BOOSTING UNDERGRADUATE STUDENTS' ACADEMIC INTEGRATION THROUGH EDUCATIONAL HACKATHONS: THE 'UTAD CODE AND CONQUER' EXPLORATORY STUDY

Abstract

This paper presents a mixed-methods pre-experimental study with a one-group pre-test-post-test design. It explores the academic integration of a sample of 29 Bachelor (BSc) Informatics Engineering students before and after participating in a 6-hour educational hackathon: the 'UTAD Code and Conquer' (UCC) hackathon. The UCC hackathon challenged a total of 63 participants to solve an adapted version of the Traveling-Salesman Problem (TSP) in heterogeneous groups of three elements, with at least one first-year BSc student. Quantitative data were collected using the reduced version of the Academic Experiences Questionnaire (QVA-r), which measures students' experiences of academic adjustment across 5 key dimensions-personal, interpersonal, course-career, study, and institutional. Despite high mean scores on the QVA-r, differences before (x̄ total = 3.582) and after the event (x̄ total = 3.787) were not statistically significant (p > 0.050). While significant correlations were observed between some QVA-r dimensions, no such correlations were found with students' gender, year of study, or place of residence (in or outside the hometown). Qualitative data were collected through participant observation, document analysis, and semi-structured interviews with the student groups pre-selected for a final pitch of the five best TSP solutions. Overall, the interviewed participants expressed positive opinions, feelings, and attitudes towards the UCC hackathon, not only by recognizing its benefits for the academic integration of first-year students, as an opportunity for the practical application of theoretical knowledge and social interaction across academic cohorts but also by identifying challenges and suggestions for improvement related to the appropriateness of the TSP scenario. Consequently, practical and research implications for the adoption of educational hackathons as an academic integration strategy by higher education institutions are discussed and outlined.

Full text

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BOOSTING UNDERGRADUATE STUDENTS' ACADEMIC
INTEGRATION THROUGH EDUCATIONAL HACKATHONS: THE
‘UTAD CODE AND CONQUER’ EXPLORATORY STUDY
G. Cruz1, R. Nunes2, T. Ferreira3, A. Reis2, J. Cravino4
1University of Trás-os-Montes and Alto Douro (UTAD), Vila Real, Portugal; Research Centre
on Didactics and Technology in the Education of Trainers (CIDTFF), Aveiro, Portugal;
Institute for Systems and Computer Engineering, Technology and Science (INESC TEC),
Porto, Portugal (PORTUGAL)
2University of Trás-os-Montes and Alto Douro (UTAD), Vila Real, Portugal; Institute for
Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal
(PORTUGAL)
3University of Trás-os-Montes and Alto Douro (UTAD), Vila Real, Portugal
4University of Trás-os-Montes and Alto Douro (UTAD), Vila Real, Portugal; Research Centre on
Didactics and Technology in the Education of Trainers (CIDTFF), Aveiro, Portugal (PORTUGAL)
Abstract
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1 INTRODUCTION
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Proceedings of EDULEARN24 Conference
1st-3rd July 2024, Palma, Mallorca, Spain
ISBN: 978-84-09-62938-1
7778

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'*.!$*9()(*+$'1!%+'+$%+$8%!2%$*5!7<i!>N>>!>+'+$%+$8%!:;6Z6Z6Z6!
3.2 Participant observation, document analysis, and semi-structured interviews
P2'1$+'+$M(!.'+'!4()(!8011(8+(.!2%$*5!&')+$8$&'*+!0A%()M'+$0*O!.082,(*+! '*'13%$%O!'*.!%(,$/%+)28+2)(.!
$*+()M$(4%!abSc6!"#(!)(%(')8#!+(',!'%%(%%(.!+#(!%+2.(*+%K!0M()'11!(*5'5(,(*+!'*.!&()90),'*8(!.2)$*5!
+#(!5)02&!40)C!%(%%$0*!+#)025#!$*!1080!0A%()M'+$0*%!'*.!$*90),'1!.$%82%%$0*%6!"#(!$**0M'+$0*!'*.!+#(!
80.(!J2'1$+3!09!('8#!&)0&0%(.!%012+$0*!+0!+#(!">N!%8(*')$0O!'%!'%%(%%(.!A3!+#(!&'*(1!09!R2.5(%O!4()(!
'1%0!80*%$.()(.6!
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+#)025#!%#0)+!&0%+/(M(*+!$*+()M$(4%6!"#(%(!S/,$*2+(!$*+()M$(4%!4()(!A'%(.!0*!+#(!901104$*5!0&(*/(*.(.!
J2(%+$0*%D!
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+#(!+')5(+(.!'2.$(*8(!09!+#(!FII!#'8C'+#0*k!!
•![#'+!4021.!302!$,&)0M(!$*!'!92+2)(!(.$+$0*!09!+#(!FII!#'8C'+#0*k!
G!)'&$.!80*+(*+!'*'13%$%!4'%!8'))$(.!02+!2%$*5!+#(!'2.$0!)(80).$*5%!09!+#(!$*+()M$(4(.!&')+$8$&'*+%!abBc6!
"#(!'2.$0!)(80).$*5%!4()(!80.(.O!'*'13](.O!'*.!+)'*%1'+(.!'880).$*5!+0!+#(!+#)((!&)(/$.(*+$9$(.!+#(,(%!
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(,&103(.O!'*.!b;!=BS6Sn?!4()(!1$M$*5!$*!%#')(.!'880,,0.'+$0*!4$+#!0+#()!F"GH!%+2.(*+%6!!
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$*+()M$(4(.6!\9!+#(%(!b:!&')+$8$&'*+%O!bZ!=WL6Ln?!4()(!,'1(O!'*.!:!4()(!9(,'1(O!4$+#!'5(%!A(+4((*!bW!
'*.!:b!3(')%6!"#(!,'R0)$+3!=BB6Xn?!4()(!%(80*./3(')!%+2.(*+%O!4#$1(!+#(!)(,'$*.()!4()(!$*! +#(! 9$)%+!
3(')!09!+#(!<>8!.(5)((6!!
4 RESULTS
H(%8)$&+$M(!'*.!$*9()(*+$'1!%+'+$%+$8%!09!+#(!&)(!'*.!&0%+/PQG/)!%80)(%!')(!9$)%+!&)(%(*+(.6!i('%2)(%!09!
8(*+)'1!+(*.(*83O! .$%&()%$0*O!$*+()*'1!80*%$%+(*83O!,('*!.$99()(*8(%O! '*.!80))(1'+$0*%!A(+4((*!PQG/)!
M')$'A1(%! ')(! (-',$*(.6! >(80*.13O! +#(! )(%21+%! 09! +#(! )'&$.! 80*+(*+! '*'13%$%! 09! +#(! %#0)+O! &0%+/(M(*+!
$*+()M$(4%!')(!'1%0!%2,,')$](.O!+05(+#()!4$+#!+#(!$*!1080!0A%()M'+$0*%!'*.!+#(!R2)3!&'*(1K%!(M'12'+$0*!
09!+#(!%+2.(*+K%!&)0&0%(.!%012+$0*%6!
4.1 Quantitative results
"'A1(! b! &)(%(*+%! +#(! .(%8)$&+$M(! %+'+$%+$8%! 09! +#(! PQG/)! .$,(*%$0*%!',0*5! +#(! &')+$8$&'*+%!=*! V! :;?!
A(90)(!'*.!'9+()!+#(!FII!#'8C'+#0*O!$*812.$*5!8(*+)'1$+3O!.$%&()%$0*O!'*.!$*+()*'1!80*%$%+(*83!,('%2)(%6!
"#(!0M()'11! ,('*!%80)(%!09!+#(!PQG/)!4()(!#$5#()!+#'*!+#(!0M()'11!,('*!09!+#(!$*%+)2,(*+6!"#$%!4'%!
&')+$821')13! 0A%()M(.! $*! +#(! EI02)%(/I')(()K! =-
U!+0+'1! V! e6ZeB?O! E7*%+$+2+$0*'1K! =-
U!+0+'1! V! L6;L;?O! '*.!
E7*+()&()%0*'1K! =-
U!+0+'1! V! L6WSb?! .$,(*%$0*%O! 4#$8#! #'.!#$5#()! ,('*! %80)(%! 4#(*! 80,&')(.! +0! +#(!
E>+2.3K!=-
U!+0+'1!V!L6SW:?!'*.!EN()%0*'1K!=-
U!+0+'1!V!L6eXW?!.$,(*%$0*%6!
Table 1. Centrality, dispersion, and internal consistency measures
of the QVA-r dimensions before and after the UCC hackathon.
Min.
Max.
Mean (x
*)
Std. Deviation (σ)
Cronbach’s Alpha (α)
Personal_Pre
2.077
4.846
3.552
.695
.828
Personal_Post
2.231
4.846
3.403
.803
.858
Interpersonal_Pre
3.000
4.923
3.851
.564
.820
Interpersonal_Post
2.923
5.000
3.851
.604
.817
Course-Career_Pre
2.308
5.000
4.032
.700
.819
Course-Career_Post
2.231
5.000
4.061
.652
.803
Study_Pre
2.154
5.000
3.533
.623
.803
Study_Post
2.538
4.846
3.631
.674
.801
Institutional_Pre
2.500
4.750
3.892
.556
.831
Institutional_Post
3.000
4.875
3.987
.580
.827
G!%1$5#+!$*8)('%(!4'%!0A%()M(.!A(+4((*!+#(!+0+'1!,('*!%80)(%!09!+#(!PQG/)!A(90)(!=-
U!+0+'1!V!L6SW:?!'*.!
'9+()!+#(!(M(*+!=-
U!+0+'1!V!L6XWX?6!@-8(&+!90)!+#(! EN()%0*'1K!.$,(*%$0*O!+#(!,('*!%80)(%!09!'11!+#(!0+#()!
PQG/)!.$,(*%$0*%!4()(!(J2'1!'*.o0)!A(++()!$*!+#(!&0%+/+(%+6!"#(!0M()'11!M')$'A$1$+3!A(+4((*!.'+'!&0$*+%!
9)0,!+#(!,('*!%80)(!4'%!,0.()'+(!=p!+0+'1!V!6Se:?O!'*.!+#(! 0M()'11!$*+()*'1!80*%$%+(*83!09!+#(!PQG/)!
.$,(*%$0*%!4'%!#$5#!=q!+0+'1!V!6WLX?6!
[#(*! 100C$*5!'+! +#(!,('*! %80)(%! 09!PQG/)! A3! 5(*.()O!3(')!09! %+2.3O! '*.!&1'8(! 09! )(%$.(*8(O!%1$5#+!
.$99()(*8(%!4()(! 902*.6!`(,'1(!%+2.(*+%!=-
U!+0+'1!V!L6;S;?!%80)(.!#$5#()!+#'*!,'1(!%+2.(*+%!=-
U!+0+'1!V!
L6X;b?O!'*.!%(80*./3(')!%+2.(*+%!=-
U!+0+'1!V!L6;;e?!%80)(.!#$5#()! +#'*! 9$)%+/3(')! =-
U!+0+'1!V!L6We;?!'*.!
+#$)./3(')!%+2.(*+%!=-
U!+0+'1!V!L6BWZ?6!>$,$1')13O!%+2.(*+%!1$M$*5!02+%$.(!+#($)!#0,(+04*!=-
U!+0+'1!V!L6WB;?!
%80)(.!%1$5#+13!#$5#()!+#'*!%+2.(*+%!1$M$*5!$*!+#($)!#0,(+04*!=-
U!+0+'1!V!L6Wbe?6!
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)(M('1(.! +#'+! 0*13! +#(! E7*%+$+2+$0*'1K! .$,(*%$0*! %$5*$9$8'*+13! .(M$'+(.! 9)0,! '! *0),'1! .$%+)$A2+$0*! =&! r!
Z6ZSZ?6!"#()(90)(O!&')',(+)$8!+(%+%!4()(!2%(.!90)!%2A%(J2(*+!$*9()(*+$'1!'*'13%$%O!(-8(&+!90)!+#$%!PQG/
)!.$,(*%$0*6! "#(! N'$)(.! %',&1(!+/+(%+!4'%!2%(.!+0!80,&')(!+#(!,('*!.$99()(*8(%! 09! +#(!($5#+! PQG/)!
.$,(*%$0*%!4$+#!*0),'1!.$%+)$A2+$0*%!A(+4((*!+#(!&)(!'*.!&0%+/+(%+! ="'A1(!:?6!\*!+#(!0+#()!#'*.O!+#(!
[$180-0*!%$5*(.!)'*C!+(%+!4'%!'&&1$(.!+0!80,&')(!+#(!,(.$'*!.$99()(*8(%!09!+#(!E7*%+$+2+$0*'1K!.$,(*%$0*!
A(90)(!'*.!'9+()!+#(!(M(*+!="'A1(!L?6!
Table 2. Paired sample test of the QVA-r dimensions
with normal distribution before and after the UCC hackathon.
Mean
Std. Deviation (σ)
t
One-Sided p
Pair 1
Personal_Pre - Personal_Post
.149
.682
1.174
.125
Pair 2
Interpersonal_Pre - Interpersonal_Post
.000
.439
.000
.500
Pair 3
Course-Career_Pre – Course-Career_Post
-.029
.476
-.330
.372
Pair 4
Study_Pre - Study_Post
-.098
.385
-1.374
.090
Table 3. Wilcoxon signed rank test of the QVA-r dimension
without normal distribution before and after the UCC hackathon.
Null Hypothesis
Test
Sig. a,b
Decision
The median of differences between
Institutional_Pre and Institutional_Pos equals 0.
Related-Samples Wilcoxon
Signed Rank Test
.101
Retain the null
hypothesis.
The significance level is .050.
Asymptotic significance is displayed.
H(%&$+(!+#(!.$99()(*8(%!0A%()M(.!$*!,('*!%80)(%!'8)0%%!+#(!PQG/)!.$,(*%$0*%!A(+4((*!+#(!&)(!'*.!&0%+/
+(%+!="'A1(!b?O!+#(%(!4()(!*0+!%+'+$%+$8'113!%$5*$9$8'*+!="'A1(!:!'*.!"'A1(!Lm!&!Y!Z6ZSZ?6!\M()'11O!&')+$8$&'*+%!
%#04(.! 80*%$%+(*+! 1(M(1%! 09! '5)((,(*+! '8)0%%! '11! +#(! PQG/)! .$,(*%$0*%! A(90)(! '*.! '9+()! +#(! FII!
#'8C'+#0*6!"#()(90)(O!+#(%(!)(%21+%!%255(%+!+#(!FII!#'8C'+#0*!.$.!*0+!%$5*$9$8'*+13!'99(8+!+#(!%+2.(*+K%!
&()8(&+$0*%!09!+#($)!(-&()$(*8(%!09!'8'.(,$8!'.R2%+,(*+!'%!,('%2)(.!A3!+#(!PQG/)!$*%+)2,(*+6!
H$99()(*+! &0%$+$M(! 80))(1'+$0*%! 4()(! 902*.! A(+4((*! %0,(! 09! +#(! PQG/)! .$,(*%$0*%!4$+#! '! *0),'1!
.$%+)$A2+$0*!="'A1(!e?6!>+)0*5!&0%$+$M(!80))(1'+$0*%!4()(!902*.!$*!+#(!&)(/+(%+!%80)(%!A(+4((*!+#(!E>+2.3K!
'*.!+#(!EI02)%(/I')(()K!.$,(*%$0*%!=&!V!Z6ZZW?6!i0.()'+(!&0%$+$M(!80))(1'+$0*%!4()(!'1%0!0A%()M(.!$*!
+#(!&)(/+(%+!%80)(%O!+#$%!+$,(!A(+4((*!+#(!E>+2.3K!'*.!+#(!EN()%0*'1K!=&!V!Z6ZbW?O!'*.!A(+4((*!+#(!E>+2.3K!
'*.!+#(!E7*+()&()%0*'1K!=&!V!Z6ZLB?!.$,(*%$0*%6!!
Table 4. Correlations between the QVA-r dimensions with
normal distribution before and after the UCC hackathon.
Personal
_Pre
Interpersona
l_Pre
Course-
Career_Pre
Study_Pre
Personal_Po
st
Interpersona
l_Post
Course-
Career_Post
Study_Post
Personal_Pre
Pearson Correlation
1
.309
.270
.436*
.594**
.184
.223
.502**
Sig. (2-tailed)
.102
.156
.018
<.001
.339
.246
.006
Interpersonal_Pre
Pearson Correlation
.309
1
.344
.391*
.040
.720**
.272
.348
Sig. (2-tailed)
.102
.068
.036
.837
<.001
.153
.064
Course-Career_Pre
Pearson Correlation
.270
.344
1
.481**
-.059
.328
.754**
.397*
Sig. (2-tailed)
.156
.068
.008
.761
.083
<.001
.033
Study_Pre
Pearson Correlation
.436*
.391*
.481**
1
.256
.515**
.657**
.827**
Sig. (2-tailed)
.018
.036
.008
.179
.004
<.001
<.001
7782

!
!
Personal_Post
Pearson Correlation
.594**
.040
-.059
.256
1
.128
.183
.355
Sig. (2-tailed)
<.001
.837
.761
.179
.508
.343
.059
Interpersonal_Post
Pearson Correlation
.184
.720**
.328
.515**
.128
1
.406*
.570**
Sig. (2-tailed)
.339
<.001
.083
.004
.508
.029
.001
Course-
Career_Post
Pearson Correlation
.223
.272
.754**
.657**
.183
.406*
1
.544**
Sig. (2-tailed)
.246
.153
<.001
<.001
.343
.029
.002
Study_Post
Pearson Correlation
.502**
.348
.397*
.827**
.355
.570**
.544**
1
Sig. (2-tailed)
.006
.064
.033
<.001
.059
.001
.002
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
>$,$1')13O! %+)0*5! &0%$+$M(! 80))(1'+$0*%! 4()(! 902*.! $*! +#(! &0%+/+(%+! %80)(%! A(+4((*! +#(! E>+2.3K! '*.! +#(!
E7*+()&()%0*'1K!=&! V!Z6ZZb?O!A(+4((*! +#(! E>+2.3K!'*.! +#(!EI02)%(/I')(()K!=&! r! Z6ZZb?O!'*.! A(+4((*!+#(!
E>+2.3K!'*.!+#(!EN()%0*'1K!=&!V!Z6ZZB?!.$,(*%$0*%6!i0.()'+(!&0%$+$M(!80))(1'+$0*%!4()(!'1%0!0A%()M(.!$*!
+#(!&0%+/+(%+!%80)(%O!+#$%!+$,(!A(+4((*!+#(!E7*+()&()%0*'1K!'*.!+#(!EI02)%(/I')(()K!.$,(*%$0*%!=&!V!Z6Z:;?6!!
[#(*!'*'13]$*5!+#(!80))(1'+$0*!A(+4((*!+#(!PQG/)!M')$'A1(%!+#'+!.(M$'+(.!9)0,!'!*0),'1!.$%+)$A2+$0*!="'A1(!
S?O!'! %+)0*5! &0%$+$M(!80))(1'+$0*!4'%!902*.!A(+4((*!+#(! E7*%+$+2+$0*'1K!'*.! +#(! EI02)%(/I')(()K!.$,(*%$0*%O!
A0+#!'+!+#(!&)(/+(%+!=&! V!Z6ZZ:?!'*.!&0%+/+(%+!=&! r! Z6ZZb?6!7*! '..$+$0*O!,0.()'+(!&0%$+$M(!80))(1'+$0*%!4()(!
'1%0! 902*.! A(+4((*! +#(! &)(/+(%+! %80)(%! 09! +#(! E7*%+$+2+$0*'1K! '*.! +#(! E>+2.3K! .$,(*%$0*%! =&! V! Z6Z:L?O! '*.!
A(+4((*!+#(!&0%+/+(%+!%80)(%!09!+#(!E7*%+$+2+$0*'1K!'*.!+#(!E7*+()&()%0*'1K!.$,(*%$0*%!=&!V!Z6Z:L?6!!
Table 5. Correlations between the QVA-r dimensions
without normal distribution before and after the UCC hackathon.
Gender
Year of
Study
Place of
Residence
Institutional_
Pre
Institutional_
Post
Spearman’s
rho
Personal_Pre
Correlation Coefficient
.092
-.163
.112
.072
-.028
Sig. (2-tailed)
.636
.398
.562
.711
.886
Interpersonal_Pre
Correlation Coefficient
-.357
-.030
.066
.333
.489**
Sig. (2-tailed)
.057
.877
.732
.078
.007
Course-Career_Pre
Correlation Coefficient
-.331
.230
.234
.554**
.471**
Sig. (2-tailed)
.079
.230
.221
.002
.010
Study_Pre
Correlation Coefficient
-.271
.048
.153
.421*
.256
Sig. (2-tailed)
.156
.805
.428
.023
.180
Institutional_Pre
Correlation Coefficient
-.245
-.060
.199
1
.722**
Sig. (2-tailed)
.200
.756
.300
<.001
Personal_Post
Correlation Coefficient
.122
.097
-.229
-.165
-.072
Sig. (2-tailed)
.528
.616
.232
.392
.711
Interpersonal_Post
Correlation Coefficient
-.173
.157
-.168
.149
.422*
Sig. (2-tailed)
.368
.415
.383
.439
.023
Course-Career_Post
Correlation Coefficient
-.225
.136
.153
.614**
.651**
Sig. (2-tailed)
.242
.483
.428
<.001
<.001
Study_Post
Correlation Coefficient
-.061
-.036
-.102
.400*
.310
Sig. (2-tailed)
.753
.855
.599
.032
.102
Institutional_Post
Correlation Coefficient
-.174
.054
.031
.722**
1
Sig. (2-tailed)
.367
.783
.874
<.001
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
7783

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!
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09! )(%$.(*8(?! '*.! +#(! PQG/)! .$,(*%$0*%O!*0! %$5*$9$8'*+! )(%21+%! 4()(! 902*.6! G1+#025#! +#(! PQG/)! ,('*!
%80)(%!09!9(,'1(O!%(80*./3(')!%+2.(*+%!1$M$*5!02+%$.(!+#($)!#0,(+04*!4()(!#$5#()!+#'*!+#0%(!09!,'1(O!9$)%+!
0)!+#$)./3(')!%+2.(*+%!1$M$*5!$*!+#($)!#0,(+04*O!+#(%(!4()(!*0+!%+'+$%+$8'113!80))(1'+(.!=&!Y!Z6ZSZ?6!!
4.2 Qualitative results
"#(! )(%21+%! 9)0,! +#(! )'&$.! 80*+(*+! '*'13%$%! 09! +#(! %(,$/%+)28+2)(.! $*+()M$(4%! ')(! %2,,')$](.6! "#(!
0&$*$0*%O!9((1$*5%O!'*.!'++$+2.(%!%#')(.!A3!+#(!&')+$8$&'+$*5!5)02&%!09!%+2.(*+%!&)(/%(1(8+(.!90)!+#(!9$*'1!
&$+8#!4()(! 80*%$%+(*+! '8)0%%! +#(! +#)((! +#(,(%! '*'13](.D! b?! +#(! $,&'8+! 09! +#(! FII! #'8C'+#0*! 0*!
%+2.(*+%K!'8'.(,$8!$*+(5)'+$0*O!:?!+#(!'&&)0&)$'+(*(%%!09!+#(!">N!%8(*')$0!90)!+#(!+')5(+(.!'2.$(*8(O!
'*.!L?!%255(%+$0*%!90)!$,&)0M(,(*+6!!
f(5').$*5! +#(! $,&'8+! 09! +#(! FII! #'8C'+#0*! 0*! +#(! '8'.(,$8! $*+(5)'+$0*! 09! %+2.(*+%O! +#()(! 4'%! '!
5(*()'1! '5)((,(*+! ',0*5! '11! %+2.(*+! 5)02&%! +#'+! +#(! (M(*+! #'.! '! &0%$+$M(! $,&'8+! 0*! $+6! "#$%! 4'%!
A(*(9$8$'1!(%&(8$'113!90)!+#(!9$)%+/3(')! %+2.(*+%O! .2(! +0! +#(! &)'8+$8'1! '*.! %08$'1! *'+2)(! 09! +#(! (M(*+O! '%!
(-&)(%%(.!A3!+#(!901104$*5!&')+$8$&'*+%D!
‘I personally liked it. It was a very good initiative by UTAD, we had a great time! Especially
for the first-year students, because it was compulsory to have one in each group. He (the
first-year student) learned something with us (…) sometimes he wanted to do it one way,
but we guided him to do it another way because of the next steps that we had to do, and
he understood.’ (Participant_1_group_2)
‘This event is interesting to integrate new students because it gives them a more practical
perspective. In the first year, students have more theoretical classes, and this allows them
to put the knowledge they are learning into practice, by applying it to a real-world problem.’
(Participant_2_group_1)
‘I (a first-year student) found it interesting because it was the first time I put into practice
what I was learning in class. I understood what I could do and what this course allowed me
to do. It was nice to work with older colleagues who have more knowledge than me.’
(Participant_3_group_4)
‘The hackathon was also socially important because it gives you a moment to know and
relate to people from the different academic years of our degree. They (the first-year
students) can ask for help for other classes, and this strengthens the relationships. I was
happy with this event, and I highly recommend it to all the students. We finally had an event
related to what we can do with our degree (usually the events are not so related). If there
is another edition, I will attend!’ (Participant_1_group_3)
7*!)(1'+$0*!+0!+#(!'&&)0&)$'+(*(%%!09!+#(!">N!%8(*')$0!90)!+#(!+')5(+(.!'2.$(*8(O!+#()(!4'%!'1%0!'!5(*()'1!
'5)((,(*+!',0*5!%+2.(*+!5)02&%!+#'+!+#(!">N!%8(*')$0!4'%!.$99$821+6!7+!4'%!&')+$821')13!.$99$821+!90)!+#(!
9$)%+/3(')! %+2.(*+%O! .2(! +0! +#(! (-$%+$*5! C*041(.5(! 5'&! A(+4((*! +#(,! '*.! +#(! %(80*.! 0)! +#$)./3(')!
%+2.(*+%O!'%!(-&)(%%(.!A3!+#(!901104$*5!&')+$8$&'*+%D!
‘We (second-year students) think that the problem should be easier if it is only aimed at the
first-year students. In terms of critical thinking level, he (the first-year student) had the same
thoughts and ideas as us; but in terms of code, we decided to code in C++, and he did not
have any background in that.’ (Participant_2_group_2)
‘We (second-year students) coded in Python, but our first-year student only knows a little
bit of C. He still does not know anything about discrete mathematics, so even the TSP
scenario was difficult.’ (Participant_1_group_4)
‘I (a first-year student) found the first phase, the algorithm, particularly interesting. But then
I got a bit lost when we started programming in Python.’ (Participant_3_group_4)
‘I (a third-year student) found the challenge easy. We work with this type of algorithm (TSP)
in our discrete mathematics classes, so we had a general idea of how it works. The first-
year student was a little bit lost because he did not know what to do – so maybe it was a
bit difficult for him (he only knows C programming and we coded in Python).’
(Participant_2_group_3)
"#025#!&0%$+$M(!'++$+2.(%! +04').%!+#(!FII!#'8C'+#0*! 4()(!(-&)(%%(.!A3!+#(!0M()'11!%+2.(*+%O!%0,(!
%255(%+$0*%!90)!$,&)0M(,(*+!4()(!,'.(!)(5').$*5!+#(!">N!%8(*')$0D!
7784

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!
‘It was our first time at an event like this and we think that more editions need to be
organized shortly! The time was sufficient and appropriate. We confronted our solutions
with other groups, and we wanted to improve them. It was a kind of competition between
the groups.’ (Participant_3_group_3)
‘We liked the way it was organized – the kick-off session, the group work, the coffee break.
In general, I really liked it! In terms of technical knowledge, it is a bit difficult for first-year
students (but adequate for older ones). It will work better in the second semester.’
(Participant_3_group_1)
‘For a first edition, it was very well organized, and it was interesting! The duration was OK.
If we had more time, it might not have been so productive. We were already a little bit tired
because we were the last to finish.’ (Participant_2_group_4)
‘It should be about something that the first-year students do in their classes because they
did not have enough knowledge to solve it. The level of difficulty should be lower. But it is
a good option for the second and third-year students.’ (Participant_1_group_4)
"#(!)(%21+%!#()($*!&)(%(*+(.!4()(!'1%0!80*%$%+(*+!4$+#!+#(!$*!1080!0A%()M'+$0*%!,'.(!.2)$*5!+#(!(M(*+!
'*.!4$+#!+#(!R2.5(K%!'%%(%%,(*+!09!+#(!%+2.(*+K%!&)0&0%(.!%012+$0*%!+0!+#(!"N>!%8(*')$0!$*!$**0M'+$0*!
'*.!80.(!J2'1$+36!"#(!5(*()'1!(*+#2%$'%,!'*.!&0%$+$M(!'++$+2.(!+04').%!+#(!(M(*+!4()(!0A%()M(.!.2)$*5!
+#(!5)02&! 40)C!%(%%$0*O!4#()(!,0%+!%+2.(*+!5)02&%!09!%+2.(*+%!4()(!'8+$M(13!(*5'5(.!2*+$1!+#(!(*.O!
'9+()!S!#02)%!'*.!LZ!,$*2+(%6!"#(!1(M(1!09!.$99$821+3!09!+#(!">N!%8(*')$0!4'%!'1%0!)(91(8+(.!$*!+#(!*2,A()!
09! 5)02&%! +#'+! 4()(! &)(/%(1(8+(.! A3! +#(! R2)3! &'*(1! 90)! +#(! 9$*'1! &$+8#D! 0*13! 9$M(! 5)02&%! %2A,$++(.! '*!
'&&)0&)$'+(!80.(/A'%(.!%012+$0*!+0!+#(!">N!%8(*')$06!G11!+#(%(!9$M(! 5)02&%!80*%$%+(.!09!0*(!9$)%+/3(')!
%+2.(*+!'*.!+40!%+2.(*+%!9)0,!+#(!%(80*.!'*.o0)!+#$).!'8'.(,$8!3(')!09!+#(!<>8!.(5)((!$*!7*90),'+$8%!
@*5$*(()$*5!'+!F"GH6!
5 CONCLUSIONS
"#(!9$*.$*5%!09!+#$%!%+2.3!&)0M$.(!M'12'A1(!$*%$5#+%!$*+0!+#(!$,&'8+!09!+#(!FII!#'8C'+#0*!0*!%+2.(*+%K!
'8'.(,$8! $*+(5)'+$0*6! <'%(.! 0*! +#(!J2'*+$+'+$M(! '*.! J2'1$+'+$M(! .'+'! '*'13%$%O! +#(! FII! #'8C'+#0*!
(,()5(.!'%! '!%$5*$9$8'*+!80*+)$A2+0)!+0!90%+()$*5!'8'.(,$8!$*+(5)'+$0*!',0*5!&')+$8$&'*+%O!&')+$821')13!
90)!9$)%+/3(')!%+2.(*+%O!A3!&)0M$.$*5!'!&1'+90),!90)!+#(!&)'8+$8'1!'&&1$8'+$0*!09!+#(0)(+$8'1!C*041(.5(!'*.!
+#(!9'8$1$+'+$0*!09!%08$'1!$*+()'8+$0*!'8)0%%!'8'.(,$8!80#0)+%6!"#$%!#$5#1$5#+%!+#(!$,&0)+'*8(!09!$*90),'1!
'*.!8011'A0)'+$M(!1(')*$*5!(*M$)0*,(*+%!$*!(*#'*8$*5!%+2.(*+%K!(*5'5(,(*+!'*.!%(*%(!09!A(10*5$*5!+0!
'*!$*%+$+2+$0*O!+#()(A3!80*+)$A2+$*5!+0!%,00+#()!+)'*%$+$0*%!A(+4((*!%(80*.')3!'*.!#$5#()!(.28'+$0*!aLcaec6!
P2'*+$+'+$M(! )(%21+%O! $*812.$*5! .(%8)$&+$M(!'*.! $*9()(*+$'1!%+'+$%+$8%! 09!+#(! &)(!'*.! &0%+/PQG/)!%80)(%O!
&)0M$.(.!'*!0M()M$(4!09!#04!%+2.(*+%!4()(!'.'&+$*5!+0!'8'.(,$8!1$9(!'8)0%%!.$99()(*+!.$,(*%$0*%6!_$5#!
,('*! %80)(%! 4()(! 0A%()M(.! $*! +#(! EI02)%(/I')(()KO! +#(! E7*%+$+2+$0*'1KO! '*.! +#(! E7*+()&()%0*'1K!
.$,(*%$0*%O!&1'8$*5!+#(,!'+!+#(!8(*+()!09!%+2.(*+%K!'8'.(,$8!'.R2%+,(*+6!_04(M()O!.(%&$+(!+#(!#$5#()!
PQG/)! ,('*! %80)(%! '9+()! +#(! FII! #'8C'+#0*O! +#(%(! .$99()(*8(%! 4()(! *0+! %+'+$%+$8'113! %$5*$9$8'*+O!
%255(%+$*5!80*%$%+(*83!$*!%+2.(*+%d!&()8(&+$0*%!A(90)(!'*.! '9+()! +#(! (M(*+6!`2)+#(),0)(O!80))(1'+$0*%!
A(+4((*! PQG/)! $*+()*'1! .$,(*%$0*%! %255(%+! %0,(!$*+()&1'3! A(+4((*! M')$02%! '%&(8+%!09! %+2.(*+!
'8'.(,$8!'.R2%+,(*+O!A2+!92)+#()!)(%(')8#!$%!%+$11!*((.(.6!
P2'1$+'+$M(!)(%21+%!9)0,!+#(!)'&$.!80*+(*+!'*'13%$%!09! &0%+/(M(*+! $*+()M$(4%! #$5#1$5#+(.!+#(!)01(! 09! +#(!
FII!#'8C'+#0*!$*!&)0M$.$*5!+#(!0&&0)+2*$+3!90)!9$)%+/3(')!%+2.(*+%!+0!8011'A0)'+(!4$+#!,0)(!(-&()$(*8(.!
&(()%!'*.!5'$*!&)'8+$8'1!$*%$5#+%!$*+0!+#($)!9$(1.!09!%+2.36!_04(M()O!8#'11(*5(%!)(1'+(.!+0!+#(!80,&1(-$+3!
09!+#(!">N!%8(*')$0!4()(!(M$.(*+6!"#()(90)(O!+#(!+'$10)$*5!09!(.28'+$0*'1!(M(*+%!1$C(!#'8C'+#0*%!+0!,((+!
+#(!.$M()%(!*((.%O!C*041(.5(O!'*.!%C$11%!09! %+2.(*+%!%#021.!A(!80*%$.()(.!+0!(*#'*8(!$*812%$M$+3!'*.!
&)0,0+(!5)('+()!&')+$8$&'+$0*!'8)0%%!'8'.(,$8!80#0)+%O!'%!%255(%+(.!A3!a;c!'*.!abZc6!`2+2)(!)(%(')8#!
$%!*((.(.!+0!(-&10)(!'1+()*'+$M(!'%%(%%,(*+!,(+#0.%! '.'&+(.!+0!%+2.(*+%d!A'8C5)02*.%O!4#$8#!8021.!
$*812.(!+#(!.(M(10&,(*+!09!'.'&+'A1(!+'%C%!4$+#!.$%+$*8+!1(M(1%!09!.$99$821+36!
H(%&$+(! +#(%(! 9$*.$*5%O! $+! $%! $,&0)+'*+!+0! '8C*041(.5(!+#(! 1$,$+'+$0*%! 09! +#(! %+2.3!$*! +(),%! 09!
5(*()'1$]'A$1$+3!'*.!)0A2%+*(%%6! "#(! 5(*.()!$,A'1'*8(!'*.!+#(!.$%+)$A2+$0*!09! &')+$8$&'*+%! '8)0%%! '5(!
'*.! '8'.(,$8! 3(')! )'$%(! 80*8()*%! 'A02+! +#(!%',&1(! )(&)(%(*+'+$M(*(%%6! 7*! '..$+$0*O! +#(! 1$,$+(.!
J2'1$+'+$M(!.'+'!9)0,!+#(!$*+()M$(4(.!&')+$8$&'*+%!%(1(8+(.!90)!+#(!9$*'1!&$+8#!,'3!$,&'8+!+#(!.(&+#!'*.!
A)('.+#! 09! $*%$5#+%! 51('*(.! 9)0,! +#$%!%+2.36! `2+2)(! )(%(')8#! %#021.! '..)(%%! +#(%(! 1$,$+'+$0*%! A3!
(,&103$*5! 0+#()! )(%(')8#! .(%$5*%O! 4$+#! .$M()%(! '*.! )(&)(%(*+'+$M(! %',&1(%O! +#2%! (*%2)$*5! ,0)(!
7785

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80,&)(#(*%$M(!.'+'!0*!%+2.(*+!0&$*$0*%O!9((1$*5%O!'*.!'++$+2.(%!+04').%!(.28'+$0*'1!#'8C'+#0*%O!+#($)!
'8'.(,$8!$*+(5)'+$0*O!'*.!'.R2%+,(*+6!
ACKNOWLEDGEMENTS
"#$%!40)C!4'%!8'))$(.!02+!'%!&')+!09!s>FI@>>\tF"GHsO!80/92*.(.!A3!+#(!_2,'*!I'&$+'1!\&()'+$0*'1!
N)05)',!=N\I_?O!N)0R(8+!g06!N\I_/Z:/SL7:/`>@/ZZZZe;O!+#)025#!+#(!@2)0&('*!>08$'1!`2*.!=@>`?6!
[(!4021.! 1$C(!+0!+#'*C!'11!+#(!%+2.(*+%!4#0!&')+$8$&'+(.!$*! +#(!FII!#'8C'+#0*6![(!4021.!'1%0!1$C(!+0!
+#'*C! '11! +#(! R2.5(%! 4#0!(M'12'+(.! +#(! ">N! %012+$0*%! %2A,$++(.! A3!+#(! %+2.(*+%O! '*.! '11! +#(! 9'821+3!
,(,A()%O!<>8O!i>8O!'*.!N#H!%+2.(*+%!$*M01M(.!4$+#!+#(!0)5'*$]'+$0*!09!+#(!FII!#'8C'+#0*!'+!F"GH6!!
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abc!i6!_6!I1')CO!>6!I6!i$..1(+0*O!H6!g523(*O!'*.!l6!^6!u4$8CO!si(.$'+$*5!f(1'+$0*%#$&%!A(+4((*!
G8'.(,$8!i0+$M'+$0*O!G8'.(,$8!7*+(5)'+$0*!'*.!G8'.(,$8!N()90),'*8(Os!Learning and Individual
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of Educational ResearchO!M016!eSO!*06!bO!&&6!W;Tb:SO!b;XS6!
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aec!i6!>8#*($.()O!'*.!`6!N)(8C(1O!sQ')$'A1(%!'%%08$'+(.!4$+#!'8#$(M(,(*+!$*!#$5#()!(.28'+$0*D!G!
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(*M$)0*,(*+!90)!'8'.(,$8!$*+(5)'+$0*Os!Higher EducationO!M016!X;O!&&6!;S/bbZO!:Z:Z6!
aBc!I6!f(**$8CO!j6!l$+%+()O!I6!I6![6!_211%O!'*.!G6!_2)%+O!sI2))$821')!_'8C'+#0*%!90)!@*5$*(()$*5!
H(%$5*!l(')*$*5D!"#(!I'%(!09!@*5$*(()$*5!H(%$5*!H'3%Os!IEEE Transactions on EducationO!M016!
BBO!*06!BO!&&6!BSe/BBeO!:Z:L6!
aXc!^6!j','O!<6!G1(*8')O!`6!I'1(5')$0O!G6!g(M(%O!'*.!N6!G1(%%$06!sG!#'8C'+#0*!,(+#0.01053!90)!
2*.()5)'.2'+(!802)%(!&)0R(8+%6s!7*!2018 IEEE Frontiers in Education Conference (FIE)O!&&6!b/;6!
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aWc!i6!<6!j')8$'O!v_'8C'+#0*%!'%!(-+)'82))$821')!'8+$M$+$(%D!F*)'M(1$*5!+#(!,0+$M'+$0*'1!0)$(*+'+$0*!
A(#$*.!%+2.(*+!&')+$8$&'+$0*Ow!Computer Applications in Engineering EducationO!M016!LZO!*06!BO!&&6!
b;ZL/b;bWO!:Z::6!
a;c!I6!['114(3O!i6!i6!l0*5,($()O!H6!_'3.(O!z6!G),%+)0*5O!f6!^'R9(]O!'*.!f6!N(1'*O!vI0*%$.()!
v_GI^>w!4#(*!.(%$5*$*5!#'8C'+#0*!8#'11(*5(%D!_00CO!'8+$0*O!8011'A0)'+$M(!C*041(.5(!%#')$*5Ow!
Frontiers in EducationO!M016!XO!&6!;SeZeeO!:Z::6!
abZc!<6!_(11()O!G6!G,$)O!f6!['-,'*O!'*.!x6!i'')'M$O!v_'8C!302)!0)5'*$]'+$0*'1!$**0M'+$0*D!1$+()'+2)(!
)(M$(4!'*.!$*+(5)'+$M(!,0.(1!90)!)2**$*5!#'8C'+#0*%Ow!Journal of Innovation and
EntrepreneurshipO!M016!b:O!*06!BO!&&6!b/:eO!:Z:L6!
abbc!i6!i6!`100.O!v"#(!+)'M(1$*5/%'1(%,'*!&)0A1(,Ow!Operations ResearchO!M016!eO!*06!bO!&&6!Bb/XSO!b;SB6!
ab:c!H6!"6!I',&A(11!'*.!z6!I6!>+'*1(3O!Experimental and quasi-experimental designs for research6!
<0%+0*O!iGD!_025#+0*!i$991$*!I0,&'*3O!b;BL6!
abLc!l6!>6!G1,($.'O!z6!G6!j6!`())($)'O!'*.!G6!N6!>0')(%O!vP2(%+$0*h)$0!.(!M$M{*8$'%!'8'.|,$8'%D!
80*%+)2}~0!(!M'1$.'}~0!.(!2,'!M()%~0!)(.2]$.'!=PQG/)?Ow!Revista Portuguesa de PedagogiaO!M016!
LLO!*06!LO!&&6!bWb/:ZXO!b;;;6!!!
abec!l6!>6!G1,($.'O!G6!N6!I6!>0')(%O!'*.!z6!G6!`())($)'O!vP2(%+$0*h)$0!.(!Q$M{*8$'%!G8'.{,$8'%!=PQG/
)?D!GM'1$'}~0!.0!'R2%+',(*+0!.0%!(%+2.'*+(%!2*$M()%$+h)$0%Ow!Avaliação PsicológicaO!M016!bO!*06!:O!
&&6!Wb/;LO!:Z::6!
abSc!z6![6!I)(%4(11!'*.!I6!g6!N0+#O!Qualitative Inquiry and Research Design - Choosing Among Five
Approaches6!"#02%'*.!\'C%O!IGD!>'5(O!:ZbW6!
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abBc!z6![6!g('1O!u6!N6!g('1O!@6!Q'*H3C(O!'*.!i6!^0)*A12#O!v@-&(.$+$*5!+#(!'*'13%$%!09!J2'1$+'+$M(!.'+'!
$*!(M'12'+$0*D!G!&)08(.2)(!90)!+#(!)'&$.!$.(*+$9$8'+$0*!09!+#(,(%!9)0,!'2.$0!)(80).$*5%!=f7"G?Ow!
American Journal of EvaluationO!M016!LBO!*06!bO!&&6!bbW/bL:O!:ZbS6!
7787