Digital learning environments, the science of learning and the relationship between the teacher and the learner

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Abstract
The relationship between teachers and their students is being increasingly mediated via educational technologies. This increased use of technologies has implications for all levels of education, perhaps most evident in a higher education context where students are spending less time on campus and more time online than they did in the past. The flexibility afforded by educational technologies is evident in the emergence of 'flipped classes', massive open online courses and a growing number of programs being offered by institutions online. Data, analytics, artificial intelligence and machine learning are also all poised to substantially influence the adaptability and capacity for personalisation of educational technologies. These trends necessitate an ongoing adjustment of the role of teachers and their relationship with students. As the relationship changes, there is a pressing need to ensure that what is understood about quality student learning remains the primary consideration. In this chapter, we will examine how research findings from the science of learning might be best used to help support learning as the relationship between teachers and learners evolves into the future.
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Preprint to appear in Carroll, A., Cunnington, R. & Nugent, A. (eds.: 2020) Learning under the lens: Applying
findings from the science of learning to the classroom.
Cite as: Lodge, J. M., Kennedy, G. & Lockyer, L. (2020). Digital learning environments, the science of learning and the
relationship between the teacher and the learner. In A. Carroll, R. Cunnington & A. Nugent (eds.) Learning under the lens:
Applying findings from the science of learning to the classroom. Abingdon, UK: CRC Press.
Digital learning environments, the science of learning and the relationship between the
teacher and the learner
Jason M. Lodge
University of Queensland
Gregor Kennedy
University of Melbourne
Lori Lockyer
University of Technology Sydney
Abstract
The relationship between teachers and their students is being increasingly mediated via
educational technologies. This increased use of technologies has implications for all levels of
education, perhaps most evident in a higher education context where students are spending less
time on campus and more time online than they did in the past. The flexibility afforded by
educational technologies is evident in the emergence of ‘flipped classes’, massive open online
courses and a growing number of programs being offered by institutions online. Data, analytics,
artificial intelligence and machine learning are also all poised to substantially influence the
adaptability and capacity for personalisation of educational technologies. These trends
necessitate an ongoing adjustment of the role of teachers and their relationship with students.
As the relationship changes, there is a pressing need to ensure that what is understood about
quality student learning remains the primary consideration. In this chapter, we will examine
how research findings from the science of learning might be best used to help support learning
as the relationship between teachers and learners evolves into the future.
Introduction: Educational technologies in the 21st Century
Educational technologies are increasingly commonplace and expected in formal learning
environments. In addition to traditional multimedia like videos and audio, these technologies
now allow for students to interact with these environments, providing much richer learning
experiences (for overview, see Freina & Ott, 2015). As these technologies continue to evolve
and become more sophisticated, it will have profound implications for formal education
environments. One of the most pressing of these implications is what these technologies will
mean for the relationship between the student and the teacher. As technology continues to
impact on the ways in which students learn, it is also, and will continue to impact on the ways
in which teachers and students interact with each other and with content. In this chapter, we
provide an overview of the impact of these technologies, particularly on higher education, and
discuss the implications of emerging educational technologies for the student-teacher
relationship. Specifically, this discussion is aligned with research from the science of learning.
The implications of emerging trends and understanding how these technologies can be best
deployed to enhance student learning need to be built on a foundation of research on how
students learn. We offer suggestions for emerging priorities for science of learning researchers
and educators.
Preprint to appear in Carroll, A., Cunnington, R. & Nugent, A. (eds.: 2020) Learning under the lens: Applying
findings from the science of learning to the classroom.
Cite as: Lodge, J. M., Kennedy, G. & Lockyer, L. (2020). Digital learning environments, the science of learning and the
relationship between the teacher and the learner. In A. Carroll, R. Cunnington & A. Nugent (eds.) Learning under the lens:
Applying findings from the science of learning to the classroom. Abingdon, UK: CRC Press.
There are obvious signs that learning, both within formal education environments and beyond,
is increasingly being mediated via technology. Mobile devices now mean that there is potential
to access a wealth of information at anytime and anywhere with a network connection. One of
the clearest examples of the impact this availability of networked devices has had is to
fundamentally alter how people go about developing certain kinds of knowledge. In order to
see how to bake a pavlova or to erect a fence, many people will now go to online videos as a
first option in order to see the process in action. Videos are particularly well-suited to this form
of procedural learning (Lee & Lehto, 2013). The availability of networked devices and
multimedia allows for easy access to demonstrations of almost any procedural task imaginable.
The ease of access to this kind of resource raises questions about how teachers and educational
institutions adapt to a world where information and knowledge are available on demand.
The emergence of new technologies has raised questions about what the impact on education
will be since the invention of the printing press (see Moodie, 2016). What is perhaps different
about the trends emerging in the 2000s and 2010s is that information and knowledge are no
longer predominantly the domain of institutions. Even after the Gutenberg’s invention made
books available to the masses, the majority of these books were still to be found within
university, monastery, or library walls. It was also only possible to carry a certain number of
books around, as anyone who attended school in the 20th Century can attest. The capacity to
both access and store vast (practically limitless) information in mobile devices is a change that
is fundamentally different to those that have come before. Students in higher education contexts
are constantly connected and are interacting with each other and with content using mobile
devices (Gikas & Grant, 2013). These trends raise questions about how these devices influence
the ways in which students acquire, store, update, and use information and knowledge. Under
what conditions do these technology tools lead to the most effective learning experiences? Do
they serve as a distraction if not deliberately integrated into learning activities? When these
devices are incorporated deliberately into learning activities, how are students using them to
make sense of ideas and apply them in practice? There is a significant role for the science of
learning in exploring and understanding these trends and unpacking the implications for
students and teachers.
While the growing use of educational technologies is evident in all levels of formal education,
it is perhaps in higher education that some of the most profound changes are taking place.
Students are increasingly engaging in their studies in ‘blended’, ‘flipped’, or online modes with
significant proportions of the learning activities they undertake occurring in digital
environments (Siemens, Gasevic, & Dawson, 2015). In particular, students increasingly engage
in acquiring information and developing knowledge online. Some commentators have
suggested that the impact of these new practices heralds the end of higher education as we
know it (e.g. Christensen & Eyring, 2011). However, as we outline in this chapter, established
and emerging research paints a far more complex and nuanced picture than a simplified
dichotomous tension between traditional and digitally-mediated educational offerings. There
are advantages and disadvantages to learning in both physical and virtual settings, with teachers
needing to employ different strategies and tactics in diverse environments.
Data, analytics, and their impact on learning and learning environments
The growing use of data, sophisticated algorithmic work and increasingly accessible and cost
effective adaptive environments are resulting in an evolution in digital and emerging
technologies. Data and analytics are being used in ever more sophisticated ways to track
Preprint to appear in Carroll, A., Cunnington, R. & Nugent, A. (eds.: 2020) Learning under the lens: Applying
findings from the science of learning to the classroom.
Cite as: Lodge, J. M., Kennedy, G. & Lockyer, L. (2020). Digital learning environments, the science of learning and the
relationship between the teacher and the learner. In A. Carroll, R. Cunnington & A. Nugent (eds.) Learning under the lens:
Applying findings from the science of learning to the classroom. Abingdon, UK: CRC Press.
students’ progress, predict their learning trajectory and inform interventions. These
developments have allowed much more targeted and personalised learning experiences which
support the development of learning complex concepts and ideas, not just procedures and
declarative facts.
The field of learning analytics, for example, has grown rapidly since the first Learning
Analytics and Knowledge (LAK) conference in 2011. Learning analytics innovations are
focussed on collecting and analysing data generated about, for and from students about various
aspects of their learning (Sclater, 2017). This includes audit trail data generated as students
interact with digital environments, personal data about who they are, what their preferences
might be and data about their knowledge and abilities generated through assessment. There are
significant ethical implications associated with the collection and analysis of these data (Slade
& Prinsloo, 2013). There are, at the same time, significant opportunities to better understand
how students learn broadly and to gain insight into how individual students learn (Lodge &
Corrin, 2017; Siemens et al., 2015). These findings can then be used in order to provide
personalised feedback and other interventions.
The initial focus for the field of learning analytics broadly was to find indicators that students
in higher education were potentially at risk and failing or withdrawing (e.g. Macfadyen &
Dawson, 2010). There were, however, also earlier attempts to draw on audit trail data to gain
insight into student learning processes (e.g. Kennedy & Judd, 2004). These studies laid a
foundation for exploration of the use of ‘big data’ and analytics to help understand how students
are learning in digital environments. In the years since the first LAK conference, there has been
increased interest in how these data might contribute to an understanding of student learning.
Aligned with this has been a trend towards integrating learning analytics with design (e.g.
Lockyer, Heathcote & Dawson, 2013) and with ideas and methods from educational
psychology (e.g. Gašević, Dawson, & Siemens, 2015). This trend was particularly apparent at
the 2018 LAK conference where the most cited articles in the proceedings were from the
educational psychology literature and not from technical domains that had, up until that point,
dominated the discussion about big data and analytics in education.
It is always difficult to predict future trends but there is reason to believe that some recent
emerging technologies, such as machine learning and artificial intelligence (AI) could follow
a similar trajectory to that of learning analytics. These technologies are poised to have a
significant effect on education in the near future, as in other domains (Jordan & Mitchell, 2015:
Luckin, 2018). Luckin (2017), for example, argues that artificial intelligence systems can and
will fundamentally change the way assessment is carried out in education. AI-based systems
will allow for continuous assessment and real-time feedback that aligns much more closely
with what is understood about quality learning and feedback. There is some conjecture about
what counts as artificial intelligence and what role it will play in education (Roll & Wylie,
2016). What is less controversial, however, is that it is likely that the advanced processing and
adaptability provided by AI platforms will contribute, as is learning analytics, to our
understanding of how students learn. There are, in parallel, also great possibilities for drawing
on the science of learning to provide personalised interventions including through feedback,
prompts and tailored learning pathways in digital environments using these same technologies
(e.g. Pardo, 2018). These trends suggest the coming to fruition of the promise of multimedia
learning; adaptability in real time and personalisation built on data mining and predictive
algorithms. It is difficult to see how the potential of these technologies will be fulfilled without
drawing on the science of learning to provide a foundational knowledge base describing how
students learn.
Preprint to appear in Carroll, A., Cunnington, R. & Nugent, A. (eds.: 2020) Learning under the lens: Applying
findings from the science of learning to the classroom.
Cite as: Lodge, J. M., Kennedy, G. & Lockyer, L. (2020). Digital learning environments, the science of learning and the
relationship between the teacher and the learner. In A. Carroll, R. Cunnington & A. Nugent (eds.) Learning under the lens:
Applying findings from the science of learning to the classroom. Abingdon, UK: CRC Press.
The realisation of the full potential of learning analytics, machine learning and AI in education
may still be a work in progress, however, there have been significant advances to date. There
are already advanced, adaptive environments available that are being used for both research
and educational purposes. Some of these systems have been in use for some time. For example,
there are already sophisticated simulation environments for training pilots (Huet et. al., 2011),
surgeons (Piromchai et al., 2017), and dentists (Perry, Bridges & Burrow, 2015). What these
environments share though is a focus on procedural tasks. It is much more complicated and
difficult to develop an environment that can facilitate learning in complex conceptual domains.
These domains include biological systems, climate, social and political phenomena as
examples. These are all phenomena that require complicated mental structures or schema in
order to understand them, which, in turn rely on or are inhibited by prior knowledge (Carey,
2009). Understanding these concepts is difficult even without considering the additional
complexity that comes with the application of this knowledge, which adds a further set of
complexities. Focussing on the acquisition and updating of complex concepts of this kind,
Dalgarno, Kennedy and Bennett (2014), for example, found that people adopt a variety of
strategies when working through simulations to help them understand complex biological and
meteorological concepts. The challenge with facilitating the learning of these more complex
ideas is that it requires some understanding or assessment of how each individual makes sense
of the concept to begin with. As the vast literature on conceptual change has demonstrated,
there are many different reasons why an individual student might misunderstand a concept
(Amin & Levrini, 2017). Each student constructs meaning in their own way (as per Bruner,
1962). Therefore, while adaptive systems have taken some forward leaps, there is still some
way to go before these environments can cope with the significant diversity in how individual
students make sense of complex ideas.
Taken together, developments in machine learning, AI, and learning analytics point to a
situation where it will be possible to acquire even complex conceptual ideas in digital
environments. However, adapting these environments on the basis of how each individual
constructs meaning and develops mental schema remains a significant challenge. For example,
it is relatively easy to see when a student might reach an impasse in a digital environment based
on their activity within the environment. It is much more difficult to make a prediction about
why (Arguel, Lockyer, Lipp, Lodge & Kennedy, 2017). Chi’s (2013) categorisation of
misconceptions partly explains what the difficulty is. Depending on how students structure
related ideas in their mind, that structure will limit the way in which new information can be
incorporated. So, one individual may see a very large dog and assume it is a horse, hence
placing the example of the dog into the wrong conceptual schema (horse). Another may see a
horse and assume it is a very large dog if they do not have a pre-existing conception of ‘horse’.
The problem with providing personalised instruction in a digital environment is therefore not
just about what the overall level of prior knowledge is but how that knowledge is structured in
students’ minds.
Helping students develop their conceptual understanding is therefore a key challenge for
developers of adaptive digital learning environments. Given the need to be able to predict not
just overt behaviour but the ways in which each student is making sense of both the ideas they
are being exposed to and developing their capacity to monitor and update their own
understanding. The research in the science of learning examining how students acquire
concepts (e.g. Schoor & Bannert, 2011), how they change their conceptual understanding (e.g.
Amin & Levrini, 2018), how they make judgements about what they know and think they know
(e.g. Lodge, Kennedy & Hattie, 2018) and how they self-regulate their learning (e.g. Broadbent
Preprint to appear in Carroll, A., Cunnington, R. & Nugent, A. (eds.: 2020) Learning under the lens: Applying
findings from the science of learning to the classroom.
Cite as: Lodge, J. M., Kennedy, G. & Lockyer, L. (2020). Digital learning environments, the science of learning and the
relationship between the teacher and the learner. In A. Carroll, R. Cunnington & A. Nugent (eds.) Learning under the lens:
Applying findings from the science of learning to the classroom. Abingdon, UK: CRC Press.
& Poon, 2015) are all critical for informing the development of these technologies. Integrating
what is known about how students learn is required here in order to make better predictions
about what students are having trouble with and provide appropriate interventions. Research
on these fundamental processes are all critical if digital environments are to be fully responsive
to student needs and learning trajectories.
Technologies that are and will continue to impact on education need to be built on a foundation
that includes a deep understanding of how students learn. Without this, the kinds of
technologies available will struggle with facilitating learning beyond procedural domains or
simple adaptations that treat all students as the same on the basis of observable behaviour rather
than the underlying cause. It will also be difficult to determine what role the teacher will need
to play working alongside these environments. The science of learning will contribute here in
two ways. First, the capacity for conducting laboratory-based experiments leads to increased
confidence that different kinds of conditions and interventions cause specific outcomes.
Second, and perhaps more importantly, if these technologies are to fulfil their potential, the
science of learning will help to better understand individual differences. With learning
scientists, designers, data scientists and developers working together with teachers, it is
possible that the potential of adaptive educational technologies will finally be realised after
what seems like decades of promise (e.g. Wenger, 1987).
Teacher and student relationships in the digital world
With machine learning and AI evolving rapidly and being used in new domains, it is tempting
to think that there will be soon be sophisticated programs and platforms that can replace
teachers altogether. One of the strongest indicators of how difficult this is likely to be comes
from a study conducted by Koedinger, Booth and Klahr (2013). Using a modelling approach,
these researchers attempted to map out the total possible number of ways in which instruction
can be delivered. This ‘teacher model’ included factors such as how and when feedback should
be delivered, how examples are used, and a multitude of other instructional factors. It quickly
became apparent that teachers are constantly navigating a decision set that is practically
infinite. The researchers abandoned the model building process about half way through coding
in all the factors with the number of possible instructional options already well over 200 trillion.
The model also did not take into account content, context, or the variability that is brought to
educational environments by students and teachers. This exercise shows how complex the task
of teaching is. It also suggests that, even when the critical social elements of teacher-student
interaction are removed, the number of decisions required to effectively deliver instruction
makes the task of teaching extraordinarily complex.
It is unlikely that technologies will be able to replace teachers or teaching in the short term
given the complexity teachers deal with in practice. However, the 4th industrial revolution is
here and digital technologies are here to stay in our virtual and physical classrooms (Aoun,
2017). The question becomes one of when and how technologies can be most effectively used,
for what, and understanding what implications this has for the teacher-student relationship. The
science of learning points to vital elements teachers bring to educational environments that are
difficult to simulate digitally. Beyond just what students know (epistemology), modelling of
knowledge and professional ways of being (ontology) are critically important to quality higher
education (Dall'Alba & Barnacle, 2007). To date, it is difficult to simulate this modelling of
professional ways of being virtually or digitally (e.g. Cunningham, 2015; Mastel-Smith, Post
& Lake, 2015). The extensive research on the contributions of social cognition to learning
Preprint to appear in Carroll, A., Cunnington, R. & Nugent, A. (eds.: 2020) Learning under the lens: Applying
findings from the science of learning to the classroom.
Cite as: Lodge, J. M., Kennedy, G. & Lockyer, L. (2020). Digital learning environments, the science of learning and the
relationship between the teacher and the learner. In A. Carroll, R. Cunnington & A. Nugent (eds.) Learning under the lens:
Applying findings from the science of learning to the classroom. Abingdon, UK: CRC Press.
across many domains (Blakemore, 2010) is one example of the importance of the interactions
between students and teachers. Many of the subtle nuances of applying knowledge in practice
in professional contexts, as explained by social cognition, require seeing these processes in
action and that means seeing them demonstrated by a teacher. Additionally, when it comes to
the direct relationship between students and their teachers, there is also great difficulty in
simulating the ability of a teacher to read and respond to student emotions. Although affective
technologies are developing rapidly (see Calvo, D'Mello, Gratch & Kappas, 2015), they do not
come close to replicating the capacity a teacher has for seeing when a student is confused or
frustrated and adequately intervening. For example, our research suggests there is potential in
further exploring how confusion can be identified and managed in digital environments (e.g.
Arguel et al., 2017). However, it will be some time before these environments can be built to
operate at a capacity nearing that of a human teacher in a face-to-face setting. What is critical
in the meantime then is to better understand how best to build environments that can respond
to students in productive ways.
The changing student-teacher dynamic in higher education
Partly in response to broader trends associated with the ubiquity of technologies, there are
already signs of significant change in policy and practice across higher education settings.
While debateable, some (e.g. Lai, 2011) have argued that the core teaching approach in
universities has not changed for centuries. In other words, while there has been some movement
away from traditional pedagogical approaches, the relationship between students and their
teachers has been predominantly through the lecture or other didactic approaches. Essentially
academics have broadcast what is in their minds to students. Mounting evidence over an
extended timeframe about the value of active learning (e.g. Bell & Kozlowski, 2008; Freeman
& Eddy, 2014), underpinned by constructivist learning theories and instructional frameworks,
has put increasing pressure on the lecture as a viable means of teaching students in universities
(French & Kennedy, 2017). A substantial proportion of this evidence can be traced back to the
science of learning. For example, Bell and Kozlowski (2008), examined how the emotional,
cognitive and motivational aspects of active learning contribute to long-term learning and
transfer. They found that a complex mix of factors including goal orientation and capacity for
metacognition influence the success of active learning activities. An overview of how research
such as this is impacting on education, including in universities, has been provided by Yates
and Hattie (2013). Thus, the science of learning has already had significant impact on notions
of effective teaching in higher education.
Lecturing as the key pedagogical approach in higher education has also come under scrutiny
over several decades due to changes in the availability of information and knowledge, as we
have previously outlined (see also Laurillard, 2002). In tandem, there has been pressure placed
on universities through increases in student numbers and a diversification of student cohorts,
often without commensurate increases in government funding for higher education
(Marginson, 2016). There has therefore been an ongoing need to enrol students in large classes
of various kinds to accommodate the growth in numbers. A tension emerges here because the
continued move from elite to mass higher education globally has meant, economically at least,
lectures have remained a central approach (French & Kennedy, 2017). Easy availability of high
quality learning resources outside the university combined with the greater understanding of
the value of active learning has created demand for more meaningful and interactive
pedagogical approaches on campus (Boys, 2015). From a student perspective, there is also
demand for more flexible learning experiences as students lead increasingly demanding lives
Preprint to appear in Carroll, A., Cunnington, R. & Nugent, A. (eds.: 2020) Learning under the lens: Applying
findings from the science of learning to the classroom.
Cite as: Lodge, J. M., Kennedy, G. & Lockyer, L. (2020). Digital learning environments, the science of learning and the
relationship between the teacher and the learner. In A. Carroll, R. Cunnington & A. Nugent (eds.) Learning under the lens:
Applying findings from the science of learning to the classroom. Abingdon, UK: CRC Press.
and have work, carer and other responsibilities competing with their studies for time and
attention (Baik, Naylor & Arkoudis, 2015).
These forces are leading to slow but fundamental change in the ways in which higher education
is being delivered. With high quality resources now freely available online and an ability to
acquire information anytime, there is a need to refocus on the value of the campus experience
(Boys, 2015; French & Kennedy, 2017). In particular, the value of interaction between students
and between students and academic teaching staff takes on a new level of importance. Sfard’s
(1998) two metaphors for learning are important here. The critical argument Sfard makes is
that there are two central narratives about what learning is. The first, acquisition, is vital but
the second, participation, is even more powerful for learning. Participation means not just
accumulating knowledge but using it in meaningful ways in collaboration with others and in
varying contexts. As technology currently stands, participation of this kind is still more difficult
in a virtual or digital environment than on campus (Kebritchi, Lipschuetz & Santiague, 2017).
Accessing opportunities for using knowledge in meaningful ways (i.e. application) has
improved through increased use of webinars, wikis and other collaborative tools. However, the
capacity to interact with qualified experts and see them model the processes of applying
knowledge is difficult to capture in a video. This modelling is highly valuable and necessary
in many instances, for example when clinical reasoning is carried out in a medical setting (e.g.
Eva, 2005). Similarly, watching a video of an experienced nurse go about their practice is not
quite the same as seeing this same practice first hand in a live classroom setting or hospital
(Mastel-Smith et al., 2015). In addition to having opportunities to use knowledge in meaningful
ways, as in active learning, immersive participation and interaction with experts is not
something that can easily be recreated in a virtual or digital setting beyond procedural tasks,
yet. Digital simulations, virtual roleplays and virtual reality environments are beginning to
bridge this gap. How much these environments can and do emulate the application of
knowledge and/or provide access to expert application of knowledge remains an open question.
Within this changing context, it is not straightforward to take findings from experimental
studies and apply them to such complex and dynamic conditions (Horvath & Donoghue, 2016)
in order to understand how student-teacher interaction will change and can be enhanced.
However, there are some key areas in which the science of learning can and is having an impact
on informing the future of higher education (Lodge, 2016). Research on the effective design of
video resources (e.g. Carpenter, Wilford, Kornell & Mullaney, 2013; Muller, Bewes, Sharma
& Reimann, 2007) is one example of a relatively straightforward translation process from
laboratory to classroom. The research of Muller and colleagues (2007) demonstrates that it is
useful to use dialogue and focus on common misconceptions in instructional videos, which
proves effective in the design of effective videos for ‘flipped’ and blended approaches. Along
similar lines, Verkade et al. (2017) have highlighted the development of instructional strategies
that are focussed on addressing student misconceptions that have a grounding in the extensive
literature on conceptual change. Both of these evidence-informed approaches incorporate
modifications to the way in which teachers mediate the interaction between students and
concepts. There are therefore already numerous examples of how the science of learning may
be used in understanding and enhancing student-teacher interaction as technology increasingly
impacts on policy and practice. These same approaches will continue to prove useful and
informative as the nature of student-teacher relationships continues to evolve.
Preprint to appear in Carroll, A., Cunnington, R. & Nugent, A. (eds.: 2020) Learning under the lens: Applying
findings from the science of learning to the classroom.
Cite as: Lodge, J. M., Kennedy, G. & Lockyer, L. (2020). Digital learning environments, the science of learning and the
relationship between the teacher and the learner. In A. Carroll, R. Cunnington & A. Nugent (eds.) Learning under the lens:
Applying findings from the science of learning to the classroom. Abingdon, UK: CRC Press.
Key priorities for the science of learning
Within this broader context of rapidly evolving technologies and a rethinking of traditional
approaches to education, there are several key areas in which the science of learning is and will
continue to contribute. We have already discussed the ways in which the science of learning is
providing a foundation for the design and use of cutting edge technologies such as data-driven
adaptive learning environments and how these environments might continue to shape the
student-teacher dynamic in education. There are also several other key areas, particularly
associated with the evaluation of new technologies, helping students to work with technologies
and how these technologies can be best deployed to function alongside teachers. We will touch
on these areas below.
Informing the development of and evaluating new technologies
Given it is seemingly inevitable that there will continue to be improvements in the capabilities
of digital technologies for facilitating learning, there will be a parallel need for informing these
developments and determining their effectiveness. This will not only be needed to better
understand how teachers and machines will work together to enhance student learning but also
to determine the effectiveness of these technologies themselves in a comprehensive way. One
of the major issues with the development of educational technologies is that the research
examining the effectiveness of the tools lags well behind the spread of their use (Lodge &
Horvath, 2017). In other words, new technologies are created and enter into widespread use
often before the educational implications of the technologies are fully understood. As
highlighted by Luckin (2017), there is great potential for continuous forms of assessment and
feedback beyond the procedural domains such as dentistry where simulations incorporating
continuous assessment and feedback are common. Development of these technologies will
inevitably rely on a sound understanding of the learning process and evaluation approaches
that are specifically designed to determine the impact on learning.
Alongside the overall need for the science of learning to help underpin the development of new
instructional technologies, this is a clear need to draw on principles of quality student learning
to determine how best to effectively combine the expertise of teachers and power of machines.
As the student-teacher dynamic evolves, it will be important to monitor and obtain rigorous
data on the best ways to deploy technologies and to set up activities and curricula designed
specifically to maximise the benefits of the tools and the teacher. Simplified dichotomies will
not sufficiently capture the complex nature of the three-way interaction of students, teachers
and machines. It would seem that the science of learning is well placed to conduct this ongoing
monitoring in concert with teachers and educational designers.
Helping students to work with technologies
Alongside a better understanding of how teachers and machines can work together to help
students, there is an ongoing need to help students to work with technologies themselves. As it
is likely that more of the acquisition side of learning, as per Sfard’s (1998) two metaphors, is
carried out by students in digital environments, there will be a need to understand how this is
occurring and to help enhance it. Students will increasingly be asked to self-regulate their own
learning in these contexts. That is, without the nuanced intervention strategies that teachers
employ in a classroom, students, in the short to medium term at least, will need to be self-
Preprint to appear in Carroll, A., Cunnington, R. & Nugent, A. (eds.: 2020) Learning under the lens: Applying
findings from the science of learning to the classroom.
Cite as: Lodge, J. M., Kennedy, G. & Lockyer, L. (2020). Digital learning environments, the science of learning and the
relationship between the teacher and the learner. In A. Carroll, R. Cunnington & A. Nugent (eds.) Learning under the lens:
Applying findings from the science of learning to the classroom. Abingdon, UK: CRC Press.
directed in their learning. This includes making sound judgements about how much they know
compared to how much they need to know, how they are progressing towards completing
quality work and whether or not they need to shift strategies if the approach to their learning is
not as effective as it could be. It is critical to determine how best to support students to do so
in the absence of a teacher to help with this. The science of learning will play a key role in both
understanding how students learn with and adapt to emerging technologies and determining
how best to equip them with the right knowledge and skills to get the most out of these
environments until such time as these environments are as sophisticated in their intervention
strategies as a live teacher is in a classroom. With teachers seemingly likely to play less of a
role in acquisition and more of a role in facilitating participation, it is critical to understand
what the implications are for student learning.
Determining how technologies can best facilitate teaching and learning
A further area in which the science of learning will assist in understanding the changing
student-teacher dynamic in education is through the implications on broader policy and
practice. Much of what we have focussed on in this chapter has been the operational aspects of
the teacher-student dynamic. Beyond this, there are implications for schools and universities,
as well as policy making bodies and government as technology increasingly encroaches on
education. The increased use of these technologies in classrooms must be driven by what is
known about quality learning and not about financial or political motives. The history of
educational technologies is littered with examples of technologies that have been implemented
for reasons other than what is best for facilitating learning (Watters, 2014). The science of
learning has a critical role to play in providing the evidence base for what works to counter
they hype so often accompanying the development and spread in the use of technologies in
education (see also Lodge & Horvath, 2017).
Conclusions
Developments in emerging educational technologies are already significantly impacting on
education. This is apparent through the changing student-teacher dynamic in all levels of
education. While it is most obvious in higher education, it is increasingly clear that teachers
will be working alongside sophisticated machine learning and AI systems to help facilitate
student learning. The science of learning has and will continue to play a pivotal role in
providing a foundation underpinning these technologies and for determining how best the
combination of teachers and machines can be deployed to enhance learning. While it has
perhaps not received the attention that other implications of emerging technologies have, we
have highlighted what these technologies mean for how students and teachers work together
and in combination with machines. The complex, social environment of the physical and virtual
classroom will continue to raise issues and problems that will necessitate investigation. As has
become apparent in the field of learning analytics, these investigations cannot rely on technical
solutions alone but must be driven through a fundamental understanding about how students
learn. So, while teachers seem unlikely to be replaced by robots anytime soon, it seems unlikely
that researchers in the science of learning will either.
Preprint to appear in Carroll, A., Cunnington, R. & Nugent, A. (eds.: 2020) Learning under the lens: Applying
findings from the science of learning to the classroom.
Cite as: Lodge, J. M., Kennedy, G. & Lockyer, L. (2020). Digital learning environments, the science of learning and the
relationship between the teacher and the learner. In A. Carroll, R. Cunnington & A. Nugent (eds.) Learning under the lens:
Applying findings from the science of learning to the classroom. Abingdon, UK: CRC Press.
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