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ARTICLE
Evolution Is not enough: Revolutionizing
Current Learning Environments to Smart Learning
Environments
Kinshuk
1
& Nian-Shing Chen
2
& I-Ling Cheng
2
&
Sie Wai Chew
1,2
#
International Artificial Intelligence in Education Society 2016
Abstract Advances in technology in recent years have changed the learning behaviors
of learners and reshaped teaching methods. This had resulted in several challenges
faced by current educational systems, such as an increased focus on informal
learning, a growing gap of prior knowledge among students in classrooms and
a mismatch between individual career choices and the development of the work
force. This paper looks at these challenges with a view towards revolutionizing
current learning environments to smart learning environments and provides new
suggestions for technological solutions. Furthermore, this paper argues for a
transformation from the current learning environments to smart learning e nvi-
ronments. This is to be achieved by reengineering the fundamental structure and
operations of current educational s ystems to better integrate these new technol-
ogies with the required pedagogical shift. The future perspectives of smart
learning environments are reviewed and shared, through examples of emerging
innovations such a s the flipped classroom, game based learning, gesture based
learning, along with pedagogical shifts, such as life-long learning portfolio
maintenance, team teaching, and separation of learning and competency
assessment.
Keywords Smart learning environment
.
Reengineering education
.
Reorganizing
schooling
.
Pedagogical innovations
.
Full context awareness
.
Learning analytics
.
Autonomous decision making
Int J Artif Intell Educ
DOI 10.1007/s40593-016-0108-x
* Kinshuk
kinshuk@ieee.org
1
Athabasca University, Athabasca, AB, Canada
2
National Sun Yat-sen University, Kaohsiung, Taiwan
Introduction
With recent advancements in learning technologies, along with the ways information
and communication technology (ICT) are implemented, many exciting possibilities
have emerged to reshape students’ learning behaviors and teachers’ teaching methods.
Regardless of how beneficial they may be, the education sector is known for being
extremely slow in embracing change and in adopting technological advancements. The
required pedagogical changes to make actual effective use of these technological
advancements are even slower. Likewise, Price (2015) indicates that Bwhile technology
has changed what is possible to learn and how students can be supported in their
learning, the principles of effective instruction have not changed^(p1). While early
adopters’ successful stories are not uncommon, current efforts for transforming existing
schools to evolve and gradually adopt to these learning technologies are simply not
enough. Though some initial efforts have already been made to reform the current
learning environments, a revolution is needed to fundamentally transform the current
learning environments towards smart learning environments. Smart learning environ-
ments go beyond simple application of technology. They enable the fusion of technol-
ogy and pedagogy to create an ecosystem that involves active participation of teachers,
parents and others in the learners’ learning process. They also provide real-time
and ongoing evidence of changes in knowledge, instilli ng skills which are
seamlessly transferred to learners as they move from one learning context to
another (Kinshuk 2016).
This paper begins with analyzing the problems of existing learning environments,
along with the need for reform and innovations. It is followed by a discussion on the
need for the emergence of smart learning environments. The paper also looks at the
interplay of pedagogy, technology and their fusion towards the advancement of smart
learning environments. From the pedagogical perspective, issues like learning para-
digms, assessment paradigms and social factors are discussed. From the technological
perspective, emerging technologies, innova tive uses of ma ture technologies, and
emerging/new technological paradigms are elaborated. Finally, from the perspective
of pedagogy and technology integration, new curriculum, changes in teaching behavior,
reengineering education, and reorganization of the existing school structure and oper-
ations are explored.
The Problems of Current Educational Systems
Over the past decade, it has been argued that technology-enhanced learning (TEL)
could respond to the needs of the new knowledge society and transform learning (Chan
et al. 2006) TEL can be perceived as a smart learning system (Hwang 2014). While
there are indeed isolated achievements, TEL becomes a key consideration in the current
revolutionizing of education and learning processes (Hwang 2014;Price2015).
This implies that a fundamental change of traditional schooling is needed: as
said by Wirth and Perkins (2008), there is a Bneed for new kinds of learning^
(p2). This section identifies three major challenges of current educational
systems that require innovative uses of learning technologies to tear down the
barriers and tackle the challenges.
Int J Artif Intell Educ
Time Allocation Shift from Formal Learning to Informal Learning
In the past, the primary channel for learners to gain knowledge was through formal
learning in schools and universities. They spent something like 90 % of their daily
learning time in formal learning and very little in informal learning. However, learners’
learning patterns and habits have been evolving along with the continuous growth of
technology and the Internet. Nowadays, learners are no longer solely dependent on
schools or universities to gain knowledge on a certain topic (Cross 2011;Belletal.
2009). As ASTD (2008)indicatesBInformal learning is used to supplement or reinforce
learning from formal programs^ (p15). Informal learning methods are easily accessible
and available to learners via the Internet, resulting in a shift in the time allocation of
formal learning to perhaps only 50 % of the learners’ daily learning time. The other
50 % (or even more) of the learners’ daily learning time is devoted to informal learning,
such as online learning, virtual classrooms, or other online materials. This trend is
moving faster than anyone could anticipate because there is an increasing number of
better quality learning contents/courses available on the Internet (ASTD 2008;Bell
et al. 2009;Conner2008). With students spending less time in formal learning, it will
only be a matter of time before informal learning replaces formal learning if formal
educational systems do not change (ASTD 2008;duBois-Reymond2003). Traditional
formal educational systems desperately need to make fundamental change to strive for
a better survival of formal education.
The Growing Gap of Prior Knowledge among Students
W ith the rapid growth of the Internet today , information is retrievable anywhere and
anytime. With a tap on the smartphone or a click on the laptop, learners are able to search
for relevant facts on any topic or subject matter (ASTD 2008). As a result, educators are
increasingly faced with the problems of preparing and structuring formal courses to
accommodate learners with different knowledge levels on a subject matter. Learners
with prior knowledge on the subject may otherwise find the course dull. As McNeely
(20 05)indicates,B… the Net Generation... all use computers in their class work and in
their hobbies. They have a wide range of interests, outside their chosen area of study…
They learn by doing, not by reading the instruction manual or listening to lectures^ (p 4.3).
Yet, there are others, who may not have been exposed to a subject and they require
appropriate content delivery from the educator. As mentioned by Clayton-Pedersen and
O’Neill (2005), Beducation must enable individuals to discover what they need to know
rather than just having static knowledge^. Hence, the increasingly large gaps in prior
knowledge about the subject matter among learners make it difficult for educators to
maintain learners’ engagement in the class.
Mismatch in Individual Career Path with the Development of the Work
Force
Development and expansion of technologies are fueling a dynamic change in our
society, including industries and corporate organizations. New forms of industries
Int J Artif Intell Educ
and new types of jobs are emerging, requiring future personnel to be well equipped to
meet the need of the expansion requirements of these industries and keep up with their
development needs. Learners and students, being the future drivers of these industries,
are the main human resource to fulfill the vacancies of these work forces. However,
with the current formal learning provided in schools and universities, learners are not
well equipped nor prepared for the new kinds of jobs, making it difficult for them to
fulfill the work-force demands of the future. Therefore, constant improvements in and
reevaluation of the curriculum taught to the learners has to be done regularly to keep the
learners up-to-date in fulfilling the requirements of these industries and corporations.
With the work demands changing at a rapid pace, corporations are instilling the
education element into their working environment by transforming their working
environment into a learning environment for their employees. For example, Google
and IKEA provide extensive learning opportunities for their employees while they are
on the job. This not only provides employees with an innovative environment to learn
and grow, but also gives them the positive urge to venture into new products and come
out with creative ideas benefiting the organizations. Universities could benefit from
these Bthinking out of the box^ practices by equipping students with work force
experience that involves more hands on task with corporations and companies. Many
universities have started to adopt co-op and internship programs to facilitate these
experiences, particularly in engineering and computer science programs. For example,
University of Waterloo offers co-op that provide students to Balternate 4-month school
terms with 4-month paid work terms in jobs relating to your program^ (https://
uwaterloo.ca/find-out-more/co-op). Universities would then be setting courses which
are more relevant to the actual field work by putting themselves in the field itself,
equipping students with all the skills and knowledge required in the work field.
Emergence of Smart Learning Environments
Due to rapid advances of information and communication technology (ICT) in recent
years, learning technologies have already impacted and changed the ed ucational
landscape significantly. In order to provide learners with a learning environment that
makes effective use of technological advances, teaching methodologies and learning
strategies also require changes. For example, cyber synchronous learning, mobile
learning, social learning, and ubiquitous learning did not exist in the past educational
landscape. As mentioned by Hwang (2014), Bnew learning modes will raise new
pedagogic issues^ (p11). Thus, new teaching methodologies and learning strategies
are desperately needed in the current education systems to support these newly
emerging modes.
This section discusses the case of employing innovative learning technologies in
ubiquitous settings, as an example of the emergence of smart learning environments.
This is where learning benefits from authentic environments. The environment is not
restricted only to formal educational activities, but also encompasses informal learning
opportunities that result in improvement of the learners’ overall knowledge and skill
levels. In other words, smart learning environments engage and integrate formal and
informal learning in order to create autonomous adaptive learning environments for
supporting individual learners with real-time and seamless learning experiences in
Int J Artif Intell Educ
ubiquitous settings. As Hwang (2014) indicates, BA smart learning environment not
only enables learners to access digital resources and interact with learning systems in
any place and at any time, but also actively provides the necessary learning guidance,
hints, supportive tools or learning suggestions … in the right place, …right time and …
right form^ (p2).
These environments typically use big data and learning analytics techniques to
synthesize the combination of real-time data and the historical datasets in order to
identify contextually meaningful learning patterns. Smart learning environments facil-
itate just-in-time learning as they can provide various levels of adaptation and precision
of diversified learning conditions (including curriculum, course content, strategy and
support, etc.) for the learners. As mentioned by Boulanger et al. (2015), Ba smart cloud
computing framework…consists of four basic services:… Bthe pull service will extract
the type of content to be delivered to the users. The prospect service is responsible for
the preparation of the learning content to comply with user context. The content service
generates the content and establishes the connection between the server and the target
device.…the push service performs the synchronized delivery of the generated content
to the target device. (p290)^.
Three major features of the development of smart learning environments are
discussed that separates smart learning environments from other advances in learning
technologies. The three features/directions are full context awareness, big data and
learning analytics, and autonomous decision making.
Full Context Awareness
Abowd et al. (1999) indicated that B…context-awareness or context-aware computing
is the use of context to provide task-relevant information and/or services to a user.
Three important context-awareness behaviors are the presentation of information and
services to a user, automatic execution of a service, and tagging of context to informa-
tion for later retrieval^ (p304–305). Traditional learning environments are not fully
context-aware systems. For example, teachers and educators are not aware of every
learner’s learning behavior, learning process and learning outcomes in real time in a
traditional learning environment.
Boulanger et al. (2015) indicated that smart learning environments involve context-
awareness that can combine a physical classroom with many virtual learning environ-
ments. This could provide full context awareness by combining smart learning envi-
ronments with holistic Internet of Things (IOT) and ubiquitous sensing devices, e.g.,
wearable technologies such as smart watches, brainwave detection, and emotion
recognition (Li et al. 2015; Hwang 2014). Full context awareness enables smart
learning environments to provide learners with authentic learning contexts and seam-
less learning experiences to fuse a variety of features in the e-learning environments.
The system includes learning management systems, mobile and ubiquitous learning
systems, various artificial intelligence based adaptive and intelligent tutoring/learning
systems. These systems would assist teachers and instructors in direct monitoring of the
learning environment, understand learners’ conditions and give learners real-time
adaptive assistance, while at the same time facilitating independent learning for the
learners (Chan et al. 2006;Hwang2014; Schilit et al. 1994).
Int J Artif Intell Educ
Big Data and Learning Analytics
Smart learning environments are expected to facilitate smart learning support that is
based on each individual learner’s learning profile. Smart learning environments need
to consider advanced data manipulation techniques such as employing big data and
learning analytics to collect, combine and analyze individual learning profiles (from
past to present) in order to scientifically generalize and infer each individual learning
need in real time in ubiquitous settings that encompass both physical and online
activities. Learning analytics by using big data can monitor individual learners’ prog-
ress and behavior continuously in order to explore factors that may influence learning
efficiency and effectiveness (Kumar et al. 2015a, b). However, providing each learner
with just in time adaptive learning support through effective application of full context
awareness is still a challenge for TEL. As Boulanger et al. (2015) pointed out, Bthe
degree of customization, the scalability of ubiquity, and the integration of learning-
related data are still key challenges facing educational technologists^ (p289). However,
Price (2015) indicated that Bto develop a smart learning environment, it is important to
monitor, refine, and improve the components of the efforts continuously. The purpose
of using advanced data techniques is to gain insights about an individual learner’s
learning experiences, to tailor instruction to individual learner’s needs, and to equip the
learning environment with relevant and/or optimal learning conditions (in terms of
curriculum, course content, strategy and support, etc.) (Boulanger et al. 2015;Kumaret
al. 2015a, b). Price (2015) further stated that Bsuccessful education reform should be
based on future outlook, and should incorporate program data from the outset in regular
evaluations and measurements …for the successful education transformation…it must
deliver sustained value and drive toward continuous improvement in student
performance^ (p12). Thus, through big data and learning analytics, smart learning
environments could derive new and more effective learning models by analyzing the
data collections of various learners and further extract valuable learning patterns, to
provide suggestions and recommendations to the learners over long periods of time,
possibly even during their future careers.
Autonomous Decision Making and Dynamic Adaptive Learning
Another important feature of smart learning environments, which is different from other
learning environments, is their autonomous knowledge management capability that
enables them to automatically collect individual learners’ life learning profiles. As Kay
(2008)mentioned,learners’ life learning profiles can track their learning progress over
long periods and across the range of sources of evidence about the learner’sprogress.
Based on individual life learning profiles and the techniques of big data and learning
analytics, smart learning environments can precisely and autonomously analyze
learner’s learning behaviors in order to decide in real time, for example, what interac-
tions with the physical environment to r ecommend to the individual le arners to
undertake various learning activities, the best location for those activities, which
problems the learners should solve at any given moment, which online and physical
learning objects are the most appropriate, which tasks are the best aligned with the
individual learner’s cognitive and meta-cognitive abilities, and what group composition
Int J Artif Intell Educ
will be the most effective for each group member’s learning process. Such autonomous
decision making and dynamic adaptivity has the potential to generalize and infer
learners’ learning needs in order to provide them with suitable learning conditions.
It is a challenge for smart learning environments to collect these data about the
learners and their environment from disparate sources in both physical and online
components of the ubiquitous settings. For smart learning environments to possess
autonomous decision making capability that could utilize learning analytics features to
provide individual adaptive learning support is important. As Spector (2014)pointsout,
it is necessary for a smart learning environment to autonomously provide Bdifferent
learning situations and circumstances, as... a human teacher or tutor… to help learners
become more organized and aware of their own learning goals, processes and
outcomes^ (p7).
Pedagogical Innovations in Smart Learning Environments
This section looks at the need for the shift in pedagogies to support new requirements
of learning. Some examples of pedagogical innovations are discussed, and while the list
is not exhaustive, it identifies critical differences that set smart learning environments
apart from traditional approaches.
Knowledge Generated from Micro-Social Interactions
Knowledge in smart learning environments is not restricted to formal learning situations
any more. There is an increasing realizationthatlearningcananddoeshappeninany
environment, interaction and conversation that the learners engage in (Dabbagh and
Kitsantas 2012). In particular , when the learners engage in social media, such as browsing
Facebook posts, reading tweets on Twitter, looking at pictures on Flickr or Instagram, or
simply having a conversation with someone on Skype or WhatsApp (Kinshuk 2014), the
challenge for a smart learning environment is to identify the moments of Bopportunistic
learning^ by analyzing those micro-social interactions for possible knowledge nuggets
andintegratingthemwiththelearner’s previously gained knowledge.
Change in Assessment Practices to Include Knowledge Generated
from Micro-Social Interactions
For effective integration of knowledge generated during micro-social interactions with
the previously gained knowledge, the assessment practices also need reconsideration.
Formal assessment methods, such as two-hour final exams in an examination room
using paper and pen, or quizzes on computer, do not provide adequate means to assess
the knowledge nuggets the learners have acquired during their social interactions or the
integration of those knowledge nuggets with previous knowledge to expand their
understanding (Kinshuk 2015). New assessment methods are needed to analyze the
knowledge gains due to social interactions as well as their impact on the overall
competence level of individual learner. Learning analytics approaches enable such
assessment by identifying patterns in the learner’s behavior and use the gained knowl-
edge at a fine grain to be able to detect the impact of micro level gains.
Int J Artif Intell Educ
Assessment in Ubiquitous Learning Environments
Recent advances in the mobile and sensor technologies have started to give rise to
research and development that takes advantage of a learner ’s location, environment,
proximity and situation to contextualize the learning process (Kinshuk 2014). Adap-
tivity and personalization in this kind of ubiquitous learning context have taken on a
new meaning by bringing authenticity (Kinshuk 2014), through seamless integration of
physical objects available in the learner’s vicinity with virtual information in real-time.
For example, environments not only break barriers to education by widening access to
those who cannot attend a p hysical classroom, but also increase the richness of
instruction by integrating multiple sources of instruction, contextualization and real-
time location-aware learning. Hence, overcoming the limitations of classroom learning,
assessment for ubiquitous learning also needs a shift from traditional assessment
approaches. Some examples of the assessment needs in ubiquitous learning environ-
ments are as follows:
(a) What capabilities and competencies do learners possess with respect to ubiquitous
learning opportunities, and what are their preferences with respect to the associ-
ated authentic learning environment and learning process?
(b) What are the ways in which meaningful movement and interaction patterns of
individual learners can be detected and analyzed? How do those movements and
interactions relate to the interactions and learning activities at a particular authen-
tic learning location?
(c) What is the influence of the technologies that are accessible to the learners at a
given point in time, on their learning experiences? What influence do the tech-
nologies surro unding th e individua l learners have on learners’ learning
experiences?
(d) How do various components of context relate to the current situation of individual
learners, and how do those components affect each other?
Real-Time Intervention in Learning
Real-time interventions in learning require mining of relevant information about the
learners from various sources and aggregating that information for Bubiquitous learning
analytics^ in order to provide real-time recommendations to improve the effectiveness
of the learning process. These information nuggets need to be presented using advanced
visualization techniques and smart technologies to enable natural interaction through
rich interfaces, and help investigate and analyze learners’ behavior, activities, and
performance in ubiquitous (field) environments (Mottus et al. 2015). Advanced data
mining techniques need to be designed to identify relevant patterns, such as where and
when learners have difficulties and where their strengths lie (Kumar et al. 2014).
Teachers in such environments are able to intervene in the learning process and provide
learners with advice and support, such as explaining the topic/tasks, pointing to
particular learning material, providing learners with the activities that could help them
understand the particular topic/task, and creating teams with complementary strengths.
In addition, teachers can receive personalized and intelligent support through analysis
Int J Artif Intell Educ
of their preferences and can adapt their teaching accordingly. Techniques also need to
be designed using, say, machine learning approaches to infer how different teachers
respond in certain situations, which kind(s) of support they provide to their learners, as
well as how a particular teacher previously responded in certain situations. Based on
this analysis, suggestions and guidelines can be offered as and when a teacher is found
in a similar situation.
Technological Innovations in Smart Learning Environments
Smart learning technology can support learners in both real and virtual environments.
The emerging technical innovations now enable us to effectively record and analyze
learners’ learning patterns and habits. Based on each learner’slearningprofile,they
provide educators and learners with a customizable and personalized approach to
learning. Using these technologies, teaching approaches can change to fit the needs
of the learners, making it possible for the teachers to monitor an individual learner’s
learning process at a much finer granularity. Moreover, learners can be given the
opportunity to choose and shape their own learning portfolios and fill such portfolios
with actual evidence of learning.
In the near future, these technologies will progress into greater maturity in executing
seamless monitoring and autonomous adapting to the learners’ learning needs. These
technologies would enable spontaneous information feeding to learners, providing
learners with learning materials fitting their needs, interests and learning habits.
Furthermore, instantaneous reflection and review of the learners’ learning progress
would be possible, along with instant feedback by the environment to assist learners
with their enquiries. This would make it possible to provide Balways-on^ learning
independent of location and device/platform.
There are thousands of technologies that have been implemented into TEL. This
section takes a look at some examples of innovative technologies used in smart learning
environments that enable learning opportunities and instructional interventions that
have been very difficult, if not impossible, in traditional settings.
Flipped Classroom
The concept of a flipped classroom has emerged in recent years as one of the popular
methods used for enhancing teaching quality and improving learners’ outcomes. The
idea is to flip the traditional in-class lecture so that the students take a vital role through
collaborative activities (McLaughlin et al. 2014). This method has gained particular
interest in K-12 education (Chen et al. 2014). The flipped classroom approach enables
teachers to provide students with different learning opportunities in the course as they
move through the curriculum (Fulton 2012). By integrating various learning technol-
ogies in the classroom, the flipped classroom approach has shifted the instruction from
a teacher centered physical classroom, to a student centered blended learning environ-
ment (Chen and Chen 2014). Flipped classrooms allow teachers to deliver the course
content in a more interactive manner, along with interactive learning activities both
inside and outside of the physical classroom (Cheng et al. 2015). They include both
formal and informal learning approaches, enriched with interesting and interactive
Int J Artif Intell Educ
learning activities to increase students’ engagement in the course and enhance their
learning process.
Massive Open Online Course (MOOC)
The concept of Massive Open Online Course (MOOC) has emerged as another popular
method in recent years, enabling learners to attend online courses anytime and anywhere.
MOOCs are designed to provide learners with free access to open education and these
courses are capable of having intakes of vast number of participants through the Internet
(Chew et al. 2015). They allow everyone across the globe to pursue more knowledge with
no limitation on the number of student intake and enrollment period (Riddle 2012). Learners
taking MOOCs have the flexibility to complete each course at their own pace, to suit with
their own schedule. MOOCs enable self-directed learning where learners can achieve their
own learning needs and goals by applying appropriate personal learning strategies (Paquette
et al. 2015). MOOCs are typically equipped with interactive video lectures and learning
activities to engage learners in the learning process. Both asynchronous learning and
synchronous learning modes are possible with MOOCs, allowing and increasing the
interaction among the learners and between the learners and the instructors (Chen et al.
2005; Hastie et al. 2010) to facilitate better learner experience.
Game-Based Learning
Game-based learning involves integrating learning and curriculum into computer
games, providing learners with a virtual environment to enjoy the game and have fun
while learning the curriculum. Compared to instructional or formal learning methods,
learners typically spend more time engaged with computer games, which has resulted
in educators and researchers examining the potential of game-based learning as a
serious component of instruction. For example, Tobias et al. (2014)havearguedthat
game-based learning can assist in enhancing learners’ cognitive processes and cultivate
multitasking skills in the learners. Furthermore, game-based learning has been found to
enhance learners’ self-efficacy and assist in increasing knowledge retention (Sitzmann
2011). It is worth noting that the work of engineering a game to meet a specific learning
task or need becomes a challenge or barrier when trying to scale it towards many
learning tasks.
Augmented Reality and Virtual Reality
Augmented reality (AR) is defined as Ba live direct or indirect view of a physical, real-
world environment whose elements are augmented (or supplemented) by computer-
generated sensory input^ (Wikipedia n.d.). Virtual reality (VR), on the other hand, is a
simulated real-world environment that is created to enable users to believe that the
environment is real. Both AR and VR can be used for education by simulating a real
environment during the instruction process, providing learners with a better under-
standing and visualization of the subject matter (Kaufmann 2003). With the pervasive
growth of the Internet of Things and wireless sensor networks, plus the wide accessi-
bility of wearable technologies/devices, AR/VR technologies can be used to convert
our physical contexts into 3D immersive learning environments that seamlessly
Int J Artif Intell Educ
integrate physical and digital worlds. A whole new learning experience with authentic,
situated and timely response features is emerging. This obviously brings with it a great
challenge of constructing a broad range of learning scenarios to be used in these virtual
learning environments.
Gesture-Based Learning
Gesture-based learning incorporates gesture-based technologies in the learning process,
allowing le arners to engage in a virtual environment by interacting with computers primarily
using their body motions and movements. Gesture-based technologies consist of various
sensors, such as gravity sensor , infrared sensor and structured-light 3D sensor , enabling
gesture recognition (body actions and movements), voice recognition, and positioning
(action acceleration and direction changes) (Johnson et al. 2012;Hungetal.2014). Through
the theory of embodied cognition used as the main pedagogical approach, gesture-based
learning provides opportunities for the learners to actively and physically engage in various
hands-on learning activities (Chao et al. 2013). Gesture-based learning enhances learners’
body-related experiences through the use of actions as part of the learning process, which
intensify the learning and cognitive processing through motor-sensory experience (Chen and
Fang 2014). The major challenge of designing gesture-based learning is the lack of design
principles and guidelines for systematic development.
Educational Robots
Robots are mechanical artificial agents guided by computer programs. They are now widely
used in various industries, such as manufacturing, production and medical sectors. With the
ability to imitate lifelike automated movements, robots are capable of assisting in solving
various problems. In terms of education, robots can provide a social and physical embodi-
ment to the concept of teaching, assisting teachers in continuous and repetitive follow ups
with the learners. For example, in language learning, robots have been successfully used to
assist learners by repeating phrases over and over again without the problem of getting tired
(Mubin et al. 2013). W ith educational robots, learners do not feel shy in asking questions and
the robots can assist learners in solving their enquiries immediately anytime and anywhere.
Besides, educational robots can enrich the subject curriculu m and increase learners’ attention
and engagement in the learning process. Thus, the robots in education have potential to play
the role of a personal tutor for learners, understanding the learning needs of each learner and
customizing the learning contents according to individual learners’ pace and requirement.
The advancement in data mining, big data analysis and learning analytics have strong
potential to become the backend engines for implementing more sophisticated intelligent
educational robots.
Future Perspectives of Smart Learning Environments
The learning process is increasingly integrated into every aspect of life including
conscious, unconscious, intentional and/or unintentional situations (Malcolm et al.
2003). It implies immersive, always-on learning that needs to happen naturally and in
such small chunks that virtually no conscious effort is needed to be actively learning
Int J Artif Intell Educ
while engaged in everyday life (Mottus et al. 2015). Smart learning environments need
to be so much in tune with their learners that they are aware of every interaction the
individual has with the environment. In particular, because smart learning environments
are personalized to cater for individual learner’s needs in all types of situations, every
bit of information that each learner comes into contact with needs to be taken into
account. To meaningfully support a learner’s learning process through smart learning
environments, assessment of suc h learn ing also requires fundamental changes in
current practices and in the mindsets of educators and other stakeholders in order to
ensure effective learning in a wide variety of contexts and for individuals with a wide
variety of prior experiences and backgrounds. This section will discuss these issues of
smart learning environments by focusing on the integration of pedagogy and technol-
ogy through a variety of perspectives.
Life-Long Profiling
Learning portfolios, that include not only the traditional learner models depicting the
change in learners’ knowledge and skills but also the evidence of learners’ competency
gains in the form of learners’ artifacts, have long been implemented in the academic
field for profiling past and ongoing learning progress, serving as meaningful tools to
assess and demonstrate one’s learning capabilities (Kay 2008). They provide learners
with opportunities to reflect on their work, access to an organized record and system-
atically demonstrate the accomplishments. So far, learning portfolios have been used on
a course-by-course basis. For example, the portfolio of a certain course is discontinued
when the learner completes that course and a new portfolio is created for the next
course the learner enrolls in. Moreover, learners’ learning could be conscious, uncon-
scious, intentional and/or unintentional (Malcolm et al. 2003). Thus, all the portfolios
created by the learner during their entire program remain disconnected. We argue that
the use of these portfolios should be extended to a life-long profiling, carried from one
course to another, bridging the learning gap between courses, or even transcending
from formal and informal education on to the continuous learning taking place later in
life that connects to the learner’s career development.
In order to achieve this, a life-long learning profile can be stored on the cloud,
combined with social media at a macro level, which uses learning analytics/big data to
automatically provide recommendations and/or record achievements within the life-
long portfolio. Such a portfolio will not only connect to a formal learning system to
gather achievement data, but it will also identify skills and knowledge gained during
any social media interactions, based on the access permissions set by the learner.
Although life-long learning may require a lot of big data techniques or AI techniques,
it will help in analyzing the learning process and product, providing both the bigger
picture and also allowing drilling down to the associated evidence. This would enable
learners to build their digital life online whereby they could consistently reflect on their
learning and share accomplishments Kinshuk (2014)
Team Teaching
In traditional schools, course teachers are solely responsible for preparing the lecture
notes, assigning homework, creating final exam questions, and so on. As is true for the
Int J Artif Intell Educ
learners, the teachers also have different individual strengths, such as specific expertise
in particular knowledge content, the ability to give lectures to large classes versus
providing explanations in small groups, the creation of open ended questions for group
based assignments versus meaningful multiple choice questions to assess individual
competence, etc. Going forward, smart learning technologies can enable teachers to
consider forming alliances with other teachers, locally as well as remotely to conduct
team teaching in order to make plans, organize teaching materials, and categorize
various teaching activities based on individual teacher’s strengths and expertise to
enhance teaching quality as well as quality of course content. Team teaching can cater
to various teaching contexts, ranging from large group activities to serving the unique
learning demands of differentiated learners since different teachers may look at a topic
from a different angle (Leavitt 2006). Although team teaching may cause some
problems, for example, when teachers debate an issue or propose different solutions
to a particular question, this can potentially result in enriching course teaching tech-
niques as well as reduction in the ever increasing teaching loads of individual teachers
(McDaniel and Colarulli 1997). Learners, moreover, could receive the best learning
experience in every learning activity, as each activity will be supported by the teacher
who is the best suited to teach that activity. In other words, team teaching allows a
learner to understand a concept/topic from multiple perspectives through different
scholars with different teaching skill sets (Leavitt 2006).
Additionally, team teaching would also solve the resource constraints traditional
schools typically experience due to restrictions imposed by the local conditions such as
classroom size. For example, a teacher may teach a maximum of 35 to 40 students in a
class due to space constraints and other similar restricting factors. Thus, it is common
for the learners to not be able to enroll in a course that they are very much interested in,
if the demand for that course is high. Team teaching, supported by the advances in
technology eliminates those restrictions, enabling thousands of learners around the
world to enroll in courses of their choice and interest. Therefore, team teaching is a way
for schools to not only provide the best education by harnessing the specific strengths
and expertise of the teachers but also to circumvent resource constraints. It is important,
however, to recognize that teaching itself is a highly professional job and requires
teachers to master many different teaching skills and to dedicate a lot of effort. It
becomes even more challenging when new technologies are involved and the diversity
of learners continues to increase.
Competency Based Assessment
Traditionally, assessment is often standardized (or formalized) in terms of both time and
location. It is also tied to the course content designed by the instructor. For example,
students undertake their final exams together at the same time in an examination room
at the end of the course. Thus, instructors play the roles of both teaching the course
contents and assessing the learners’ learning outcomes. However, Bassessment involves
more than just measurement. …to systematically collecting and analyzing information
… interpreting and acting on information about learners’ understanding and/or perfor-
mance relative to educational goals^ (Shute and Ventura 2013, p9). With advances in
information technology, a number of changes can be observed in education that have
impact on these assessment roles of the instructors. For example, course materials can
Int J Artif Intell Educ
now be delivered and shared through the Internet where the materials can be accessed
anytime and anywhere. The structure of the learning contents is typically broken into
smaller units, and learners can choose units that suit their levels and interests. This also
reduces the length of learning time for each unit and allows for learning to take place at
the time and pace suitable to individual learner’s needs. The timeline of studying a
particular course and for an overall program would also be reduced, making it different
from one learner to another. This has direct implications for assessment, as it does not
fit the traditional form of assessment that requires all students to complete assessment at
the same time (and in the case of final examination, at the same location). The
assessment should be different from this traditional form. As Shute and Ventura
(2013) observe, B…having different purposes and procedures for obtaining information
assessments may be differentially referenced or interpreted – for instance, in relation to
normative data or a criterion^ (p10).
We propose that, in future, the assessment process would be separated from courses
or programs to align with the changes in the learning process. This is similar to
evidence-centered assessment design (ECD) that informs the design of valid assess-
ments and yields real-time estimates of students’ competency levels across a range of
knowledge and skills (Shute and Ventura 2013, p24). It also considers learners’
multidimensional competencies by collecting and assessing their knowledge, skills,
and other attributes (Shute and Ventura 2013). The roles of the instructors would also
change from both teaching the course content and assessing performance to primarily
supporting the learners in developing certain levels of competence. A possible solution
is for the formal assessment to be conducted by an independent unit or organization.
They would design, organize, and conduct assessments of individual learner’scompe-
tency (or of small groups, as the need may be). They would track the competency
changes not only for individual subjects but over the overall learning portfolio men-
tioned above, using analytics techniques, in order to identify the issues that transcend
beyond single subjects and individual teachers. This is similar to the approach typically
used in the domain of learning English as a second language. Learners can take
language courses, or go through self-learning by accessing learning content from
YouTube, audio tapes and other resources. Their English proficiency level is then
assessed by an authorized organization, such as Educational Testing Service (ETS),
International English Language Testing System (IELTS) and alike.
In summary, as Woolf (2010) indicate, assessment of learners’ knowledge, skills,
and other attributes will be seamless and ubiquitous. The assessment methods need to
respond to the changing learning processes. They should focus on assessing compe-
tencies that include both knowledge and skills, compared to traditional assessment
approaches that have largely focused on memorization and conceptual understanding
(Woolf 2010). The learner models will transform into competency based assessment in
order to adapt to the nuances of changing needs of the learners, such as being able to
undertake assessment anytime and anywhere, support assessment of competencies in
collaborative activities, and developed in such a way that they are Bplagiarism-proof.^
Education in the Service Economy
While the world is increasingly becoming flat and the service economy has swept all
over the world (Friedman 2005), our presen t education systems still embrace a
Int J Artif Intell Educ
traditional style. Traditional education has focused on a BProduct-Oriented^ approach
that puts more importance on static instructional quality delivery than on the perception
of the learners. This philosophy may have worked in a closed and domestic context, but
it is not feasible in today’s developed global environment.
Education itself needs to be opened to the world and be operated globally, making
education a global market. From a marketing point of view, business models nowadays
have forced educators to rethink how they can educate learners to be successful in the
new global village. Competition is inevitable. There is growing impact of new business
models, such as MOOCs – the newly emerging education system that does not require
any tuition fees from students unlike traditional schools. This is forcing educators to
rethink the way education is delivered. These new systems rely on revenue from
certification, advertisement and profiling. While these systems suffer, at least for now
from the lack of personalization, high dropout rates, difficulty of teaching hands-on
topics, such as programming, where individual feedback is critical for learners in their
impasses, and so on, they have still proven to be quite successful; an open course can
accommodate hundreds and thousands of students, ten or even hundred times more
than traditional schools. Learners are turning their heads towards these trendy initia-
tives, and in turn, making them financially much stronger than traditional schools. If the
old-fashioned schools do not adapt to such changes and reengineer their approaches,
they will face the risk of being diminished from this global and open market.
A new concept of BEducation-as-a-Service^ is emerging as an approach to deal with
the challenges of global and open market. Educational resources in this approach are
made easily accessible to global learners by delivering them as a service. From this
perspective, one can expect the traditional education organizational structure and
teaching processes to be radically changed, where configurable and modularized
functional units are identified, divided, remixed, reused and rearranged, in order to
provide the best possible learning experience to the learners. For example, lectures may
be separated from the course itself. Some of the lectures may be given by a teacher
other than the teacher responsible for the course. This is to leverage on the specific
expertise of different teachers. Assessments may be separated too, where a third party
may conduct the tests instead of the course teacher, in similar fashion as Scholastic
Aptitude Test (SAT) or Graduate Record Examination (GRE). Social interaction could
also be monitored to analyze even the smallest knowledge that learners gain during
social interactions, and how that knowledge can be linked with the learner’spreviously
learnt knowledge. So, the BEducation-as-a-Service^ approach can reengineer the tradi-
tional organizational structure of academia, which embeds all functions in one envi-
ronment, to a federated approach where many different providers provide different
functional education units, according to their unique expertise.
Whil e BEducation-as-a-Service^ takes an organizational view, BLearning-as-a-
Service^ takes the learner’s viewpoint and considers the learner’s learning experience.
Focusing on learner-centered learning, the learner’s knowledge map becomes more
self-directed than teacher-instructed. This change requires reconsideration of service
flow (aka the learning process). In smart learning environments, learners would have
different service choices at different learning stages, where these services are provided
by different educational facilities, either online or physically. For example, learners
could choose to learn BIntroduction to Calculus^ from one school with a teacher known
for introducing the topic with fun. They can then continue on with learning Differential
Int J Artif Intell Educ
Calculus from another school from a teacher who is famous for serious teaching using
practical examples and exercises. After learning the contents, the students may go to an
organization for t he ir as sess men t and get a certificate. These ne w sc enar ios o f
BEducation-as-a-Service^ and BLearning-as-a-Service^ provide learners with more
flexibility than traditional schools. This allows them to aggregate different services at
their own pace, resulting in the learners following different learning paths and solu-
tions. Analyzing how the learners (aka consumers in the Bservice^ approach) experi-
ence the service of learning holistically and how they reflect and stick to a service, will
inform new business models for the education-as-service providers.
The Reorganization of Schooling
Last but not least, smart learning environments require critical analysis and most likely,
reorganization of existing educational systems with a view to benefit from the recent
advancements in technologies that support learning in pervasive and ubiquitous envi-
ronments. This section will examine several examples of these new approaches that
may result in the formation of new schooling approaches.
Self-Directed Schooling
Students in traditional schools are typically compelled to accept courses, examination
periods, and even teachers, as they are pre-determined by the school. The absence of
self-directed schooling in typical traditional schooling means the learners are forced to
study what schools can offer and how they decide to teach. These practices do not
allow for customization of learning processes, taking into account the individual
learner’s strengths and weaknesses, which has implications on the future career choices
of the learners. Self-directed schooling is a self-initiated process of learning that stresses
the ability of individuals to plan and manage their own learning, an attribute or
characteristic of learning with personal autonomy as shared by Caffarella (1993). It
has the potential to shrink the learning gaps between students and provide better
alignment to a school’s curriculum to differences in individual learners. Although there
is some truth in Kirschner and van Merriënboer (2013) skepticism about whether
learners know best (indicated in their paper –BDo Learners Really Know Best? Urban
Legends in Education^) we still think that smart learning environments of the future
should employ a self-directed schooling approach to enable learners to pursue their own
options about what, how and when they want to learn. Enabling learners to exercise
their own options will not only contribute towards better customization of content,
timing and course plan to suit the needs of the individual learners, but will also improve
the learning experience where learners will enjoy the flexibility of choosing the group
mates of their own choice and their favorite/suitable teachers. We also believe that
having self-directed schooling would have direct impact on motivation and other
affective aspects of the learners.
Adaptation to Individual Differences
Learners nowadays have multiple sources at their disposal to retrieve learning re-
sources, such as open educational resource repositories, Wikipedia, MOOCs, and even
Int J Artif Intell Educ
content from TED talks, YouTube, and iTunes U. Accessing these additional learning
resources has never been so easy. Learners have the opportunity to acquire mastery of
different topics with ease, without having to rely on the teacher in a classroom. This
situation makes learners much more diverse than ever in how they go about achieving
their learning objectives. Individual differences nowadays are caused not only by
learners’ background, competence, gender, personality, cognitive and metacognitive
ability and learning progress, but also by the possibility of being able to access different
learning resources easily (Kinshuk 2014). Providing an instructional design that can
accommodate the needs of all the learners is a very challenging task. The availability of
alternative learning resources is continuing to grow in all subject areas and is forcing
traditional schools to reconsider how they support the learning process.
Due to the diversity of individual differences, instructors in traditional schools face
difficulty in designing curriculum that can cater for every single learner in the class-
room. Learners’ learning autonomy is a trend that would define the future educational
approaches. Schools need to respond to this trend by adopting various features of smart
learning environments that support adaptive and personalized learning through inno-
vations in technologies. For example, current learning management systems (LMS)
could evolve into intelligent personal learning environments (iPLE) that are capable of
personalizing instruction to each learner’s needs. Learning methods could also aim at
providing intelligent learning supports (iLS) for individual learners. Hence, future
schools need to consider how to effectively utilize learning technologies and provide
a curriculum that meets the needs and demands of different learners.
Formal Learning Revolution
Traditional educational approaches are categorized as formal education and informal
education. Formal education is classified as the learning that takes place within a school
curriculum, whereas informal education is identified as everything else. With advances
in technology and the emergence of Bopportunistic learning^ situations in ubiquitous
environments, this distinction is proving to be very artificial. Increasingly, there is
ubiquitous availability of mobile devices at almost every location and situation, giving
ready access to information as and when the learners need to learn. Learners have
frequent interactions in a variety of social media, resulting in micro-learning opportu-
nities, and an abundance of resources available through the always-on Internet. This
means that the ratio between formal learning and informal learning is changing rapidly
(ASTD 2008;duBois-Reymond2003). Even in the past learners had already spend
significant time on informal learning activities, and the ratio of informal learning to
formal learning is only going to increase in the future with increasing availability of
higher bandwidth, richer media, more communication channels, and greater content
variety. For example, learners may increasingly decide to spend more time learning
from informal channels, e.g. MOOCs or game-based learning, than learning from
formal channels such as attending lectures in a classroom. This tendency will squeeze
the time of formal learning even further. Schools will need to adapt to this change to
remain viable in future.
Once formal learning and informal learning interweave, schools and teachers would
need to embrace this combination and find their way out of a mindset where formal
learning was the primary means for learning. Smart learning environments treat this
Int J Artif Intell Educ
trend of rising informal learning positively and not as a threat. With diminishing
boundaries between formal and informal learning, and the focus on informal learning
increasing, we may not need to distinguish these two learning formats separately in the
future. Today’s informal learning would become tomorrow’sformallearning.Thereare
two possible directions this development can take. As a result, formal learning and
informal learning may merge and become one Bseamless-learning ^ environment.
Various methodologies and approaches of these two learning formats may combine
to provide a more comprehensive learning experience to the learners.
A second possibility is that there would always be a unique space for informal
learning, but the applicable domain for informal learning would be in Bjust-in-time^
settings. In such contexts, people will learn certain knowledge in a timely manner
whenever needed, for example, learning how to bake a cake for Christmas, or fixing a
kitchen sink when it breaks. Nevertheless, the emergence of informal learning along
with the awareness among teachers and learners, suggests that formal and informal
learning will be balanced in the overall future education system. It is also becoming
clear that the decision regarding whether to use formal learning or informal learning
will be based on the needs of the individual learners and not on some pre-defined
arrangement of schools and instructors. As a consequence, the need for a new kind of
schooling is on the rise.
Conclusion
This paper discusses a futuristic vision for smart learning environments. These envi-
ronments are expected to break the boundaries of the classroom and enable the
detection of the learner’s location, environment, proximity and situation. This would
provide a fully contextualized learning process in order to provide learners with
learning scenarios in their own living and work environments, leading to significantly
better learning experiences.
Research on smart learning environments is reaching a maturity point where it will
be able to provide authentic learning through seamless integration of physical objects
available in the learner’s vicinity with virtual information in real-time. Also, compe-
tency is becoming a key indicator of success while performing tasks. Workforce
training an d performance ap praisal are indispensable components in technology-
enabled and resource-rich industries, as they are inherently linked with recruitment,
retention, succession planning and other talent management considerations in human
resource management. From this perspective, education is becoming a global com-
modity, which will attract private sector involvement, pushing future educational
processes through reengineering (whether automated or not) into processes that cater
for individual needs and the requirements of individual career development. One
possible scenario is the separation of instructors’ teaching process, learners’ learning
process, assessment and accreditation. In other words, learners would be controlling
their own learning process, choosing their own teachers, and undertaking assessment
and accreditation at their own time. This phenomenon, enhanced by the emergence of
technological advances, has given rise to smart learning environments, where learning
and working are merged together. These environments hold the key to the future of
education.
Int J Artif Intell Educ
While advances in technology and computing research in recent years are promising
for the implementation of smart learning environments, there are technological as well
as social challenges that researchers and practitioners will need to overcome. Learning
analytics, for example, is still very much in its infancy, particularly with respect to
scaling up the real time processing of big data in order to understand the immediate
context of a large number of individual learners and to enable teachers to help them.
These environments also challenge the existing educational practices that have been
used for decades if not centuries and push teachers as well as educational experts out of
their comfort zone by making them aware of the limitations of teaching and assessment
practices and how they can be improved through these new possibilities. Existing
policies also become a barrier to improving education that these environments allow, as
they are primarily geared towards old educational paradigms. Tackling these challenges
will require concerted efforts of various stakeholders at different levels: teachers at the
grassroots level, policy makers, children and parents and in fact all people, industry and
professions.
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