86 M. Eisenberg and Z. Jacobson-Weaver
5.2.1 L eisure-Oriented Technologies for Adults
In one sense, the appropriation of professional technologies by youngsters might be
interpreted as simply a by-product of a larger phenomenon by which such technolo-
gies are “democratized” more generally (cf. Mumford [3], p. 278). After all, not
only do modern-day children use computers, but so do adults of all ages, and for an
endless variety of (serious and “nonserious”) purposes. Thus, we might argue that it
is primarily adults, not children, that have led the way in the de-professionalization
of computing technology. In the more recent case of 3D printing, children are being
introduced to the technology in tandem with its adoption by the “maker movement”
of hobbyists and amateur builders. In such cases, then, we might argue that the real
phenomenon of interest is the (adult) movement of professional activities toward
independent, leisure-oriented uses.
Some of these themes are suggested by Rachel Manes’ [4] Hedonizing
Technologies, an indispensable book-length treatment of the transition of certain
activities from industrial to leisure contexts. Manes’ focus is almost exclusively on
adults, with a focus on the (primarily female) audience for textile crafts such as knit-
ting and embroidery. Manes coins the term “hedonizing” to denote the process by
which formerly work-related activities become pursued primarily for fun, as hobbies.
Her discussion of the process is especially perceptive and suggests some avenues of
continuity with children’s technology: “Unlike industrial workers, hobby artisans
have complete control over what tools and materials they use, and since efficiency of
production and marketability of product are rarely issues, they buy or make the tools
and materials they most enjoy using. Leisure theory and sociological sources
illuminate the kinds of pleasures artisans experience in their craft, which include
control and mastery, the beauty or the individuality of the product, the sensual enjoy-
ment of the task…the escapist quality of immersion in voluntarily chosen work, and
socialization opportunities with other crafts enthusiasts.” (Manes, [4], pp. 13–14).
Clearly, there are parallels here with the transitions common to children’s
technology, but there are also interesting points of difference. For one thing, as
Manes notes, many of the hedonized technologies whose history she traces are in
fact not state-of-the-art industrial technologies, but rather old-fashioned t echnologies
made obsolete by the process of high-volume industrialization. Thus, leisure-oriented
textile work represented a pleasurable return to an older, pre-factory style of a ctivity,
and one can identify (as Manes notes [p. 122]) an element of nostalgia in at least
some instances of democratized technology.
With children, the emphasis is different. Youngsters, after all, are not particularly
given to nostalgia, and often, the cultural emphasis in adapting professional technolo-
gies for kids is expressly to celebrate the very novelty of the technology, and quite
possibly the presumed job skills associated with its mastery. (No parent would object
to his child’s playing with 3D printers on the grounds of the devices’ uselessness or
obsolescence.) Moreover, even when focusing on (say) computers, the rethinking of
the professional technology has a somewhat different flavor when the audience is
composed of children. A particularly vexing element is the (sometimes tense) rela-
5 The Work of Children: Seeking Patterns in the Design of Educational Technology 87
tionship between true leisure activity for children–what one might call the “hedonized”
version of children’s computing–and school or classroom usage. A “child’s com-
puter” might look like a somewhat different instrument when associated with home
or hobbyist usage and with classroom usage. Adult leisure technologies rarely exhibit
this kind of dual vision in the lives of their users.
5.2.2 Consumer Technologies and Creative Technologies
A second theme worth touching upon in this discussion is the distinction between
certain types of consumer technologies for children (notably television, electronic
toys, and video games) and technologies that, adopted from professional settings,
thereby expand the presumed capacities and expressive range of children. To inter-
pret the distinction in a somewhat broad-brush way, the advent of children’s radio
(cf. Cross [5], p. 107) or television shows, while of historical interest, is not an
especially apt example of the pattern we are focusing on in this chapter. In these
consumer-oriented cases, some advanced technology is adopted primarily for chil-
dren’s play, entertainment, or education, but there is no reason to think that children
are modeling or reinventing professional or adult creative activities for themselves.
This difference, between “consumer technologies” and “creative technologies”
for children, is crucial to our discussion, though in practice it is not always entirely
clear-cut. Indeed, the distinction is particularly blurred on those occasions when
adults note with admiration the facility of children with novel technology. Children
might be said to “naturally gravitate” toward mobile phone and devices, for e xample,
but rarely is that phrase meant to suggest that children do anything other than use
the phones as consumer devices. Children may be familiar with texting or video
games in ways that their parents are not, but that familiarity is rooted in a consum-
er’s understanding–this phone has a bigger screen than that one, this game player is
faster and has higher resolution than that one, and so forth. The children know
things about the technologies that their parents don’t, but the things they know
hardly deserve the name of “deep understanding” or creative mastery. Adult praise
in this context often seems to make little distinction between children-as-savvy-
audience and children-as-professional-apprentices.
Significantly, when adults don’t employ praise–when they instead worry about
children’s use of such technologies–the language employed is quite different from
that of Markoff’s “high priests” mentioned earlier. The adults don’t take the role of
professionals worried about the improper, incautious use of high-powered devices:
they don’t fret about whether children are ready for, or worthy of, adult technolo-
gies, as in the case of the earliest computers. Rather, they are concerned about over-
use. The question for such consumer technologies is: should children be spending
so much time texting, or playing video games, or surfing the Web? Or–to take
another current example–should teenagers spend so much of their time on social
media? (Cf. Freitas [6]).
88 M. Eisenberg and Z. Jacobson-Weaver
We can put this distinction into sharper focus by taking the venerable example of
television: adult anxiety about children’s television might be said to center on (men-
tal or emotional) safety concerns. Is the programming appropriate for children? Are
shows too violent or disturbing? Are they harming the child’s attention span? These
are the sorts of arguments that have historically raged around children’s television
(and have been revived in the case of video games and social media), but they have
a different flavor than the anxiety of the high priests, who were focused on the safety
of the device. No one worries that a child watching television is potentially risky for
the TV, and by and large, the worry is that television will “dumb children down,”
rather than expect too much of them.
For an illuminating contrast with children’s consumer technologies, consider the
physicist Freeman Dyson’s [7] advocacy for placing the tools of current-day bio-
technology in the hands of children: “The final step in the domestication of biotech-
nology will be biotech games, designed like computer games for children down to
kindergarten age but played with real eggs and seeds rather than images on a screen.
Playing such games, kids will acquire an intimate feeling for the organisms that they
are growing.” ([7], p. 3) Dyson’s vision is squarely within the “creative technology”
camp: he sees children as apprentice colleagues in the larger scientific enterprise,
rather than as a profitable audience to be wooed. Dyson’s recommendations may or
may not sound desirable (or practical) to some readers, but the anxiety that they
conjure is not that children will spend too much time thinking about biology; rather,
the anxiety is that these are tools and techniques for credentialed professionals only.
5.2.3 F antasy (Imitative) Technologies and Child-Adapted
Technologies
A final theme worth mentioning here is that there is a subtle, and again not always
clear-cut, distinction in this discussion between children’s technology as unexpect-
edly appropriated by children (as in the case of the computer or 3D printer) and
children’s artifacts that imitate adult technology (as in, say, toy cars, or faux kitchen
appliances, or model erector sets). Historically, many toys of the previous century
have been based on the assumption (often realistic) that children wish to mimic the
work and activities of their elders; thus, a small child might pick up a toy plastic saw
and pretend to do “real” carpentry. This sort of design might be called imitative
design in that it allows or encourages children to pretend to do adult work.
There are two contrasts between, on the one hand, “imitative design” for children
and, on the other, the sorts of advanced technology artifacts that we have in mind. First,
in many commercial instances of imitative design for children’s toys and games, the
technology (where it occurs) is generally imaginary, fantasy-oriented, or miniaturized.
A fantasy toy oven, for instance, might be nonoperational or operate only at safely low
(light-bulb-produced) temperatures. A fantasy carpentry set employs blunted saws and
chisels. The weapons of a fantasy soldier are (thank goodness) nonoperational.
5 The Work of Children: Seeking Patterns in the Design of Educational Technology 89
In contrast, the type of artifact that we have in mind here is a true, professional
device; in this sense, the examples of interest are similar to musical instruments for
children. Typically, a child learning to play piano or violin employs a real piano or
violin; the instrument may be (as in the case of the violin) physically implemented
at a smaller scale, but it is nonetheless a genuine instrument. In a similar vein, chil-
dren’s computers are real computers; indeed, the current versions are machines of
far greater power and versatility than those that Markoff’s “high priests” themselves
used. Similarly, the adoption of such devices as 3D printers for children represents
a true democratization of technology: the printers might be less powerful and high-
resolution than their professional cousins, but they are most definitely real printers.
The second contrast to be drawn between imitative and advance-technology arti-
facts is perhaps more interesting and subtle. Briefly, the idea is that imitative tech-
nologies tend to be interpreted as signals of social continuity: the child is feeling his
or her way into the established roles of adults in the community. Advanced technol-
ogy for children, however, seems to augur a break with the past and to establish a
disconnect between generations.
The importance of this distinction is worth elaborating. We might begin by not-
ing that imitative design is in fact anthropologically venerable. In hunter-gatherer
societies, children often play with smaller or cast-off versions of the sorts of tools
(arrows, for instance) of the type used by their elders, or they reenact play scenarios
based on the interactions of the adults that they observe (([8], Chap. 5), [9]). The
children in these societies are in fact doing much the same thing that urbanized
children do when they use toy ovens or “play house.” Arguably this sort of imitative
play is biologically “hard wired” into children; at the very least, its prevalence in
(current-day) hunter-gatherer communities strongly suggests that this sort of play
would not have been unfamiliar to children even in the long prehistorical human
period before the advent of agriculture. Moreover (though the sources do not focus
on this issue), in hunter-gatherer societies, the children’s activity is presumably met
with approval from adults, inasmuch as the youngsters are learning to carry on the
activities of the community. The adults in these societies show little or no anxiety
about children’s implements; indeed, as Lancy [9] notes, their lack of anxiety about
the risk of tool use is positively startling to the eyes of urbanized visitors:
While not universal, in many societies tools are made for children to play with and/or they
are given cast-off tools to convert into toys, and/or the raw materials to make their own…
I’ve found many cases where children are free to handle and play with adult-sized tools.
Most striking are children literally “playing with knives” …. Appearances to the contrary, I
do not think this nonchalant attitude reflects indifference or callousness toward the fate of
one’s children. Rather, this extreme laissez-faire attitude reflects a bedrock belief in the
power of children to learn autonomously (Lancy [9], pp. 54–55.)
The issues involved with advanced children’s technology, however, are quite
different and anthropologically recent. The problem is that children now begin to
make use of technology that is unfamiliar to most adults (all except, perhaps, a
caste of professionals). That is, if children begin to use novel technologies such
as DNA sequencing, nanotechnology, or “smart” materials, they are not perceived
as conserving or carrying on an existing culture, but of heralding an unknown
90 M. Eisenberg and Z. Jacobson-Weaver
new one. As a consequence, the attitude of many observing adults is discomfort
and anxiety. The future world intimated by children playing with DNA could, in
time, be a very u nfamiliar place. This scenario, of children using technology
unfamiliar to their parents, is perhaps not unique to the modern era (conceivably,
some fifteenth-century European children apprenticed in printing presses to the
consternation of their parents), but it is far more prevalent and emotionally
charged now than in earlier times.
There is much more to say about all these themes (and still others left unmen-
tioned), but for now we can sum up the implications of our discussion for design so
far: namely, that we can look to the professional or industrial world for powerful
technologies that can be adapted, without excessive loss of power or performance,
to the creative activities of children. The express goal of this sort of design is to
expand the creative and intellectual capacities of children–to make their worlds
more enjoyable and challenging–while taking due account of the cognitive and
physical limitations (and sometimes advantages) of children vis-à-vis adults. The
following section of this chapter presents some speculative suggestions on the sorts
of designs we have in mind, and in the final section of this chapter we present an
illustrative prototype of this approach.
5.3 E merging “Professional” Technologies for Children
Having arrived at this point in our discussion, it could now be asked–almost as an
exercise for the reader–what sorts of professional technologies might today elicit
the same sorts of anxious adult reactions when presented to children? In this sec-
tion, we sketch several possible directions for adapting technologies for children,
using current professional practice as a jumping-off point.
Biotechnology. Earlier, we noted that an essay by the physicist Freeman Dyson
advocated for biotechnological artifacts to be made available for children. In the
time since the essay was first written (in 2007), Dyson’s scenario looks significantly
less futuristic than it did originally. Certainly, within the larger “maker movement,”
there is an increasingly prominent subculture of “do-it-yourself (DIY) biology” in
which hobbyists garner (or build) the laboratory resources to conduct such activities
as genetic analysis [10]. Likewise, the popular worldwide iGEM competition enlists
teams of undergraduate college students, and high school students as well, to create
novel multicomponent biological processes out of a growing set of relatively simple
“BioBricks” that can be linked together in sequences [11]. To date, these cultural
phenomena are not associated with children (or, at least, with preteens), but it is
hardly a stretch to imagine the design of usable “kits” of the sort that Dyson envi-
sions. One might begin, for instance, by envisioning child-friendly technology for
rapid DNA sequencing and analysis (perhaps extending more traditional children’s
artifacts such as terraria, fish tanks, ant farms, butterfly collections, and so forth).
Sensorimotor extension and augmentation. One facet of the DIY biology subcul-
ture is the growing community of hobbyists involved in rethinking the nature of the
5 The Work of Children: Seeking Patterns in the Design of Educational Technology 91
human body itself. In a recent book, Platoni [12], for example, describes a group of
young “body-modders” who experiment with techniques such as implanting small
magnets within their fingers (to gain an embodied sense for ambient magnetic
fields). More generally, the growing interest in what has come to be called
“transhumanism” increasingly takes the form of experimental technologies that (in
the form of wearables) work on the surface of the body or (as in the case of the
aforementioned magnetic implants) work within the body itself. These technologies
are in the main intended to extend or augment the sensory or muscular capacities of
the human body.
The issues surrounding this technological movement are varied, controversial,
and complex, but for the purposes of this chapter it should be noted that there is no
reason to believe that transhumanist technology will not, in some form, be a matter
of interest to children as well as adults. (Cf. Eisenberg [13].) One rather small-scale
foray into this territory might take the form of designing wearable sensory exten-
sions for children in the context of science education, allowing children to tempo-
rarily extend their senses to, e.g., hear frequencies outside the normal human range,
to “see” ultraviolet or infrared light, to “feel” electrical conductance in surfaces, and
so forth.
Tools for fashioning/outputting/customizing novel materials. The world of
“ children’s materials,” over the past few decades, has come to include substances
like conductive threads, inks, and ceramics–all suitable for integration with small-
scale, hobbyist-level electronic devices. Indeed, the field of educational electronic
textiles is inextricably based on the availability of such new materials. (Cf. Buechley
et al. [14]) This is not the occasion for a thorough review of novel materials that
could be made available to children for craftwork and construction projects; none-
theless, it should at least be noted that the landscape of powerful new materials is
expanding in so many directions that designers should be alert to the possibilities
they afford for children’s activities. Possibilities include such materials as carbon
nanotubes (which, among many other things, can be employed in conductive fab-
rics), biomimetic materials (an enjoyable summary is provided by Drake [15]), con-
ductive materials for 3D printing (a relatively recent development facilitating the
integration of hobbyist electronic devices with 3D printed forms), and many more.
In keeping with the arguments of this chapter, such materials could be made avail-
able to children, along with ancillary technologies for customizing, decorating, and
shaping such materials.
5.4 A Children’s Pick-and-Place Device
The previous discussion might conceivably be interpreted as either somewhat
“theoretical” (looking at general patterns by which professional tools become
reinterpreted for children) or else somewhat futuristic (as in the examples of the previ-
ous section). The essential point throughout, however, is that exploring these patterns
and possibilities can act as a springboard for new design. In our own case, we used
92 M. Eisenberg and Z. Jacobson-Weaver
Fig. 5.1 A working mechanism (a “children’s pick-and-place prototype”) that picks up and moves
tiles. At left, the “picker” (the small orange cylinder visible just underneath the larger metallic
cylinder toward the front of the photo) is positioned over a stack of plastic hexagonal chips. At
right, we see a close-up of the means by which a chip is picked up by the device, as outlined in the
accompanying text [16]
these observations to work on an illustrative prototype of a “professional-device-
made-for-children”: a pick-and-place machine. In industrial settings, such devices are
employed to rapidly place objects (such as electronic components) onto surfaces with
high precision; in essence they are automatic assembly devices for complex multipart
objects. The child-oriented pick-and-place machine that we originally envisioned (and
indeed still envision for future work) is a device that children can program and through
which they can rapidly create complex 3D constructions made of standardized sets of
small pieces (such as hexagonal chips, cubic elements, or mosaic tiles).
The prototype that we in fact built was only an early and partial realization of this
idea. (For a full description of the working device, see Jacobson-Weaver and
Eisenberg [16].) Our device, shown in Fig. 5.1, was created by repurposing the
mechanical elements of an older 3D printer. Essentially, the device was able to pick
up hexagonal chips under computer control from specified locations on a printing
table (one could specify x, y, and z coordinates); in the figure at left, the small
orange “picker” has been placed directly above a stack of chips, ready to pick up the
topmost chip. Each chip in the device is fashioned with a small hole in its center;
when the picker is moved downward to pick up the chip, the picker activates a small
inflatable balloon within the chip’s hole. The inflated balloon can then be used to
pick up a single chip as shown toward the right of Fig. 5.1; then a new computer
command can tell the device to move the chip to a new location in the printing area.
Figure 5.2 shows the result of a series of such manipulations: a set of adjoining
stacks of hexagonal tiles. Overall, then, the purpose of the device (even in its primi-
tive state) was to build patterned forms, under program control, composed of dis-
crete stackable pieces. More generally, the device was intended to demonstrate that
an “industrial” tool could be re-envisioned as a children’s tabletop device, thus illus-
trating the design strategy that we have advocated in this chapter.
Our pick-and-place device never advanced beyond the “proof of concept” stage
(though we still entertain hopes for pursuing the project in the future). Even had it
done so, any single device is itself only one example–an individual instantiation of a
5 The Work of Children: Seeking Patterns in the Design of Educational Technology 93
Fig. 5.2 Stacks of
hexagonal tiles created
from the plastic chips
manipulated by the
pick-and-place prototype
much broader agenda. For our purposes, that agenda is to integrate, over time, the
world of “professional” tools and the world of “children’s” tools. There are u ndeniably
caveats and limitations to this approach: we do not, for instance, advocate that
children should be, e.g., driving cars or operating heavy construction machinery.
Naturally, there are safety concerns involved in pursuing this strategy of design–for
children, for the community at large, and (occasionally) for the devices themselves.
Nonetheless, we tend to think that the adult (“professional”) world has a penchant for
alarmism when children are given the opportunity to create and express themselves
with new technologies. Just as children play “adult” musical instruments (generally
to play simpler music) or program “adult” computers (generally to produce simpler
programs), one can envision well-designed child-friendly activities involving such
emerging technologies as biotechnology, sensory augmentation, and novel materials,
among many others. This transition–from professional to children’s technology–
should, we believe, provide a rich source of inspiration for future designers.
Acknowledgments The work described in this chapter has been supported in part by the National
Science Foundation under grant IIS1736051, in collaboration with Ben Shapiro, Joseph Polman,
and Nicholas Gonyea. Thanks especially to Jeffrey LaMarche for his creative input to the pick-
and-p lace machine described in this chapter.
References
1. Markoff, J. (2005). What the Dormouse Said. New York: Viking.
2. HP Computer Museum. A description of “Squirt”, and links to advertisements, are at: http://
www.hpmuseum.net/display_item.php?hw=303.
3. Mumford, L. (1934). Technics and civilization. San Diego: Harcourt Brace & Company.
Harvest edition, 1963.
4. Manes, R. (2009). Hedonizing technologies. Baltimore: Johns Hopkins University Press.
5. Cross, G. (1997). Kids’ stuff. Cambridge, MA: Harvard University Press.
6. Freitas, D. (2017). The happiness effect. New York: Oxford University Press.
7. Dyson, F. (2015). Dreams of earth and sky. New York: New York Review of Books.
8. Diamond, J. (2012). The world until yesterday. New York: Viking Books.
9. Lancy, D. (2017). Raising children. Cambridge, UK: Cambridge University Press.
94 M. Eisenberg and Z. Jacobson-Weaver
10. Wohlsen, M. (2011). Biopunk: DIY scientists hack the software of life. New York: Penguin.
11. iGEM competition. (2017). Description at: igem.org.
1 2. Platoni, K. (2015). We have the technology. New York: Basic Books.
13. Eisenberg, M. (2017). The binding of Fenrir: Children in an emerging age of transhumanist
technology. In Proceedings of Interaction Design and Children (IDC 2017), pp. 328–333.
14. Buechley, L., Peppler, K., Eisenberg, M., & Kafai, Y. (Eds.). (2013). Textile messages:
Dispatches from the world of E-Textiles and Education. New York: Peter Lang Publishing.
1 5. Drake, N. (2013). Strange biology inspires the best new materials. Wired. www.wired.
com/2013/03/biomimetic-materials/Wired article.
16. Jacobson-Weaver, Z. & Eisenberg, M. (2015). The voxel printer: steps toward a 3D pick-and-
place printer for children. In AACE Proceedings of EdMedia 2015, pp. 1429–1434.
Chapter 6
How Do High School Students Prefer
To Learn?
Leila A. Mills, Laura Baker, Jenny S. Wakefield, and Putthachat Angnakoon
Abstract Learning preference among a group of high school students was
e xamined in order to determine how students feel about options for learning within
the integrated communications technology-mediated spaces of our time. Learning
preference is presented as student-chosen learning and a way to examine student
attitudes, within the affective and cognitive domains of learning outcomes. Learning
preferences, specifically student attitudes and feelings, are neglected yet important
aspect of learning. One specific student learning preference examined in this study
was choice of learning mode by degree of technology applied, ranging from learning
in Internet spaces via online interactions to learning in the more traditional c lassroom
setting. Students’ information behavior was also examined in relation to the
information search theory, to gain insight on how students focus their activities in
Internet virtual learning spaces. The Information Communications Technology
Learning (ICTL) survey was used to examine differences in high school students’
information behavior for seeking and sharing information. A total of 88 students,
from a predominantly African American high school in the southern United States,
participated in the study. The major questions asked were: Can we identify trends in
student-chosen learning preference for learning with technology by gender and is
there a relationship between information behavior and students’ choice of STEM
academic major? Findings revealed that high school girls and students with Science,
Technology, Engineering, and Math (STEM) interest preferred learning in a
traditional classroom setting.
L. A. Mills (*) · L. Baker 95
St. Edward’s University, Austin, TX, USA
e-mail: [email protected]
J. S. Wakefield
Dallas County Community College, Dallas, TX, USA
e-mail: [email protected]
P. Angnakoon
Faculty of Learning Sciences and Education, Thammasat University – Rangsit Campus,
Pathumthani 12121, Thailand
© Springer International Publishing AG 2018
D. Sampson et al. (eds.), Digital Technologies: Sustainable Innovations for
Improving Teaching and Learning, https://doi.org/10.1007/978-3-319-73417-0_6
96 L. A. Mills et al.
6.1 Introduction
“Educators have, for many years, noticed that some students prefer certain methods
of learning more than others” ([1], p. 130) and that students learn and construct
knowledge based on unique experiences [2]. Differences in student learning have
been examined from many perspectives, and research theories related to styles of
learning have emerged to explain differences in learning outcomes that appear to be
the result of intrinsic individual differences. There is some research to indicate that
variations in learning can be depicted as differing learning styles. Learning styles,
viewed as human cognitive abilities, serve as the basis for theories of teaching and
learning such as the theory of multiple intelligences [3] which has been widely
applied to instruction. Jonassen and Grabowski [4] stated that learning styles reflect
the learners’ mental abilities as cognitive controls and styles. They posited that
learning styles allow learners to interact with instruction. The major focus of this
research is learning preference (not learning style). Learning preference, another
aspect of learning outcomes, encompasses student attitudes and feelings regarding
learning. In psychological theories of learning, learning preference would generally
be considered an aspect of the affective domain of learning outcomes whereas
learning style would generally be considered to be an aspect of the cognitive domain
of learning outcomes.
Since the 1950s, research on student learning has focused on the domains of
learning that can identify the main categories of learning outcomes. Three domains,
the cognitive, affective, and psychomotor, were initially identified [5]. These three
domains, Cognitive, mental skills; Affective, growth in feelings or emotional areas;
and Psychomotor, manual or physical skills, continue to serve as a taxonomy of
learning outcomes and are embedded in such works as the revised Bloom’s
Taxonomy [5]. Blooms’ cognitive taxonomy has served as a reference for teacher
training and preparation in education for almost 60 years.
The cognitive and affective domains of learning were the first to be theorized in
the 1950s, and the psychomotor domain was added to the three-component model
by the 1970s. Harrow [6] provided the research that confirmed the psychomotor
domain. This domain depicts the use of physical action to achieve the desired
cognitive objectives.
However, already in the 1960s, Gagné had presented a theory of instruction, a
taxonomy of learning outcomes, consisting of the three abovementioned domains,
and he described the affective domain of learning outcomes rather simplistically as
learner attitudes [7]. By 1972, Gagné presented an expanded taxonomy of learning
outcomes stating that “there are five major categories of learning outcomes: (1)
verbal information, (2) intellectual skills, (3) cognitive strategies, (4) attitudes, and
(5) motor skills” ([7], p. 348). Important literature such as Jung’s theory of
personality [8] and Gagné’s theory of instruction [9] recognized the importance to
individual learning of both cognition (individual mental action toward acquiring
and understanding—learning style) and affect (emotions and feeling toward
something—learning preference).
6 How Do High School Students Prefer To Learn? 97
However, research on the cognitive domain, as learning styles, has attracted most
of the attention and research for decades. Willingham et al. [10] explained that
learning style theories generally hold “that people learn in different ways and that
learning can be optimized for an individual by tailoring instruction to his or her
style” (p. 266), meaning a learning style is a personal constant and would be more
effective when it is consistent with what a person prefers. Some theorists hold that
little scientific research, however, supports the theory of learning styles, and
Willingham and his colleagues conclude in their publication that the popularity of
learning styles is based mostly on popular belief.
A relatively smaller body of research has evolved to explain learning and the
affective domain of learning outcomes, student attitudes/preferences. Research on
the psychology of learning indicates that learner attitudes relate to learning outcomes
[9]. For example, we know that motivation for learning-related activities has been
linked to academic performance [11] and that learner aptitude and academic
m otivation [12] are important elements of student learning. We also know that a
teacher may avoid problem behaviors by rearranging the learning environment [13].
There is some research to indicate that there is a relationship between students’ pref-
erence, and resulting choice of different learning environment modality, and learning
outcomes [14–16]. Curry [14] noted learning preference as a choice to, for example,
choose to “read to attending a course” (p. 535). Owen and Straton [16] stated that “an
important variable in the effectiveness of learning is the preference of the student for a
mode of learning, i.e., cooperative, competitive, or individualized” (p. 141).
Such reports by researchers indicate that it is important to seek to understand
students’ learning preferences in relation to how they will make choices to q uestions
in teaching in learning that will support positive educational outcomes in K-12 and
higher education.
As for research on learning preferences related to educational implications of
choices for new modes of learning with technology, both the existing studies of
learning styles and learning preferences tend to focus on identifying possible
relationships between learners’ characteristics and specific aspects of student
achievement (such as outcomes in a specific course or of academic completion rates).
Additional research is needed to inform educators and instructional designers on
considerations related to learning preferences and learning modes/methods; the
match of learner to mode of learning [14–16] has been noted to influence learning
and academic goals as new waves of technology continue to redefine the boundaries
for dissemination of knowledge and information. Of particular concern is the trend
toward the adoption of new instructional modes and technology tools for (daily or
continuous) use in K-12 and higher education without sufficient understanding of
the impact of learning mode, learner characteristics, and learning outcomes.
Within the realm of formal learning, online and e-learning options are now available
for compulsory education years (K-12) as well as in Higher Education. Many students
use Information Communication Technology (ICT) options for rapid information
access, messaging, and online connection in their personal lives as tools for informal
communications, and many would welcome opportunities to use these networking
tools for formal-to-informal learning [17]. However, not all students welcome the
98 L. A. Mills et al.
move toward technology-mediated learning and not all students tend to engage in
information-seeking activities in Internet spaces which are associated with knowledge
construction. While there is a trend toward more readily available online learning
options, we have yet to confirm that these options match learners’ preference.
There is much left to be known regarding the affective domain, the cognitive
domain, learning preference, and learner needs in traditional and technology-
mediated learning environments [14]. Important questions remain to be answered as
students participate in learning interactions in online, blended, and traditional envi-
ronments. The objective to have validated instruments, especially those that are spe-
cifically designed to gauge student preferences for technology-enhanced learning
options of the twenty-first century, will help to answer important questions such as
can all students be reasonably expected to succeed in learning in new technology-
mediated learning spaces? Do students who succeed in online courses have a prefer-
ence toward a certain classroom environment? Do they tend to feel more positive
about learning in virtual learning spaces rather than the traditional classroom? How
important is a student’s preferred mode of learning to student success in learning?
How can we gauge learning domains such as the cognitive and affective? One
method is by collection and analysis of self-report data with validated survey
instruments. Validated survey instruments have demonstrated reliability in detecting
differences in learning characteristics. One such instrument, the MBTI, was developed
on the framework of Jung’s personality theory. Cano and Garton [18] utilized the
MBTI survey instrument and reported that learning style is related to students’ learn-
ing outcomes and achievement. The MBTI learning scales combines aspects of learn-
ing style (cognition) with aspects of learning preference (affect). Another popular
survey instrument, the Kolb Learning Style Inventory (LSI) [19], focuses on cognitive
learning style and has been used in research on learning style and distance learning.
For example, LSI data gathered for a study of community college students enrolled in
a distance course was analyzed to detect trends in style of learning among students
identifying students who might be at risk of failure in the course. Learning style dif-
ferences in were found between students who were successful and those who were not
successful in the course. Students with higher preference for LSI concrete experiences
tended to be at risk of being unsuccessful in the distance learning course format [20].
This research reports the validation of a learning preference instrument for use
with high school students. The instrument, the Information Communications
Technology Learning instrument, was introduced in studies on learning preference
of adult learners [21, 22]. The ICTL has demonstrated reliability in gauging learning
preferences and student-chosen interaction with information for information
b ehaviors in technology-enhanced Internet spaces. The ICTL was validated here by
confirmatory factor analysis for the subjects of the study—students approaching
adulthood. Findings are presented on high school students’ preference for learning
with technology and student information behavior—specifically on how positive
students are toward learning in the traditional classroom and how students interact
with technology for seeking and sharing information in Internet spaces.
The students in this study were in high school, the highest grades of compulsory
education in the United States. These student subjects, in grades 7–12, reported their
feelings (affect) about learning with technology and interacting with information via
6 How Do High School Students Prefer To Learn? 99
the Internet using various devices and communications tools. The authors suggest that
studies on student attitudes toward learning with technology in educational learning
environments are of value not only to educators and instructional designers but also to
the learning technologists who are charged with supporting technology-enhanced
learning. Focusing on student learning, and how it is impacted by shifts in educational
technology and the melding of formal and informal learning, is necessary to advance
educational practices to best suit student learning preferences. The general question
asked in the study presented in this chapter was: Are there identifiable learning prefer-
ence trends among the high school subjects of this study?
6.2 C onceptual Rationale
6.2.1 L earning Options
The rapid growth of personal use technologies throughout the world and the rapid
emergence of mobile technologies and applications [23, 24] provide an array of
rapidly changing technology-mediated communications tools. These tools can be
used for interaction with online information and informal to formal learning
exchanges that support knowledge construction [21].
Communication, an integral aspect of learning [25], is exceedingly well supported by
the Internet (e.g., Skype, Facetime, Zoom, Collaborate). Recent decades have introduced
previously unimagined ICT tools that offer a multitude of options for interaction with
information as well as communication with many audiences. As the newest personal
technology tools blur the line between informal and formal learning [21, 23, 24, 26], and
mobile technologies are gradually becoming more geographically dispersed [27], there
is talk of a revolution in education in what is now an Internet connected world [28]. The
wave of Internet and technology-e nhanced tools for teaching and learning have resulted
in new options and difficult decisions for teaching and learning. Student learning options
have been expanded in many circumstances, but the changing face of traditional e ducation
may not fit the needs of all students. In light of the changes in teaching and learning, it is
important to understand the implications of match or mismatch between learning options,
learning methods [14–16], and student learning preferences. Learning technologies
research interests are understandably shifting from a focus on what channels and
resources are used for emphasis on student attitudes toward options to encounter, interact,
and interpret information and the use of ICT tools [29].
6.2.2 L earner Characteristics
The foundations for understanding learner characteristics such as learning style and
learner preferences are related to broader theories of educational psychology. Witkin
[30] reported a link between students’ cognitive style and academic achievement
and choice of major. Zajonc reported experimental results of research which
100 L. A. Mills et al.
suggested that affective judgments precede and are fairly independent of perceptual
and cognitive operations and that:
It is concluded that affect and cognition are under the control of separate and partially inde-
pendent systems that can influence each other in a variety of ways, and that both constitute
independent sources of effects in information processing. ([31], p. 151)
Learner attitudes are described in Jung’s personality theory, in which he depicted
learner characteristics along a dichotomous scale from introversion to extroversion.
Jung’s personality theory describes the individual person’s general attitude and
sheds light on the individual’s leaning to general interest [8]. Research on learner
characteristics has also focused on depictions of personality that lend themselves to
theories on learning. A review of the literature reveals a large body of research on
aspects of how students learn (for example, [32–35]). Along this line, we also find a
taxonomy of learning, referred to as Blooms Taxonomy. This framework classifies
statements of what we expect or intend students to do to show mastery after
assessment of learning, using verbs as objectives, ([36], p. 212).
Research on styles of learning indicates that learning style is related to students’
learning outcomes and achievement [18]. Cano [37] conducted a study to examine learn-
ing style in relation to academic achievement among students at Ohio State University.
Cano found that learning style positively influenced academic achievement in the
College of Food, Agricultural, and Environmental Science at Ohio State University. For
that study, learning style was gauged by use of the MBTI instrument [38, 39]. The MBTI
instrument scales were designed to incorporate aspects of Jung’s [8]Theory of Personality
Type. The MBGI elicits student responses on four dichotomous scales: Extraversion/
Introversion; Sensing/Intuition; Thinking/Feeling; and Judging/Perceiving. Learning
style is gauged by the combination of the scales of perception (Sensing/Intuition) and
judgment (Thinking/Feeling). The authors note that the MBTI incorporates facets of
both affective and cognitive domains in assessing their measure of learning style.
6.2.3 L earning Preference and Effective Learning
Orhun stated that, “learning can be expressed as gathering information, processing
information, the improvement of thinking, and the method of selection for attaining
knowledge” ([40], p. 1159). Student preference for a mode of learning is an important
variable in the effectiveness of learning [16]. Further, learning preference is a facet of
how we learn and has been defined in a few different ways. Keefe [15], for example,
defined learning preference in relation to learning style and methods, as how a learner
perceives, interacts, and responds to learning opportunities. Grasha [41] defined learn-
ing preference as “the preference students have for thinking, relating to others, and
particular types of classroom environments and experiences” (p. 26). Mayer and Massa
examined learning preference along with aspects of cognitive ability and cognitive
style. They described learning preference as a distinct aptitude and “property of the
learner’s interaction with a particular learning situation” ([42], p. 838). Learning
6 How Do High School Students Prefer To Learn? 101
preference research is gradually emerging with support for the notion that learning
preference is an important aspect of learning outcomes that relates to academic
p erformance. Orhun’s [40] research revealed that preferred learning style is potentially
a tool for improvement of mathematic performance. Learning preference, as how
s tudents prefer to attain knowledge, was found to be a predictor of academic p erformance
in a study of nursing students, for those students who reported a preference for multiple
approaches for learning [43]. Additionally, Hong and Milgram [11] reported that
learning preference is related to motivation for homework and out-of-school learning.
6.2.4 R esearch Questions
The research questions addressed here were related to the extent to which high school
students like to learn with technology and identifiable preference trends related to
information behavior. Survey data gathered with scales of the Information
Communications Technology Learning (ICTL) instrument were analyzed to v alidate
the constructs of the ICTL for high school students and to examine students’ information
behavior (information seeking and information sharing) in technology-mediated ICT
learning environments, as well as students’ liking of classroom learning. Student data
were analyzed by trends with demographic data that included student gender and
academic fields of study. Academic interest was examined in order to shed light on
learning preferences of students who have goals of study and participation in Science,
Technology, Engineering, and Math (STEM) disciplines.
The specific research questions examined were:
1. How are male and female students different in their learning preferences and
information behaviors?
2 . How are students who have academic interest in STEM courses of study and
careers different in their learning preference and information behaviors?
6.3 R esearch Methods
6.3.1 Participants
Institutional Review Board approval was granted for this study of learning preference
among high school students. Data was gathered using paper and pencil surveys. All
high school students, with the exception of those who were not eligible for a school
field trip, from one rural school district’s one and only high school were invited to
participate in the study. Eighty-eight of the 296 students who attended the field trip
returned permission forms and surveys. Therefore, this study is based on data from
88 student subjects. Participants were 58% male (n = 51) and 42% female (n = 37),
attending high school grades 9–12. Fifty percent of students (n = 44) indicated that
they are interested in seeking a STEM-related career.
102 L. A. Mills et al.
6.3.2 Measurement and Instruments
The Information Communications Technology Learning (ICTL) survey was
designed and validated to address questions relating to students’ preferences in
u tilizing ICT and to assist in understanding individual differences in information
behavior. Instrument development included analysis for internal consistency
reliability, principal components exploratory factor analysis, multidimensional
scaling, and higher order factor analysis [22]. The original version of the ICTL
survey consisted of 16 items to assess three constructs: two of the three are informa-
tion seeking and information sharing [21]. A new construct, classroom learning,
emerged in the analysis of the ICTL for the participants of this study. These
c onstructs were measured on a five-point Likert scale ranging from (1) “strongly
disagree” to (5) “strongly agree.” Exploratory factor analysis indicated an original
model for constructs wherein the information seeking construct was developed from
six items (ic8, ic10, ic11, ic14, ic2, ic13), the information sharing construct was
created from six items (ic3, ic6, ic15, ic4, ic1, ic16), and the classroom learning
construct was created from four items (ic9, ic12, ic5, ic7).
The CFA was then performed with the 16 items using the Analysis of Moment
Structure (AMOS) statistical package with the estimation method of the Maximum
Likelihood. To modify the model, the observed variables that had produced low
factor loading (<0.5) were eliminated. The modification indices were consulted for
the model modification.
Figure 6.1 presents the final items for each construct and the factor loadings. The
models were considered acceptable if CFI ≥0.90, GFI ≥0.90, AGFI ≥0.90, RMSEA
≤0.08, and SRMR ≤0.10 [44, 45]. Table 6.1 presents the model fit indices. Based
on the outputs, the model had adequate fit with the given data set and was valid and
acceptable for measurement.
All of the Cronbach’s alpha results ranged from 0.82 to 0.89. According to
Devellis [46], these reliability estimates can be considered very good. Cronbach’s
alphas suggest that the items in each scale were consistent with each other.
Convergent validity was examined from the construct reliability and the Average
Variance Extract (AVE) of each construct (See Table 6.2). All three constructs dem-
onstrated adequate convergent validity with an AVE greater than 0.5. However, the
constructs demonstrated low discriminant validity because the information sharing
and the information seeking constructs are highly correlated. The correlation
between these two scales indicates that the two aspects being measured, information
sharing and information seeking are similar in nature (Table 6.3).
Table 6.1 Goodness-of-fit indices for CFA model fit indices
Acceptable if CFI GFI AGFI RMSEA SRMR
ICTL <0.080
≥0.900 ≥0.900 ≥0.900 ≤0.100
0.970 0.898 0.806 0.083 0.042
Note: Refs. [44, 45]
6 How Do High School Students Prefer To Learn? 103
e4
Information .79 ic11 e3
Seeking .68 ic13 e1
.83 ic14
e11
.90 Information .80 ic6 e10
.84 Sharing .87 ic15 e8
ic1
.81
.79 ic9 e16
.83 ic12 43
.80 ic5
ic7 e15
Classroom .77 15
Learning .77
e14
29
e13
Fig. 6.1 CFA measurement model
Table 6.2 Average variance extract and discriminant validity
Information seeking Information seeking Information sharing Classroom learning
Information sharing 0.628
Classroom learning 0.596 0.679
0.814 0.618
0.699
Note: In the diagonal-running cells, the average variance extracted (AVE) is shown; the lower-left
half of the table shows the squared inter-construct correlation estimates (SIC)
Table 6.3 Reliability Cronbach’s alpha
(reliability coefficient)
Constructs
0.82
Information seeking 0.86
Information sharing 0.89
Classroom learning
104 L. A. Mills et al.
6.3.3 Analysis
Among the student respondents, 58% of the respondents were male (n = 51) and 42%
of the respondents were female (n = 37). The majority of the respondents are between
15 and 17 years old (n = 49). When asked about their learning behaviors, students
reported an average of 3.53 (SD = 1.12) of level of agreement on information sharing,
3.91 (SD = 0.96) on information seeking, and 4.05 (SD = 0.86) on classroom learning,
respectively. These constructed variables were the combination of multiple items. The
information seeking construct was developed using three statements: More classroom
learning should use technology tools; I learn more when I am free to search for the
answers on my own; and I use the Internet to keep current on important topics. The
information sharing construct was developed using three statements: I learn many
things by interacting with other Internet users; I like to share information on the Internet
with posts and tweets; and I would like to be a member of an Internet learning online
community. The classroom learning construct was developed using four statements: I
learn best in the classroom setting; the things I need to know are taught in the classroom;
I like to take classes to learn new things; and I learn many things in the classroom.
A Pearson’s Product Moment Correlation analysis revealed significant positive cor-
relations between these aspects of information behavior (r = 0.672–0.754, p < 0.001).
To answer research question 1, multiple analysis of variance (MANOVA) was per-
formed to determine the effects of a categorical independent variable (Gender: Male
and Female) on three continuous dependent variables including information seeking,
information sharing, and classroom learning. Although the groups for the male and
female respondents were unequal (51 male students; 37 female students), the Box’s M
test (Box’s M = 13.869; F = 2.206, p = 0.04) was found to be non-statistically signifi-
cant based on Huberty and Petoskey’s guideline [47], indicating that the variance–
covariance matrices between the groups were equal for the MANOVA purpose.
Generally, female students reported having a higher level of agreement on all
three aspects of information behaviors. The one-way MANOVA on learning prefer-
ence revealed a significant multivariate effect with Wilks’ lambda = 0.87,
F(3,84) = 4.18, p = 0.008, η2 = 0.130. Approximately, 13% of the multivariate vari-
ance of the learning preference construct was associated with student gender. The
univariate effects revealed statistically significant differences on classroom learning
preference where F(1,86) = 10.36, p = 0.002. To be specific, female students had
significantly higher levels of preference for the classroom learning construct
(M = 4.38, SD = 0.65), indicating that their perceptions on classroom learning are
more positive than those of male students (M = 3.81, SD = 0.91). See Table 6.4.
The one-way MANOVA revealed a nonsignificant multivariate effect with Wilks’
lambda = 0.924, F(3,84) = 2.29, p = 0.084, η2 = 0.076. However, through the
examination of the univariate effects, we found that there was a statistically significant
differences on “classroom learning” preference between STEM and non-STEM
students, F(1,86) = 6.22, p < 0.05. To be specific, students with STEM major
d emonstrated higher level of classroom learning preference (M = 4.3, SD = 0.73)
than those of students with non-STEM major (M = 3.83, SD = 0.92). See Table 6.5.
6 How Do High School Students Prefer To Learn? 105
Table 6.4 Learning preference by gender
Learning preference n Mean SD F Sig. Effect size (Cohen’ s d)
2.72 0.103 0.36
Information
1.05 0.309 0.21
Male 51 3.77 0.98
10.36 0.002 0.72
37 4.11 0.91
Information
Male 51 3.43 1.08
37 3.67 1.18
Classroom
Male 51 3.81 0.91
37 4.38 0.65
Table 6.5 Learning preference by STEM major
Learning preference n Mean SD F Sig. Effect size (Cohen’s d)
Information seeking 1.91 0.171 0.25
Non-STEM 44 3.77 0.99
44 4.01 0.92
Information sharing 3.77 0.056 0.45
Non-STEM 44 3.30 1.15
44 3.80 1.06
Classroom learning 6.22 0.015 0.53
Non-STEM 44 3.83 0.92
44 4.27 0.73
Note: Means on ratings from 1 to 5 where 1 = strongly disagree and 5 = strongly agree
6.3.4 L imitations
This research is limited by the constraints of self-reported survey data. Additionally,
findings from this research would be considered to have limited generalizability,
and only to similar populations, because the subjects are all from one school com-
munity. Additional research is underway with other student groups using the same
instrument measurement scales.
6.4 Discussion and Conclusion
It is important to incorporate consideration of the affective domain of learning,
learner attitudes, as learning preference—feelings or emotions toward a fact or
state—when designing instruction or deciding what modes of learning should be
made available for students attending compulsory and higher education. Learning
preference should be reflected not only in the design of instruction but also in the
definition of best educational practices. Research suggests that understanding
106 L. A. Mills et al.
students’ learning preferences together with learning styles—the underlying char-
acteristics related to individual cognition—can help to answer questions related to
student achievement and learning in new environments and with new technologies.
Links between learning style and academic achievements have been reported [37]
when learning preference is viewed as student dispositions toward certain methods
of learning which form a student’s unique learning preference [1].
There are many questions to be answered in regard to teaching and learning as
new technology-mediated informal and formal learning options are available in
technology-mediated environments. A Pew Internet project survey analysis of 2054
students among college students enrolled in degree seeking programs at 27 institu-
tions of higher education reported that the degree to which college students use the
Internet as a source of information and reference strongly indicates that learners
will continue to use the Internet for information in the future [48]. The report indi-
cates that 68% of college students subscribed to at least one academically oriented
mailing list, and used connections made online to participate in discussions on aca-
demic topics that were related to the content of college classes in which they were
enrolled. This Pew survey analysis also reported that while the Internet has enhanced
education:
There appears to be little interest among traditional college students (those 18–22 years old)
to abandon the classroom and take courses online. Only 6% of students took online courses
for college credit, and of those only half (52%) thought the online course was worth their
time. Half of the students who took an online course said they believed they learned less
from the online course than they would have from an on-campus one. ([48], pp. 12).
While many in education are still examining the extent to which e-learning can
replace or differ in classroom instruction [49–51], the trend toward movement of
formal face-to-face learning courses to the distance learning modality continues at
many institutions around the world. Increasingly, the Internet is not only used to
supplement and enhance students’ academic activities and provide alternatives for
learning interactions but also to move course offerings from the traditional class-
room format to virtual learning spaces.
While we cannot question the popularity of new ICT tools for communications and
information exchange in Internet-mediated virtual spaces, there are many questions
regarding relationship to new modes and options for learning that will support the best
educational outcomes that we need to consider. Within formal education, students
must make choices related to educational options and learning interactions based on
offerings and features of courses designed by instructional designers, provided by edu-
cators, institutions. Therefore, it is essential that those who are charged with decisions
regarding instructional options consider the relationship or match between student
feelings/preferences and learning options for formal to informal learning. For exam-
ple, the Pew Internet project survey analysis student data [48], based on undergraduate
and graduate students data, reveals that students have distinct preferences in how they
feel about informal and formal communications and learning with technology.
The research presented here extends understanding of students’ preferences and
information behavior in technology-mediated, Internet-connected information
6 How Do High School Students Prefer To Learn? 107
learning spaces in relation to effective learning. The ICTL, a validated survey
instruments, was used to examine high school students’ preferences and information
behavior. Students’ preferences for use of ICT tools and their information behaviors
warrant careful examination because they reveal how students can be expected to
interact with information in different learning environments. Information behavior
in Internet spaces can be used to examine learner tendencies to participate in
self-directed learning via ICT. This research validated constructs of the ICTL infor-
mation seeking, information sharing, and classroom learning scales. The scales
were found to be consistent and worthy of use to gauge students’ learning prefer-
ences. The ICTL constructs were designed with consideration to the general and
group specific factors that will support a theoretical framework for research and
experimentation [52] on information behavior within the conceptual framework of
Kuhlthau’s [53] information behavior theory. The authors posit that the ICTL scales
offer a gauge that provides information on student feelings and attitudes that may
bridge the affective and cognitive domains of learning outcomes.
Findings indicated that regardless of gender and STEM interest, high school students
tend to be positive in their perceptions of classroom learning and ICT-mediated infor-
mation seeking and sharing. As a group, students were most positive toward classroom
learning, followed by ICT information seeking, and then information sharing. The
standout for the students in this study was the classroom learning construct, which when
taken into consideration by gender and study major, indicated significant differences of
learning preference between groups. To be specific, female students were found to have
a significantly higher preference for classroom learning than male students.Additionally,
students with STEM-related academic goals were found to have a significantly higher
preference for classroom learning than non-S TEM major students.
Research indicates that teaching and learning are best facilitated when learning
preference can be matched to methods [14–16]. Students’ information behavior has
been related to critical thinking skills as important aspect of knowledge construc-
tion. Weiler [54] pointed out that we assume that students will be effective in seek-
ing information, yet we should be prepared to guide students in developing essential
information and critical thinking skills. Additional research is needed to guide edu-
cators and instructional designers in designing and implementing educational
options that will support academic goals, outcomes, and student success in learning.
Additional methods of observation and longitudinal studies are also needed to pro-
vide a fuller view of the relationship of learner characteristics to student learning
outcomes. The ICTL scale was used in this study to gauge learner attitudes which
relate to effective learning within the cognitive and affective domains of learning
outcomes, as depicted in Gagné’s model of instruction. Learning preference and the
role that student dispositions play in successful learning warrant attention in light of
reports that “the entire world, is currently in the middle of a massive and wide rang-
ing shift in the way knowledge is disseminated and learned” [54]. Trends toward
online public charter school options for students in grades K-12 and massive online
open courses (MOOCs), popularized in 2011 [55] which provide online learning
opportunities from major research universities to a global audience, clearly evidence
the massive shift in the dissemination of instruction.
108 L. A. Mills et al.
Acknowledgments This research was made possible by the Laser Interferometer Gravitational
Wave Observatory (LIGO) Science Education Center (SEC) in Livingston, LA. Funding was pro-
vided by NSF grants, awards PHY0917587 and PH-0757058, to the Baton Rouge (Louisiana,
USA) Area Foundation and the LIGO Cooperative Research Agreement with Caltech and MIT.
References
1. Diaz, D. P., & Cartnal, R. B. (1999). Students' learning styles in two classes: Online distance
learning and equivalent on-campus. College Teaching, 47(4), 130–135.
2. Jonassen, D. H. (1991). Objectivism versus constructivism: Do we need a new philosophical
paradigm? Educational Technology Research and Development, 39(3), 5–14.
3. Gardner, H. E. (2008). Multiple intelligences: New horizons in theory and practice. New York:
Basic books.
4. Jonassen, D. H., & Grabowski, B. L. (2012). Handbook of individual differences, learning, and
instruction. New York: Routledge.
5. Krathwohl, D. R., & Anderson, L. W. (2010). Merlin C. Wittrock and the revision of Bloom's
taxonomy. Educational Psychologist, 45(1), 64–65.
6. Harrow, A. (1972). A taxonomy of psychomotor domain: A guide for developing behavioral
objectives. New York: David McKay.
7. Driscoll, M. P. (2000). Psychology of learning for instruction (2nd ed.). Pearson, Needham
Heights: Allyn & Bacon.
8. Jung, C. G. (1971). Psychological types in collected works of C. G. Jung, 6, Chapter X.
Retrieved from http://www.humanmetrics.com/personality/type.
9. Driscoll, M. P. (2005). Psychology of learning for instruction (3rd ed.). Pearson, Needham
Heights: Allyn & Bacon.
10. Willingham, D. T., Hughes, E. M., & Dobolyi, D. G. (2015).The scientific status of learning styles
theories. Teaching of Psychology, 42(3), 266–271. https://doi.org/10.1177/0098628315589505
11. Hong, E., & Milgram, R. M. (2000). Homework: Motivation and learning preference.
Westport: Greenwood Publishing Group.
12. Schunk, D. H. (1991). Self-efficacy and academic motivation. Educational Psychologist,
26(3–4), 207–231.
1 3. Osher, D., Bear, G. G., Sprague, J. R., & Doyle, W. (2010). How can we improve school
discipline? Educational Researcher, 39(1), 48–58.
1 4. Curry, L. (1981). Learning preferences and continuing medical education. [Editorial].
Canadian Medical Association Journal, 124(5), 535.
15. Keefe, J. W. (1979). Learning style: An overview. In Student learning styles: Diagnosing
and prescribing programs (pp. 1–17). Reston, VA: National Association of Secondary School
Principals.
16. Owens, L., & Straton, R. G. (1980). The development of a co-operative, competitive, and
individualized learning preference scale for students. British Journal of Educational
Psychology, 50(2), 147–161.
1 7. Rennie, F., & Morrison, T. M. (2013). E-learning and social networking handbook: Resources
for higher education. New York: Routledge.
18. Cano, J., & Garton, B. L. (1994). The relationship between agriculture preservice teachers’
learning styles and performance in a methods of teaching agriculture course. Journal of
Agricultural Education, 35(2), 6–10.
19. Kolb, A. Y. (2005). The Kolb learning style inventory-version 3.1 2005 technical specifications
200, 72. Boston: Hay Resource Direct.
20. Dille, B., & Mezack, M. (1991). Identifying predictors of high risk among community college
telecourse students. American Journal of Distance Education, 5(1), 24–35.
6 How Do High School Students Prefer To Learn? 109
2 1. Mills, L. A., Knezek, G., & Khaddage, F. (2014). Information seeking, information sharing,
and going mobile: Three bridges to informal learning. Computers in Human Behavior, 32,
324–334.
2 2. Mills, L. A., Knezek, G. A., & Wakefield, J. S. (2013). Understanding information seeking
behavior in technology pervasive learning environments of the 21st century. Turkish Online
Journal of Educational Technology, 12(4), 200.
23. Henríquez, P., & Organista, J. (2012). Theoretical Considerations for Mobile Learning
Approach: Teaching-Learning Mobile Model (TLMM) For the Higher Education Level. Paper
presented at the INTED 2012: 6th International Technology, Education and Development
Conference, Valencia, Spain.
2 4. Khaddage, F., & Knezek, G. (2012). Convert your thinking! Creativity and imagination using
mobile applications. In W. Dowling (Ed.), E-learning in action 1 (pp. 69–77). Abu Dhabi:
Pearson/HCT Press.
25. Warren, S. J., & Wakefield, J. S. (2012). Learning and teaching as communicative actions:
Social media as educational tool. In K. Seo (Ed.), Using social media effectively in the class-
room: Blogs, wikis, twitter, and more (pp. 98–114). New York: Routledge.
2 6. Cox, M. J. (2012). Formal to informal learning with IT: Research challenges and issues
for E-learning. Journal of Computer Assisted Learning, 29(1), 85–105. https://doi.
org/10.1111/j.1365-2729.2012.00483.x
27. Castells, M. (2011). The rise of the network society. The information age. In Economy, society,
and culture (Vol. 1). West Sussex: National Academy Press.
2 8. Bonk, C. J. (2009). The world is open: How web technology is revolutionizing education. John
Wiley & Sons, Inc..
2 9. Ilgaz, H., Mazman, S. G., & Altun, A. (2015). A cross cultural perspective on information
communication technologies learning survey. Technology, Knowledge and Learning, 20(2),
1–10.
3 0. Witkin, H. A. (1973). The role of cognitive style in academic performance and in teacher-
student relations. ETS Research Report Series, 1973(1), i–29.
31. Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. American
Psychologist, 35(2), 151–175. https://doi.org/10.1037/0003-066X.35.2.151
32. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman and Co..
33. Bransford, J. D., Brown, A. L., Anderson, J. R., Gelman, R., Greenough, W. T., et al. (2000).
How people learn: Brain, mind, experience, and school. Washington, DC: National Academy
Press.
34. Clark, R. C., & Mayer, R. E. (2003). E-learning and the science of instruction: Proven guide-
lines for consumers and designers of multimedia learning. San Franscico: Preiffer.
3 5. Snowman, J., & McCown, R. (2015). Psychology applied to teaching (14th ed.). Stanford:
Cengage Learning.
3 6. Krathwohl, D. R. (2002). A revision of Bloom's taxonomy: An overview. Theory Into Practice,
41(4), 212–218.
3 7. Cano, J. (1999). The relationship between learning style, academic major, and academic
performance of college students. Journal of Agricultural Education, 40, 30–37.
3 8. Myers, I. B. (1990). Introduction to type: A description of the theory and application of the
Myers-Briggs type indicator. Palo Alto: Consulting Psychologists Press, Inc..
3 9. Myers, I. B., & Briggs, I. (1962). The Myers Briggs type indicator manual. Princeton:
Educational Testing Service.
40. Orhun, N. (2013). The effects of learning styles on high school students’ achievement on a
mathematics course. Educational Research and Reviews, 8(14), 1158–1165.
4 1. Grasha, A. F. (1990). Using traditional versus naturalistic approaches to assessing learning
styles in college teaching. Journal of Excellence in College Teaching, 1, 23–38.
42. Mayer, R. E., & Massa, L. J. (2003). Three facets of visual and verbal learners: Cognitive
ability, cognitive style, and learning preference. Journal of Educational Psychology, 95(4), 833.
110 L. A. Mills et al.
43. Koch, J., Salamonson, Y., Rolley, J. X., & Davidson, P. M. (2011). Learning preference as a
predictor of academic performance in first year accelerated graduate entry nursing students: A
prospective follow-up study. Nurse Education Today, 31(6), 611–616.
4 4. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure
a nalysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1),
1–55.
4 5. Jöreskog, K., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS
command language. Chicago, IL: Scientific Software International Inc..
4 6. DeVellis, R. F. (1991). Scale development. Newbury Park, NJ: Sage.
4 7. Huberty, C. J., & Petoskey, M. D. (2000). Multivariate analysis of variance and covariance. In
H. Tinsley & S. Brown (Eds.), Handbook of applied multivariate statistics and mathematical
modeling. New York: Academic Press.
4 8. Jones, S. (2002). The internet goes to college: How students are living in the future with
today’s technology. [Report]. Pew Internet Project.
4 9. Boonsue, W., Jansem, A., & Srinaowaratt, S. (2015). International patterns in face-to-face and
synchronous computer-mediated communication in problem-based learning contexts. Journal
of Language Teaching and Research, 6(1), 99–110.
50. Quintas, C., Fernandes Silva, I., & Teixeira, A. (2017). Assessing an e-learning and b- learning
model—A study of perceived satisfaction. International Journal of Information and Education
Techology, 7(4), 265–268.
51. Zhang, D., Zhao, J. L., Zhou, L., & Nunamaker, J. F., Jr. (2004). Can e-learning replace
classroom learning? Communications of the ACM, 47(5), 75–79.
5 2. Cattell, R. B. (1987). Intelligence: Its structure, growth and action. Waltham: Elsevier.
5 3. Kuhlthau, C. C. (1993). A principle of uncertainty for information seeking. Journal of
Documentation, 49(4), 339–355.
54. Weiler, A. (2005). Information-seeking behavior in generation Y students: Motivation, critical
thinking, and learning theory. The Journal of Academic Librarianship, 31(1), 46–53.
5 5. Siemens, G. (2015). The role of MOOCs in the future of education [Foreword 1]. In C. Bonk,
M. M. Lee, T. Reeves, & T. H. Reynolds (Eds.), MOOCs and open education around the
world. New York: Routledge.
Chapter 7
Students’ Self-Regulated Learning
Through Online Academic Writing
in a Course Blog
Athanassios Jimoyiannis, Eleni I. Schiza, and Panagiotis Tsiotakis
Abstract This chapter presents an investigation on students’ self-regulation prac-
tices associated with online academic writing and inquiry through blogging, in the
context of a blended postgraduate course. The design framework of this particular
intervention was determined by the assumption that online writing in course blogs
could be a means of increasing students’ academic writing skills. The key aspects of
students’ engagement, peer interaction, and reflection were directed by the princi-
ples of self-directed and self-regulated learning. Students’ contributions to the
course blog were analyzed by using two schemas, i.e., Community of Inquiry and
Learning Presence. Descriptive and content analysis revealed important information
regarding individual inquiry and performance, peer interaction and reflection, as
well as self-regulated learning actions exhibited by the participants within a com-
munity of inquiry. The results provided supportive evidence that online academic
writing was an effective learning activity and promoted students’ cognitive presence
as well as their learning presence. Students’ self- and co-regulated learning appeared
to be addressed to the dimensions of monitoring, strategy use, and reflection.
7.1 Introduction
In the last years, a new generation of e-learning programs appears to dynamically
evolve in higher education by harnessing the core features of Web 2.0 applications
as well as the open educational resources available for both educators and students.
These emerging learning environments have fundamentally changed our thinking
about e-learning, pedagogical strategies, students’ activities and learning out-
comes. They provide multiple opportunities for sharing content and resources,
communication and dialogue, self-directed and reflective learning, and collabora-
tive and ubiquitous learning [1–3]. Therefore, they challenge educators and institu-
tions to consider new ways of delivering educational programs by (a) extending
A. Jimoyiannis (*) · E. I. Schiza · P. Tsiotakis
Department of Social and Educational Policy, University of Peloponnese, Korinthos, Greece
e-mail: [email protected]; [email protected]
© Springer International Publishing AG 2018 111
D. Sampson et al. (eds.), Digital Technologies: Sustainable Innovations for
Improving Teaching and Learning, https://doi.org/10.1007/978-3-319-73417-0_7
112 A. Jimoyiannis et al.
learning beyond time and space bound classroom places and (b) adopting new
pedagogical models which offer enhanced opportunities for authentic learning
through self-d irection, participation, collaboration, social networking and commu-
nity building [4].
Among Web 2.0 learning environments, educational blogs are very popular in
higher education. There are two main types of participant contributions (publica-
tions) in a blog, i.e., content posts which typically include content information (text,
visual, audio, video, web links) and comments to previous posts. Due to the user-
friendly format and their organizational features (e.g., arrangement of comments in
reverse chronological form, archives and thematic organization, taglines, permanent
links, etc.), blogs are widely considered as participatory, open, reflective, and col-
laborative learning environments [5]. They are used to promote students’ critical
thinking and knowledge construction as members of an online community of learn-
ing created within the blog space [1, 6].
Therefore, educational blogs are deemed to construct dynamic learning environ-
ments that have the affordances (a) to improve students’ authoring and communica-
tions skills [7–10], and (b) to promote students’ expressing and exchanging ideas,
resources sharing, critical and reflective thinking, and self-directed and collabora-
tive learning [11–14].
Current trends in higher education promote the notion that university students
should be prepared to effectively connect content knowledge (classroom instruc-
tion, readings, and online search) and their writing skills.
Traditionally, academic writing has followed a “content-textual” approach which
appears to ignore the learning context and the practices used by the students. In this
direction, Wingate et al. [15] suggested that academic writing is both a process and
a textual product, i.e., students’ written artifacts and essays. Students must have
enhanced opportunities, as well as the appropriate guidance, to carry out systematic
review and scientific inquiry through detailed and analytical reading of literature
papers, critical thinking, argumentation and documentation, expressing ideas and
discussion, in order to revise misconceptions, to internalize new concepts, and to
use problem-solving techniques [16]. Moreover, the students involved in online aca-
demic writing activities are expected to draw new forms of scientific discourse by
critically reflecting on their own academic writing and interacting with their class-
mates [15, 17, 18].
On the other hand, the research regarding online learning has revealed that many
higher education students enrolled in online courses face difficulties to achieve a
good understanding of the critical differences between online learning and the tra-
ditional classroom settings [19]. Unlike traditional classroom settings, online learn-
ing environments lack structured guidance and they demand students’ readiness and
ability for self-directed engagement. In addition, many students are not well pre-
pared to effectively participate in student-centered, autonomous, and collaborative
learning activities. Existing research has also revealed that students’ academic per-
formance in online learning is associated to their adoption and effective application
of self-regulated learning strategies [20–22]. It seems that students with strong self-
regulation tended to promote motivation, to persist with learning in challenging
7 Students’ Self-Regulated Learning Through Online Academic Writing in a Course… 113
tasks, to put more efforts into assignments and learning activities, and, finally, to
demonstrate higher learning outcomes and achievements.
Currently, self-regulated learning in online environments is a topic of great
research interest and a range of issues are still open for investigation [19]. In par-
ticular, relatively few studies have explored the dynamics of students’ discourse,
critical thinking, interpersonal interaction and reflection, as well as the emergence
of self-regulated learning in online collaborative environments. Another major chal-
lenge for research is related to shedding light on the individuals’ shared reflection
actions in online academic writing.
In response to the issues above, this chapter reports upon the design and the con-
sequent implementation of an instructional intervention regarding students’ engage-
ment, interaction, and learning presence in a course blog aiming to support their
academic writing and argumentation skills, in the context of a masters’ course. The
study was designed to explore the different forms of learning presence exhibited by
the students, through the lens of an online community of inquiry, which was
expected to be created around academic writing in the course blog. Additionally,
this chapter has the ambition to contribute by presenting a qualitative analysis based
on the learning presence schema, proposed by Shea and Bidjerano [23, 24], to reveal
valuable information regarding the dynamics of social interaction among students
and their self-regulated actions within an online community. Source data were the
content artifacts appeared in the blog in the form of students’ academic articles and
discussion comments that represent discourse, peer feedback, and reflection.
The main assumption addressing this particular study was that the participants,
as experienced learners, have developed a coherent base of content knowledge as
well as the learning, cognitive, and metacognitive skills that could help researchers
to reveal valuable information about self-regulated learning in online collaborative
environments. Therefore, this study has a twofold objective: (a) to explore the effec-
tiveness of students’ academic writing and scientific argumentation in a blogging
initiative, embedded in a master’s course, (b) to analyze students’ self-directed and
self-regulated performance by identifying critical indicators representing their
learning presence within a learning community (i.e., motivation, argumentation,
and critical writing). In accordance with the research objectives, the following
research questions were addressed:
• To what extent the course blog afforded an effective environment that promoted
students’ online academic writing within a community of inquiry?
• To what extent self-regulated learning and co-regulated learning can be achieved
in the context of online academic writing in the course blog? Which forms of
learning presence were prominent among the participants?
The chapter is structured as following. The theoretical foundations of online aca-
demic writing and self-regulated learning that addressed the design framework of
the present intervention are presented in detail. The methodological issues of the
research as well as the preliminary findings of both descriptive and content analysis
are presented to depict students’ learning presence, interaction and collaboration.
114 A. Jimoyiannis et al.
Finally, conclusions are drawn for future development and research with regard to
online academic writing and self-directed learning.
7.2 Theoretical Framework
7.2.1 Content 2.0 and Online Writing
The debate about the adoption of e-learning in higher education, in both blended
and distance learning formats, is mainly addressed by the affordances of the existing
Web 2.0 tools to support learner-centered and personalized forms of learning. A
radical shift in online pedagogy is quite apparent from individual to collaborative
approaches that put emphasis on interaction and learning within a community of
learners who share the same interests and goals [3, 4, 25].
In this perspective, Goodfellow [26] suggested that academic writing, in many
subject areas, should be an integral to learning, in a sense that the development of
students’ writing ability is practically seen as a strong indicator of knowledge acqui-
sition. In addition, she argued that students’ induction into academic culture and
communities of discourse is the principal way to demonstrate the knowledge and
skills they have acquired during their studies. Likewise, Wingate et al. [15] sug-
gested that writing instruction could be embedded in higher education contexts.
They also reported that the integration of writing tasks, both in-class and online, and
assessment feedback was effective to support the development of students’ abilities
in academic writing.
Academic writing skills are key competencies that both undergraduate and mas-
ter students need to develop [16, 26]. Their development is based on critical and
analytical reading, dynamic discourse, and feedback among peers; eventually, it is
the outcome of critical thinking. In this perspective, the use of Web 2.0 environ-
ments, like blogs and wikis, as online writing spaces in blended and fully online
courses creates a new form of learning environment and an emergent research topic.
Because of their key features (i.e., participation, openness, interactivity, collabora-
tion, and sociability), these tools can operate as dynamic collaboration spaces sup-
porting long-term, online academic writing activities. A course blog, for example,
offers to the students a common space operating, in an integrated manner, as a
content composition system, an online discussion tool, and a literature (content
source) repository.
Student generated content (referred also as Content 2.0) is a critical factor and a
new notion in e-learning, since it is a new type of content authentically created
through students’ engagement, reflection, and collaboration within online commu-
nities [27]. By incorporating both content evolution and community features,
Content 2.0 reflects both the product and the process of learning in online collabora-
tive environments [25]. Therefore, Content 2.0 is totally different in nature compar-
ing to the officially provided content in conventional e-learning programs.
7 Students’ Self-Regulated Learning Through Online Academic Writing in a Course… 115
Independent studies have shown that blogs promote students’ reflective and col-
laborative creation skills while they can improve critical thinking and writing skills.
For example, Kung [10] explored students’ perceptions, motivation, and confidence
along with their perceived strengths and weaknesses of learning academic writing
through blog-assisted language learning. Likewise, Novakovich [11] conducted a
comparative study on students’ academic writing and peer feedback by using tradi-
tional in-class methods and blog-mediated writing practices. The findings suggested
improved quality in students’ writing in the blog and increased peer feedback reflec-
tion in the form of critical and directive comments, which promoted students’ self-
assessment and metacognitive self-awareness. Kathpalia and See [17] showed that
class blogs were efficient to enhance students’ scientific argumentation with valid
claims, evidence, and rebuttals in a critical writing course. In addition, they advo-
cated that specific pedagogical strategies, like argumentation prompts and peer-
evaluation schemes, are necessary to enhance student argumentation through blogs.
By extending previous research findings, Jimoyiannis and Tsiotakis [28] have found
that students’ learning in educational blogging was achieved as the outcome of
reflection and collaboration among active participants in an online community.
Blogs in particular offer an ideal community space for student generated content,
through participatory, reflective, and constructive ways, which direct the transition
from tutor-led to open learning approaches where content is constructed by the
learners themselves in a dynamic-evolving manner [25]. The primary objective of
student generated content is to stimulate shared and collective knowledge by (a)
making transparent the individual learning trajectories and (b) promoting students’
collaboration, creativity, and community identity. In particular, blogs offer enhanced
opportunities to combine individual students’ creative thinking with peer interac-
tions through ideas’ presentation, argumentation, group dialogue, shared reflection,
and regulation, features that are expected to support high quality learning [29].
In an educational blog, student contributions are evolving artifacts constituted by
an initial (starting) article or post and the related peer or tutor’s comments. This is
an integration of content and interaction, both appeared in the same space (the blog),
which offers an overview of the topics under study and the ideas therein, students’
individual contributions and reflection, peer feedback, the overall meaning, and the
collective knowledge constructed within the community [29]. In online academic
writing, student performance content constitutes the main challenge which deter-
mines the transition from tutor-led, professional content delivered to learners to
open learning approaches, where content will be constructed by the learners them-
selves in a dynamic and evolving manner. Under well-designed learning situations,
student performance content elaborates students’ knowledge construction through
their personal inquiry, article writing, ideas evolution, peer reflection, and co-
creation. This is a long-term, evolving process that combines multiple forms of
learning actions, i.e., formal, personal, and social ones. Ultimately, these help online
learners to transform their performance content into professional content, collabora-
tively constructed as active members of a learning community (Fig. 7.1).
Student performance content is dynamically generated by the students them-
selves in the course blog. It includes both the written assignments as well as e vidence
Community learning 116 A. Jimoyiannis et al.
Formal learning Personal learning
Classroom sessions Community space Personal space
Educational material
Student discussion Student inquiry
Professional content and blog articles
content
Fig. 7.1 Content 2.0 and online academic writing in blogs
7 Students’ Self-Regulated Learning Through Online Academic Writing in a Course… 117
of the individual process of learning adopted (e.g., successive contributions, drafts
of solutions, descriptions of mistakes, difficulties encountered, peer interaction and
feedback, collaboration indicators, etc.). Discussion content is produced through
blog comments which represent student communication and discourse. Typically,
they include questions and replies, proposals and guidelines, new ideas and content
resources, feedback, or comments to existing content contributions. Therefore, stu-
dents are engaged in critical discourse which represents their collaborative attempts
to jointly understand a specific topic, independently of possible guidance or support
provided by their instructor.
The cognitive benefits of peer interaction are determined by the assumption that
classmates’ contributions can provide additional information, new perspectives,
reminders, constructive feedback, new modes of reasoning and critical thinking,
etc., all expected to enhance learning and enrich knowledge through guidance, scaf-
folding, and peer support. Chi [30] has explained that this type of interaction
involves co-construction of knowledge and enhances understanding by allowing
learners to do things like build upon each other’s contributions, defend and argue
positions, challenge and criticize each other on the same concepts or points, ask and
answer each other’s questions. Therefore, in online collaborative environments,
peer interaction is constructive, by nature, because learners are generating knowl-
edge that goes beyond the information that would typically be provided by the
learning materials.
7.2.2 The Notion of Learning Presence
Online learners are usually engaged in discourse that includes collaborative attempts
to understand a specific topic independently or with guidance and support provided
by peers and the instructor. The Community of Inquiry (CoI) model articulates that
online learners create a community of inquiry with a shared goal of achieving their
learning outcomes as a result of collaborative work among active participants [31].
The fundamental idea in online collaborative environments is that three mutually
related components (presences) are required to enable meaningful learning experi-
ences and successful knowledge construction, i.e., teaching, social, and cognitive
presence.
Social presence is related to the ability of a student to identify and generate a
sense of social interaction with other members of the learning community (i.e.,
classmates and instructor). Cognitive presence relates to the extent to which learn-
ers construct knowledge through active discourse and individual and shared reflec-
tion. Ultimately, cognitive elements are activated when students are challenged and
intellectually stimulated, throughout a dynamic process, to achieve a meaningful
learning experience.
Self-regulated learning is currently considered as a sound theoretical framework
for describing the critical aspects of inquiry and reflective learning activities that
118 A. Jimoyiannis et al.
emerge within online learning environments. The critical role of self-regulation in
online learning has been widely acknowledged [20–22, 32–34].
Shea and Bidjerano [23, 24] have introduced and investigated the construct of
learning presence (LP) as a moderator in the Community of Inquiry model. In their
perspective, the notion of LP is considered as a new dimension of the CoI frame-
work with the aim to explain the attitudes, abilities, and behaviors that active and
engaged students bring to their individual and collaborative activities in online envi-
ronments [19, 35]. Learning presence was grounded in the theoretical approach of
Zimmerman [36] about self-regulation, which is generally based on a cyclical pro-
cess of learning that includes planning (forethought), performance (monitoring and
strategy use), and reflection. Learning presence is defined by four mutually related
pedagogical notions: (a) forethought and planning, (b) monitoring, (c) strategy use,
and (d) reflection. The LP notion forms a sound theoretical construct towards
describing the significant aspects of the learning processes, which relate specific
learning outcomes to learners’ goals, motivations, volitions, and actions.
Within online learning environments, learning presence is simultaneously predi-
cated by individual efforts and the group dynamics [19]. In this context, emphasis is
placed on three, mutually related, dimensions of regulation [37, 38]: (a) self-
regulation, an individual is looking after his own activities; (b) co-regulation, an
individual is scaffolding and regulating another’s learning initiatives; and (c) shared
regulation, individuals are working together to regulate each other’s learning.
Given the self-directed and social nature of students’ online academic writing, in
the intervention regarding presented in this chapter, it is very important to examine
the different aspects of students’ self- and co-regulation in the course blog, espe-
cially as they are tightly related to expected higher levels of cognitive presence,
according to the CoI framework. As proposed by Zimmerman [36], self-regulated
learning theory extends conceptions of learning beyond the cognitive processes by
recognizing the influential roles of motivation, emotion, metacognition, and strate-
gic behaviors. What is important and original to be investigated in online learning
environments, under the lens of self-regulated learning, is, therefore, to identify
motivational, behavioral, and metacognitive traits in learning situations that are
directed and controlled by the students themselves as online learners.
7.3 C ourse Design and Research Method
7.3.1 Context and Participants
The present intervention and the study followed were conducted at the Department
of Social and Educational Policy, University of Peloponnese, in Greece, in the con-
text of a masters’ degree course entitled “e-learning and ICT in education”. A total
of 20 students, all having a bachelor degree in various educational disciplines, were
7 Students’ Self-Regulated Learning Through Online Academic Writing in a Course… 119
enrolled in this course. The majority of the participants (17) were in-service teach-
ers in primary and secondary schools.
The course was designed in a blended format including five face-to-face sessions
properly interwoven with online work, both individual and collaborative, in Mahara
e-portfolio. The main objectives of the course were to enhance students’ theoretical
knowledge in e-learning, their learning design skills, as well as their competency in
conducting field research and communicating their achievements to a scientific
audience consisted of peers and the instructor.
The instructor was acting as e-moderator [39] by shaping an emerging reflective
and collaborative framework with the aim to support dialogue and peer interaction
and promote students’ self-directed and self-regulated learning. He encouraged the
students not only to restrict their activity in publishing individual articles in the
course blog but also to post critical comments and comprehensive argumentation on
peer articles as well as to debate on current research topics. Guidelines were also
given to the students with regard to searching and referencing on scientific litera-
ture, doing critical evaluation of primary research articles, and writing synthesizing
literature reviews. In addition, examples of academic writing strategies and sugges-
tions to avoid plagiarism were given.
7.3.2 C ourse Workflow and Students’ Performance
The course was thematically structured into two parts. Individual and collaborative
coursework were properly interwoven and expected to contribute towards achieving
the learning objectives of the course. In the first part, between the starting and sixth
week of the course, the participants were asked to get involved in individual inquiry
and make literature search in various topics of their choice, related to e-learning and
ICT in education.
In the second part of the course (7th to 14th week), each student was advocated
to write three academic articles (of 1000–1500 words) in the form of blog publica-
tions, hosted in the Mahara platform area. These articles should be related to course
topics and the literature review individually implemented by the students. In addi-
tion, the students were advised to use a proper way of writing with the aim to com-
municate their outcomes and share their knowledge with peers. Students were asked
to be actively engaged into the course blog activities, on a systematic and regular
basis. They were expected a) to reflect upon peer contributions through comment-
ing, criticizing, expressing alternative opinions, and expanding ideas or themes and
b) to create a culture of collaboration with the aim to develop a common space of
valuable academic content.
120 A. Jimoyiannis et al.
7.3.3 Analysis Framework
The primary research data were collected by monitoring and recording students’
online activities in the blog area of the online platform along the 8 weeks of the
second part of the course. Existing content analysis procedures, well-known and
applied in the analysis of asynchronous online interactions, were used. Every blog
publication was considered as the unit of analysis [29]. Students’ contributions were
divided into articles and comment posts; typically, the latter included questions,
replies, new ideas, argumentation, or criticism regarding a particular article and
previous comments.
In order to codify students’ interaction and self-regulation within the blog com-
munity of academic writing, the learning presence framework was adopted [23].
Since the LP construct addresses the regulatory processes students display, instruc-
tor postings were excluded from our analysis. Content analysis of students’ contri-
butions to online writing included four mutually related dimensions, according to
the learning presence model: (a) forethought and planning, (b) monitoring, (c) strat-
egy use, (d) reflection.
Once the coding scheme was established, two independent researchers analyzed
all contributions in the course blog. The researchers met to compare results of cod-
ing and established an inter-rater reliability. Initial and negotiated inter-rater agree-
ment was established to refine and finalize the coding scheme. Members’
contributions (articles and comments on peer articles) were recorded and analyzed
using NVivo software (ver. 10). Data reduction, thematic interpretation, as well as
the relevant categories and indicators of the learning presence framework were
emerged from a detailed sententious analysis.
7.4 F indings
7.4.1 S tudents’ Engagement in Online Writing
The majority of the students appeared to be effective, and they contributed to both
dimensions of the online writing activities, individual and collaborative. They pub-
lished their academic articles and discussed on critical theoretical, pedagogical, and
learning design issues that emerged in face-to-face course sessions and online dis-
cussions. They appeared as active members of a community of inquiry by exchang-
ing ideas, sharing educational material and experiences, suggesting resources,
drawing conclusions, and, eventually, co-constructing new content knowledge. In
some cases, they moved their work forward by creating specific groups of interest
and designing educational scenarios applicable in school practice. Figure 7.2 shows
a screenshot presenting a typical student article related to the topic of “Learning
Analytics” and the discussion among classmates that was evolving through peer
feedback and reflection postings.
7 Students’ Self-Regulated Learning Through Online Academic Writing in a Course… 121
Fig. 7.2 A typical student article and the related peer reflection postings
Table 7.1 shows the results of students’ activity in the blog which depict an over-
all picture of the academic writing part of the course and the main contributions of
each individual. A total of 52 original articles were published online until the end of
the course. They were related to various course topics, for example, e-learning,
learning design, Web 2.0 tools in educational practice, new pedagogy and ICT, dis-
tance learning, collaborative learning, learning communities, open educational
resources and digital content, MOOCs, Learning Analytics, etc. Comprehensive
discussions were spontaneously evolving around the various articles as indicated by
the comments published in the blog area by the students.
The majority of the students (16) were actively involved in the online academic
writing in the course blog. They performed very well in both learning dimensions
122 A. Jimoyiannis et al.
Table 7.1 Students’ writing Participant Articles Comments
activities in the course blog
S1 3 43
S2 3 24
S3 3 20
S4 3
S5 3 3
S6 0 31
S7 0
S8 3 0
S9 1 2
S10 3 28
S11 3 3
S12 3 51
S13 2 61
S14 3 63
S15 3 10
S16 3 8
S17 3 100
S18 3 24
S19 3 41
S20 3 64
T 1 58
Total 52 32
30
696
expected to appear, i.e., preparing their own articles and contributing to constructive
online discussions with classmates regarding the various blog articles. Some stu-
dents, for example, S15, S18, S12, S11, S19, and S10, had a significant number of
comprehensive comments (more than 50). On the other hand, two students (S9,
S13) were below the expected level of performance, since they wrote one or two
articles and they uploaded few comments to other articles. In addition, students S6
and S7 were not essentially involved in the online writing activities, mainly because
of personal reasons and problems.
The presence of the instructor (T) was intentionally reduced with the aim to cul-
tivate a culture of self-directed and self-regulated learning among the students. He
provided an article as example of what was expected from the students and a total
of 30 comments representing his role as moderator in students’ academic writing
and critical interaction processes. The instructor’s initiatives were restricted to
encourage students’ active engagement; to provide examples and indicate signifi-
cant issues, proper articulation, mindful argumentation, or important literature
sources; to make connections with classroom sessions; etc. He was also advising
them to apply techniques of self- and co-regulation to promote their learning.
7 Students’ Self-Regulated Learning Through Online Academic Writing in a Course… 123
90 Frequency (%) Teaching presence
80
70 CogniƟve presence
60
50
40
30
20
10
0
Social presence
Fig. 7.3 Classification of student comments in CoI categories
7.4.2 Learning Presence Analysis of Student Publications
Figure 7.3 presents the classification of students’ publications (articles and com-
ments) into the elements of the Community of Inquiry model [31]. It is quite clear
that the majority of them (609 contributions) represent students’ cognitive presence
within the community of academic writing. On the other hand, 102 comments were
assigned to social presence and 36 comments, mainly coming from the instructor,
were classified into the teaching presence category.
Content analysis of students’ discourse revealed, in addition, two modes of peer
interactions around the published articles in the blog. In many cases, the students
exhibited a high level of cognitive presence through postings that projected elabora-
tions, justifications, thoughtful argumentation, critical thinking, ideas negotiation,
and inferences, which are assumed to contribute to knowledge construction within
a community of inquiry. They all provided strong evidence of knowledge transfor-
mation, exchanging ideas and different perspectives, connection to previous content
knowledge, and integration within students’ existing cognitive structures. In con-
trast, low level of students’ cognitive presence appeared to refer to sharing informa-
tion or resources, expressing awareness, asking questions, clarifying new terms and
definitions, etc.
In the context of self-regulated learning, Shea and Bidjerano [24] identified
learning presence as an important moderator between teaching and social and cog-
nitive presence. They also advocated that, in the absence of satisfactory teaching
presence, measures of significant learning reflected in the cognitive presence factor
(up to 82% of the comments in the present case) are contingent upon student ability
to self-regulate their learning. In this sense, the notion of learning presence was
addressing our analysis further by assuming that it could potentially shed light into
the critical aspects of student self-directed learning actions.
124 A. Jimoyiannis et al.
Frequency (%)
60
50
40
30 Monitoring Strategy use ReflecƟon
20
10
0
Forethought and
planning
Fig. 7.4 Classification of student comments in learning presence categories
Following, students’ comments were classified into the various categories and
the consequent indicators of the learning presence framework. By combining the
two schemas, i.e., CoI and LP, our analysis of students’ performance revealed that
they were able to achieve high level of cognitive results due to self-regulated and
co-regulated actions. Figure 7.4 outlines the frequencies of student comments with
regard to the constructs of the learning presence notion. The majority of them were
assigned to the categories of Monitoring (48.6%) and Strategy use (39.8%), fol-
lowed by the Reflection (10.4%) and Forethought and planning (1.2%). According
to Shea et al. [19], the latter phase includes students’ interaction with regard to plan-
ning, coordinating, and delegating or assigning online tasks in the early stages of an
online course or a specific learning activity. Considering the blended nature of this
particular course and the personal features that determine students’ choices and
planning activities during academic writing, it is quite reasonable to record low
students’ activity in this dimension.
Table 7.2 presents the results of students’ contributions to the academic writing
project classified into the four phases and the related indicators of the learning pres-
ence framework. The findings demonstrated that most students, as members of a
community, were deeply engaged in a process of managing their inquiry activities,
thinking, writing and expressing ideas, as well as their emotions.
The indicators of monitoring category showed that students’ self- and co-
regulated learning was mainly addressed by advocating effort or focus, quality eval-
uation,appraisingpersonalinterest,engagementorreaction,andobserving–monitoring
during performance. They regulated their online activities by monitoring both their
individual performance and their peer contributions by interacting with their class-
mates, checking for understanding of specific topics, noting use of strategies,
encouraging and scaffolding peer efforts, and writing contributions. Moreover, the
participants managed their time efficiently in terms of literature search, preparation,
and writing tasks. On the other, the amount of time devoted to this collaborative
project was, in some cases, expanded beyond the official end of the course.
7 Students’ Self-Regulated Learning Through Online Academic Writing in a Course… 125
Table 7.2 Learning presence analysis of students’ publications in the course blog Comments
5
Category Indicator 1
2
Forethought Goal setting 2
Planning 5
Coordinating, delegating, or assigning tasks to self and others 3
Monitoring Checking for understanding 88
Identifying problems or issues 24
Noting completion of tasks
Evaluating quality 67
Observing or monitoring during performance and taking corrective 7
action 98
Appraising personal interest, engagement, or reaction 30
Recognizing learning behavior of self or group 16
Advocating effort or focus 22
Noting use of strategies 30
66
Strategy use Seeking, offering, or providing help 131
Recognizing a gap in knowledge 23
Reviewing 46
Noting outcome expectations 666
Seeking/offering additional information
Reflection Change in thinking
Causal attribution of results to personal or group performance
Total
It was also demonstrated that the active students were mindful of the strategies
they used and, therefore, they were able to recognize a gap in their knowledge in
specific topics, to seek for help in the community or offer additional information
and support to their classmates. In some cases, interesting regulative actions
appeared and provided the students the feedback necessary to reinforce them
towards a new cyclical process of inquiry. As students’ participation in the commu-
nity proceeded, they identified the relevance of the discussions taken place within
the blog articles with regard to the course and the new ideas that appear in the litera-
ture. In addition, they were also motivated to personally continue research and sci-
entific inquiry to promote their progress in new knowledge fields. More precisely,
these students were aware of and adopted effective online strategies in the blog, and
they demonstrated self-regulation by means of interaction and reflection with peers.
They were able to adopt new pedagogical ideas and address new applications in
teaching practice, based on the abilities and experiences shared with classmates in
the online community of academic writing.
A significant number of comments (69) were classified in the category of reflec-
tion. This finding indicates that the students were able to reflect on their actions and,
therefore, to evaluate their performance with regard to the expected learning out-
comes as well as the best writing and peer interaction strategies they should adopt
and apply. Students’ active engagement indicates their motivation to learn which
was cultivated through self- and peer-reflection in the blog.
126 A. Jimoyiannis et al.
Overall, the results demonstrated that online writing in blogs is an efficient
reflection process that supports students to manage and motivate their engage-
ment, to share experiences, to give and receive feedback, and to regulate their
practices and use their own strategies, in order to achieve their goals as members
of a learning community. Eventually, the students successfully directed them-
selves to online academic writing and shaped their own learning trajectories. They
also expressed their satisfaction of this particular learning experience and the
learning outcomes they achieved. In some cases, they pointed out their intention
to embrace similar practices in their professional and instructional choices in
classroom practice.
7.5 C onclusions
This study reported on students’ self-regulated learning in an online academic writ-
ing program through a course blog. Our analysis has shown that students’ publish-
ing of their academic work on the course blog offered enhanced opportunities, in
terms of managing individual work, peer feedback and interaction, supportive dia-
logue and reflection, sharing ideas, evaluation criteria and strategies, and critical
thinking and metacognition, all the above having a positive impact on the quality of
academic writing. Confirming existing research findings [10, 11, 17], our results
provided supportive evidence that online academic writing, embedded in higher
education courses through blogs, can promote students’ reflective engagement, crit-
ical thinking, writing skills, as well as collaborative construction of knowledge as
active members of an online community.
Using the learning presence schema, as the fourth dimension of the Community
of Inquiry theoretical framework, our analysis revealed important information with
regard to the regulatory processes the students displayed during the online academic
writing phase of the course. The findings provided a useful mapping of students’
performance within an online community of blog. The majority of their contribu-
tions were addressed to the categories of monitoring and strategy use, which
appeared to be prominent among the participants.
In comparing the distribution of the four LP categories, our findings are very
similar to the results reported by Shea et al. [33]; the monitoring construct was the
most frequent (48.6%) while strategy use (39.8%) occurred more frequently than
reflection (10.4%). Despite that one would expect to see more student activities
classified in the reflection construct; reflective strategies are actually demanding and
require appropriate students’ preparation in terms of high order of cognitive, meta-
cognitive, collaboration and critical thinking skills.
In terms of self-regulation, our findings showed that the course blog, as online
writing environment, offered enhanced opportunities to the students for cognitive
presence and self-regulated learning through peer communication, feedback, and
social support. In addition, in the absence of continuous teaching presence, origi-
nated from the instructor’s side, students’ learning is reflected on their cognitive
7 Students’ Self-Regulated Learning Through Online Academic Writing in a Course… 127
presence which was contingent upon individual ability to self-regulate their
learning actions. In this sense, our results regarding the indicators of students’
learning presence in online academic writing seem to confirm the assumption of
Shea and Bidjerano [24] that self-regulated learning approaches can effectively
“compensate the lack of adequate teaching presence” in fully online and blended
courses.
The majority of the participants demonstrated enhanced interest and individual
engagement in the community of inquiry, which was built within the course blog
through peer interaction, reflection, and support. Online learning communities
should be, in many senses, self-directed, self-organized, and dynamic. However,
online environments do not generally lead, by definition, to significant learning out-
comes as indicating by the peripheral role exhibited by four students. Our findings
indicate that a balance between openness and obligatory contributions is necessary
to effectively organize, support, and constructively influence students’ engagement
in communities of learning [40].
Considering the pivotal role of self-regulation in online learning future research,
particularly in higher education contexts, could be directed to identifying pedagogi-
cal strategies that foster students’ self- and co-regulatory behaviors in blended and
fully online learning environments. Deploying effective pedagogical practices can
help to leverage the affordances of blogs as online writing environments that sup-
port self-regulated learning and knowledge construction through social interaction,
peer feedback, and reflection within communities of learning.
The findings of this study may not be generalizable to other contexts because of
the specific sample, the context of implementation, and the learning strategy
deployed. However, as long as the limitations are recognized, we hope that this
study could be of value for educators and researchers and stimulate dialogue towards
exploring the nature of self-regulated learning strategies. The new idea that, hope-
fully, could contribute to the existing literature of online learning is related to the
integration of academic writing and self-regulated approaches to create effective
learning activities in online environments.
Further investigation and empirical testing could prove the validity of the find-
ings and the proposed design framework about online academic writing in higher
education. Our future research will be directed to the structure of students’ learning
presence, evolving within the blog communities, with the aim to reveal more infor-
mation about the salient differences among self-regulation, co-regulation, and
shared regulation advocated by Volet et al. [41]. In addition, combining social net-
work analysis with qualitative content analysis representing online students’ learn-
ing presence, we expect to shed light into the information flow in the course blog,
students’ connections and groups developed therein, as well as the power and the
influence each student had within the community of online writing. Thus, we expect
to map an overall view of learners’ knowledge construction and the different modes
of engagement, interaction and control, as well as the regulatory and supportive
practices shared among students, necessary to keep learning in online collaborative
environments sustainable.
128 A. Jimoyiannis et al.
References
1. Jimoyiannis, A., Tsiotakis, P., & Roussinos, D. (2013). Social network analysis of students’
participation and presence in a community of educational blogging. Interactive Technology
and Smart Education, 10(1), 15–30.
2. Jimoyiannis, A., Tsiotakis, P., Roussinos, D., & Siorenta, A. (2013). Preparing teachers to inte-
grate web 2.0 in school practice: Toward a framework for pedagogy 2.0. Australasian Journal
of Educational Technology, 29(2), 248–267.
3. McLoughlin, C., & Lee, M. J. W. (2010). Personalised and self-regulated learning in the web
2.0 era: International exemplars of innovative pedagogy using social software. Australasian
Journal of Educational Technology, 26(1), 28–43.
4. Dede, C. (2011). Reconceptualizing technology integration to meet the challenges of educa-
tional transformation. Journal of Curriculum and Instruction, 5(1), 4–16.
5. Angelaina, S., & Jimoyiannis, A. (2011). Educational blogging: Developing and investigating
a students’ community of inquiry. In A. Jimoyiannis (Ed.), Research on e-learning and ICT in
education (pp. 167–180). New York: Springer.
6. Tang, E., & Lam, C. (2014). Building an effective online learning community (OLC) in blog-
based teaching portfolios. The Internet and Higher Education, 20, 79–85.
7. Chang, Y. J., & Chang, Y. S. (2014). Assessing peer support and usability of blogging in hybrid
learning environments. Interactive Learning Environments, 22(1), 3–17.
8. Deng, L., & Yuen, A. H. K. (2013). Blogs in pre-service teacher education: Exploring the par-
ticipation issue. Technology, Pedagogy and Education, 22(3), 339–356.
9. Kerawalla, L., Minocha, S., Kirkup, G., & Conole, G. (2009). An empirically grounded frame-
work to guide blogging in higher education. Journal of Computer Assisted Learning, 25,
31–42.
10. Kung, F.-W. (2015). Assessing an innovative advanced academic writing course through blog-
assisted language learning: Issues and resolutions. Innovations in Education and Teaching
International. https://doi.org/10.1080/14703297.2015.1108213
1 1. Novakovich, J. (2016). Fostering critical thinking and reflection through blog-mediated peer
feedback. Journal of Computer Assisted Learning, 32(1), 16–30.
1 2. Paulus, T., & Spence, M. (2010). Using blogs to identify, misconceptions in a large under-
graduate nutrition course. TechTrends, 54(5), 62–68.
13. Tan, S. M., Ladyshewsky, R. K., & Gardner, P. (2010). Using blogging to promote clinical rea-
soning and metacognition in undergraduate physiotherapy fieldwork programs. Australasian
Journal of Educational Technology, 26(3), 355–368.
1 4. Robertson, J. (2011). The educational affordances of blogs for self-directed learning.
Computers & Education, 57(2), 1628–1644.
15. Wingate, U., Andon, N., & Cogo, A. (2011). Embedding academic writing instruction into
subject teaching: A case study. Active Learning in Higher Education, 12(1), 69–81.
16. North, S. (2005). Different values, different skills? A comparison of essay writing by students
from arts and science backgrounds. Studies in Higher Education, 30(5), 517–533.
1 7. Kathpalia, S. S., & See, E. K. (2016). Improving argumentation through student blogs. System,
58, 25–36.
18. Tuomainen, S. (2016). A blended learning approach to academic writing and presentation
skills. International Journal on Language, Literature and Culture in Education, 3(2), 33–55.
1 9. Shea, P., Hayes, S., Uzuner-Smith, S., Gozza-Cohen, M., Vickers, J., & Bidjerano, T. (2014).
Reconceptualizing the community of inquiry framework: An exploratory analysis. The Internet
and Higher Education, 23, 9–17.
20. Artino, A. R., & Stephens, J. M. (2009). Academic motivation and self-regulation: A compara-
tive analysis of undergraduate and graduate students learning online. The Internet and Higher
Education, 12, 146–151.
2 1. Cho, M.-H., & Shen, D. (2013). Self-regulation in online learning. Distance Education, 34(3),
290–301.
7 Students’ Self-Regulated Learning Through Online Academic Writing in a Course… 129
22. Sun, J. C.-Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-
regulation: Their impact on student engagement in distance education. British Journal of
Educational Technology, 43(2), 191–204.
2 3. Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-
regulation, and the development of a communities of inquiry in online and blended learning
environments. Computers & Education, 55, 1721–1731.
24. Shea, P., & Bidjerano, T. (2012). Learning presence as a moderator in the community of
inquiry model. Computers & Education, 59, 316–326.
25. Jimoyiannis, A. (2015). TPACK 2.0: Towards a framework guiding web 2.0 integration in
educational practice. In M. S. Khine (Ed.), New directions in technological pedagogical con-
tent knowledge research multiple perspectives (pp. 83–108). Charlotte, NC: Information Age
Publishing.
2 6. Goodfellow, R. (2005). Academic literacies and e-learning: A critical approach to writing in
the online university. International Journal of Educational Research, 43(7–8), 481–494.
2 7. Ehlers U.-D. (2013). Open Learning Cultures: A Guide to Quality, Evaluation, and Assessment
for Future Learning. Berlin Heidelberg: Springer.
28. Jimoyiannis, A., & Tsiotakis, P. (2017). Beyond students’ perceptions: Investigating learning
presence in a community of educational blogging. Journal of Applied Research in Higher
Education, 9(1), 129–146.
29. Jimoyiannis, A., & Angelaina, S. (2012). Towards an analysis framework for investigating stu-
dents’ engagement and learning in educational blogs. Journal of Computer Assisted Learning,
28(3), 222–234.
30. Chi, M. T. H. (2009). Active-constructive-interactive: A conceptual framework for differentiat-
ing learning activities. Topics in Cognitive Science, 1, 73–105.
31. Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environ-
ment: Computer conferencing in higher education. The Internet and Higher Education, 2,
87–105.
3 2. Bol, L., & Garner, J. K. (2011). Challenges in supporting self-regulation in distance education
environments. Journal of Computing in Higher Education, 23(2–3), 104–123.
33. Shea, P., Hayes, S., Uzuner-Smith, S., Vickers, J., Bidjerano, T., Gozza-Cohen, M., … Tseng,
C.-H. (2013). Online learner self-regulation: Learning presence viewed through quantitative
content and social network analysis. International Review of Research in Open and Distance
Learning, 14(3), 427–461.
34. Winters, F. I., Greene, J. A., & Costich, C. M. (2008). Self-regulation of learning within
computer-based learning environments: A critical analysis. Educational Psychology Review,
20(4), 429–444.
3 5. Shea, P., Hayes, S., Uzuner, S., Vickers, J., Bidjerano, T., Pickett, A., … Jian, S. (2012).
Learning presence: Additional research on a new conceptual element within the community of
inquiry (CoI) framework. The Internet and Higher Education, 15(2), 89–95.
3 6. Zimmerman, B. (2008). Investigating self-regulation and motivation: Historical background,
methodological developments, and future prospects. American Educational Research Journal,
45(1), 166–183.
37. Grau, V., & Whitebread, D. (2012). Self and social regulation of learning during collaborative
activities in the classroom: The interplay of individual and group cognition. Learning and
Instruction, 22(6), 401–412.
38. Volet, S., Summers, M., & Thurman, T. (2009). High-level co-regulation in collaborative learn-
ing: How does it emerge and how is it sustained? Learning and Instruction, 19(2), 128–143.
39. Salmon, G. (2003). E-Moderating: The key to teaching and learning online. London:
Routledge.
40. Tsiotakis, P., & Jimoyiannis, A. (2016). Critical factors towards analysing teachers’ presence
in on-line learning communities. The Internet and Higher Education, 28, 45–58.
4 1. Volet, S., Vauras, M., & Salonen, P. (2009). Self-and social regulation in learning contexts: An
integrative perspective. Educational Psychologist, 44(4), 215–226.
Chapter 8
Digital Tool Use and Self-Regulated
Strategies in a Bilingual Online
Learning Environment
Ulla Freihofner, Chris Campbell, and Simone Smala
Abstract This chapter details the investigation into how Year 9 students experience
and negotiate a technology-enhanced learning environment in their bilingual class-
room. The study investigated how their translanguaging practices (using both
German and English to communicate in bilingual education settings) contribute to
the self-regulation of their learning in a scientific open inquiry process. Data for this
study were collected via voice recordings, a student-designed questionnaire, and
focus group interviews with 22 Year 9 students who studied 18 Biology lessons dur-
ing 6 weeks and over 2 consecutive years. The study revealed that students’ self-
regulatory practices during open inquiry processes developed in specific ways
through the exposure to a bilingual classroom setting, for example, by being exposed
to unknown terms in German which led students to search for translations and then
on to further self-initiated and self-regulated research to find explanations online.
Students favored the teacher prepared German language biology content in guided
customized simulations using computer software than their own self-initiated prac-
tices. The tool use also appeared to be reliant on students’ prior disposition to using
such a tool. Thus, the results of this study have implications for the future custom-
ization of online learning spaces for high school students and educators in bilingual
settings as well as in other fields.
U. Freihofner · S. Smala 131
School of Education, The University of Queensland, Brisbane, QLD, Australia
e-mail: [email protected]; [email protected]
C. Campbell (*)
Griffith University, Nathan, QLD, Australia
e-mail: [email protected]
© Springer International Publishing AG 2018
D. Sampson et al. (eds.), Digital Technologies: Sustainable Innovations for
Improving Teaching and Learning, https://doi.org/10.1007/978-3-319-73417-0_8
132 U. Freihofner et al.
8.1 Introduction
In the recent past, student learning environments have changed due to the introduc-
tion of educational technology or information and communication technologies
(ICT) into classrooms [1] with many schools throughout the world, including in
Australia, now teaching using much more educational technology as a tool.
Empirical research in regards to best practice for teaching students has mainly
focused on the delivery of information with less focus on the pedagogy involved in
using electronic learning technologies with little attention being given to how chil-
dren handle this learning [2]. Similarly, in the bilingual context, research has pre-
dominately focused on teacher-centered issues, disregarding communication
processes for meaning making from a student viewpoint [3]. Cook-Sather [4], in
particular, has argued for authorizing student perspectives as this would also intro-
duce missing perspectives of those who experience curriculum everyday [4]. Further
to this, the field of bilingual learning environments using an online learning envi-
ronment as a tool for learning has been much less studied. Although there has been
research into the design of online learning spaces for content and language inte-
grated learning (CLIL) environments [5–7] these have been teacher-centered rather
than student-centered research. Ludwig, Finkbeiner and Knierim [8] published pre-
liminary results on the application of self-regulation strategies from middle school
students while reading a text in a foreign language. Student results were derived
from a laboratory setting [8], removed from an authentic classroom situation. De
Diezmas [9] investigated the development of emotional competence in a CLIL set-
ting through a diagnostic assessment [9]. Even though both studies looked at differ-
ent aspects of self-regulation in combination with language learning, the outcomes
were not based on student voices reflecting on their use of technology in an authen-
tic CLIL classroom environment. As this research focuses on capturing student
opinion or voice about their experiences working independently in an online bilin-
gual learning environment, this research, therefore, is an important contribution to
the literature in this area.
This study investigated student perceptions on their use of an online learning
environment in a bilingual biology class, which allowed for considerations to the
course’s design. Importantly, the design features needed to permit strategies for
student self-regulation [10] as well as scientific open inquiry [11]. Self-regulation
from a social-cognitive perspective according to Zimmerman [12] incorporates self-
generated thoughts, feelings, and actions that are planned and continually adapted
and adjusted to the achievement of personal goals, depending on self-belief and
affective reactions, such as doubts and fears about a specific performance context
[12]. The phases of scientific open inquiry are paralleling the phases of self-
regulation; these are forethought, planning, goal setting, performance, and reflec-
tion. In the process of scientific open inquiry, the student has agency to decide on
their own learning path, in formulating the research questions and choice of investi-
gative methods as well as incorporating feedback to design a new investigation [10,
13]. In this study, students were able to choose the biology topic to be investigated
8 Digital Tool Use and Self-Regulated Strategies in a Bilingual Online Learning… 133
within human body systems, built a 3D–human body system model, and researched
the functions and malfunctions, which were presented as a group project to their
peers. This study afforded the students the opportunity to explore and experience
learning using this student-centered approach. To support self-regulation and open
inquiry, scaffolded learning activities and feedback loops were incorporated into the
online learning environment, thus encouraging goal setting, planning, and reflection
on achievement [13, 14]. Customized online software applications such as Education
Perfect [15] supported the use and production of the foreign language through help-
ful language, vocabulary lists, and fact sheets. Web links coupled with scaffolded
activities and work sheets gave the students the opportunity to research the topic
further for knowledge creation. These applications were optional for the students;
however, some were utilized during the lesson activities initiated by the teacher as
this allowed for modeling of effective practice for language and knowledge acquisi-
tion. The online learning experience design also afforded the teacher to step back in
her role as the gatekeeper of knowledge and student regulator [16], again, facilitat-
ing a student-centered approach. The specific online learning environment design
for this particular bilingual classroom setting was key to the investigation of student
opinions. It aimed to position the students to be self-motivated, self-regulated, and
encouraged scientific open inquiry strategies, which are all traits that will benefit
students in a range of learning situations.
Summarizing the previous studies, it is important to note that this study took
place in a CLIL biology classroom, where the students explored a new online
learning environment customized to allow scientific open inquiry and self-regula-
tion. This setting inspired the investigation into student voices as technology
users and language learners and how this setting influenced student actions in
regard to employing strategies of self-regulation. Previous research studies have
failed to address authentic student experiences in this setting. The next section
outlines the CLIL approach linked to different CLIL discourses. It further elabo-
rates the theoretical lens of Bakhtin’s dialogism and heterology used to analyze
the students’ voices engaged in the different CLIL discourses, thereby expressing
their opinions on technology use to further their foreign language and biology
content acquisition.
8.2 T heoretical Framework
In the CLIL approach, the specific content and the language are taught explicitly as
a synergy [17] or as one complimenting each other. This synergy happens in the
context of dialogic learning, because the dialogue of learning uses an additional
language and focuses on quality discourses between learners, as well as between
learners and teachers [18]. The students engage in both languages, and their voices
are expressing experiences that involve personal, classroom, and knowledge aspects.
To be able to analyze the student dialogues about their experiences, the theoretical
lens of dialogism and heterology developed by Bakhtin [19, 20] and
134 U. Freihofner et al.
translanguaging practices [21] have been adopted. The theory of dialogism provides
the starting point by looking at dialogues as interacting forces of monoglossic (sci-
entific discourse) and heteroglossic (individual discourse) language. The students’
dialogues occur using the two languages (German and English) and involve trans-
languaging practices for all students in the class.
Bakhtin argued that the production of thought and self-awareness can only hap-
pen through contact with the “Other” [22]. This conceptualization of language has
implications for this study because language production is seen as a social and
historical process that is used to create specific cultural spaces through the interac-
tion with the unknown. The students in this CLIL Biology classroom were exposed
to the “Other” through the science content in German in the online learning envi-
ronment, in their personal engagement producing German language in both peer
and teacher communication, and producing German language in the Biology con-
tent domain. The merger of dialogism and bilingual communication comes to the
fore in the students’ translanguaging practices. Garcia and Wei [21] refer to trans-
languaging as language practices of plurilingual individuals where they travel
between the different languages to complete the meaning making process [21]. For
example in this bilingual classroom, two languages were required to communicate
meaning. If the two languages cannot stand alone, they become a complete inte-
grated system and consequently a translanguaging strategy [23]. By using translan-
guaging strategies, students were appropriating the content and the languages and
also negotiating different cultures apparent in the monoglossic and heteroglossic
discourses [24].
This study suggests that the expert student voices provide new and deeper
insights into Year 9 students’ dialogic bilingual engagement and understanding of
self-regulated learning, open inquiry, technology-use, and translanguaging pro-
cesses. The following research questions were useful in the design and analysis of
this investigation:
1 . How do Year 9 students in a bilingual environment use and perceive the online
learning environment design for scientific open and guided inquiry?
2 . How do students use their student voice as language and content learners to
reflect on becoming self-regulated and effective learners within the online learn-
ing environment?
8.3 Methodology
This study has used a multiple case study design [25] as this has allowed the dynamic
CLIL online learning environment to be captured in detail from a student’s view
point. The initial case study was used to ascertain the validity of the research ques-
tions and the feasibility of the research methods used in the study. The subsequent
study allowed for the fine-tuning of the methods and established further support and
explanations of new discoveries. Qualitative methodology was chosen to illuminate
8 Digital Tool Use and Self-Regulated Strategies in a Bilingual Online Learning… 135
the student voices and therefore the methods selected were able to capture the stu-
dents’ dialogues and specific ally their use of monoglossic and heteroglossic lan-
guage discourses specific to the CLIL setting.
This study involved 22 participants who were in Year 9 studying Biology. The
participants were Australian native speakers, aged 13 and 14 years, and enrolled in
a CLIL program for their second year at a Queensland high school. The ethnicity of
the two groups included five students with parents from Germany, Switzerland,
Eastern Europe, the Philippines, and South Africa and 17 students with parents from
Australia. The participants worked through a Biology topic, Human Body Systems,
over 6 weeks. The students had 370 min of CLIL science lessons per week in vari-
ous classrooms and laboratories and 11 other CLIL lessons each week. The Year 9
students were enrolled for the second year into the CLIL program and were used to
engaging in German monoglossic texts, vocabulary training, and bilingual partici-
pation in the lessons. All participants had access to their own laptop and the Internet
during each lesson and at home. The two-year 9 cohorts were chosen to represent a
student group newly engaging with the laptop tool and the online learning environ-
ment as required by the Department of Education [1]. Ethical clearance was obtained
from the university and subsequently all participants and their guardians gave
approval for the study.
The CLIL classes chosen were determined by the fact that the Year 9 students in
that high school received new laptops as their learning tools at the beginning of that
school year. The new laptops combined with new strategies in the CLIL online
learning environment meant that new insights into the students’ lived experiences of
getting used to a new learning environment and formulating new learning strategies
for language and science content learning were thus offered.
The online learning environment, called the Learning Place [26], was available
in Queensland schools and offered secure access for the students and customization
through a so-called EdStudio for the teacher/researcher. In this study, the design for
the EdStudio was based on considerations for teaching using appropriate technol-
ogy, pedagogy, content, knowledge—TPACK, as well as learning activities and stu-
dent engagement [27] and a social infrastructure framework [28]. The key points
arising from these two frameworks for the design of the EdStudio aimed to support
a bilingual setting, self-regulated learning strategies, and a scientific open inquiry
process; they were as follows:
• Customization of content
• Scaffolding to reach specific learning goals and to support self-regulated learn-
ing strategies in the open inquiry process
• Ensuring collaboration between students
• Providing authentic learning activities to connect the classroom with the online
learning environment and vice versa
The EdStudio design incorporated all learning content in German monoglossic
science language. However, it was modified slightly from 2014 to 2015 to accom-
modate changes that arose from school-based adaptations to the science curriculum.
In 2015, a significant difference occurred in the customization of the Education
136 U. Freihofner et al.
Perfect website [15], the vocabulary training site for the students. A new feature
called smart lesson was introduced, and the teacher/researcher was able to custom-
ize learning content by combining fact sheets with vocabulary lists, close exercises,
and quizzes. The Education Perfect smart lessons gave students one-on-one auto-
mated feedback, as they attempted the exercises, quizzes, and learned the v ocabulary.
This differed from 2014, where only a vocabulary learning function was available.
Enabling the portrayal of student voices, data collection tools were chosen that
highlighted their opinions and afforded students’ participation as experts [29].
Triangulation of data occurred through the use of voice recordings backed up by
video footage, a student-designed questionnaire, and focus group interviews.
8.3.1 Data Collection Tools
8.3.1.1 Voice Recordings and Video Footage
Data collection was organized during each lesson as three iPads, one iPod touch,
and two cameras were used to record student voices and actions. These voice
recordings offered a unique insight into the student’s learning journey and the tran-
scripts provided particular clues, e.g., think aloud phrases and student peer conver-
sations for evidence of student conversations in two languages. Because of possible
sound loss in the voice recordings, video footage was used as a potential back up of
the data.
8.3.1.2 Student-Designed Questionnaire and Response Schedule
Placing the students in the expert role, a student-designed questionnaire was devel-
oped and managed before and after each research phase. The teacher/researcher
initiated a class discussion to stimulate the students’ thought processes in regard to
exploring learning interests together with their peers. To eliminate the intrusion into
the privacy of participants a questionnaire could present [30], the questions were
designed by the students and targeted towards peers. The questions were written by
the students anonymously, gathered, and typed up by the teacher/researcher. In the
first case, the students compiled 55 questions and in the second case the students
wrote 45. This process offered unique insight into the students’ understanding of
learning strategies linked to self-regulation, translanguaging practices, and lan-
guage acquisition. The student voices provided themes emphasizing learning with
technology, learning German, learning strategies, organization, time management,
learning environment, and motivation for learning. Learning strategies, learning
German, and motivation for learning were most prevalent for both years. Learning
with technology was a prominent issue in the first case, but was hardly mentioned
by the second cohort. These themes confirm that Year 9 students are conscious of
different aspects of self-regulation.
The words you are searching are inside this book. To get more targeted content, please make full-text search by clicking here.
Home
Explore
Demetrios Sampson,Dirk Ifenthaler,J. Michael Spector,Pedro Isaías (eds.) - Digital Technologies_ Sustainable Innovations for Improving Teaching and Learning-Springer International Publishing (201
View in Fullscreen
Demetrios Sampson,Dirk Ifenthaler,J. Michael Spector,Pedro Isaías (eds.) - Digital Technologies_ Sustainable Innovations for Improving Teaching and Learning-Springer International Publishing (201
Discover the best professional documents and content resources in AnyFlip Document Base.
Search