Tinkering in Informatics as Teaching Method

Angelika Mader, Ansgar Fehnker and Edwin Dertien

University of Twente, Enschede, The Netherlands

Keywords:

Tinkering, Programming Education, Teaching Methods.

Abstract:

Our university offers an IT-based design programme, with an engineering background in Computer Science

and Electrical Engineering. Its focus on design and creativity, as well as the diversity of the students, requires

an approach in informatics courses different from classical computer science programmes. While tinkering is

an increasingly popular approach in STEM stimulation and education outside university, we argue that also in

an academic setting a tinkering mindset has a relevant contribution. In this paper, we identify key elements in

setting up tinkering sessions and report on their implementation for a course on algorithms. We will present

and discuss results and observations of our teaching method, that are promising to continue and extend the

tinkering approach in an academic setting.

1 INTRODUCTION

Our university offers an IT-based design programme

with an engineering background in Computer Science

and Electrical Engineering. It is one of the fasted

growing programmes of our faculty. In this bachelor

programme, students learn how to employ the latest

technology to develop interactive installations geared

towards an impact on human lives, in contexts such as

health, entertainment, or environment. Knowledge,

interest and skills in design, culture, human-centred

problem solving, art and creativity are essential for

this programme, next to sound technical knowledge.

The program attracts a very diverse student popu-

lation. Many students have little STEM background.

For example, in a 2019 questionnaire among ﬁrst-year

students of the algorithms course – which was com-

pleted by 46 of the 107 students – 39% reported that

they had never, and 30% that they had rarely pro-

grammed before entering the degree. Over the ﬁve

years from 2014 to 2018, 35% of all enrolments were

female students. This compares to 10% in informat-

ics, and 8% in the electrical engineering degree at our

university. A similar share of students in our degree

has an international background, which also means

that they come from different education systems.

While we embrace the diversity for the originality

and quality of the results our students deliver, it also

poses a challenge for us. Especially in the courses on

mathematics, informatics and electrical engineering,

the prior knowledge of entering students varies con-

siderably.

In this programme, we consider informatics as a

means for students to express there ideas and concepts

and focus less on the scientiﬁc questions of informat-

ics. Still, students need to have an understanding of a

computer and software, and in particular of program-

ming. They need to be familiar with decomposition,

structure, and abstraction and need to be able to apply

non-trivial concepts of programming, such as classes

and methods.

This setting of a programme with a very diverse

student population and its speciﬁc mindset and con-

tent geared towards creativity requires a different ap-

proach to teaching informatics than classical infor-

matics programmes. In this paper, we describe our ap-

proach in introducing tinkering as a teaching method

for informatics that caters to the diversity of the stu-

dents. Here we take the example of a ﬁrst-year course

on algorithms. We also report on our ﬁndings and ex-

periences from almost 10 years.

Section 2 describe tinkering, emphasising both the

academic and teaching perspective. Section 3 de-

scribes the principles of the tinkering process and

the core ingredients for setting up tinkering sessions.

How we implement them for a course on algorithms

is elaborated in Section 4. Evidence and observation

will be presented in Section 5, followed by Section

6 discussing the approach. Section 7 concludes this

paper.

450

Mader, A., Fehnker, A. and Dertien, E.

Tinkering in Informatics as Teaching Method.

DOI: 10.5220/0009467304500457

In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 1, pages 450-457

ISBN: 978-989-758-417-6

2 TINKERING

By tinkering, we understand a self-directed, playful

exploration of material. Often starting with a seem-

ingly undirected investigation of the material, after a

while self-chosen goals are set, experiments are de-

ﬁned and executed to achieve the goal. Subsequently,

observations and interpretations lead to the next goal

to choose. In an iterative process the tinkerer ex-

plores the material of a given toolbox and the pos-

sibilities how to get it working, by a series of exper-

iments and interpretations of the results of the exper-

iments (see (Resnick and Rosenbaum, 2013)). This

process is guided by serendipity rather than structure

(Libow Martinez and Stager, 2013) having trial and

error at its core.

Conceptually, the actual notion of tinkering is

shaped by the maker movement

. This movement was

initiated by the MIT with its ﬁrst fablab in 2002. Fa-

blabs provide tools for prototyping, next to classical

workshop environment also laser cutters, 3D-printers

and open source hardware, and make them accessible

for the public. Makers are often amateurs and hob-

byists who create new products, for their individual

usage, or for value in the community. Tinkering and

making are related, where tinkering emphasises the

explorative aspect of making.

The material part of tinkering is often viewed as

physical. However, according to (Resnick and Rosen-

baum, 2013): We see tinkering as a style of making

things, regardless of whether the things are physical

or virtual. You can tinker when you are programming

an animation or writing a story, not just when you

are making something physical. The key issue is the

style of interaction, not the media or materials being

used. One abstraction level higher, tinkering would

also be possible with concepts or in completely dif-

ferent domains (e.g. gaming, health, music, philoso-

phy). In the context of this paper, we use algorithms

and data structures as material to tinker, which is ex-

plicitly non-physical.

We understand tinkering as a mindset for learning

(Libow Martinez and Stager, 2013). The goal of the

tinkering process for the student is to create an origi-

nal prototype. Our, the teachers’, goal is that the stu-

dents get familiar with the material of the toolbox and

learns to apply it, as well as training their serendipity

and creativity.

From the maker perspective tinkering is at the

heart of making things. The maker movement

seems

to have adopted tinkering as one of its core activities,

as the way of learning new skills, working with phys-

https://makerfaire.com/maker-movement/

See https://en.wikipedia.org/wiki/Maker culture

ical materials and source of inventiveness. For exam-

ple The Exploratorium (Wilkinson and Petrich, 2014)

has been developing kits, books and materials aimed

at STEM education for all ages, focusing on sparking

curiosity, enabling creativity and making fun. Tin-

kering from a maker perspective usually starts with

(re)building an existing idea or concept (from a kit,

book or other sources) and use that as ’seed’ or start-

ing point for new things to build.

From an engineering perspective mastering mate-

rial is a key skill. Mastering the material, knowing its

properties, potential, and how to use it to get things

to work, is essential for any design or engineering,

being at a university or outside. Engineering typi-

cally strives for an effective solution to a problem. We

are convinced that the tinkering approach is a key to

mastering material. Exploration and experimentation

are at the core of tinkering and results precisely in

the knowledge about the material. It generates hands-

on knowledge and reﬂection on the experiments con-

ducted, (as knowing in action in (Sch

on, 1983)), it

cultivates serendipity for what could be a working so-

lution. Reﬂection on the experiments is an important

step in the learning process, as in (Tawﬁk and Kolod-

ner, 2016) stated: the more effort the reasoner has

put into identifying what can be learnt from experi-

ence and when the lessons might be useful to pertain,

the better the learner will be able to label the expe-

riences and apply them for future use. In addition,

mature tinkerers and engineers have a technological

theory and scientiﬁc foundations in their toolbox, and

also know how to apply these within their design pro-

cesses (Boon, 2006).

From an academic perspective the tinkering mind-

set also stimulates core scientiﬁc activities, such as

raising questions, performing experiments, observ-

ing, interpreting, and theory forming. These skills

come short in curricula that are dominated by courses

that focus on teaching existing theory, but not how to

go beyond or how to apply it. Tinkering requires these

activities on a small scale (depending on the level of

material provided) and trains them. Especially, set-

ting up a meaningful experiment also requires tinker-

ing. Therefore, we consider tinkering as an essential

part of academic education, next to science. To go

even a step further on the perspectives on engineering

and academia, in (Libow Martinez and Stager, 2013)

(p41) the claim can be found that tinkering is exactly

how real science and engineering are done.

From a teaching perspective we understand tin-

kering as a mindset for learning (Libow Martinez and

Stager, 2013). For the students, the goal of the tinker-

ing process is to create an original prototype. Our, the

teachers’, goal is in the ﬁrst place that the students get

Tinkering in Informatics as Teaching Method

451

familiar with the material of the toolbox and learn to

apply it. In the second place, we intend it as training

of their serendipity and creativity, as well as their abil-

ity to reﬂect. i.e., to internalise the tinkering mindset.

A tinkering approach supports ownership. As sug-

gested in (Savery and Duffy, 1995), learners must

have ownership of the learning or problem-solving

process as well as having ownership of the problem it-

self. Taking own decisions is a cornerstone of owner-

ship and, with it, motivation. This is also one of the ar-

guments in project-based learning (Savery and Duffy,

1995; Savery, 2006): students deciding about their

own process and solutions have a more active learning

experience. Often, classical teaching is about telling

students solutions to problems they do not have. Ac-

cordingly, the understanding of the solution and its

peculiarities is not deep. Instead, having a problem

gives a completely different understanding of the pos-

sible solutions and their different qualities. Tinkering

even allows to deﬁne the own problems, and also how

to solve them. Problem, solution and the process are

in the hands of the student, the setting is what has to

be provided by the teaching environment.

Another factor in the teaching perspective is the

possibility of making mistakes. Understanding the

nature of mistakes can be very effective for the learn-

ing process. In contrast, in classical teaching making

mistakes has no single positive connotation. In tinker-

ing the whole concept of iteration is built on mistakes

and improvement, thus also here tinkering contributes

to a better learning process.

Obviously, tinkering also is an excellent approach

to cope with diversity: within the setting given, stu-

dents can choose their problems individually, as well

as their speed, experiments, depth, horizon etc.

From the life long learning perspective self-

directedness is a main ingredient (Loyens, 2008). As

an element of tinkering will be a crucial method to

master new technologies and explore its potential.

Due to fast technological progress and severe global

problems, students and the whole society has to keep

learning all life. School and universities are not the

places where students can learn everything they need

later in life. Education here can strive for giving a

basis and teach students to ﬁnd their own ways and

methods of learning, and foster serendipity. In the

context of informatics, there is continuous innovation

in languages, paradigms, and platforms. A student

having internalised a tinkering mindset will always

ﬁnd her/his way in these new developments.

Figure 1: The iterative process of tinkering prompts explo-

ration of the toolbox.

3 PRINCIPLES OF TINKERING

EDUCATION

In this section, we ﬁrst describe shortly the ingredi-

ents of a tinkering setting in general. While we use

these ingredients for tinkering approaches in several

courses, this paper focuses here on a ﬁrst-year course

on algorithms. For this course, we describe the imple-

mentation chosen in the next section.

Setting up education with a tinkering approach

can be done by following a number of principles, here

considered in a broad context of application domains.

Building upon results of (Mader and Dertien, 2016),

we consider the principles of tinkering education con-

sisting of the following:

The Seed. A stimulant serving as a starting point.

This may be a ﬁrst goal, it may be a toolset or mate-

rial, or a theme (e.g., tinkering with time). The nature

of a seed depends very much on the maturity of the

participants: those used to a tinkering mindset need

less, e.g. just a new building block, than beginners in

tinkering.

The Discovery. The self-deﬁned outcome of the

process. It may be a new product or prototype, or a

new way of doing things, a new concept or the (even

intended) learning outcomes proposed by the teacher

or facilitator.

The Toolbox. It includes physical or conceptual

building blocks, but also skills, knowledge and tem-

plates. A toolbox is for one part provided in a dedi-

cated tinkering session, for another part it is the expe-

rience and knowledge an individual participant brings

in.

CSEDU 2020 - 12th International Conference on Computer Supported Education

452

The Process. This is the path from the seed to the

discovery, guided by the tinkering mindset, it is a

hands-on approach, playful and driven by serendipity,

performed in an iterative way.

The Facilitators. Are guarding the process and the

mindset, set the mood, introduce the seed, keep the

threshold low and the ceiling high, give feedback,

keep the ﬂow, and stimulate reﬂection as an impor-

tant part of the learning process (Tawﬁk and Kolod-

ner, 2016).

The Playground. Although tinkering can be

viewed as a mindset (Libow Martinez and Stager,

2013) - as activity or process it is usually bounded

in time and place. With the playground, the situat-

edness of the activity is deﬁned. For example, the

environment where tinkering takes place should be

supportive and inspiring (Doorley et al., 2011).

Figure 1 illustrates the iterative nature of the tinker-

ing approach. The process is driven by core tinkering

activities that use trial and error to explore and ex-

tend the toolbox. Prior seeds and discoveries become

part of the toolbox, new iterations are fuelled by seeds

emerging from prior discoveries through applying the

toolbox, thus closing the loop for the next iteration.

4 CHOICES AND

IMPLEMENTATION

In this section, we describe the choices taken to im-

plement the principles of tinkering education men-

tioned above in an informatics context. We use tin-

kering in a number of different courses; for this paper,

we focus on a course on algorithms, offered at the end

of the ﬁrst year of the programme.

The Seed. The programming environment chosen is

Processing, a language built on Java, and originally

developed for artists and designers. While classical

concepts of programming languages are present (data

structures, object orientation, recursion, etc.), Pro-

cessing is tailored for graphical output and user in-

teraction. Both of these ingredients make it an attrac-

tive programming environment for education: code

produces visible output and interaction can easily be

added, which can be rewarding for the student from

the ﬁrst exercises on, and has certainly a motivating

effect. From a teaching aspect, the language offers

the concepts relevant at this stage of informatics lec-

turing. It comes with a great number of examples that

demonstrate the scope of applications and stimulate

to set levels.

The Discovery. The students have to deﬁne their in-

dividual design goals for their ﬁnal assignment - they

do not have to solve a given problem. The framing

of the individual design goal is such that they have to

write their own program using elements from differ-

ent sets of building blocks, i.e. algorithms that were

covered in the course. From a given set of building

blocks treated in the course, they may choose a subset

for their ﬁnal assignment, or use other algorithms of

comparable complexity. Moreover, their program has

to satisfy given rules of programming style and com-

plexity. The program is assessed during an individ-

ual oral exam at the end of the course. The students

know this setting from the beginning of the course.

They can start from the beginning with ideation and

use (almost) each building block they create during

the course for their individual end assignment. The

assignments are given each week allow for some free-

dom in the solution, e.g. the assignment attach your

particle system to a moving object lead to solutions

consisting a ﬂying rocket with smoke as particles, and

of a unicorn with glitters as particles, both equally

good.

The Toolbox. The toolbox consists of a range of al-

gorithms that are arranged in different topics, such as

“ randomness”, “ forces”, “particle systems”, “mass-

spring-damper systems”, etc. Roughly once per week,

a new topic is introduced, and a number of assign-

ments are included for each topic. Assignments typ-

ically consist of a ﬁxed part (such as to use certain

laws of physics to build code for a catapult) and a part

that can be individually designed (such as the form of

the catapult and the interaction of a user with it).

In addition to programming concepts and design

that have to be covered here, much focus is on an ap-

pealing selection of algorithms, stimulating the play-

fulness. while the individual applications of the algo-

rithms may differ, the students learn, e.g., about how

to structure code into different classes, dependencies

between classes, methods and value passing, and get a

ﬁrst idea of complexity when a huge number of parti-

cles needs to be supported by array management, and

particle systems. Each assignment done is considered

as a building block for the end assignment, where stu-

dents also can select from different topics.

The Facilitator. These are here the lecturer(s) to-

gether with a great number of student assistants. The

student assistants are students of higher years, some

of them already ﬁnished with our study programme

Tinkering in Informatics as Teaching Method

453

and following a different master programme. They

are all experienced, with toolbox and mindset, and

most are truly enthusiast about programming and the

tinkering way of working. In contrast to other infor-

matics courses, we always have a lecturer present, and

sufﬁciently many assistants to prevent too long wait-

ing periods, which might be demotivating to students.

Grades are only given for the end assignments. Dur-

ing the course only material is provided, explained,

feedback and help given.

The Playground. Tinkering takes place in time and

space. Typically in education, this is bound to lecture

rooms and scheduled hours. In general, a stimulat-

ing environment with ﬂexible working space would

be ideal. Practically, we get only minimal require-

ments fulﬁlled such as that each student is physically

accessible by a student assistant or a teacher.

Next to these general ingredients of a guided tin-

kering process identiﬁed, we found a couple of con-

cepts useful extending the line of education described

so far.

Ambition Levels. As described above, the varia-

tion in the student population is very high, ranging

from students with no programming experience be-

fore entering university, to students who have already

completed a semester or bachelor in informatics or

another technical programme that includes program-

ming courses. The other dimension of variation is

the ambition level. Some students try to optimise

their work effort to just pass the course. Motiva-

tion can reach some of them, but many stick to their

mindset. Others are highly motivated, independent

of their prior knowledge. Experience from more tra-

ditional courses shows that often the little ambitious

students consume a lot of attention to bring them to

a course average, and they determine the overall level

and speed.

All these observations together led to the introduc-

tion of three ambition levels, where each student can

choose which level she or he wants to follow. Assign-

ments are offered for each ambition level. With the

lowest level assignments, a student can pass, with the

highest level they can reach the highest grade, with

the medium level in between. As said above, the as-

signments specify the building blocks the ﬁnal assign-

ment is expected to include, which is for a major part

evaluated according to the level of the building blocks

used. For students, this choice supports ownership,

as they have made the decision themselves. Further-

more, it provides a clear setting for both the students

and the lecturer, by explicitly managing expectations,

and thus it reduces discussions about effort and am-

bition. A third positive effect is that for the very

good and/or ambitious students, ﬁrst, challenges can

be provided that increase also their motivation, and

second also more time for attention becomes avail-

able.

Personal Support. Only a few lectures are given,

just for the introduction of each new topic, which are

in most cases no longer than 20 minutes. The rest

of the time students have time to work on the assign-

ments and to get help when needed. A team of student

assistants and the lecturer(s) are busy with individual

support. The driving insight here is that students have

a much deeper understanding of a solution when they

struggle with a problem to solve, than when getting a

solution without understanding the problem in depth.

Additionally, as described above, the variation in the

student population is very high, which also shows in

the different speed students work. With individual

help, we can much better adapt to the individual speed

and needs of the students.

The tinkering approach was also applied to the

two programming courses that precede the course

used for the discussion in this chapter. The ﬁrst in-

troductory programming course includes two main it-

erations. Students are given as ﬁrst seed the goal

to make an interactive creature. Every week a new

concept is introduced to the toolbox, from variables,

loops, decisions to classes and objects. The second

iteration is the ﬁnal project. The seed is the goal to

animate an artwork, each year with a new theme. For

the ﬁnal students have to use everything that was in-

troduced previously, with the addition that the new

animation should incorporate proper composition of

objects. The second course adds various aspects of

physical computing to the toolbox, and the seed de-

ﬁned by a theme, for example, to build a musical in-

strument. This illustrates that the tinkering approach

can be implemented in different contexts for students

of different maturity.

5 EVIDENCE AND

OBSERVATIONS

The impact is about didactic methods, about the moti-

vation of students, ownership, dealing with diversity,

and ﬁnally about getting talented female students on

board.

Learning Tinkering. In general, while tinkering

seems to be a natural way of getting to know the

material, it is not a way students are used to when

CSEDU 2020 - 12th International Conference on Computer Supported Education

454

they enter university. Seemingly, school education fo-

cuses much more on the reproduction of content than

curiosity-driven learning. As a consequence, students

have to learn tinkering. This does not take only one

informatics course but includes several courses and

projects. The course in the focus of this paper is the

third course in the programme where tinkering con-

cepts are used. Students who are motivated for learn-

ing, have taken up the concept.

Diversity in Working. During the tutorials the vari-

ation in speed, strategies in accessing the assign-

ments, learning styles (ranging from using video tu-

torial, existing examples, tinkering, to discussion with

fellow students), is overwhelming, supporting the ap-

proach that respects the diversity of the students.

Variety in Solutions. The solutions students

present in the end also show a huge variation. Popu-

lar themes are storytelling, games, or simply aesthetic

scenarios, most often a mixture of two or three of

these elements. It is not uncommon that novel types

of solutions emerge in a course.

Plagiarism. The ﬁnal assignments are all differ-

ent, which suggests little copying behaviour of stu-

dents. Checked with a plagiarism detector we found

no shared code between students, except for the pairs

that worked together, and sources allowed. However,

there are still students who let friends or family write

their code when they ﬁnd it too difﬁcult. Some also

do not add sufﬁcient own content beyond the sources

allowed.

Individual Support. Students often do not ask for

help even if they got stuck. Waiting for questions of-

ten does not provide much interaction with a lecturer

and student assistants. Instead, walking around and

asking students what they are busy with results often

in interesting and fruitful discussions.

Maturity in Responsibility. While all students are

positive about the possibilities in choices of ambition

levels, speed and order of doing assignments, and the

possibility to get help, not all of them can really cope

with the responsibility well. Some students use the

freedom to postpone and trying to catch up in the last

weeks achieving overall weaker results. Among the

students being present and working actively, a higher

percentage of female students is present than male

w.r.t. the distribution in the whole population. It

seems that this way of learning ﬁts better to their way

of working.

Debugging. Most students show poor skills in de-

bugging. Typically, they build a piece of software and

call for help when it is not working.

Quality of Results. Considering the results of the

course in the past 3 years (see table 1), an overall fail-

ure rate below 20% and seem to be in the line of gen-

eral introductory courses in Informatics (Bennedsen

and Caspersen, 2007). In the years analysed, female

students, have a slightly lower failure rate than males.

The average grades between male and female stu-

dents seem not to differ signiﬁcantly. For a number of

reasons, this is a very positive result. First of all, girls

and women, on average, have a lower self-image con-

cerning their abilities in STEM subjects, which is es-

pecially manifest in the Netherlands (C. Booy and van

Schaik, 2012) (which is the origin of the majority of

the students). A low self-image, typically, has also ef-

fect on the results achieved in these subjects (C. Booy

and van Schaik, 2012; Rubiol et al., 2015). If there is

no gender gap observable for the results, the teaching

approach has compensated for that. In (Rubiol et al.,

2015) the authors report that a physical computing ap-

proach closed the gender gap in their programming

course. By similar reasons, we assume that the tin-

kering approach with playful algorithms increases the

motivation of female students, and provides a context

where they can give meaning to the learning content,

which is especially relevant for women in the STEM

context. This assumption is conﬁrmed by a testimo-

nial of a female student (see next item below). A sec-

ond reason, why the absence of the gender difference

in grades is a positive result, is the prior knowledge.

The percentage of girls choosing for a STEM focus

during school in the Netherlands is lower than the one

of boys (27% versus 42% for boys, (C. Booy and van

Schaik, 2012) p22). Accordingly, it could be expected

fewer girls enter the university with a STEM back-

ground. For programmes where only a few women

enrol, these few are better than the average among

girls. In our case, the percentage of female students

is about a third, which suggests that much more of

the average background is represented here. Still, we

cannot observe a possible inﬂuence of a lower STEM

background in the grades.

On a more qualitative level, we have the following

observations: Students with little or no prior knowl-

edge in programming can achieve excellent results.

Surprisingly, students with a background in informat-

ics (e.g. a semester or a bachelor in informatics)

come up with well-structured programs, but in av-

erage little creativity. Obvious was that the students

with successful results were driven by enthusiasm to

make their self-chosen concept work. These con-

Tinkering in Informatics as Teaching Method

455

Table 1: Gender separated results for the course “Algorithms”.

Year Gender

Number

Students

Precentage

of Total

Average

Grade

Precentage

Failed by

Gender

Precentage

Failed

Total

2015

f 27 34% 7.3 0%

13%

m 53 66% 6.4 19%

2016

f 27 32% 6.9 7%

m 57 68% 7.4 7%

2017

f 32 42% 7.3 3%

m 45 58% 7.4 13%

2018

f 45 43% 6.9 4%

m 59 57% 6.8 8%

2019

f 36 34% 6.5 17%

19%

m 71 66% 6.6 20%

cepts included game elements, aesthetic animations,

storytelling, or pure quantity, all outside the scope

of programming. Surprising were also a number of

assignments constructed from simple building blocks

but beautifully arranged to complex and sophisticated

combinations with original concepts of interaction.

Testimonials of Students. Maaike (second-year

student): Before I began the study Creative Technol-

ogy I had never programmed before. I started with

programming at a low level in the ﬁrst year and it

builds up to harder assignments such as simulating

physical systems. When I started programming I re-

alised that it is not just for male students and that it is

really enjoyable. With the tinkering method Creative

Technology offers, I learned to think out of the box,

solve a lot of my own problems through systematic

debugging and to make more creative programs.

Margot (master student after having passed the

Creative Technology bachelor, also working as a soft-

ware developer next to her study): I always felt that

the goal of our programming courses was not teach-

ing us how to best program; instead the goal was to

learn how to create visuals for your digital products

and art installations. Programming just happened to

be the right tool. I think that approach still sets me

apart when I now collaborate with traditionally edu-

cated computer scientists, and with designers.

You don‘t learn an algorithm just because the as-

signment tells you to – you learn it because it helps

you show something on screen. Seeing your code

turn into for example physics, natural looking ﬂocks

of birds, and modern works of art is very motivating.

It‘s like learning how to translate aspects of the world

around you to code.

6 DISCUSSION

With our approach, we reach many students, but not

all of them. Still, there is a number who just want

to pass the course with minimal effort, who are not

taking the step to playing and tinkering, and often

do not show up for lectures. We expect that we still

could reach some of them using different, or addi-

tional strategies.

Teaching students debugging remains an ongoing

problem. In interviews student assistants, also from

informatics courses, report this as a basic shortcom-

ing in the skill-set of most students. However, for

a proper tinkering process identifying the errors and

bugs, reﬂecting and learning from them, and improv-

ing on them are core elements.

The toolbox for a course does not contain a ﬁxed

collection of building blocks. For the course on al-

gorithms, often enough solutions for assignments pop

up on the internet about two years after the assign-

ment came into the course. This means that there is

a need for a continuous update, ﬁnding or developing

new, appealing assignments or increase the level of

difﬁculty for some assignments.

The main question, however, is, how tinkering can

be realised in a setting where teaching goals have to

be achieved and work graded, in a given time frame,

contrary to the openness of the process. We are try-

ing to circumvent this contradiction by giving open

assignments with the requirement to use a subset of

the building blocks of the toolbox, and teachers and

students assistants steering in the direction of quality

of results or giving students a choice in the ambition

level.

Teaching tinkering to students is a time-intensive

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456

process for lecturers and student assistants. Individ-

ual trajectories require individual feedback, and, in

the end, individual evaluation. It is obvious that this

approach does not scale up straightforwardly. When

the team of teaching assistants is growing, also con-

sistency is becoming an increasing problem. To over-

come this, we currently are working on a tool that sup-

ports teaching assistants in giving feedback and shar-

ing it with each other.

Seeing surprising work of students is very reward-

ing. Also, seeing students who are surprised about

their own achievements makes this time-intensive

way of teaching worthwhile. In the ﬁnal projects of

the study programme, we also see that many students

have a tinkering mindset, even if for some of them this

was not obvious in the preceding courses.

The promising results and observations presented

in the previous section, like the quality of results, the

possibility to cater populations with a diverse back-

ground, the varieties in solutions and working styles

observed, plagiarism nearly eliminated, convince us

that this is the right approach to follow, keeping work-

ing on shortcomings.

7 CONCLUSION

In this paper, we reported on our approach to apply-

ing a tinkering mindset in informatics teaching. We

elaborated the role of tinkering in teaching in general,

at academia and for engineering. For the implementa-

tion of tinkering in teaching, we identiﬁed key ingre-

dients, such as the toolbox, the seed, the design goal

and the facilitator. While we use this schema or ele-

ments from it in a number of courses, we focused in

this paper on the implementation in a ﬁrst-year course

on algorithms. The results experienced so far are very

promising, little drop-out, gender-differences in grad-

ing are disappearing, no plagiarism due to individual

assignments, and a number of very enthusiast students

with surprising results.

Still, there are challenges to work on, like get-

ting more students in a mindset of playing. The next

step we will address is the scalability of the approach:

the process is feedback intensive which cannot be ex-

tended straightforwardly to higher numbers of stu-

dents. Currently, we are identifying elements of feed-

back that can be tool-supported, giving more space

for the inherently necessary feedback on the individ-

ual learning processes.

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