Deep Meaningful Learning

Abstract

Definition
Deep meaningful learning is the higher-order thinking and development through manifold active intellectual engagement aiming at meaning construction through pattern recognition and concept association. It includes inquiry, critical thinking, creative thinking, problem-solving, and metacognitive skills. It is a theory with a long academic record that can accommodate the demand for excellence in teaching and learning at all levels of education. Its achievement is verified through knowledge application in authentic contexts.

Full text

Entry
Deep Meaningful Learning
Stylianos Mystakidis 1,2


Citation: Mystakidis, S. Deep
Meaningful Learning. Encyclopedia
2021,1, 988–997. https://doi.org/
10.3390/encyclopedia1030075
Academic Editors: Chia-Lin Chang,
Michael McAleer and Philip
Hans Franses
Received: 16 August 2021
Accepted: 16 September 2021
Published: 18 September 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1School of Natural Sciences, University of Patras, 26504 Rio, Greece; smyst@upatras.gr
2School of Humanities, Hellenic Open University, 26335 Patras, Greece
Definition:
Deep meaningful learning is the higher-order thinking and development through mani-
fold active intellectual engagement aiming at meaning construction through pattern recognition and
concept association. It includes inquiry, critical thinking, creative thinking, problem-solving, and
metacognitive skills. It is a theory with a long academic record that can accommodate the demand
for excellence in teaching and learning at all levels of education. Its achievement is verified through
knowledge application in authentic contexts.
Keywords:
pedagogy; instructional design; teaching; deep learning; meaningful learning; significant
learning; deeper learning
1. Introduction
Equitable quality education and life-long learning opportunities for all is one of the
United Nation’s seventeen global goals for sustainable development [
1
]. These goals
comprise a compass for all countries and citizens for peaceful, global development and
transformation by 2030. Quality higher education provides graduates with a robust combi-
nation of durable competencies, theoretical knowledge and procedural skills [
2
]. Life-long
learning is of growing importance for the reskilling and upskilling of the workforce in
the era of the fourth industrial revolution [
3
]. In the context of the COVID-19 pandemic
and the imposed social distancing measures, there is also an acute need to improve the
quality of distance education by transforming emergency remote teaching into deep online
e-learning [4].
2. Model and Influences
2.1. Deep Learning
Deep learning originates from the research on the mental processing strategies by
Marton and Säljö in Sweden [
5
]. In a series of experiments, they examined students’
approaches to learning when prompted to reply to comprehension questions after reading
a text. They discovered two distinct behaviors; some students strived to store isolated facts
without any reflection (surface approach). Others processed them critically and attempted
to connect the new information with existing knowledge (deep approach). A student,
employing deep learning approaches directs her own learning, attempts to comprehend
the learning content and procedure, and modify accordingly his/her beliefs, behavior and
values [
5
]. On the opposite end of the spectrum, a learner with a surface approach is rather
apathetic towards the studied domain, driven by exam pressure or stress and hence opts
to rote facts memorization. Beyond these two orientations, there is evidence of another,
superseding pragmatic dimension towards short-term performance dictated by course
assessment requirements, namely a strategic approach to learning [6].
The differences between a deep and a surface approach to learning are illustrated
in the following example: John and Melissa attend the obligatory, core course on fluid
mechanics towards a degree of Mechanical Engineering. John has a strong interest in
industrial engineering and does not see how this course can be of any use to him in the
short or long run. Therefore, he skips or is rather inattentive in classes and study. He
Encyclopedia 2021,1, 988–997. https://doi.org/10.3390/encyclopedia1030075 https://www.mdpi.com/journal/encyclopedia

Encyclopedia 2021,1989
intends to perform the bare minimum possible to get a passable grade in the final exam.
Melissa is fascinated by the course’s links to previous courses on mathematics as well as
future applications in various fields. She takes notes during lectures, asks questions, and
is driven to search and study additional material beyond the course’s textbook. John’s
attitude is an example of a surface approach to learning while Melissa exhibits a deep or
meaningful, in-depth approach to learning.
The same researchers went on to formulate a hierarchy of six conceptions of learning,
phases that students experience during their study [
7
]. The lowest three conceptions consist
of surface approaches to learning: quantitative knowledge accumulation, memorization
and storing, fact acquisition for future utilization. The next three phases are typical of
the deep learning approach: sense-making through abstraction, reconceptualizing reality
interpretation, and finally holistic person growth [7].
In addition, there is an alternative view towards deep learning. More specifically,
Ohlsson conceptualized deep learning as the ability to perform essential, non-monotonic,
cognitive development and change [
8
]. Among others, he identified three categories of
non-monotonic mental shift:
(i).
capability to produce new solutions to problems and reach creative insights,
(ii).
adaptation of cognitive competencies through repetitive experimentation, and
(iii).
shift in values and perceptions through critical thinking [8].
Deep learning happens through active student engagement and especially in mean-
ingful construction activities [
9
]. Deep learning is associated with polymorphic thinking
(i.e., creative, critical, reflective, and caring) [
10
] and problem-solving processes and capa-
bilities [
11
]. The notion of in-depth learning should not be confused with deep learning
computational processing techniques used for data analysis and representation in the field
of artificial intelligence.
2.2. Meaningful Learning
Meaningful learning, according to Ausubel [
12
], should be the hallmark of formal
higher education, which is achieved through sustained critical discourse. Meaningful
learning construction is linked with teaching methods such as inquiry and problem solv-
ing resulting in the ability to identify and analyze the underlying structure and connect
existing with new concepts [
13
,
14
]. Educators who intend to offer meaningful educational
experiences to their students are invited to contemplate and design teaching and learning
around the following attributes: active, constructive, intentional, authentic, cooperative, or
relational [15,16].
•
Active: Learning is an active cognitive procedure where the student is the protagonist.
This dimension signals the active participation of learners by interacting with content
and the learning environment, and engaging with a subject matter so as to make a
personal cognitive contribution.
•
Constructive: Learners are expected to construct continuously their own meaning
by interpreting and reflecting on observed phenomena, content and the results of
their actions.
•
Intentional: Learners are encouraged to exhibit individual ownership, agency, be
self-directed, set goals consciously and commit emotionally.
•
Authentic: Meaningful learning requires tasks linked to an authentic experience or
simulated, realistic context so that they become personally significant and transferable.
•
Cooperative/relational: Human learning is also a social process involving learners and
teachers. Group collaboration and peer conversation occur naturally in knowledge-
building communities. Additionally, engaged, passionate teachers contribute signifi-
cantly to the emotional involvement of learners.
Meaningful learning depends primarily on course design linking theory and practice
with strong experiences where both teachers and students feel free to express their positive
or negative emotions [17].

Encyclopedia 2021,1990
2.3. Deep and Meaningful Learning
Deep learning and meaningful learning have structural similarities that signal high
quality in education and thus are integrated into the term deep and meaningful learning
(DML) [18].
3. Related Theories
DML overlaps with other relevant concepts and theoretical frameworks with similar
epistemological underpinnings in literature. These are significant learning, transformative
learning, generative learning, deeper learning, and transfer of learning.
3.1. Significant Learning
Significant learning generates durable knowledge that can be applied in authentic
contexts. It is achieved through student-centered teaching experiences driving personal
learner cognitive development [
19
]. Significant learning requires multilevel mental student
engagement across several categories [
20
]. Fink [
21
] proposed a taxonomy of the follow-
ing six critical categories that can be used to formulate intended learning outcomes for
interactive learning experiences:
1.
Foundational knowledge; remembering and understanding the fundamental concepts
in the core of an educational program’s content.
2.
Application; identifying, analyzing a problem and solving it by applying the basic
knowledge or skills.
3.
Integration; building conceptual connections between new and existing knowledge
and experiences.
4.
Human dimension; recording an insight in the social dimension in relation to the self
and other.
5.
Caring; an emotional shift in regarding their values, perceptions and interest towards
the studied domain.
6.
Learning how to learn: acquiring domain-specific self-regulation skills to pursue
life-long learning.
Educators seeking to ensure significant learning are encouraged to design and plan
various learning activities across all categories.
3.2. Transformative Learning
Mezirow’s transformative learning is a much researched and studied adult education
theory based on the critical theory [
22
]. Critical theory takes a clear stance towards the
progressive transformation and emancipation of persons and society as a whole. It strives
to discover the underlying or served interests in studied situations. It notes for example that
the selection of information and methods in curriculum design is an ideological action [
23
].
Transformative learning emphasizes personal development, the evolution of worldview
and perspectives through critical discourse and rational thinking [
24
]. This path of attitude
transformation includes several steps: quandaries to trigger self-reflection leading to
realizations and new decisions, exploring new, better and valid choices and devising plans
towards behavioral change, putting new resolutions and values into action [25]
3.3. Generative Learning
Generative learning is based on the constructivist premise that knowledge is con-
structed through active student agency and participation [
26
]. Wittrock’s generative
learning model includes four main stages: motivation, learning strategy, generation, and
knowledge creation. However, one essential element is that learners need to assume respon-
sibility, control and direct their own learning. For example, deep learning is more probable
when learners are prompted to produce their own replies in the form of a written text to
address an open question rather than select one option in a close-format multiple-choice
question [27]. Generative learning involves active sense-making activities [28].

Encyclopedia 2021,1991
3.4. Deeper Learning
Deeper learning advocates learning beyond rote, superficial fact accumulation. Deeper
learning is associated with higher-order thinking skills and mastery of transversal skills [
29
].
Deeper learning has the potential to deliver desirable effects such as enhanced information
recall, intrinsic incentives, lasting knowledge and a structured comprehension of the cardi-
nal propositions of the conceptual and procedural phenomena under scrutiny [
30
]. It aims
at the development of six core competencies: proficiency of core academic content; critical
thinking and complex problem solving; cooperation; communication; life-long learning;
academic mindset. To cultivate these competencies teaching strategies such as problem-
based and project-based learning have been found effective [
31
]. Active, student-centered
instructional approaches are recommended including authentic case studies, small group
work, interdisciplinary projects, mentorships, open-ended exploration, knowledge applica-
tion outside of the classroom boundaries, personalized learning according to individual
needs [32].
3.5. Transfer of Learning
Educational transfer or the transfer of learning is the phenomenon where a learner has
the capability to demonstrate competencies, knowledge, skills, and values, acquired from
educational settings to novel, unprecedented situations, and ill-defined problems [
33
]. For
transfer to take place, learning needs to be organized as an active and dynamic process that
is influenced by learners’ motives [
34
]. Educational transfer is considered a top priority in
continuous professional development and corporate training programs.
4. Application
How could DML be facilitated in the context of formal education? DML frame-
works conceptualize education quality as the cognitive, affective, and social skills activa-
tion [
21
,
35
]. DML success in physical and online contexts depends on every individual’s
idiosyncratic attributes in terms of personalities, abilities, perceptions, and goals [
14
].
Hence DML on scale requires adaptation and differentiation to accommodate personalized
needs. Education stakeholders need to orchestrate litanies of activities and experiences to
foster deep learning approaches [
36
]. DML from the educator’s angle is a tough challenge
as it entails the expenditure of extra energy for sophisticated planning, patience, mindful-
ness, and diligence [
14
]. Information and communication technology could support DML
when the latter is used for teaching and learning strategies such as knowledge synthesis,
discussion, articulation, cooperation, and reflection [13,15,37].
DML is even harder to achieve and maintain in online learning where learners’ dy-
namic emotional and motivational fluctuations are sometimes neglected [
38
]. For instance,
curiosity, interest, and goal orientation are essential as they influence directly cognitive
learning procedures [
39
]. Quality e-learning towards higher-order processes should be
organized around learner-centered meaningful, demanding activities assisting students to
build associations of new information with existing knowledge and experiences [40].
More specific, DML is influenced by factors of three types: learners’ individual traits
(e.g., personality, skills, emotions, motivation), contextual (e.g., teaching methods, as-
sessment, teacher, class), and perceived contextual factors (e.g., workload, usefulness,
relevance) [
38
]. In the context of distance education, a systematic review has integrated
fifteen influencing factors into a blended model for deep and meaningful e-learning in
social virtual reality environments [
41
]. Factors are organized in three classes: in relation
to the learner (e.g., perceptions, technical skills), the implemented instructional design
according to teacher perceptions and beliefs (e.g., learning theory, environment, activities),
and the used technology (e.g., access, usability), before and during learning.
Hence, the community of inquiry theory was formulated to promote DML in ter-
tiary education [
42
]. Deriving from a social constructivist epistemology, its empirically
supported premise is that effective distant educational experiences should combine three
crucial components: teaching, cognitive, and social presence. Teaching presence comprises

Encyclopedia 2021,1992
the responsibilities and actions of educators such as instructional design, direct instruction,
and online facilitation. Cognitive and social presence relates to student behavior. Cognitive
presence is “the extent to which the participants in any particular configuration of a com-
munity of inquiry are able to construct meaning through sustained communication” [
35
].
Social presence is achieved when learners communicate purposively and build collectively
shared identities in an environment of trust.
Online learning features principally flexible, self-regulated study. Even when learning
features synchronous virtual meetings, i.e., teacher-led tutorials or group work, learner
isolation is an inherently inhibiting factor [
37
,
43
,
44
]. Active, challenging activities, cooper-
ative problem-based tasks, and emotional empowerment are recommended to promote
DML [
45
]. Additionally, overlooking the importance of internal student incentives in
distance education leads to high course attrition rates [
46
]. When distance students can-
not interact socially with their fellows they have a higher probability of abandoning a
course [
47
]. This effect has been observed on a magnified scale in Massive Open Online
Courses (MOOCs). Global enrollment in each MOOC rose to thousands and even hundreds
of thousands but completion rates typically do not exceed ten percent [14,48].
Excessive coursework is one common, DML blocking mistake educators commit de-
spite their benevolent intentions is. Too much work inevitably pushes students towards
a surface approach to learning due to time pressure. Hence, reducing content is recom-
mended so that learners have the time to reflect on the studied subject [
18
]. Another
universal teacher recommendation towards DML is to allow students to confront their
own misconceptions. Learners should be animated to demonstrate comparatively their
constructed meaning and interpretations of the studied domain and debate with each
other [18].
DML proposes an outcome or competency-based design approach in e-learning [
49
].
Research in distance education connects DML with active learning, peer communica-
tion, and collaboration [
50
] as well as high levels of teaching and social presence [
14
,
51
].
Meaningful e-learning relies on the quality rather than the quantity of meaningful online
interactions of learners with content, instructors, and peers [
52
]. These interactions should
be designed around realistic experiences necessitating complex knowledge construction
tasks with ample cooperation and reflection opportunities [
14
,
53
,
54
]. Game-based and
gamified interventions such as serious games in physical and online, virtual settings have
produced supporting evidence of DML [
55
,
56
]. Distance courses designed with construc-
tivist principles integrating community interactions, open-ended discussions, and team
assignments into a flexible curriculum with fluid content achieve higher levels of learner
satisfaction and deep learning [57].
5. Evaluation
Summative student assessment in formal education serves one main purpose: to
ascertain the degree to which course participants have achieved the intended learning
outcomes. Its format, however, constitutes an indirect hint to students as what is deemed
of the highest value to focus on and learn [
58
]. Hence, a course aiming at deep meaningful
knowledge development should examine higher-order competencies. Proposed evaluation
strategies include authentic, realistic performance tasks, self-evaluation, and peer assess-
ment [
59
,
60
]. Suggested assessment methods to encourage deep learning approaches are
catalytic assessment, concept maps, problem-based learning, and e-portfolios [18,61].
Catalytic assessment starts with a question that students have to tackle [
61
]. The quest
to find the right answer triggers first individual exploration and then discourse, often in
dyads or larger teams where students present and defend their choices. Catalytic assess-
ment can be applied in large audiences in physical and online settings as demonstrated by
the peer instruction method [62].
Although concepts maps are learning resources, their creation by students can be a
form of assessment [
63
]. Concept maps demonstrate a person’s cognitive organization of
comprehension of a topic. Building links, hierarchical structures, and branches among

Encyclopedia 2021,1993
related concepts, processes, and categories allows the accurate representation of students’
mental models.
Problem-based learning is a learner-centered method that starts with a real, ill-defined
problem [
11
]. In order to solve the problem, students have to take initiative and direct
their own learning in multiple ways: analyze the situation, identify its components, study
sources, collect evidence, formulate and test hypotheses, communicate with peers, argue
and take decisions, experiment, and validate their beliefs and assumptions.
Learning portfolios are collections of nowadays mostly digital artifacts (e.g., essays,
papers, projects, digital files, etc.) that students build gradually throughout the course [
59
].
Portfolios, similarly to PBL, place the responsibility and initiative of learning to each learner.
Moreover, they strengthen learners’ agency and relatedness with personally meaningful
values and connections. E-portfolios have the additional advantage that they can be
transferable to other digital platforms and visible to social networks and other outlets
enabling a seamless transition from educational to professional roles and settings [
64
]. In
this way, portfolios encourage students’ intrinsic goal orientation.
6. Research Instruments
In an attempt to describe and classify the level, depth, complexity and quality of
student learning and understanding, Biggs and Collis formulated the Structure of the
Observed Learning Outcome taxonomy (SOLO), a hierarchy of five stages for learning
outcomes [65]. These categories are the following from lowest to highest order:
1. Prestructural: Unstructured, inappropriate work.
2. Unistructural: Appropriate presentation of one relevant subject aspect.
3.
Multistructural: Appropriate presentation of several relevant but unconnected
subject aspects.
4. Relational: Integration of several relevant subject aspects.
5.
Extended Abstract: Creation of a coherent, holistic approach at a new
abstraction level.
SOLO taxonomy distinguishes two phases in student learning, intended or recorded.
In the lowest, quantitative phase (stages 1 to 3), learning is mainly superficial, additive. In
the qualitative phase (stages 4 and 5), learning results in advanced, deeper understanding,
the ability of application, reflective abstraction and transfer. SOLO categories have corre-
spondences with the six levels of Bloom’s revised taxonomy (remembering, understanding,
applying, analyzing, evaluating, creating) [
66
]. SOLO can be used by educators in the
design and assessment stage of education: to formulate learning objectives, techniques,
activities, evaluation methods and to assess students’ outcomes and performance [67].
DML can be researched both with qualitative and quantitative methods. A qualitative
DML research approach is phenomenography [
68
]. It constitutes a new research paradigm
aiming at interpreting differences in thought and experiences based on the descriptions of
understanding [69].
Validated quantitative research instruments to measure subjectively DML include
the Study Process Questionnaire SPQ [
70
], the Approaches and Study Skills Inventory for
Students (ASSIST) [
71
], the Motivated Strategies for Learning Questionnaire (MSLQ) [
72
],
and the Community of Inquiry framework survey [73].
SPQ and more specifically the Revised Two-Factor Study Process Questionnaire (R-
SPQ-2F) is a questionnaire developed by Biggs that measures two factors, deep and surface
study approach [
70
]. It consists of twenty items, e.g., “my aim is to pass the course while
doing as little work as possible” (surface study approach), “I feel that virtually any topic
can be highly interesting once I get into it” (deep study approach). Students’ replies are
scored on a five-point scale from “this is never or very rarely true of me” to “this always
or almost always true of me”. R-SPQ-2F can be combined with SOLO taxonomy to link
student study strategies to learning outcomes [74].
ASSIST is a self-reporting questionnaire that reflects relative student preferences
towards three studying approaches: deep, surface and strategic, stemming from the work
of Entwistle and Ramsden [
71
]. It contains three sections with the main section being

Encyclopedia 2021,1994
the Revised Approaches to Studying Inventory (RASI). RASI includes 52 items, e.g., “I
tend to read very little beyond what is actually required to pass” (surface approach),
“Before tackling a problem or assignment, I first try to work out what lies behind it” (deep
approach), I organize my study time carefully to make the best use of it (strategic approach).
Students are invited to mark their degree of (dis)agreement across a five-level Likert type
scale: agree, agree somewhat, unsure, disagree somewhat, agree.
MSLQ is based on Pintrich’s socio-cognitive assumption on learning depending pri-
marily on the dynamic and contextual interplay between cognitive learning strategies
and motivation orientation [
75
]. MSLQ can be used to measure 15 different motivation
and learning strategy scales that can be used collectively or separately, e.g., intrinsic and
extrinsic goals, self-efficacy, critical thinking, self-regulation, management of resources [
72
].
It contains 81 statements students assess ranging from 1 (not at all true of me) to 7 (very
true of me), e.g., “I’m confident I can learn the basic concepts taught in this course”, “When
studying for this course, I often try to explain the material to a classmate or friend”.
The Community of Inquiry framework survey was developed to measure the three
primary scales of the studied model: cognitive, teaching, and social presence [
73
]. It
comprises 34 items—statements such as “The instructor clearly communicated important
course goals” and “Course activities piqued my curiosity”. Respondents are scored from
0 (strongly disagree) to 4 (strongly agree).
7. Conclusions and Prospects
Life-long learning in the context of an information-centered society through continu-
ous professional development is ubiquitous [
76
]. The quality of life-long learning is vital for
the effectiveness of upskilling and reskilling professional development initiatives. Learning
interventions and educational programs of high quality lead to DML. Future research lines
could investigate the intersection of DML and behavioral change in blended and distance
education with emerging technologies such as extended, cross, augmented, mixed, virtual
reality as well as digital games [
77
], big data and learning analytics [
78
]. In a macroscopic
view, DML is not an end, it is the beginning of passionate engagements of students with
domains of knowledge fueled by inspiration through inquiry and experimentation leading
to creativity, polymorphic innovation and solutions to pressing problems.
Funding: This research received no external funding.
Conflicts of Interest: The author declares no conflict of interest.
Entry Link on the Encyclopedia Platform: https://encyclopedia.pub/15416.
References
1.
United Nations General Assembly. Transforming Our World: The 2030 Agenda for Sustainable Development; UN: New York, NY, USA,
2015.
2.
Greiff, S.; Wüstenberg, S.; Csapó, B.; Demetriou, A.; Hautamäki, J.; Graesser, A.C.; Martin, R. Domain-general problem solving
skills and education in the 21st century. Educ. Res. Rev. 2014,13, 74–83. [CrossRef]
3.
Gleason, N.W. Higher Education in the Era of the Fourth Industrial Revolution; Springer Nature: Berlin/Heidelberg, Germany, 2018;
ISBN 9789811301940.
4.
Schultz, R.B.; DeMers, M.N. Transitioning from Emergency Remote Learning to Deep Online Learning Experiences in Geography
Education. J. Geog. 2020,119, 142–146. [CrossRef]
5.
Marton, F.; Säljö, R. On Qualitative Differences in Learning—II Outcome as a Function of the Learner’s Conception of the Task.
Br. J. Educ. Psychol. 1976,46, 115–127. [CrossRef]
6.
Miller, C.M.L.; Parlett, M.R. Up to the Mark: A Study of the Examination Game. In Research into Higher Education Monographs;
Society for Research into Higher Education: Guildford, UK, 1974; ISBN 9780900868375.
7. Marton, F.; Säljö, R. Approaches to Learning. In The Experience of Learning; Marton, F., Hounsell, D., Entwistle, N., Eds.; Scottish
Academic Press: Edinburgh, UK, 1997; pp. 39–58.
8.
Ohlsson, S. Deep Learning: How the Mind Overrides Experience; Cambridge University Press: Cambridge, UK, 2011;
ISBN 9781139496759.
9.
Hay, D.B.; Kehoe, C.; Miquel, M.E.; Hatzipanagos, S.; Kinchin, I.M.; Keevil, S.F.; Lygo-Baker, S. Measuring the quality of e-learning.
Br. J. Educ. Technol. 2008,39, 1037–1056. [CrossRef]

Encyclopedia 2021,1995
10.
Valtanen, J.; Berki, E.; Kampylis, P.; Theodorakopoulou, M. Manifold Thinking and Distributed Problem-Based Learning: Is There
Potential For ICT Support? In Proceedings of the E-Learning’08 Conference, Las Vegas, NV, USA, 14–17 July 2008; Volume I,
pp. 145–152.
11.
Dolmans, D.H.J.M.; Loyens, S.M.M.; Marcq, H.; Gijbels, D. Deep and surface learning in problem-based learning: A review of the
literature. Adv. Heal. Sci. Educ. 2016,21, 1087–1112. [CrossRef]
12. Ausubel, D.P. In Defense of Verbal Learning. Educ. Theory 1961,11, 15–25. [CrossRef]
13.
Jonassen, D.H. Learning to Solve Problems with Technology: A Constructivist Perspective, 2nd ed.; Merrill: Upper Saddle River, NJ,
USA, 2003; ISBN 9780130484031.
14.
Mystakidis, S.; Berki, E.; Valtanen, J.-P. The Patras Blended Strategy Model for Deep and Meaningful Learning in Quality
Life-Long Distance Education. Electron. J. e-Learning 2019,17, 66–78. [CrossRef]
15.
Howland, J.L.; Jonassen, D.H.; Marra, R.M. Meaningful Learning with Technology, 4th ed.; Pearson: London, UK, 2011;
ISBN 9780132565585.
16.
Mystakidis, S. Motivation Enhanced Deep and Meaningful Learning with Social Virtual Reality; University of Jyväskylä: Jyväskylän
yliopisto, Finland, 2019.
17.
Kostiainen, E.; Ukskoski, T.; Ruohotie-Lyhty, M.; Kauppinen, M.; Kainulainen, J.; Mäkinen, T. Meaningful learning in teacher
education. Teach. Teach. Educ. 2018,71, 66–77. [CrossRef]
18. Rourke, L.; Kanuka, H. Learning in Communities of Inquiry: A Review of the Literature. J. Distance Educ. 2009,23, 19–48.
19. Rogers, C. Client-Centered Therapy; Houghton-Mifflin: Boston, MA, USA, 1951.
20.
Delotell, P.J.; Millam, L.A.; Reinhardt, M.M. The Use of Deep Learning Strategies in Online Business Courses to Impact Student
Retention. Am. J. Bus. Educ. 2010,3, 49–56. [CrossRef]
21.
Fink, L.D. Creating Significant Learning Experiences: An Integrated Approach to Designing College Courses; Jossey-Bass: San Francisco,
CA, USA, 2003.
22. Mezirow, J. Transformative Learning as Discourse. J. Transform. Educ. 2003,1, 58–63. [CrossRef]
23. Cohen, L.; Manion, L.; Morrison, K. Research Methods in Education, 7th ed.; Taylor and Francis: London, UK, 2013.
24.
Christie, M.; Carey, M.; Robertson, A.; Grainger, P. Putting transformative learning theory into practice. Aust. J. Adult Learn.
2015
,
55, 10–30. [CrossRef]
25.
Illeris, K. Transformative Learning in the Perspective of a Comprehensive Learning Theory. J. Transform. Educ.
2004
,2, 79–89.
[CrossRef]
26. Wittrock, M.C. Learning as a generative process. Educ. Psychol. 1974,11, 87–95. [CrossRef]
27.
Slamecka, N.J.; Graf, P. The generation effect: Delineation of a phenomenon. J. Exp. Psychol. Hum. Learn. Mem.
1978
,4, 592–604.
[CrossRef]
28. Fiorella, L.; Mayer, R.E. Eight Ways to Promote Generative Learning. Educ. Psychol. Rev. 2016,28, 717–741. [CrossRef]
29.
Martinez, M.; McGrath, D. Deeper Learning: How Eight Innovative Public Schools Are Transforming Education in the Twenty-First
Century; EBL-Schweitzer; New Press: New York, NY, USA, 2014; ISBN 9781595589941.
30.
Dede, C.; Grotzer, T.A.; Kamarainen, A.; Metcalf, S. EcoXPT: Designing for Deeper Learning through Experimentation in an
Immersive Virtual Ecosystem. J. Educ. Technol. Soc. 2017,20, 166–178.
31.
Sergis, S.; Sampson, D.G. Teaching and Learning Analytics to Support Teacher Inquiry: A Systematic Literature Review. In
Learning Analytics: Fundaments, Applications, and Trends; Springer Nature: Cham, Switzerland, 2017; pp. 25–63.
32. Dede, C. The Role of Technology in Deeper Learning; Jobs for the Future: New York, NY, USA, 2014.
33. Ellis, H.C. The Transfer of Learning; Macmillan: Oxford, UK, 1965.
34. Pugh, K.; Bergin, D. Motivational influences on transfer. Educ. Psychol. 2006,41, 147–160. [CrossRef]
35.
Garrison, D.R.; Anderson, T.; Archer, W. Critical Inquiry in a Text-Based Environment: Computer Conferencing in Higher
Education. Internet High. Educ. 1999,2, 87–105. [CrossRef]
36.
Entwistle, N.; Peterson, J.; Elizabeth, R. Promoting deep learning through teaching and assessment: Conceptual frameworks
and educational contexts. In Proceedings of the Teaching and Learning Research Programme (TLRP) Conference, Leicester, UK,
9–10 November 2000; pp. 9–20.
37.
Koszalka, T.A.; Pavlov, Y.; Wu, Y. The informed use of pre-work activities in collaborative asynchronous online discussions: The
exploration of idea exchange, content focus, and deep learning. Comput. Educ. 2021,161, 104067. [CrossRef]
38.
Baeten, M.; Kyndt, E.; Struyven, K.; Dochy, F. Using student-centred learning environments to stimulate deep approaches to
learning: Factors encouraging or discouraging their effectiveness. Educ. Res. Rev. 2010,5, 243–260. [CrossRef]
39. Schiefele, U. Interest, Learning, and Motivation. Educ. Psychol. 1991,26, 299–323. [CrossRef]
40.
Bonk, C.J.; Reynolds, T.H. Learner-centered Web instruction for higher-order thinking, teamwork, and apprenticeship. In
Web-Based Instruction; Khan, B.H., Ed.; Educational Technology Publications: Englewood Cliffs, NJ, USA, 1997; pp. 167–178.
41.
Mystakidis, S.; Berki, E.; Valtanen, J.-P. Deep and Meaningful E-Learning with Social Virtual Reality Environments in Higher
Education: A Systematic Literature Review. Appl. Sci. 2021,11, 2412. [CrossRef]
42.
Garrison, D.R.; Anderson, T.; Archer, W. The first decade of the community of inquiry framework: A retrospective. Internet High.
Educ. 2010,13, 5–9. [CrossRef]
43.
Paulus, T.; Scherff, L. Can Anyone Offer any Words of Encouragement? Online Dialogue as a Support Mechanism for Preservice
Teachers. J. Technol. Teach. Educ. 2008,16, 113–136.

Encyclopedia 2021,1996
44.
Mystakidis, S.; Berki, E.; Valtanen, J.-P.; Amanatides, E. Towards a Blended Strategy for Quality Distance Education Life-Long
Learning Courses—The Patras Model. In Proceedings of the 17th European Conference on e-Learning (ECEL), Athens, Greece,
1–2 November 2018; pp. 408–416.
45.
Hacker, D.J.; Niederhauser, D.S. Promoting deep and durable learning in the online classroom. New Dir. Teach. Learn.
2000
,84,
53–63. [CrossRef]
46.
Tyler-Smith, K. Early attrition among first time eLearners: A review of factors that contribute to drop-out, withdrawal and
non-completion rates of adult learners undertaking eLearning programmes. J. Online Learn. Teach. 2006,2, 73–85.
47.
Willging, P.A.; Johnson, S.D. Factors that Influence Students’ Decision to Dropout of Online Courses. J. Asynchronous Learn.
Networks 2009,13, 115–127.
48.
Jordan, K. Massive open online course completion rates revisited: Assessment, length and attrition. Int. Rev. Res. Open Distrib.
Learn. 2015,16, 341–358. [CrossRef]
49.
Guàrdia, L.; Maina, M.; Sangrà, A. MOOC Design Principles. A Pedagogical Approach from the Learner’s Perspective. eLearning
Pap. 2013,33, 1–6.
50.
Morin, D.; Thomas, J.D.E.; Raafat, G.S. Deep Learning and Virtual Environment. Int. J. Psychol. Behav. Sci.
2012
,6, 31–63.
[CrossRef]
51.
Bangert, A. The influence of social presence and teaching presence on the quality of online critical inquiry. J. Comput. High. Educ.
2008,20, 34–61. [CrossRef]
52. Yoon, S. In search of meaningful online learning experiences. New Dir. Adult Contin. Educ. 2003, 19–30. [CrossRef]
53.
Woo, Y.; Reeves, T.C. Meaningful interaction in web-based learning: A social constructivist interpretation. Internet High. Educ.
2007,10, 15–25. [CrossRef]
54.
Garrison, D.R.; Cleveland-Innes, M. Facilitating Cognitive Presence in Online Learning: Interaction Is Not Enough. Am. J. Distance
Educ. 2005,19, 133–148. [CrossRef]
55.
Mystakidis, S.; Cachafeiro, E.; Hatzilygeroudis, I. Enter the Serious E-scape Room: A Cost-Effective Serious Game Model for
Deep and Meaningful E-learning. In Proceedings of the 2019 10th International Conference on Information, Intelligence, Systems
and Applications (IISA), Patras, Greence, 15–17 July 2019; pp. 1–6.
56.
Pellas, N.; Mystakidis, S.; Christopoulos, A. A Systematic Literature Review on the User Experience Design for Game-Based
Interventions via 3D Virtual Worlds in K-12 Education. Multimodal Technol. Interact. 2021,5, 28. [CrossRef]
57. Ke, F.; Xie, K. Toward deep learning for adult students in online courses. Internet High. Educ. 2009,12, 136–145. [CrossRef]
58. Biggs, J. What the Student Does: Teaching for enhanced learning. High. Educ. Res. Dev. 1999,18, 57–75. [CrossRef]
59.
Gikandi, J.W.; Morrow, D.; Davis, N.E. Online formative assessment in higher education: A review of the literature. Comput. Educ.
2011,57, 2333–2351. [CrossRef]
60.
Nieminen, J.H.; Asikainen, H.; Rämö, J. Promoting deep approach to learning and self-efficacy by changing the purpose of
self-assessment: A comparison of summative and formative models. Stud. High. Educ. 2019, 1–16. [CrossRef]
61.
Draper, S.W. Catalytic assessment: Understanding how MCQs and EVS can foster deep learning. Br. J. Educ. Technol.
2009
,40,
285–293. [CrossRef]
62. Crouch, C.H.; Mazur, E. Peer Instruction: Ten years of experience and results. Am. J. Phys. 2001,69, 970–977. [CrossRef]
63.
Novak, J.D.; Ridley, D.R. Assessing Student Learning in Light of How Students Learn. In AAHE Assessment Forum; American
Association for Higher Education: Washington, DC, USA, 1988.
64.
Gibson, D.; Ostashewski, N.; Flintoff, K.; Grant, S.; Knight, E. Digital badges in education. Educ. Inf. Technol.
2015
,20, 403–410.
[CrossRef]
65.
Biggs, J.B.; Collis, K.F. Evaluating the Quality of Learning: The SOLO Taxonomy; Elsevier: Amsterdam, The Netherlands, 1982;
ISBN 0120975505.
66.
Anderson, L.W.; Krathwohl, D.R.; Airasian, P.W.; Cruikshank, K.A.; Mayer, R.E.; Pintrich, P.R.; Raths, J.; Wittrock, M.C. A
Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, Abridged Edition; Pearson:
London, UK, 2000; ISBN 080131903X.
67.
Leiva-Brondo, M.; Cebolla-Cornejo, J.; Peiró, R.; Andrés-Colás, N.; Esteras, C.; Ferriol, M.; Merle, H.; Díez, M.J.;
Pérez-de-Castro, A.
Study Approaches of Life Science Students Using the Revised Two-Factor Study Process Question-
naire (R-SPQ-2F). Educ. Sci. 2020,10, 173. [CrossRef]
68. Marton, F. Phenomenography—Describing conceptions of the world around us. Instr. Sci. 1981,10, 177–200. [CrossRef]
69.
Marton, F. Phenomenography—A research approach to investigating different understandings of reality. J. Thought
1986
,21,
28–49.
70.
Biggs, J.; Kember, D.; Leung, D.Y.P. The revised two-factor Study Process Questionnaire: R-SPQ-2F. Br. J. Educ. Psychol.
2001
,71,
133–149. [CrossRef]
71.
Entwistle, N.J.; McCune, V.; Tait, H. The Approaches and Study Skills Inventory for Students (ASSIST); Centre for Research on
Learning and Instruction, University of Edinburgh: Edinburgh, UK, 1997.
72.
Pintrich, P.R.; Smith, D.A.F.; Garcia, T.; Mckeachie, W.J. Reliability and Predictive Validity of the Motivated Strategies for Learning
Questionnaire (Mslq). Educ. Psychol. Meas. 1993,53, 801–813. [CrossRef]

Encyclopedia 2021,1997
73.
Arbaugh, J.B.; Cleveland-Innes, M.; Diaz, S.R.; Garrison, D.R.; Ice, P.; Richardson, J.C.; Swan, K.P. Developing a community of
inquiry instrument: Testing a measure of the Community of Inquiry framework using a multi-institutional sample. Internet High.
Educ. 2008,11, 133–136. [CrossRef]
74.
Rossum, E.J.; Schenk, S.M. The Relationship between Learning Conception, Study Strategy and Learning Outcome. Br. J. Educ.
Psychol. 1984,54, 73–83. [CrossRef]
75.
Duncan, T.G.; McKeachie, W.J. The Making of the Motivated Strategies for Learning Questionnaire. Educ. Psychol.
2005
,40,
117–128. [CrossRef]
76.
Bragg, L.; Walsh, C.; Heyeres, M. Successful design and delivery of online professional development for teachers: A systematic
review of the literature. Comput. Educ. 2021, 104158. [CrossRef]
77. Grande-de-Prado, M.; García-Martín, S.; Baelo, R.; Abella-García, V. Edu-Escape Rooms. Encyclopedia 2021,1, 4. [CrossRef]
78.
Christopoulos, A.; Mystakidis, S.; Pellas, N.; Laakso, M.-J. ARLEAN: An Augmented Reality Learning Analytics Ethical
Framework. Computers 2021,10, 92. [CrossRef]