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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.
Deep Meaningful Learning
Stylianos Mystakidis 1,2
Citation: Mystakidis, S. Deep
Meaningful Learning. Encyclopedia
2021,1, 988–997.
Academic Editors: Chia-Lin Chang,
Michael McAleer and Philip
Hans Franses
Received: 16 August 2021
Accepted: 16 September 2021
Published: 18 September 2021
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1School of Natural Sciences, University of Patras, 26504 Rio, Greece;
2School of Humanities, Hellenic Open University, 26335 Patras, Greece
DefinitionDeep 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.
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 [
]. 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 [
]. Life-long
learning is of growing importance for the reskilling and upskilling of the workforce in
the era of the fourth industrial revolution [
]. 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 [
]. 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 [
]. 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.
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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 [
]. 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 [
]. Among others, he identified three categories of
non-monotonic mental shift:
capability to produce new solutions to problems and reach creative insights,
adaptation of cognitive competencies through repetitive experimentation, and
shift in values and perceptions through critical thinking [8].
Deep learning happens through active student engagement and especially in mean-
ingful construction activities [
]. Deep learning is associated with polymorphic thinking
(i.e., creative, critical, reflective, and caring) [
] and problem-solving processes and capa-
bilities [
]. 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 [
], 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 [
]. 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].
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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 [
]. Significant learning requires multilevel mental student
engagement across several categories [
]. Fink [
] proposed a taxonomy of the follow-
ing six critical categories that can be used to formulate intended learning outcomes for
interactive learning experiences:
Foundational knowledge; remembering and understanding the fundamental concepts
in the core of an educational program’s content.
Application; identifying, analyzing a problem and solving it by applying the basic
knowledge or skills.
Integration; building conceptual connections between new and existing knowledge
and experiences.
Human dimension; recording an insight in the social dimension in relation to the self
and other.
Caring; an emotional shift in regarding their values, perceptions and interest towards
the studied domain.
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 [
]. 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 [
Transformative learning emphasizes personal development, the evolution of worldview
and perspectives through critical discourse and rational thinking [
]. 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 [
]. 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].
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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 [
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 [
]. 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 [
]. 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 [
]. For
transfer to take place, learning needs to be organized as an active and dynamic process that
is influenced by learners’ motives [
]. 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 [
]. DML success in physical and online contexts depends on every individual’s
idiosyncratic attributes in terms of personalities, abilities, perceptions, and goals [
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 [
]. 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 [
]. 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 [
]. For instance,
curiosity, interest, and goal orientation are essential as they influence directly cognitive
learning procedures [
]. 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) [
]. 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 [
]. 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 [
]. 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
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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” [
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 [
]. Active, challenging activities, cooper-
ative problem-based tasks, and emotional empowerment are recommended to promote
]. Additionally, overlooking the importance of internal student incentives in
distance education leads to high course attrition rates [
]. When distance students can-
not interact socially with their fellows they have a higher probability of abandoning a
course [
]. 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 [
]. 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 [
Research in distance education connects DML with active learning, peer communica-
tion, and collaboration [
] as well as high levels of teaching and social presence [
Meaningful e-learning relies on the quality rather than the quantity of meaningful online
interactions of learners with content, instructors, and peers [
]. These interactions should
be designed around realistic experiences necessitating complex knowledge construction
tasks with ample cooperation and reflection opportunities [
]. Game-based and
gamified interventions such as serious games in physical and online, virtual settings have
produced supporting evidence of DML [
]. 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 [
]. 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 [
]. 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 [
]. 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 [
]. 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 [
]. 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 [
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 [
]. 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.
Multistructural: Appropriate presentation of several relevant but unconnected
subject aspects.
4. Relational: Integration of several relevant subject aspects.
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) [
]. 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 [
]. 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 [
], the Approaches and Study Skills Inventory for
Students (ASSIST) [
], the Motivated Strategies for Learning Questionnaire (MSLQ) [
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 [
]. 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 [
]. 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 [
]. 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 [
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 [
]. 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 [
]. 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 [
], big data and learning analytics [
]. 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:
United Nations General Assembly. Transforming Our World: The 2030 Agenda for Sustainable Development; UN: New York, NY, USA,
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]
Gleason, N.W. Higher Education in the Era of the Fourth Industrial Revolution; Springer Nature: Berlin/Heidelberg, Germany, 2018;
ISBN 9789811301940.
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]
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]
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.
Ohlsson, S. Deep Learning: How the Mind Overrides Experience; Cambridge University Press: Cambridge, UK, 2011;
ISBN 9781139496759.
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
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.
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]
Jonassen, D.H. Learning to Solve Problems with Technology: A Constructivist Perspective, 2nd ed.; Merrill: Upper Saddle River, NJ,
USA, 2003; ISBN 9780130484031.
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]
Howland, J.L.; Jonassen, D.H.; Marra, R.M. Meaningful Learning with Technology, 4th ed.; Pearson: London, UK, 2011;
ISBN 9780132565585.
Mystakidis, S. Motivation Enhanced Deep and Meaningful Learning with Social Virtual Reality; University of Jyväskylä: Jyväskylän
yliopisto, Finland, 2019.
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.
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]
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.
Christie, M.; Carey, M.; Robertson, A.; Grainger, P. Putting transformative learning theory into practice. Aust. J. Adult Learn.
55, 10–30. [CrossRef]
Illeris, K. Transformative Learning in the Perspective of a Comprehensive Learning Theory. J. Transform. Educ.
,2, 79–89.
26. Wittrock, M.C. Learning as a generative process. Educ. Psychol. 1974,11, 87–95. [CrossRef]
Slamecka, N.J.; Graf, P. The generation effect: Delineation of a phenomenon. J. Exp. Psychol. Hum. Learn. Mem.
,4, 592–604.
28. Fiorella, L.; Mayer, R.E. Eight Ways to Promote Generative Learning. Educ. Psychol. Rev. 2016,28, 717–741. [CrossRef]
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.
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.
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]
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]
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.
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]
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]
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.
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]
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]
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
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.
Hacker, D.J.; Niederhauser, D.S. Promoting deep and durable learning in the online classroom. New Dir. Teach. Learn.
53–63. [CrossRef]
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.
Willging, P.A.; Johnson, S.D. Factors that Influence Students’ Decision to Dropout of Online Courses. J. Asynchronous Learn.
Networks 2009,13, 115–127.
Jordan, K. Massive open online course completion rates revisited: Assessment, length and attrition. Int. Rev. Res. Open Distrib.
Learn. 2015,16, 341–358. [CrossRef]
Guàrdia, L.; Maina, M.; Sangrà, A. MOOC Design Principles. A Pedagogical Approach from the Learner’s Perspective. eLearning
Pap. 2013,33, 1–6.
Morin, D.; Thomas, J.D.E.; Raafat, G.S. Deep Learning and Virtual Environment. Int. J. Psychol. Behav. Sci.
,6, 31–63.
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]
Woo, Y.; Reeves, T.C. Meaningful interaction in web-based learning: A social constructivist interpretation. Internet High. Educ.
2007,10, 15–25. [CrossRef]
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]
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.
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]
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]
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]
Draper, S.W. Catalytic assessment: Understanding how MCQs and EVS can foster deep learning. Br. J. Educ. Technol.
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]
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.
Gibson, D.; Ostashewski, N.; Flintoff, K.; Grant, S.; Knight, E. Digital badges in education. Educ. Inf. Technol.
,20, 403–410.
Biggs, J.B.; Collis, K.F. Evaluating the Quality of Learning: The SOLO Taxonomy; Elsevier: Amsterdam, The Netherlands, 1982;
ISBN 0120975505.
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.
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]
Marton, F. Phenomenography—A research approach to investigating different understandings of reality. J. Thought
Biggs, J.; Kember, D.; Leung, D.Y.P. The revised two-factor Study Process Questionnaire: R-SPQ-2F. Br. J. Educ. Psychol.
133–149. [CrossRef]
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.
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
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]
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]
Duncan, T.G.; McKeachie, W.J. The Making of the Motivated Strategies for Learning Questionnaire. Educ. Psychol.
117–128. [CrossRef]
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]
Christopoulos, A.; Mystakidis, S.; Pellas, N.; Laakso, M.-J. ARLEAN: An Augmented Reality Learning Analytics Ethical
Framework. Computers 2021,10, 92. [CrossRef]

Supplementary resource (1)

... This idea is relevant to educators who wish to encourage in-depth knowledge and application of theoretical information around a taxonomy (Mystakidis, 2021b). It requires subject-matter and basic editing skills in 3D virtual worlds. ...
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Distance education in 3D virtual worlds can open up new horizons of student-centered pedagogies with collaborative problem-based activities in the Metaverse. The Metaverse is an interconnected web of social, networked immersive environments in persistent multiuser platforms. Easy to set up activities without special or expensive resources can be organized in social VR platforms without advanced programming skills to facilitate the application of complex theoretical academic concepts towards durable, in-depth learning.
... Students enjoy the learning process that follows their interests and talents. Mystakidis (2021) defined Deep meaningful learning as higher-order thinking by involves active intellectuals in constructing meaning through pattern recognition and concept association. These include inquiry, critical thinking, creative thinking, problem-solving, and metacognitive skills. ...
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Independent Learning Independent Campus (MBKM) is a program to develop an education system that aligns graduate capacity with industrial needs. The compatibility between educational programs and the business world is a challenge in realizing Indonesia as a developed country in the era of the industrial revolution 4.0. This study aims to analyze the impact of the MBKM program on improving the performance of Ibn Khaldun University Bogor. The sample that participated in this study consisted of all active students, lecturers, and administration staff. Questionnaires were distributed to 6100 students, 233 lecturers, and 150 administration staff. The stages of research implementation are as follows: socializing the understanding of MBKM, filling out the survey, calculating the distribution of respondents' filling, and concluding the assessment category using the weighted mean score (WMS) method. It can conclude that MBKM improves the performance of Ibn Khaldun Bogor University with an average percentage of assessment criteria above 75%. From the integration of the weighted mean score assessment criteria and the IPA model, the attribute that needs to get priority for improvement is increasing students' soft and hard skills and the capacity and expertise of lecturers. The implementation of MBKM that can improve graduate learning achievement must be maintained. Received: 21 April 2022 / Accepted: 29 June 2022 / Published: 5 July 2022
... Learners' perceptions of the learning process reflect how they guide their learning in the brain. It is of vital significance as it is related to learners' approaches to learning (deep vs. surface) [12,13]. Therefore, their learning experiences and preference for learning methods contribute to the formation of their learning concepts, and different learners often show different conceptions of learning science. ...
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Augmented reality (AR) demonstrates great promise in science education. However, students’ conceptions of learning when they learn science using AR are currently unclear. This study aimed to analyze learners’ views and scientific epistemic beliefs on learning science. Eighty-two elementary school students in grades 4–6 participated in a two-week course on the introduction to sound. The intervention adopted inquiry-based learning utilizing three AR software programs that integrated multisensory channels. The data were collected through Cheng’s Conceptions of Learning Science by AR (CLSAR) questionnaire and Learners’ Scientific Epistemic Beliefs (SEB) questionnaire. The results show that students in this study generally had positive conceptions of learning science and a high level of scientific epistemic beliefs. Moreover, gender differences existed in the relationship between CLASR and SEB. This study contributed to the currently unresolved discussion of the impact of demographic differences on students’ learning, indicating that AR can be used to enhance senior students’ learning of science in elementary schools.
... An earlier/better understanding of necessary procedural elements can help the students to reach higher levels of skill acquisition [10]. By helping the students experience how all parts of the procedure relate to each other, DGBL can be a valuable tool for providing opportunities for meaningful learning [11,12]. Thus, this study focuses on how DGBL can better support gaining procedural knowledge compared to traditional elements in BLS education. ...
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Practice-based training in education is important, expensive, and resource-demanding. Digital games can provide complementary training opportunities for practicing procedural skills and increase the value of the limited laboratory training time in biomedical laboratory science (BLS) education. This paper presents how a serious game can be integrated in a BLS course and supplement traditional learning and teaching with accessible learning material for phlebotomy. To gather information on challenges relevant to integrating Digital Game-Based Learning (DGBL), a case was carried out using mixed methods. Through a semester-long study, following a longitudinal, interventional cohort study, data and information were obtained from teachers and students about the learning impact of the current application. The game motivated students to train more, and teachers were positive towards using it in education. The results provide increased insights into how DGBL can be integrated into education and give rise to a discussion of the current challenges of DGBL for practice-based learning.
... CAVE-VR systems have been used to recreate environments and allow users to observe and interact with objects in project screens in various fields such as marine archeology [16] and specialized vehicle safety training [17]. Although learning should prioritize high-end immersion hardware [8], desktop-based VR lab simulation games can be enhanced using motion trackers such as Kinect [28]. Various efforts have been undertaken to develop realistic and accurate simulators that visualize aspects of fire dynamics in VR [29]. ...
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Immersive virtual reality (VR) is a technology that can be effective for procedural skills training through game-based simulations such as serious games. The current study describes the instructional design, development, and evaluation of the FSCHOOL fire preparedness serious game in a cave automatic virtual environment (CAVE-VR) for elementary school teachers. The main game mechanics include a storytelling scenario, enhanced realism, freedom of movement, levels, and points corresponding to the learning mechanics of instruction, action, simulation, discovery, repetition, and imitation. The game was developed in Unity 3D with the help of the Fire Dynamics Simulator and a script to emulate and visualize fire propagation. The game featured three levels to respond to school fire safety regulations and was evaluated by elementary school teachers (N = 33) in Greece. A comparative quantitative study was conducted with experimental and control groups. The results indicate that the VR serious game is appropriate for training, providing challenge, enjoyment, and mastery.
... Participants work on specific projects, addressing challenges and needs linked with the current and future business development. New technological trends and concepts are central in the TF and LF activities and they are defined considering the feedback of manufacturing companies [18]. ...
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Following the concepts introduced by Industry 4.0, manufacturing sector is moving towards digitalization, supported by advanced manufacturing technologies. Despite globalization and easy access to information, digitalization has not advanced in a similar way among different countries, but also within the industries of the same country. According to the Digital Economy and Society Index (DESI) 2020, all the countries that are part of the Regional Innovation Scheme (RIS) exhibit lower performance than the EU average regarding advanced digital skills and lower adoption of advanced technologies in manufacturing companies than the rest of the EU countries. Since the operation of digital technologies require a new set of skills from the workforce, there is a growing demand for enhanced innovation capacity on behalf of EU RIS countries by adapting their educational model and boosting their digital skills in order to face the manufacturing challenges of the future. To that end, Education 4.0 ecosystems must be used to enhance the skills and competences of both students and manufacturing employees of the new era. This work presents an educational program where students, researchers, and manufacturing companies work together to develop their skills through co-created solutions to real industrial challenges. Under the Teaching and Learning Factory concepts, a proposed framework for learning, tailor made to the needs of the manufacturing companies, is presented. On this basis, description and analysis of the implementation of the program is included, as well as the definition of the Teaching Factory (TF) and Learning Factory (LF) network and the use of Information and Communication Technology (ICT) tools that enable Teaching Factory activities between academia and industry. Case studies are also included to support the implementation and integration of the framework in pilot cases.
... They stressed further the importance of being able to experiment and even fail purposefully in some activities, as students often do so as to test the system. Learning by trial and error and repeated engagement was seen as a reinforcing factor of deeper comprehension [44]. Several of them expressed interest in designing and building their own educational escape rooms. ...
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Science, technology, engineering, and mathematics (STEM) is a meta-discipline employing active, problem-centric approaches such as game-based learning. STEM competencies are an essential part of the educational response to the transformations caused by the fourth industrial revolution, spearheaded by the convergence of multiple exponential technologies. Teachers’ attitude is a critical success factor for any technology-enhanced learning innovation. This study explored in-service teachers’ views on the use of a digital educational escape room in virtual reality. Forty-one (n = 41) K-12 educators participated in a mixed research study involving a validated survey questionnaire instrument and an online debriefing session in the context of a teacher training program. The key findings revealed that such alternative instructional solutions can potentially enhance the cognitive benefits and learning outcomes, but further highlighted the shortcomings that instructional designers should consider while integrating them in contexts different than the intended. In line with this effort, more systematic professional development actions are recommended to encourage the development of additional teacher-led interventions.
Training in virtual reality (VR) is a valuable supplementing tool for advancing knowledge transfer that results in increased efficiency and accuracy of technicians in fieldwork. However, COVID-19 pandemic restrictions made it impossible for VR training centers to operate on a full scale, forcing traditional face-to-face learning sessions to become remote. In this article, we investigate the asymmetric use of a VR training solution—among devices with different levels of immersion and control—to enrich the content of remote training sessions. The VR in this case can be seen as a source of visual and other contextual information to advance the effects of situated learning and enhance knowledge transfer. To evaluate this approach, we conducted a remote user study with ten industrial maintenance and installation experts. We also introduce the “Research Panel” tool to gather reactions of learners during the remote training session. The expert user study results demonstrate the usefulness and relevance of asymmetric VR to improve remote training sessions and other application industrial scenarios, while the “Research Panel” data provided detailed insight into the session flow. Building on the qualitative findings, we present design guidelines to aid the adoption of asymmetric VR in the industrial context.
Background Online learning is prevalent among nursing students, but the effect of online learning seems not as good as expected. Deep learning, as a learning approach that could help people solve complex problems and make innovative decisions, is associated with individual behavior and psychology. However, from the perspective of individual behavior and psychology to explore the potential influence mechanism of deep learning in online courses is little, in China or indeed internationally. Objectives The purpose of this study is to explore the relationship between online learning engagement, academic self-concept and deep learning in online courses for Chinese nursing students, and the mediating effect of academic self-concept on the relationship between online learning engagement and deep learning in online courses of Chinese nursing students. Design A cross-sectional electronic survey. Settings and participants The study was conducted using a convenience sample of 617 nursing students in five schools in eastern, central, and western China from September 2021 to October 2021 (the number of eligible students in the five schools was 2065). Methods The data were collected with the College students' learning engagement scale in cyberspace, Academic self-concept scale and Deep learning scale in online courses, and analyzed by correlation analysis, univariate analysis, multiple linear regression and PROCESS macro. Results 594 valid questionnaires were collected (effective response rate: 96.2 %). High online learning engagement and high academic self-concept were correlated with a high level of deep learning in online courses (correlation coefficient: 0.731 to 0.800). Part of the influence of online learning engagement on deep learning in online courses was mediated by academic self-concept, and the indirect effect accounts for 39.75 % of the total effect. Conclusions Chinese nursing students' online learning engagement may partially influence deep learning in online courses through academic self-concept.
This work aims to analyze and explain the didactic strategies used to achieve meaningful learning. It begins under the assumption that meaningful learning is created if students are given freedom and confidence. In this situation, they can find their own answers and develop their knowledge, both in the classroom and in practical life. The method used is an analytical-descriptive one of the reviews of the literature of the main authors who have given rise to this approach, its elements, and the didactic strategies used. It is concluded that the design and implementation of didactic strategies focused on meaningful learning with the application of active didactic methodologies and strategies in meaningful learning processes depending on the context in which it takes place. They obtain better results in the training of professionals.
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The emergence of the Learning Analytics (LA) field contextualised the connections in various disciplines and the educational sector, acted as a steppingstone toward the reformation of the educational scenery, thus promoting the importance of providing users with adaptive and personalised learning experiences. At the same time, the use of Augmented Reality (AR) applications in education have been gaining a growing interest across all the educational levels and contexts. However, the efforts to integrate LA techniques in immersive technologies, such as AR, are limited and scarce. This inadequacy is mainly attributed to the difficulties that govern the collection and interpretation of the primary data. To deal with this shortcoming, we present the “Augmented Reality Learning Analytics” (ARLEAN) ethical framework, tailored to the specific characteristics that AR applications have, and focused on various learning subjects. The core of this framework blends the technological, pedagogical, and psychological elements that influence the outcome of educational interventions, with the most widely adopted LA techniques. It provides concrete guidelines to educational technologists and instructional designers on how to integrate LA into their practices to inform their future decisions and thus, support their learners to achieve better results.
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A substantial body of literature has well-documented and demonstrated the potential of using three-dimensional (3D) virtual worlds (VWs) across various learning subjects and contexts in primary and secondary (K-12) education. However, little is known when it comes to issues related to child-interaction research and the impact that design decisions have on the user experience (UX), especially when game-based learning approaches are employed in 3DVWs. Hence, in this systematic literature review, we appraise and summarize the most relevant research articles (n = 30) conducted in K-12 settings, published between 2006-2020 and that elicit information related to (a) the interaction design (ID) of game events and trends associated with game elements and features that were utilized for the development and creation of game prototypes, (b) the research methods which were followed to empirically evaluate their teaching interventions, and (c) the design-related issues and factors affecting ID and UX by identifying the most frequent set of learning and game mechanics that were adopted in various game prototypes in different learning subjects. The vast majority of game prototypes enhanced students' engagement and participation, affecting their achievements positively. This systematic literature review provides clear guidelines regarding the design decisions that educational stakeholders should consider, and provides recommendations on how to assess and evaluate the students' learning experience (i.e., performance, achievements, outcomes) using 3DVWs.
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Deep and meaningful learning (DML) in distant education should be an essential outcome of quality education. In this literature review, we focus on e-learning effectiveness along with the factors and conditions leading to DML when using social virtual reality environments (SVREs) in distance mode higher education (HE). Hence, a systematic literature review was conducted summarizing the findings from thirty-three empirical studies in HE between 2004 (appearance of VR) and 2019 (before coronavirus appearance). We searched for the cognitive, social, and affective aspects of DML in a research framework and studied their weight in SVREs. The findings suggest that the use of SVREs can provide authentic, simulated, cognitively challenging experiences in engaging, motivating environments for open-ended social and collaborative interactions and intentional, personalized learning. Furthermore, the findings indicate that educators and SVRE designers need to place more emphasis on the socio-cultural semiotics and emotional aspects of e-learning and ethical issues such as privacy and security. The mediating factors for DML in SVREs were accumulated and classified in the resultant Blended Model for Deep and Meaningful e-learning in SVREs. Improvement recommendations include meaningful contexts, purposeful activation, learner agency, intrinsic emotional engagement, holistic social integration, and meticulous user obstacle removal.
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Escape Rooms are cooperative games in which players must find clues, solve puzzles, and perform a variety of tasks within a limited time. The goal is usually to escape or leave a room, place, or environment. When the Escape Rooms have a pedagogical purpose, they are usually called Edu-Escape Rooms and can be related to gamification and Game-Based Learning. The potential for student engagement and motivation is one of the main advantages of Edu-Escape Rooms.
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Recent events resulting from the Covid-19 pandemic precipitated a triage-like environment wherein experienced faculty were forced to convert courses rapidly to online venues. This unexpected circumstance forced educators to adopt different learning theories of which they were largely unaware. The results were predominantly unsatisfactory for both learner and educator. This paper provides perspectives to this unfortunate circumstance, describes positive and negative aspects of the experiences, presents best practices for deep online learning, and challenges geography educators to learn how instructional design for online courses can be leveraged. The goal is to provide a forum for online learning in geography education.
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Students’ approaches to learning can vary between students of different ages, genders, years, degrees, or cultural contexts. The aim of this study was to assess the approaches to learning of different students of life science degrees. The Revised Two-Factor Study Process Questionnaire (R-SPQ-2F) has been used to assess the approaches to learning of 505 students of thirteen different subjects of four different degrees at Universitat Politècnica de València in order to study the factors that influence their approaches. Results show a higher deep approach of the students. Differences were observed between subjects and gender, not related to level (bachelor or master) or year. The item reliability analysis showed a high consistency for the main scales, but not for the secondary scales of the R-SPQ-2F questionnaire. High correlation between the deep and surface scales were observed. These data can provide more information to the teachers, which may help them to develop strategies focused on promoting a deeper approach to learning for the students, more adapted to their subject, level, and year.
The growth in online professional development opportunities for teachers, due to the COVID-19 pandemic, prompts us to question what the most effective practices of facilitating professional development online are and what design elements of online professional development (OPD) programs improve teachers’ content and pedagogical content knowledge (PCK). These questions are critical to the successful design and delivery of OPD for teachers. To date, there is no systematic review that provides answers to these questions. Hence, this review presents a synthesis of 11 studies that systematically examine experimental and observational studies that tested or evaluated formal OPD programs for teachers. Eight studies were quantitative and three were mixed methods detailing evidence of teachers’ OPD program effectiveness, including design elements, that lead to teachers’ improved: content knowledge; PCK; beliefs about teaching; self-efficacy; and instructional practices. Design elements identified included a focus on learner supports, further acquisition or development of PCK, engagement, flexibility, individual difference in learners and learning styles, practical learning activities, reflection, relevance and application of knowledge and skills. The analysis uncovers a primary issue that few available publications of teachers’ OPD are strong methodologically. This systematic review’s findings report on design elements that lead to effective OPD learning experiences for teachers.
Asynchronous online discussions (AODs) can fail to benefit student learning in online classes if they are not designed to promote higher-order thinking. AODs can be designed as collaborative learning events that prompt students to go beyond the reproduction of basic content. The purpose of this study was to describe student participation, interaction, and levels of learning in AODs following pre-work activities. The pre-work in this study was conceptualized as socio-cognitive scaffolding. It engaged students in common activities where everyone experienced the same content preparation prior to the AOD. During the AOD students were prompted to share, discuss, and validate their understanding of the content. The study was conducted with 49 students in a graduate-level project management course. Results revealed that students were engaged in discussions of content-related material and showed evidence of deep learning during the AODs. Specifically, students analyzed, evaluated, and synthesized information during the collaboration. Results suggested that pre-work activities can be a promising strategy in the design of AODs.
Many teachers see major difficulties in maintaining academic standards in today's larger and more diversified classes. The problem becomes more tractable if learning outcomes are seen as more a function of students’ activities than of their fixed characteristics. The teacher's job is then to organise the teaching/learning context so that all students are more likely to use the higher order learning processes which “academic” students use spontaneously. This may be achieved when all components are aligned, so that objectives express the kinds of understanding that we want from students, the teaching context encourages students to undertake the learning activities likely to achieve those understandings, and the assessment tasks tell students what activities are required of them, and tell us how well the objectives have been met. Two examples of aligned teaching systems are described: problem-based learning and the learning portfolio.
Current online teaching and learning practices in distance education face limitations in terms of quality and effectiveness. The theories of deep and meaningful learning have the potential to address these challenges by placing emphasis on the cognitive, social and affective aspect of learning by engaging the person holistically. New e-learning models and frameworks are needed to develop and sustain learners’ high levels of motivation, engagement and satisfaction. This dissertation’s focus is on the motivation enhancement methods for deep and meaningful learning in distant education. The overall goal is to find out the effect of motivation-enhancement approaches using social virtual reality environments in e-learning and open education. Game-based approaches for enhancing intrinsic motivation include playful design, gamification and serious games. Previous empirical research in attendance-based, blended learning and online settings has shown promising results. However, there is a need for researching the effect of motivation enhancement methods in e-learning regard-ing the quality of learning. Can we improve learning quality and help learners achieve deep meaningful learning when instructional design and teaching focuses on intrinsic motivation? To understand the effect of motivation enhancement, eight articles were authored using research designs based on qualitative and quantitative methods. The dissertation proposes four tentative frameworks towards deep and meaningful e-learning utilizing game-based motivation enhancement methods; OpenQuest, Serious E-scape Room, the Blended Model for Deep & Meaningful E-learning in Social Virtual Reality Environments and the Patras Blended Strategy Model. The results from this study can accelerate the improvement of e-learning quality to address pressing societal and economic educational needs that affect the future of higher education and life-long learning. Facilitating deep and meaningful learning in online education to provide high-quality, flexible, personalized and transformative learning for large audiences could open new educational frontiers towards new milestones of economic growth, social progress and well-being.