Computer Science Attitude as a Descriptor to Understand Inclusion in

Non-Conventional Learning Experiences

Ilenia Fronza

1 a

and Luis Corral

2 b

Free University of Bozen/Bolzano, Italy

ITESM Campus Queretaro, Mexico

Keywords:

Coding Camps, Inclusion, Computer Science Attitude (CSA), Non-Conventional Learning Experience.

Abstract:

Non-conventional learning experiences (e.g., hackathons and coding camps) are increasingly popular to

broaden participation in computing. It is relevant to analyze the proﬁle of participants of non-conventional

learning experiences to outline better whether they efﬁciently attract proﬁles that can enrich future profes-

sional proﬁles in Computer Science (CS) with an inclusive and diverse approach. Picking up from that need,

this paper attempts to shed light and better understand the original attitudes toward CS that participants display

upon joining an informal CS-relevant educational activity. To this end, we analyze, as a compelling case, the

participants’ attitudes of two coding camps carried out recently. This analysis permits us to discuss what type

of students are attracted by these events, provide a more detailed analysis of the participants’ proﬁles, and

better understand whether informal educational events effectively thrive diversity in science. The compelling

case presented in this paper promotes discussion and raises questions for future research.

1 INTRODUCTION

Attracting curiosity and prospective career develop-

ment on STEM topics (in particular, Computer Sci-

ence) has been a genuine concern that, in recent times,

has gained major attention from education, govern-

mental, and industrial sectors. The growing need

for well-trained professionals to sustain the demands

of the sector and the lack of diversity in computing

(including gender, racial minorities, people with dis-

abilities, and other dimensions of diversity (Rankin

and Thomas, 2020)) motivate the wide variety of

outreach activities that attempt to broaden participa-

tion in computing (DeWitt et al., 2017; Liebenberg

et al., 2015) and increase its popularity (Decker et al.,

2015; Champagne, J., 2016). In particular, non-

conventional learning experiences (i.e., experiences

that do not necessarily issue a diploma, degree, or

record) provide curricular ﬂexibility, appropriate staff

capacity, infrastructure access, and access to effec-

tive programs. Examples are camps, hackathons, and,

in general, “short-time collaborative innovation activ-

ity focusing on some use of computer skills” (Porras

et al., 2019).

https://orcid.org/0000-0003-0224-2452

https://orcid.org/0000-0002-9253-8873

Getting closer to younger generations and protect-

ing minorities is a job that can trace its origin to the

very roots of formal education. The K-12 time span

is an ideal period to build excitement with computing

(Solyst et al., 2022) since research shows that K-12

learners develop life aspirations that eventually are

translated to career choices and selection of majors

(Jackson et al., 2011). Thus, the design, implemen-

tation, supervision, and follow-up of formal or non-

conventional learning experiences in this early phase

of education should be taken very seriously, as they

may lay foundations or create seed effects toward se-

lecting a relevant major.

The real impact of non-conventional learning ex-

periences on very young students can be understood

only with time. Analyzing, in the long, run the ca-

reer choices that participants eventually make is of

great relevance to research and understand better the

growing offer of non-conventional learning experi-

ences, as well as their impact on the attitudes and per-

spectives that participants display. Moreover, it is of

great relevance to analyze the proﬁles of participants

taking part in these experiences to outline more pre-

cisely whether these learning experiences efﬁciently

attract diverse proﬁles that can enrich future profes-

sional proﬁles in Computer Science (CS) with an in-

clusive approach.

Fronza, I. and Corral, L.

Computer Science Attitude as a Descriptor to Understand Inclusion in Non-Conventional Learning Experiences.

DOI: 10.5220/0012616800003693

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 2, pages 509-516

ISBN: 978-989-758-697-2; ISSN: 2184-5026

509

In this paper, we present the experience of a com-

pelling case by discussing the insight gained running

a coding camp targeted at high school students to indi-

cate how it identiﬁes and promotes questions for fu-

ture research to answer. The coding camp has been

offered for the last ten years (including two online

editions), and through that experience, several aspects

and traits of its execution can be discussed. In par-

ticular, we elaborate on the participants’ attitudes to-

wards CS before participating in the coding camp. In

this way, we lay the basis for discussing what type

of students are attracted by non-conventional learning

experiences to understand better whether they are ef-

fective in thriving diversity in science, in particular,

CS and software development.

The rest of the paper is organized as follows: Sec-

tion 2 provides background and an overview of related

work; Section 3 presents the experience of a com-

pelling case; Section 4 reports the results of the anal-

ysis performed during the considered case; Section

5 discusses the presented case and Section 6 raises

questions for future research to answer.

2 STATE OF THE ART

Inclusiveness is considered a crucial characteristic for

experiences that, through an informal educational en-

vironment, make an intent to attract talent to scientiﬁc

and technological subjects (Warner and Guo, 2017)

because participants can improve their skills, feel part

of a community, build their network (Chen and Kelly,

2013), and have an impact in communities (Mtsweni

and Abdullah, 2015).

Non-conventional learning experiences (such as

hackathons and coding camps) commonly attempt to

be inclusive and serve as an instrument of science and

technology outreach, including specializing in attract-

ing minorities and fostering diversity. While diver-

sity improves, in general, the learning environment

(Paloheimo and Stenman, 2006), it is especially ben-

eﬁcial for underrepresented and non-traditional stu-

dents (Hardin, 2021), who may ﬁnd technology a

tool to grow their capacities, extend a network, and

grow personally and professionally toward a career

of high impact and added value for themselves and

their communities. Nevertheless, research shows that

hackathons have had limited participation from un-

derrepresented groups and non-traditional students

(Kos, 2018).

Most existing studies focus on logistics guidelines

for non-conventional learning experiences (Fronza

et al., 2020; Gama, 2019; Nandi and Mandernach,

2016; Lara and Lockwood, 2016; Happonen et al.,

2020; Schulte and Knobelsdorf, 2007). The studies

focusing on inclusiveness mainly consider registra-

tion numbers (Hardin, 2021), while few studies col-

lected data on the experiences the participants had

and the barriers they faced. For example, participants

consider extra-curricular learning experiences more

open and inclusive (Thayer and Ko, 2017). How-

ever, stereotypes of nerdiness and intelligence exist

(Thayer and Ko, 2017) as in other computing educa-

tion contexts (Lewis et al., 2016); moreover, partici-

pants need considerable perseverance and conﬁdence

(Thayer and Ko, 2017) and educational beneﬁts are

unequal between genders (Hardin, 2021).

Regarding gender-related issues, Kovaleva et al.

focused on the lack of gender diversity in hackathons:

the authors summarized the literature-based solutions

and suggested female-inclusive measures to improve

gender diversity (Kovaleva et al., 2022). Several

factors affect participation and success in computing

programs, including background experience (Biggers

et al., 2008; Wilson and Shrock, 2001) and sense of

belonging and stereotypes (Hardin, 2021; Cheryan

et al., 2013; Lewis et al., 2016).

COVID-19 resulted in using technologies and in-

novative pedagogies to facilitate the transition to an

online environment (Siegel et al., 2021). As a re-

sult, some research works have explored remote non-

conventional learning from different angles. For ex-

ample, researchers analyzed how these events involve

less-conﬁdent students (so often female, especially

in CS) (Davies, Madeleine, 2021) and foster profes-

sional skills and collaboration (Steglich et al., 2021;

Gama et al., 2021; Afﬁa et al., 2022) while keep-

ing an element of fun (Fronza et al., 2022) and be-

ing culturally responsive (Solyst et al., 2022). Other

works focus on the effect of hackathons and coding

camps on the perception of CS (Lusa Krug et al.,

2021; Ma et al., 2022) and self-efﬁcacy in commu-

nication (Begel et al., 2021).

However, research reported several issues that

need to be solved when running non-conventional

events online, including communication issues (Herb-

sleb and Moitra, 2001), lack of a sense of belong-

ing (Mooney and Becker, 2021), lack of engagement

(Powell et al., 2021), fatigue due to prolonged com-

puter use (Yousof et al., 2021), and reproducing the

face-to-face dynamics (Fronza et al., 2022). A recent

systematic review collected the best practices for or-

ganizing online/remote hackathons and code camps

(Happonen et al., 2021).

Finally, we noticed that research works fall short

of characterizing the type of participants according

to their preferences and original approach to science,

particularly in a longitudinal manner, i.e., by compar-

CSEDU 2024 - 16th International Conference on Computer Supported Education

510

ing different editions of the same learning experience.

This work picks up from that need, attempting to shed

light and better understand the original attitudes and

approaches that participants have upfront upon join-

ing a non-conventional learning experience that will

get them closer to Computer Science. Identifying this

open avenue, we frame the goal of this work to an-

alyze the attitude of participants of two recent cod-

ing camps. This analysis will permit us to discuss

what type of students are attracted by these events and

provide a more detailed analysis of the participants’

proﬁles to understand better whether informal educa-

tional events effectively thrive diversity in science.

3 COMPELLING CASE

Understanding better the attitudes that participants

show during learning experiences may help explain

the approach learners construct upon career choices

in science and technology majors. To elaborate on

this crucial open item in literature, we focus the goal

of this work on analyzing the participants’ attitude

toward Computer Science at the beginning of two

editions of an online coding camp directed to high

school students. In this coding camp, participants

learn Software Engineering practices and use didac-

tic tools for software development that expose them

for the ﬁrst time to a software development project,

taking particular care in developing high-quality soft-

ware (Fronza et al., 2022). In particular, participants

learn how to build mobile applications with Thunk-

able (https://thunkable.com), i.e., using a drag-

and-drop feature to build user interfaces and a puzzle

metaphor to code the functionality.

The coding camp (https://mobiledev.inf.unib

z.it) is free of charge and takes place yearly at the

Free University of Bozen/Bolzano, Italy. There are

no selection criteria or restrictions on the attended

high school to create a truly interdisciplinary envi-

ronment. Registrations are accepted on a ﬁrst-come,

ﬁrst-served basis.

This paper reports two subsequent editions of the

coding camp, hereafter referred to as CodingCamp1

and CodingCamp2, respectively. Both were online

editions due to the pandemic emergency (Fronza

et al., 2022). In the two editions, we surveyed the par-

ticipants about their attitudes toward Computer Sci-

ence, which permitted us to describe a general proﬁle

of the participants and use such proﬁle to adjust com-

munication, messages, complexity, and teaching style

to the speciﬁc needs voiced by the class.

3.1 Instructional Strategy

The coding camp consists of twenty hours of activity

over ﬁve days, divided into ﬁve sessions:

• Session 1 (4 hours): foundations of logical think-

ing, structured sequencing, and data abstraction;

• Sessions 2-4 (12 hours in total): iterative develop-

ment of mobile apps;

• Session 5 (4 hours): completion and presentation.

Each element of the strategy (Table 1) fosters eX-

treme Programming (XP) practices (Fronza et al.,

2022). Games allow participants to release energy be-

fore focusing again on the online session to help re-

duce fatigue due to prolonged computer use (Yousof

et al., 2021). After each game, 15 minutes are re-

served for reﬂections on the takeaway message. Thus,

each game requires around 20-30 minutes.

3.2 Participants

The coding camp targets high school students (aged

15-19) with diversiﬁed disciplinary backgrounds. As

a common characteristic, participants have little or no

previous software development knowledge.

The communication strategy to reach potential

participants was the same for both editions. All

schools in the area received communication via email;

the coding camp was also promoted through pre-event

press coverage, which included newspapers (includ-

ing online versions) and major social media. Coding-

Camp1 and CodingCamp2 were the ninth and tenth

editions of the coding camp; thus, the event has

achieved a certain relevance and can rely on word of

mouth among students, families, and teachers.

3.3 Computer Science Attitude

The literature review presented by (Washington et al.,

2016) identiﬁed several computing and engineering-

related surveys that measure students’ attitudes to-

ward and interest in CS and engineering. Among

the surveys that measure attitudes toward Computer

Science, the one introduced by Hoegh and Moskal

(Hoegh and Moskal, 2009) was proven both reliable

and valid; moreover, the tool targets ﬁrst-year ma-

jors and non-majors, which can be considered close

enough to our target audience (i.e., students in the

second part of high school). Finally, it has been suc-

cessfully used as a basis for creating other tools, such

as the one measuring Computer Science Attitude and

Identity (Washington et al., 2016). Based on these

considerations, we derived the survey we used in this

paper from (Hoegh and Moskal, 2009) by identifying

Computer Science Attitude as a Descriptor to Understand Inclusion in Non-Conventional Learning Experiences

511

Table 1: Elements of the instructional strategy (adapted from (Fronza et al., 2022)).

Element Session Length

(min.)

XP Practice Description

Manipulatable

examples

1-5 – User stories Manipulatable examples (Burnett and My-

ers, 2014) allow participants to explore

ideas from the perspective of learning by

doing, i.e., by creating new conﬁgurations

and designs by tailoring software compo-

nents in their software environments.

Focus on

problem-

solving

1-5 – Small releases, test-

ing

The coding camp supports an opportunistic

and incremental (Burnett and Myers, 2014)

working style by focusing on problem-

solving rather than on SE lifecycle.

Alert with-

out impos-

ing

1-5 – Refactoring, testing We alert participants to dependability

problems and assist them with their ex-

plorations into those problems to whatever

extent they choose to pursue such explo-

rations.

We are here

to help

1-5 – Small releases, team-

work, on-site cus-

tomer (i.e., one of the

facilitators)

Participants ask for support (using the ded-

icated button in Zoom) by ﬁrst describ-

ing the attempted solutions. Student tutors

visit the assigned breakout rooms regularly

(Fronza et al., 2021).

Block-

Based

Program-

ming

2-5 – Continuous integra-

tion, refactoring,

testing

Thunkable (https://thunkable.com) fosters

problem-driven learning and XP practices

(Corral et al., 2021; Fronza et al., 2022); it

builds apps both for iOS and Android and

an emulator on PC is available.

Teamwork 1-5 – Collective owner-

ship, pair program-

ming, metaphor and

coding standard

Facilitators form teams (Oakley et al.,

2004) of three students from different

schools; mixed teams include two females

(Gammie and Matson, 2007). Teams

choose the logo/name. They can collabo-

rate on the same code or develop software

parts individually. When not in plenary,

teams work in Zoom breakout rooms.

Game: Pa-

per tower

2 18 Prototyping and iter-

ating, quick collabo-

ration, simple design,

teamwork

Building the tallest freestanding tower us-

ing 20 A4 paper sheets. Takeaway mes-

sages: prototyping/iterating, collaborating,

the value of cross-functional teams.

Game:

Color wheel

3 15 Simple design, team-

work, user stories

Creating a color wheel using the highest

number of colors and objects. The take-

away message is to work together toward a

solution by identifying small steps.

Game:

Thirty items

4 15 Prototyping/iterating,

quick collaboration,

teamwork

Finding 30 items with given characteris-

tics. Takeaway message: the importance

of understanding ambiguous requirements

(e.g., is an object valid for more than one

category?) and team self-organization.

Game:

Boosting

attention

3-4 10 Teamwork, simple

design

Who likes what? Participants mark their

preferred hobby/activity on a shared bingo-

like screen. Gimme ﬁve. Participants high-

ﬁve the persons right next to them on the

screen.

CSEDU 2024 - 16th International Conference on Computer Supported Education

512

the following three constructs as a focus for our sur-

vey:

• Conﬁdence Construct (C): students’ conﬁdence in

their ability to learn Computer Science skills;

• Interest Construct (I): students’ interests in Com-

puter Science;

• Professional Construct (P): students’ beliefs

about professionals in Computer Science.

Table 2 shows the questions from the original tool

(Hoegh and Moskal, 2009) we used in this work. A

total of 22 randomly-ordered questions were included

in the survey, which was anonymous and did not col-

lect demographic information. A four-point Likert

scale was used to ensure participants chose a positive

or negative response to each question.

4 RESULTS

Table 3 shows the number of participants and survey

respondents with respect to the total number of partic-

ipants in the two editions. In CodingCamp2, we im-

proved the communication strategy related to the sur-

vey, i.e., we explained how the survey would be help-

ful for us to shape the proposed activities. This may

explain the higher response rate in CodingCamp2.

Table 3 also shows the representation of gender

within the two editions. Marketing and communica-

tion strategies have stayed the same, so the authors

could not ﬁnd any explanation for the increase in fe-

male participants at CodingCamp2.

To analyze survey responses, we assigned each

item a numerical score (from 1 to 4), with reverse

scoring of negatively worded questions (such as C2

and I1); then, we calculated the CS attitude score for

each respondent by summing the values of each ques-

tion. Thus, the CS attitude score ranges from a mini-

mum of 22 (i.e., the respondent answered “1” to each

of the 22 questions) to a maximum of 88 (i.e., the re-

spondent answered “4” to each of the 22 questions).

Figure 1 compares the CS attitude of the participants

in the two editions of the coding camp under investi-

gation: CodingCamp1 and CodingCamp2 have nearly

identical medians (i.e., 66 and 67, respectively) and

comparable variability.

5 DISCUSSION

Table 3 and Figure 1 permit us to build up an insight

into the attitude of participants and hence permit us to

draw a high-level line about the diversity of proﬁles

attracted by this coding camp:

Table 2: Constructs and survey questions (derived from

(Hoegh and Moskal, 2009)).

Conﬁdence construct

C1 I am comfortable with learning computing

concepts.

C2 I have little self-conﬁdence when it comes

to computing courses.

C3 I do not think that I can learn to understand

computing concepts.

C4 I can learn to understand computing con-

cepts.

C5 I can achieve good grades (C or better) in

computing courses.

C6 I am conﬁdent that I can solve problems by

using computer applications.

C7 I am not comfortable with learning com-

puting concepts.

C8 I doubt that I can solve problems by using

computer applications.

Interest construct

I1 I would not take additional computer sci-

ence courses if I were given the opportu-

nity.

I2 I think computer science is boring.

I3 I hope that my future career will require the

use of computer science concepts.

I4 The challenge of solving problems using

computer science does not appeal to me.

I5 I like to use computer science to solve

problems.

I6 I do not like using computer science to

solve problems.

I7 The challenge of solving problems using

computer science appeals to me.

I8 I hope that I can ﬁnd a career that does not

require the use of computer science con-

cepts.

I9 I think computer science is interesting.

I10 I would voluntarily take additional com-

puter science courses if I were given the

opportunity.

Professional construct

P1 A student who performs well in computer

science will probably not have a life out-

side of computers.

P2 A student who performs well in computer

science is likely to have a life outside of

computers.

P3 Students who are skilled at computer sci-

ence are less popular than other students.

P4 Students who are skilled at computer sci-

ence are just as popular as other students.

Computer Science Attitude as a Descriptor to Understand Inclusion in Non-Conventional Learning Experiences

513

Table 3: Number of participants and survey respondents

with respect to total number of participants.

CodingCamp1 CodingCamp2

Participants 80 100

Male 66 (82.5%) 69 (69.0%)

Female 14 (17.5%) 31 (31.0%)

Respondents 62 (77.5%) 95 (95%)

CodingCamp1 CodingCamp2

50 60 70 80

CS attitude

Figure 1: Attitude toward Computer Science of the partic-

ipants in the two editions of the online coding camp under

consideration.

• The gender diversity of attracted participants de-

notes consistency with the trend in STEM roles

in the European Union, which ranges from 22 to

46 percent in 2022

. Indeed, the proportion of

women participating in the coding camp spans 17

to 31 percent.

• The learning experience of the coding camp at-

tracts students with a Computer Science attitude

rather medium. This is an indicator of success in

the aspect that being a technical learning experi-

ence that attempts to broaden participation in CS,

the target audience that effectively gets attracted

is not only a population that already has a dispo-

sition to programming or science (which would

have been observed as a higher CS attitude).

• The fact that the learning experience does not

have any pre-requisite effectively attracts a diver-

sity of proﬁles; however, Figure 1 shows that few

students have a low CS attitude. A larger pop-

ulation with a low CS attitude would be a good

descriptor of higher success in attracting skeptical

or less science-prone proﬁles.

• It is pertinent to note that the effort to publicize

https://ec.europa.eu/eurostat/web/products-eurostat-

news/-/edn-20220211-2

the coding camp is minimal, and the lack of pub-

licity also cuts out a possible bias given by the fact

that advertising targets an audience, which in this

case is a more science or engineering-prone pop-

ulation, that may skew the data towards higher CS

attitudes.

• The two editions of the coding camp yield simi-

lar results in terms of CS attitude. Observing this

trend through time (acknowledging that the anal-

ysis is limited to two editions) sheds light on the

aspect that the behavior of the data is not limited

to what happened once.

• As the trend of coding camps, hackathons, and

similar experiences is relatively recent, the need

for longitudinal studies that permit the analysis of

trends through time is evident.

6 CONCLUSION AND

QUESTIONS FOR FUTURE

RESEARCH TO ANSWER

In this paper, we show the insight collected by sur-

veying the Computer Science attitude of participants

of two editions of a coding camp. This analysis at-

tempts to motivate a discussion about what type of

students are attracted by these events and better un-

derstand whether non-conventional learning experi-

ences effectively foster diversity in talent attracted to

science and technology. Although the study presented

in this paper represents an early analysis, we can iden-

tify the following questions that motivate future work

and deeper discussion on the subject:

• What are the best strategies to attract volume and

diversity into STEM subjects? Is a high-ranked

attitude a good descriptor of targeted selection, or

is a medium-ranked attitude an indicator of diver-

sity, inﬂuencing and convincing towards Science?

• What is the difference posed by conducting these

learning experiences online or face to face? Is

there any inﬂuence or impact on gender diversity,

attitudes, and other participants’ characteristics?

• What are characteristics, beyond gender, that ef-

fectively represent diversity in science (for in-

stance, attitude, localization, citizenship, and eth-

nic background) that should be part of a validated

measurement tool?

• The importance of conducting longitudinal stud-

ies on the subject. An analysis like the underlying

work of this paper represents a picture of a situa-

tion observed in a particular context at a speciﬁc

CSEDU 2024 - 16th International Conference on Computer Supported Education

514

moment in time. A long-term analysis enables the

study of other factors that can impact the attitude

and the proﬁle of participants and the evolution of

these factors through time.

Non-conventional learning experiences in early

education can be a precious resource to broaden par-

ticipation in CS. The insight collected by this work

shows promising views that the audience attracted is

not only a population that already has a disposition to

CS but also different kinds of proﬁles, which eventu-

ally contributes to fostering diversity in science and

technology.

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