The evolving learning environments
Unarguably, technology has become an irreversible force driving the transformation
of teaching and learning practices. Cloud computing, learning analytics, big data,
and artificial intelligence are being adopted in today’s teaching and learning, though
to different extents. For over a decade, educational researchers have been exploring
how different innovative means could be integrated into traditional learning in order
to enrich learning experience and enhance learning effectiveness. Enabled by various
pedagogical and technological innovations, brand new learning environments can be
created to optimize learners’ ability to learn. They are collectively referred as
the commonly known “smart learning environments” which can best delineate the future
learning environments. Embracing a variety of concepts, including but not limited
to flexible learning, personalized learning, mobile learning, adaptive learning, and
blended learning, for obvious reasons, there are no one single form of smart learning
environments. The concepts, and even definitions, of smart learning environments have
continuously emerging.
A smart learning environment can be conceptualized as a learning environment that
emphasizes learning flexibility, effectiveness, efficiency, engagement, adaptivity,
and reflectiveness (Spector, 2014), where both formal learning and informal learning
are integrated (Gros, 2016). It is basically an adaptive system that improves learning
experience based on learning traits, preferences and progress, features increased
degrees of engagement, knowledge access, feedback and guidance, and uses rich-media
with a seamless access to pertinent information, real-life and on-the-go mentoring
with the use of technologies to continuously enhance the learning environment (Singh
& Hassan, 2017). In recent years, educational researchers have been actively investigating
a smart learning environment. As of March 2021, a simple search of the keyword, “smart
learning environment”, from Google Scholar and Scopus yields 1990 and 1773 results
respectively. Over 80% of these results are published within 5 years, and almost all
refer to the tertiary education settings, including higher education, further education
and open education.
This thematic issue entitled Future Learning Environment: Pedagogical and Technological
perspectives aims to report the latest research findings and share good practices
on creating brand new learning environments that emphasize learning effectiveness,
efficiency, flexibility and engagement. It started to invite submissions in April
2020. By the submission deadline in December 2020, over 160 submissions had been received.
After a rigorous and highly selective review process, 6 papers were finally accepted
to this thematic issue. All of them are well written with significant original contributions
that would provide excellent references for educational researchers and practitioners
in the pursuit of the best learning environments.
Serving as the editorial introduction, this position paper first discusses the new
challenges and opportunities in designing and implementing smart learning environments
in the higher education context, and then highlights the key findings reported by
each article appearing in the thematic issue.
Current issues and new challenges
Today, the education system has been undergoing major changes brought about by emerging
educational concepts and technological reforms. These emerging concepts and reforms
pose a number of new challenges on smart learning environments, as enumerated below.
Pedagogical approaches
Many advances in education will be brought about by the further integration of personalized
learning and intelligent learning environments (Price, 2015). Chatti et al. (2010)
pointed out that learning is personal, social, distributed, universal, flexible, dynamic,
and complex. In a smart learning environment, a fundamental shift is needed towards
a more personalized, social, open, dynamic, emergent, and knowledge-pull model for
learning, as opposed to the one-size-fits-all, centralized, static, top-down, and
knowledge-push models of traditional learning solutions (Chatti et al., 2010). To
achieving this goal, new pedagogical approaches are required regarding the effective
application of integrating technologies into the curriculum in a smart learning environment,
to improve the effectiveness and efficacy of students’ learning.
Personalized adaptive learning
In a smart learning environment, more attention has been paid to individual needs
of students. According to Hwang and Fu (2020), a smart learning environment is regarded
as a learning system for facilitating efficient personalized learning. Adaptive learning
provides technical and methodological support for personalized learning. Personalized
adaptive learning makes adaptive adjustments according to the individual characteristics
of learners to promote the individualized development of students.
Smart devices and intelligent technologies in smart learning environments can be used
to promote the development of personalized learning and adaptive learning for students.
The smart learning environment has a large potential to effectively promote the development
of personalized learning and adaptive learning (Peng, Ma & Spector, 2019). Thus, how
to design learning ecosystems that integrate smart learning to personalize and self-regulated
learning will be a key challenge (Gros, 2016). The following efforts could be made:
monitoring learners’ differences and changes in individual characteristics, individual
performance, personal development, and adapting teaching strategies (Peng, Ma & Spector,
2019).
Affective interaction
New knowledge is constructed through social interaction. Just because the smart learning
environment makes it technically possible, it does not mean that social interaction
will necessarily occur (Feidakis, et al., 2013). Emotion is a kind of psychological
response of human beings, which can influence and regulate cognitive activities such
as attention, perception, representation, memory, thinking, and language. It occurs
in the social interaction between students. In the traditional face-to-face learning
environment, affective interaction occurred among teachers and students at a very
high frequency, while smart learning environments focus more on imparting knowledge
than affective interaction. Therefore, how to improve the affective interaction within
the smart learning environment is an important challenge nowadays. One effective solution
is to construct a comprehensive and dynamic learner model, which can incorporate learners’
learning emotions as a more important influencing factor (Hwang & Fu, 2020).
Assessment method
Despite advances in psychological research and educational technology, assessment
practices in educational institutions have remained unchanged for decades. Under a
smart learning environment, there is an urgent need to go beyond traditional forms
of assessment and use new methods to evaluate the effectiveness of the smart learning
environment. The formative assessment might be an effective approach. It can enhance
students’ ability to change from passive learners to active learners, where they can
understand their strengths and weaknesses, recognize gaps in learning and develop
solutions (Price, 2015).
Integration of formal learning and informal learning
In the past, the channels for students to acquire knowledge were formal school and
university studies, but now through the Internet, students can easily obtain and use
informal learning methods, which leads to formal learning time allocation may only
account for 50% of learners’ study time (Kinshuk et al., 2016). However, due to the
blurring of the boundaries between formal and informal learning and the increasing
attention to informal learning, the smart learning environment must integrate formal
and informal learning to create an autonomous learning environment to support individual
learners (Gros, 2016).
Learning data
Education (in whatever form) has always used data (such as demographic and behavioral
data) to plan, operate and teach, and smart technology offers new opportunities to
extend the "data gaze" (Kwet & Prinslo, 2020). In a smart learning environment, a
large amount of learner behavior data is generated. However, it is important to note
that the data collected for these forms of delivery may vary depending on technology,
background, institutional characteristics, and pedagogical strategies (Broughan &
Prinsloo, 2020; Pink, et al., 2018). Therefore, how to integrate data in different
scenarios, build data-centric smart education, and provide learners with a seamless
learning experience and personalized customized services is also a big challenge (Zhu,
Yu & Riezebos, 2016). It is also a challenge to collect and use these learning data,
while observing relevant data protection principles and guidelines. Learning analysis
could be used to process learning data, monitor learning progress, and provide feedback
to the system, teachers, and students. However, the current studies on the design
and implementation of learning analytics as reported in the literature are found to
be largely driven by researchers in areas of computer science and decision sciences
(Lee et al., 2020). The focus is on the applications of analytics to teaching and
learning more from the technological perspectives that the pedagogical perspectives.
Opportunities and development trends
In the last decade, innovations have emerged into teaching and learning practices
at an ever accelerating rate. The latest advances in pedagogies and technologies have
brought new opportunities on the development of smart learning environments in two
aspects, namely, performance evaluation and instructional design. The following discusses
these opportunities with suggestions.
Evaluation of a smart learning environment
Evaluation on learning performance would be more accurate
Through artificial intelligence technologies in a smart learning environment, such
as the internet of things, perception technology, video recording technology, image
recognition technology and platform acquisition technology, multi-source, heterogeneous,
multi-modal big data (for example, raising hands, facial expressions, bodily postures,
and discussion) concerning with students’ learning process could be collected (Beer,
2019; Chatterjee et al., 2019; Kwet & Prinsloo, 2020). Such big data would generate
new insights about students’ behavior and learning performance in the smart learning
environment, which makes it possible to better understand and optimize the learning
process and the teaching environments (Shorfuzzaman et al., 2019; Syafrudin et al.,
2018). For instance, the “artificial intelligence smart classroom” solution by Intel
partner Corerain utilizes video analytics to detect and identify students’ positive
actions, such as participation, hand raising, and standing up, and negative actions,
such as turning around and resting their head on the table. Then, these actions would
be traced, recorded, and visualized in a dashboard to determine students’ engagement
situations (Intel, 2019).
Feedback and intervention would be more timely
With the help of learning analytics, a smart learning environment could monitor students’
learning process, alert possible academic failures, conduct timely and effective interventions
for learning problems, and provide students with personalized support services (Pardo
et al., 2019; Tempelaar et al., 2021). Specifically, with the application of machine
learning and predictive modeling techniques, learning analysis could help to identify
students at risk of failure or dropping out, and provide special support, such as
course recommendation, instructional design (Sclater, 2017; Xing, et al., 2019). For
instance, in response to the declining freshman retention rate, Purdue University
launched the course signal system, which could collect and analyze data, such as student
course performance, learning behavior, previous academic history, learner characteristics,
etc., to realize real-time prediction of a course. Since students’ performance would
be indicated by different signals, teachers can appropriately intervene in students’
learning by sending emails, text messages, and face-to-face interviews according to
the signals. Furthermore, teachers can also guide learners through recommending appropriate
learning resources of the system to promote their success in learning (Arnold & Pistilli,
2012).
Instructional design in a smart learning environment
Instructional resources would be more equitable
In a typical smart learning environment, digital cameras and recording or casting
equipment, multiple student-controlled interactive whiteboards or touch screen televisions,
mobile devices that are compatible to connect with student-controlled displays, wireless
Internet, and educational management software are ubiquitously available (MacLeod
et al., 2018). These equipped resources and technologies could ensure all students
in a smart learning environment have the access to engage in different kinds of instructional
resources regardless of race, gender, learning differences, socio-economic status,
or background.
Instructional approaches would be more student-centered and flexible
With the help of smart technologies, existing researches have shown that active learning
approaches, including inquiry learning, collaborative learning, group learning, and
so on, are increasingly ubiquitous (Ellis & Bliuc, 2016). With the continued maturing
of smart technologies, these student-centered instructional approaches could be more
common. With the ability to store, collect, compute and analyze the massive data of
learners to do the optimized pedagogical decisions (Li, Kong & Chen, 2015), a smart
learning environment could push personalized learning plans for every student, at
the same time, students could interact with the smart learning management system to
adjust the learning plan. Besides interaction between students and the system, interactions
between students and teachers, students and parents would be more convenient and timely,
since the smart learning system could assist teachers in mastering students’ conditions
and in adjusting teaching in real-time (Dai, 2019).
What’s more, the ubiquitous instructional resources in a smart learning environment
make it possible for students to conduct any learning activities with their preferential
learning approaches at anytime and anywhere they wanted (Hwang, 2014). Students could
choose their classmates by themselves, some in a face-to-face environment whilst some
others in the cloud. Compared with the fixed time and fixed classroom in the traditional
instruction, the instructional approach in the smart learning environment would be
more flexible.
Instructional objectives would be more ability-centered
Previous studies have also indicated that a smart learning environment can stimulate
students’ learning motivation, promote active learning, improve academic performance
and stimulate higher-order thinking skills (Jena, 2013; Liu et al., 2011; Lu et al.,
2021). With the tendency that the instructional approaches to be more student-centered,
with the interaction between students, teachers, parents, and learning system to be
more convenient, students would have more free space to develop and conduct learning
activities by communicating and collaborating with their classmates, or seeking help
from their teachers. This active learning process can not only help students gain
new knowledge, but also cultivate their cognitive, behavioral, and emotional skills.
As a final note, while learning environments continue to evolve, the learning process
itself is inevitably undergoing different levels of transformation. It is also about
time for the learning process to be reviewed or even re-defined.
Papers in this thematic issue
The upcoming 6 papers collectively attempted to address the challenges and evaluate
the effectiveness of learning environments, as well as to develop new instructional
design approaches and technological measures.
The first upcoming paper entitled, “Past, Present, and Future of Smart Learning: A
Topic-based Bibliometric Analysis”, provides a literature review of smart learning.
The authors conducted a topic-based modelling analysis on the publications relevant
to smart learning. The major research topics on smart learning were identified, for
example, interactive learning, multimedia learning, STEM (science, technology, engineering,
and mathematics) education, blended learning, affective and biometric computing. Some
emerging topics, such as learning analytics, IoT (Internet of things), could computing,
MOOCs (massive open online courses), and feedback and assessment, were also identified.
The authors attempted to explain how these topics evolved over the years. The findings
help educational researchers, practitioners and policy makers better understand the
past, present, and future of the development of smart learning and smart learning
environments.
The second paper entitled, “Technology Acceptance of Four Digital Learning Technologies
(Classroom Response System, Classroom Chat, E-Lectures, and Mobile Virtual Reality)
after Three Months’ Usage”, provides a reflection of how the four popular learning
technologies are compared under a technology acceptance model. The study was carried
out through a survey conducted to the students of a university in Switzerland. Three
core factors, namely, perceived usefulness, perceived ease of use, and behavioural
intention, were considered. The results showed that classroom response systems had
the highest level of acceptance, followed by e-lectures, and then classroom chat,
and then mobile virtual reality. The authors admitted that the low level of acceptance
for mobile virtual reality was surprising and went contrary to their expected results.
Feedbacks from students were studied, revealing a substantial drop in perceived usefulness
and behavioural intention.
The paper to follow is entitled, “Transitioning to the New Normal of Learning in Unpredictable
Times: Pedagogical Practices and Learning Performance in Fully Online Flipped Classrooms”.
The authors shared their successful experience in transforming two conventional flipped
classes into fully online flipped classes with the help of a cloud-based video-conferencing
app, in order to cope with the immediate switching of classes to online delivery modes
due to the COVID-19 outbreak. The transformation was explained, based on the 5E (Engage,
Explore, Explain, Elaborate, and Evaluate) framework for flipped classes. The effect
of fully online flipped classes on learning performance was evaluated. The results
showed that the online flipped classroom approach can be as effective as the conventional
flipped classroom approach. A number of good practices for using video-conferencing
tools to support online flipped classrooms were proposed. Useful guidelines on the
implementation of online flipped classes were provided for reference.
In the fourth paper entitled, “Examining the Key Influencing Factors on College Students’
Higher-Order Thinking Skills in the Smart Classroom Environment”, the authors conducted
a structural equation modelling analysis to study the relationships between key factors
that influence students’ learning and higher-order thinking skills in a smart classroom
environment. It was revealed that peer interaction and learning motivation had a direct
impact on higher-order thinking skills. Indirect effects were found between students’
learning strategy and higher-order thinking skills through the mediator peer interaction,
and between smart classroom preferences and higher-order thinking skills through the
learning motivation, the combination of learning strategy and peer interaction, and
the combination of learning motivation, learning strategy and peer interaction. Accordingly,
recommendations were made for teaching higher-order thinking skills in a smart classroom
environment.
The next two papers shift the focus on improving the learning environments with technologies
such as virtual reality and lecture capturing systems, where the benefits and advantages
are illustrated.
The fifth paper entitled, “Benefits of Immersive Collaborative Learning in CAVE-based
Virtual Reality”, demonstrated the use of immersive virtual reality in learning complex
subjects for more engaging, motivating and effective learning experience. Taking neuroanatomy
as an example of a visually and spatially complex subject, a virtual reality game
was developed in a cave automatic virtual environment or CAVE for learning brain structures,
their interconnections and broader spatial relationships. The game consisted of an
interactive virtual learning environment which employed all four walls of a CAVE to
provide an immersive and engaging experience to groups of learners. Constructivist
elements, such as free exploration, knowledge construction and collaboration, were
incorporated. It was found that learning in a CAVE yielded higher learning gains,
as compared to the conventional textbooks, and that low spatial ability learners could
benefit most from the strong spatial cues provided by immersive virtual reality in
term of improvement in performance.
The last paper entitled, “Investigating the use of a lecture capture system within
pharmacy education: Lessons from an internationally accredited undergraduate pharmacy
program”, discussed the use of a lecture capture system to assist students in grasping
difficult concepts. The authors conducted an analysis of 18 courses over three academic
years. The results showed that year-1 students viewed lecture captures most frequently
at the beginning of the academic year, followed by year-2 students, and then year-3
students, and that such pattern was further underscored by the class of 2020. Based
on the findings, the authors proposed professional development for faculty to showcase
the advantages of the lecture capture system and the benefits of a multitude of learning
and teaching styles and methods, while also suggesting further quantitative and qualitative
studies to help grasp the students’ motivations for use, and their attitudes and perceptions
towards the system.
All these papers would contribute to help shape the future learning environments with
various smart elements from both pedagogical and technological perspectives. We hope
that you would enjoy reading the papers.