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Br J Educ Technol. 2025;00:1–24.
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wileyonlinelibrary.com/journal/bjet
Received: 2 2 June 2024
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Accepte d: 19 April 2025
DO I: 10 .1111/ bje t .13 5 97
ORIGINAL ARTICLE
The roles of technology efficacy and
networking agency in elementary students'
engagement in online and face- to- face
technology- mediated learning
Wonjoon Cha1 | Minxuan Hong2 | Michael Glassman1 |
Eric M. Anderman1 | Tzu- Jung Lin1
This is an op en access article under t he terms of t he Creative Commons Attribution-NonCommercial-NoDerivs License, which
permits use and dist ribution in any medium, provide d the orig inal work i s proper ly cited, t he use is non -commercia l and no
modifications or adaptations are made.
© 2025 The Author(s). British Journal of Educational Technology published by Joh n Wiley & Sons Ltd on behalf of British
Educational Research Association.
1Department of Educ ational S tudies, T he
Ohio State University, Columbu s, Ohio,
USA
2Depar tment of Statistic s, Rutgers The
State University of New Jersey, New
Brunswi ck, New Je rsey, USA
Correspondence
Wonjoon Cha, Department of Educational
Studies, The Ohio State Univer sity,
Columbus, OH, USA.
Email: cha.170@osu.edu
Funding information
Institute of Education Sciences, Grant/
Award Number: R305 A20 0364
Abstract
Despite the growing use of learning technology in
classrooms, factors predicting young students' en-
gagement in contexts fully or partially mediated by
technology remain understudied. This study investi-
gated how fourth and fifth grade students' technol-
ogy self- efficacy (ie, confidence in utilizing learning
management systems) and networking agency (ie,
comfort in collaborating and communicating with
others online) predicted students' engagement in
technology- mediated instruction in online and face-
to- face environments. Hierarchical regression and
moderation analyses were employed to examine
the independent and joint effects of technology self-
efficacy and networking agency in two cohorts of
public elementary school students from a Midwestern
US city. For Cohort 1 students primarily receiving
synchronous online instruction in 2020–2021, higher
networking agency predicted greater engagement in
online learning. For Cohort 2 students primarily re-
ceiving face- to- face instruction in 2021–2022, only
technology efficacy significantly predicted student
engagement. As for task- specific engagement in
small group activities, however, which involved heav-
ier use of the learning management systems and
applications, networking agency was a significant
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CHA et al.
INTRODUCTION
With advances in educational technologies and their widespread integration into ev-
eryday classroom activities (eg, personal and classroom- wide digital devices, online
learning management systems), educational technologies are becoming an increasingly
moderator. Notably, as students felt less comfortable
with online collaboration/communication (ie, lower
networking agency), technology self- efficacy had
a stronger association with students' engagement.
These findings suggest that while technology self-
efficacy is crucial, it alone does not ensure engage-
ment. Educators should cultivate both technical skills
and a supportive, collaborative online environment to
enhance engagement across technology- mediated
learning contexts.
KEYWORDS
engagement, networking agency, technology self- efficacy
(technology efficacy), technology- mediated learning
Practitioner notes
What is already known about this topic
• Technology (self- )efficacy, or students' confidence in using online learning tools, is
crucial for their engagement in technology- mediated learning.
• Student engagement in technology- mediated learning varies between online and
face- to- face environments.
What this paper adds
• In online environments, networking agency or comfort in online collaboration and
communication predicts engagement more strongly than technology efficacy.
• Technology efficacy consistently predicts young learners' engagement in
technology- mediated learning in face- to- face classrooms.
• Networking agency can interact with technology efficacy in predicting elementary
students' engagement in technology- mediated activities in face- to- face
classrooms.
Implications for practice and/or policy
• Teachers should consider students' technological competence and how they
construe online collaboration and interactions to effectively engage them in
technology- mediated instruction.
• When implementing technology, teachers should help students view the online
learning environment as a safe and collaborative space.
• Familiarizing young learners like elementary school students with digital tools and
online spaces can enhance their engagement in technology- mediated instruction.
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TECHNOLOGY EFFICACY AND NETWORKING
valued component within education (Escueta et al., 2017). The outbreak of COVID- 19
in 2020 accelerated this trend, pushing teachers and students, even young learners at
the elementary school level, to use learning management systems and digital programs
both in in- person and online learning environments (Lubis & Dasopang, 2021; Maatuk
et al., 2022). However, the empirical evidence and research to help guide the use of
learning technologies for elementary students are lacking (Johnson et al., 2023; Liao
et al., 2021).
The integration of learning applications and mobile devices into instruction is known
to have different influences on adult learners depending on their technology efficacy (ie,
perceived competencies in technology use) and networking agency (ie, comfort in online
communication and collaboration) (Courtney et al., 2022; Pellas, 2014; Wang et al., 2013).
However, these findings may not be readily translatable to young learners due to their unique
developmental stage and learning experiences (Huang et al., 2020). As compared to adult
learners, for example, children have more difficulties self- directing their learning (Wesarg-
Menzel et al., 2023); consequently, their lack of perceived competence and negative attitudes
towards online interaction may have a greater negative impact on their learning processes
(Johnson et al., 2023). Conversely, many students who have been exposed to digital de-
vices and applications since a young age, sometimes referred to as digital natives (Boie
et al., 2024), may see advanced technologies as a natural component of learning regardless
of their technological competence and perceptions of online spaces (Flewitt et al., 2024).
Focusing on student engagement, which is crucial to positive academic outcomes both in
online (Fredricks et al., 2004; Tay et al., 2021) and technology- enhanced learning (Schindler
et al., 2017), we examined how technology efficacy and networking agency predicted ele-
mentary school students' engagement in technology- mediated instruction.
Engagement
Engagement is a broad term that has been conceptualized and operationalized in vari-
ous ways (Reschly & Christenson, 2012). For example, Fredricks et al. (2004) defined
engagement as a multifaceted construct encompassing cognitive, behavioural and emo-
tional dimensions, whereas Reeve (2013) introduced agentic engagement as an additional
component. While different typologies have been posited, researchers have also consid-
ered engagement a highly malleable state that can vary by developmental competencies,
self- appraisals and contexts (Martins et al., 2022; Wang et al., 2019). The development
of students' cognitive ability and emotion regulation may prevent younger students from
distinguishing subtle differences between dimensions of engagement (Carter et al., 2012).
When investigating younger students, researchers often view engagement as a broader
construct rather than focusing on its sub- dimensions (eg, Baroody et al., 2016; Reyes
et al., 2012). In this study, engagement is conceptualized as a general construct that
incorporates students' active behavioural and emotional involvement in classes (Skinner
et al., 2009).
More importantly, student engagement can be context- or task- dependent. The nature of
an academic task can heighten or lower students' expectation for success or task values,
which then affect students' level of engagement (Wang et al., 2019). Indeed, some research-
ers have conceptualized engagement more specifically as task- oriented endeavours involv-
ing the use of technology (O'Brien & Toms, 2008). Abbasi et al. (2017), for instance, gauged
engagement in the context of video gameplay. Such task- specific engagement measures
can provide additional information about student learning that cannot be revealed when as-
sessing general engagement only (eg, students who report feeling disengaged when asked
about their general engagement at school may actually show greater engagement when
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CHA et al.
learning with technology). Thus, along with general engagement (Fredricks et al., 2004), we
evaluated task- specific engagement when students used multiple digital tools. Technologies,
such as web- conferencing tools and blogs (or weblogs), may have positive influences on stu-
dent engagement because they provide functionalities, such as messaging and commenting
features that foster active interactions (Schindler et al., 2017). For example, students' use
of discussion boards in online collaborative problem solving may increase engagement be-
cause it opens up new communication possibilities (eg, asynchronous discussion, where
students do not have to respond immediately and can comment on others' posts over time,
as well as a continuous, permanent archive of discussion points). Immersive graphics and
digital artefacts may also facilitate students' active involvement in knowledge construction
(Godsk & Møller, 2024; Kuznetcova et al., 2023). The use of technology, however, may not
uniformly guarantee active participation among diverse learners with different competen-
cies and perceptions of online spaces, which necessitates the need to examine personal
factors, such as technology efficacy and networking agency (Courtney et al., 2022; Wang
et al., 2013).
Technology efficacy and engagement in technology- mediated
instruction
A critical variable influencing engagement is self- efficacy, which refers to one's self-
assessment of their perceived capability to successfully carry out the necessary actions to
achieve specific outcomes or reach certain goals (Bandura, 1977). Self- efficacy is generally
thought to be domain- or task- specific (Schunk et al., 2014). Computer self- efficacy, for
instance, is defined as ‘a judgment of human's capability to use a computer’ (Compeau &
Higgins, 1995, p. 192). The effective use of technologies is critical for students' success in
technology- enhanced learning environments (Heo et al., 2021). Indeed, with the increasing
prevalence of online applications and programs, many studies have demonstrated positive
associations between technology efficacy and online learning engagement. For example,
computer self- efficacy was found to be positively associated with online discussion
performance and course satisfaction (Wei & Chou, 2020) and with learning performance and
engagement (Chen, 2017). These findings, however, were based on adult populations. On
the one hand, young learners may find it more difficult than adult learners to overcome the
challenges arising from technology use because of their developing competencies (Johnson
et al., 2023). On the other hand, young learners, who use digital devices for socializations
and entertainment, might be more resilient when faced with technical challenges (Ventouris
et al., 2021). For this reason, it is important to examine the association between elementary
school students' technology efficacy and their engagement. In this study, we conceptualized
technology efficacy as perceived competence in utilizing learning management systems
(LMSs), since most instructions and activities took place based on the functionalities
afforded in the LMSs.
Networking agency as a form of digital citizenship
In an attempt to explore individuals' use of internet tools (eg, social media, blogs, search
engines) in online environments (Choi, 2016), Choi et al. (2017) developed the Digital
Citizenship Scale (DCS). The scale intended to capture how respondents perceive
internet applications as tools for civic problem solving, especially in terms of their online
competencies fostering civic agency. Five digital citizenship factors were developed based
on three theoretical foundations: (1) Feenberg's (1991) critical approach to technology, which
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TECHNOLOGY EFFICACY AND NETWORKING
argues that technology can promote human autonomy but can also be used in a controlling
way; (2) Castells's (1996) idea of an increasingly networked society where he suggests
that the Internet creates new contexts and processes for individual users to interact and
communicate; and (3) Glassman's (2013) concept of open source educational processes
(OSEP) which focuses on the development of individuals' competencies for navigating
and sustaining online communities/networks (see Choi et al., 2017 for more details).
Digital citizenship is often studied as an outcome influenced by demographics (eg, gender,
grade level), psychological factors (eg, internet anxiety) and experiences with technology
(eg, internet skills, daily computer use) (Ali et al., 2023). When examined as a predictor,
research typically focuses on digital citizenship ‘education’ and its impact on problematic
online behaviours, such as cyberbullying and online harassment, rather than on learning
processes (Lu & Gu, 2024).
Networking agency is one dimension of the DCS that measures media and information
literacy, encompassing one's comfort and willingness to collaborate and communicate in an
online environment (Cha et al., 2023). In this study, we focused on students' perceived net-
working agency as it may lead to active participation in online activities (Choi & Park, 2023;
Roberts et al., 2023). As with technology efficacy, few studies have examined the role of
networking agency in elementary students' academic engagement (Hong, 2022). The as-
sumption behind networking agency, however, is that anticipated successful collaborations
based on trust and reciprocity will lead to greater likelihood of engagement in shared online
activities (Glassman et al., 2021). Considering this factor was designed to assesses individ-
uals' civic competencies for exploring, developing and/or sustaining online problem- solving
communities specifically related to citizenship issues (Glassman, 2013), it should theoreti-
cally and positively predict students' engagement in learning environments where technol-
ogy is used as a tool for collaborative and interactive activities (Kim et al., 2025). Networking
agency might also interact with technology efficacy in predicting learners' engagement in
technology- assisted activities because it is easier to connect with others when one is more
confident in one's abilities to complete essential tasks. Students who perceive themselves
as more skilled in using internet applications (ie, high technology efficacy) may also find it
more comfortable to navigate what can be complex information landscapes (ie, high net-
working agency). However, even if students have low technology efficacy, high networking
agency may still create productive pathways for technology- mediated communication, mo-
tivating them to continue using digital tools and online applications in collaborative problem
solving. By examining networking agency as a predictor of engagement in technology-
mediated learning environments, we aimed to expand the applicability of digital citizenship
literature and find nuanced implications for researchers and practitioners (Cha et al., 2024;
Shi et al., 2023).
Technology- mediated learning in online and face- to- face environments
In this study, we defined ‘technology- mediated learning’ (TML) as instruction that uses
technology to convey information and link people together (Bower, 2019). TML can involve
various educational technologies (eg, discussion boards, video- based communication ap-
plications, shared digitally enhanced learning environments) which enable teachers and
students to connect and collaborate in the learning processes even when separated by
natural boundaries of space and time. Digital connections may be synchronous, with in-
teractions occurring in real- time, or asynchronous, where students communicate across
time frames (DiFrancesca & Spencer, 2022; Watson et al., 2013). TML can occur in single,
enclosed spaces (eg, a physical classroom) where some instruction is mediated through
online activities by digital tools but can also stretch across ecological settings (eg, a local
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CHA et al.
library, community centre, a kitchen table) (Bower, 2019). While TML requires basic digital
competencies for navigating online spaces, it is important to recognize that online and face-
to- face learning environments can be very different in the barriers they propose to learn-
ing. Therefore, we differentiated these two learning contexts to examine the relationships
of technology efficacy and networking agency with students' engagement in technology-
mediated instruction.
Research questions
We examined whether technology efficacy and networking agency independently or jointly
predicted students' engagement in technology- mediated instruction in online (Year 1) and
face- to- face classrooms (Year 2) while controlling for demographic and contextual factors
(ie, gender, grade and school district). In Year 2, we additionally investigated how these
same factors predict students' task- specific engagement in activities more heavily medi-
ated by technology in face- to- face classrooms. We addressed three research questions.
RQ1: How do technology efficacy and networking agency independently or jointly predict
students' general engagement in technology- mediated instruction in online classes? RQ2:
How do technology efficacy and networking agency independently or jointly predict stu-
dents' general engagement in technology- mediated instruction in in- person classrooms?
RQ3: How do technology efficacy and networking agency independently or jointly predict
students' task- specific engagement in activities involving more active use of digital tools in
in- person classrooms?
We hypothesized that technology efficacy and networking agency would positively pre-
dict students' engagement in technology- mediated instruction in both online (RQ1) and
in- person contexts (RQ2). Perceived competence in technology and positive construal
of online interactions could serve as valuable resources for young learners to engage in
technology- mediated learning (Rahman et al., 2023). In addition, we assumed an interac-
tion between these two constructs in predicting engagement, exploring a potential comple-
mentary effect (Al- Zahrani, 2015). Students with lower technology efficacy may still engage
in technology- mediated learning if they have higher networking agency. Conversely, stu-
dents with lower networking agency may find it easier to engage in technology- mediated
learning activities if they have higher technology efficacy. We tested the same hypothe-
ses for students' task- specific engagement in activities within face- to- face technology-
mediated instruction that require greater use of digital tools and learning applications
(RQ3) because the associations may differ in activities that are more heavily mediated by
technology (Bower, 2019).
METHOD
Study context
The study was conducted as part of the Digital Civic Learning (DCL) project and ad-
hered to all applicable laws and institutional guidelines for study implementation and
data collection involving children. Initial approval was obtained from the Institutional
Review Board (2020B0181) on June 24, 2020. In this classroom- based research project,
we collaborated with teachers to co- design a social studies curriculum based on social
constructivist theories (Vygotsky & Cole, 1978) and technology- enhanced immersive
learning principles to help students develop civic competencies and improve academic
achievement (Kim, 2023). Over 2 years, four units were developed and incorporated
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into schools' social studies curricula for fourth and fifth graders (MeanAge = 9.92,
SDAge = 0 .71).1 Each unit lasted for 10 school days, during which students used digital de-
vices (eg, Chromebooks) and web- based applications (eg, Google Slides; see Figure 1)
for their coursework. Students engaged in activities, such as small group discussions
and collaborative problem solving via multimedia incorporated into the schools' learning
management systems (Kuznetcova et al., 2023). This technology- mediated curriculum
took place mostly online due to the pandemic in Year 1 (2020–2021) and face- to- face in
Year 2 (2021–2022). Online classes in Year 1 mainly consisted of synchronous meetings
complemented by asynchronous assignments. These were facilitated through learn-
ing management systems (LMSs) and video conferencing platforms, such as Microsoft
Teams. Students participated in activities, including collaborative group discussions,
problem- solving tasks using Google Slides and online discussion boards (eg, Flip). In
contrast, during Year 2, students mostly learned in face- to- face environments, where
technology- mediated activities were integrated into traditional classroom settings. For
example, students used LMSs for resource sharing and assignment submissions, while
small group projects incorporated shared digital tools to facilitate in- person collabora-
tion enhanced by technology. The learning experience of Year 1 participants in online
learning may be qualitatively different from that of Year 2 participants, who returned to
in- person classes. Therefore, we investigated the association that technology efficacy
and networking agency have with engagement separately with participants from each
year.
Participants
In Year 1 (2020–2021), 87 students in four fourth and fifth grade classrooms from two
Midwestern elementary school districts consented to participate. Students who had sub-
stantial missing data (eg, completed <10% of the survey, only filled out name and ID with no
responses on the rest of the survey items) were excluded, resulting in a final sample of 80
students. In Year 2 (2021–2022), the project was scaled up and the final consented sample
was 151 students from eight classrooms in the same two districts. Students' socio- economic
status (SES) was relatively higher in District 1 compared with other school districts in the
region. For instance, in 2021–2022, only 4% of students qualified for the Free and Reduced-
price Lunch (FRL) programme. District 2 was more racially diverse and relatively low in SES
(57.1% of students qualified for FRL). For more information about sample demographics,
please refer to Tables S1 and S2 in Supporting Information.
FIGURE 1 Online class in Year 1 (2020–2021) and in- person class in Year 2 (2021–2022).
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Measures
We selected measures suited to students' developmental levels to explore their experiences
in the technology- mediated DCL curriculum. Students completed a battery of assessments
through Qualtrics surveys before, during and after the intervention. In Year 1, the variables
of interest were fully available only after the intervention. In Year 2, the data were available
before (and at the early phase of) and after the intervention (Figure 2). Cronbach's alpha for
the scales at each time point ranged from 0.70 to 0.95, indicating good internal consistency
(Kline, 2000). For the full list of items, reliability and descriptive statistics, please refer to the
Tables S2–S9 in Supporting Information.
The Technology Efficacy scale was adapted from the Internet Self- efficacy Scale (Kim &
Glassman, 2013), which is comprised of items developmentally appropriate for elementary stu-
dents and focuses on students' competence in diverse use of learning management systems.
Students reported their degree of confidence on a scale of 1 (not at all confident) to 7 (very
confident). There were 6 items (Tables S3 and S4), including questions like ‘I can use Microsoft
Teams to connect with my classmates and teachers’. Networking agency was measured by a
scale adapted from the Digital Citizenship Scale (Choi et al., 2017) and consists of four items
(Tables S5 and S6) that assess students' comfort in collaborating and communicating with oth-
ers online (eg, ‘I enjoy collaborating with my classmates online more than I do offline’). Students
rated their networking agency on a scale of 1 (strongly disagree) to 7 (strongly agree).
The General Engagement scale was adapted from Skinner et al. (2009). This self- report
scale has been widely used with many age groups, including elementary students (eg,
McLean et al., 2023), but its dimensionality may vary by age (Carter et al., 2012). Considering
that elementary school students may find it difficult to differentiate between sub- dimensions
of engagement (Fredricks et al., 2004; Martins et al., 2022), we created our engagement
scale by combining only the behavioural and emotional engagement items (eg, ‘I tried hard
to do well in class.’, ‘I enjoyed learning new things in class.’; Tables S7 and S8). Students
indicated how much they agreed with each statement on a four- point Likert scale, ranging
from 1 (not at all true) to 4 (very true).
In Year 2, to investigate students' Task- specific Engagement (Specific Engagement,
henceforth), we used a composite measure of behavioural and emotional engagement
items, which were specifically assessed for technology- mediated activities. We focused on
specific engagement in small group discussions/projects as these activities accompanied
more extensive use of technology, such as Google Slides and online discussion boards
FIGURE 2 Data sources on timeline.
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TECHNOLOGY EFFICACY AND NETWORKING
(eg, ‘I tried hard to do well when we had small- group discussion in class’, ‘I enjoyed learn-
ing new things when we worked on small group projects in class’; Table S9). Unlike the
other variables of interest (ie, technology efficacy, networking agency and general engage-
ment), specific engagement was measured after each unit to gauge students' involvement
in technology- mediated activities. We used the data measured after the first and last units
for analyses, as these were most proximal to the timepoints when technology efficacy and
networking agency were measured. Covariates included contextual and demographic vari-
ables, which were dummy coded—district (0 = wealthier district and 1 = less affluent district),
grade (0 = fourth and 1 = fifth) and gender (0 = boys, 1 = girls).
Data analysis
To investigate how students' technology efficacy, networking agency and their interaction
predict general and specific engagement in technology- mediated instruction, we conducted
three- step hierarchical multiple regression analyses. For each regression analysis, students'
demographic information (ie, grade, gender and school districts) was entered in the first step as
covariates aiming to account for potential confounding effects of contextual and demographic
variables on students' engagement (Matthews et al., 2009; Reardon, 2011; Sirin, 2005).
Subsequently, mean- centred composite scores for the primary predictors—technology effi-
cacy and networking agency—were introduced to examine their unique contributions beyond
demographics. Lastly, for exploratory purposes, the multiplicative interaction of technology
efficacy and networking agency was added in the third step to explore whether networking
agency moderates the association between technology efficacy and engagement.
Since participating in the technology- mediated curriculum (ie, Digital Civic Learning) itself
may influence how students' technology efficacy and networking agency predicted their en-
gagement, we analysed the pre- (early- ) intervention and post- intervention data in separate
models. We chose not to conduct pre–post change analyses, such as repeated measures
ANOVA, because our research questions mainly focused on the relationships among tech-
nology efficacy, networking agency and student engagement during the time when technol-
ogy efficacy and networking agency were assessed. It was beyond the scope of this work to
examine how student engagement changed from pre- to post- intervention as a function of
technology efficacy or networking agency.
Missing data
In Year 2, considering that some students might not have any experience with online plat-
forms, we added the response option of ‘I have not used online platforms in class yet’ and
later treated this response as a missing value. This resulted in a higher missing rate in the
pre- intervention data. To address this missingness, we conducted hierarchical regression
with Mplus Version 8.4 using the full information maximum likelihood (FIML) estimator, which
utilizes all available data to determine the model parameters (Schafer & Graham, 2002).
FIML is known to outperform pairwise and listwise deletion even in small sample size
(Bowen & Guo, 2011; Enders & Bandalos, 2001). Mplus does not provide ΔR2, ΔF value and
the associated p- value of ΔF when comparing a simpler, nested model to a more complex,
full model. Therefore, we calculated these values from the R2 of each model to examine
whether the addition of predictors and the interaction term explained additional variance
in student engagement (Figure 3). The complete case analysis using listwise deletion
yielded the consistent main findings, and the results are available in the Tables S10– S19 of
Supporting Information.
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RES U LTS
RQ1. General engagement in technology- mediated learning in online
classes
We used hierarchical regressions to systematically examine the independent and joint
effects of technology efficacy and networking agency in student general engagement
in technology- mediated learning in online classes. In Year 1, the analysis was con-
ducted only with post- intervention data, due to the lack of availability of the variables
of interest at earlier time points. Table 1 shows the Pearson correlation coefficients
between the measured variables. The initial regression model, including demographic
and contextual variables, indicated that these covariates made a statistically significant
contribution to the model (F(3,76) = 9.09, p < 0.0 1, R2 = 0.264) accounting for 26.4% of
the variance in online engagement. The second step model, where the two primary pre-
dictors (ie, technology efficacy, networking agency) were introduced, accounted for an
additional 16.0% of the variance in online engagement (ΔF(2,74) = 10.28, p < 0. 01) . Th e
addition of the interaction between technology efficacy and networking agency did not
result in a significant improvement from the previous model (ΔF(1,73) = 0.90, p = 0.3 6).
Therefore, the second step model without the interaction term was chosen as the final
model in which school district and networking agency predicted student engagement in
online classes.
As presented in Table 2, networking agency, but not technology efficacy, positively
predicted engagement in the online environment (B = 0 .16 , p < 0.01). Students with
FIGURE 3 Formula of F- statistics and F- change. mA, nested model; mB, full model; n, sample size; k,
number of parameters.
TAB LE 1 Pearson correlation coefficients among measured variables in Year 1.
1 2 3 4 5 6
1. District (1 = low SE S) –
2. Grade (1 = fi ft h) −0.12 –
3. Gender (1 = girls) −0.25 0 .10 –
4. Posttest technology Efficacy −0 . 41** 0 .17 0.24*–
5. Posttest networking Agency −0.21 0.02 0.18 0.56** –
6. Posttest general Engagement −0.50** 0 .1 9 0.14 0.50** 0.45** –
Mean 5.36 4.77 3.09
SD 1.65 1.3 5 0.78
α0.95 0.79 0.92
*p < 0.05; **p < 0.01.
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higher networking agency tended to be more engaged in technology- mediated on-
line classes. Residing in the less wealthy school district was negatively associated
with engagement (B = −0.55, p < 0.01) while grade and gender did not predict student
engagement.
RQ2. General engagement in technology- mediated learning in
in- person classes
We examined how students' general engagement is predicted by technology efficacy and
networking agency in technology- mediated in- person classes. In 2021–2022, networking
agency and technology efficacy were measured both before and after implementation of
the curriculum. Table 3 shows the Pearson correlation coefficients between the measured
variables. As students' technology efficacy and networking agency may vary due to
participation in the DCL curriculum, we conducted separate analyses for the pre- intervention
and post- intervention data, rather than conducting longitudinal analyses.
Pre- intervention engagement
The base model containing only the demographic and contextual covariates explained 5.7%
of the variance in students' engagement (F(3 ,147) = 2.96, p = 0.03, R2 = 0.057). In the second
step, technology efficacy and networking agency significantly explained additional 23.4%
of the variance in engagement (F(5,145) = 11. 9 0, p < 0.01), while technology was the only
significant predictor. Adding the interaction term to the model accounted for an additional
2.5% of variance and the change in R2 was significant (ΔF(1,1 4 4 ) = 5.26, p = 0.02). However,
the interaction itself (B = 0.05, p = 0.06) did not significantly contribute to predicting students'
TAB LE 2 Hierarchical regression model of engagement in online class (Year 1).
BSE βp R2ΔR2 (p)
Model 1 0.264
District (1 = l ow S ES) −0. 74 0.16 −0.48 <0.01
Grade (1 = fif t h) 0.24 0 .15 0 .15 0.11
Gender (1 = girls) −0.02 0 .16 − 0.01 0.92
Model 2 0.424 0 .16 0 (<0. 01)
District −0.55 0 .1 5 −0.35 <0.01
Grade 0.17 0.14 0.11 0.21
Gender −0.09 0 .14 −0.06 0.52
Technology efficacy 0.1 0 0.05 0. 21 0.07
Networking agency 0.1 6 0.06 0.28 0.01
Model 3 0.431 0.007 (0.36)
District −0.54 0.1 5 −0.35 <0.01
Grade 0.18 0 .14 0.11 0.20
Gender −0.06 0 .14 −0.04 0.67
Technology efficacy (TE) 0.12 0.06 0.25 0.04
Networking agency (NA) 0.17 0.06 0.29 0.01
TE × NA interaction 0.03 0.03 0.10 0.33
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TAB LE 3 Pearson correlation coefficients among measured variables in Year 2.
1 2 3 4 5 6 7 8 9 10 11
1. District (1 = low SE S) –
2. Grade (1 = fi ft h) −0.48** –
3. Gender (1 = girls) −0.03 0.06 –
4. Pretest technology efficacy −0.32** − 0.01 − 0 .17 –
5. Pretest networking agency −0.01 −0.09 −0.35** 0.29** –
6. Pretest general engagement −0.12 −0 .10 −0.1 6 0.58** 0.20 –
7. Early- intervention specific engagement −0.14 −0.07 − 0.15 0 .41 ** 0.34** 0.46** –
8. Posttest technology efficacy 0.01 −0.02 − 0.09 0.47** 0.1 6 0.46** 0 .47** –
9. Posttest net working agency 0.06 −0.07 −0.04 0 .11 0.21 0.14 0.26*0.24** –
10. Posttest general engagement 0.1 0 −0.20 −0.08 0.17 0.25*0.34** 0.29** 0.61** 0.37** –
11. Posttest specific engagement −0.08 0.07 0.0 0 0.19 0 .14 0.26*0.46** 0.49** 0.33** 0.46** –
Mean 4.87 4.40 3.34 3.43 5.56 5.00 3.49 3.54
SD 1.39 1.26 0.44 0.57 1. 21 1.28 0.48 0.48
α0.89 0.70 0.71 0.88 0.88 0.75 0.78 0.86
*p < 0.05; **p < 0.01.
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TECHNOLOGY EFFICACY AND NETWORKING
engagement. Therefore, to ensure a clear and interpretable model, the second model with-
out the interaction term was chosen as the final model (Table 4).
Technology efficacy was the singular significant predictor positively associated with
students' engagement (B = 0 .16 , p < 0.01), indicating that students reporting higher tech-
nology efficacy tended to be more engaged in class. Networking agency (B = 0.0 3,
p = 0.30) and covariates (ie, district, grade and gender) did not demonstrate a significant
effect.
Post- intervention engagement
The base model with only demographic covariates was not statistically significant
(F(3 ,147) = 0.90, p = 0.44). Upon introducing technology efficacy and networking agency
as predictors, the model became significant (F(5,145) = 1 7. 09 , p < 0.01), with an increase
in R2 of 0.363 (ΔF(2 ,14 5 ) = 42.52, p < 0.01), indicating that these primary predictors
additionally explained 36.3% of the variance in students' engagement. The addition of
the interaction did not yield a significant increase in explanatory power (ΔF(1,14 4) = 1.1 7,
p = 0.28). Consequently, the second model without the interaction term was selected as
the final model (Table 5).
Consistent with the pre- intervention results, technology efficacy was the sole significant
predictor, suggesting a positive relationship with students' engagement (B = 0 .24, p < 0. 01) .
This finding indicates that students reporting higher technology efficacy are more likely to
be more engaged in class. Networking agency was not a significant predictor (B = 0 .01,
p = 0.74). The result also revealed a positive association between engagement and residing
in the less wealthy school district (B = 0.25, p < 0.01) while grade and gender did not predict
student engagement.
TAB LE 4 Hierarchical regression model of engagement in in- person class (Year 2, pretest).
BSE βp R2ΔR2 (p)
Model 1 0.057
District (1 = l ow S ES) −0.20 0.09 −0.20 0.02
Grade (1 = fif t h) −0.10 0.08 − 0.1 2 0.18
Gender (1 = girls) − 0.12 0.07 − 0 .14 0 .1 0
Model 2 0.291 0. 234 (<0. 01)
District 0.01 0.09 0.01 0.93
Grade −0.05 0.07 −0.06 0.48
Gender −0.02 0.07 −0.02 0.83
Technology efficacy 0.16 0.03 0.50 <0.01
Networking agency 0.03 0.03 0.09 0.30
Model 3 0.316 0.025 (0.02)
District 0.02 0.09 0.02 0.84
Grade −0.08 0.08 −0.09 0.31
Gender 0.00 0.07 −0.01 0.95
Technology efficacy 0.15 0.03 0.47 <0.01
Networking agency 0.03 0.03 0.09 0.28
TE × NA interaction 0.05 0.02 0 .17 0.0 6
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CHA et al.
RQ3. Specific engagement in technology- mediated activities in
in- person classes
We further explored how technology efficacy and networking agency predicted students'
specific engagement in small group activities that involved active use of learning applica-
tions (eg, group projects mediated by Google Slides, discussion activities occurring in Flip)
in technology- mediated f2f classes. Specific engagement data measured after the first and
last units were used, as these were assessed most closely to the pre- intervention and post-
intervention data respectively.
Early- intervention specific engagement
The base model comprising only covariates explained a small but significant percentage (6%)
of the variance in specific engagement measured after the first unit (F(3 ,147) = 3.1 3, p = 0.0 3,
R2 = 0.060). The subsequent model that incorporated technology efficacy and networking
agency explained an additional 15.8% of the variance (ΔF(2,145) = 14.65, p < 0.01). Notably,
a significant improvement in model fit was observed (ΔF(1,14 4) = 12 .18 , p < 0. 01, ΔR2 = 0. 0 61)
when the interaction term was added. The final model explained 27.9% of the variance in
students' specific engagement in technology- mediated small group activities (Table 6).
To further investigate the interaction, a simple slope analysis was tested for low (−1 SD),
moderate (mean) and high (+1 SD) levels of networking agency (Figure 4). At low levels of
networking agency, students' perceived efficacy in using technologies significantly predicted
their engagement in small group activities (B = 0.28, t = 4.55, p < 0.01), indicating a posi-
tive relationship between technology efficacy and specific engagement. For students with
a moderate level of networking agency, the simple effect of technology efficacy on small
group activity engagement (B = 0.15, t = 3.71 , p < 0.01) exhibited a diminished but still positive
TAB LE 5 Hierarchical regression model of engagement in in- person class (Year 2, posttest).
BSE βp R2ΔR2 (p)
Model 1 0.018
District (1 = l ow S ES) 0.01 0 .10 0.01 0.90
Grade (1 = fif t h) −0.08 0.09 −0.08 0.37
Gender (1 = girls) − 0 .10 0.08 − 0 .10 0. 24
Model 2 0.381 0.363 (<0. 01)
District 0.25 0.09 0.23 <0.01
Grade −0.09 0.07 −0.09 0.20
Gender −0.06 0.07 −0.07 0.34
Technology efficacy 0.24 0.03 0.63 <0.01
Networking agency 0.01 0.03 0.03 0.75
Model 3 0.386 0.005 (0.28)
District 0.24 0.09 0.23 0.01
Grade −0.08 0.07 −0.09 0.22
Gender −0.04 0.07 −0.05 0.52
Technology efficacy 0.26 0.04 0.66 <0.01
Networking agency 0.01 0.03 0.03 0 .74
TE × NA interaction 0.02 0.02 0.08 0.32
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TECHNOLOGY EFFICACY AND NETWORKING
relationship between technology efficacy and specific engagement. However, when net-
working agency was high, the main effect was not statistically significant (B = 0.03, t = 0.52,
p = 0. 60) ( Table 7).
Post- intervention specific engagement
The base model, consisting solely of demographic covariates, failed to reach statistical signif-
icance (F(3 ,14 6 ) = 0.45, p = 0.72). By introducing technology efficacy and networking agency
in the subsequent model, the model obtained statistical significance (F(5,145) = 10.62 ,
p < 0.01), with a change in ΔR2 of 0.259 (ΔF(2 ,1 4 5 ) = 25.65, p < 0.01). The addition of the
interaction term did not explain any additional variance (ΔF(1 ,14 4) = 3 .42, p = 0.07). Thus,
the second model, which explained 26.8% of the variance in students' specific engagement,
TAB LE 6 Hierarchical regression model of specific engagement in Year 2 (early- intervention).
BSE βp R2ΔR2 (p)
Model 1 0.060
District (1 = l ow S ES) −0.32 0 .12 −0.24 0.01
Grade (1 = fif t h) −0.08 0 .1 0 −0.07 0.39
Gender (1 = girls) −0.1 0 0 .10 − 0.08 0. 31
Model 2 0.218 0 .158 (<0. 01)
District − 0 .12 0 .13 −0.09 0.37
Grade −0.04 0 .1 0 −0.03 0.69
Gender 0.03 0.09 0.02 0.78
Technology efficacy 0.1 4 0.04 0.35 <0.01
Networking agency 0.09 0.04 0.1 8 0.05
Model 3 0.279 0.061 (<0.01)
District − 0 .16 0 .13 − 0.12 0.24
Grade −0.01 0.1 0 −0.01 0.93
Gender 0.02 0.09 0.02 0.80
Technology efficacy 0.1 5 0.04 0.39 <0.01
Networking agency 0.10 0.04 0.22 0.02
TE × NA interaction − 0.1 0 0.03 −0.29 <0.01
FIGURE 4 Moderation of technology efficacy by networking agency (NA) in predicting specific
engagement.
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CHA et al.
was retained (Table 8). Technology efficacy emerged as the singular positive predictor
(B = 0.20, p < 0. 01) .
DISCUSSION
We examined technology efficacy and networking agency as predictors of general engage-
ment in technology- mediated instruction in online (RQ1) and in- person classes (RQ2). We also
examined technology efficacy and networking agency as the predictors of specific engagement
in activities involving greater use of technology occurring in technology- mediated in- person
classes (RQ3). Overall, our findings support the hypothesis that technology efficacy and net-
working agency contribute to students' engagement in technology- mediated instruction, but
their associations may vary depending on the contexts. In online classes, networking agency—
an understudied dimension of digital citizenship—was a significant predictor of student en-
gagement. In face- to- face classes, technology efficacy consistently predicted both general and
specific engagement. However, technology efficacy was a necessary but insufficient predictor.
Networking agency served as a complementary predictor of specific engagement in activities
requiring more extensive use of digital tools. Students with high networking agency reported
strong specific engagement in these activities, even when their technology efficacy was lower.
TAB LE 7 Simple slopes analysis on specific engagement in Year 2 (early- intervention).
Networking agency BSE t p
Low (−1 SD) 0.28 0.06 4.55 <0.01
Moderate (M) 0.1 5 0.04 3 .71 <0.01
High (+1 SD) 0.03 0.06 0.52 0.60
TAB LE 8 Hierarchical regression model of specific engagement in Year 2 (posttest).
BSE βp R2ΔR2 (p)
Model 1 0.009
District (1 = l ow S ES) −0.10 0 .10 −0.09 0.35
Grade (1 = fif t h) 0.01 0.09 0.02 0.87
Gender (1 = girls) − 0.01 0.08 − 0.01 0.9 0
Model 2 0.268 0.259 (<0. 01)
District 0.10 0.09 0.1 0 0.27
Grade 0.01 0.07 0.01 0.89
Gender 0.01 0.07 0.01 0.93
Technology efficacy 0.20 0.04 0.54 <0.01
Networking agency 0.00 0.04 0.00 0.98
Model 3 0.285 0.02 (0.07)
District 0.10 0.09 0.09 0.30
Grade 0.02 0.07 0.02 0.7 7
Gender 0.04 0.07 0.04 0.58
Technology efficacy 0.23 0.04 0.61 <0.01
Networking agency 0.00 0.04 0.01 0.96
TE × NA interaction 0.03 0.02 0 .15 0 .11
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TECHNOLOGY EFFICACY AND NETWORKING
These findings suggest that technology efficacy alone may not be enough to sustain high stu-
dent engagement in technology- mediated instruction, especially in the contexts where students
have limited face- to- face interactions or lack relational foundations.
General engagement in online versus technology- mediated F2F
learning environments
In the prediction of engagement in online classes during Year 1 (2020–2021) (RQ1),
only networking agency was significantly associated with students' engagement. The
prominence of networking agency over technology efficacy suggests that in the context of
technology- mediated learning in the online environment, social interactions and community-
building were more predictive of engagement than mere technological proficiency (Choi &
Park, 2023). Since networking agency can be the foundation of learners' active collaboration
and interaction in online spaces (Choi et al., 2017 ), it might have facilitated their engagement
by enabling them to connect with peers, participate in online discussions and contribute
actively to the learning community (Kim et al., 2025).
The absence of an interaction between networking agency and technology efficacy may
initially seem counterintuitive. Networking agency taps into students' comfort with commu-
nication and collaboration in an online environment, which may explain why some students,
even though they perceived themselves as being able to successfully use information tech-
nologies, did not engage in online learning as actively as others who possessed similar
levels of technology efficacy. However, the lack of interaction may reflect the essence of net-
working agency, which involves the ability to develop and sustain community relationships
(Choi et al., 2017). Early studies of social network sites (SNS), which are online social media
platforms used for building social connections, demonstrated that people used SNS technol-
ogy to support and maintain pre- existing social relationships in offline contexts, rather than
building new online relationships (Boyd & Ellison, 2007; Ellison et al., 2007). Due to COVID
restrictions, students in Year 1 had minimal face- to- face classroom interactions. These stu-
dents may have lacked the relational foundations to employ their networking agency regard-
less of their technology efficacy.
In Year 2 (2021–2022), when the technology- mediated curriculum was implemented in
face- to- face classrooms, only technology efficacy was a direct and positive predictor of
general engagement, both at the beginning and end of the intervention (RQ2). Students'
networking agency did not predict their engagement even though they participated in the
curriculum where they needed to work using digital devices and learning applications. This
implies that online collaboration and communication in the in- person classroom may not
require as much networking as in fully online classes. This difference in the salience of
networking agency aligns with prior studies that emphasize the importance of social pres-
ence (ie, the ability to perceive others in an online environment) (Richardson et al., 2017).
Students may need to more actively communicate and collaborate with others to keep them-
selves engaged in their learning processes in contexts where face- to- face interactions are
limited (Turk et al., 2022).
Specific engagement in technology- mediated activities in in- person
classrooms
Since how technology efficacy and networking agency predict engagement may change
when it comes to students' involvement in small group activities that are more heavily
mediated by learning management systems and online applications (O'Brien & Toms, 2008),
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CHA et al.
we investigated the associations among technology efficacy, network agency and specific
engagement (RQ3). Notably, for students who felt more comfortable with communicating and
collaborating in the online space, their technical skills demonstrated a weaker association
with their engagement in these activities. A potential implication is that, when implementing
technology- mediated activities in face- to- face learning environments, educators should
create a safe online space where learners can freely communicate and collaborate.
Students less skilful with technology can seek help from the other group members who
are more adept at using technology. Therefore, as long as they enjoy collaborating and
communicating using the applications and programs, they could actively engage in small
group activities (Choi & Park, 2023).
At the same time, technical efficacy appeared to compensate for the lack of networking
agency in students' engagement in technology- mediated activities, since students could
still communicate and collaborate with their peers face- to- face if necessary. For instance,
even though students may find communication and collaboration in an online environment
uncomfortable, as long as they feel efficacious about commenting on their peers' discussion
posts and Flip, they may further engage in the discussion through face- to- face conversa-
tions. Students who are able to manipulate shared Google Slides may be more capable of
participating in decision- making processes by communicating with their peers sitting right
next to them. Proficiency in technology use could have a positive influence on engagement
for adult learners (Pellas, 2014). Similarly, by helping students become familiar with tech-
nological tools and learning applications, educators can better engage young learners in
technology- mediated activities (Johnson et al., 2023).
However, the significant interaction effect between networking agency and technology
efficacy was not found at the end of the intervention in in- person classrooms. This implies
that networking agency may function differently based on the dynamics of the technology-
mediated learning environment. At the start of the academic term, students were less
familiar with the technology- mediated intervention or their peers. During this phase, net-
working agency may have played a more crucial role in helping students overcome the
initial challenges associated with this unfamiliar environment. As the semester progressed
and students gradually acclimated to the technology- mediated instruction, the formerly
conspicuous effect of networking agency appeared to diminish. Students in general might
have become more adjusted to working in technology- mediated activities. In physical
classrooms, they might find ways to replace online communication and collaboration with
face- to- face interactions. In essence, the initial significance of networking agency in pro-
moting engagement gradually subsided as students collectively adjusted to the learning
procedures.
Technology efficacy may not be enough to sustain engagement in
technology- mediated instruction
Different patter ns of how technology eff icacy and networking agency predict students' general
engagement in technology- mediated instruction in online and in- person environments,
and specific engagement during technology- mediated small group activities suggest
that more nuanced research and practices should be implemented to engage students
across diverse technology- mediated contexts. For young students, perceiving the online
learning environment as safe and collaborative may be more important for engagement
than simply possessing technical skills (Wang et al., 2019). Self- efficacy is critical but is
one of many factors influencing academic engagement (Skinner & Pitzer, 2012); therefore,
although technology efficacy is essential, it may not be sufficient on its own to drive student
engagement in technology- mediated instruction.
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TECHNOLOGY EFFICACY AND NETWORKING
We propose networking agency as a complementary factor of student engagement.
Research on digital citizenship (Choi et al., 2017) highlights the necessity of a baseline
level of technological skills that enable individuals to feel comfortable with online collabora-
tion and communication. However, the development of technological skills and networking
agency may be reciprocal. As individuals become more comfortable in their online activities,
they may seek to enhance their basic abilities to navigate online contexts. Therefore, teach-
ers should foster a learning community where students feel safe to collaboratively construct
knowledge (Kim et al., 2025) while ensuring that they are prepared to use the digital tools and
applications required for instruction (Heo et al., 2021; Johnson et al., 2023). In technology-
mediated learning environments where students lack relational resources (eg, online class
with limited face- to- face interactions, the beginning of a semester when students are unfa-
miliar with one another), it is crucial to foster a safe and collaborative atmosphere to make
the most out of interactivity and connectedness afforded by learning technology (Johnson &
Johnson, 2008; Kim et al., 2025).
Engagement varied by school district (socio- economic context)
Though it is not the focus of the study, it is worth mentioning that students in the less
affluent school district reported significantly lower engagement in fully online classes in
Year 1, whereas this did not occur during Year 2. This aligns with the previous studies that
demonstrate the disproportionate negative impacts of COVID- 19 on disadvantaged students
(Fahle et al., 2023). However, students from the less wealthy school district actually showed
greater general engagement after the intervention than students from the wealthier district
in Year 2, when the technology- mediated instruction took place in face- to- face classrooms
where students had better access to technological affordances and received immediate
support from teachers.
LIMITATIONS AND FUTURE DIRECTION
Several limitations should be acknowledged in this study. First, while we extended existing
literature by examining how engagement is associated with technology efficacy and
networking agency among young learners, the generalizability of our findings is limited as
the sample population only targeted elementary students from two school districts within a
single state. Still, by conducting studies in two school districts differing in socio- economic
status, we were able to include linguistically/culturally diverse learners in our sample. Second,
students' experiences of online learning in Year 1 varied across classrooms due to schools'
differing pandemic- related policies. For instance, some students had fully online learning
only during the first semester and then returned to face- to- face classrooms in the second
semester. DiFrancesca and Spencer (2022) defined online learning during the pandemic as
emergency remote learning, differentiating it from true online learning. Still, even if students'
online learning experiences in Year 1 may not be generalizable, we believe that it is worth
investigating how technology efficacy and networking agency predict students' engagement
under that unique circumstance.
We suggest future directions to expand this research. First, more research examining
how diverse learners engage in technology- mediated learning, especially in elementary
school settings, should be conducted. While the use of technology and online applications
has become common for young learners, most of the published research is drawn from the
higher education context (eg, Barbour, 2018; Means et al., 2009). In addition, we call for
attention to whether technology efficacy that taps into students' technological proficiency
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CHA et al.
is enough to capture student engagement in technology- mediated instruction. Considering
that students actively collaborate and communicate in online spaces, other factors, such as
networking agency and digital citizenship, should also be examined to better understand
predictors of active engagement.
CONCLUSION
Regarding students' engagement in technology- mediated learning, researchers and
practitioners tend to solely focus on students' perceived technological competencies, or
whether they can use devices and applications proficiently (Chen, 2017; Wei & Chou, 2020).
However, the results of our multi- year study indicate that considering how students construe
online environments may also be an important factor that predicts student engagement in
online classes and activities that are more heavily mediated by technology. The late childhood/
early adolescent participants in this study are entering perhaps some of the most critical
educational periods for developing as digital learners and citizens. To foster engagement in
technology- mediated instruction, educators should equip them with technological skills and
facilitate positive collaborative experiences in online spaces.
ACKNOWLEDGEMENTS
The authors would like to thank our current and former project team members, district
partners, teacher collaborators and student participants. This project is funded by an
Innovation and Development grant (R305A200364) from the Institute of Education Sciences
(IES).
CONFLICT OF INTEREST STATEMENT
No conflict of interest was reported by the authors.
DATA AVAILABILITY STATEMENT
Study data are available by emailing the first author.
ETHICS STATEMENT
The study was reviewed by the Institutional Review Board at The Ohio State University. No
violation of human research ethics was found during the study.
ORCID
Wonjoon Cha https://orcid.org/0009-0007-5532-671X
Minxuan Hong https://orcid.org/0009-0009-9332-631X
Michael Glassman https://orcid.org/0000-0003-3870-8760
Eric M. Anderman https://orcid.org/0000-0002-3473-0971
Tzu- Jung Lin https://orcid.org/0000-0002-4525-1001
Endnote
1 Students' age was measured in Year 2.
REFERENCES
Abbasi, A. Z., Ting, D. H., & Hlavacs, H. (2017). Engagement in games: Developing an instrument to measure
consumer videogame engagement and its validation. International Journal of Computer Games Technology,
2017, 7363925. https:// doi. org/ 10. 1155/ 2017/ 7363925
|
21
TECHNOLOGY EFFICACY AND NETWORKING
Ali, I., Butt, K., & Warraich, N. F. (2023). Factors effecting digital citizenship in education sector: A systematic
review and future direction. Education and Information Technologies, 28(12), 1578 9 –15821. https:// doi. org/
10. 1007/ s1063 9- 023- 11811 - 8
Al- Zahrani, A. (2015). Toward digital citizenship: Examining factors affecting participation and involvement in the
internet society among higher education students. International Education Studies, 8(12), 203–217. https://
doi. org/ 10. 5539/ ies. v8n12 p203
Bandura, A. (1977). Self- efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2),
191–215. https:// doi. org/ 10. 1037/ 0033- 295X. 84.2. 191
Barbour, M. K. (2018). The landscape of K- 12 online learning: Examining what is known. In Handbook of distance
education (pp. 521–542). Routledge.
Baroody, A. E., Rimm- Kaufman, S. E., Larsen, R. A ., & Curby, T. W. (2016). A multi- method approach for describ-
ing the contributions of student engagement on fifth grade students' social competence and achievement
in mathematics. Learning and Individual Dif ferences, 48, 54 –60. https:// doi. org/ 10. 1016/j. lindif. 2016. 02. 012
Boie, M. A. K., Dalsgaard, C., & Caviglia, F. (2024). Digital instinct—A keyword for making sense of students'
digital practice and digital literacy. British Journal of Educational Technology, 55(2), 668 – 686. https:// doi.
or g / 10 . 1111/ bje t . 13 3 9 8
Bowen, N. K., & Guo, S. (2011). Structural equation modeling. Oxford University Press.
Bower, M. (2019). Technology- mediated learning theory. British Journal of Educational Technology, 50(3), 1035–
1048 . h t tps: / / d o i . or g / 10. 1111 / bje t . 12 7 71
Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-
Mediated Communication, 13(1), 210–230. https:// doi. org/ 10. 1111/j. 1083- 6101. 2007. 00393. x
Carter, C. P., Reschly, A. L., Lovelace, M. D., Appleton, J. J., & Thompson, D. (2012). Measuring student engage-
ment among elementary students: Pilot of the student engagement instrument—Elementary version. School
Psychology Quarterly, 27(2), 61–73. https:// doi. org/ 10. 1037/ a0029229
Castells, M. (1996). Rise of the network society: The information age: Economy, society and culture. Blackwell
Publishers, Inc.
Cha, W., Glassman, M., Anderman, E. M., & Lin, T.- J. (2024, April 11–14). Exploring how technology self- efficacy
and online digital citizenship predict engagement in technology- mediated civic discussion activities [Poster
presentation]. American Educational Research Association 2024 Annual Meeting, Philadelphia, PA.
Cha, W., Tilak, S., Wen, Z., Kim, S., Sheng, Y., Anderman, E. M., Lin, T.- J., & Glassman, M. (2023, April 13–16).
Flipgrid versus discussion board: Students' modality choice depending on online digital citizenship [Poster
presentation]. American Educational Research Association 2023 Annual Meeting, Chicago, IL.
Chen, I. S. (2017). Computer self- efficacy, learning performance, and the mediating role of learning engagement.
Computers in Human Behavior, 72, 362–370. https:// doi. org/ 10. 1016/j. chb. 2017. 02. 059
Choi, M. (2016). A concept analysis of digital citizenship for democratic citizenship education in the internet age.
Theory & Research in Social Education, 44(4), 565– 607. http s:// doi. org/ 10. 1080/ 00933 10 4. 2016. 121054 9
Choi, M., Glassman, M., & Cristol, D. (2017). What it means to be a citizen in the internet age: Development of a
reliable and valid digital citizenship scale. Computers & Education, 107, 100–112. https:// do i. org/ 10. 1016/j.
compe du. 2017. 01. 002
Choi, M., & Park, H. J. (2023). Korean adolescents' profiles of digital citizenship and its relations to internet eth-
ics: Implications for critical digital citizenship education. Cambridge Journal of Education, 53(4), 567–586.
https:// doi. org/ 10. 1080/ 03057 64X. 2023. 2191929
Compeau, D. R., & Higgins, C. A. (1995). Computer self- efficacy: Development of a measure and initial test. MIS
Quarterly, 19(2), 189–211. https:// doi. org/ 10. 2307/ 249688
Courtney, S. A., Miller, M. E., & Gisondo, M. J. (2022). The impact of COVID- 19 on teachers' integration of digital
technology. Contemporary Educational Technology, 14(4), ep387. https:// doi. org/ 10. 30935/ cedte ch/ 12420
DiFrancesca, D., & Spencer, D. (2022). The new normal: How virtual learning can benefit K- 12 students. Theor y
Into Practice, 61(4), 362–372. https:// doi. org/ 10. 1080/ 00405 841. 2022. 2107345
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “ friends:” social capital and college
students' use of online social network sites. Journal of Computer- Mediated Communication, 12 (4 ), 1143 –
116 8 . http s : // d o i. o r g / 10 . 1111/j. 1 0 8 3 - 6 10 1. 20 0 7. 0 0367. x
Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information ma ximum likelihood estima-
tion for missing data in structural equation models. Structural Equation Modeling, 8(3), 430– 457. https:// doi.
org/ 10. 1207/ S1532 8007S EM 0803_ 5
Escueta, M., Quan, V., Nickow, A. J., & Oreopoulos, P. (2017). Education technology: An evidence- based review.
NBER Working Paper No. W23744. https:// doi. org/ 10. 3386/ w23744
Fahle, E. M., Kane, T. J., Patterson, T., Reardon, S. F., Staiger, D. O., & Stuart, E. A. (2023). School district and
community factors associated with learning loss during the COVID- 19 pandemic. Center for Education Policy
Research at Harvard University.
Feenberg, A. (1991). Critical theory of technology (Vol. 5). Oxford Universit y Press.
22
|
CHA et al.
Flewitt, R ., El Gemayel, S., Arnott, L., Gillen, J., Goodall, J., Winter, K., Dalziell, A., Liu, M., Savadova, S., &
Timmins, S. (2024). Toddlers, tech and talk: Very young children's language and literacy learning at home in
a post- digital age [Summary report]. Manchester Metropolitan University.
Fredricks, J. A ., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of
the evidence. Review of Educational Research, 74(1), 59–109. ht tps:// doi. org/ 10. 3102/ 00346 54307 400105 9
Glassman, M. (2013). Open source theory. 01. Theory & Psychology, 23(5), 675– 692. https:// doi. org/ 10. 1177/
09593 54313 495471
Glassman, M., Kuznetcova, I., Peri, J., & Kim, Y. (2021). Cohesion, collaboration and the struggle of creating
online learning communities: Development and validation of an online collective efficacy scale. Computers
and Education Open, 2, 100031. https:// doi. org / 10. 1016/j. cae o. 2 021. 100031
Godsk, M., & Møller, K. L. (2024). Engaging students in higher education with educational technology. Education
and Information Technologies, 30, 1–36. ht tps:// doi. org/ 10. 1007/ s1063 9 - 024 - 12901 - x
Heo, H., Bonk, C. J., & Doo, M. Y. (2021). Enhancing learning engagement during COVID- 19 pandemic: Self-
efficacy in time management, technology use, and online learning environments. Journal of Computer
Assisted Learning, 37(6), 1640–1652. ht t p s : // d o i. o r g / 10 . 1111/ j c a l . 12 6 0 3
Hong, M. (2022). Technology self- efficacy and digital citizenship as predictors of elementary students' online
learning engagement (Master's thesis). The Ohio State University.
Huang, K. T., Ball, C., Cotten, S. R., & O'Neal, L. (2020). Effective experiences: A social cognitive analysis of
young students' technology self- efficacy and STEM attitudes. Social Inclusion, 8(2), 213–221. https:// doi. org/
10. 17645/ si. v8i2. 2612
Johnson, C. C., Walton, J. B., Strickler, L., & Elliott, J. B. (2023). Online teaching in K- 12 education in the United
States: A systematic review. Review of Educational Research, 93(3), 353– 411. https:// doi. org/ 10. 3102/ 00346
54322 1105550
Johnson, D. W., & Johnson, R. T. (2008). Social interdependence theor y and cooperative learning: The teacher's
role. In R. M. Gillies, A. F. Ashman, & J. Terwel (Eds.), The teacher's role in implementing cooperative learn-
ing in the classroom (pp. 9 –37). Springer.
Kim, S. C., Sheng, Y., Cha, W., Lin, T. J., & Glassman, M. (2025). Using immersive civic discussion to cultivate
civic purpose in elementary schools during politically volatile times. Theory Into Practice, 64. https:// doi. org/
10. 1080/ 00 405 841. 2025. 2453370
Kim, S. B. (2023). Enriching civic learning experiences in elementary social studies classrooms to prepare stu-
dents for purposeful citizenship (Doctoral dissertation). The Ohio State Universit y.
Kim, Y., & Glassman, M. (2013). Beyond search and communication: Development and validation of the Internet
Self- Efficacy Scale (ISS). Computers in Human Behavior, 29(4), 1421 –1429. ht tps:// do i. or g/ 10. 1016/j. ch b.
2013. 01. 018
Kline, P. (2000). Handbook of psychological testing (2nd ed.). Routledge.
Kuznetcova, I., Tilak, S., Wen, Z., Glassman, M., Anderman, E., & Lin, T. J. (2023). Making immersive stor ytelling
accessible: Interactive low- tech implementation in elementary school civic learning. Convergence. https: //
doi. org/ 10. 1177/ 13548 56523 119996
Liao, Y.- C., Ottenbreit- Lef twich, A., Zhu, M., Jantaraweragul, K., Christie, L., Krothe, K ., & Sparks, K. (2021). How
can we support online learning for elementary students? Perceptions and experiences of award- winning K- 6
teachers. TechTrends, 65(6), 939–951. https:// doi. org/ 10. 1007/ s1152 8- 021- 00663 - z
Lu, C., & Gu, M. M. (2024). A systematic review and meta- analysis of factors and outc omes of digital citizen-
ship among adolescents. Asia Pacific Journal of Education, 1–16. https:// doi. org/ 10. 1080/ 02188 791. 2023.
2296352
Lubis, A. H., & Dasopang, M. D. (2021). Online learning during the covid- 19 pandemic: How is it implemented in
elementary schools. Premiere Educandum: Jurnal Pendidikan Dasar dan Pembelajaran, 11(1), 120–134.
http:// repo. uinsy ahada. ac. id/ id/ eprint/ 757
Maatuk, A. M., Elberkawi, E. K., Aljawarneh, S., Rashaideh, H., & Alhar bi, H. (2022). The COVID- 19 pandemic
and e- learning: Challenges and opportunities from the perspective of students and instructors. Journal of
Computing in Higher Education, 34(1), 21–38. https:// doi. org/ 10. 1007/ s1252 8- 021- 09274 - 2
Martins, J., Cunha, J., Lopes, S., Moreira, T., & Rosário, P. (2022). School engagement in elementary school: A
systematic review of 35 years of research. Educational Psychology Review, 34(2), 793–849. https:// doi. org/
10. 1007/ s1064 8- 0 21- 09642 - 5
Matthews, J. S., Ponitz, C. C., & Morrison, F. J. (2009). Early gender dif ferences in self- regulation and academic
achievement. Journal of Educational Psychology, 101(3), 689–704. https:// doi. org/ 10. 1037/ a0014240
McLean, L., Granger, K. L., & Chow, J. C. (2023). Associations between elementary teachers' mental health and
students' engagement across content areas. Contemporary Educational Psychology, 75, 102231. https:// doi.
org/ 10. 1016/j. cedps ych. 2023. 102231
Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of evidence - based practices in
online learning: A meta- analysis and review of online learning studies. US Depar tment of Educ ation. ht tps://
repos itory. alt. ac. uk/ id/ eprint/ 629
|
23
TECHNOLOGY EFFICACY AND NETWORKING
O'Brien, H. L., & Toms, E. G. (2008). What is user engagement? A conceptual framework for defining user en-
gagement with technology. Journal of the American Society for Information Science and Technology, 59(6),
938–955. https:// doi. org/ 10. 1002/ asi. 20801
Pellas, N. (2014). The influence of computer self- efficacy, metacognitive self- regulation and self- esteem on stu-
dent engagement in online learning programs: Evidence from the virtual world of second life. Computers in
Human Behavior, 35, 157–170. https:// doi. org/ 10. 1016/j. chb. 2014. 02. 048
Rahman, M. K., Bhuiyan, M. A., Mainul Hossain, M., & Sifa, R. (2023). Impact of technology self- efficacy on online
learning effectiveness during the COVID- 19 pandemic. Kybernetes, 52(7), 2395 –2415. https:// doi. org/ 10.
1108/ K- 07- 20 22- 1049
Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidenc e
and possible explanations. In R. Murnane & G. Duncan (Eds.), Whither opportunity? Rising inequality and
the uncertain life chances of low- income children (pp. 91–116). Russell Sage Foundation Press.
Reeve, J. (2013). How students create motivationally supportive learning environments for themselves: The con-
cept of agentic engagement. Journal of Educational Psychology, 105 (3), 579– 595. https:// doi. org/ 10. 1037/
a0032690
Reschly, A. L., & Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evolution and future direc-
tions of the engagement construct. In S. Christenson, A. Reschly, & C. Wylie (Eds.), Handbook of research
on student engagement (pp. 3–19). Springer. https:// doi. org/ 10. 1007/ 978- 1- 4614- 2018- 7_ 1
Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., & Salovey, P. (2012). Classroom emotional climate, student
engagement, and academic achievement. Journal of Educational Psychology, 104(3), 700 –712. https:// doi.
org/ 10. 1037/ a00 27268
Richardson, J. C., Maeda, Y., Lv, J., & Caskurlu, S. (2017). Social presence in relation to students' satisfaction and
learning in the online environment: A meta- analysis. Computers in Human Behavior, 71, 402–417. https:// doi.
org/ 10. 1016/j. chb. 2017. 02. 001
Roberts, M. J. D., Connolly, R., Conley, J., & Miller, J. (2023). Digital citizenship and the big five personality traits.
Informatics, 10(3), 58. https:// doi. org/ 10. 3390/ infor matic s1003 0058
Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the ar t. Psychological Methods, 7(2),
147–177. https:// d oi. o rg/ 10. 1037/ 1082- 98 9X.7. 2. 147
Schindler, L. A., Burkholder, G. J., Morad, O. A., & Marsh, C. (2017). Computer- based technology and student
engagement: A critical review of the literature. International Journal of Educational Technology in Higher
Education, 14(25), 1–28. https:// doi. org/ 10. 1186/ s4123 9- 017- 0063- 0
Schunk, D. H., Pintrich, P. R., & Meece, J. L. (2014). Motivation in education: Theory, research, and applications.
Pearson.
Shi, G., Chan, K. K., & Lin, X. F. (2023). A systematic review of digital citizenship empirical studies for practitioners.
Education and Information Technologies, 28(4), 3953–3975. https:// doi. org/ 10. 1007/ s1063 9 - 0 22- 11383 - z
Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta- analytic review of research. Review
of Educational Research, 75(3), 417–453. https:// doi. org/ 10. 3102/ 00346 54307 5003417
Skinner, E. A., Kindermann, T. A., & Furrer, C. J. (2009). A motivational perspective on engagement and disaf-
fection: Conceptualization and assessment of children's behavioral and emotional participation in academic
activities in the classroom. Educational and Psychological Measurement, 69(3), 493–525. https:// doi. org/ 10.
1177/ 00131 64408 323233
Skinner, E. A., & Pitzer, J. R. (2012). Developmental dynamics of student engagement, coping, and everyday
resilience. In S. Christenson, A. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement
(pp. 21–44). Springer. https:// doi. org/ 10. 1007/ 978- 1- 4614- 2018- 7_ 2
Tay, L. Y., Lee, S. S., & Ramachandran, K. (2021). Implementation of online home- based learning and students'
engagement during the COVID- 19 pandemic: A case study of Singapore mathematics teachers. The Asia-
Pacific Education Researcher, 30, 299–310. https:// doi. org/ 10. 1007/ s4029 9- 021- 00572 - y
Turk, M., Heddy, B. C., & Danielson, R . W. (2022). Teaching and social presences supporting basic needs satisfac -
tion in online learning environments: How can presences and basic needs happily meet online? Computers
& Education, 180, 104432. https:// doi. org/ 10. 1016/j. compe du. 2022. 104432
Ventouris, A., Panourgia, C., & Hodge, S. (2021). Teachers' perceptions of the impact of technology on chil-
dren and young people's emotions and behaviours. International Journal of Educational Research Open, 2,
100081. https:// doi. org/ 10. 1016/j. ijedro. 2021. 100081
Vygotsky, L. S., & Cole, M. (1978). Mind in society: Development of higher psychological processes. Har vard
University Press.
Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students' characteristics, self- regulated learning, technology
self- efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323. https:// doi. org/
10. 1080/ 01587 919. 2013. 835779
Wang, M. T., Degol, J. L., & Henry, D. A. (2019). An integrative development- in- sociocultural- context model for
children's engagement in learning. American Psychologist, 74(9), 1086 –1102. https:// doi. org / 10. 1037/ amp 00
00522
24
|
CHA et al.
Watson, J., Murin, A., Vashaw, L., Gemin, B., & Rapp, C. (2013). Keeping pace with K- 12 online & blended learn-
ing: An annual review of policy and practice. 10 year anniversary issue. Evergreen Education Group.
Wei, H. C., & Chou, C. (2020). Online learning performance and satisfaction: Do perceptions and readiness mat-
ter? Distance Education, 41(1), 48– 69.
Wesarg- Menzel, C., Ebbes, R., Hensums, M., Wagemaker, E., Zaharieva, M. S., Staaks, J. P. C., van den Akker,
A. L., Visser, I., Hoeve, M., Brummelman, E., Dekkers, T. J., Schuitema, J. A., Larsen, H., Colonnesi, C.,
Jansen, B. R. J., Overbeek, G., Huizenga, H. M., & Wiers, R. W. (2023). Development and socialization of
self- regulation from infancy to adolescence: A meta- review differentiating between self- regulatory abilities,
goals, and motivation. Developmental Review, 69, 101090. https:// doi. org/ 10. 1016/j. dr. 2023. 101090
SUPPORTING INFORMATION
Additional supporting information can be found online in the Supporting Information section
at the end of this article.
How to cite this article: Cha, W., Hong, M., Glassman, M., Anderman, E. M., & Lin,
T.-J. (2025). The roles of technology efficacy and networking agency in elementary
students' engagement in online and face- to- face technology- mediated learning.
British Journal of Educational Technology, 00, 1–24. ht t p s: // d o i . o rg/10 .1111 / b j et .13 5 9 7
