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Continuing write the research essay based on existing 1300 words. My assessment is a 2500 words critical analysis and reflection based on my group work, and proofreading.Requirement: Critical analysis and reflection on the design process and its outcome based on the design case group work.Describe, support with literature, and critically reflect on your design case. More information about the requirement, design method journal, the example given by module, etc. I will ATTACH ALL, please read carefully before you start, thank you!!! :)





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Our group opted for a very demanding, but even more interesting topic,
which is self-regulation. The exact problem we finally ended up
addressing was how to support self-regulated learning in collaborative
contexts. We did not come up with an actual tangible technological tool,
due to limitations and deficiencies on which I will elaborate later in this
essay, but we managed to create a very concrete and justifiable design
path to be followed for a self-regulation technology.
Design Case
Self-regulation is an issue that has lately been drawing a lot of attention.
In the educational domain, it can be defined as an active process during
which learners plan, monitor and reflect not only on their learning, but
also on their motivation and emotions (Law, Ge and Eseryel, 2016).
Educational settings are now being revolutionized by the idea that
learning “content” is not the educational objective anymore. The ultimate
goal of education now is, or should be, to enable students apply their
learning skills in any new situation they will have to face. The goal of
education is now the preparation for future learning” (Stanford, 2016).
One could argue that self-regulation is an indispensable part of the above
educational revolution, while it addresses students’ metacognitive ability
to become fully aware of “how they learn”, or “how to teach themselves”.
Indeed, self-regulation encompasses a variety of behaviors, such as “selfcontrol”, which are foundational for generalizing individual learning
techniques to new contexts and situations (Oreck, 2004). Hence,
supposing that learning consists of a cognitive level and a metacognitive
one, the domain knowledge we used to create our technological tool,
addresses the metacognitive level. Metacognitive knowledge includes
monitoring of one’s learning, performance and cognition (Azevedo, 2013).
Metacognitive knowledge is achieved when a set of metacognitive subskills is realized (Oreck, 2004). Two skills that function at the
metacognitive level of human mind and play a significant role at the
realization of self-regulation are self-awareness and emotional awareness.
To clarify, emotional awareness and self-awareness enable learners
monitor and control the effect of emotions on their performance, set
goals and behavioral intentions (Lin, Fan and Chau, 2014). On top of that,
one of the most important characteristics of self-regulation, which also
played a major role in our design, is its cyclical nature. Learners are
involved in a repetitive, self-reflective process during which they generate
thoughts, feelings and actions and, after receiving feedback, they reflect
on them and re-adapt them, in order to reach their personal goals
(Zimmerman, 2000). These changes are re-adaptations that appear to be
critical to the development of self-regulation, because behaviors,
thoughts, feelings and even knowledge, change and improve during the
course of learning.
In our design we aimed to address a research gap regarding selfregulation, which is related to collaboration. Collaborative self-regulation
has been proved to have beneficial outcomes but has not yet been
thoroughly examined. Research has shown that a deficiency or
malfunction in social learning experiences can provoke “self-regulatory
dysfunctions” (Zimmerman, 2000). As Vygotsky has also emphasized, the
social environment plays a crucial role in creating contexts, influential to
any type of learning process, such as the acquisition of self-regulatory
skills. It is true that until now, most research has focused on the individual
and not on the student as part of a community of learners. (Beishuizen
and Steffens, 2011). The research that has been conducted in the area of
co-regulation has proven that when students work collaboratively, their
opportunity to develop co-regulation skills, entails the potential to
improve learners’ self-regulation (DiDonato, 2013).Hence, co-regulation,
the ability to monitor others, provide and receive feedback, understand
and examine their thoughts and intentions, will be taken into
consideration in our design as a transition stage, after which, the learners
can reach self-regulation while they collaborate. It is true that selfregulation entails the cognitive dimension, the metacognitive dimension,
but also the social dimension, which has to be further supported (Law,
Ge and Eseryel, 2016)
The importance of such a technological tool lies on the fact that selfregulation is considered an important skill to be developed not only for
low achieving but also high achieving students. Both of them are prone
to experience motivational and achievement problems and selfregulation appears as a potential solution to overcome these problems
(Livingstone, 2012). Moreover, the process of acquiring self-regulatory
skills plays an essential role in creating the student’s identity as a learner.
Our target age group is early secondary students, as well as their teachers.
At first, our aim was to compare face-to-face and online students, but
during our journey through the module, we realized that the emphasis
should be given on face to-face collaboration alone. Given that coregulation emerges as necessary to the development of self-regulatory
skills in collaborative situations, and since co regulation depends a lot on
gestures, facial expressions, dialogue, argumentation and action, it would
be better facilitated by the physical presence of all group participants.
Hence, in an effort to keep our design simple but more concrete,
functionable and efficient, we narrowed our participants down to the
face-to-face group.
Theoretical underpinnings
Given that our challenge was to improve self-regulation, which is an
individual skill, in a collaborative situation, the learning theory that would
best fit our design is constructivism which falls under the socio-cultural
model of learning. Adapting a Vygotskian perspective, sociocultural
approaches highlight the interrelation between individual and social
processes to the construction of knowledge (John-Steiner and Mahn,
1996). The cultural and social context play an important part in learning
and development. Hence, constructivist instructional technologies, like
the one we were aiming to design, facilitate personal improvement
through experiences and interactions with others, by creating situated
and novel ways of understanding (Cox, 2008). As a result, following the
theory of constructivism is the best choice which allows the creation of a
group situation and the examination of self-regulation skills through
interaction. The instructional goal of constructivist technologies is to
support knowledge construction, rather than knowledge communication,
to offer authentic and learner-centered tasks, entailing reflection on
experiences and intrinsic motivation. The role of the teacher is not
eliminated but changed, while he/she functions as a guide and not as an
The knowledge type we aim to address through our technology is
epistemological knowledge. Epistemological knowledge is knowing
about “knowledge”, which perfectly fits the metacognitive learning skills
we aim to improve. It consists of sub categories, from which, the most
useful for our design, are strategic and situational knowledge (De Jong
and Ferguson-Hessler, 1996). Situational knowledge is knowledge about
the situations that emerge in a particular domain. Since context plays
such an important role in our design, situational knowledge would be
collaboration. Strategic knowledge helps students monitor and organize
the steps they should take to solve a given problem or a task. This type
of knowledge is more than necessary not only for self-regulation, but also
for co-regulation and collaboration.
Design Rationale
In our design, we decided to use Knowledge Representation (KR). When
we first started thinking about our technology, we decided to create an
intelligent system.
Without having something concrete in mind, and after a lot of thought
and discussion, we agreed that the first step we would take is to have a
knowledge representation for this intelligent system. According to
Szolovits, Shrobe and Davis (1993), KR is an expression of an intelligent
reasoning that represents the fundamental conceptions of this reasoning
and how they are related. As he proposes, KR is a semantic map that aids
efficient computation, while it guides the experts on how to categorize
information and produce a tool that accomplishes the required steps and
inferences. What is very critical about KR, which we also used as a guide
when trying to depict ours, is that we should not confuse the
representational choices for KR, with data structures. Before having the
actual data structure, one should create a semantic diagram that carries
the meaning of concepts we want to convey. This is the KR.
However, during the process of thinking our KR, we encountered a
problem. Self-regulation, as also mentioned above, functions at the
metacognitive level. Thus, since our domain knowledge, the skill we
aimed to teach was self-regulation, we should find a cognitive level, on
which we would base the activities of our design, which we should then
give to students to work on. At first, we decided to have the cognitive
level of second language acquisition and create a tool that teaches selfregulation through second language acquisition activities. We eventually
diverged from this idea and the reasons will be explicated later in this
Therefore, our conclusion was that we should create a KR for selfregulation skills. Winne (2015), has proposed some phases to reach selfregulation, which we slightly altered according to our goals and
adaptations for our KR. In Phase 1, the learners make decisions regarding
factors that could influence their learning and they set their goals and
make some initial strategic decisions. In Phase 2, they make decisions on
how to achieve those goals, they gather and filter information. In Phase
3, the learners interpret the information, start asking questions to
monitor their own learning and progress so far and adjust their strategies.
In Phase 4, we have added a collaborative stage and self-assessment
through offering and receiving feedback. Those four phases take place
reciprocally. Finally, there is the final Phase 5, of reflection and
metacognition, where the initial self-regulation takes place. However, as
mentioned above this is a constant cyclical process which should be
repeated according to given feedback, in order to reach the final selfregulation. This cyclical process is represented in the following figure:
At the next stage we decided to include participatory design. This
design’s goal is to take into account the users’ perspective and reflect it
in the design of the technology (Cocea and Magoulas, 2015). In order to
inform our design, we decided to apply knowledge elicitation techniques.
More specifically, a human-tutor example would provide us with rich
information on some aspects of our design. For instance, we encountered
a paper from McQuirter Scott and Meeussen (2017), in which a teacher
was teaching self-regulation skills to her students in her classroom. This
situation helped us raise questions like “How can our technology
replace the role of the teacher in scaffolding?” or “How can our
technology provide feedback to students?”.
To elicit the above information, we would apply techniques like classroom
observations, video recording analysis and questionnaires. After eliciting
knowledge from the teacher student interaction, we decided to include a
participatory phase with the students. This stage aids the learner
modelling process. Part of our design would be an intelligent learner
model, while such technology allows the system to adapt to individual
needs and interacts with individual learners (Cocea and Magoulas, 2015),
a process that seems necessary as one of the methods of addressing the
participation as a mutual-learning process, during which both the
designers and the participants are positively affected (Halskov and
Hansen, 2015). From our perspective, the participation of students and
teachers would provide us with information on how to develop our tool
eventually, according to their preferences and needs. From the
participants’ perspective, both teachers and students would acquire
experience on aspects of technological design. At the same time, given
that our learning objective is self-regulation and that the design process
involves testing ideas, brainstorming and implementing initial prototypes,
they would also acquire domain knowledge on how to test their teaching
skills and self-regulation skills, accordingly (Good and Robertson, 2006).
Our next step would be to apply user-centered design to the process.
User-centered design addresses the end-users of the product, which in
our case are again both students and teachers and facilitates an iterative
design process in which the end users influence the shape of the final
design, by providing the designers with meaningful feedback (McLoone
et al., 2010). For instance, after creating a prototype of our tool we could
test the usability of our technology by asking teachers and children to
use it. This step would give us feedback on technical as well as more
commercial characteristics of the output, like the appearance of the
interface and how it appeals to the end-users. The application of user
centered design would be essential to finalize our design as efficiently as
possible, because this process would help us evaluate its usability and
functionality by applying the structure in a real classroom situation and
re-design it until we reach the final product.
After a lot of thinking and discussion, and since our domain knowledge
is self regulation, which addresses students’ metacognitive level, we came
up with the solution of creating a design that would not be applied only
to second language acquisition contexts, but to any learning context. This
is one of the biggest strengths of our design-thinking process for our
tool. Moreover, I believe that our design thinking process is quite lucid
and clear and could properly guide a programming expert to come up
with a tangible tool which we do not have the knowledge and expertise
to imagine. Such an effort to develop an actual product for our goal
would be more than meaningful because self-regulation fosters the
differentiation of learning by addressing individual student needs and
collaboration appears as significant to trigger students’ motivation and
better learning outcomes.
However, one of our design’s most critical weaknesses is that we did not
come up with a particular and tangible technological tool. The only
aspect which we were able to identify is the existence of an intelligence
system that would be able to support orchestration within the classroom
and facilitate teachers’ classroom management. In fact, one of the most
critical limitations and difficulties that we faced throughout the whole
process is the complexity of our design case, which consists of two
“layers”, a cognitive one and a metacognitive one (Bruin and Gog, 2012).
Thus, this challenged our effort to create a piece of technology and
provoked many obstacles, both at the theoretical level of knowledge
representation, knowledge elicitation and design processes, and at the
practical level of being able to imagine and present a tangible tool. As a
group, we spent a lot of time trying to figure out on which level our focus
should be placed, and thus we run out of time for focusing on the actual
tool interface. Finally, the design case itself challenges the creation of the
technological tool. The initial goal we set, to entail in the tool both a
cognitive level, like ESL, and the metacognitive level of self-regulation,
was very far-fetched. This is the reason why we managed to focus only at
the metacognitive level and provide some decent and in-depth
guidelines for a potential design, instead of coming up ourselves with a
proposal of a technology.
Part A: Critical Analysis of the Group Design Process
For our group project, we initially chose self-awareness as our
design case, with a focus on exploring the difference between face to
face interaction and online engagement for young learners’
development of self-awareness. We chose this theme collectively,
considering our general interests across the design case options.
This democratic decision process aligned with our shared theoretical
position within an interpretivist paradigm and an ontology of
relativism, where “truth is a consensus formed by co-constructors”
(Pring in Scotland, 2012: 12), as well as with social constructivist
theories of knowledge construction. We initially focussed on
investigating how group awareness might affect the development of the
self-regulated learning (SRL) skill of children aged 12-14 years old.
Although there may be great differences between children of the same
age in terms of their self-regulation ability (Wigfield, Klauda &
Cambria, 2013), we believed this age group would be appropriate as
young people around this age may begin to develop more nuanced social
skills and negotiate knowledge of the self, though this may be
dependent on specific socio-cultural contexts (Shaffer, 2000;
Vygotsky, 1978). In this sense learners of this age group, as they
approach adolescence, may experience more tangibly an awareness of
the social negotiation of learning (Scardamalia & Bereiter, 1991).
Therefore, we believed our design would be especially helpful for
this age group.
After some discussion, we were concerned our approach might require a
positivist design, in order to measure the variables for the two
conditions (face to face and online), therefore requiring a
quantifiable measurement of the different levels of mutual or selfawareness skills. Due to our interpretivist, relativist position, we
therefore, shifted our focus from attempting to measure these skills
as competencies, to a socio-cultural model of learning, wherein
learning is a less rigid phenomenon that happens between people, not
just in the mind (Scardamalia, & Bereiter, 1991). Furthermore, we
envisioned that learners might construct knowledge through an
‘existential matrix’ of sensory input, experience and reflection in a
social-cultural context (Dewey in Maddux & Donnett, 2015), where
knowledge “emerges from an intentional process of creating
connections between actions and consequences” (Xyst, 2016: 16).
Therefore, we focused on the possibilities for supporting the
development of mutual and self-regulation through social
constructivist learning situations, consistent with our
epistemological orientation as co-constructors.
As a result of discussing methods to support the development of self
and mutual regulation, we decided to focus on self-regulation (SRL).
We realised we were interested in providing a situated learning
context as a conduit to support the development of SRL skills, using
a socio-cultural learning model. We also wanted to incorporate
constructivist theories concerning the situativity of knowledge
acquisition, via active learning strategies, which consider the
context and culture in which knowledge occurs (Durning & Artino,
2011; Tobias & Duffy, 2010; Vygotsky, 1978). Initially we were
distracted by technological features, rather than purpose. This
changed as we were introduced to knowledge representation and
knowledge elicitation as design approaches, as well as contemplating
texts that were presented. For example, Kirkwood and Price (2014)
highlight the importance of determining the purpose of educational
technologies, which is especially important considering the contested
agency of technology for enhancing learning directly (Selwyn, 2 …
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