Peer-learning and Talents Exchange in Programming: Experiences and

Challenges

Corinna Kr

ohn and Barbara Sabitzer

School Of Education, Johannes Kepler University Linz, Altenbergerstraße 69, Linz, Austria

Keywords:

STEM, Programming, Peer-learning, Talents Exchange, COOL, Neurodidactics.

Abstract:

“Programming is difﬁcult” society tells us. However, this is only partly due to the subject itself. Very often,

the tasks in software development are related to mathematical problems, which themselves are considered as

difﬁcult because they have no relation to the student’s world of experience. Programming exams contribute

to high drop-out rates and often students choose a different subject or even stop their university education. A

possible gap could be closed by supporting beginners with the help of the COOL (cooperative open learning)

concept that has already found its way into one course in the Business Informatics bachelor program at our

university. In addition to this mandatory class, a peer-teaching course has been establishes. Students meet on

a weekly basis to work together on the problems of software development based on the COOL concept. Two

PhD candidates supervise the students, although they only act as tutors and do not provide any solutions. The

approach described above is now being carried out for the third semester in a row and the drop-out rate has

been reduced. Of course, further observation must be done to be able to draw concrete conclusions.

1 INTRODUCTION

Whether at school or university, learning to code can

be very difﬁcult. High drop-out rates and a major

gender-gap verify these experiences. There already

exist several different approaches to support begin-

ners like tutoring systems or extra training courses but

nonetheless there are still many students that do not

pass exams.

Figure 1: An information processing model (Wolfe, 2010)

adapted by the authors.

One possible way to improve the learning out-

come is to consider how the brain works and ap-

ply teaching methods investigated by Neurodidac-

tics (Sabitzer et al., 2020). Neurodidactics combines

Neuroscience and Didactics and presents a relatively

young science that is yet not very established. Of

course Neurodidactics cannot be transferred to higher

education lessons one to one but it can give propos-

als for effective learning and teaching and can pro-

vide information of how the brain works (Sabitzer and

Pasterk, 2015).

One of the key ﬁndings from Neurodidactics is

that knowledge cannot be transferred but has to be

newly created in each student’s brain. That proposes

that the learning process is an active one which also

agrees with ﬁndings in pedagogy and constructivism.

Our memory is a highly complex system (see ﬁgure

1) that also includes unconscious processes. The hu-

man long-term memory seems to be static but in fact

is dynamic which concludes that each recall causes a

re-encoding (Sabitzer, 2011).

Teaching methods suggested for effective learning

and stimulating the long-term memory are the follow-

ing (Sabitzer et al., 2013):

Discovery Learning: rests on ﬁnding solutions on

your own and not getting instructions from teach-

ers.

Social or Observational Learning: is based on so-

called mirror neurons that are active when stu-

dents observe or imitate others.

466

Kröhn, C. and Sabitzer, B.

Peer-learning and Talents Exchange in Programming: Experiences and Challenges.

DOI: 10.5220/0009472004660471

In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 1, pages 466-471

ISBN: 978-989-758-417-6

Learning by Teaching: Students slip into the role of a

teacher and explain things to others. This method

restarts the memory process and enhances reten-

tion.

Learning by Doing: inspires the student to be more

active and is more effective than teacher-centered

training.

COOL – Cooperative Open Learning: was initiated

by a team of Austrian teachers and is described

in one of the following sections.

This paper describes the evolution of a tutoring

group to a peer-teaching class for programming be-

ginners based on COOL concepts at our university.

Sections 2 and 3 describe the underlying theoretical

ideas, whereas section 4 concentrates on the detailed

explanation of the development process of the differ-

ent approaches to help students. It also presents expe-

riences of students and teachers and concludes with

ideas to further integrate the tested concepts in other

university classes.

2 PEER-LEARNING AND

ASSESSMENT

2.1 Deﬁnitions

Peer-learning is the use of teaching and learning

strategies in which students learn with and from each

other without any direct intervention of a teacher. Ex-

amples of peer-learning are student-led workshops,

team projects, study groups or student-to-student

learning partnerships. In reciprocal peer-learning

students act as both teachers and learners (Boud et al.,

1999).

In contrast to peer-learning peer-teaching also in-

volves advanced students in class who act as tutors.

The term “collaborative learning” is used to refer to

peer-teaching as well (Boud et al., 1999).

In Pair programming two programmers work to-

gether at one computer on the same algorithm. One

person is the “driver” whereas the other is the “nav-

igator”. The driver types at the computer and per-

forms the physical input while the navigator observes

and comments the role of the driver. Literature rec-

ommends to alternate roles during the software devel-

opment process. Research has shown that this con-

cept improves design quality, produces higher qual-

ity code, and improves communication among team

members. But it has to be mentioned that pair pro-

gramming only works if people are matched with a

partner of comparable ability (Han et al., 2010).

2.2 Why Focusing on Peer-learning?

There are pragmatic as well as principled reasons for

concentrating on peer-learning at university. One ma-

jor problem of universities is lack of staff. As peer-

learning and peer-teaching both concentrate on stu-

dents, universities can easily increase the number of

students in classes without more input from staff. Of

course, it is also being argued that collective forms of

learning may better suit women and minority groups

than the traditional individualistic teaching methods

because they concentrate on cooperation rather than

competition (Boud et al., 1999). Considering the

gender-gap, more and more of these teaching and

learning concepts should be taken into examination.

Another important advantage is to foster students’

practice in planning and teamwork because in peer-

learning it is necessary to work together. It also en-

gages reﬂection as well as exploration of ideas when

the teacher is not immediately present. Moreover,

students achieve more practice in the communication

in the subject area compared to traditional teaching

methods (Boud et al., 1999).

2.3 Assessment

When considering peer-learning, assessment needs to

be taken into account. Assessment works as a form

of academic currency and provides compensation for

efforts of students. Of course it is also important not

to misuse assessment as an instrument to get students

to join peer-learning activities (Boud et al., 1999).

Assessing peer-learning has to be done with cau-

tion. The teachers will not be able to assess the out-

come of peer-learning groups directly as it restricts

the beneﬁts of peer-learning itself. But there is a tradi-

tion of individual and competitive assessment in most

universities which implies competition against others,

rather than cooperation and collaboration is seen as

cheating (Kohn, 1992).

Boud et al. recommend the following forms of

assessment when peer-learning or peer-tutoring is in-

volved (Boud et al., 1999):

Group Assessment: If teamwork and collaborative

working is valued then each member of the

group should receive the same grade. To prevent

freeloading, the group assessment could also be

the sum of each member’s assessment.

Peer Feedback and Self-assessment: Switching out-

comes of given tasks and grading classmates (or

oneself) is a common approach

Assessment of Process: shifting the main focus from

the learning outcomes towards the learning pro-

cess

Peer-learning and Talents Exchange in Programming: Experiences and Challenges

467

Negotiated Assessment: all parties involved agree on

the assessment process

Use of Cumulative Rather than Weighted Assessment:

If the weighting of one element of the course is

very low, students tend to ignore it or put little

effort in it. Peer-learning is often one of those

aspects with little weight. But if it is only possible

to pass the course by completing all of the tasks,

motivation could rise to attain positive results in

the smaller tasks as well.

3 COOL CONCEPTS: COOL

INFORMATICS AND COOL

PROGRAMMING

COOL is an acronym for Cooperative Open Learn-

ing, Computer-(science)-supported Open Learning,

as well as cool with the meaning of interesting or

“in” (Sabitzer, 2014). The COOL concept was de-

veloped by Austrian teachers in 1996 and then ex-

tended to COOL Informatics which was ﬁrstly pre-

sented in the habilitation thesis of one of the authors

(Sabitzer, 2014). The four main principles are Dis-

covery, Cooperation, Individuality, and Activity (see

ﬁgure 2). Each of those concepts is then related to

various teaching and learning methods that rest upon

Neurodidactics (Sabitzer and Pasterk, 2015).

COOL Programming concentrates on these de-

scribed neurodidactical concepts and was installed in

one of the beginners programming classes in our uni-

versity’s Business Informatics bachelor program. The

lessons as well as tasks were designed based upon the

following ﬁndings:

1. New content is always built on existing knowl-

edge and learning occurs through association

(Sousa, 2009) (Spitzer, 2006).

2. Knowledge cannot be transferred but must be con-

structed in each student’s brain (Roth, 2004).

3. Learning occurs through imitation (Sousa, 2009).

4. The brain recognizes and produces patterns, cate-

gories, and rules itself (Spitzer, 2006).

5. The instruction method impacts the retention of

new information (Sousa, 2009).

6. Double-coded is double-saved (multimedia ef-

fect) (Roth, 2004).

Each of the 90-minutes-lessons in the designed course

was divided into three parts: Questioning (10-15 min-

utes), Discovering (10-15 minutes), and Pair Pro-

gramming (60-70 minutes). More precisely, the stu-

dents started with an open round of questions that

Figure 2: Brain-based teaching concepts (Sabitzer, 2014)

adapted by the authors.

they could ask their professor. The next phase was

dedicated to extract structures, rules, and other essen-

tial elements from Java Code by using reading cor-

ners, sample solutions, puzzles (see ﬁgure 3), or step-

by-step examples (see ﬁgure 4). Afterwards students

worked together on various programming tasks, fol-

lowing the rules of pair-programming (Sabitzer et al.,

2020).

Figure 3: Datatype puzzle (Sabitzer et al., 2019).

CSEDU 2020 - 12th International Conference on Computer Supported Education

468

Figure 4: Excerpt from a step-by-step programming exam-

ple (Sabitzer et al., 2019).

4 DEVELOPMENT OF A

PEER-LEARNING/

PEER-TEACHING GROUP

Even though one of the beginners programming

classes switched to the concepts of COOL Program-

ming, there were many other courses that required

support for students who had a hard time starting to

code. More and more students created learning and

tutoring groups on their own but they lacked loca-

tions, a ﬁxed time, and experienced tutors. There-

fore, students came to our department and asked if

we could help them. Consequently, in winter term of

2018 we created a peer-learning group that met each

week in the conference room of our department to dis-

cuss the upcoming programming tasks. One student

who already worked as a software developer in differ-

ent companies supported them as tutor. They met on

a regular basis but the number of students varied de-

pending on how hard the task was or if the exam was

close. Accordingly, some of the students had to alter

programming partners and sometimes there was only

one student and the tutor present.

In summer term 2019 the ﬁrst ofﬁcial peer-

learning class was installed. Students could take that

class voluntarily but still get ECTS for it. In that

semester there were only 8 students who enrolled for

class. Students were expected to actively participate

and attendance was mandatory. The group split up

on their own in various smaller groups – most of the

time pairs – to work together on the given program-

ming tasks. Two PhD-students acted as tutors but did

not solve the coding tasks step-by-step. The problem

with this class was that the students attended three dif-

ferent mandatory programming courses at university.

One was the beginners class from the Computer Sci-

ence bachelor program, the second one was the class

of Business Informatics described in section 3 that

is based on COOL Informatics, and the third group

consisted of students from an advanced software de-

velopment class. The problem occurring was obvi-

ous: whether the students nor the teachers could an-

swer all the upcoming questions with such a variety

of given tasks. But still the survey at the end of the

course showed that all students approved and wanted

the class to be continued. Fortunately, each one of

them passed the ﬁnal programming exam.

The following winter term the class was restricted

to students who were enrolled in either the begin-

ners programming class of the Computer Science or

the Business Informatics bachelor program. The sys-

tem also changed from peer-learning to peer-teaching,

as two advanced students were involved in the same

group. At the beginning 23 students attended the

class, whereas only 14 completed it due to lack of

time or interest. The group was split into three smaller

study groups: one group consisted only of Computer

Science students, the other two of Business Informat-

ics students. Each of the two Business Informatics

groups was assisted by one of the advanced students.

Due to the lack of one more experienced student the

group of students from Computer Science had no des-

ignated external tutor. Of course the two PhD candi-

dates who oversaw the class provided help to each of

the groups but still the main focus was on the peers.

As already described in section 2.3, assessment of

such peer-learning or peer-teaching classes is hard.

As all students of the course participated actively in

class, the teachers decided on assessing them accord-

ing to their attendance. The end results of all the ﬁnal

exams are not published yet and therefore questioning

students about this class has to await the outcome but

we already know that at least half of the group passed.

5 EXPERIENCES OF STUDENTS

AND TEACHERS

The main motivation for changing the ongoing system

of programming education at our university was the

close contact to students. At least once a semester the

professor (and one of the authors) ofﬁcially opens her

ofﬁce for ﬁrst semesters and talks about their prob-

lems and wishes. Students as well as teachers ex-

pressed their experiences towards the implemented

courses:

Peer-learning and Talents Exchange in Programming: Experiences and Challenges

469

5.1 Report of a Female Student

“I started the studies of Computer Science with no

prior programming experiences. As the introductory

software-engineering course started, I soon found out

that I was (1) one of the few students with no prior

programming experiences and (2) one of the very few

girls, which was quite intimidating. Because of the

high demands and rapid pace, students dropped out

week per week. In my opinion, these are the main

reasons for the high drop-out rate:

Collaboration: Students had to complete weekly as-

signments independently. Collaboration was not

allowed and in case of similar codes, the home-

work of all the students involved did not count.

However, especially beginners beneﬁt from col-

laboration and improve their skills by learning

from and talking to each other. It could be ar-

gued, that students can indeed collaborate, they

only have to develop different algorithms. Nev-

ertheless, for students who are also not familiar

with algorithmic thinking, this can be a huge chal-

lenge. And in reality, aren’t software project usu-

ally planned and realized in teams?

Pace and Demands: As a programming beginner, I

needed to dedicate the major part of my time to

this course to be able to keep up the speed. Be-

sides my job and other courses, this meant very

long nights and long weekends. Furthermore, stu-

dents had to positively complete 80% of the as-

signments and at the same time, reach a certain

amount of points. These demands are very hard

to achieve and put a high pressure on the students.

Fortunately, this was the ﬁrst semester, where a

weekly peer-learning group was offered for CS

students of the teacher training program. In these

weekly meetings, we worked on our own algo-

rithms, but in a very supportive environment. In

other words, in case we were stuck, there was a

tutor who led us to the right direction and gave us

very helpful advice. Luckily, in my case it was

worth the effort and I passed the course.

However, success very much depends on external sup-

port. Without professional support, like we had in the

peer-learning group, I would not have been able to

complete the course, which probably would also have

been the end of my computer science career.”

5.2 Report of a Teacher of the

Peer-learning Class

“As a teacher of the new peer-tutoring course, I

learned software engineering the classical way and

was challenged with difﬁcult tasks, an extremely high

drop-out rate and hours of searching through the In-

ternet for answers. Teaching this course was a com-

plete new role for me. As a teacher I usually give

answers to questions. In this course I had to help the

students to ﬁnd the answers on their own. This lim-

ited way to support the students worked better than

expected. It showed that a lot of difﬁculties were

the results of theoretical problems like “how does a

list work”. Furthermore, students started to help each

other, since they worked on the same task, the prob-

lems were quite similar. The course was offered to

students of two different Computer Science classes,

the basic one and the advanced. During the course I

realised that it is very challenging to stay up to date

with all tasks and to switch during the lesson in an-

swering questions to the basic and the advanced tasks.

Altogether, the new way in teaching showed me, that

students can help their fellow students in a very ef-

fective way and they have a lot of patience in helping

each other.”

6 CONCLUSION AND OUTLOOK

In this paper we have presented the journey from a

tutoring group to a peer-teaching course in program-

ming. We had high drop-out rates in our program-

ming classes for beginners at our university. More and

more students created learning groups on their own

but they lacked experienced tutors. At ﬁrst attempt,

we installed a peer tutoring group that met weekly. In

summer term 2019 the ﬁrst peer-learning class based

on COOL Informatics and COOL Programming was

created that could be visited voluntarily. After two

semesters, we can already identify improvement in

terms of drop-out rates, exam attendance, and home-

work submissions.

We have identiﬁed the need for more collabora-

tion than just working alone on tasks therefore com-

petition has to be decreased. One remaining question

is the assessment of the programming tasks. If stu-

dents work in pairs or in groups on one project they

will hand in similar or even equal solutions. How can

a tutor distinguish between group work and cheating?

In the upcoming months, we will concentrate on

evaluating the survey and the exam grades to further

investigate the needs of programming beginners. We

also need to collect more data about the background

of our students, as their personal factors inﬂuenced

their learning outcomes. We do not have enough in-

formation to answer questions like:

1. How do prior programming knowledge and drop-

out likelihood correlate?

CSEDU 2020 - 12th International Conference on Computer Supported Education

470

2. How does the type of high-school-diploma and

drop-out rate correlate?

3. Does peer-learning or peer-teaching reduce the

gender-gap?

Summarizing, we want to expand our concepts to ad-

vanced programming courses and provide more than

one peer-teaching class.

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