Conference PaperPDF Available

The Practices of Programming


Abstract and Figures

How diverse are the ways that programming is done? While a variety of accounts exist, each appears in isolation, neither framed in terms of a distinct practice, nor as one of many such practices. In this work we explore accounts spanning software engineering, bricolage/tinkering, sketching, live coding, code-bending, and hacking. These practices of programming are analyzed in relation to ongoing research, and in particular HCI’s ‘practice turn’, offering connections to accounts of practice in other contexts than programming. The conceptualization of practice helps to interpret recent interest in program code as craft material, and also offers potential to inform programming education, tools and work as well as future research.
Content may be subject to copyright.
978-1-5090-0252-8/16/$31.00 ©2016 IEEE
The Practices of Programming
Ilias Bergström
MobileLife group,
KTH Royal Institute of Technology
Stockholm, Sweden
Alan F. Blackwell
Computer Laboratory
University of Cambridge
Cambridge, UK
Abstract How diverse are the ways that programming is done?
While a variety of accounts exist, each appears in isolation, neither
framed in terms of a distinct practice, nor as one of many such
practices. In this work we explore accounts spanning software
engineering, bricolage/tinkering, sketching, live coding, code-
bending, and hacking. These practices of programming are
analyzed in relation to ongoing research, and in particular HCI’s
‘practice turn’, offering connections to accounts of practice in other
contexts than programming. The conceptualization of practice helps
to interpret recent interest in program code as craft material, and
also offers potential to inform programming education, tools and
work as well as future research.
Keywords— Practices of Programming; Digital Craft; Material;
Practice Turn; Material Turn; Parameter Mapping; Mutable
Mapping; Creative Coding; Programming as Art; Tinkering;
Bricolage; Sketching; Code-Bending; Hacking; Live Coding;
Software Engineering; Education.
A key achievement of the VL/HCC conference series has
been the recognition that there are different kinds of
programmers: people write programs for different reasons, and
do so in different ways. The essence of a ‘human-centric’
approach is to better understand people’s needs and behavior,
hopefully allowing us to make tools better fitted to those needs.
Past successes from this human-centered strategy have
included better understanding of novice programmers (students
who benefit from languages more specifically designed to help
them learn), and of end-user programmers who are not
specialists, but do programming tasks in the context of carrying
out their regular jobs (e.g. a teacher making a spreadsheet
grade book).
The goal of this paper is to look forward and ask how we
might recognize and understand further human-centric models
of programming. On the basis of past research achievements,
we can anticipate what the interesting outcomes might be of
such an investigation - we are interested in learning more about
different ways in which people approach programming, leading
to new strategies, new preferences, and new design trade-off
choices for software tool developers. In order to gain a new
perspective, we draw here on a current trend in HCI research
more broadly described as “the practice turn” [1]–[3].
Kuutti & Bannon [3], coin the term “interaction
paradigm”, to refer to HCI studies focusing on momentary,
ahistorical situations, disconnected from a particular time and
space. Such research traditionally draws from the methodology
of psychological sciences, using controlled, short-term
laboratory studies. This contrasts to the “practice paradigm”:
studying longer-term actions, situated in time and space, and
richly dependent on their material and cultural environment.
Such research methodologically derives from design and social
sciences, involving qualitative, observational modes of
knowledge production. The focus is widened, to “studying an
overall activity, involving people, artifacts, organizational
routines and daily practices” [3]. The shift from the
interactional to the practice paradigm, then, is what constitutes
HCI’s practice turn.
The practice turn is influenced by theories of tool-use, the
nature of knowledge, the structuring of society into
professions, and other dynamics in philosophy and social
science. In this paper, we will draw on these theoretical
developments, but will also pay close attention to the existing
literature and communities in which people have already
described the practices of programming. We acknowledge and
celebrate the success of VL/HCC research into novice and end-
user programming, but in this paper, we wish to look
elsewhere, understanding other descriptions of what the
practices of programming might be.
We believe that the practice turn will allow us to explore
some critical recent questions. At VL/HCC 2015, Aghaee et al
opened up the very general question of why people engage in
programming, through broad analysis of motivations and
personality, challenging and extending beyond typical
conceptions of why programmers program [4]. In this paper we
might be said to consider how people do programming, once
again extending beyond typical conceptions - or perhaps more
provocatively (because one might think we know the answer
already), what people do when they are doing programming.
This echoes challenges to foundational questions such as ‘What
is Programming’ [5]? However, where earlier research of that
kind has considered programming as an individual and
cognitive activity, our analysis in this paper makes no such
The practice turn in HCI derives from work such as that of
Lave, who observed that practice consists of activities situated
in a social and material world [6]. Rather than explicitly
articulated ‘knowledge’, professional practice is a ‘craftwork’
in which knowledge is constructed and transformed in use, and
always complexly problematic. Lave warns that things
assumed to be natural categories, such as “bodies of
knowledge,” “learners,” or “cultural transmission,” require
reconceptualization as cultural, social products. In order to
escape previous conceptions, we therefore adopt new research
approaches, complementary to existing concerns of VL/HCC.
Our method in the current piece of research is analytic,
rather than empirical. The world of practice is one of
embedded and lived experience, not always directly accessible
to observation and measurement. As a result, the turn to
practice in HCI has been associated with giving priority to the
reflective writing of practitioners, and more recently with the
traditional analytic method of the ‘essay,’ itself situated within
an existing body of humanistic literature [7]–[9]. Our evidence
in this work is therefore based in accounts of practice, from as
broad a perspective as we could find, with the intention of
discovering new insights beyond the well-established
achievements in VL/HCC and cognate human-centric fields
(such as empirical studies of software engineering, and
psychology of programming).
We aimed not to be distracted by definitional questions,
taking the inclusive approach to definitions of programming
that has been advocated in previous research such as [5]. For
the purposes of HCI research, Kuutti and Bannon describe
practices as “(…) routines consisting of interconnected and
inseparable elements: physical and mental activities of human
bodies, the material environment, artifacts and their use,
contexts, human capabilities, affinities and motivation.
Practices are wholes, whose existence is dependent on the
temporal interconnection of all these elements, and cannot be
reduced to, or explained by, any one single element [3].
As source material for our analysis, we therefore collected
accounts of how programming is done, using an inclusive
interpretation of the term, but noting ways in which each
practice described is explicitly delineated and named by the
authors. We considered books, journals and conferences in
specialist fields extending to software engineering, human
computer interaction, interaction design and digital humanities,
as well as emergent interdisciplinary ventures such as Creative
Coding [10] and Art-Science [11].
During this process, we organized our observations
according to some broad categories informed by the historical
development of distinctive programming practices. In our
overview of the practices in the next section, we will group
these under the terms Software Engineering,
Bricolage/Tinkering, Sketching, Live Coding, Hacking, and
Code-Bending. We will also consider the relationship of these
practices to particular kinds of tool, through consideration of
the affordances of those tools. However, before reporting those
findings, we provide a brief overview of the historical and
theoretical contexts for our analysis.
A. Software Engineering and Cognitive Ergonomics
The term Software Engineering was coined in response to
the 1960s ‘software crisis’, and advocated that the increasing
difficulty of delivering software within budget should be
addressed by more disciplined, systematic software
development and maintenance [12], [13]. The
acknowledgement that software development was a human
problem soon led to a concern with understanding human
performance. This branch of human factors research moved
beyond previous research into physical ergonomics, accuracy
and reaction times, to address ‘cognitive ergonomics’ of the
machines. Interdisciplinary investigations into the Psychology
of Computer Programming [14] developed from the
“(…) that programming tools and technologies
should not be evaluated based on their computational power
only, but also on their usability from the human point of view,
that is, based on their cognitive effects” [15].
Concern with improving the efficiency of human
performance was associated with emphasis on methods for
creating and reusing standardized engineering components,
extending beyond program code, to processes and models [12].
Standardized processes proposed ways to organize the
formulation of requirements, design, implementation, testing
and maintenance of each new software system [16]. Different
models of component interaction were associated with
alternative paradigms for expressing problem structure such as
Structured, Declarative, Object Oriented, Event-Driven, or
Dataflow. Specific processes and paradigms were often
encapsulated in software development tools extending beyond
the programming language to support libraries, debuggers,
analyzers, modelers, unit test tools, Integrated Development
Environments (IDEs), Rapid Application Development (RAD)
tools, etc.
B. Beyond Software Engineering
The earliest development of software engineering was
associated with an assumption that direct interaction with
computers would remain a specialist technical task. Books such
as Weinberg’s Software Psychology [14] made little distinction
between user and programmer. However, the increasing
ubiquity of computers that came with decreasing size and cost
led to an understanding that computers would be used for
purposes beyond technical ones, and to a research concern with
the extent to which computer users of all ages might be able to
become programming-literate, as in Alan Kay’s Dynabook
proposal [17, p. 393].
The Smalltalk system created by Kay and Goldberg did
indirectly lead to greatly increased computer usage through its
introduction of the WIMP paradigm (Windows, Icons, Menus,
Pointer), but the resulting direct manipulation interfaces
ironically became increasingly distanced from programming.
The efforts towards reintroducing automation and abstract
functionality into this more graphical environment, have
become associated with End-User Programming (EUP), and a
full-circle return to End-User Software Engineering that offers
more systematic processes and tools to non-experts [18]. For
our purpose, the key question is whether such tools are
associated with novel practices distinguishable from software
The Smalltalk project placed an early emphasis on use of
the computer as a creative tool, rather than simply engineering
and business. The painting, publishing and music applications
that Kay and Goldberg envisaged are now ubiquitous. The use
of programming in professional arts contexts has also increased
in popularity, and has been described as creative coding [10].
This development naturally follows the many algorithmic
artforms predating the computer, including textile weaving,
Islamic and Celtic decorative patterns, or musical change-
ringing. In modern times, designers and artists have followed
in the footsteps of pioneers Ben Laposky and John Whitney
[19], creating procedural art using computers.
Finally, the study of programming (as opposed to its
application) has also in recent years extended beyond the
technical concerns of software engineering. The broad area of
Digital Humanities concerns itself with all research where the
fields of computing and the humanities intersect, often also
involving the study of programming and program code, as in
the fields of Software Studies, Computational Culture or the
code aesthetics of M.J. Black [20]. Aesthetic computing, or “the
application of the theory and practice of art to the field of
computing” [21], widens the scope of aesthetics in computing,
emphasizing how artistic aesthetics may inform all computing
C. Affordances in Programming
These historical shifts in audiences and objectives are
reflected in the programming paradigms, languages and tools
that have developed to support them. Each of these ensembles
is characterized by a distinctive set of properties. We describe
these joint technical properties of paradigms, languages and
tools as affordances, used here in the original sense introduced
by Gibson [22]: a relation between an object or environment
and an organism, that affords the opportunity for that organism
to perform an action. McCullough [23] discusses affordances
as “the workable capacities in a medium”, relating them to the
notion of a medium’s constraints. Together these form a
medium’s structure of expression, establishing what that
medium can and cannot be used for.
A familiar example illustrating the constraints and
affordances of different software environments is the trade-off
between those that afford high computational efficiency on one
hand and those that prioritize rapid development and
experimentation on the other. The dataflow paradigm affords
fluid manipulation and reconfiguration of signal flow, but
limited opportunity to work with the kinds of dynamic data-
structure traditionally handled with recursive algorithms [24]
Programmers might attempt to circumvent such limitations by
implementing some components in a language using a different
paradigm, but system design spanning multiple paradigms
quickly becomes cumbersome.
Two particularly interesting affordances are Directness [25]
and Liveness [26] “Directness means a user interface designer
can initiate the process of examining or changing the
attributes, structure, and behavior of user interface
components by pointing at their graphical representations
directly, as opposed to navigating through an alternate
representation. Liveness means the user interface is always
active and reactive - objects respond to user actions,
animations run, layout happens, and information displays are
updated continuously. Directness and liveness are properties of
the physical world: to examine and change a physical object,
you manipulate it directly while the laws of physics continue to
operate” [27]. The implications of these affordances for
programming more generally have been explored by Tanimoto
[28], and have currency through increasing interest in
environments that allow changes to be made and observed in a
program already executing [29]. These affordances, which
have long suggested that the boundary between interaction and
programming needs to be redefined, are gaining further
attention in HCI [30].
V. T
We now describe the broad categories of distinctive
programming practices that we have identified in the literature
spanning these historical and theoretical contexts.
A. Established Software Engineering Practice
By this term, we refer to the practice discussed in the
previous section, where, in some form, a specification has been
formulated, and is implemented, evaluated, and refined,
through defined phases, at varying levels of granularity
depending on which of the many existing software engineering
processes is followed. In the ‘waterfall model’ this cycle is
supposed to be repeated once, in the ‘spiral model’ [31] several
times, while in ‘agile development’ [32] it may be repeated
with intervals of weeks or even days. Nevertheless, the notion
of phase and progression, and of distinction between
specification and implementation, is always present. This
established practice is well-documented, so we include it for
completeness of analysis rather than for new insight.
B. Bricolage & Tinkering
The earliest suggestions that an alternative approach to
programming might be desirable, different to that of
professional software engineering, appeared in the context of
teaching programming to the general public, and particularly to
children. What has later been named Bricolage and Tinkering
approaches were advocated by the creators of the first
programming languages intended for teaching, e.g. LOGO
[33], Smalltalk [34], and their many descendants.
Turkle and Papert [35] introduced the notion of Bricolage
programming, adopting the term from anthropologist Lévi-
Strauss [36]. Turkle and Papert provide the following
definition: “The bricoleur resembles the painter who stands
back between brushstrokes, looks at the canvas, and only after
this contemplation, decides what to do next. For planners,
mistakes are missteps; for bricoleurs they are the essence of a
navigation by mid-course corrections. For planners, a
program is an instrument for premeditated control; bricoleurs
have goals, but set out to realize them in the spirit of a
collaborative venture with the machine. For planners, getting a
program to work is like "saying one's piece"; for bricoleurs it
is more like a conversation than a monologue.”
Note that the loop between distinct phases of designing and
then implementing is largely done away with. There may well
be no plan at all in bricolage, or the plan may be made up at the
very instant that the program is entered. The process is as much
happy accidents, trying things out and seeing what happens, as
it is deliberate action with a particular outcome in mind. The
practice has also been related to the word tinkering [37], [38], a
casual kind of mechanical play or dabbling that is associated
with amateurs and hobbyists, but is also characteristic of
curiosity and invention. The tinkerer is not an engineer, and
may be unsure of how material will react. She learns through
action, trying different manipulations and tools, and responds
as her material takes different forms.
C. Sketching with Code
In art and design, sketching is central to how ideation is
carried out [39]. Through freely and quickly trying out a large
number of variations of a loosely defined idea, a more concrete
conception of what is desired takes form. The sketches can
either then be discarded, having served their purpose, or chosen
ones kept, if they sufficiently represent their author’s
communicative intent. Note the crucial difference to the role of
prototypes in software engineering, where in processes such as
Boehm’s spiral model [31], or agile development [32] a
prototype is developed as a single, well-thought out design that
is to be evaluated for its soundness, with its re-evaluation
occurring through a clear cycle, and where each iteration of the
prototype serves as the concrete representation of the
continuously refined design under evaluation.
An early advocate of sketching as an approach to
programming was Miller Puckette, originator of the visual
languages Max/MSP and Pure Data that are now widely used
for music and other creative applications [40]. Puckette states
that he emphasized the sketching analogy by presenting users
with a blank canvas, on which the dataflow program can be
incrementally built up as a directed graph (Figure 1). Sketching
also features prominently in the Processing language and
environment [41], which expands on ideas in John Maeda’s
Design by Numbers language, created for teaching the “idea of
computation to designers and artists” [42]. Others have since
created alternative programming environments with sketching
specifically in mind, e.g. Baader and Bødker [43] and
Blackwell [44]. In all of these environments, the goal has been
to facilitate creative practices in programming, by analogy to
artists and designers working in traditional media.
Figure 1 – The visual language of Max/MSP ‘patches’ were described by
Miller Puckette as supporting sketch (this example created by IB).
In the words of the Processing authors [45]: “It is necessary
to sketch in a medium related to the final medium so that the
sketch can approximate the finished product. Painters may
construct elaborate drawings and sketches before executing the
final work. Architects traditionally work first in cardboard and
wood to better understand their forms in space. Musicians
often work with a piano before scoring a more complex
composition. To sketch electronic media, it’s important to work
with electronic materials. Just as each programming language
is a distinct material, some are better for sketching than others,
and artists working in software need environments for working
through their ideas before writing final code. Processing is
built to act as a software sketchbook, making it easy to explore
and refine many ideas within a short period of time”.
D. Live Coding
The implications of liveness are taken to an extreme in the
practice of live-coding, where artists write code as a means of
performance, with an audience experiencing the output of the
program at the same time as viewing a large-screen projection
of the continuously modified program code [46].
Predominantly, live-coding is used in musical performance, but
visual or audiovisual performances are not uncommon (Figure
2), and there is nothing to keep the practice from being applied
to any other context in which the output of generative
algorithms is presented to an audience (e.g. dance, textiles)
Live Coding is closely reliant on the availability of a
programming language that affords liveness. Otherwise, it is
impossible to program and immediately perceive the result,
without interrupting output and re-executing the program. So,
live-coding currently requires the use of specialized
programming environments capable of interpreting the code on
the fly as it is entered by the performer, without restarting or
recompiling the whole program. While several environments
already had this capability (including Max/MSP and Pure
Data), many have been created specifically with live-coding in
mind (for example ChucK, Impromptu, Fluxus and the JITlib
facility for SuperCollider [48] as well as a large number of
more recent examples).
Perhaps uniquely to live-coding, this programming practice
is not carried out with the ultimate goal of realizing some
design outcome, but is instead a continuous performance, with
the journey itself being the principal intended outcome. To
stress this point, early live-coding performers often ended their
acts by purposefully breaking what they had created: inserting
faults into their code, which crash, disrupt or delete their
program, ideally producing interesting visual and audio glitch
effects as it dies.
Figure 2 – Benoit and the Mandelbrots, from the Supercollider Symposium
2012 "Livecode Evening": “Codefaced people hacking music live in front of
your eyes. Live coding is a new direction in electronic music and video: live
coders expose and rewire the innards of software while it generates
improvised music and/or visuals” (Image copyright Steve Welburn, CC BY-
E. Hacking
The word hacking has often been used as a casual reference
to an informal style of programming. However, it has been
appropriated for many other purposes, extending to criminal
activity, political ethos, technocratic subculture, critical theory
and others [49]. We are concerned specifically with the
practice of hacking, and not these many other senses. Even
then, the term may vary greatly in definition depending on
context. Erickson [50] writes: “Hacking is the art of creative
problem solving, whether that means finding an
unconventional solution to a difficult problem or exploiting
holes in sloppy programming. Many people call themselves
hackers, but few have the strong technical foundation needed
to really push the envelope”.
Informal practices of creative problem solving, while an
important aspect that is consistent with the historical origins
and self-identity of ‘hacker’ communities, overlap with the
other categories of practice we have identified. It is therefore
useful to focus specifically on the second sense identified in
Erickson’s definition: modifying or otherwise interfering with
a pre-existing piece of software, in order to make it perform
differently from what its original designers intended. This
specific practice can be seen as an originating concept for the
many other interpretations of the word ‘hacker’: hacking
requires deep understanding of computers and software; it may
involve machine-level programming (to intervene when source
code is protected or hidden); this might be legitimate reverse
engineering [51], but in many cases will be illegal, violating
license agreements or copyright legislation. These illicit
connotations of hacking practices give the word a romantic
‘underground’ flavor, providing counter-cultural and anti-
authoritarian anarchist credentials even for mainstream and
publicly-funded artists (see for example the 2011 Netherlands
Media Art Institute exhibition “the art of hacking” [52]).
F. Code-Bending
This practice is named by analogy to Circuit-Bending, a
term coined by Reed Ghazala [53] to refer to creative
experimental modification of an electronic device. One well-
known circuit-bending target is Mattel’s Speak & Spell, which
can be changed into a musical instrument producing
otherworldly vocal sounds (Figure 3). It is not assumed that
circuit benders understand how the circuitry at hand works:
although such knowledge is undeniably beneficial, lack of
training is not a barrier to entry. Ghazala refers to his technique
as anti-theoretical: not in the sense of rejecting theoretically
informed practice, but providing a complementary alternative
to it.
Where circuit-bending opens up a plastic case to access
electrical connections, in code-bending [54], the internal API
of open-source software is re-purposed, so that instead of
fulfilling its originally intended internal function, it is used for
external communication. While internal interfaces are not
usually accessible in closed-source programs, with open-source
software, one can figuratively “lift the lid” to experiment with
these interfaces, even if the APIs are not fully documented.
Existing software can thus, in a comparatively rapid, playful
manner, be repurposed, encouraging an explorative approach to
Figure 3 – A Circuit-Bent Speak & Spell, as exhibited at the London Science
Museum. Switches, buttons and potentiometers have been added to the device,
controlling the modifications made to its circuits (Image copyright Loz
Pycock, CC BY-SA).
Code-bending may involve mapping of variables across
separate parameter spaces [55], or mutable mappings [56] that
are gradually altered, created and destroyed. Code-bending is
conducted in two phases: first exposing the previously
inaccessible contact points in open-source programs, for
example using the Open Sound Control (OSC) protocol, and
then executing these programs, so that while they are running,
one can experiment with altering mappings and adding data
sources; either in search of a new mapping to subsequently
finalize, or continuously, as a form of performance.
Code-bending has points of similarity to Opportunistic
Software Development [57] through integration and re-use of
existing systems; and to Mashup programming [58], an
approach to end-user web-development by combining data
and/or functionality from different, originally unrelated online
data sources or services.
We do not expect that the above survey of practices will be
definitive. However, it does provide sufficient range to assess
the potential value that might be realized from describing
programming as practice. In particular, whether or not the
categories we have identified represent a complete set, we have
confirmed that a diversity of practices does exist. These offer
the potential for new insights, with regard to alternative
practices that might be borrowed or adapted by other kinds of
user – for example, even expert software engineers will on
occasion need to engage in creative exploration, or tinker with
an unfamiliar tool or undocumented API. The following
observations consider ways in which these findings might
therefore be applied, and what new insights might be gained.
A. Practices are Best Practiced in Combination
These practices need not be mutually exclusive. A software
engineer might identify parts of a project where she cannot
write a specification, so instead sketches out alternative ideas.
During live coding performance, the second (mapping) stage of
code-bending may take place as part of the act. Code-bending
might also be used as a sketching technique, or as a design
evaluation strategy within a software engineering process.
Finally, a programmer might combine tinkering and hacking:
she tinkers with a piece of closed source software to familiarize
herself with the material at hand, having the goal of then
hacking, exposing functionality originally intended to remain
inaccessible. Further combinations are certainly possible.
B. Social Relations Around Practice
While software engineering might in principle be practiced
by a single person working in isolation, much of its literature is
concerned with how groups can successfully coordinate their
efforts [59]. In contrast, some of the practices identified within
creative fields are developed and refined by individual
practitioners pursuing personal objectives, with individual
creative attribution of the process and outcomes.
There is also wide variation in the audience for the
programming effort, with the intended audience influencing the
practices that are chosen. In software engineering, the
‘audience’ is often a paying client, and then perhaps the end-
users to whom the client intends to deliver the product. The
resulting expectations in this set of commercial relations (ease
of use, quality, value for money, etc), are worlds apart from the
requirements and expectations of visitors at a temporary
interactive installation, a permanent museum exhibit, or in the
audience of a live-coded ‘algorave’.
C. The Boundaries are Unclear: is Debugging a Practice?
There are cases where distinctions are less clear-cut: is
debugging a programming practice in itself, or a sub-practice
of software engineering, or perhaps a superset of practices?
The act of finding a bug is very different to writing the code.
But then, debugging can be carried out in many ways does
this make it a superset of distinct alternative practices [60]? On
the other hand, a mistake in bricolage programming, sketching,
or live coding, may never need to be debugged. If it gives rise
to interesting results, the programmer might want to understand
what happened, and to harness it - but would this still be
debugging? And if a live coder makes a mistake, she needs to
carry on with the performance. She has no time for debugging,
but must instead improvise, incorporating the mistake into the
performance. All of the above are equally valid, thus making
debugging difficult to singularly characterize across all
D. Away from the Process Cycle: Programming as Craft
It is essential to software engineering processes that a
specification is formulated, describing what the programming
effort should achieve. In some approaches the specification is
expected to be more refined than in others, and the frequency at
which this goal is reviewed and revised throughout the process
varies wildly between the waterfall model at one end, and
extreme programming at the other. But specification always
precedes the act of programming a solution. Not defining a
specification for the programming effort is considered very bad
practice, referred to with derogatory terms such as ‘Cowboy
Coding’ [12]. The dichotomy has even been formulated,
between the ‘correct way of doing things, and its antithesis,
the incorrect approach, pointedly referred to as craft. Dijkstra
declared that programming is a discipline and not a craft [61],
precisely to stress how programming shouldn’t be done [12],
In contrast to this justifiable engineering philosophy, the
emergence of the Interaction Design (IxD) field has
encouraged an explicit shift in focus, toward programming as
the craft [62] that is associated with a new design practice [63],
responding to the pragmatic engineering challenge that
requirements of interactive artefacts cannot always be defined
a priori [12].
E. Links to Craft Practices in HCI and IxD
The themes of this paper have emerged from consideration
of the practice turn as a matter of current concern in HCI. It is
often the case that application of HCI principles to
programming draws attention to specific opportunities that can
provoke further innovation in HCI tools themselves. For this
reason, we believe that there is useful potential for comparison
of this work to recent thinking on craft practices in IxD, such as
Vallgårda and Fernaeus’ [64] discussion of bricolage, Buxton’s
account of sketching [65], and understanding of specific
materials, such as Bdeir’s account of sketching with electronics
[66]. There will certainly be overlap with these accounts, but
we should not expect that the practices for programming will
translate to IxD, or to its various composing disciplines and
materials, without further research.
F. Practices and Materiality
One specific theoretical concern in HCI, to which this
account of programming practices does contribute, relates to
the materiality of program code [67]. The perspective in which
IxD, as a design discipline, incorporates a craft element,
intersects with the observations that we reported from Lave at
the start of this paper, situating practice in terms of craft
knowledge that is embedded in a material context.
The interest in materiality for HCI more broadly is derived
from ubiquitous, tangible and embodied computing. Work in
these domains draws increased attention to craft aspects in IxD,
as part of a material move [68], [69]. Craft practitioners
interact with, learn about and reflect on their materials. In this
context, questions are raised about the materiality of program
code, with much discussion, and divergent views. Vallgårda &
Redström [70], posit that program code has to be “(…)
combined with other materials to come to expression as
material”. Löwgren and Stolterman, describe information
technology as a “material without properties” [71]. A
contrasting conception of material is advanced by Lindell [67],
and by Dourish and Mazmanian [72]: material as an abstract
construct, grounded only in its usefulness to the practitioner for
understanding her practice, and its usefulness to the
perceiver/consumer in making sense of the result. Material is in
other words not manifest in some physical reality, but is a
purely mental construct. Blackwell and Aaron suggest that
even this abstract materiality exhibits a resistance to the
intentions of the programmer, generating new knowledge
through the ‘mangle of practice’ [73]. These perspectives, as
revealed in programming practices, can be inspected through
the lens that Ingold [74] describes as the “two faces of
materiality”: “On one side is the raw physicality of the world’s
‘material character’; on the other side is the socially and
historically situated agency of human beings who, in
appropriating this physicality for their purposes, are alleged to
project upon it both design and meaning in the conversion of
naturally given raw material into the finished forms of
Adopting Ingold’s terms, it is this second face of
materiality that we find particularly relevant to program code:
collecting around it a set of socially constructed traditions and
connotations, properties towards its use, and the practices these
afford. It is here that attention to practices makes a
contribution, because the understanding of a material is
incomplete without the practices in which it is employed.
These constructs are useful for informing practitioners’ work,
reflection and discussion. They also provide observers with
another lens, a way in, to understanding the work of the
Treating software as a material brings the consequence that
the granularity of material is not fixed, but depends entirely on
the intent of the practitioner, and context at hand. It may be a
composite material, comprising physical materials, electronics
and code; it may be written in a number of programming
languages; or it may simply be a single program. In the digital
realm, it is rarely clear where the digital tools applied to the
digital material end, and where the digital material begins [23].
But as observed by Ingold, what is most interesting is not what
a material is, but what it can do in the hands of an artisan.
G. Embodied Programming Practices for Physical Domains
While the immaterial-materiality of program code is subtle
and disputed, an equally neglected but undisputable
consideration is the fact that the programmer does have a body.
The embodiment of the programmer can be brought to the fore,
when developing for and through various forms of physical
performance during the development [75]. When creating a
system for a domain which requires full body interaction,
whether a golf-training simulator, or a software instrument for
live musical performance, how do the practices from the
application domain inform the choice of which programming
practices to employ during development? Assuming the
developer does acquire distinct practices from the application
domain, she might then adopt different programming practices
as lenses through which to better her understanding of the
application domain. After reflection, these application domain
practices may even inspire new programming practices,
physical or otherwise.
H. Practices of Programming in Education
Mathematics educators understand that there is a huge gulf
between the subject as it is taught in the classroom, and the
mathematical practices of the outside world [76]. This
recognition, that a body of knowledge is associated with a
diverse set of professional and life practices, is essential in
making the transition from teaching a specialist elite subject to
a broad population literacy.
Even for students who do intend to become professional
specialists, whether software engineers, interaction designers,
or artists, exposure to a range of different practices will
enhance their development as reflective practitioners. They
might start by tinkering during early stages of familiarization
with a new language; sketch to understand different ways that a
problem can be approached (possibly coding live, for
especially fluid feedback); then apply software engineering
methods to structure a large and complex development effort.
Understanding these practices from early on will help dispel
the misunderstanding that the only correct way of
programming is software engineering – and instead convey that
the choice of which practice to use is contingent on the goal
adopted in a particular programming effort.
The first author has designed an introductory programming
course that applies concepts of varying programming practices
early on, with students’ activities throughout the course
structured around the practice most relevant to that stage of the
learning activity. It is designed as a contextualized course [77],
[78], with all activities grounded in an application domain that
provides students with a meaningful context for their learning.
It currently uses the Processing language, facilitating
applications with interactive graphics and sound. The course
benefits from existing teaching materials for Processing that
emphasize contextualized learning, such as Daniel Shiffman’s
books Learning Processing [79] and The Nature of Code [80].
The intention is that explicit description of the practices of
programming, discussing the contexts where they might be
advantageous, will allow students to use these reflectively, both
separately and in combination. Findings from this teaching
experiment will be published in the future.
I. Contrasting and Comparing Practices
In the previous section, we discussed how programming
practices can be used in combination. It is also interesting to
reflect on how the practices differ, and over which dimensions:
What differentiates tinkering from hacking? Tinkering from
sketching? Hacking from code-bending? In this section, we
draw some comparisons.
In tinkering, achieving a final product is usually a
secondary objective, if it is a goal at all. With no intended
target-state for the material to reach, people engage in tinkering
to gain familiarity with material. In hacking on the other hand,
there is a clear goal: exposing and taking advantage of
“exploits” in closed source software.
While in tinkering the material is unfamiliar to the
practitioner, in sketching, it is the end-result that is unfamiliar –
a goal is acknowledged, but underspecified. So a programmer
that sketches is familiar with her materials and tools, and rather
than exploring them, is exploring a design space.
Hacking addresses a closed codebase, a program binary not
meant to be altered. Code-bending on the other hand, uses code
that is open to read and modify, and instead concerns itself
with finding new ways in which the code can be used.
With the distinctions outlined above as a starting point, we
find the following dimensions across which practices vary.
- What the end-goal is: perhaps to learn, whether about the
material (tinkering) or about the design space (sketching);
perhaps to create a final product (e.g. engineering); or
perhaps to produce an experience, with no end product at
all (e.g. live-coding).
- The extent to which the end-goal is defined: in engineering
the goal specification may span thousands of pages; in
sketching, it may be a brief, abstract description of a
design space; in live-coding, it may range from a carefully
rehearsed performance, to free improvisation; and in
tinkering, there need not even be an end goal at all.
- The extent of programming effort, over time, and in the size
of the resulting codebase: an engineering effort can extend
over decades, producing millions of lines of code;
sketching can last weeks or days, producing thousands of
lines; tinkering and live-coding may last only a few
minutes, producing no codebase whatsoever.
- The extent of familiarity with codebase and tools: varying
greatly from very low (tinkering), to very high (hacking,
with respect to the tools but not codebase, and engineering,
with respect to both tools and codebase).
- The relation between intended and subsequent use of the
program: In engineering, the intended use is defined, and a
program is made to satisfy this specification. A hacked
program on the other hand, is definitely not used as
originally intended. And, while a code-bent program starts
from open-source code, the resulting use was not one this
code was originally designed to cater for.
A dedicated examination of the dimensions across which
programming practices vary is most certainly needed, but until
that research is done, we let the above points stand as initial
groundwork towards such an effort.
In summary, what do we gain from explicitly talking about
Practices of Programming, as an umbrella term under which to
identify, name and gather information about the specific
characteristics of distinctive practices?
The descriptions and analyses above seem to be usefully
orthogonal to earlier considerations at the VL/HCC conference
of different kinds of programmers (e.g. novices and end-users),
and different motivations for doing programming (e.g. in terms
of attention investment and personality types).
Consideration of programming practices has also shown us
that, although the social and craft context of these practices can
be identified with previously identified research concerns (such
as data flow languages for sketching or level 4 liveness for live
coding), specific practices are in no way constrained to a
particular user community or tool set.
On the contrary, analysis of practices draws attention to
new opportunities for reflective discussion about the act of
programming, and informed choices of what practices to
follow during the programmer’s development as an artist,
craftsman or engineer, and at different stages of a particular
The practices of programming are a useful lens, in other
words, on the one hand for programmers and educators to
consciously choose how to best move through the phases of a
project and through their own career development, and on the
other hand for reflective practice, towards expanding our
understanding of what the activity of programming may entail.
IB’s work has been supported by an EU Presenccia grant, Spanish
INNPACTO Melomics project IPT-300000-2010-010, and a Swedish
ICT TNG grant “Music in Somaesthetic Design”. AB’s work has
been supported by a grant from the Boeing Corporation. The
authors would like to thank IB’s colleagues at KTH, for their
comments on earlier versions of this article.
[1] Y. Fernaeus, J. Tholander, and M. Jonsson, “Towards a new set of
ideals: consequences of the practice turn in tangible interaction,” in
Proceedings of the 2nd international conference on Tangible and
embedded interaction, 2008, pp. 223–230.
[2] R. Miettinen, D. Samra-Fredericks, and D. Yanow, “Re-turn to
practice: an introductory essay,” Organ. Stud., vol. 30, no. 12, pp.
1309–1327, 2009.
[3] K. Kuutti and L. J. Bannon, “The turn to practice in HCI: Towards a
research agenda,” in Proceedings of the 32nd annual ACM conference
on Human factors in computing systems, 2014, pp. 3543–3552.
[4] S. Aghaee, A. F. Blackwell, D. Stillwell, and M. Kosinski,
“Personality and intrinsic motivational factors in end-user
programming,” in Visual Languages and Human-Centric Computing
(VL/HCC), 2015 IEEE Symposium on, 2015, pp. 29–36.
[5] A. F. Blackwell, “What is Programming,” in 14th workshop of the
Psychology of Programming Interest Group, 2002, pp. 204–218.
[6] J. Lave, “The practice of learning,” Contemp. Theor. Learn., pp. 200–
208, 2009.
[7] K. Williams, “An Anxious Alliance,” Aarhus Ser. Hum. Centered
Comput., vol. 1, no. 1, p. 11, 2015.
[8] J. Bardzell, “HCI and the Essay: Taking on ‘Layers and Layers’ of
Meaning,” presented at the CHI 2010 Workshop on Critical Dialogue,
[9] J. Bardzell, Humanistic HCI. Morgan & Claypool Publishers, 2015.
[10] J. Maeda, Creative code. Thames & Hudson London, 2004.
[11] J. Ox, “Art-Science Is a Conceptual Blend,” Leonardo, vol. 47, no. 5,
pp. 424–424, 2014.
[12] B. Boehm, “A view of 20th and 21st century software engineering,” in
Proceedings of the 28th international conference on Software
engineering, 2006, pp. 12–29.
[13] N. Wirth, “A Brief History of Software Engineering,” IEEE Ann. Hist.
Comput., vol. 1, no. 3, pp. 32–39, 2008.
[14] G. M. Weinberg, The psychology of computer programming. Van
Nostrand Reinhold New York, 1971.
[15] J. Sajaniemi, “Psychology of programming: Looking into
programmers’ heads,” Probl. Prof., p. 3, 2008.
[16] I. Sommerville, Software Engineering, 9th ed. Addison Wesley, 2010.
[17] N. Wardrip-Fruin and N. Montfort, The NewMediaReader, vol. 1.
MIT Press, 2003.
[18] A. J. Ko, R. Abraham, L. Beckwith, A. Blackwell, M. Burnett, M.
Erwig, C. Scaffidi, J. Lawrance, H. Lieberman, B. Myers, M. B.
Rosson, G. Rothermel, M. Shaw, and S. Wiedenbeck, “The State of
the Art in End-user Software Engineering,” ACM Comput Surv, vol.
43, no. 3, pp. 21:1–21:44, Apr. 2011.
[19] “Digital Art Museum (DAM).” [Online]. Available:
[Accessed: 11-Oct-2012].
[20] M. J. Black, “The art of code,” 2002.
[21] P. A. Fishwick, Ed., Aesthetic Computing. The MIT Press, 2008.
[22] J. J. Gibson, “The theory of affordances,” Hilldale USA, 1977.
[23] M. McCullough, Abstracting craft: The practiced digital hand. MIT
press, 1998.
[24] W. M. Johnston, J. R. P. Hanna, and R. J. Millar, “Advances in
dataflow programming languages,” ACM Comput Surv, vol. 36, no. 1,
pp. 1–34, 2004.
[25] B. Shneiderman, “Direct manipulation: A step beyond programming
languages,” in ACM SIGSOC Bulletin, 1981, vol. 13, p. 143.
[26] S. L. Tanimoto, “VIVA: A visual language for image processing,” J.
Vis. Lang. Comput., vol. 1, no. 2, pp. 127–139, 1990.
[27] J. H. Maloney and R. B. Smith, “Directness and liveness in the
morphic user interface construction environment,” in Proceedings of
the 8th annual ACM symposium on User interface and software
technology, 1995, pp. 21–28.
[28] S. L. Tanimoto, “A perspective on the evolution of live
programming,” in Live Programming (LIVE), 2013 1st International
Workshop on, 2013, pp. 31–34.
[29] D. Ungar and R. B. Smith, “The thing on the screen is supposed to be
the actual thing,” in Proceedings of LIVE 2013, Workshop on Live
Programming, San Francisco, CA, 2013.
[30] J. Hook, G. Schofield, R. Taylor, T. Bartindale, J. McCarthy, and P.
Wright, “Exploring HCI’s Relationship with Liveness,” in CHI ’12
Extended Abstracts on Human Factors in Computing Systems, New
York, NY, USA, 2012, pp. 2771–2774.
[31] B. W. Boehm, “A spiral model of software development and
enhancement,” Computer, vol. 21, no. 5, pp. 61–72, 1988.
[32] M. Fowler and J. Highsmith, “The agile manifesto,” Softw. Dev., vol.
9, no. 8, pp. 28–35, 2001.
[33] W. Feurzeig, S. Papert, M. Bloom, and R. Grant, “Programming-
Languages as a Conceptual Framework for Teaching Mathematics.
Final Report on the First Fifteen Months of the LOGO Project.,” Nov.
[34] A. C. Kay, “The early history of Smalltalk,” in History of
programming languages—II, 1996, pp. 511–598.
[35] S. Turkle and S. Papert, “Epistemological pluralism: Styles and voices
within the computer culture,” Signs, pp. 128–157, 1990.
[36] C. Lévi-Strauss, The savage mind. University of Chicago Press, 1968.
[37] A. F. Blackwell, “Gender in domestic programming: From bricolage
to séances d’essayage,” in CHI’2006 workshop on End User Software
Engineering, 2006.
[38] J. Maloney, M. Resnick, N. Rusk, B. Silverman, and E. Eastmond,
“The Scratch Programming Language and Environment,” Trans
Comput Educ, vol. 10, no. 4, pp. 16:1–16:15, Nov. 2010.
[39] C. Eckert, A. Blackwell, M. Stacey, C. Earl, and L. Church,
“Sketching across design domains: Roles and formalities,” Artif.
Intell. Eng. Des. Anal. Manuf., vol. 26, no. 03, pp. 245–266, 2012.
[40] M. Puckette, “Pure Data: another integrated computer music
environment,” Proc. Second Intercollege Comput. Music Concerts,
pp. 37–41, 1996.
[41] C. Reas and B. Fry, Processing: A Programming Handbook for Visual
Designers and Artists. The MIT Press, 2007.
[42] J. Maeda, Design by numbers. The MIT Press, 2001.
[43] S. Baader and S. Bødker, “SketchCode – An Extensible Code Editor
for Crafting Software,” in End-User Development, P. Díaz, V. Pipek,
C. Ardito, C. Jensen, I. Aedo, and A. Boden, Eds. Springer
International Publishing, 2015, pp. 211–216.
[44] A. F. Blackwell, “Palimpsest: A layered language for exploratory
image processing,” J. Vis. Lang. Comput., vol. 25, no. 5, pp. 545–571,
[45] C. Reas and B. Fry, “Processing: Programming for Designers and
Artists,” Des. Manag. Rev., vol. 20, no. 1, pp. 52–58, Jan. 2009.
[46] N. Collins, A. McLean, J. Rohrhuber, and A. Ward, “Live Coding in
Laptop Performance,” Organised Sound, vol. 8, no. 03, pp. 321–330,
[47] E. Cocker, “Live Notation:–Reflections on a Kairotic Practice,”
Perform. Res., vol. 18, no. 5, pp. 69–76, 2013.
[48] J. Rohrhuber and A. de Campo, “Just in time programming,”
SuperCollider Book MIT Press Camb. Mass., 2011.
[49] M. Wark, A Hacker Manifesto. Cambridge: Harvard University Press,
[50] J. Erickson, Hacking: the art of exploitation. No Starch Press, 2008.
[51] E. J. Chikofsky, J. H. Cross, and others, “Reverse engineering and
design recovery: A taxonomy,” Softw. IEEE, vol. 7, no. 1, pp. 13–17,
[52] “The Art of Hacking Exhibition.” [Online]. Available: [Accessed: 11-Oct-
[53] Q. R. Ghazala, “The Folk Music of Chance Electronics: Circuit-
Bending the Modern Coconut,” Leonardo Music J., pp. 97–104, 2004.
[54] I. Bergstrom and R. B. Lotto, “Code Bending: A new creative coding
practice,” Leonardo, vol. 48, no. 1, pp. 25–31, 2015.
[55] E. R. Miranda and M. M. Wanderley, New digital musical
instruments: control and interaction beyond the keyboard, vol. 21. AR
Editions, Inc., 2006.
[56] I. Bergstrom, A. Steed, and B. Lotto, “Mutable Mapping: Gradual Re-
routing of OSC Control Data as a Form of Artistic Performance,” in
Proceedings of the International Conference on Advances in
Computer Entertainment Technology, New York, NY, USA, 2009, pp.
[57] C. Ncube, P. Oberndorf, and A. W. Kark, “Opportunistic Software
Systems Development: Making Systems from What’s Available,”
IEEE Softw., vol. 25, no. 6, pp. 38–41, Nov. 2008.
[58] J. Wong and J. I. Hong, “Making mashups with marmite: towards
end-user programming for the web,” in Proceedings of the SIGCHI
conference on Human factors in computing systems, 2007, pp. 1435–
[59] F. P. Brooks, The mythical man-month, vol. 1995. Addison-Wesley
Reading, MA, 1975.
[60] E. Regelson and A. Anderson, “Debugging practices for complex
legacy software systems,” in Proceedings of International Conference
on Software Maintenance, 1994, pp. 137–143.
[61] E. W. Dijkstra, Notes on structured programming. Technological
University Eindhoven Netherlands, 1970.
[62] R. Lindell, “The Craft of Programming Interaction,” presented at the
7th Nordic Conference on Human-Computer Interaction, 2012, pp.
[63] D. Fallman, “The interaction design research triangle of design
practice, design studies, and design exploration,” Des. Issues, vol. 24,
no. 3, pp. 4–18, 2008.
[64] A. Vallgårda and Y. Fernaeus, “Interaction Design as a Bricolage
Practice,” in Proceedings of the Ninth International Conference on
Tangible, Embedded, and Embodied Interaction, 2015, pp. 173–180.
[65] B. Buxton, Sketching user experiences: getting the design right and
the right design: getting the design right and the right design. Morgan
Kaufmann, 2010.
[66] A. Bdeir, “Electronics as Material: LittleBits,” in Proceedings of the
3rd International Conference on Tangible and Embedded Interaction,
New York, NY, USA, 2009, pp. 397–400.
[67] R. Lindell, “Crafting interaction: The epistemology of modern
programming,” Pers. Ubiquitous Comput., vol. 18, no. 3, pp. 613–
624, 2014.
[68] Y. Fernaeus and P. Sundström, “The material move how materials
matter in interaction design research,” in Proceedings of the
Designing Interactive Systems Conference, 2012, pp. 486–495.
[69] M. Wiberg, “Methodology for materiality: interaction design research
through a material lens,” Pers. Ubiquitous Comput., vol. 18, no. 3, pp.
625–636, 2014.
[70] A. Vallgårda and J. Redström, “Computational composites,” in
Proceedings of the SIGCHI conference on Human factors in
computing systems, 2007, pp. 513–522.
[71] J. Löwgren and E. Stolterman, Thoughtful interaction design: A
design perspective on information technology. Mit Press, 2004.
[72] P. Dourish and M. Mazmanian, “Media as material: Information
representations as material foundations for organizational practice,”
Matter Matters Objects Artifacts Mater. Organ. Stud., vol. 3, p. 92,
[73] A. F. Blackwell and S. Aaron, “Craft Practices of Live Coding
Language Design.”
[74] T. Ingold, Making: Anthropology, archaeology, art and architecture.
Routledge, 2013.
[75] M. Jonsson, J. Tholander, and Y. Fernaeus, “Setting the stage–
Embodied and spatial dimensions in emerging programming
practices,” Interact. Comput., vol. 21, no. 1–2, pp. 117–124, 2009.
[76] T. Nunes, A. D. Schliemann, and D. W. Carraher, Street mathematics
and school mathematics. Cambridge University Press, 1993.
[77] O. Bälter and D. A. Bailey, “Enjoying Python, Processing, and Java in
CS1,” ACM Inroads, vol. 1, no. 4, pp. 28–32, Dec. 2010.
[78] C. B. Price and W. R. Worcester, “Can the Fine Arts Inform Software
Development,” in JICC Conference, LMU, 2007.
[79] D. Shiffman, Learning Processing: A Beginner’s Guide to
Programming Images, Animation, and Interaction. Morgan
Kaufmann, 2009.
[80] D. Shiffman, S. Fry, and Z. Marsh, The nature of code. D. Shiffman,
... It is emerging as a distinct way of programming. It does not quite strike us as a distinct practice of programming, as that term has been applied to communities of programmers united by similar ethos and aims, such as enterprise software engineers, bricoleurs, live coders, and code benders; but as Bergström & Blackwell (2016) note, there are no clear criteria by which we can define the boundaries of a practice. Nor does it strike us as being a new activity of programming as per the cognitive dimensions framework, since AI assistance is clearly orthogonal to authoring, transcription, and modification, being applicable to each of these activities and others besides. ...
Full-text available
Large language models, such as OpenAI's codex and Deepmind's AlphaCode, can generate code to solve a variety of problems expressed in natural language. This technology has already been commercialised in at least one widely-used programming editor extension: GitHub Copilot. In this paper, we explore how programming with large language models (LLM-assisted programming) is similar to, and differs from, prior conceptualisations of programmer assistance. We draw upon publicly available experience reports of LLM-assisted programming, as well as prior usability and design studies. We find that while LLM-assisted programming shares some properties of compilation, pair programming, and programming via search and reuse, there are fundamental differences both in the technical possibilities as well as the practical experience. Thus, LLM-assisted programming ought to be viewed as a new way of programming with its own distinct properties and challenges. Finally, we draw upon observations from a user study in which non-expert end user programmers use LLM-assisted tools for solving data tasks in spreadsheets. We discuss the issues that might arise, and open research challenges, in applying large language models to end-user programming, particularly with users who have little or no programming expertise.
... As discussed in case studies one to three, sketching is often overlooked in many applications and disciplines, it is often referred to as a "soft" skill and as such direction is often not provided in teaching and learning settings (universities and adult learning institutions). Although, it is proven that sketching can support students, researchers and practitioners in HCI to ideate, collaborate, document, and explore complex topics, themes, feelings, attitudes, opinions, and experiences of ourselves and others, e.g., code (Bergström and Blackwell, 2016), rapid prototyping (Cottam and Wray, 2009), algorithmic recognition (Johnson et al., 2012), and a digital representation (Igarashi et al., 2006). ...
Full-text available
Sketching is recognised as an important tool in the journey of research and practical processes of Human Computer Interaction (HCI) and User Experience Design (UX). However, it is not always included in higher education curriculum, in which HCI and UX is often a single module in one year group amongst more “traditional” approaches in computer science. The benefits of sketching and visualisation practice can be used by students across the board in computing degrees, but especially so within HCI and UX, where novel approaches and ideation are valued and practiced. By the time learners leave higher education, they may or may not have engaged with this valuable skill. HCI has a lot in common with UX, and the two are commonly conflated to be the same thing, though despite this, there is not a focus on practical sketching and visualisation skills. In comparison, within the UX workplace environment, sketching is part of design thinking and vital for the structuring of ideas, storyboards, user journey maps and more. We focus on the incorporation and exploration of sketching as an educational tool, technique and output within HCI, and how this learning is given and received over a number of contexts. This paper outlines case studies where sketching has been included in both formal and informal learning with both undergraduate, postgraduate, and post education populations, and how this knowledge exchange has been both enhanced and changed by the recent compulsory move to online teaching during the COVID-19 pandemic. We discuss practice and learning in the context of four case studies: Data-Sketching in a First Year Minor; Sketching in a 2nd Year HCI Cohort; Sketching as a Foundational Tool for MSc User Experience Design; and, Sketching in HCI for Peer-to-Peer Learning. Further, we make recommendations for incorporating sketching practice and theory into both undergraduate and postgraduate university programs, as well as for peer-to-peer learning in both public and private contexts.
... Previous works have developed programming languages and environments that lower the barriers to programming (Kelleher and Pausch, 2005;Sim and Lau, 2019). Other studies have examined error and notification messages (Becker et al., 2019), debugging strategies (Murphy et al., 2008), emotions (Kinnunen and Simon, 2010), practices of programming (Bergström and Blackwell, 2016), and programming patterns in block-based and text-based programming languages (Weintrop and Holbert, 2017). Previous research has also put forward pedagogical strategies for teaching children to code (Bers, 2019), examined the difficulties non-English speaking people experience while learning programming (Guo, 2018;Pal, 2016;Dasgupta and Mako, 2017;Vogel, 2020), analyzed the benefits of learning a visual programming language over a traditional text-based language (Noone and Mooney, 2018;Weintrop and Wilensky, 2017), reported on what programming languages developers use and why (Pang et al., 2018), and examined creative coding (Li et al., 2020). ...
Computer programming is widely regarded as a key skill in the 21st century. Yet, and despite a growing aging population and interest in promoting computer programming for all, research on this topic with older people (60+) is scant in the Human-Computer Interaction literature. This paper presents a qualitative case study aimed to explore the rst experiences of computer programming of a group of older active computer users with low levels of educational attainment (i.e., primary school / K-12). Over a 6-month period, we provided a hands-on introduction to several textual and visual programming languages and environments to (N = 29) older and adult people in three courses in an adult educational center. We reveal and explain relevant factors that shape, and help us understand, the participants’ computer programming learning experiences, including their moti- vations, dif culties, and identity, along with strategies that hindered and fostered empowerment. Implications for research and design are discussed.
... One such hurdle that causes frustration in courses is understanding topics [2]- [8]. Although people learn to program in different ways, such as with graphical programming blocks, text, or both together [8]- [10], and for different reasons [11], few of them consider it an easy task [3], [5]. This can be perceived in failure and dropout rates, which reach about 28% among undergraduate students [12]- [14]. ...
... This large variance makes running quantitative user studies very challenging. Range of working styles: Bergström and Blackwell described a diverse collection different approaches to programming problems [4], such as bricolage/tinkering and engineering. These different styles may be used even by different people using the same language, impeding a designer's attempts to anticipate a user's strategy or behavior. ...
Programming language designers commonly guess what language designs would be best for their users and create languages accordingly. The outcome of this is languages that are difficult to use and error-prone. In particular, blockchain programs have been plagued by serious bugs. Although techniques from the theory of programming languages can detect many of these kinds of bugs, languages that use these techniques have been too difficult for programmers to use effectively. We have developed Obsidian, which integrates a strong, static type system that detects many of these bugs, using a new user-centered design approach. In this paper, we describe the formative and summative methods we have developed for user-centered design of programming languages and how we have applied them to create Obsidian. This includes a usability study, which demonstrates the effectiveness of our design methods to obtain a usable language.
... These new informal, experimental and everyday practices of coding treat code as a kind of craft material, and discuss programming skill and experience in relation to other craft practices in which malleable materials are explored toward creative ends (Bergstrom and Blackwell, 2016). The 29th annual meeting of PPIG in 2018 was convened together with the Art Worker's Guild of London, a respected establishment of the 19th century English Arts and Crafts movement. ...
This paper reflects on the evolution (past, present and future) of the ‘psychology of programming' over the 50 year period of this anniversary issue. The International Journal of Human-Computer Studies (IJHCS) has been a key venue for much seminal work in this field, including its first foundations, and we review the changing research concerns seen in publications over these five decades. We relate this thematic evolution to research taking place over the same period within more specialist communities, especially the Psychology of Programming Interest Group (PPIG), the Empirical Studies of Programming series (ESP), and the ongoing community in Visual Languages and Human-Centric Computing (VL/HCC). Many other communities have interacted with psychology of programming, both influenced by research published within the specialist groups, and in turn influencing research priorities. We end with an overview of the core theories that have been developed over this period, as an introductory resource for new researchers, and also with the authors’ own analysis of key priorities for future research.
Context: The concept of software craftsmanship has early roots in computing, and in 2009, the Manifesto for Software Craftsmanship was formulated as a reaction to how the Agile methods were practiced and taught. But software craftsmanship has seldom been studied from a software engineering perspective. Objective: The objective of this article is to systematize an anatomy of software craftsmanship through literature studies and a longitudinal case study. Method: We performed a snowballing literature review based on an initial set of nine papers, resulting in 18 papers and 11 books. We also performed a case study following seven years of software development of a product for the financial market, eliciting qualitative, and quantitative results. We used thematic coding to synthesize the results into categories. Results: The resulting anatomy is centered around four themes, containing 17 principles and 47 hierarchical practices connected to the principles. We present the identified practices based on the experiences gathered from the case study, triangulating with the literature results. Conclusion: We provide our systematically derived anatomy of software craftsmanship with the goal of inspiring more research into the principles and practices of software craftsmanship and how these relate to other principles within software engineering in general.
Making offers a series of profound reflections on what it means to create things, on materials and form, the meaning of design, landscape perception, animate life, personal knowledge and the work of the hand. It draws on examples and experiments ranging from prehistoric stone tool-making to the building of medieval cathedrals, from round mounds to monuments, from flying kites to winding string, from drawing to writing. The book will appeal to students and practitioners alike, with interests in social and cultural anthropology, archaeology, architecture, art and design, visual studies and material culture.
Conference Paper
Most ideas come from previous ideas. The sixties, particularly in the ARPA community, gave rise to a host of notions about "human-computer symbiosis" through interactive time-shared computers, graphics screens, and pointing devices. Advanced computer languages were invented to simulate complex systems such as oil refineries and semi-intelligent behavior. The soon to follow paradigm shift of modern personal computing, overlapping window interfaces, and object-oriented design came from seeing the work of the sixties as something more than a "better old thing." That is, more than a better way: to do mainframe computing; for end-users to invoke functionality; to make data structures more abstract. Instead the promise of exponential growth in computing/$/volume demanded that the sixties be regarded as "almost a new thing" and to find out what the actual "new things" might be. For example, one would compute with a handheld "Dynabook" in a way that would not be possible on a shared main-frame; millions of potential users meant that the user interface would have to become a learning environment along the lines of Montessori and Bruner; and needs for large scope, reduction in complexity, and end-user literacy would require that data and control structures be done away with in favor of a more biological scheme of protected universal cells interacting only through messages that could mimic any desired behavior.Early Smalltalk was the first complete realization of these new points of view as parented by its many predecessors in hardware, language, and user interface design. It became the exemplar of the new computing, in part, because we were actually trying for a qualitative shift in belief structures---a new Kuhnian paradigm in the same spirit as the invention of the printing press---and thus took highly extreme positions that almost forced these new styles to be invented.
Conference Paper
We explore the factors that determine whether individuals are likely to experience intrinsic motivation in end-user programming (EUP). We report two experiments: one that tests whether there are reliable psychometric constructs that describe different aspects of intrinsic motivation, and one that tests whether these constructs are successful in predicting individuals' own self-reported intrinsic motivation after using a popular EUP product. We conclude that there are identifiable and distinct motivational factors in EUP, and that these are associated with particular psychometric personality traits. We offer several suggestions for future research that could apply these findings to improve uptake and quality of user experience for educational and general-purpose EUP applications.