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Collaborative-constructivist online learning appears well-aligned with Ukraine’s post-revolutionary aspirations for globalized and transformed, higher education. This study explores digital competencies of students and professors at Kyiv National Economic University, Ukraine, to probe readiness for fully online collaborative learning. The General Technology Competency and Use profile tool was completed by 244 participants to measure digital experience and confidence across four categories of human-computer activity. To assess readiness, reported levels of competencies were related to the three dimensions of successful collaborative learning described by the Community of Inquiry model. Despite some key differences between students and teachers, general findings include moderate-to-low levels of self-reported technical, social and informational competency, accompanied by consistently low levels of epistemological competency. These findings suggest neither students nor teachers are adequately prepared for achieving high levels of social, cognitive and teaching presence in a fully online learning environment. It is recommended that digital-competency development become an educational priority.
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This is the Authors’ Original Manuscript (AOM) of this published
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article:
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Blayone, T., Mykhailenko, O., VanOostveen, R., Grebeshkov, O., Hrebeshkova, O., &
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Vostryakov, O. (2017). Surveying digital competencies of university students and
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professors in Ukraine for fully online collaborative learning. Technology,
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Pedagogy and Education, 1-18. doi:10.1080/1475939X.2017.1391871
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Please do NOT distribute or cite this manuscript. Readers are
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encouraged to consult the published version above, in which several
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corrections/revisions were made to the text and the reporting of
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tabular data.
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Surveying digital competencies of university students and professors in
1
Ukraine for fully online collaborative learning
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Collaborative-constructivist online learning appears well-aligned with Ukraine’s
3
post-revolutionary aspirations for globalized and transformed, higher education.
4
This study explores digital competencies of students and professors at Kyiv
5
National Economic University, Ukraine, to probe readiness for fully online
6
collaborative learning. The General Technology Competency and Use profile tool
7
was completed by 244 participants to measure digital experience and confidence
8
across four categories of human-computer activity. To assess readiness, reported
9
levels of competencies were related to the three dimensions of successful
10
collaborative learning described by the Community of Inquiry model. Despite
11
some key differences between students and teachers, general findings include
12
moderate-to-low levels of self-reported technical, social and informational
13
competency, accompanied by consistently low levels of epistemological
14
competency. These findings suggest neither students nor teachers are adequately
15
prepared for achieving high levels of social, cognitive and teaching presence in a
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fully online learning environment. It is recommended that digital-competency
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development become an educational priority.
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Keywords: digital competence; fully online learning; educational transformation;
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Ukraine; higher education.
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Context
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Ukraine, a country with a declining population of about 44 million people
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(Worldometers, 2017), is a transitional post-Soviet nation (Roztocki & Weistroffer,
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2015). Despite making strides towards democratization since independence in 1991, full
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integration with Europe, and Western levels of socio-economic development have not
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been realized (Nikolayenko, 2009; Wilson, 2013). Researchers have described
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Ukrainians as experiencing low self-confidence and pessimism about the future
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(Nikolayenko, 2009), low levels of trust (Rose-Ackerman, 2001), acute social
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atomization (Pjesivac, 2014), and weak civil engagement (Way, 2014). One positive
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Soviet legacy is mass education: almost 30% of Ukrainians have completed higher
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education (Wilson, 2013).
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In early 2014, two million Ukrainian protesters, initially led by university
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students, ousted the Russian-backed President, Viktor Yanukovych, after he refused to
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sign a major economic agreement with the EU (Onuch, 2014). This Euromaidan
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revolution crystallized democratic valuesincluding freedom of expression, self-
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direction, universalism and openness to change (Sviatnenko & Vinogradov, 2014). In
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post-Maidan Ukraine, educational reform efforts are a high priority (Kovtun & Stick,
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2009; Kutsyuruba, 2011; Kutsyuruba & Kovalchuk, 2015). New learning models
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aligned with democratic transformation and digital innovation are especially coveted
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(Powell, Kuzmina, Yamchynska, Shestopalyuk, & Kuzmin, 2015; Sharkova, 2014).
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Purpose
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Although not focused on cross-cultural comparison, this study evolved from
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transnational conversations between Canadian digital-learning researchers at the
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EILAB, University of Ontario Institute of Technology (UOIT), and several Ukrainian
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professors at the Faculty of Economics, Kyiv National Economic University (KNEU).
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After a partnership was established (UOIT & KNEU, 2015), a seminal research
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question was formulated: Might fully online learning, like that practiced at UOIT
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(vanOostveen, 2015; vanOostveen & Desjardins, 2013; vanOostveen, DiGiuseppe,
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Barber, Blayone, & Childs, 2016), offer Ukrainians an effective model for
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democratizing learning and transforming education “from below”? In order to pursue
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this question, an online pilot course was conceived, and a research team was formed to
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survey the digital competencies of students and professors at the host Ukrainian
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institution. This preparatory step was considered vital because: (a) digital competencies,
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developed through experience and confidence (Akaslan & Law, 2012), represent a key
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facet of online-learning readiness (Borotis & Poulymenakou, 2004; Machado, 2007;
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Mosa, Naz’ri bin Mahrin, & Ibrrahim, 2016); (b) digital competency levels have a
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significant impact on group functioning in online-learning communities (Gunawardena
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et al., 2001); and (c) profiling digital competencies has proven useful at UOIT to
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support students and faculty in fully online degree programs (Barber, DiGiuseppe,
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vanOostveen, Blayone, & Koroluk, 2016; Desjardins, vanOostveen, Bullock,
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DiGiuseppe, & Robertson, 2010; DiGiuseppe, Partosoedarso, vanOostveen, &
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Desjardins, 2013).
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The guiding research questions were formulated as follows:
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(1) What digital competencies are most relevant to achieving successful
10
participation in a fully online collaborative learning environment?
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(2) What levels of these digital competencies do Ukrainian students and professors
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report?
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(3) Are there significant differences between reported student and professor
14
competencies?
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(4) Do the competency profiles of Ukrainian students and professors suggest
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readiness for successful functioning in a fully online collaborative learning
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environment?
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Theoretical frameworks
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In order to address these questions, a theoretical apparatus was constructed from the
20
literature. First, the General Technology Competency and Use (GTCU) framework
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(Desjardins, 2005; Desjardins, Lacasse, & Belair, 2001; Desjardins & Peters, 2007) was
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selected to conceptualize and measure digital competencies. Second, the Community of
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Inquiry (CoI) digital-learning model (Garrison, 2011, 2013, 2016) was selected to
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conceptualize successful social-constructivist learning. Finally, the constituent
1
dimensions of both models were related so that reported digital-competencies
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(measured by the GTCU) could be interpreted by the researchers as indicators of
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readiness (and non-readiness) for successful, collaborative-constructivist learning (as
4
defined by the CoI).
5
The General Technology Competency and Use framework: A model for
6
conceptualizing and measuring digital competencies
7
The GTCU framework was selected owing to several strengths. First, it is an
8
extensively theorized and operationalized, digital-competency framework developed in
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a university context of fully online education (Desjardins, 2005; Desjardins, Davidson,
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Blayone, vanOostveen, & Childs, 2015). Second, the GTCU’s survey instrument has
11
been used successfully over time to probe the digital readiness of both students and
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teachers (Barber et al., 2016; Desjardins & vanOostveen, 2015; Desjardins et al., 2010;
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DiGiuseppe et al., 2013; DiGiuseppe, vanOostveen, Childs, Blayone, & Barber, 2017).
14
Finally, by incorporating behavioural and attitudinal indicators, and associating items
15
with specific types of devices, this instrument provides a depth of data beyond other e-
16
learning readiness instruments (Blayone, vanOostveen, Mykhailenko, & Barber,
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2017b).
18
The GTCU offers a highly-reduced model that remains stable over time despite
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ongoing changes to digital devices, software functionality and human motives for
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technology use (Desjardins et al., 2015). This is achieved by conceptualizing three
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foundational “orders” (dimensions) of digital competency using the IEEE’s definition of
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computer hardware: “physical equipment used to process, store, or transmit computer
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programs or data” (IEEE, 1990). As shown in Figure 1, Desjardins (2001) called these
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competency orders: epistemological (process), informational (store) and social
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(transmit). Knowledge and abilities in each of these orders support effective functioning
1
as digital learners and teachers. A secondary technical order was added to acknowledge
2
operational competencies that are prerequisite to successful interaction in the three
3
primary orders.
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Figure 1. Dimensions of digital competency according to the GTCU.
5
6
The epistemological order of use is theorized as those interactions in which a
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person assigns cognitive (algorithmic) tasks to a computer using a programming
8
language or the features of a software application (e.g., a spreadsheet or concept-
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mapping tool). Key competencies relate to effective data analysis, problem solving and
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hypothesis development (Desjardins, 2005). The informational order relates to
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interactions between humans and information objects. Key competencies relate to
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effective searching, evaluation, synthesis and the production of new knowledge
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(Desjardins, 2005). The social order addresses competencies related to effective digital
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communication, collaboration and publication (Desjardins, 2005).
15
Person
Other
People
Digital
Knowledge
Artifacts
Knowledge
Processor
Technical Informational
General Technology
Competency and Use
(GTCU)
interact through
interact with
Four Orders of Competency
Community of Inquiry: A model for fully online collaborative learning
1
Many online learning models, like massive open online courses (MOOCs), emphasize
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open access to expert content and individualized learning (Dalsgaard & Paulsen, 2009;
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De Corte, Engwall, & Teichler, 2016; Paulsen, 2008; Siemens, Gašević, & Dawson,
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2015). Others focus on building highly collaborative learning communities. The
5
Community of Inquiry model (Garrison, 2011, 2013) belongs to the latter group, and
6
was selected as a guiding model for the Ukrainian pilot course. Like some other
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collaborative-constructivist models (Blayone, vanOostveen, Barber, DiGiuseppe, &
8
Childs, 2017; Gunawardena et al., 2004; Gunawardena et al., 2006), the CoI offers a
9
foundation for democratized education at the level of course interactions. It builds on
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the theories of Dewey (1897), Piaget (1959) and Vygotsky (1978) to envision learners
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as “post-industrial” inquirers (Swan, 2010). More specifically, the CoI models ideal
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learning communities (Garrison & Archer, 2000), which:
13
integrate individual and social priorities, incorporating the fundamental ideals
14
and values of participatory democracy (Dewey, 1897, 1916; Garrison, 2013).
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promote “deep learning” instead of rote learning, fostering reflective thinking
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and cognitive agility (Akyol & Garrison, 2011; Garrison, Anderson, & Archer,
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2001).
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facilitate active collaboration, freedom of expression, and deliberation vital for
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effective entrepreneurship, innovation and social development (Garrison, 2016).
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adapt to the cultural values of participants, recognizing experience as an
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essential foundation for new knowledge (Dewey, 1897).
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emphasize learner-driven, problem-based learning and group inquiry
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(vanOostveen & Desjardins, 2013).
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Not only does the CoI appear well-aligned with Ukrainian aspirations for educational
1
transformation, it provides detailed, and empirically researched, descriptions of
2
successful functioning in fully online learning communitiesorganizing successful
3
collaborative learning into three “presences,” for which instructors and students share
4
responsibility.
5
Relating digital competencies and fully online collaborative learning
6
The CoI’s three presences address social, cognitive and teaching transactions essential
7
to optimal community functioning. To conceptualize the prerequisite digital
8
competencies required to achieve high levels of each presence, the researchers
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conceptually mapped the GTCU’s orders of digital competency to the CoI presences, as
10
shown in Figure 2.
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Figure 2. Digital competencies for successful, collaborative online learning.
12
13
Social presence (SP) encompasses interpersonal, open and cohesive
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communication, thus facilitating trust and community cohesion, and aligning directly
15
with the GTCU’s social dimension (Garrison, 2011). Cognitive presence (CP), includes
16
SOCIAL
PRESENCE
COGNITIVE
PRESENCE
TEACHING
PRESENCE
Supporting
Discourse
Setting
Climate
Selecting
Content
EDUCATIONAL
EXPERIENCE Social Presence Activities
Interpersonal communication
Open communication
Cohesive communication
Phatic (purely social) salutations
Cognitive Presence Activities
Triggering inquiry
Exploration
Integration
Resolution
Teaching Presence Activities
Design/organization
Facilitating discourse
Direct instruction
Informational
Competencies
Epistemological
Competencies
Social
Competencies
Technical
Competencies
General Technology
Competency and
Use (GTCU)
Community
of Inquiry
(CoI)
Four Orders Three Presences
facets of critical inquiry (e.g., exploration, synthesis and resolution), and relies heavily
1
on informational and epistemological competencies, supported by social competencies
2
for effective collaboration (Garrison, 2011). Teaching presence (TP), conceptualized as
3
a shared function, refers to curriculum design, activity facilitation, and instruction
4
(Garrison, 2011), thus facilitating cognitive outcomes and requiring high levels of
5
competency across all GTCU orders in fully digital learning environments.
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Method
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In May and June 2015, data were gathered from students and teachers at KNEU in
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Kyiv, Ukraine, using the GTCU profile tool. Because the GTCU is an extensive
9
instrument, indicators most relevant to successful online learning were selected for
10
analysis based on two, data-reduction processes. Scores on these indicators were
11
analysed in SPSS. Significant differences between students and professors were
12
identified using the Mann-Whitney-U test for comparing non-parametric means.
13
The GTCU profile tool
14
Based on an earlier instrument (Desjardins et al., 2001), the GTCU profile tool is a
15
custom, online application developed by the EILAB, UOIT, and used at institutions of
16
higher education around the world. Version 1, used in this study, offered survey and
17
profile-visualization functionality (Figure 3), and a persistent data repository,
18
facilitating cross-contextual and longitudinal analyses. Version 2, renamed the Digital
19
Competency Profiler, offers a substantially redesigned interface, new profile-
20
visualization algorithms, and profile-comparison functionality at both the group and
21
individual levels (R. vanOostveen, personal communication, September 1, 2017).
22
23
Figure 3. Representation of personalized feedback in the GTCU profile tool.
1
2
The GTCU profile tool collects three sets of data: (a) socio-demographic; (b)
3
digital device access and general purposes of use (i.e., work, personal, teaching or
4
studying); and (c) frequency and confidence performing digital activities, measured on
5
5-point Likert scales. Frequency is measured using never, a few times a year, a few
6
times a month, a few times a week, daily. Frequency of use is an important indicator of
7
competency because transferable procedural knowledge is reinforced through repeated
8
use (i.e., practice leads to acquired ability). Confidence is measured using do not know
9
how to use; not confident, require assistance to use; confident, can solve some
10
problems; fairly confident, can use with no assistance; very confident, can teach others
11
how to use. Confidence of use is an important predictor, not necessarily of acquired
12
ability, but rather of an individual’s willingness to explore novel situations and extend
13
abilities already acquired (Bandura, 1993). Both frequency and confidence are
14
measured in relation to specific devices because device size, degree of mobility and
15
target audience all influence use.
16
In total, the GTCU gathers 337 points of data. To prepare the GTCU profile tool
1
for application in Kyiv, a Ukrainian localization file was created by a co-investigator,
2
and validated by two other Ukrainian academics.
3
Validation
4
The originating GTCU instrument of 20 activity items in French underwent content
5
validation through the participation of 10 Canadian teachers and parents (Desjardins et
6
al., 2001). Subsequently, six experts joined Desjardins, Lacasse, & Belair (2001) in a
7
process of construct validation, which included statistical investigation of correlation
8
matrices. All retained items related well to their conceptualized dimension (Desjardins
9
et al., 2001). Cronbach’s alpha was used to confirm the reliability of the scales in a later
10
study involving 225 Canadian teacher-participants (Desjardins, 2005). The current,
11
updated GTCU profile application of 26 activity items (available in several languages)
12
is based directly on this validated instrument. Furthermore, a recent study compared the
13
reported GTCU competencies of individuals to digitally-recorded performances of
14
authentic online-learning scenarios. This study found generally high positive
15
correlations between reported competency levels and performance quality, suggesting
16
the instrument’s predictive value as an online-learning readiness tool (Blayone,
17
vanOostveen, Mykhailenko, & Barber, 2017a).
18
Participants and setting
19
Students and professors were recruited from the Department of Enterprise Strategy at
20
the Faculty of Economics and Management, Kyiv National Economic University
21
(KNEU), Ukrainethe sponsoring department of the planned digital-learning pilot
22
course. Twenty professors (two thirds of departmental teachers) and 224 students
23
(almost one third of those registered in the faculty) volunteered to complete the GTCU
24
online survey. Student participants were primarily undergraduate students in their
1
twenties. Two thirds were female, which represents a demographic trend in non-
2
technical fields (O. Mykhailenko, personal communication, April 3, 2016). Full
3
participant characteristics (N=244) are provided in Table 1. The logic of collecting data
4
from both students and professors flowed from a collaborative-constructivist ideal that,
5
despite role and identity distinctions, students and teachers share responsibility for
6
developing successful, fully online learning communities.
7
Table 1. Socio-demographic characteristics of participants.
8
Variables
Values
N
%
Gender
Male
81
33
Female
163
67
Age group
19-25
205
84
26-35
23
9
36-49
12
5
50+
4
2
Role
Student
224
92
Professor
20
8
Specialty
Economics
122
50
Other
12
5
Not reported
110
45
Highest degree held
Diploma
80
33
Bachelor
131
54
Masters
16
6
Doctoral
17
7
9
Consent and collection period
10
Having obtained approval from UOIT’s Research Ethics Board and KNEU’s Academic
11
Council, this project proceeded under an international partnership (UOIT & KNEU,
12
2015). In May 2015, a co-investigator presented the project to colleagues at KNEU, and
13
several professors opted both to participate and share the opportunity with students.
14
Data was collected online from the mid-May until mid-June 2015.
15
Findings and discussion
1
GTCU data-reduction strategy
2
In order to identify those GTCU items most pertinent to the research questions, the data
3
were reduced to a manageable and highly relevant subset prior to completing a full,
4
statistical analysis. This was achieved through two data reduction procedures.
5
The first procedure reduced the number of items by reviewing reported device
6
usage, and selecting the device items most relevant to the educational research context.
7
Almost all participants (97%) owned a computer with about half (52%) reporting
8
“learning” as the primary context of use. The same amount (97%) also owned a cell
9
phone or smartphone, but 82% used them primarily for personal purposes. Similarly,
10
very few participants (18%) used a tablet for learning. Therefore, those activity items
11
related to desktop/laptop computers were selected as key indicators. By treating the
12
mobile-device data as supplementary, the primary data set was reduced to 52 items,
13
measuring frequency and confidence for 26 activities.
14
The second procedure required two of the authors with online facilitation
15
experience to review all 26 activity items independently, and list those most relevant to
16
fully online collaborative learning, as modelled by the CoI. These reviewers then met to
17
discuss differences between the two lists, and negotiate a consensus. Within the
18
technical dimension, both reviewers dropped two items (T2 and T5, as shown in Table 2
19
below). They agreed that T2 was a specialized proxy for technical competence, and T5
20
was related to a digital lifestyle foreign to the research context. Items T1, T3 and T4
21
were retained (bolded in Table 2). Within the social dimension, all items were retained
22
(Table 3). Within the informational dimension, I1, I4 and I5 were dropped by both
23
reviewers. After lengthy discussion, I7 was dropped because it related to algorithmic
24
information aggregation (Bucher, 2012; Mager, 2014), rather than learner selection of
25
information based on critical analysis of context and purpose. This left three items
1
(bolded in Table 4). Within the epistemological dimension, E1, E2 and E3 were
2
independently included by both reviewers. Both reviewers marked items E5 and E6 for
3
discussion. These items were included by reflecting on a recent online group activity
4
incorporating analyses of World Values Survey data
5
(http://www.worldvaluessurvey.org/). This resulted in five items being retained,
6
displayed in bold type in Table 5.
7
In the end, 36 items, measuring frequency and confidence for 18 activities using
8
a laptop/desktop computer, were considered most relevant to fully online collaborative
9
learning in this context, and most capable of functioning as general readiness indicators.
10
Interpreting activity frequency and confidence of use values
11
Students and teachers were analysed as separate groups in order to explore significant
12
differences. A group Mean value of three or above (indicating a frequency of activity
13
from “a few times month” to “daily”) was defined as a good level of experience.
14
Supported by adequate levels of confidence, defined as a Mean value of three or above
15
(indicating “some confidence” to “high level of confidence”), participants were
16
considered likely to possess the necessary motivational resources to apply their skills to
17
new situations and increase abilities through active participation.
18
Values are presented in Tables 2-5 below. Bolded items are those selected as
19
most relevant to fully online collaborative learning. Italicized items indicate values that
20
fall at or below the competency threshold defined above. Asterisked items represent
21
significant differences as reported in the text. The findings of the mediating technical
22
order are reported first, followed by those of the higher-level orders.
23
Technical competencies
1
Activity items in the technical dimension address an individual’s operational abilities
2
with the rationale that frequently performing simple, file creation and management tasks
3
implies the acquisition of underlying skills. Of the three indicators selected as most
4
relevant in contexts of online learning (bold items in Table 2), participants create
5
documents (T1) and manage online accounts (T4) at least a few times a month.
6
However, they create multimedia (T3) less frequently, perhaps suggesting less
7
familiarity with more complex software interfaces and diverse file types (e.g., graphic
8
formats and video codecs). Professors create documents significantly more frequently
9
than students (MeanRank: Profs=164, Students=118.8; Sig.=.001), suggesting a slightly
10
greater level of operational experience.
11
Table 2. Technical activities: Frequency and confidence results.
12
Activity Item
Segment
N
M
(Frequency)
SD
M
(Confidence)
SD
T1. Create/edit documents
All
244
3.4
1.0
3.9
1.0
Students
224
3.4*
1.0
3.9
1.0
Professors
20
4.0*
.0
4.0
1.0
T2. Create/edit audio
All
244
1.4
1.3
2.7
1.4
Students
224
1.5
1.3
2.8
1.4
Professors
20
1.0
1.1
2.3
1.4
T3. Create/edit multimedia
All
244
2.2
1.2
3.5
1.2
Students
224
2.1
1.2
3.5
1.2
Professors
20
2.3
1.0
3.3
1.2
T4. Manage online accounts
All
244
3.1
1.3
3.9
1.3
Students
224
3.0
1.3
3.9
1.3
Professors
20
3.6
.8
3.0
1.0
T5. Operate other devices
All
244
.4
1.0
1.5
1.2
Students
224
.5
1.0
1.6
1.2
Professors
20
.4
.9
1.3
.8
13
Social competencies
14
All participants communicate using text messaging (S1) and email (S2). They also use
15
social networks (S5) at least a few times per month (Table 3). Cell phones and
16
smartphones are used about the same amount for text messaging (Mean frequency=3.5;
17
SD=1.2) and social networks (Mean frequency=3.2; SD=1.5), and somewhat less for
1
email (Mean frequency=2.5; SD=1.6). As a cross-cultural point of reference, Canadian
2
students have reported texting almost daily (Mean frequency=4.76), but the frequency
3
of email (Mean frequency=3.92) and social media (Mean frequency=3.8) uses were
4
only marginally higher than that of Ukrainian students (DiGiuseppe et al., 2013).
5
Ukrainian professors use email significantly more than Ukrainian students (MeanRank:
6
Profs=168.1, Students=118.4; Sig.=.001), but students possess more confidence than
7
professors for sending and receiving text messages (MeanRank: Profs=76.6,
8
Students=126.6; Sig.=.001), and participating in social networks (MeanRank:
9
Profs=95.1, Students=124.9; Sig.=.049). These differences mirror broader usage trends
10
(Pew Research Center, 2016).
11
Table 3. Social activities: Frequency and confidence results.
12
Activity Item
Segment
N
M
(Frequency
)
SD
M
(Confidence)
SD
S1. Send/receive text messages
All
244
3.1
1.4
4.0
1.4
Students
224
3.1
1.4
4.0*
1.4
Professors
20
3.0
1.1
3.5*
1.1
S2. Audio chat or conference
All
244
2.3
1.4
3.6
1.4
Students
224
2.3
1.4
3.6
1.4
Professors
20
2.4
.9
3.4
1.1
S3. Video chat or Web conference
All
244
2.0
1.3
3.5
1.4
Students
224
2.0
1.3
3.5
1.4
Professors
20
2.3
1.0
3.3
1.0
S4. Communicate with email
All
244
3.0
1.3
4.0
1.2
Students
224
3.0*
1.3
4.0
1.2
Professors
20
3.7*
1.0
4.1
1.0
S5. Participate in social networks
All
244
3.3
1.3
4.1
1.3
Students
224
3.3
1.3
4.1*
1.3
Professors
20
3.7
.6
3.9*
.9
S6. Share files and collaborate
All
244
2.2
1.3
3.2
1.3
Students
224
2.1
1.3
3.3
1.4
Professors
20
2.5
1.3
3.0
1.1
S7. Publish media and ideas online
All
244
.9
1.2
2.1
1.4
Students
224
.9
1.2
2.1
1.5
Professors
20
.9
1.1
2.0
1.1
13
All participants use computers to communicate via audio (Mean frequency=2.3)
1
and video (Mean frequency=2.0) only a few times per year. The secondary, smartphone
2
data show somewhat less activity levels for video (Mean frequency=1.5; SD=1.5), but
3
usage jumps to a few times per month for audio communication (Mean frequency=3.0;
4
SD=1.5). Therefore, the phone remains the preferred device for voice communication.
5
As a cross-cultural point of comparison, the usage of Canadian students for voice
6
communication with smartphones was highercloser to a few times per week (Mean
7
frequency=3.7). Frequency of video communication with smartphones among
8
Canadians was similar to that reported by Ukrainian students (Mean frequency=2.2)
9
(DiGiuseppe et al., 2013). This may be related to bandwidth costs, and the fact that it is
10
easier to communicate with audio alone while mobile.
11
Two items in the social dimension highly relevant to online learning involve
12
sharing files, collaborating, and publishing ideas/content online. The Ukrainian
13
participants collaborate on files only a few times per year (Mean frequency=2.2), with
14
no significant difference in frequency between students and professors. However, they
15
report being confident in this area. They almost never publish content and ideas online
16
(Mean frequency=.9), with the same results for both students and professors. Moreover,
17
both groups report low levels of confidence in this area of activity.
18
Informational competencies
19
The informational indicators most relevant to collaborative-constructivist fully online
20
learning relate to searching, finding, evaluating, selecting and appropriating content in a
21
variety of formats (Table 4). Finding and using online news/journal articles (I2) and
22
digital books (I3) are foundational research skills. As a group, participants access
23
articles less than a few times a month (Mean frequency=2.7). However, professors
24
report significantly more activity in this area than students (MeanRank: Profs=157.5,
25
Students=119.4; Sig.=.02), suggesting that the research practices of Ukrainian
1
university teachers has shifted to the digital space. Confidence for this activity is high
2
for all participants. Both students and professors access digital books only a few times
3
per year (Mean frequency=1.8), which suggests that digital books are seldom used for
4
academic purposes. This may relate to the scant financial resources available in the
5
Ukraine for digitizing scholarly books. Finally, participants view online videos (like
6
those on YouTube) a few times a month (Mean frequency=2.7). For all of these
7
activities confidence is high, suggesting a willingness to develop requisite skills as
8
opportunities arise.
9
Table 4. Informational activities: Frequency and confidence results.
10
Activity Item
Segment
N
M
(Frequency)
SD
M
(Confidence)
SD
I1. Use maps or GPS
All
244
1.6
1.3
3.0
1.6
Students
224
1.6
1.3
3.1
1.6
Professors
20
1.5
1.2
2.7
1.5
I2. Find and use articles or news
All
244
2.7
1.3
3.8
1.3
Students
224
2.6*
1.3
3.8
1.3
Professors
20
3.4*
.6
4.1
1.0
I3. Find and view short videos
All
244
2.7
1.3
3.8
1.3
Students
224
2.7
1.4
3.8
1.4
Professors
20
3.0
.7
4.0
.9
I4. Watch or download movies
All
244
2.4
1.2
3.9
1.3
Students
224
2.4
1.2
3.9
1.3
Professors
20
2.1
.9
3.7
1.1
I5. Listen or download music
All
244
2.2
1.4
3.6
1.5
Students
224
2.3*
1.4
3.7*
1.5
Professors
20
1.4*
1.2
2.9*
1.5
I6. Read or download digital books
All
244
1.8
1.3
3.3
1.5
Students
224
1.8
1.3
3.3
1.5
Professors
20
1.9
1.0
3.4
1.3
I7. Automate information sources
All
244
.7
1.2
1.8
1.3
Students
224
.8
1.2
1.8
1.3
Professors
20
.5
.9
1.6
1.1
11
Epistemological competencies
12
Using the information-processing power of computers to solve problems, map concepts,
13
mine data, perform statistical analyses, or engage in collaborative decision-making,
14
dramatically increases the functioning of most researchers. Five of the seven GTCU
15
indicators in this dimension were considered relevant to effective fully online learning.
1
These indicators relate to managing one’s schedule (E1), organizing and presenting
2
complex information (E2 and E3), and producing knowledge from numerical data (E5
3
and E6), as shown in Table 5.
4
Table 5. Epistemological activities: Frequency and confidence results.
5
Activity Item
Segment
N
M
(Frequency)
SD
M
(Confidence)
SD
E1. Use calendar or organizer
All
244
.8
1.3
1.9
1.4
Students
224
.8
1.3
2.0
1.4
Professors
20
1.0
1.3
1.8
1.0
E2. Use concept map or flow chart
All
244
.9
1.2
2.1
1.3
Students
224
.9
1.2
2.1
1.3
Professors
20
1.0
1.2
2.0
1.1
E3. Create figures and diagrams
All
244
1.7
1.2
3.0
1.3
Students
224
1.7*
1.2
3.0
1.3
Professors
20
2.3*
.7
3.5
1.0
E4. Sort large amounts of data
All
244
1.7
1.3
3.0
1.4
Students
224
1.7
1.3
3.0
1.4
Professors
20
1.6
1.4
2.7
1.3
E5. Generate graphs from numbers
All
244
1.7
1.2
3.1
1.3
Students
224
1.7
1.2
3.0
1.3
Professors
20
2.0
.9
3.2
1.1
E6. Automate complex calculations
All
244
1.7
1.4
2.8
1.4
Students
224
1.7
1.4
2.8
1.4
Professors
20
1.5
1.1
2.6
1.4
E7. Program or automate procedures
All
244
.6
1.1
1.6
1.1
Students
224
.6
1.1
1.7*
1.1
Professors
20
.3
1.0
1.1*
.2
6
Participants reported never using a calendar or organizer (Mean frequency=.8),
7
and possessing low confidence related to this activity. Concept mapping is often vital
8
for effective knowledge construction, academic writing and problem solving. Yet, this
9
activity was performed seldom (Mean frequency=.9), and participants reported low
10
confidence. Professors reported creating figures and diagrams a few times a year (Mean
11
frequency=2.3), which is significantly more often than students (MeanRank:
12
Profs=159.9, Students=119.2; Sig.=.01). Both groups, however, reported good levels of
13
confidence. Although neither professors nor students generate graphs from numbers or
14
automate complex calculations more than a few times per year (Mean frequency=1.7),
1
both groups report a good level of confidence.
2
As a footnote in this dimension, although neither programing nor scripting was
3
considered highly relevant to successful, collaborative online learning, and therefore
4
excluded as a key activity, students showed significantly more confidence in this area
5
than professors (MeanRank: Profs=91.5, Students=125.3; Sig.=.011).
6
Assessing readiness for fully online collaborative learning
7
Having reviewed the reported competencies of Ukrainian students and professors in
8
each GTCU dimension, we turn to interpreting the findings in relation to readiness. As
9
noted, the CoI identifies three major dimensions of successful functioning in fully
10
online learning communitiessocial presence, cognitive presence and teaching
11
presence. This readiness assessment is organized accordingly.
12
Readiness for social presence
13
Social presence relates to interpersonal, open and cohesive communication, aimed at
14
building trusting relationships and a cohesive learning community (Garrison, 2011).
15
Importantly, deep and meaningful, learning outcomes in a community of inquiry are
16
facilitated through purposeful and authentic, social interaction, leading to mutual
17
respect, trust, and a sense of safety (Armellini & De Stefani, 2015). The prerequisite
18
competencies supporting the development and maintenance of social presence relate
19
primarily to the effective use of digital communication affordances.
20
Encouragingly, good levels of activity and confidence were reported for text-
21
based communication via messaging and email, and social-network participation. The
22
latter is becoming increasingly significant as publically-accessible affordances, such as
23
Facebook Groups, emerge as potential learning spaces (Dickie & Meier, 2015; Ellefsen,
24
2015). However, the low levels of activity reported for communication via audio and
1
video may pose some challenges because these competencies support successful
2
participation in web-conferencing environments, which offer rich forms of interaction
3
and were selected for use in the pilot course.
4
Readiness for cognitive presence
5
Cognitive presence addresses processes of critical inquiry (Garrison, 2011). Readiness
6
for building cognitive presence implies rich capabilities to engage effectively in
7
processes of digitally-mediated collaboration, information selection, and data analysis.
8
Readiness for collaboration
9
In fully online learning environments, successful collaboration requires competencies
10
related to file sharing, collaborative editing, and self-expression. Unfortunately, both
11
teachers and students report low levels of competency using major collaboration tools
12
such as Dropbox, Google Drive and Google Docs. Additionally, they report little
13
experience and confidence for publishing content and expressing ideas online.
14
Reflecting on the Ukrainian context, low self-expression may be related to a collectivist
15
orientation in which maintaining face within a group tends to be a significant concern
16
(Hofstede, 2001).
17
In order to nurture these competencies, one opportunity is to build upon
18
participants social-media experience. Although Facebook is still less popular in
19
Ukraine than the Russian VKontakte ("Facebook," 2016), it is used by a sizable number
20
of Ukrainians. A Facebook Group, for example, can be closed to a particular
21
community, and leveraged as a tool for sharing experience, exchanging ideas, and
22
promoting group interaction (Dickie & Meier, 2015; Ellefsen, 2015; Wang, Woo, Quek,
23
Yang, & Liu, 2012).
24
Readiness for interacting with digital information
1
A strong capacity to interact with information, including educational videos, peer-
2
reviewed articles and other digital publications is a core requirement of social-
3
constructivist online learning. Ideally, all members of an online-learning community
4
would have rich capacities for accessing, evaluating and appropriating diverse source
5
materials. Activity levels for the three selected indicators in the informational
6
dimension, however, appear low. Only professors achieved a moderate score in relation
7
to accessing online articles, reporting significantly more experience than students. With
8
high confidence levels also reported, responsibility may fall on facilitators to encourage
9
effective processes of online research and information access.
10
Readiness for analysing, mapping and processing data
11
Previous results from GTCU surveys in Canada suggested participants might report less
12
activity and confidence in the epistemological dimension (Desjardins et al., 2010). In
13
other parts of the world, significant gaps have also been found between computational
14
skills and other digital abilities of students (Jun, Han, Kim, & Lee, 2014). Of the five
15
activities judged relevant for online learning, the Ukrainian professors reported modest
16
familiarity with creating diagrams and generating graphs, suggesting some base of
17
experience from which to build.
18
A major challenge is that the development and application of epistemological
19
competencies have relatively high cognitive demands. Effective use of concept maps,
20
flow charts, visualization tools, spreadsheets and data-analysis applications requires
21
thoughtfulness, focus and a systematic application of logical proceduresin addition to
22
the requisite operational skills. The payoff for diligence in this area is increased access
23
to a computer’s most powerful capabilityto function as a cognitive partner for
24
tackling complex problems (Desjardins, 2005). This function has become increasingly
1
relevant as complex datasets are made available online (e.g., The World Values Survey
2
at http://www.worldvaluessurvey.org/), and “big data” are generated through endless
3
online interactions.
4
Readiness for teaching presence
5
Teaching presence refers to curriculum design, activity facilitation, and direct
6
instruction (Garrison, 2011). Although conceptualized as a shared function, the
7
professional facilitator typically bears responsibility for TP in new, fully online learning
8
communities. The digital competencies required for success are spread across the
9
GTCU spectrum of social, informational, epistemological and technical.
10
Reported digital competencies of the professors indicate readiness is generally
11
low. However, two findings show promise. Professors reported a higher level of
12
competencies than students on items related to (a) finding academic information online,
13
and (b) completing technical procedures.
14
Overall general readiness
15
In the end, overall general readiness for successful functioning in a fully online
16
collaborative learning community appears somewhat low for Ukrainian students and
17
teachers. Consequently, it is recommended that digital-competency development
18
become an educational priority prior to launching the pilot course.
19
Contributions and limitations
20
We believe that this conceptual and empirical analysis contributes to research in three
21
ways. First, by extending the GTCU aggregate data set internationally, it creates new
22
opportunities for cross-cultural comparisons, enabling deeper analyses of relationships
23
between digital practices and socio-cultural contexts.
1
Second, this study bridges a gap between the domains of digital-competency
2
and digital-learning research, and thus, opens the door for additional synergistic studies.
3
Indeed, the Fully Online Learning Community (FOLC) model (vanOostveen et al.,
4
2016) is a recently published model that bridges these domains, diverging from the CoI
5
by treating digital competencies as endogenous variables in relation to fully online
6
learning (Blayone, vanOostveen, Barber, et al., 2017).
7
Finally, this study offers an effective methodology for assessing digital-learning
8
readiness in a post-Soviet nation, using an established online application. This may
9
inspire other researchers to leverage the GTCU profile tool, or other existing Western
10
research assets, to support educational transformation in contexts of transition.
11
With respect to limitations, first, self-reported digital abilities are sometimes
12
difficult to interpret in relation to performance. The literature reports some
13
misalignments between perceived abilities and observed performance using some other
14
survey instruments (Bradlow, Hoch, & Hutchinson, 2002; Hargittai & Shafer, 2006;
15
Litt, 2013). For example, Hargittai and Schaffer (2006) found misalignments along
16
gender lines. Although both men and women varied in their performance-based
17
abilities, women consistently perceived their abilities to be lower than men. Other
18
challenges with self-report instruments relate to conceptual ambiguity, incompleteness
19
and over-simplification (van Deursen, Helsper, & Eynon, 2015). Additionally, one
20
would reasonably expect cultural patterns of self-perception, levels of confidence and
21
reporting patterns to differ across national contexts (Hofstede, 2001; Minkov, 2012).
22
Second, the GTCU profile tool is a multi-dimensional instrument for collecting a
23
large set of technology-use data. Its use was defended on three grounds above.
24
However, to use it effectively as a readiness tool, data-reduction procedures were
25
required. Future research might develop a short version of the instrument aimed at the
1
specific context of use to increase data-collection efficiency and obviate the need for
2
data reduction.
3
Finally, the GTCU profile tool was developed within a Canadian, socio-
4
economic and technological context, and thus, makes references to 4G, TLE, wearable
5
computers and digital appliances. A Ukrainian economics professor at KNEU rightly
6
inquired if Canadian researchers were aware how typical incomes in Ukraine relate to
7
prices of devices “they ask how often we use” (N. Yevdokymova, personal
8
communication, June 15, 2015). In response, the researchers seek to balance sensitivity
9
to specific contexts with the ability to compare data from multiple contexts. Such
10
comparative data is vital for describing and addressing, differences and disparities,
11
across national, regional and organizational contexts of application.
12
A postscript
13
In early 2016, the researchers conducted the planned, fully online collaborative pilot
14
course over 10 weeks. Data captured from this course has been discussed at a Canadian
15
academic conference (Mykhailenko, Blayone, & VanOostveen, 2016), and reported in
16
the Ukrainian media (Mykhailenko & Blayone, 2016; Mykhailenko, Hrebeshkova, &
17
Blayone, 2016). Further analysis of learning interactions in relation to cultural
18
dimensions and transformative outcomes is underway. As predicted by this study, using
19
a web-conferencing environment and collaborative tools effectively, particularly in
20
small groups, presented significant challenges for most students and participating
21
professors over the duration of the course. Moreover, role perceptions, occasional
22
student resistance to highly participatory activities, and institutional resistance to online
23
learning, also emerged as important facets of successful functioning. Nevertheless,
24
tremendously positive feedback from students regarding the digital tools, collaborative
25
activities, and support for open discussion and self-expression, was an encouraging
1
outcome.
2
Acknowledgements
3
The researchers thank students and professors at the Department of Enterprise Strategy,
4
Kyiv National Economic University for their enthusiastic support for this project. We
5
also thank researchers around the world who provided feedback on the instrument.
6
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Author Bios
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1. Todd J.B. Blayone, EILAB, UOIT and Collaboritsi.com
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Corresponding Author: todd.blayone@collaboritsi.com
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Todd Blayone is a professional researcher, educator and technologist exploring digital readiness
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and post-industrial learning in post-Soviet contexts for individual development and social
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progress. Living in Kyiv, Ukraine and Toronto, Canada, he is co-founder of Collaboritsi.com, a
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collaborative-learning and digital-readiness consultancy, and an Associate Researcher at the
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EILAB, UOIT, Canada.
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2. Olena Mykhailenko, EILAB, UOIT and Collaboritsi.com
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Dr. Olena Mykhailenko is a trilingual educator, economist, former government advisor and
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consultant living in Kyiv, Ukraine and Toronto, Canada. As co-founder of Collaboritsi.com and
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an Associate Researcher at the EILAB, UOIT, Canada, she gives workshops for educators and
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business professionals on collaborative learning and culture. Her diverse publications span
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development economics, cross-cultural analysis and post-industrial, educational transformation.
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3. Roland vanOostveen, University of Ontario Institute of Technology, Canada
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Dr. Roland van Oostveen holds a PhD in Curriculum, Teaching and Learning from the Ontario
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Institute for Studies in Education (OISE) at the University of Toronto. He is director of the
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Educational Informatics Laboratory (EILAB) and the Educational Studies and Digital
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Technology programs at the Faculty of Education, UOIT. His research explores digital
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competency and technology use in learning, and the development of fully online learning
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environments.
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4. Oleksiy Grebeshkov, Kyiv National Economic University, Ukraine
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Dr. Oleksiy Grebeshkov is Associate Professor at the Faculty of Economics and Management,
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and Head of Innovation at the Educational Technologies Laboratory (IETLab) at Kiev National
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Economic University, named after Vadim Hetman. A cloud solutions architect, he is an online-
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course author with over 4000 students from around the world. He is co-organizer of scientific
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conferences on digital educational innovation.
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5. Olena Hrebeshkova, Kyiv National Economic University, Ukraine
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Dr. Olena Hrebeshkova is an Associate Professor at the Faculty of Economics and Management,
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Kiev National Economic University, named after Vadim Hetman. She is author of more than
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100 scientific and educational works, coordinator of the project for the implementation of
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Microsoft Office 365 at the Faculty of Economics and Management, and a Udemy Premium
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Instructor. She is founder and co-organizer of international scientific conferences on digital
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educational innovation.
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6. Oleksandr Vostryakov, Kyiv National Economic University, Ukraine
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Dr. Oleksandr Vostryakov is Dean of the Economics and Management Department of the Kyiv
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National Economic University, named after Vadim Hetman. He is a member of the Scientific
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and Methodological Council of the Ministry of Education and Science of Ukraine.
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... Inspired by educational reform efforts in post-revolutionary Ukraine (Blayone, Mykhailenko, vanOostveen, Grebeshkov, et al., 2018;Mykhailenko et al., 2016), I published a preliminary theorisation of deeply democratized digital learning as a 'loose boundary concept' (Blayone et al., 2017). Löwy (1990) notes that such concepts often emerge through cross-disciplinary inquiry and facilitate innovations in research and praxis because they are not narrowly defined. ...
... In conclusion, the primary purposes of this post-doctoral research program are to (a) theorise 'deeply democratised' digital learning; (b) examine the complex socio-political role of Facebook as a democratising educational technology; (c) systematically investigate a substantial body of learning transactions to investigate the educational value of learner discourse in Facebook, and (d) provide practitioners with an original vision for democratised, socially 'close' and globalised online learning. This program will contribute original perspectives and a wealth of new empirical data extending a six-year international program of digital-learning research (Blayone, 2017;Blayone, 2019;Blayone, Mykhailenko, Kokhan, et al., 2018;Blayone, Mykhailenko, vanOostveen, Grebeshkov, et al., 2018;Blayone & vanOostveen, 2020;Blayone et al., 2017;Mykhailenko et al., 2020). Moreover, it will aim to help educators envision original, emancipatory and engaging digitalised educational experiences at a time when such help is vitally needed. ...
Research Proposal
The primary purposes of this post-doctoral research program are to (a) theorise ‘deeply democratised’ digital learning; (b) examine the complex socio-political role of Facebook as a democratising educational technology; (c) systematically investigate a substantial body of learning transactions to investigate the educational value of learner discourse in Facebook, and (d) provide practitioners with an original vision for democratised, socially ‘close’ and globalised online learning. This program will contribute original perspectives and a wealth of new empirical data extending a six-year international program of digital-learning research . Moreover, it will aim to help educators envision original, emancipatory and engaging digitalised educational experiences at a time when such help is vitally needed.
... With few exceptions (van Deursen and Mossberger 2018;Blayone et al. 2020), however, specialised digital-competence researchers have not explored the ability requirements of digitalised industrial work. Instead, they have investigated mainstream digital competences of students and citizens from operator-tool perspectives misaligned with intelligent systems and new forms of human-machine partnering (van Deursen, Helsper, and Eynon 2016;Ferrari 2013;Eshet 2012;Blayone et al. 2018c). ...
... This model is a necessary first step by the Educational Informatics Lab (EILAB), Ontario Tech University, Canada toward implementing an online application for generating and aggregating Industry 4.0 readiness profiles of individuals around the globe, supporting self-diagnosis and ongoing research to inform higher education, employee (re)training, human resource management and policymaking. Having already implemented a global readiness application for measuring the digital competences of students, teachers and knowledge professionals (Blayone 2018;Blayone et al. 2018aBlayone et al. , 2018bBlayone et al. , 2018c, this project pivots to the development needs of industrial workers and the requirements of digitalised manufacturing. ...
Article
Full-text available
Within Industry 4.0 research, the spotlight shines on technological and organisational challenges. This study shifts the focus to worker readiness, beginning with an analysis of twenty-three models to establish the state of research. Findings demonstrate that existing models are mostly early-stage proposals addressing competences featured in mainstream 21st-century and digital-competence frameworks. Worker-level factors explicitly aligned with emerging cyber-physical systems receive little attention. To construct a worker-readiness model calibrated to the needs of Industry 4.0, the authors devised a research procedure based on a two-phase integrative review of 135 publications. Firstly, they deployed an activity-system apparatus to produce a structured description of the target environment. Secondly, major worker competence groupings, aligned with this target, were extracted, tagged and reduced to five dimensions. The resulting model consolidates prior research and introduces two original competence groupings addressing human-machine partnering and decision-making in Industry 4.0. This study is a foundational step by the Educational Informatics Lab, Ontario Tech University, Canada, toward deploying a global online profile tool for generating, analysing and aggregating worker readiness profiles. This cross-disciplinary project will help researchers, educators, corporate trainers, human resource managers, policymakers, and systems designers more effectively diagnose the readiness of workers for Industry 4.0.
... This model is a necessary first step by the Educational Informatics Lab (EILAB), Ontario Tech University, Canada toward implementing an online application for generating and aggregating Industry 4.0 readiness profiles of individuals around the globe, supporting self-diagnosis and ongoing research to inform higher education, employee (re)training, human resource management and policymaking. Having already implemented a global readiness application for measuring the digital competences of students, teachers and knowledge professionals Blayone et al. 2018aBlayone et al. , 2018bBlayone et al. , 2018c, this project pivots to the development needs of industrial workers and the requirements of digitalised manufacturing. ...
... With few exceptions (van Deursen and Mossberger 2018;, however, specialised digital-competence researchers have not explored the ability requirements of digitalised industrial work. Instead, they have investigated mainstream digital competences of students and citizens from operator-tool perspectives misaligned with intelligent systems and new forms of human-machine partnering(van Deursen, Helsper, and Eynon 2016;Blayone et al. 2018c). ...
Thesis
This thesis showcases a substantially new approach to exploring the preparedness of humans for the psychologically challenging and technologically disruptive requirements of digitalised learning and work. It consolidates a five-year research program, conducted in Canada and Eastern Europe, catalysed by an interventionist case study exploring democratised digital learning as a pathway to post-revolutionary educational reform in Ukraine. Findings from this study showed that despite initial enthusiasm, many participants struggled to engage successfully in fully online activities. Thus, the author’s attention shifted to investigating the preparedness of students and professionals for successful functioning within increasingly digitalised environments. Seven strategically interwoven investigations were conducted to explore this concern conceptually and empirically, resulting in an equal number of published works. As an original contribution to knowledge, this thesis frames, synthesises, defends and (re)presents these works as exemplary expressions of a unified research program. More specifically, Section 1 introduces the catalysing circumstances, epistemological perspectives informing the works, and the overall structure of this thesis. Section 2 organises the seven-constituent works, provides an unambiguous statement of authorship and presents newly formulated abstracts and backstories. Section 3 critically reviews the research methods and apparatuses deployed. Section 4 articulates and defends significant contributions to knowledge and measured impacts within the global scientific community. Section 5 addresses limitations and projected next steps be-fore offering a consolidating statement. Part Two, inclusive of sections 6-12, present the constituent works in their original published formats (with formal publisher approvals). Finally, Part Three offers a retrospective theoretical afterword that positions the selected works within their ‘thought worlds’ and demonstrates deep interaction with crucial ideas and thinkers across multiple domains.
... Investigation of the levels of Polish university teachers' digital competences based on percentiles shows that near 50% of the respondents are at the medium level in the examined range for both factors (PK and TK), near 30% demonstrated low level and less than 20% are in the higher level of the digital competences. These results might be opposite to the statement about the lower rating of the technological knowledge of the university teachers (Blayone et al., 2018;Castéra et al., 2020;Cubeles et al., 2018). ...
Conference Paper
Full-text available
The purpose of this study is to examine levels of academic teachers' digital competences regarding the demographics and professional backgrounds in Polish educational context. The 2-factor (Pedagogical and Technological knowledge) TPACK model is using in the study. A survey was administered to 103 academic teachers from Polish universities. Descriptive analysis indicated a significant negative correlation between some demographic variables (age, years of teaching, titles and degrees) and domains from both factors. However, some positive associations with certain variables from the professional background (using for teaching online learning environments, digital quizzes or polls, interactive apps or games; providing on-line courses; creating videos for teaching) were also noticed. Implications for professional development and suggestions regarding teachers' digital competences and TPACK have been discussed.
... Much research has been conducted to conceptualise and measure the technologyrelated capacities of students around the world ( van Deursen and van Diepen 2013;Calvani et al. 2008;Eshet-Alkalai and Amichai-Hamburger 2004;Blayone et al. 2018b). Digital skills and competencies have garnered tremendous attention, but numerous attitudinal complexes have also been investigated (Litt 2013). ...
Article
Full-text available
Global processes of digitalisation are transforming learning and work. University students in all nations are under pressure to develop positive and productive technology-related skills and dispositions. This study investigates the attitudes of 1,006 Latvian and Ukrainian university students toward information technology. Survey responses from the Attitudes toward Information Technology scale were collected, validated, analysed and interpreted. By generating group-response profiles and conducting multivariate analyses of variance, the attitudinal orientations of participants were compared, and significant differences between gender and nation subgroups identified. From a gender perspective, one noteworthy finding is that males in both countries expressed a significantly higher interest in learning about IT than females. From a national perspective, Ukrainians reported significantly higher optimism about IT in the workplace than Latvians. This study produces several novel findings addressing the attitudes of Eastern European university students toward information technology and their readiness for digitalised learning and work.
... Because of these benefits, educational researchers have examined what and how individual factors are related to students' collaborative learning experiences, including learners' emotion and affect (Reis et al. 2018), self-efficacy (Wilson and Narayan 2016), selfregulation behaviors (Kwon et al. 2014), metacognitive skills (Akyol and Garrison 2011), and digital competence (Blayone et al. 2017). ...
Article
Full-text available
While collaboration is an important and key attribute for medical students in order to prepare them to perform well in health care teams, how to effectively develop and assess such skills is challenging. The current widespread practice of using Likert-scale questionnaire only to measure the quantity of collaboration at course and/or program level appears to be insufficient to provide an evidence-base for what counts desirable collaborative learning experience. Drawing on research into student approaches to learning and social network analysis, this study investigates differences in collaborative learning configurations amongst 217 Australian medical students. Based on students' learning orientations (i.e., 'understanding' and 'reproducing') and their choice of collaborations (i.e., whether to collaborate or not, with whom to collaborate, and mode of collaboration), the analyses found five configurations of collaborations differing in a number of features. The most desirable collaborative experience was a configuration of collaborations formed by students with an 'understanding' orientation. This configuration revealed a strong tendency towards intensive pair work with measurable differences in how easy and effectively they collaborated. The results of the study not only have practical implications for teaching and curriculum design for collaborative learning, but also have significant implications for assessing students' collaborative learning experiences.
... Ubiquitous learning is a new educational paradigm in which the student is positioned to learn from a more global perspective and where physical space is not a determining variable for learning [2]. Non-formal environments and places -the coffee shop, the street, the means of transportation, the home, the social network, the playground, the media and popular culture, the workplace, etc.become new learning scenarios [3]. This type of society is called the "Society of Ubiquity" [4]. ...
Article
Full-text available
Mobile digital devices are at the same time a tool for social interaction, an individual learning resource and can be a valuable contribution in the context of higher education to develop and promote new teaching and learning models. Recent studies show that both the more traditional pedagogical models of face-to-face teaching and distance teaching mediated by Virtual Learning Environments (VLE) can be enhanced by the use of these devices on and off campus. Likewise, the current context of Higher Education urges university institutions to promote a series of generic and specific competencies, where the use of these devices in a personal, academic and professional way acquires an outstanding value in the European Higher Education Area (EHEA), and represents an enrichment of university educational practice. This paper presents a study of the didactic and social use made by Hispanic American university students in 10 universities in several areas in order to establish common and divergent patterns of use so that useful conclusions can be extrapolated to improve the educational context of Higher Education in the Hispanic world.
... Este dato indica que solo en esta competencia se encontraron diferencias individuales. Es decir, que el profesorado masculino tiene una mayor autopercepción en esta Competencia Digital, dato que coincide con los estudios de (Blayone et al. 2018) y (Cabero, Llorente, Puentes, Marín y Cruz 2011). En el resto de casos no se encontraron diferencias significativas, resultados que se corroboran con otros trabajos (Aguaded, Tirado y Hernando 2011;Corredor 2014;Echeverri 2018;Pérez 2016;Revelo 2017). ...
Research Proposal
The tangible benefits and socio-material ‘divides,’ rewarding and thwarting people’s use of digital technology, are well established. Numerous digital skills frameworks have been developed to conceptualise and measure those abilities considered essential for successful citizenship, learning and work. These frameworks are based on mainstream technology-use scenarios in which humans are positioned as intelligent subjects and their desktops, laptops and mobile devices (running standard applications), as responsive tools. However, new forms of digitalised activity are being envisioned and implemented by global innovation programs like Industry 4.0. Hence, researchers must consider the feasibility of current skills models and instruments for addressing human readiness for digitalised learning and work. This task is necessary because, within contexts of digitalised activity, (a) machines increasingly exhibit sensory, cognitive and physical capabilities formerly available only to humans, leading to partnership, augmentation and shared-agency configurations; (b) the interfaces enabling human-machine interaction are incorporating cognitive capacities such as ‘natural language’ and even more direct thought processing; and (c) intelligent computational functionality is extending to objects of all kinds. As a researcher at Ontario Tech University’s, Educational Informatics Lab (EILAB), I was tasked with exploring conceptual and operational gaps between (a) current digital-competence models and instruments, and (b) the dynamics of human-computer interaction described in the Industry 4.0 re-search. A scoping review of the literature suggested that significant gaps existed between mainstream digital-skills models and the affordances and professional functions presented to humans by digitalised systems. Two large competency complexes relating to human-machine partnering and hybrid problem-solving were almost completely underserved by existing frameworks. Based on these findings, this research synthesis extends the cross-disciplinary task of developing a conceptual model and instrument, well-grounded in current technology research, addressing those individual readiness requirements aligned with digitalised learning and work. The formal research questions, to be addressed through a structured research synthesis, are: • What are the specific technological affordances in digitalised environments that constitute a paradigm shift in human-machine interaction? • What are the constituent abilities of those individual readiness complexes, which are vital to digitalised work and overlooked by mainstream digital and 21st-century skills models? • What is the state of development and validation of survey measures for these constituent abilities, and what subscales should be included in a robust self-report tool? • What published case studies and observational research is available to triangulate self-report measures? • Can the resulting conceptual model and proposed instrument function as an extension to digital and 21st-century skills frameworks, or should it be positioned, like Industry 4.0 itself, as a next-stage readiness model? With this study completed and a well-researched readiness model in place, funding will be sought to develop an online self-diagnostic application for measuring readiness for digitalised activity. Based on the EILAB’s GREx engine, this application would be used to (a) facilitate self-diagnosis, (b) develop a global database of data addressing human readiness for digitalised activity, (c) inform policy in higher and vocational learning institutions, and (d) shape the design of learning experiences that prepare students and workers for the new world of digitalised learning and work.
Article
Full-text available
Readiness for digital learning is an international research domain exploring the preparedness of people and contexts for successful technology-rich education. This study, focused on higher education, reviews the readiness literature and positions digital competencies as factors within it. A key limitation identified is that despite the position of digital knowledge, skills and attitudes as highly significant factors, readiness instruments are limited by inconsistent conceptualization and unidimensional operationalization of digital competencies. In response, the General Technology Competency and Use (GTCU) framework and accompanying online Digital Competency Profiler (DCP) application, developed in Canada and used over a decade in several national contexts, are reviewed as an alternative readiness-assessment apparatus. Findings from a pilot observational study comparing: (a) self-reported digital competencies (using DCP indicators), and (b) the quality of authentic digital-learning performances, are highlighted in support. Through continued application development and international collaboration, the researchers are pursuing contextually-sensitive and non-exclusionary readiness assessment tools that help individuals and groups better prepare for successful digital learning. 426
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A video presentation available online: https://www.youtube.com/watch?v=6_xOb3LpLfY#t=3h4m56s.