=Paper=
{{Paper
|id=Vol-2437/paper2
|storemode=property
|title=Pedagogy-informed Design of Conversational Learning at Scale
|pdfUrl=https://ceur-ws.org/Vol-2437/paper2.pdf
|volume=Vol-2437
|authors=Mike Sharples,Rebecca Ferguson
|dblpUrl=https://dblp.org/rec/conf/ectel/SharplesF19
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==Pedagogy-informed Design of Conversational Learning at Scale==
https://ceur-ws.org/Vol-2437/paper2.pdf
Pedagogy-informed Design of Conversational Learning at
Scale
Mike Sharples, Rebecca Ferguson
Institute of Educational Technology, The Open University, UK
mike.sharples@open.ac.uk
rebecca.ferguson@open.ac.uk
Abstract. This paper examines how an explicit theory of learning as conversation
has informed design of the FutureLearn MOOC platform. We describe a process
of pedagogy-informed systems design and show how Conversation Theory has
provided a framework for design that combines learning as conversation with
instruction through structured content. The paper compares performance metrics
across three MOOC platforms. Results show higher levels of social engagement,
with comparable completion rates, for the FutureLearn platform.
Keywords: Adult Learning, Learning at Scale, Learning as Conversation, Co-
operative and Collaborative learning, Distance Education, Pedagogical Issues.
1 Background
This paper describes how the FutureLearn MOOC platform was designed from an ex-
plicit pedagogy of learning through conversation. It addresses the question: Can a free
open platform based on a pedagogy of learning as conversation enhance learning at
large scale?
In order to answer the question, we describe the educational design of the Future-
Learn platform and learner experience. We also cover: the process for designing, im-
plementing and testing new elements of the FutureLearn learner experience, theories of
learning and interaction that informed the platform design, evidence of effectiveness of
the design decisions based on a comparison of key performance indicators for MOOC
platforms, and indications of the learner and educator experience based on learner com-
ments.
We should make clear from the start that the aim of this paper is not to discuss
whether one MOOC platform is better than another. Instead, the claim is that the per-
formance of learners with FutureLearn is comparable to two other major platforms
(Coursera and edX) and, in addition, FutureLearn enables a form of learning through
asynchronous conversation that works at scale and complements direct instruction.
Learning by building shared understanding through conversation offers a distinctly dif-
ferent experience to learning through direct instruction.
The focus of this paper is pedagogy-informed design of educational technology,
whereby a study of existing theory and practice of learning informs a series of design
experiments, each producing new implementations, practices and theories of learning.
Copyright © 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0)
2
There are many pedagogies that can inform development of future tools for learning,
including game-based learning, experiential learning, inquiry-led learning and adaptive
teaching. Understanding which of these can work at scale and how they can be incor-
porated into existing platforms can provide a rationale for design of new technology-
enhanced learning.
2 Methods
The method of pedagogy-informed design has similarities to contextual design [1]. It
can also incorporate elements of design-based research [2] and Lean UX [3] as appro-
priate for the project. All these methods are intended to develop interactive systems by
understanding and working with intended users. They involve iterative cycles of design,
implementation and testing by a multi-disciplinary team of domain experts, software
designers and experts in user experience. In addition, pedagogy-informed design in-
cludes understanding in depth how relevant elements of the science of learning [4] and
previous implementations of similar educational technology can inform initial require-
ments and software architecture.
In early 2013, the software development team at FutureLearn was challenged to im-
plement a robust and scalable platform capable of supporting courses provided by the
initial 12 partner universities, and further anticipated institutions, with the first course
running within 10 months (by October 2013). The company established an Agile soft-
ware development process with Scrum [5]. The basis of Scrum is that a nominated
Product Owner produces a prioritised list of proposed software features called the
‘product backlog’. In this case, the backlog consists of features to support effective
learning. The development team works in short time-bounded ‘sprints’ (for Future-
Learn, these are two weeks) to develop and implement these features. During sprint
planning, the team pulls some items from the backlog and decides how to implement
these in the time available. At the end of each sprint, the work should be ready to deliver
on the platform. Each sprint ends with a sprint review and retrospective.
Scrum has the advantage of delivering rapid incremental changes to the software
product, providing clarity and evidence that the software is being implemented on
schedule. It also places severe constraints on innovation. Items added to the product
backlog must be capable of implementation within a two-week timespan, requiring a
rapid iterative process of design, implementation and testing. This approach can lead to
‘hillclimbing’ with each incremental feature added to the previous ones, missing op-
portunities for more radical innovation in a different part of the design space.
Thus, to enable pedagogy-informed design of the FutureLearn platform, it was nec-
essary to provide the software development team with a framework that instantiated a
theory of effective learning at scale that could also be implemented in parts, with each
part being developed during a two-week sprint session. Since the sprint process was
being used to deliver the entire product, not just the educational elements, the educa-
tional software design had to fit with the Scrum approach. Through discussion between
advisors from The Open University and the software team, a framework of learning
through conversation was adopted, described below.
3
To evaluate effectiveness of the pedagogy-informed design of FutureLearn, we pre-
sent a comparison of high-level indicators (registrants, learners, active learners, social
learners, and completing learners – these categories are defined below) across three
major MOOC platforms: FutureLearn, Coursera and edX. To provide further compar-
ative data with control for international differences, a comparison is made of courses
run by The University of Edinburgh, which offers MOOC courses on both the Coursera
and FutureLearn platforms.
The MOOC platforms have evolved over time, incorporating features from other
MOOC providers. Coursera and edX now include elements of conversational learning
(such as conversation alongside content) and FutureLearn has added elements of direct
instruction. Thus, to give the most direct answer to our research question, we use his-
toric data (from 2013-2014) to compare pedagogies at the time when the platforms were
first implemented.
3 Learning as conversation
An important point to note as we outline learning as conversation is that this is a com-
prehensive theory of the cognitive and social processes of learning, not simply a de-
scription of online discussions. It is based on a cybernetic systems theory of learning
that stands alongside behaviourist, cognitivist and socio-cultural theories. As initially
formulated by Pask [6,7] and reworked by Laurillard [8], Conversation Theory provides
a scientific account of how interactions between language-oriented systems (which may
be human or machine-based) can enable a process of learning: a process of ‘coming to
know’ by reaching mutual agreements [6]. Examples of human language-oriented con-
versational systems range from tutorial groups to scientific communities.
Cybernetics is the study and design of systems for regulation and control, not
through external diktat or command, but from self or mutual feedback and modification.
In cybernetic terms, learning through conversation is more than an exchange of
knowledge – it is a self-regulating process in which intelligent organisms employ a
mutually evolving language to share and negotiate differences in understanding, with
the aim of constructing new knowledge and reaching agreements.
A cybernetic conversation is a recursive process of coordinating understanding at
differing levels of abstraction. Higher level coordinations (mutual reflections) refer to
lower-level coordinations (shared objects and events). For their interactions to consti-
tute a conversation, learners must be able to formulate descriptions of their reflections
on actions, explore and extend those descriptions, and carry forward the understanding
to a future activity. This is the basis for human action-oriented dialogue – for example,
when two students perform an experiment together, discuss the results and what went
wrong, then plan how to re-run the experiment.
Fig. 1 depicts learning as conversation, based on diagrams from Scott [7] and Lau-
rillard [8]. An individual learner’s process of reflection and enactment has similarities
to Kolb’s [9] constructivist learning cycle. Concrete experience, reflection on experi-
ence, abstract conceptualisation, and active experimentation drive forward learning,
based on internalised reflection and action. In Pask’s terms, this is a conversation with
4
oneself, requiring an internalised language that is employed to interpret and adapt ac-
tions.
Fig. 1. A framework for learning as conversation (adapted from [7] and [8]).
Conversations with others can occur at the levels of actions and descriptions. Each
level requires a shared medium and an evolving language. At the level of actions, a
learner and one or more partners (who may be other learners, teachers, or even a com-
puter-based tutoring system) discuss a practical activity or model of the world. For ex-
ample, a teacher may set a maths problem to solve, or a historical event to interpret, or
a computer may provide a runnable model of a mechanical system to explore. The
learners converse in the context of that model or problem, asking ‘how’ questions, shar-
ing experiences and interpretations. The aim is to coordinate the action so that the learn-
ers’ expectations and understandings mesh with the teaching materials. Not only must
the teaching content and models be appropriately designed and relevant, they must pro-
voke reflective conversation. For example, learning computer science involves under-
standing the structure and process of computer code alongside terms such as ‘variable’,
‘conditional’ and ‘recursion’. If the learners find it difficult to converse, because their
shared language cannot adequately express ways in which materials and understanding
are coordinated, then learning may not take place.
At the level of descriptions, learners converse about why things happen, offering
conceptions of their learning and questioning the understanding of others, in attempts
to reach agreement about their reflective understandings. A different shared medium is
5
needed for these conversations, one that can support a process of coming to know
through constructive argumentation, where each learner expresses and adjusts concep-
tions in relation to the expressed understanding of others.
At both levels, learners need to agree on clear goals and objectives. Although the
process of learning through conversation is exploratory, with learners managing their
own activities and reflective discussions, there is a strong role for an educator in pro-
posing goals and objectives, creating suitable activities and models to explore, facili-
tating discussions and prompting reflection.
4 Implications for massive open online courses
What makes the conversational framework different to other theories of experiential
and reflective learning, and learning through mutual discussion, is that it is intended to
be an implementable model for learning mediated by technology. Pask was an educa-
tional technologist. He designed adaptive teaching systems and examined how people
engage in self-managed learning of formalised topic maps. He also studied systems of
conversation in theatre, art, architecture and music.
The formation of FutureLearn provided an opportunity to rethink the Pask/Laurillard
framework for learning at large scale. A group of academics from The Open University,
led by the authors, advised the FutureLearn software product team on pedagogy-in-
formed design of the platform. Together, they considered a range of social constructiv-
ist learning theories and chose to implement Conversation Theory, as it provides ex-
plicit guidance on how to implement a process of learning through social interactions.
The framework, as outlined above and depicted in Fig. 1, indicates that MOOCs, to
support effective conversations for learning at scale, should satisfy the following re-
quirements:
Consistent language: Platform developers and educators should develop a consistent
language to describe educational actions and descriptions and this should be made ex-
plicit to learners. This is more than a software usability principle – the shared language
of pedagogy is the means for educators and learners to discuss their progress towards
mutual understanding.
Pedagogy elements: The platform should distinguish different types of pedagogic
media and practice (e.g. narrative, interactive, communicative, argumentative, as-
sessing) so that educators can design for appropriate conversational sequences.
Conversations for action and description: The platform should distinguish conver-
sations for learning at the levels of action and description, with the former directed
towards understanding topics and solving problems, and the latter aimed at exploring
differences in conception and reaching agreements.
Conversations in context: Conversations at the level of action should be directed
towards specific topics and conducted in the context of narrative and interactive media.
Reflective conversations: The platform and learning design should prompt internal
reflective conversations as well as social interactions. Learning can occur by analysing
and modifying one’s own actions, and by reflecting on the conversations of others.
6
Conversations for coordinating agreements: Conversations at the level of descrip-
tions should be initiated and structured to enable a process of ‘coming to know’ by
sharing perspectives, synthesising new knowledge, and reaching agreements.
Conversational media: Each conversation should be associated with a medium that
allows open investigation of the topic, revealing the contribution made by each learner
to the evolving discussion.
Explicit objectives, outcomes and goals: Educators should set specific objectives and
outcomes for a course, and provide a medium that enables learners to negotiate personal
and shared goals.
Facilitation: The primary role of educators should be to facilitate conversations for
learning around well-structured content.
Tracking conversations: Educators should be supported to understand the learners’
conceptions and, where appropriate, to adjust learning objectives as the conversations
progress.
This is a distinctly different foundation for design of a MOOC platform and courses
to the ones that were in currency when FutureLearn was developed. At that point, con-
nectivist cMOOCs were based on learning through active connection of knowledge ob-
jects [10], and instructivist xMOOCs provided personalised interaction with instruc-
tional materials [11]. An important difference is that effective learning through conver-
sation requires learners to reach agreements through a process of facilitated interaction
and conversation, within a well-structured domain.
Learning through conversation on MOOCs is not a replacement for direct instruc-
tion, but an adjunct to it. The shared medium comprises video, audio and text materials
that are designed for individual reflective learning as well as prompted discussion.
5 Design of the FutureLearn platform and courses
Design of the FutureLearn platform and its courses is directly informed by the require-
ments for a conversational framework outlined in the previous section.
Consistent language: FutureLearn has developed a consistent ‘pattern language’ for
its pedagogy, as well as visual design and interaction 1. The courses are designed ac-
cording to principles of effective learning through conversation, narrative, visible learn-
ing and shared celebration of progress. Each new feature on the platform is accompa-
nied by a terminology and set of evidence-based concepts that are communicated to
course developers, educators and learners 2.
Pedagogy elements: The platform was designed to support construction of courses
using pedagogy building blocks (named ‘steps’) that can be put together in different
combinations to form learning activities. The main FutureLearn step types are: article,
video & audio, discussion, peer review, quiz, poll, and exercise. A FutureLearn ‘activ-
ity’ is the minimum complete pedagogy element. It is associated with learning objec-
tives, enacted through a sequence of learning steps.
1 https://www.futurelearn.com/pattern-library
2 https://www.futurelearn.com/using-futurelearn/why-it-works
7
Fig. 2. A FutureLearn video step for the Understanding IELTS course, with linked conversation.
Conversations for action and description: The platform supports learning as conver-
sation in various ways. Associated with each step in the course is a flow of comments
and replies (Fig. 2). A learner can read the comments, leave a new comment, or reply
to a previous comment. An educator can also add comments and replies. In addition to
this conversation at the level of actions, discussion steps provide spaces for conversa-
tion at the level of descriptions. Typically, learners are guided to reflect on their recent
activity, share current understanding, and engage in constructive argument.
Conversations in context: To stimulate conversations alongside article, audio and
video steps, course designers are encouraged to pose questions that can be answered by
a short comment building on a learner’s prior experience or current knowledge (see Fig.
2 for an example). The platform is designed so that learners add short comments along-
side the teaching content to continue the flow of conversation.
Reflective conversations: A fundamental design principle is that learners should not
be required to interact socially: it should be possible to complete any course without
contributing to conversation, but by reading and reflecting on the conversations of oth-
ers. Contributing to conversations with others enhances this internal process.
8
Conversations for coordinating agreements: Peer review is designed as a conversa-
tion to reach agreements. Learners are asked to respond to assignments from other
learners, following a three-part rubric, typically: ‘How clear is the argument’, ‘How
well has the writer demonstrated a knowledge of the concepts?’, and ‘How well has the
writer expressed the argument in a coherent way?’. Peer review is typically followed
by a discussion step, where learners are encouraged to reflect on and discuss their as-
signments and reviews. In quizzes and tests, the responses to correct and incorrect an-
swers are presented as educator comments, continuing the conversational style of inter-
action. More recently, Study Groups have been implemented to enable conversation
within small groups aimed at reaching agreements in relation to key questions or prob-
lems.
Conversational media: Each learning element (step) is associated with a distinct me-
dium where learners can engage in conversation, usually with a question from the edu-
cator prompting the addition of comments based on personal experience or response to
the teaching materials (see Fig. 2). Learners (who are aged 13 or over) are asked to be
open in revealing their real names and are encouraged to introduce themselves and post
relevant details on their personal profile.
Explicit objectives, outcomes and goals: The course team is encouraged to provide
objectives for a course in the form of ‘big questions’ that are presented to learners in
the course description. In the platform’s course creator tool, the academic team sets
explicit learning outcomes using a structured format. At the start of a course, learners
have an opportunity to set personal goals that not only provide a reference for them to
reflect on progress but also provide data to the course team about how learner goals
align with course objectives.
Facilitation: A course team can assign specific roles to members, including the roles
of educator – with specialist knowledge of the subject – and mentor to guide discus-
sions. These roles are visible to learners (see Fig. 2). Team members can facilitate study
group discussions through broadcast prompts and by contributing to any discussion.
Tracking conversations: The course team has access to a dataset that includes each
comment, its associated step, timestamp and author. A dashboard allows the team to
track the progress of each study group conversation.
The FutureLearn platform was designed from the outset to be scalable and respon-
sive, with cloud hosting to cope with a large number of users and an interface that can
be accessed on mobile devices. It was not, however, possible to predict in advance the
scale and nature of the conversations for learning. Contingencies were made for abusive
comments (through reactive moderation of learner comments) and lack of engagement
(by enabling the contextual commenting to be switched off). What was not anticipated
by the FutureLearn design team was the large scale of positive commentary. During
early course runs, in autumn 2013, some steps of a course attracted over 1,000 com-
ments and replies.
To manage this scale of learner contribution, the FutureLearn team added elements
of social networking. Learners can ‘follow’ their peers and educators. They can ‘like’
comments and replies, and filter comments in various ways, including by ‘most liked’
(see Fig. 2). Each personal profile shows the learner’s recent comments with a link from
each to its place in the conversation, as well as followers and people being followed.
9
A typical interaction with a course step is to read or view the teaching content ending
with the educator’s prompt for conversation, click to view the most recent items in the
flow of comments and replies (perhaps ‘liking’ a good comment and ‘following’ its
author), click ‘Most liked’ to see favoured comments, then add a reply or post a new
comment to the flow. Learners are encouraged to follow educators and can receive
email notifications of new comments by people they follow. If an educator replies to a
posting, followers are notified of that action, meaning that the reply is likely to attract
viewings and likes, raising it up the list of most-liked comments. These social network
mechanisms enable comments favoured by educators and other learners to ‘rise to the
top’ of the conversation so, for any step in the course, a learner can switch between its
current conversational flow and its prioritised comments and replies.
6 Evaluation
Not only the design of the FutureLearn platform, but also the courses that run on it,
have been informed by a pedagogy of learning as conversation. In this paper, we focus
on the conversational and social networked learning aspects of FutureLearn. However,
for comparison with other aspects of the learning experience, Fig. 3 shows learner re-
sponses in a typical large FutureLearn course to the post-course survey item: “Please
rate from ‘strongly disliked’ to ‘strongly liked’ how you felt about learning on Future-
Learn”. A majority of respondents strongly liked to learn by reading articles and watch-
ing videos. Sixty percent of respondents liked or strongly liked to learn by reading the
comments of other learners. Thirty percent liked or strongly liked to learn by online
discussion with other learners.
Fig. 3. Responses to post-course survey question on aspects of the learning experience (N=2504).
Open responses to survey questions and in User Voice feedback indicate that some
learners find the large number of comments and responses daunting. Others view the
contextual conversation as an additional resource for learning.
FutureLearn educators now routinely design activities to support learning through
conversation. To give one example, the course “Rome: A Virtual Tour of the Ancient
city” from the University of Reading is based on an interactive 3D model of ancient
Rome. For each location, such as the Colosseum, learners are asked to explore the
10
environment, then discuss their personal experiences and how these changed their un-
derstanding of how people lived in the city.
7 Comparison with other MOOC platforms
There can, in general, be no direct measures of learning on MOOC courses, since learn-
ers volunteer to study and their topic knowledge is not compared at the start and end of
a course. Instead, all major course providers use measures of activity, engagement and
course completion as proxies for learning. For this paper we also provide a comparison
of social engagement as a proxy for learning through conversation.
Table 1 presents a top-level view of these analytics. In FutureLearn, of those who
register for a course (‘registrants’), a mean of 53% visit at least one step (‘learners’).
The number of learners on a course is taken as the baseline for further analysis.
FutureLearn learners are asked to mark each step as complete when they finish it
(see Fig. 2). The table shows the percentage of learners who: mark as complete one step
or more (active learners), post one or more comments (social learners), or finish the
course by marking at least half the steps as complete and submitting all assessment
(completing learners).
Table 1 offers comparative analytics with the two largest MOOC platforms,
Coursera and edX [12, 13] for the five major performance indicators: registrants, learn-
ers, active learners, social learners and completing learners. The comparison is for
courses during 2013 and 2014. More recently, both Coursera and edX have incorpo-
rated elements of social networked learning into their platforms, including support for
comments and replies linked to elements of the course as well as face-to-face study
groups [14]. To address the research question, we compare courses on the platforms as
they were in 2014. This shows how a platform designed using a pedagogy of learning
as conversation compares with ones designed for personalised instruction.
To provide a fair comparison, only courses lasting five weeks or more and offering
tests were included for FutureLearn, since this is the typical format for courses on the
other platforms. The figures for all FutureLearn courses in 2014 (including shorter
courses and those without tests) are more favourable for FutureLearn: mean registrants:
12,354; learners: 52%; active learners: 82%; social learners: 39%; completing learners:
23%.
Published data for Coursera are only available for a single course on Business Strat-
egy. However, a general figure for the percentage of Registrants who completed
Coursera courses was given in 2013 as 5%: “In total, roughly 5 percent of students who
signed up for a Coursera MOOC earned a credential signifying official completion of
the course” [15]. Translated into a percentage of learners, rather than a percentage of
registrants, that would indicate a general figure for Completing Learners on Coursera
of about 9%, similar to that on edX.
11
Table 1. Comparison of mean top-level metrics for MOOC platforms, including figures for the
22 Coursera and 2 FutureLearn MOOCs run by University of Edinburgh prior to 1 March 2015.
Group Description Future- Coursera edX 5 Edin- Edin-
Learn 3 4
burgh burgh
Coursera Future-
Learn
Regis- Mean number 12,753 87,000 52,605 44,373 24,728
trants of people
who register
for a course
Learners Registrants 53% 54% 6 65% 7 54% 57%
(% of who visit the
regis- course
trants)
Active Learners who 83% 8 83% 9 N/A 78% 81%
learners engage with
(% of course
learners) material
Social Learners who 36% 9% 10 12% 11 13% 20%
learners post at least
one comment
Complet- Learners who 17% 12 5% 13 8% 14 15% 12%
ing learn- complete the
ers course
3 Data provided to the authors by FutureLearn from all 51 courses starting between 29 July
2013 and 1 December 2014, lasting five weeks or more, and offering tests.
4 Published data from a single Coursera course on Business Strategy [15].
5 Figures from 16 courses by Harvard and MIT for 2012-2013 academic year [13].
6 Registrants who “logged into the course’s website at least once” [15].
7 From Table 2 of [13]. (Registered - Only Registered) / Registered x 100.
8 Learners who mark at least one step (learning element) as complete. The FutureLearn platform
differs from the others in having a ‘mark as complete’ button for each step of the course.
9 Learners who “viewed or downloaded at least one of the lecture segments” [13].
10 Learners who “created at least one post or comment in the online discussion forums” [15].
11 From Tables 2 and 6 of [13]. Numbers of Registrants with ≥1 Post on Forums / Learners x
100.
12 Learners who mark at least half the steps as complete and complete all assessments.
13 Learners who “received a nonzero score in the course, implying that they submitted at least
one quiz or the final project” [15].
14 From Table 2 of [13]. Certified / Learners x 100. A less stringent measure of completion is
those learners who either explored (accessed more than half of the available chapters in the
courseware) or who earned a certificate (Certified + Only Explored / Learners x 100). That
figure is 14%. There is no comparable edX figure for learners who not only completed half
the available chapters but also earned a certificate.
12
The results show that the number of registrants for FutureLearn during 2013-14 was
smaller than for Coursera and edX, as the platform had been more recently established.
The figures for learners (those who started a course) and active learners (learners who
engaged with the course material) are comparable across the three platforms. The figure
of 17% who complete a FutureLearn course compares well with other major MOOC
platforms and suggests that a MOOC platform based on learning as conversation is
overall more effective in retaining learners than those founded on instructivist ap-
proaches. The most striking difference is in the percentage of learners who post at least
one comment, with a figure of 36% for FutureLearn, compared to 9% for Coursera and
12% for edX. This indicates that a substantial proportion of learners are engaging ac-
tively in learning through conversation.
The University of Edinburgh has provided the authors with unpublished data on the
22 Coursera courses it had run by 1 March 2015. These data offer an even closer com-
parison of platforms, since Edinburgh is both a partner of FutureLearn and of Coursera
and all its courses are designed to foster community and engagement. The figures are
broadly similar, except for social learners, with 13% for Edinburgh Coursera and 20%
for Edinburgh FutureLearn courses.
8 Discussion
We return to the question posed at the start of the paper: Can a free open platform based
on a pedagogy of learning as conversation enhance learning at large scale?
Evidence of effectiveness comes from the scale of contributions plus data from the
post-course survey. From the outset, FutureLearn courses generated a large number of
comments and replies, especially when compared to other MOOC platforms. Because
of this, mechanisms from social networks – including liking, following, user profiles
and filters – were added to manage social engagement. Surveys of learners who have
completed courses show that approximately two-thirds like or strongly like reading
comments and one third like contributing to discussion online. FutureLearn partners are
guided in designing courses that can appeal to the two-thirds who do not contribute,
while offering easy ways to participate in conversation for people with widely differing
skills and backgrounds, as well as opportunities to learn by viewing and browsing con-
tributions from peers.
We have conducted a fair comparison of learner experience with the two other larg-
est MOOC platforms, by covering courses during 2013 and 2014 (before other plat-
forms adopted some facilities for social networked learning). Table 2 only includes
those FutureLearn courses that lasted five weeks or more and offered tests, since that
was the standard format for courses on Coursera and edX. A separate comparison was
made with Coursera courses offered by the University of Edinburgh, which is also a
partner in FutureLearn. The comparisons show a similar or greater retention rate for
FutureLearn courses and much higher active social engagement. That social engage-
ment continues in current courses, with a current average of 38% of learners making
one or more contributions.
13
The conversational framework could be further extended for large-scale learning.
Pask proposed that learners should interact not only with individual instructional mate-
rials but also with ‘entailment structures’ of interlinked topics, similar to concept maps.
Structural relations between topics, similar to Pask’s entailment structures, already un-
derlie adaptive teaching systems such as Knewton (www.knewton.com) and ALEKS
(www.aleks.com). These topic networks could be made visible to learners, allowing
them more agency to choose strategies for exploring the learning content.
A challenging new area of research is to explore how providing tools for personal-
ised exploration of topics can be reconciled with supporting social and collaborative
learning. When learners take different paths through a course, or adopt different strate-
gies for exploration, they may find it difficult to identify a shared basis for discussion.
Yet if many learners from diverse backgrounds can be helped to explore a complex
subject along differing pathways, they could each contribute a perspective to an emer-
gent conversation. By focusing design effort on the interwoven conversations for learn-
ing (rather than on individuals or content) and enabling many people to contribute in
individual ways, we may be able to create a MOOC equivalent to Wikipedia, where
people learn by building, intersecting, discussing and sharing pathways to learning.
9 Conclusion
Pickering [16] describes how the work of Pask and other British cyberneticians chal-
lenges assumptions of traditional education in three ways. First, it asserts that variety is
good in that it helps us to adapt to an uncertain future. By attempting to understand the
world from multiple perspectives, we can develop a society that is more resilient when
it encounters unexpected shocks. Second, cybernetics offers a performative take on de-
mocracy, where respectful interaction among thousands of people to explore a topic or
solve a problem may not only reveal novel solutions, but also say something new about
the performers themselves. Third, it offers ways to act differently, by designing systems
that confront or extend traditional methods of instruction. Conversation Theory can en-
hance learning at scale through a process of mutual coordination and development of
shared meaning.
Other pedagogies could also offer variety in learning at scale and allow new forms
of respectful interaction. Inquiry-based learning supports learners in thinking like sci-
entists. Game-based learning is based on learners working together to solve problems
and create self-organising communities. Experiential learning builds knowledge from
the shared experiences of learners. Collaborative design thinking encourages learners
to work together on creative projects.
These pedagogies privilege exploration, diverse opinions and play over structured
instruction and assessment of learning outcomes. In developing the FutureLearn plat-
form and courses we have attempted to provide a space for a process of learning through
global conversation alongside a system of direct instruction through online video, text
and formative assessment.
14
Acknowledgements
We thank members of the FutureLearn advisory group, including Russell Beale, Simon
Buckingham Shum, Andrew Law, Patrick McAndrew, Peter Scott and Martin Weller,
as well as colleagues at FutureLearn including Laura Kirsop, David Major, Simon Nel-
son, Kathryn Skelton and Matt Walton.
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