DEEP
LEARNING
THE
KEY TO UNLOCKING HIGHER ORDER THINKING SKILLS
Hamdani,
Education Supervisor at Bekasi Regency west Java, Indonesia
hamdani.5ht@gmail.com
Deep learning
actually is one of approaches in learning that can be applied or used by
teachers to unlock or develop the students’ critical thinking skill. To make it
easier to be comprehended by the readers so the writer divides into several chapters,
namely from chapter one to chapter five as conclusion.
CHAPTER I. INTRODUCTION
In today’s fast-changing world, where
information is more accessible than ever, merely recalling facts is no longer
adequate. Success in education, professional environments, and everyday life
increasingly relies on higher-order thinking skills (HOTS) (Anderson, 2001).
These skills enable individuals to assess complex situations, solve intricate
problems, and make well-informed decisions (Ennis, 2018). In a rapidly evolving
knowledge-based economy, adaptability and innovation are highly valued
(Rotherham & Willingham, 2009), making HOTS indispensable. By developing
these abilities, individuals can move beyond memorization and engage with
knowledge in a deeper, more meaningful way.
HOTS are not just crucial for career
success—they play a fundamental role in everyday decision-making. Critical
thinking, for example, helps individuals analyze information objectively and
form independent judgments (Facione, 2011), allowing them to critically engage
with news, research, and public discourse. Similarly, metacognition, or the
ability to reflect on one’s own thinking, enhances self-awareness and promotes
more effective learning and problem-solving (Flavell, 1979). Developing HOTS
ensures that individuals are not just informed but truly knowledgeable,
empowering them to make meaningful contributions to society. This recognition
has led educators to emphasize the development of HOTS from an early age.
But what exactly are higher-order
thinking skills? Unlike rote memorization, which focuses on simple recall, HOTS
require deeper cognitive engagement (Bloom, 1956). Instead of just remembering
information, individuals must actively process, connect, and apply their
knowledge in new ways (Krathwohl, 2002). Bloom’s Taxonomy provides a useful
model for understanding these skills, categorizing them within the higher
levels of analysis, evaluation, and creation (Anderson & Krathwohl, 2001).
Specific examples include critical thinking, metacognition, inference,
synthesis, evaluation, application, and deep comprehension (Ennis, 2018;
Flavell, 1979; Bloom, 1956).
With the growing importance of HOTS,
the challenge is determining how best to develop them. Deep learning emerges as
a key strategy in this regard. Defined by active engagement, meaningful
connections, and real-world applications (Schank, 1999), deep learning
encourages learners to move beyond surface-level understanding. It fosters an
in-depth exploration of concepts and helps individuals relate knowledge to
their own experiences (Bereiter & Scardamalia, 1985). Unlike memorization,
which focuses on short-term recall, deep learning promotes a strong grasp of
fundamental principles and their application to new situations (Marton &
Säljö, 1976).
Therefore, deep learning serves as a
powerful approach to nurturing higher-order thinking skills. By incorporating
deep learning techniques, educators can help students develop critical
thinking, problem-solving abilities, and a mindset of lifelong learning
(Resnick, 1987). As the world becomes increasingly complex, the ability to
think critically and deeply will be essential for success. Deep learning is not
just an educational trend but a necessary tool for preparing individuals to
navigate challenges, seize opportunities, and contribute to a better future
(Hmelo-Silver et al., 2007; Wiggins, 1998)
CHAPTER II. WHAT IS DEEP LEARNING?
A. Distinguishing
Deep and Surface Learning
Deep learning differs significantly from
surface learning, as they represent contrasting methods of acquiring knowledge
(Marton & Säljö, 1976). Surface learning is often characterized by passive
intake of information, with an emphasis on rote memorization of facts and
procedures rather than genuine comprehension of underlying concepts (Biggs,
1987). While this method may assist students in recalling information for
exams, it does not foster a lasting understanding or the ability to apply
knowledge in complex and unfamiliar contexts. Essentially, surface learning
focuses on what is being learned while overlooking the reasoning and deeper
connections behind it (Entwistle & Ramsden, 1983).
Whereas surface learning treats
information as isolated and disconnected (Pask, 1976), deep learning actively
engages with new knowledge, integrating it into existing cognitive frameworks.
A fundamental characteristic of deep learning is the ability to establish
connections between concepts (Ausubel, 1968). Unlike superficial knowledge
transfer—where students apply basic facts and procedures only in specific
situations—deep learning fosters meaningful transfer, allowing learners to use
their conceptual understanding across diverse contexts (Bransford &
Schwartz, 1999). This process involves questioning assumptions, evaluating
evidence, and linking ideas, ultimately transforming information into
applicable knowledge (Brown et al., 1989).
The long-term effects of surface and
deep learning highlight their key distinctions. Knowledge gained through
surface learning tends to be forgotten quickly or becomes passive and unused
(Whitehead, 1929), whereas deep learning fosters enduring comprehension and
mastery of fundamental principles within a subject (Wiggins & McTighe,
2005). As Mehta and Fine (2019) emphasize, "Deeper Learning is the
understanding of not just the surface features of a subject or discipline, but
the underlying structures or ideas" (p. 12). While surface learning may
offer short-term advantages for tasks requiring memorization, deep learning
develops the expertise and adaptability necessary for navigating an
increasingly complex world.
B. Definition:
Active Engagement, Making Connections, and Application
Deep learning is fundamentally rooted
in active participation with the material being studied (Chi et al., 2009).
Instead of passively receiving information, students take an active role in
building their own understanding (Jonassen, 1999). This involves posing
questions, considering multiple perspectives, and identifying connections
between new concepts and prior knowledge. Educators facilitate this process by
designing learning experiences that cater to students' strengths and needs,
promote real-world problem-solving, and encourage students to explore their
passions and potential (Darling-Hammond et al., 2020).
A central aspect of deep learning is
the ability to establish relationships between facts, processes, and
foundational principles within a discipline (Donovan et al., 1999). This requires
going beyond memorization to grasp the reasoning behind concepts. For example,
rather than merely recalling the equation for velocity, a student engaged in
deep learning would explore its connections to acceleration, momentum, and
real-world physics applications. Furthermore, deep learning enables students to
analyze complex topics and make meaningful connections within and across
different subject areas (Bereiter & Scardamalia, 1985).
Deep learning extends beyond
understanding individual subjects; it also focuses on applying knowledge to new
and unfamiliar situations (Anderson, 1983). This means that students can
transfer what they have learned to solve real-world problems and generate new
insights. Through this approach, deep learning not only builds knowledge but
also cultivates essential skills such as problem-solving, critical thinking,
collaboration, and creativity—skills necessary for lifelong learning and
adaptability (Collins, 1988; Partnership for 21st Century Skills, 2019).
C. Outcome:
Achieving Deeper Understanding
The key outcome of deep learning is the
development of a thorough and multifaceted understanding of a subject (Schank,
1999). This approach moves beyond simply memorizing facts or procedures,
focusing instead on grasping fundamental concepts and principles. As a result,
students not only meet academic benchmarks but also gain the ability to
critically engage with and apply their knowledge across various contexts,
including real-world situations. Deep learning nurtures students' capacity for
critical thinking and problem-solving (National Research Council, 2012).
A deeper understanding enables
students to transfer knowledge to new situations and approach challenges they
have never encountered before (Sternberg, 1985). They develop the ability to
critically analyze information, construct well-reasoned arguments, and solve
problems independently. Moreover, deep learning encourages learners to
recognize the relationships between different concepts, allowing them to see
broader connections and overarching themes (Wiggins & McTighe, 2005).
Beyond improving academic outcomes,
deep learning equips students with skills essential for success beyond the
classroom (Darling-Hammond, 2010). By fostering critical thinking, effective
communication, and problem-solving abilities, deep learning prepares students
to contribute meaningfully to society. Thus, the emphasis on deeper
understanding is not merely an academic goal but a crucial step toward lifelong
success and personal fulfilment.
CHAPTER III. DEEP LEARNING & HIGHER ORDER THINKING
A. Analysis:
How Deep Learning Fosters Analysis, Evaluation, and Creation
Deep learning methodologies inherently
promote higher-order thinking skills such as analysis, evaluation, and creation
(Bloom, 1956). Traditional teaching methods often emphasize the acquisition of
knowledge through memorization (Ausubel, 1968), whereas deep learning
encourages students to actively engage with the material by breaking down
complex concepts into smaller, more manageable parts (Anderson & Krathwohl,
2001). This analytical process allows students to understand the underlying
structure of information, identify relationships, and draw meaningful
conclusions. For instance, students might be asked to analyze the components of
a complex system, such as a business model or scientific experiment, identifying
the key variables and their interdependencies.
Deep learning fosters evaluation by
encouraging students to assess the credibility, relevance, and validity of
information (Ennis, 2018). Students learn to critically examine sources,
identify biases, and weigh evidence to form their own informed judgments. This
evaluative process is essential for developing sound reasoning and
problem-solving skills (Facione, 2011). For example, students might be asked to
compare and contrast different theories, evaluate the strengths and weaknesses
of various arguments, or assess the potential consequences of different courses
of action. By engaging in evaluative thinking, students develop the ability to
make informed decisions and solve complex problems.
Deep
learning extends beyond analysis and evaluation to foster creation by
encouraging students to generate new ideas, develop innovative solutions, and
express themselves in original ways (Sternberg, 1999). This creative process
involves combining existing knowledge with new insights to produce something
novel and valuable. For example, students might be asked to design a new
product, develop a marketing campaign, or write a persuasive essay. Students
will learn through practice, how to select and apply appropriate tools for deep
learning. By engaging in creative thinking, students develop the ability to
innovate and contribute to society in meaningful ways.
B. Application:
Moving Beyond Recall to Apply & Evaluate
A core tenet of deep learning is
moving students beyond the simple recall of facts and procedures to apply and
evaluate knowledge in meaningful contexts (Bransford et al., 2000). Traditional
assessment methods often focus on testing students' ability to remember
information, whereas deep learning emphasizes the ability to use knowledge to
solve real-world problems and make informed decisions. This shift in focus
requires students to develop a deeper understanding of the subject matter, as
well as the ability to transfer their learning to new and unfamiliar situations
(Perkins & Salomon, 1992).
Deep learning encourages the
application of knowledge by providing students with opportunities to use what
they have learned in authentic and relevant contexts (Herrington et al., 2014).
This might involve working on real-world projects, participating in
simulations, or engaging in case studies. These experiences allow students to
see the practical value of their learning and develop the skills necessary to apply
their knowledge in professional and personal settings. They can also provide
the motivation necessary to pursue further knowledge.
In addition to applying knowledge,
deep learning also emphasizes the importance of evaluation (Schön, 1983). Students
are encouraged to reflect on their own learning, assess the effectiveness of
different strategies, and identify areas for improvement. This evaluative
process helps students to become more self-aware learners and develop the
skills necessary to monitor and regulate their own learning. By combining
application and evaluation, deep learning empowers students to become lifelong
learners who are able to adapt to change and thrive in a complex world.
C. Examples:
Activities Promoting Critical Thinking/Problem-Solving
Numerous activities can be
implemented to promote critical thinking and problem-solving within a deep
learning framework (Hmelo-Silver, 2004). Problem-based learning (PBL) presents
students with complex, ill-structured problems that require them to apply their
knowledge and skills to develop solutions (Barrows & Tamblyn, 1980). Case
studies provide students with real-world scenarios that they must analyze and
evaluate to make informed decisions (Yin, 2018). Project-based learning (PBL)
engages students in extended, in-depth investigations that culminate in the
creation of a tangible product or presentation (Thomas, 2000).
Simulations provide students with
opportunities to experiment with different strategies and observe the consequences
of their actions (Gredler, 1996). Debates encourage students to research and
present different viewpoints on a controversial issue, fostering critical
thinking and communication skills (Snider & Schnurer, 2006). Research
projects challenge students to formulate research questions, gather and analyze
data, and draw meaningful conclusions. Furthermore, digital tools can
facilitate this by allowing them to improve their digital literacy.
Collaborative learning activities,
such as group projects and peer tutoring, promote critical thinking and
problem-solving by encouraging students to share ideas, challenge assumptions,
and learn from one another (Johnson & Johnson, 2009). These activities can
be structured to promote digital literacy in addition to the academic subjects.
By implementing a variety of engaging and challenging activities, educators can
create a deep learning environment that fosters critical thinking,
problem-solving, and creativity
CHAPTER IV. IMPLEMENTING
DEEP LEARNING
A. Strategies:
Practical Applications in Education
To effectively implement deep learning,
educators must employ strategies that promote active engagement, critical
thinking, and a comprehensive grasp of concepts (Hmelo-Silver et al., 2007). A
blended learning approach that integrates pre-class preparation, interactive
classroom activities, and post-class reflection can be highly effective (Means
et al., 2013). This method utilizes online resources for preliminary learning,
facilitates classroom discussions to reinforce connections between new and
existing knowledge, and concludes with post-class activities designed to assess
and apply learning (Garrison & Vaughan, 2008).
Inquiry-based learning, where students
take the lead in their own learning process, is another essential approach
(Bruner, 1961). Providing meaningful, hands-on experiences allows students to
actively engage with the material. Additionally, designing lessons that
interconnect different subject areas can enhance learning outcomes (Wiggins
& McTighe, 2005). Project-based learning and real-world applications, such
as internships, further encourage students to transfer knowledge across
disciplines (Thomas, 2000).
To cultivate deep learning, teachers
should establish clear learning objectives (Mager, 1997), present complex and
thought-provoking material, and create authentic learning experiences that
require students to apply knowledge in real-world situations (Herrington et
al., 2014). Encouraging collaboration through learning communities fosters
engagement and constructive peer feedback (Vygotsky, 1978). Additionally,
integrating digital tools enhances research, problem-solving, project
management, teamwork, and communication, making technology an essential
component of deep learning (Partnership for 21st Century Skills, 2019).
B. Guidance:
The Role of Educators
Teachers play a vital role in
supporting deep learning by providing guidance and fostering a dynamic learning
environment (Darling-Hammond et al., 2020). They act as facilitators by
designing meaningful learning experiences, curating high-quality resources, and
encouraging student participation. The use of multimedia tools, such as
instructional videos and interactive courseware, can significantly enhance engagement
and comprehension (Guo et al., 2014).
Beyond delivering content, teachers
should promote group discussions that help students process concepts,
strengthen connections between prior and new knowledge, and activate existing cognitive
frameworks (Ausubel, 1968). Additionally, they should emphasize the development
of key competencies such as subject mastery, teamwork, analytical thinking, and
problem-solving (National Research Council, 2012).
One of the educator’s main
responsibilities is to help students transition from passive recipients of
information to self-directed learners (Knowles, 1975). Establishing guidelines
for constructive feedback, self-reflection, and collaboration can create an
environment that supports deep learning. To enhance this process, teachers
should continually assess, plan, implement, and refine their instructional
strategies based on student progress and learning needs (Schön, 1983).
C. Assessment:
Fostering Deep Learning and Higher-Order Thinking
Assessment strategies should align
with deep learning objectives by evaluating students’ ability to apply
knowledge, analyze information critically, and solve complex problems (Wiggins
& McTighe, 2005). Evaluations should be embedded throughout the learning
process, both before and after classroom instruction, to monitor progress and
reinforce understanding. These assessments should encourage students to reflect
on and take responsibility for their own learning (Black & Wiliam, 1998).
An effective assessment approach allows
students to take an active role in designing, monitoring, and evaluating their
own progress. Additionally, teachers should collaborate to review instructional
methods, make improvements, and adapt their teaching strategies to better
support student learning (Shepard, 2000).
Educators should focus on
identifying and enhancing the conditions that support deep learning (Hattie,
2009). A balanced assessment strategy that integrates digital tools with
face-to-face instruction ensures continuous reinforcement of critical thinking
and problem-solving skills, which are essential for higher-order learning.
CHAPTER V.
CONCLUSION
In today's rapidly evolving world,
critical and creative thinking is essential. Traditional education, focused on
rote memorization, often fails to prepare students for an unpredictable future.
Deep learning offers a transformative approach by fostering higher-order
thinking skills (HOTS), encouraging active engagement, meaningful connections,
and real-world applications.
Unlike surface learning, deep learning
requires students to analyze, evaluate, and synthesize information, leading to
stronger critical thinking and problem-solving abilities. Educators play a
crucial role as facilitators, creating dynamic learning environments and
shifting assessments toward real-world applications rather than rote recall.
Effective strategies such as
problem-based learning, case studies, and project-based tasks help students
apply knowledge innovatively. Collaborative learning further strengthens
critical thinking, communication, and teamwork. However, challenges like
resistance to change and resource limitations must be addressed to ensure
successful implementation.
Ultimately, deep learning represents a
vital shift in education, equipping students with the skills needed to thrive
in a complex world. By fostering deep engagement and lifelong learning, it
prepares future generations to think critically, solve problems creatively, and
contribute meaningfully to society.
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