill (2019) echoed Lim and Richardson’s ﬁndings with an

empirical review of the educational aordances of the SNS WeChat, which

is highly popular in Asia. Xue and Churchill found that SNSs have four

components that assist learning: (a) resources (access and sharing), (b)

activity (authentic learning, motivating environment), (c) support (col-

laboration and community building), and (d) evaluation (feedback and

administration of learning).

SNS for Educational Purposes and Affective Outcomes  233

Although some researchers acknowledge the educational aordances

that social media brings to collaboration and teamwork (Klamma et al.,

2007; Qi, 2019; Xue & Churchill, 2019), others have conﬂicting (Keles,

2018; Manca & Ranieri, 2016a) or negative (Kirschner & Karpinski, 2010;

Liu et al., 2017) opinions about SNS use in formal education. Some re-

searchers (e.g., Huang, 2018; Liu et al., 2017; Marker et al., 2018) have

investigated the relationship between academic achievement and SNSs in

empirical studies. For example, Marker et al. (2018) examined the eect

of SNS use in academic achievement in a meta-analysis that grouped 50

studies based on three types of use: general use, multitasking, and use to

support learning. They found small negative or little eects in relation to

academic achievement when examining SNS use in general (r = −.07) and

multitasking (r = −.10). They also reported almost no eect (r = .07) when

learners used SNSs to support learning.

However, shedding light upon student outcomes other than achieve-

ment is also vital to obtain a complete picture of the role of SNSs in student

learning. Educational researchers often use multiple indices for evaluating

learning experiences (e.g., satisfaction, reaction, retention) beyond grades

(Bacon, 2016; Elbeck & Bacon, 2015; Sitzmann et al., 2010). For example,

Strelan et al. (2020) evaluated the eectiveness of ﬂipped classrooms from

the students’ perspective by examining their satisfaction levels towards the

course and the instructor. Massive open online courses are often evaluated

based on student retention (Padilla Rodriguez et al., 2020). Therefore, al-

ternative measures are necessary to evaluate SNSs. Given this, we believe

that it would be beneﬁcial to extend the investigation of alternative mea-

sures to evaluate SNSs and that by not doing so we would continue to con-

clude that SNS use is detrimental for learning, thereby potentially missing a

rich opportunity. Moreover, we need to investigate the role of SNSs in learn-

ers’ aective outcomes considering that SNSs are an outlet for expressing

emotions and creating a sense of community (Manca & Ranieri, 2016b; Na-

zir & Brouwer, 2019; Song & Xu, 2019; Tess, 2013; Xue & Churchill, 2019).

Therefore, our systematic review focused on aective learning outcomes to

provide further information on how and when SNSs can be beneﬁcial to

student learning.

SOCIAL LEARNING AND SNS

Bandura (1971) explained learning as the process in which an individual

acquires new knowledge by observing others and modeling their behaviors.

The process is called social learning, as observation and modeling occur

in social contexts after learners imitate consciously or unconsciously the

example from others. Pahl-Wostl and Hare (2004) explained that “[social

234  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

learning] assumes an iterative feedback between the learner and their

environment, the learner changing the environment, and these changes

aecting the learner” (p. 194). Therefore, social learning implies a per-

manent multi-actor negotiation process between learners and the context

that involves them to, ultimately, co-create knowledge (Deaton, 2015; Sol

et al., 2013). However, such modeling is not limited to physical behavior or

mimicry. Abstract modeling is observational learning from thinking skills.

Bandura (2001) explained in his social cognitive theory that “observational

learning of thinking skills” (p. 275) occurs when learners verbalize thoughts

while problem-solving, which allows for internalization of societal rules.

Taking the potential of SNSs to provide learners with many opportuni-

ties to model behavior, we frame this study under the social cognitive theory

to examine the relationship between SNS use for academic purposes and

perceived learning and student satisfaction. Yilmaz et al. (2019) argued that

social media oers vicarious experiences in which learners beneﬁt from

seeing others fail and succeed. SNSs allow users to interact fully and col-

laborate through multiple forms of user-created content (e.g., videos, im-

ages, audio, text posts). As such, users can model behaviors from videos or

pictures generated or shared by other users in their network (Peng et al.,

2019). As users become learners, they can discover underlying cultural rules

from text posts (Deaton, 2015) that enunciate other users’ problem-solving.

SNS AND STUDENT OUTCOMES

Researchers and educators use SNSs for academic purposes (Huang, 2018;

Liu et al., 2017; Manca & Ranieri, 2016b; Marker et al., 2018; Tess, 2013)

in multiple disciplines, including architecture (Awidi et al., 2019), history

(McKenzie, 2014), second language learning (Miller et al., 2018), education

(Abella-García et al., 2019; Cedar & Singhara, 2017), and business (Wan-

kel, 2009). Traditionally, researchers determine the eectiveness of SNS

use by examining its relationship with academic achievement (i.e., grades).

Rosen et al. (2013) argued that SNS use has adverse eects on academic

achievement due to multitasking and distraction. They observed learners

and tallied the number of times participants accessed Facebook while study-

ing. Rosen et al. concluded that learners who used Facebook one or more

times while studying had a lower GPA. A previous meta-analysis on the re-

lationship between SNS general use and achievement reported no eect

on grades when learners used Facebook (r = –.02) and only a small but

negative eect when learners use other types of SNSs (r = –.12) (Huang,

2018). Liu et al. (2017) also reported a small negative correlation (r = –.08)

between SNS use and GPA. However, they found almost no correlation be-

tween SNS use and language tests (r = .05). Similarly, Marker et al. (2018)

AU: please add to

references

SNS for Educational Purposes and Affective Outcomes  235

reported almost no relationship when learners intentionally use SNSs for

academic purposes (r = .07). The negative small or no eect results con-

tradict the assumption that people learn by modeling abstract behaviors

observed through SNSs. Given the interactive and highly emotional nature

of SNSs, they can be related to aective learning outcomes like perceived

learning and satisfaction.

STUDENT PERCEIVED LEARNING AND SATISFACTION

Perceived learning refers to students’ self-reported level of knowledge gain

(Bacon, 2016). Contrary to actual learning, perceived learning is not mea-

sured using direct methods like tests or oral examinations (Elbeck & Bacon,

2015). Perceived learning is an indirect measure of knowledge gain based

on students’ introspection and reﬂection (Elbeck & Bacon, 2015; Joosten et

al., 2019; Mutambuki et al., 2019). Although researchers have determined

perceived learning gain to be a weak cognitive measure (Sitzmann et al.,

2010), this construct is important because self-perception of learning re-

quires learners to critically evaluate their learning as they build lifelong

learning habits (Sullivan et al., 1999). Perceived learning gain is indeed

positively correlated with aective measures like reaction (r = .51) (i.e., sat-

isfaction) and motivation (r = .59) (Sitzmann et al., 2010). Perceived

learning serves as a measure of course design eectiveness, intervention

implementation (Richardson et al., 2010), and curriculum development

(Mutambuki et al., 2019).

Student satisfaction is the extent to which students or learners are satis-

ﬁed and positively assess their learning experience (Arquero et al., 2017;

Joosten et al., 2019; Li et al., 2017). Orawiwatnakul and Wichadee (2016)

described student satisfaction as students’ impression of the level of ef-

fectiveness of the learning process and the extent to which the process is

advantageous. For example, student satisfaction in online learning is a

promising estimate of how likely learners are to continue enrolling in on-

line courses (Arbaugh, 2000) as student satisfaction is associated with per-

sistence, motivation, and retention (Alqurashi, 2017; Strelan et al., 2020).

Allen et al. (2015) invited researchers to shift the focus when investigat-

ing student satisfaction from the broad examination of online learning to

speciﬁc technological applications. Furthermore, instructors are not con-

strained to use learning management systems (i.e., Blackboard, Moodle)

to host online learning experiences (Allen et al., 2015) because they can

use other online platforms like SNSs. Allen et al. (2015) also argued that

research synthesis, in particular, a meta-analysis, is necessary to determine

the impact of various online environments, including the use of SNSs, on

student satisfaction, and its variation in learning conditions.

236  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

CURRENT STUDY

To determine the impact of SNSs in education, we aimed to systematically

synthesize study ﬁndings focused on educational SNS use and students’ af-

fective learning measures. More speciﬁcally, with this systematic review, we

ﬁrst aimed to identify how researchers, educators, and students are using

SNSs for learning purposes. For example, Manca and Ranieri’s (2016b)

critical review examined Facebook use and its aordances for learning.

Although their analysis was comprehensive, they did not consider other

popular SNSs used for learning purposes (e.g., Twitter). Likewise, previ-

ous meta-analyses that examined multiple SNSs did not focus on the rela-

tionship with aective learning outcomes but with dierent measures like

actual learning (Huang, 2018; Liu et al., 2017; Marker et al., 2018). Liu

et al. (2017) called for future synthesis on the relationship between spe-

ciﬁc SNS uses (e.g., educational purposes) and gratiﬁcation and academic

performance. Using academic achievement as the sole reference point can

provide a reductionist conclusion to the eect of SNS use on the learning

process. Also, these reviews (Huang, 2018; Liu et al., 2017; Marker et al.,

2018) were limited to studies with enough quantitative data to calculate ef-

fect size or have a speciﬁc research design (i.e., comparing experimental to

control group), so the implication of their ﬁndings may be limited.

Studies with qualitative designs often provide more in-depth insights

and high-quality interpretative research evidence (Yore & Lerman, 2008).

Therefore, a systematic process that reviews empirical ﬁndings regardless of

the research design will provide a more comprehensive account of the tar-

get phenomenon, that is, how researchers and practitioners are using SNSs

for academic purposes and how SNSs are related to student aective learn-

ing outcomes. As a part of the systematic review, we also conducted a meta-

analysis to investigate the strength of the relationship between SNS use and

students’ perceived learning and satisfaction with a small subset of studies

with quantitative evidence. This meta-analysis addresses this knowledge gap

by investigating the eect of academic SNS use on perceived learning and

satisfaction. Given this, our research questions were as follows:

1. How are SNSs incorporated to support aective outcomes (i.e., per-

ceived learning and satisfaction)?

2. What is the context of academic SNS use (i.e., type of SNS, learn-

ing management system [LMS] integration, optional use, academic

domain, and type of course)?

3. To what extent does the use of SNSs for educational purposes re-

late to an increase in students’ perceived learning and satisfaction?

Is there a signiﬁcant variability in these relationships across studies?

SNS for Educational Purposes and Affective Outcomes  237

METHOD

A systematic review attempts to make a comprehensive synthesis of studies

on a topic by systematically identifying previous research that meets explicit

predeﬁned criteria (Uman, 2011). As part of this systematic review, we also

conducted a meta-analysis with a subset of quantitative studies to estimate

the strength of the relationship between SNS use and aective learning

outcomes. Borenstein et al. (2009) explained that a meta-analysis is a piv-

otal part of the systematic review as “it is the statistical synthesis of the data”

gathered during the review (p. xxiii).

The systematic review process has multiple steps (e.g., Higgins & Green,

2009; Newman & Gough, 2020). First, we formulated the research ques-

tions to focus on the educational use of SNSs and the desired learning

outcomes (satisfaction and perceived learning). Second, we deﬁned our

inclusion and exclusion criteria to identify the studies to include. Third, we

developed a search strategy by deﬁning search terms and databases. Fourth,

after selecting a comprehensive list of primary studies, we extracted the

data using the ﬁnal coding scheme. Fifth, we synthesized empirical ﬁndings

from primary studies in the systematic review. Then, results from studies

that provided enough quantitative information were included in the meta-

analysis. Finally, we evaluated our ﬁndings for validity. The details of the

process are described below.

Study Inclusion and Exclusion Criteria

Empirical primary studies reported between 1997, when SNSs ﬁrst

launched to the public, and January 2020 were eligible for the systematic

review, regardless of their reporting format. Any literature that only reports

theoretical discussions, essays, reviews, or non-empirical ﬁndings was ex-

cluded. Studies were included using the following criteria:

1. Authors examined the relationship between the use of SNSs for

educational purposes and students’ perceived learning or satisfac-

tion in their study.

2. The study used SNSs for formal or informal educational purposes.

3. The study was reported in either English or Spanish for the re-

searchers to be able to read them.

We included other educational settings, including government and mili-

tary educational settings, as undergraduate populations and higher educa-

tion settings dominated previous meta-analyses (Huang, 2018; Liu et al.,

2017; Marker et al., 2018). Studies that reported attitudes towards SNSs or

238  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

student motivation to use SNSs were excluded (e.g., Bardwell, 2017; Kan-

thawongs et al., 2016; Zainuddin et al., 2017). Likewise, research focused

on games embedded within SNSs were also excluded (e.g., Krom, 2012; La-

bus et al., 2015). Research focused on actual learning using direct measures

(e.g., exams, GPA) was not included (e.g., Slim & Hafedh, 2019). Similarly,

studies that measured students’ satisfaction with an advisor (e.g., Junco et

al., 2016) or factors external to the course (e.g., Wang, 2013) were exclud-

ed. Finally, our systematic review excluded studies reporting on the social

network aordances of LMSs because such environments are speciﬁcally

developed for learning (e.g., Ellahi, 2018).

Study Search Process

Figure 8.1 is a PRISMA chart (preferred reporting items for systematic

reviews and meta-analyses) that summarizes the sampling process (Moher

et al., 2009). The search process had four phases: identiﬁcation, screening,

eligibility, and inclusion. In the identiﬁcation phase, we used speciﬁc key

terms listed below to ﬁnd potential research studies to include in the sys-

tematic review. In this ﬁrst phase, we identiﬁed studies from the following

sources: electronic databases, conference proceedings, references of rele-

vant articles, and referrals from experts. The ﬁrst author identiﬁed relevant

studies in October 2019 using the following databases: ERIC from EBSCO,

PsycINFO, Communication & Mass Media Complete, Education Full Text, Edu-

cation Source, APA PsycArticles, and APA PsycInfo. Search terms used were a

combination of terms related to SNSs, perceived learning, and satisfaction.

The following are three groups of key terms associated with each variable:

1. “Social network sites” [SNS, social media, social networks, Face-

book, Twitter, Pinterest, LinkedIn, Instagram, MySpace, Weibo,

Renren, StudiVZ, Google+]

2. “Perceived learning” [learning]

3. “Satisfaction” [learner satisfaction, student satisfaction]

We manually searched unpublished conference papers presented at two

prominent academic conferences: the American Educational Research As-

sociation (records available between 2010 and 2019) and the Association

for Educational Communications and Technology (records available be-

tween 1998 and 2018). Also, we manually searched the reference lists of

previous research syntheses (Huang, 2018; Liu et al., 2017; Manca & Ran-

ieri, 2016b; Marker et al., 2018). We reviewed relevant literature about per-

ceived learning and satisfaction as indicators of student outcomes (Allen et

SNS for Educational Purposes and Affective Outcomes  239

al., 2012; Alqurashi, 2017; Elbeck & Bacon, 2015; Joosten et al., 2019; Li et

al., 2017; Richardson et al., 2010; Sitzmann et al., 2010).

The identiﬁcation phase produced 574 citations of research studies from

database searching. Of those, 514 were excluded because these sources did

not have the key terms associated with the study (i.e., SNS, perceived learn-

ing, or satisfaction), but the databases associated them as synonyms. The

database search yielded several studies from the ﬁeld of medicine that were

excluded because the keyword “perceived” was related to perceived anxiety

and the keyword “satisfaction” to satisfaction with life.

In the screening phase, the identiﬁed studies were evaluated based on

their abstracts. We retained papers when at least one of the associated

terms related to the target phenomenon (i.e., the use of SNSs for educa-

tion purposes) and at least one of the terms related to the target outcomes

PL k = 7

Included Eligibility Screening Identiﬁcation

SS k = 8

Records identiﬁed

through database

searching

(n = 433)

Records identiﬁed

through references

of relevant articles

(n = 12)

Records identiﬁed

through conference

proceedings

(n = 120)

Records identiﬁed

through other

sources

(n = 9)

Records screened

(n = 574)

Records excluded

(n = 514)

Full-text articles excluded,

with reasons

(n = 29)

• Duplicates (n = 4)

• Articles did not

examine students’

perceived learning

or satisfaction

(n = 25)

Full-text articles assessed for eligibility

(n = 60)

Studies included in systemic

narrative review

(n = 31)

Unique studies included in quantitative

summary (meta-analysis)

(k = 12)

Figure 8.1 PRISMA ﬂow diagram for study selection. Note: Studies included in

the quantitative summaries are also part of the systematic review of SNSs used for

learning purposes and learning outcomes. PL k: Studies from which a perceived

learning eect size was extracted. SS k: Studies from which a student satisfaction

eect size was extracted.

240  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

(i.e., students’ perceived learning or satisfaction) were present in the ab-

stract of each of the research papers. The screening phase resulted in 60

research studies to be further assessed for eligibility. Forty-one of those 60

articles came from our database search, four from conference proceedings,

six from reference lists, and nine from external identiﬁcation (e.g., authors

recommended, suggested by the database during search).

Full-text articles of these 60 studies were accessed during the eligibility

phase. The ﬁrst author retrieved each document and assured that it met

all the inclusion criteria. Twenty-ﬁve of these studies did not explore the

relationship between educational SNS use and perceived learning or sat-

isfaction, and four additional articles were duplicates of studies already in-

cluded. The inclusion phase resulted in a ﬁnal sample of 31 studies for the

systematic review.

Data Extraction and Coding Reliability

for Systematic Review

While selecting studies, we also developed and piloted a coding scheme

to extract data from the primary studies. The ﬁrst author developed an

initial coding scheme. The second and third authors reviewed the coding

scheme, and adjustments were made accordingly. The ﬁrst author and a

graduate researcher piloted the revised coding scheme. Inter-rater reli-

ability for the revised coding scheme was calculated with the “irr” package

(Gamer et al., 2019) in R software (R Core Team, 2019). The two coders

achieved 91% agreement, Kappa = .90, z = 33.6, p < .01. Coders met to dis-

cuss disagreement and coding scheme areas for improvement. With the fur-

ther revisions on the coding scheme and piloting, the two coders achieved

93.8% agreement, Kappa = .93, z = 33.9, p < .01.

Meta-Analysis of Quantitative Studies

Eect Size Calculation

We used Pearson’s correlation coecient (r) as the eect size for the

meta-analysis. Borenstein et al. (2009) explained that measurement scales

change between studies when researchers use dierent psychometric or

educational instruments to assess an outcome. A correlation coecient is a

standardized eect size measure that “take[s] account of dierent metrics

in the original scales” (Borenstein et al., 2009, p. 41). The interpretation

of Pearson’s correlation is intuitive, ranging from –1 to +1, with zero repre-

senting no relationship (Durlak, 2009). The estimates of eect sizes were

either directly obtained from primary studies or computed from available

SNS for Educational Purposes and Affective Outcomes  241

quantitative data provided in the primary studies. Pearson’s correlations

were converted to Fisher’s z scale for normalizing its sampling distribution

and then converted back to the original r metric for reporting purposes

(Borenstein et al., 2009).

Handling Dependence of Eect Sizes

A total of 28 eect sizes were originally extracted from the 12 prima-

ry studies included in the meta-analysis portion of the systematic review.

Five studies (Frisby et al., 2016; Kucuk & Sahin, 2013; Nalbone et al., 2016;

Pishock, 2018; Thoms & Eryilmaz, 2015) reported multiple independent ef-

fect sizes. However, the eect sizes extracted from Kucuk and Sahin (2013)

and Pishock (2018) were considered statistically dependent because they

were obtained from the same samples. While variant methods are avail-

able for handling dependent eect sizes (e.g., Card, 2012; Cooper, 2010;

Hedges et al., 2010; Olkin & Gleser, 2009; Sutton et al., 2000; Van den

Noortgate et al., 2013, 2015), we decided to calculate an average of the

eect size weighted by its precision within each study (e.g., Borenstein et

al., 2009; Cooper, 2010). The decision was made because (a) the method

has been most widely used in meta-analysis (Hedges et al., 2010), and (b)

alternative methods, like a multivariate method, require additional data

knowledge (e.g., covariances among dependent eect sizes, Olkin & Gle-

ser, 2009), which is often not reported in primary studies, or requires strict

data assumptions for unbiased estimations (e.g., the application of the use

of multilevel modeling, Van den Noortgate et al., 2013, 2015) and thereby

it was not feasible to apply to the current meta-analysis. It also helped us

to avoid losing information by simply selecting one eect size per study.

To aggregate the dependent eect sizes, we used the MAd R package (Del

Re et al., 2011) aggregation function that implements the Borenstein et al.

(2009) procedure. A total of 19 eect sizes was extracted for synthesis after

aggregating dependent eect sizes. Of those, 10 eect sizes accounted for

student satisfaction, and nine accounted for perceived learning.

Data Analysis

We adapted the random-eects model as our methodological frame-

work because the ﬁxed-eect model omits potential meaningful variation

between studies (Hunter & Schmidt, 2000). Hunter and Schmidt (2000)

recommended that researchers use a random-eects model when conduct-

ing meta-analytic computations as it allows researchers to make generaliz-

able conclusions that allow for future replication. Three statistics were used

to investigate the variation of eect sizes across studies. First, we quantiﬁed

heterogeneity using an τ2 statistic to show the percentage of variance among

studies due to reasons other than chance. Second, we tested for homoge-

neity using a Q test (Hedges & Olkin, 1985). Finally, the between-groups

242  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

variance statistic τ2 was calculated (Higgins et al., 2003). The average cor-

relation was computed by weighting each eect size for its precision (Bo-

renstein et al., 2009). We used the “meta” (Schwarzer, 2019) and “metafor”

(Viechtbauer, 2010) statistical packages to perform the meta-analysis in R

(R Core Team, 2019).

Sensitivity Analysis

The ﬁle drawer problem, also known as publication bias (Sutton, 2009),

means that studies that do not result in statistically signiﬁcant results are less

likely to be published than studies that show statistical signiﬁcance (Rosen-

thal, 1979). This may lead the present meta-analysis to have publication bias

assuming that the majority of published studies show statistically signiﬁcant

results. We attempted to make a comprehensive gathering of studies that

resulted in nine published journal articles and three unpublished disserta-

tions. To examine possibilities of publication bias, we plotted the eect sizes

as a function of their standard errors in a funnel plot for visual inspection

(Egger et al., 1997). The trim and ﬁll analysis was also used to estimate the

number of studies missing in the funnel plot to make it more symmetric

and explore publication bias.

RESULTS

Characteristics of Included Studies

in the Systematic Review

The studies included in the systematic review were conducted in the

United States (n = 14), Israel (n = 3), Spain (n = 3), Turkey (n = 3), Thai-

land (n = 2), Australia (n = 1), Cyprus (n = 1), Iraq (n = 1), Saudi Arabia

(n = 1), Taiwan (n = 1), and United Kingdom (n = 1). The combined to-

tal sample size was 4,552 participants. Table 8.1 summarizes the studies

included in the systematic review. The mean age of participants was 23.4

years. Gender was fairly even across the sample of studies. Twenty-one stud-

ies reported participants’ gender, and the mean percentage of female par-

ticipants across studies was 57.3%. Only ﬁve studies included data on par-

ticipants’ race. The mean percentage of White participants was 62.43%.

Twelve studies out of 31 studies reported student satisfaction as the aective

outcome. Likewise, 12 studies investigated perceived learning as the aec-

tive outcome. A subset of seven studies studied both student satisfaction

and perceived learning as aective outcomes.

Facebook was the most reported SNS used for academic purposes

(n = 21), followed by Twitter (n = 13). Studies included in the systematic re-

view could examine the relationship between SNSs and aective outcomes

SNS for Educational Purposes and Affective Outcomes  243

TABLE 8.1 Summary of Studies on the Relation Between Social Network Sites Use for Academic Purposes

and Affective Outcomes

Author(s) and Year Type NSNS(s)

Optional/

Mandatory

Academic

Domain

Type of

Course r SS r PL

Affective

Outcome

Abella-García et al. (2019) J 202 T M ED F2F — — PL

Al-Azawei (2019) J 143 F O IT F2F — — SS

Arquero et al. (2017) J 202 F, T O B F2F — — SS

Awidi et al. (2019) J 60 F M A B — — PL

Cedar & Singhara (2017) J 30 F M SLL B — — SS, PL

*Clarke & Nelson (2012) J 81 T O B F2F .28 .19 SS, PL

Davidovitch & Belichenko (2018) J 150 F O M F2F — — SS, PL

*Eastman (2015) D 40 T M B F2F — .20 PL

*Frisby et al. (2016) #1 J 10 F M LA F2F .31 –.16 SS, PL

*Frisby et al. (2016) #2 J 15 T M LA F2F .18 .34 SS, PL

Homan (2009) CP 51 N M ED O — — SS

Hurt et al. (2012) J 107 F M LA F2F — — PL

Ioannou et al. (2016) J 60 F M M F2F — — SS, PL

*Kucuk & Sahin (2013) J 109 F M ED B .24 — SS

Kurtz (2014) J 134 F O ED B — — PL

Lowe & Laey (2011) J 123 T O B F2F — — SS

Lowenthal & Dunlap (2010) CP 17 T O ED O — — SS, PL

Meishar-Tal et al. (2012) J 50 F O ED F2F — — PL

Miller et al. (2019) J 35 F, T M SLL F2F — — PL

(continued)

244  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

TABLE 8.1 Summary of Studies on the Relation Between Social Network Sites Use for Academic Purposes

and Affective Outcomes (continued)

Author(s) and Year Type NSNS(s)

Optional/

Mandatory

Academic

Domain

Type of

Course r SS r PL

Affective

Outcome

*Nalbone et al. (2016) #1 J 568 F O M F2F –.01 — SS

*Nalbone et al. (2016) #2 J 465 F O M F2F .17 — SS

Orawiwatnakul & Wichadee (2016) J 82 F M SLL F2F — — SS

Ozan (2013) J 48 F, T O ED F2F — — PL

*Pishock (2018) D 300 F M MS F2F .65 — SS

Porath (2018) J 26 T, G+ O ED O — — PL

*Powless (2011) D 799 F O M F2F .05 — SS

*Rinaldo et al. (2011) J 113 T M B F2F — .22 PL

*Rueda et al. (2017) J 94 F, T, P, L, G+ O B F2F –.28 –.23 SS, PL

Salameh (2017) J 145 F O SLL F2F — — SS

Shih (2013) J 111 F M SLL B — — SS

*Strahler (2014) D 39 T O ED O — .81 PL

*Thoms & Eryilmaz (2015) #1 J 23 T M CS O — .26 PL

*Thoms & Eryilmaz (2015) #2 J 24 T M CS F2F — .15 PL

*Uzun et al. (2014) J 96 F M ED F2F .01 — SS

Note: The 12 unique studies included in the meta-analytic review are indicated with an asterisk. Number after publication year indicates that multiple

independent eect sizes were extracted from the same study. J = Journal Article; D = Dissertation; CP = Conference Proceeding; F = Facebook;

T = Twitter; G+ = Google+; P = Pinterest; L = LinkedIn; N = Ning; ED = Education; IT = Information Technology; B = Business; A = Architecture;

SLL = Second Language Learning; M = Mixed Academic Domains; LA = Liberal Arts; MS = Military Science; F2F = Face-to-face; OL = Online

Learning; B = Blended Learning; PL = Perceived Learning; SS = Student Satisfaction. Dash indicates that an eect size was not obtained from the

research study.

SNS for Educational Purposes and Affective Outcomes  245

with more than one SNS platform. For example, four studies investigated

both Facebook and Twitter (Arquero et al., 2017; Frisby et al., 2016; Miller

et al., 2018; Ozan, 2013). For that reason, the aforementioned numbers

add up to more than the 31 studies included. Homan (2009) focused on

Ning while Porath (2018) focused on Google+ in addition to Twitter. One

study reported that learners used multiple SNSs simultaneously (Facebook,

Twitter, Pinterest, LinkedIn, Google+; Rueda et al., 2017). Most studies did

not integrate an LMS with the educational use of SNSs (n = 22).

While disciplinary variation was observed among conducted studies, the

available research was mainly limited to undergraduate students and face-

to-face learning environments. More speciﬁcally, 22 of 31 studies were con-

ducted with an undergraduate population in various disciplines: education

(n = 5, Abella-García et al., 2019; Homan, 2009; Kucuk & Sahin, 2013;

Ozan, 2013; Uzun et al., 2014), business (n = 5, Arquero et al., 2017; Clarke

& Nelson, 2012; Eastman, 2015; Rinaldo et al., 2011; Rueda et al., 2017),

second language learning (n = 3, Miller et al., 2018; Orawiwatnakul &

Wichadee, 2016; Salameh, 2017), liberal arts (n = 2, Frisby et al., 2016; Hurt

et al., 2012), architecture (n = 1, Awidi et al., 2019), information technol-

ogy (n = 1, Al-Azawei, 2019), computer science (n = 1, Thoms & Eryilmaz,

2015), military science (n = 1, Pishock, 2018), and mixed disciplines (n = 3,

Davidovitch & Belichenko, 2018; Nalbone et al., 2016; Powless, 2011). In

contrast, limited studies are available with graduate student populations

(n = 6; Ioannou et al., 2016; Kurtz, 2014; Lowe & Laey, 2011; Lowenthal

& Dunlap, 2010; Meishar-Tal et al., 2012; Shih, 2013) and with the govern-

ment sta (n = 1, Cedar & Singhara, 2017). Studies in an informal learning

setting were also limited (n = 2, Porath, 2018; Strahler, 2014).

Five of the studies were based in a blended learning environment (Awi-

di et al., 2019; Cedar & Singhara, 2017; Kucuk & Sahin, 2013; Kurtz, 2014;

Shih, 2013) and another ﬁve were based in online learning environments

(Homan, 2009; Lowenthal & Dunlap, 2010; Porath, 2018; Strahler, 2014;

Thoms & Eryilmaz, 2015); the remaining 21 studies were focused on face-to-

face learning environments. Mandatory or optional use of SNSs was relatively

equal across studies. Participants were required to use SNSs in 16 studies,

whereas 15 studies gave students a choice to use or not use SNSs for the class.

Relationship Between SNS Use and Student Satisfaction

Facebook (n = 14) was the most used SNS, followed by Twitter (n = 6), to

examine its relationship with student satisfaction. The SNS Ning was used

in one study (Homan, 2009) to support learning. Given the predomi-

nance of Facebook and Twitter, the results are divided on the type of af-

fective learning outcome and use of Facebook and Twitter for educational

246  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

purposes. Furthermore, we believe it will be more practical for practitio-

ners and researchers alike to ﬁnd the results by SNS when looking for

recommendations.

Facebook to Support Student Satisfaction

Overall, researchers reported that using Facebook for learning purposes

increased student satisfaction (n = 11). Not all researchers reported the

standard deviation of quantitative ﬁndings. Consequently, we are report-

ing the number of items of the scale used to measure satisfaction when

the standard deviation was not available. Nalbone et al. (2016) observed

improvement on student satisfaction in an experimental study in which

Facebook was used to create a learning community among ﬁrst-year un-

dergraduate students (M = 2.28, SD = .52) when comparing to a control

group (M = 2.11, SD = .45). In this case, Facebook was used to help ﬁrst-

year students transition to college life. Similarly, Cedar and Singhara (2017;

M = 4.58, scale 1–5), Davidovitch and Belichenko (2018) (M = 2.72, scale

1–4), and Salameh (2017; M = 4.66, scale 1–5) also reported positive re-

sults regarding student satisfaction. Orawiwatnakul and Wichandee (2016;

M = 3.76, scale 1–5) explained that the positive results could be due to the

high level of familiarity with Facebook and its widespread use. Facebook

helped create bonding among students by allowing them to disclose infor-

mation (e.g., hobbies, interests) that otherwise would not be shared with

peers (Frisby et al., 2016). Having opportunities to discuss topics dierent

from the content area (Arquero et al., 2017) allowed participants to build

interpersonal ties with their peers (Frisby et al., 2016) and increased enthu-

siasm towards the learning experience (Kucuk & Sahin, 2013).

Even though 11 studies showed how Facebook supported student satisfac-

tion, four studies did not ﬁnd positive or statistically signiﬁcant results. For

example, Uzun et al. (2014) reported that there was no statistically signiﬁcant

dierence between experimental (Facebook) and control groups regarding

student satisfaction, t(96) = 0.08, p = .94. Al-Azawei (2019) found a positive

relationship (r = .80) between experience using Facebook and student sat-

isfaction. Al-Azawei suggested that further training on using SNS for edu-

cational purposes would increase satisfaction. Consequently, learners who

are unfamiliar with SNSs should receive guidelines on how to use them to

improve student satisfaction. Rueda et al. (2017) showed a weak negative cor-

relation (r = –.28) between SNS use and student satisfaction. However, this

study allowed learners to use multiple SNSs in addition to Facebook and did

not provide details on how they were used in the classroom.

Twitter to Support Student Satisfaction

All but one (Rueda et al., 2017) study that used Twitter to support student

satisfaction (n = 6) yielded positive results. Two of those studies achieved

SNS for Educational Purposes and Affective Outcomes  247

statistical signiﬁcance (Clarke & Nelson, 2012; Lowe & Laey, 2011), and

three showed a mean increase in satisfaction (Arquero et al., 2017; Frisby

et al., 2016; Lowenthal & Dunlap, 2010). Twitter use for learning purposes

promotes positive attitudes towards the overall learning experience and

promotes a sense of classroom community (Clarke & Nelson, 2012). The

positive relationship between Twitter use for learning purposes and student

satisfaction is related to the use of clear course guidelines. For example,

Clarke and Nelson (2012) established a course hashtag to follow course

tweets, provided speciﬁc criteria to follow course tweets, and asked partici-

pants to tweet at least three times about topics relevant to the course.

Still, not all studies provided learners with guidelines on how to use Twit-

ter, yielding mixed or negative results. For example, Lowenthal and Dunlap

(2010) used Twitter as an optional just-in-time tool for course interaction.

Lowenthal and Dunlap reported that learners were very satisﬁed (M = 3.63,

0–4 scale) with the learning experience when using Twitter, given that it

provided a “more intimate [communication] than mandatory weekly dis-

cussions” (p. 6). As seen with Facebook, learners also reported establishing

closer connections with peers by sharing personal information (Lowenthal

& Dunlap, 2010). However, the follow-up interviews also revealed students’

discomfort with receiving continuous updates from classmates and the in-

structor. As one student put it: “I was not interested in knowing what my

instructor was doing each minute of the day. I don’t want to know intimate

details about my instructors and peers” (Lowenthal & Dunlap, 2010, p. 13).

Only Rueda et al. (2017) reported a negative correlation between SNS use

and student satisfaction. However, guidelines on how Twitter was used in

the course are not explicit. Therefore, we presume that the use of multiple

SNSs and lack of guidance may have led to a negative relationship.

Meta-Analytic Results on the Relationship Between SNS Use and

Student Satisfaction

Studies investigating SNS use for academic purposes and student satis-

faction showed correlations ranging from r = –.28 (Rueda et al., 2017) to

r = .65 (Pishock, 2018). The 10 studies represented a total of 3,595 students.

Figure 8.2 is a forest plot that shows the correlation coecients, their

weights, and 95% conﬁdence intervals. The vertical line at zero indicates no

eect or no relationship. The forest plot shows that most eect sizes were

positive and had large variation. The exceptions were Nalbone et al. (2016)

#1 (ﬁrst iteration) on using Facebook to support college transition and

Rueda et al. (2017) who used multiple unguided SNSs to support learning.

The weighted average correlation was r = .17, which is a small but posi-

tive correlation, according to Cohen’s (1992) guideline. The I

2 = 95% (with

the CI of [92%, 96%]) indicated that the relationship between academic

use of SNSs and student satisfaction is heterogeneous due to reasons other

248  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

than sampling error variation. The statistically signiﬁcant result from the

heterogeneity test, Q(9) = 168.88 (p < .01), 𝜏² = 0.07 also supports the het-

erogeneity of the relationship. However, further analysis with the modera-

tors was not possible due to the small number of eect sizes. The results of

the meta-analysis are consistent with the ﬁndings of studies only included in

the systematic review. The eect of SNS use on student satisfaction is posi-

tive when it is used for learning purposes.

Relationship between SNSs and Perceived Learning

Researchers used Twitter and Facebook almost exclusively to research

the relationship between academic SNS use and perceived learning. Only

Rueda et al. (2017) included other SNS options (Pinterest, Linkedin,

Google+). However, they also researched Twitter and Facebook in their

study. Results are presented by SNS type, like in the case of student satisfac-

tion above.

Facebook to Support Perceived Learning

Similar to student satisfaction, most studies (n = 7) reported a positive

relationship between Facebook use for learning purposes and perceived

learning. Facebook created a dynamic online place for intensive and col-

laborative learning experiences (Ioannou et al., 2016; Meishar-Tal et al.,

2012). Researchers created these collaborative online spaces using Face-

book as an alternative to the institutional LMS or course website (Hurt et

al., 2012; Kurtz, 2014; Meishar-Tal et al., 2012; Ozan, 2013). Meishar-Tal et

al. (2012) created a private Facebook group speciﬁcally to serve as a LMS

Figure 8.2 Forest plot of relationship between SNS use for academic purposes

and student satisfaction.

SNS for Educational Purposes and Affective Outcomes  249

in the course, and in another study, Facebook served as a course reposi-

tory to share course materials (e.g., presentation, documents) (Awidi et al.,

2019). It was also the communication channel among students when do-

ing group work (Frisby et al., 2016). Similarly, instructors used Facebook

to communicate with students individually (Hurt et al., 2012). Facebook

was also heavily used for peer-assessment and online discussions (Meishar-

Tal et al., 2012) and learners reacting to others’ comments in active (re-

plying) and passive (liking) ways (Meishar-Tal et al., 2012). Unlike a LMS,

Facebook cannot guarantee participants’ privacy. Therefore, when using

Facebook for educational purposes, researchers protected learners’ privacy

by creating closed groups in which only course members could have access

(Miller et al., 2018). Contrary to Twitter, in which tweets are publicly avail-

able and can be found using the course hashtag, participants could restrict

their Facebook posts.

Participants reported high perceived learning in second language learn-

ing when using Facebook (Cedar & Singhara, 2017; Miller et al., 2018). Face-

book improved perceived learning in reading and writing skills (Miller et al.,

2018). Participants also stated that using Facebook to support oral communi-

cation skills improved their English pronunciation (e.g., “I could speak more

correctly and more clearly” (Cedar & Singhara, 2017, p. 131). Researchers

explained that the positive relationship of Facebook with perceived learning

is due in part to instructors’ good discussion facilitation (Awidi et al., 2019).

Unlike the previous studies, Hurt et al. (2012) did not yield statistically sig-

niﬁcant results on the use of Facebook and perceived learning when compar-

ing an experimental and control group. However, Hurt et al. clariﬁed that

they intervened as little as possible in Facebook interactions. Therefore, we

conclude that participants of the Facebook group had little support in the

learning experience resulting in sparse perceived learning.

Twitter to Support Perceived Learning

Nine studies out of 12 found Twitter to increase perceived learning.

Twitter was ideal for participants to share experiences and have discussions

(Abella-García et al., 2019), especially for informal learning settings. For

example, Strahler (2014) reported a high correlation between perceived

learning and Twitter use for learning purposes (r = .81). Participants of the

Twitter conversation #edtechchat discussed educational technology topics

in this study. The hashtag was used to follow asynchronous conversations

and provide answers to questions posted by moderators. Porath (2018) in-

vestigated the use of the hashtag #CyberPD by a community of educators

who were members of a book club. Participants of the informal commu-

nity followed book authors and even had synchronous conversations with

them. Porath clariﬁed that the books’ content became a secondary topic

because most of the learning was reported from interactions with peers.

250  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

As the learning community was established, participants stated that they

implemented strategies learned from the book club and discussions in their

teaching practices.

Twitter also increased perceived learning in formal educational settings.

For example, Rinaldo et al. (2011) reported a statistically signiﬁcant in-

crease (t = 3.41, p < .001) over time in a business course that used Twitter.

Rinaldo et al. conceptualized Twitter as an experiential learning tool in

which business learners could see the principles of marketing being ap-

plied by companies’ accounts that they followed on Twitter. Thoms and

Eryilmaz (2015) also found an increase in perceived learning when using

Twitter to illustrate ethical concepts to computer science and business stu-

dents. Yet Clarke and Nelson (2012) suggested that instructors needed to

emphasize how Twitter bridges theory and practice to avoid seeing Twitter

as a merely social activity.

A positive relationship was also discovered regarding perceived learn-

ing when using Twitter for second language learning. Miller et al. (2018)

found Twitter to be positive for perceived learning in writing, grammar,

and vocabulary. However, Frisby et al. (2016) argued that Twitter hinders

the transmission of academic ideas due to the character limit. Miller et al.

explained that limiting characters forced learners to write short sentences

in the target language, making it easier for them to communicate simple

ideas correctly. Lowenthal and Dunlap (2010) found that Twitter’s charac-

ter limit fosters more natural communication among learners who could

share personal details about their family and school life.

Meta-Analytic Results on the Relationship Between SNS Use and

Perceived Learning

A total of nine correlations from 464 individuals represented the re-

lationship between academic use of SNSs and perceived learning with a

range from r = –.23 (Rueda et al., 2017) to r = .81 (Strahler, 2014). A forest

plot with the nine independent eect sizes (see Figure 8.3) indicates that

the majority of the eect sizes show a positive relationship between SNS use

and perceived learning, except for Frisby et al. (2016) #1 and Rueda et al.

(2017). A small but positive average correlation (r = .23) was found. The

2 = 84% statistic was high, with a 95% conﬁdence interval of 72% and 91%,

indicating that the studies investigating the relationship between academic

use of SNSs and perceived learning are heterogeneous. The result of a het-

erogeneity test Q(8) = 50.84 (p < .01), 𝜏² = .12 showed that there was a statis-

tically signiﬁcant variation among studies under the random-eects model.

However, further analysis with the moderators was not possible due to the

small number of eect sizes. Like student satisfaction, SNSs also had a posi-

tive eect on perceived learning when purposefully used for education.

SNS for Educational Purposes and Affective Outcomes  251

Sensitivity Analysis for Meta-Analysis

Figure 8.4 shows the funnel plots referenced to explore publication bias

in academic SNS use and aective learning outcomes. Duval and Tweedie’s

trim and ﬁll procedure (Duval, 2005) was conducted to estimate the ef-

fect size assuming that potential missing studies were published for both

student satisfaction and perceived learning. The trim and ﬁll test in the

student satisfaction data shows that after adding three studies with small to

moderately large negative eect sizes (r = –.15, r = –.19, r = –.56), the sum-

mary eect decreases from r = .16 to r = .05. In the perceived learning data,

the average correlation would decrease from r = .23 to r = .08 after adding

two studies with negative eect sizes with the size of r = –.18 and r = –.74.

Because the sample sizes for both meta-analyses are small, adding small

numbers of the studies with a negative correlation would nullify the overall

Figure 8.3 Forest plot of the relationship between SNSs use for academic pur-

poses and perceived learning.

Figure 8.4 Funnel plot for correlations of academic SNS use and students’ satis-

faction and perceived learning.

252  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

summary of the relationships. However, based on our ﬁndings from the

systematic review, it is unlikely to ﬁnd studies with such large negative cor-

relations. While we acknowledge the potential threat to publication bias

and the need for replication of meta-analysis with a larger sample size, we

consider the results of the current meta-analytic ﬁndings to have scholarly

merit for interpretation.

DISCUSSION AND CONCLUSIONS

Leary and Walker (2018) stated that the systematic report of previous re-

search in the ﬁeld of instructional design and technology is urgent to deep-

en the understanding of prior research and improve theory. Failure to ﬁnd

scholarly agreement results in the use of ineective strategies and unneces-

sary replication of work. The ambivalent opinions of researchers and the

lack of systematic attempts to condense results indicate a need for another

method that brings agreement, at least to some extent, among scholars. Re-

searchers explored student satisfaction and perceived learning in empiri-

cal studies comparing face-to-face environments to online environments

(Allen et al., 2015). To respond to the accelerated speed of technology,

researchers should examine multiple forms of learning in online environ-

ments (Allen et al., 2015). To date, no systematic eorts have synthesized

the ﬁndings of empirical studies examining the relationship between SNS

and perceived learning and satisfaction. Researchers use meta-analytic

methods to review and synthesize past literature in a systematic way (Hattie

et al., 2014). Field (2005) explained that a meta-analysis is “used to discover

how big an eect actually is and what factors moderate that eect” (p. 1).

Therefore, the present meta-analysis addressed the eect of using SNSs for

academic purposes on aective learning outcomes.

We found that rules on how to use SNSs are necessary to avoid adverse

eects on learning outcomes. The few studies that reported negative re-

sults did not provide enough support and guidance to participants on how

to use SNSs for learning purposes. For example, Rueda et al. (2017) al-

lowed learners to use multiple SNSs. However, they do not report providing

explicit instruction to learners on best practice uses. We infer that satura-

tion of media (various SNSs) and lack of guidance resulted in a negative

eect for both perceived learning r = –.23 and satisfaction r = –.28. Hurt

et al. (2012) also reported a negative eect of SNS use on the aective

measure perceived learning. In this case, researchers purposefully avoided

interacting within the SNS (Facebook). Therefore, it is the intentional and

structured use of SNSs for learning that beneﬁts participants’ perceived

learning and satisfaction. Researchers and practitioners should set clear

guidelines for SNS use to support the learning experience. As a reference,

SNS for Educational Purposes and Affective Outcomes  253

Lowe and Laey (2011) provided a set of recommendations on how to use

Twitter in business courses that can be applied in other disciplines. Recom-

mendations include providing a short debrieﬁng on the SNS as a learning

tool, assigning a course hashtag, and exemplifying course content through

tweets (Lowe & Laey, 2011). Oering an introductory session on how to

use SNSs is a useful instructional method to communicate guidelines to

participants.

We found positive results when learners used SNSs to support language

learning when learners received enough guidance. Facebook supported

oral communication skills (Cedar & Singhara, 2017) and reading and writ-

ing skills (Miller et al., 2018). The beneﬁts of Twitter for language learning

focused on writing, grammar, and vocabulary (Miller et al., 2018) despite

the character limit that hindered complex language production (Frisby et

al., 2016). It could be argued that Facebook oers a broader array of af-

fordances than Twitter, like in the case of Cedar and Singhara (2017) who

required learners to share audio ﬁles via a private group wall on Facebook.

Still, users can upload videos, images, and gifs to Twitter via public tweets

or private messages. Despite Liu et al. (2017) studying actual achievement,

our positive ﬁndings agree with their results that established SNS use was

positively associated with academic performance in language tests. Liu et al.

discussed that SNSs prompted students who did not produce or consume

written material to read and write actively. However, the quality of such

processes is yet to be seen. Therefore, providing guidelines and rubrics that

explicitly communicate standards to learners is a persistent need.

Another crucial ﬁnding of this systematic review and meta-analysis was

the use of SNSs as an alternative to universities’ LMSs. Facebook was the

predominant option to substitute for an LMS because it served as an infor-

mation repository and had a space for online discussions (i.e., Facebook

wall). Although it brings privacy concerns to participants (Pishock, 2018),

Facebook can be a cost-eective option to host and facilitate learning expe-

riences, particularly in countries and institutions with limited technologi-

cal infrastructure and resources. Meishar et al. (2012) ﬁrst studied the use

of Facebook groups as an LMS in a case study ﬁnding that learners per-

ceived Facebook to promote collaborative work better than a traditional

LMS. Unlike an LMS, Facebook allowed active (i.e., comments) and pas-

sive (i.e., likes) participation (Meishar et al., 2012). Despite Facebook not

oering an advanced ﬁle sharing organization like traditional LMSs that

restricts the timing of assignment submissions (Kalelioğlu, 2017), learners

and instructors can still take advantage of comments and chat timestamps.

We believe that multiple LMS aordances can be emulated on Facebook if

clear procedures are stated beforehand. Like with any LMS, external plat-

forms like Google Drive can complement Facebook’s capabilities. Future

work could focus on how developing countries are using Facebook as LMSs.

254  D. CASTELLANOS-REYES, Y. MAEDA, and J. C. RICHARDSON

Hattie et al. (2014) argued that ﬁnding a study that supports any method

or defending any claim is an easy task. The challenge is to ﬁnd to what ex-

tent such a claim is valid for a larger population and similar studies. They

also argued that conducting empirical studies in education with “zero” ef-

fect as a baseline is misleading because it makes all studies set to have posi-

tive outcomes (Hattie et al., 2014). Therefore, they concluded that an aver-

age eect size of d = 0.4 “should be used as the benchmark to judge eects

in education” (p. 202). Following the guidelines of Hattie et al., the eect

size of academic SNS use on student satisfaction (r = .17) does not meet

the required guidelines. However, given that previous meta-analyses on the

relationship between SNS general use and student outcomes yielded eect

sizes close to zero (Huang, 2018; Marker et al., 2018), we can say that there

is a noticeable eect on the relationship between academic SNS use and

student satisfaction. We infer that it is the intentionality of using SNSs for

academic purposes that increased the eect in this particular set of studies.

Thus, we can conclude that the eect of purposeful academic SNS use is

small to moderate on students’ satisfaction. In a similar fashion to student

satisfaction, the eect size of academic SNS use on perceived learning was

positive r = .23 and larger than meta-analyses that used actual achievement

as student outcomes (Huang, 2018; Marker et al., 2018). We conclude that

the eect of academic SNS use on student satisfaction and perceived learn-

ing is positive and should be further considered.

The meta-analysis section of this systematic review included a small

sample of studies. The trim and ﬁll procedure conducted during sensitivity

analysis shows that more studies could have reduced the eect of academic

SNS use on student satisfaction. However, this was not the case for per-

ceived learning, which showed that no additional studies were needed. In

any case, the limited number of primary studies showed that more empiri-

cal research should be done on the relationship between academic SNS use

and student satisfaction and perceived learning under various academic

settings and contexts. A major limitation to including more studies in this

meta-analysis was that researchers failed to report quantitative data neces-

sary to compute eect sizes. For example, 19 studies failed to report suf-

ﬁcient data to make calculations. Regarding the systematic review, some

researchers also failed to provide details on how SNSs were used for learn-

ing purposes. Davidovitch and Belichenko (2018) and Rueda et al. (2017)

did not elaborate on how SNSs were incorporated into the learning ex-

perience. Richardson et al. (2017) concluded that lack of attention when

“reporting design elements may imply the limited application of the rel-

evant theory to practice” (p. 414). As a result, it was not possible to obtain a

complete picture of how SNSs were or were not useful to support aective

measures in their case.

SNS for Educational Purposes and Affective Outcomes  255

Researchers could also explore other subjective variables highly corre-

lated to the aective domain like motivation (Sitzmann et al., 2010). Future

work could also examine learning that occurs in SNS use, using distance

learning theory like the community of inquiry framework, as in Du Bois

et al. (2019). These researchers studied comments in a Facebook group

where learners of massive open online courses discussed course related top-

ics. Speciﬁcally, Du Bois et al. examined the role of social presence when us-

ing SNSs for learning purposes. Furthermore, the three subcomponents of

social presence (i.e., aective communication, open communication, and

cohesive responses; Garrison, 2017) would better inform researchers of the

use of SNSs to promote students’ aective learning outcomes. This meta-

analysis only included studies that used SNS platforms designed for purpos-

es other than learning. In future studies, researchers can explore the eect

of educational SNSs purposefully created for learning, such as EDMODO,

on student satisfaction or perceived learning (Ellahi, 2018; Yildiz, 2019).

Moreover, this systematic review’s ﬁndings could relate to better course

design by informing instructional designers on how SNS integration is most

advantageous for learners. To date, no meta-analysis has summarized the

results of empirical studies that investigated the relationships between SNS

use for academic purposes and perceived learning and satisfaction. This

meta-analysis provides a comprehensive summary to researchers, instruc-

tors, and instructional designers on the relationship with subjective student

outcomes when SNSs are intentionally used for academic purposes. Prac-

titioners can use this review to make decisions relevant to course design

concerning SNSs and their net beneﬁts in student outcomes. They can also

use this study to make informed decisions on the potential educational af-

fordances that SNSs oer based on the course design. Finally, researchers

can use this review to eectively gauge relevant research on the topic and

potential gaps in the literature for future work. It is pivotal for educational

researchers and practitioners to investigate the educational aordances of

alternative platforms like SNSs. We may ﬁnd them as allies in the quest to

provide high-quality engaging education rather than seeing them as merely

distractions for learners.

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The Impact of OER’s Continuous Improvement Cycles on Students’ Performance: A Longitudinal Analysis of the RISE Framework

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Open educational resources (OER) have been praised for revolutionizing education. However, practitioners and instructors battle keeping OER updated and measuring their impact on students’ performance. Few studies have analyzed the improvement of OER over time in relation to achievement. This longitudinal study uses learning analytics through the open-source Resource Inspection, Selection, and Enhancement (RISE) analysis framework to assess the impact of continuous improvement cycles on students’ outcomes. Panel data (i.e., performance and use) from 190 learning objectives of OER of an introductory sociology course were analyzed using a hierarchical linear model. Results show that more visits to an OER do not improve student achievement, but continuous improvement cycles of targeted OER do. Iterative implementation of the RISE analysis for resource improvement in combination with practitioners’ expertise is key for students’ learning. Given that the RISE classification accounted for 65% of the growth of students’ performance, suggesting a moderate to large effect, we speculate that the RISE analysis could be generalized to other contexts and result in greater student gain. Institutions and practitioners can improve the OER’s impact by introducing learning analytics as a decision-making tool for instructional designers. Yet, user-friendly implementation of learning analytics in a “click-and-go” application is necessary for generalizability and escalation of continuous improvement cycles of OER and tangible improvement of learning outcomes. Finally, in this article, we identify the need for efficient applications of learning analytics that focus more on “learning” and less on analytics.

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The aim of this investigation was to gain a broad sense of the implementation of digital social networks for teaching and learning by instructors in higher education. We were particularly interested in examples of instructors' use of digital social networks in their courses, the benefits and challenges of specific platforms for teaching and learning purposes, and the perceived role of digital social networks in teaching and learning during the first years of the COVID-19 pandemic. The participants in this investigation (n=336) were faculty and graduate students. Data was collected via an electronic questionnaire with closed and open-ended questions using Qualtrics. The quantitative analysis included descriptive statistics. The data analysis also included qualitative data analysis of textual responses provided to various questionnaire items. The results illustrate the ways in which instructors successfully apply or integrate digital social networks into teaching and learning, broadly and with specific digital social networks. Yet, the findings also highlight several complexities that instructors have experienced when considering digital social networks in their teaching practices such as data privacy issues, misinformation, user interface issues, and the willingness of students to use less trendy digital networks, among other challenges.

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Global Perspectives on Educational Innovations for Emergency Situations

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Xun Ge

This is an open access book which can be downloaded at https://link.springer.com/book/10.1007/978-3-030-99634-5#about-this-book. It focuses on making the transition from in-person, classroom education to other feasible alternative modes and methodologies to deliver education at all levels. The book presents and analyzes research questions to explore in this arena, including pedagogical issues relating to technological and infrastructure challenges, teacher professional development, issues of disparity, access and equity, and impact of government policies on education. It also provides unique opportunities and vehicles for generating scholarship that helps explain the varied educational needs, perspectives and solutions that arise during an emergency and the different roles educational institutions and educators may play during this time. Developed from a highly successful Presidential Session at the annual meeting of the Association for Educational Communications and Technology (AECT), this edited volume presents AECT and its membership as the premier organization focusing on the provision of educational communications and technology leadership. In addition, it functions as a contemporary document of this global crisis as well as a rich resource for possible future emergency scenarios in the educational arena.

Online Learning Environments and Student Engagement: Meeting the Psychological Needs of Learners during the COVID-19 Pandemic

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Hoi Vo Ngoc

This chapter reports the results of a qualitative study on students’ perception of the characteristics of the online learning environment and on how those characteristics affect their basic psychological needs and engagement during the emergency transition to online learning due to the Covid-19 pandemic. Semi-structured interviews with 24 students from a higher education institution in Vietnam revealed three main factors, including the instructor, peer groups, and technologies that either undermined or satisfied students’ basic psychological needs and affect students’ engagement in online learning. The findings shed light on the contextual factors that fulfill students’ needs for competence, autonomy, and relatedness, thus the findings provide instructors, course designers, and institutional leaders with useful information for the manipulation of instructional strategies and online learning conditions that promote student engagement in online learning in time of crisis.

Translating Distance Education Theory into Practice: Developing an Emergency Teaching Framework for a Caribbean University

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LeRoy Hill

The COVID-19 pandemic compelled universities worldwide to address the issue of continuity of teaching and learning. The request for teaching and learning continuity took many by surprise and as such, many universities were unprepared for the rapid shift. The transition from face to face to remote teaching therefore required a quick, but careful consideration to the planning, design, implementation and evaluation of remote teaching and learning. Notwithstanding this challenge, it becomes necessary to ground decisions within theoretical contexts that support and advance effective remote teaching and learning practice. Theory is well established as a tool to shape the effective teaching and learning within the higher-education setting and while there are frameworks that support traditional teaching and learning, not much attention is given to distance education frameworks. This chapter therefore intimates reflection on the utility of distance education theoretical frameworks to advance and sustain remote teaching practice at a Caribbean university.

A Reflection on Online Teaching and Learning Through the Pandemic: Revisiting Creativity

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Jin Mao

Teaching through the pandemic has revealed critical educational issues related to online learning as well as the importance of considering contextual influences and creative solutions. The purpose of this chapter is to reflect on those issues and discuss creative alternatives in response to the changing social, cultural, and technological systems. The reflection centers around three themes with a focus on recommendations for the future based on what we can learn from the emergency remote teaching and learning (ERTL) experience. The ideas proposed in the reflection themes can help establish the needed mindsets and generate creative approaches to ERTL during crisis times. Creativity is discussed and redefined within the context of ERTL during the pandemic. We should develop creative thinking, creative mindset, and creative design in re-conceptualizing assessment activities and the assessment culture for online learning as well. Recommendations to help sustain the impact of creative solutions include the need for solid network infrastructure, an innovative mindset for assessment, and a need for educational design research on creative solutions to online learning problems.

Chilean Perspectives on Educational Experiences and Innovations in Emergency Contexts

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Emergency remote teaching began suddenly and widely by March 2020 after the World Health Organization declared the coronavirus pandemics Chilean education, as many other countries around the globe, adopted the methodology of remote teaching with little no previous experience and a lack of adequate and pertinent administrator, teacher, student, and parent preparation. This chapter presents, analyzes, and discusses the Chilean higher education experiences and innovations in emergency contexts during the COVID-19 pandemic. Actual and future perspectives are discussed on the basis of education in a country with frequent emergency contexts such as earthquakes, tsunamis, floods, fires and now a pandemic. We applied a systematic review research method to the higher education data and information that emerged during the coronavirus pandemic in Chile during the last year. As a result, we ended up drawing some trends, issues, and perspectives on Chilean higher education teaching and learning activities and innovation in emergency contexts. Finally, considering the results obtained, we discuss the main themes that emerged, and some lessons learned by Chilean education in times of pandemic, in order to be better prepared and equipped to face emergency situations with less disruption and detriment to teaching and learning in the future.

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Interaction is an essential element of online learning and researchers had use Social Learning Analytics (SLA) to understand the characteristics of meaningful interaction. While the potential for network analysis in education (i.e., SLA) is valuable, limited research has considered how best to use this emerging field to inform meaningful interaction in online settings. Online learning researchers need a concise and simplified framework for SLA to support interaction in online learning environments. Therefore, we present a conceptual framework to make SLA accessible for researchers investigating learners' interactions in online learning. The framework includes concepts from network theory and the online learning literature integrated into a new perspective to analyze learners' online behaviors and interactions. We analyzed existing models and frameworks to show how network analysis has been used in online learning resulting in a conceptual environment to investigate learner interaction. The proposed i-SUN framework has four main steps: (1) interaction, (2) social presence alignment, (3) unit of analysis definition, and (4) network statistics and inferential analysis selection. We also identified five ways in which the i-SUN model contributes to the advancement of SLA in online interaction research and provide recommendations for empirical validation. As part of a sequence of manuscripts, we seek to offer a unique perspective to online learning researchers and practitioners by focusing on the social and pedagogical implications of applying network analysis to understand online learning interaction.

Student satisfaction with courses and instructors in a flipped classroom: A meta‐analysis

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J COMPUT ASSIST LEAR

The flipped classroom model challenges the way educators structure and implement their courses, and how students learn. Although there is burgeoning evidence for the effectiveness of flipped classrooms for improving student performance, student satisfaction is also an important consideration insofar as student evaluations can affect uptake of new education approaches. Yet we know little about the extent to which students are satisfied with the flipped model. To address this discrepancy, this article reports a world‐first meta‐analysis of student satisfaction with flipped classrooms and teachers relative to traditional teaching approaches across disciplines and education level (overall 53 studies; 8,429 students). Overall, the flipped classroom had a weak–moderate positive effect on student satisfaction with courses (k = 50; g = 0.36) and instructors (k = 26; g = 0.40). We also report moderating effects relating to discipline, education level, pre‐class testing, in‐class activities, group size, and sophistication of flipped designs. Lay Description What is already known about this topic • The flipped classroom model is now widely employed around the world • There is increasing evidence that it is associated with improved student performance, relative to traditional teaching approaches • Student satisfaction with teaching has implications for student motivation and performance; teacher‐related outcomes; and institution recruitment and funding What this paper adds • Reports a world‐first meta‐analysis of effects of the flipped classroom model on student satisfaction, relative to traditional teaching approaches (overall 53 studies; 8,429 students) • The flipped classroom has a weak‐moderate positive effect on student satisfaction with courses (k = 50; g = 0.36) and instructors (k = 26; g = 0.40) • Even a classroom that is only ‘minimally’ flipped is associated with higher levels of student satisfaction, relative to traditional teaching models Implications for practice and/or policy • Flipped classroom designs should be encouraged on both pedagogical and pragmatic grounds; in the case of the latter, students prefer a flipped classroom approach • Students are likely to respond positively to teachers and institutions who employ a flipped classroom approach

A Cross-institutional Study of Instructional Characteristics and Student Outcomes: Are Quality Indicators of Online Courses Able to Predict Student Success?

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Dec 2019

A study was conducted to examine instructional characteristics and their relationship to student outcomes in online courses at a 2-year and 4-year higher educational institution. Instructional characteristics included learner support, course design and organization, content design and delivery, interactivity (student-instructor and student-student), and assessment and evaluation. A student survey instrument was created that captures student perceptions of the instructional characteristics of their course, their learning, and their satisfaction with the course. The data collected from the student survey was merged with data from institutional student information systems (e.g., demographics and course grade).This article examines the relationship between these instructional characteristics, sometimes referred to as indicators of online course quality, and their relationship to student outcomes for all students and for underrepresented students. Significant findings from multiple regression analyses are reported. Additional analyses were conducted to examine differences among underrepresented students (minorities, first-generation, low-income, students with impairments/disabilities) using MANOVA. No significant differences are reported.

Learner engagement, retention and success: why size matters in massive open online courses (MOOCs)

Article

Full-text available

Sep 2019

Massive open online courses (MOOCs) are usually six to ten weeks long. Participation tends to decrease as the courses progress, leading to low completion rates. This led to the question: Could shorter MOOCs contribute to learners’ engagement, retention and success? This paper compares two versions of Study Skills MOOC, which shared the same content but were delivered in different length formats. One was deployed as a single six-week course and the other as two three-week blocks. In total, 617 people registered for the two versions. Data sources included learning analytics, surveys and the Spanish version of the General Self-Efficacy Scale. Both versions of the Study Skills MOOC resulted in increased participants’ self-efficacy. However, learners enrolled in the version composed of two three-week blocks were also more engaged with course content, other students and the facilitators. Their retention and completion rates were higher than those in the longer version of the course. Reasons linked to goal proximity, motivation, interactions and social modelling are discussed

What Drives Successful Social Media in Education and E-learning? A Comparative Study on Facebook and Moodle

Article

Full-text available

Jun 2019

Ahmed Al-Azawei

Aim/Purpose This research investigates the success variables affecting the adoption of social networking sites (SNS), namely Facebook, and learning management systems (LMS), specifically Moodle, in developing countries. Background In contemporary education, universities invest heavily in the integration of LMS with traditional classrooms. Conversely, such technologies face a high rate of failure and not all learners are satisfied with LMS services. In turn, this leads to the exploitation of SNS interactive features and services, which are subsequently included in the process of teaching and learning. However, the success of both SNS and LMS has rarely been studied in the context of developing nations.

A review of empirical studies of affordances and development of a framework for educational adoption of mobile social media

Article

Full-text available

Jun 2019

As one of the most widely adopted mobile and social media applications, Tencent WeChat ® (‘WeChat’) has been increasingly used in education at all levels in Asia, and in China in particular. However, only a small number of studies have been conducted to explore educational affordances of WeChat. In this paper, these affordances are defined as opportunities for an educational activity that are determined and supported by perceived and actual features of a technology tool or an environment (Gibson in The ecological approach to visual perception, Houghton Mifflin, Boston, 1979; Norman in The psychology of everyday things, Basic Books, New York, 1988; Sanders in Ecol Psychol 9(1):97–112, 1997). The authors conducted a review of 21 studies out of a pool of 1984 identified publications on the topic to examine existing practices, empirical studies and recommendations for the uses of WeChat, and with the over-reaching aim of articulating a framework for the adoption of educational affordances of mobile social media. Such framework will serve practice as well as research on educational uses of mobile social media and help extend theory of affordances in this domain. A total of seven categories of educational affordances of WeChat were explicated and included in this framework: Resources Sharing, Authentic Learning, Collaboration, Community Building, Motivating Environment, Evaluation and Feedback, and Administration for Learning. Guidelines for the adoption of this framework are developed, and suggestions for future research are proposed.

Research Synthesis and Meta-Analysis: A Step-by-Step Approach

Book

Jan 2017

Harris Cooper

A Bluffer’s Guide to Meta-Analysis 1

Article

Sep 2006

Andy Field

The Relation Between Social Learning and Visual Culture

Article

Mar 2019

Integrating Authentic Research Experiences into the Quantitative Analysis Chemistry Laboratory Course: STEM Majors’ Self-Reported Perceptions and Experiences

Article

May 2019

Integrating authentic research-based experiences that mimic real-world scientific practices and relevance to student personal life into a chemistry curriculum can make abstract chemical principles more palpable to students. Unfortunately, many reported research-based experiences chemistry laboratory experiments lack relevance to a student’s personal life. Additionally, the impact of a“relevant”authentic research-based experience curriculum on students’ affective outcomes beyond the General Chemistry courses has been overlooked. Two authentic research experiences modules developed around nanotechnology applications were implemented in a Quantitative Analysis Chemistry laboratory course for STEM majors. A follow-up study assessed the STEM majors’ perceptions of the learning environment and the organization of lab after exposure to both conventional experiments and authentic research-based experiences modules, as well as their perceived learning gains and the relevance of the laboratory experiments. Data were collected through validated surveys, and open-ended survey items and classroom observations. There were 55 students who participated in the study. Results showed significant improvements of students’ perceptions of learning environment and organization of the laboratory, favoring authentic research experiences modules over the conventional experiments. Self-reported learning gains and relevance of the experiments to students were also associated with the authentic research-based experiments. Students’ perceptions favoring the intervention modules related to learning through scientific inquiry, content relevance to real-world applications and student personal life, and use of real-world materials and a wide array of chemical instruments and techniques. Results imply the need to implement authentic research-based experiences centered on real-world issues and student personal life in the chemistry laboratory courses.

Comparing and Modeling via Social Media: The Social Influences of Fitspiration on Male Instagram Users’ Work out Intention

Article

May 2019
COMPUT HUM BEHAV

This study examines the effects of Instagram fitspiration images on male viewers' work out intention by integrating the processes of social comparison and social cognitive theory. The results from 1428 Instagram users in Taiwan, with the directions of comparison (upward, lateral, and downward) manipulated, indicate that upward comparisons with attractive models would strengthen self-improvement motives related to working out. In addition, pleasant affective responses to the images and one's self-efficacy for working out were also significant predictors. However, model attractiveness revealed a negative, direct effect on work out intention, suggesting that a seemingly rewarding model behavior itself discourages viewer imitation. Implications are discussed to contribute the understanding about male audiences' reactions to the same-sex body images associated with physical training. The findings verifying both the psychological and behavioral impacts of social influence also help demonstrate a more comprehensive picture about promoting exercise as a positive outcome of fitspiration imagery exposure.

The relationship between social network sites and perceived learning and satisfaction: A systematic review and meta-analysis.

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