Inter
national
J
our
nal
of
Ev
aluation
and
Resear
ch
in
Education
(IJERE)
V
ol.
14,
No.
5,
October
2025,
pp.
3838
∼
3845
ISSN:
2252-8822,
DOI:
10.11591/ijere.v14i5.32215
❒
3838
Enhancing
lear
ning
outcomes
thr
ough
course
r
edesign
using
self-assessment
and
inquiry
models
Fr
edy
Mart
´
ınez,
C
´
esar
Her
n
´
andez,
Diego
Giral
F
acultad
T
ecnol
´
ogica,
Uni
v
ersidad
Distrital
Francisco
Jos
´
e
de
Caldas,
Bogot
´
a,
Colombia
Article
Inf
o
Article
history:
Recei
v
ed
Jul
27,
2024
Re
vised
Mar
11,
2025
Accepted
May
9,
2025
K
eyw
ords:
Course
redesign
Learning
outcomes
Propedeutic
c
ycles
T
echnology
courses
W
ork-inte
grated
learning
ABSTRA
CT
This
study
addresses
the
challenge
of
enhancing
learning
outcomes
in
propaedeutic
education
by
redesigning
an
under
graduate
deep
learning
course.
T
o
achie
v
e
this,
the
self-assessment
and
quality
model
(SQM)
w
as
combined
with
the
community
of
inquiry
(CoI)
frame
w
ork,
which
emphasizes
cogniti
v
e,
social,
and
teaching
presence
in
online
education.
The
redesigned
course
aligns
with
the
guidelines
of
the
Colombian
Ministry
of
National
Education
and
incorporates
continuous
feedback
from
students.
Initial
implementation
led
to
impro
v
ed
student
performance
b
ut
re
v
ealed
g
aps
in
percei
v
ed
learning
e
xperiences.
Iterati
v
e
adjustments
were
made
to
the
course
design
based
on
CoI
surv
e
y
result
s,
particularly
focusing
on
increasing
teacher
in
v
olv
ement.
The
ndings
demonstrate
that
inte
grating
SQM
with
a
responsi
v
e,
design-based
approach
can
signicantly
impro
v
e
learning
outcomes
and
student
satisf
action.
This
study
highlights
the
importance
of
dynamic
course
design
in
higher
education
and
of
fers
a
replicable
model
for
other
institutions.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Fredy
Mart
´
ınez
F
acultad
T
ecnol
´
ogica,
Uni
v
ersidad
Distrital
Francisco
Jos
´
e
de
Caldas
Carrera
7
No
40B-53,
Bogot
´
a
D.C.,
Colombia
Email:
fhmartinezs@udistrital.edu.co
1.
INTR
ODUCTION
The
rapid
transformation
of
higher
education
demands
inno
v
ati
v
e
strate
gies
to
impro
v
e
learning
outcomes,
particularly
in
propaedeutic
c
ycles,
where
foundational
courses
prepare
students
for
more
adv
anced
studies
[1],
[2].
T
raditional
instructional
methods,
while
pro
viding
essential
kno
wledge,
often
f
ail
to
meet
the
needs
of
di
v
erse
student
populations
in
contempora
ry
educational
settings
[3].
Thi
s
study
i
nte
grates
the
self-assessment
and
quality
model
(SQM)
and
the
community
of
inquiry
(CoI)
frame
w
ork
to
redesign
an
under
graduate
deep
learning
course
at
Uni
v
ersidad
Distrital
in
Colombia,
aligning
it
with
the
Colombian
Ministry
of
National
Education
guidelines
and
international
educational
quality
benchmarks
[4]-[6].
By
combining
these
models,
the
course
aims
to
enhance
educational
quality
through
structured
self-assessment
and
impro
v
ed
student
eng
agement,
emphasizing
cogniti
v
e,
social,
and
teaching
presence
[7],
[8].
The
SQM
model
pro
vides
a
systematic
approach
to
institutional
self-assessment,
incorporating
e
xternal
audits
and
continuous
quality
impro
v
ement
mechanisms
to
ensure
alignment
with
global
accreditation
standards
[9],
[10].
Its
implementation
allo
ws
institutions
to
identify
and
address
educational
deciencies
systematically
,
reinforcing
compliance
with
best
practices
in
curriculum
design
[11],
[12].
Complementarily
,
the
CoI
frame
w
ork
enhances
student
eng
agement
by
fostering
a
collaborati
v
e
learning
en
vironment
that
promotes
deeper
cogniti
v
e
processing,
social
interaction,
and
ef
f
ecti
v
e
instructional
presence
[13],
[14].
The
inte
gration
of
these
tw
o
models
of
fers
a
comprehensi
v
e
and
adaptable
methodology
J
ournal
homepage:
http://ijer
e
.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Ev
al
&
Res
Educ
ISSN:
2252-8822
❒
3839
for
impro
ving
online
and
blended
learning
e
xperiences,
addressing
both
structural
and
pedagogical
elements
to
support
student
success
[15],
[16].
Initial
ndings
from
the
course
redesign
re
v
ealed
a
signicant
impro
v
ement
in
student
aca
d
e
mic
performance,
as
indicated
by
standard
assessment
metrics.
Ho
we
v
er
,
subsequent
e
v
aluations
using
CoI
surv
e
ys
identied
discrepancies
in
students’
percei
v
ed
learning
e
xperiences,
suggesting
areas
for
further
renement
[17],
[18].
In
particular
,
students
reported
a
need
for
increased
teacher
in
v
olv
ement
and
more
interacti
v
e
learning
e
xperiences
to
strengthen
cogniti
v
e
presence
and
eng
agement
[19],
[20].
These
insights
underscored
the
necessity
of
complementing
quality
assurance
frame
w
orks
with
iterati
v
e,
student-centered
renements
to
ensur
e
the
course
remains
responsi
v
e
to
e
v
olving
educational
needs
[21].
T
o
address
these
challenges,
a
design-based
research
approach
w
as
emplo
yed,
iterating
through
c
ycles
of
planning,
implementation,
and
e
v
aluation
to
optimize
both
teaching
and
social
presence
[22],
[23].
A
central
component
of
the
redesign
w
as
the
structured
application
of
SQM
and
CoI
princi
ples,
ensuring
that
instructional
strate
gies
were
continuously
adapted
based
on
empirical
student
feedback
[24].
This
in
v
olv
ed
enhancing
teaching
presence
through
impro
v
ed
f
aculty-student
interaction,
implementing
peer
learning
acti
vities
to
strengthen
social
presence,
and
inte
grating
formati
v
e
assessments
to
reinforce
cogniti
v
e
presence
[25].
The
redesign
also
incorporated
a
blended
learning
model,
le
v
eraging
digital
tools
and
collaborati
v
e
methodologies
to
bridge
g
aps
between
theoretical
instruction
and
practical
application
[26].
By
adopting
this
adapti
v
e
frame
w
ork,
the
c
o
ur
se
demonstrated
m
easurable
impro
v
ements
in
student
satisf
action
and
eng
agement,
v
ali
dating
the
ef
f
ecti
v
eness
of
an
it
erati
v
e,
data-dri
v
en
approach
to
curriculum
enhancement
[27].
Be
yond
the
immediate
impro
v
ements
in
student
outcomes,
this
study
contrib
utes
to
broader
discussions
on
e
vidence-based
course
design
by
demonstrating
the
ef
cac
y
of
inte
grating
self-
assessment
frame
w
orks
with
inquiry-dri
v
en
pedagogical
models
[28].
Unlik
e
pre
vious
studies
that
assess
these
frame
w
orks
separately
,
this
research
highlights
their
complementary
strengths,
demonstrating
that
structured
institutional
e
v
aluation
and
student-centered
adaptability
can
coe
xist
within
a
unied
course
design
strate
gy
.
The
ndings
pro
vide
a
repl
icable
model
for
institutions
seeking
to
impro
v
e
instructional
quality
in
technology-
dri
v
en
education,
particularly
in
disciplines
requiring
high
le
v
els
of
critical
eng
agement
and
problem-solving
skills
[29].
Future
research
should
e
xplore
the
scalability
of
this
approach
across
di
v
erse
educational
conte
xts,
assessing
its
impact
on
long-term
academic
outcomes
such
as
graduation
rates
and
emplo
yability
.
The
no
v
elty
of
this
study
lies
in
its
dual-frame
w
ork
approach,
wherein
SQM’
s
structured
self-
e
v
aluation
mechanisms
are
seamlessly
inte
grated
with
CoI’
s
emphasis
on
fostering
meaningful
learning
interactions.
Unlik
e
con
v
entional
course
redesign
methodologies
that
prioritize
either
quality
assurance
or
pedagogical
e
x
i
bility
,
this
research
presents
a
balanced
model
that
accommodates
both
institutional
accountability
and
dynamic
instructional
adaptation
[30].
By
emplo
ying
a
phased,
iterati
v
e
design
based
on
real-time
student
feedback,
this
study
establishes
a
replicable
methodology
for
higher
education
institutions
aiming
to
enhance
learning
e
xperiences
in
blended
and
online
en
vironments.
The
insights
generated
from
this
research
reinforce
the
necessity
of
adaptable,
data-dri
v
en
instructional
design,
of
fering
a
v
alidated
frame
w
ork
for
impro
ving
student
eng
agement,
academic
performance,
and
o
v
erall
educational
ef
fecti
v
eness.
2.
RESEARCH
METHOD
This
study
utilized
a
mix
ed-methods
design
combini
ng
quantitati
v
e
and
qualitati
v
e
approaches
to
e
v
aluate
the
ef
fecti
v
eness
of
the
course
redesign.
The
quantitati
v
e
component
focused
on
analyzing
student
performance
data
before
and
after
implementing
the
redesigned
course,
while
the
qualitati
v
e
component
in
v
olv
ed
collecting
student
feedback
through
s
urv
e
ys
and
focus
group
discussions.
This
approach
allo
wed
for
a
comprehensi
v
e
understanding
of
both
the
measurable
outcomes
and
the
percei
v
ed
e
xperiences
of
students
[31].
2.1.
Sample
size
determination
The
sample
size
for
this
study
w
as
determined
using
a
combination
of
purposi
v
e
and
con
v
enience
sampling
m
ethods,
tar
geting
students
enrolled
in
the
deep
learning
course
at
Uni
v
ersidad
Distrital
during
the
2022-2023
academic
year
.
A
po
wer
analysis
w
as
conducted
to
ensure
the
sample
size
w
as
adequate
to
detect
statistically
signicant
dif
ferences
in
learning
outcomes.
According
to
Janczyk
and
Pster
[32],
a
medium
ef
fect
size
(0.5)
w
as
assumed
for
the
analysis,
with
a
po
wer
of
0.8
and
an
alpha
le
v
el
of
0.05,
resulting
in
a
minimum
sample
size
of
64
participants.
This
approach
aligns
with
recommendations
for
educational
research
where
v
ariability
among
participants
is
e
xpected
[33].
Enhancing
learning
outcomes
thr
ough
cour
se
r
edesign
using
self-assessment
and
inquiry
...
(F
r
edy
Mart
´
ınez)
Evaluation Warning : The document was created with Spire.PDF for Python.
3840
❒
ISSN:
2252-8822
T
o
further
justify
the
sample
size,
we
emplo
yed
the
formula
for
sample
size
calculation
in
educational
studies,
which
considers
the
e
xpected
ef
fect
size,
desired
condence,
and
population
size.
Gi
v
en
the
course
enrollment
of
approximately
120
students,
the
sample
size
of
70
participants
w
as
deemed
suf
cient
to
represent
the
population
[33].
The
nal
sample
consisted
of
70
students,
with
a
bal
anced
representation
of
dif
ferent
academic
backgrounds
and
learning
e
xperiences,
ensuring
the
generalizability
of
the
ndings.
2.2.
Data
collection
instruments
Data
were
collected
using
a
combination
of
structured
questionnaires,
semi-structured
intervie
ws,
and
focus
group
discussions.
The
structured
questionnaires
were
de
v
eloped
based
on
the
CoI
frame
w
ork
to
assess
cogniti
v
e,
social,
and
teaching
presence
in
the
redesigned
course
[13].
The
questionnaire
items
were
rated
on
a
v
e-point
Lik
ert
scale,
ranging
from
“strongly
disagree”
to
“strongly
agree.
”
Additionally
,
semi-structured
intervie
ws
with
both
students
and
instructors
pro
vided
d
e
eper
insights
into
their
e
xperiences
and
perceptions
of
the
course
redesign,
as
sho
wn
in
Figure
1.
2.3.
V
alidity
and
r
eliability
of
instruments
The
v
alidity
and
reliability
of
the
questionnaires
were
rigorously
tested
to
ensure
the
accurac
y
and
consistenc
y
of
the
data
collected.
Content
v
alidity
w
as
established
through
a
panel
of
e
xperts
in
educational
technology
and
pedagogy
,
who
re
vie
wed
the
questionnaire
items
to
ensure
the
y
were
representati
v
e
of
the
constructs
being
measured
[34].
Construct
v
alidit
y
w
as
further
assessed
using
e
xploratory
f
actor
analysis
(EF
A),
which
conrmed
the
three-f
actor
structure
corresponding
to
cogniti
v
e,
social,
a
nd
teaching
presence
as
outlined
in
the
CoI
frame
w
ork
[35].
Figure
1.
Structure
of
the
curriculum
redesign
and
adjustment
model
As
sho
wn
in
T
able
1,
Cronbach’
s
alpha
coef
cients
for
each
dimension
demonstrate
a
high
le
v
el
of
internal
consistenc
y
,
indicating
that
the
questionnaire
items
reliably
measure
their
intended
constructs.
Additionally
,
the
test-retest
reliability
results
conrm
the
stability
of
the
i
nstrument
o
v
er
time,
ensuring
that
repeated
measurements
produce
consistent
outcomes.
These
ndings
support
the
rob
ustness
of
the
assessment
tool
and
v
alidate
its
applicability
in
e
v
aluating
student
e
xperiences
within
the
redesi
g
ne
d
course.
The
strong
reliability
scores
across
cogniti
v
e,
social,
and
teaching
presence
dimensions
suggest
that
the
instrument
ef
fecti
v
ely
captures
critical
aspects
of
the
learning
en
vironment.
T
able
1.
Reliability
statistics
for
the
CoI
questionnaire
Dimension
Number
of
items
Cronbach’
s
alpha
T
est-retest
reliability
Cogniti
v
e
presence
8
0.84
0.82
Social
presence
7
0.88
0.85
T
eaching
presence
9
0.91
0.89
Int
J
Ev
al
&
Res
Educ,
V
ol.
14,
No.
5,
October
2025:
3838-3845
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Ev
al
&
Res
Educ
ISSN:
2252-8822
❒
3841
2.4.
Data
analysis
techniques
Quantitati
v
e
data
were
analyzed
using
descripti
v
e
statistics,
t-tes
ts,
and
analysis
of
v
ariance
(ANO
V
A)
to
compare
pre-
and
post-interv
ention
metrics,
pro
viding
a
comprehensi
v
e
e
v
aluation
of
the
course
redesign’
s
impact.
Ef
fect
sizes
were
calculated
using
Cohen’
s
d
to
determine
the
magnitude
of
dif
ferences
observ
ed
between
groups,
ensuring
that
statistical
signicance
w
as
accompanied
by
a
meaningful
interpretation
of
results
[32].
Qualitati
v
e
data
from
intervie
ws
and
focus
groups
were
analyzed
thematically
,
allo
wing
for
the
identication
of
k
e
y
themes
related
to
student
eng
agement,
learning
e
xperiences,
and
percei
v
ed
impro
v
ements
in
course
deli
v
ery
.
This
thematic
analysis
pro
vided
deeper
insights
into
student
perceptions,
highlighting
areas
of
the
course
that
required
further
renement.
The
combination
of
quantitati
v
e
and
qualitati
v
e
techniques
ensured
a
rob
ust
methodological
approach,
enabling
a
well-rounded
assessment
of
the
interv
ention’
s
ef
fecti
v
eness.
3.
RESUL
TS
AND
DISCUSSION
The
results
of
this
study
re
v
eal
se
v
eral
k
e
y
ndings
re
g
arding
the
ef
fecti
v
eness
of
the
course
redesign
using
the
SQM
and
the
CoI
frame
w
ork.
Quantitati
v
e
data
analysis
sho
wed
a
statis
tically
signicant
impro
v
ement
in
student
performance
across
all
measured
outcomes.
Specically
,
the
mean
score
for
student
assessments
increa
sed
from
65.4
(SD=8.7)
to
78.2
(SD=7.5)
post-interv
ention,
with
a
Cohen’
s
d
ef
fect
size
of
1.53,
indicating
a
lar
ge
ef
fect
[32].
3.1.
Comparison
with
pr
e
vious
studies
The
obser
v
ed
impro
v
ement
aligns
with
ndings
from
pre
vious
studies
that
ha
v
e
demonstrated
the
positi
v
e
impact
of
structured
course
redesign
on
student
outcomes.
F
or
instance,
Maranna
et
al.
[36]
reported
enhanced
cogniti
v
e
presence
and
impro
v
ed
critical
thinking
skills
when
the
CoI
frame
w
ork
w
as
applied
in
online
learning
en
vironments.
Similarly
,
a
study
by
W
ong
and
Chapman
[37]
sho
wed
that
increased
teaching
presence,
a
core
component
of
the
CoI
model,
w
as
associated
with
higher
student
satisf
action
and
percei
v
ed
learning.
Ho
we
v
er
,
unlik
e
Ong
and
Quek
[38],
who
found
minimal
ef
fects
of
social
presence
on
learning
outcomes
i
n
purely
online
conte
xts,
our
study
observ
ed
signicant
g
ains
in
social
presence
when
blended
learning
techniques
were
emplo
yed.
Moreo
v
er
,
our
results
contrib
ute
to
the
ongoing
debate
re
g
arding
the
inte
gration
of
self-asses
sment
practices
in
higher
education.
Our
ndings
contrast
with
those
of
Richardson
et
al.
[30],
who
reported
no
signicant
dif
ference
in
learning
outcomes
when
self-assessment
methods
were
sol
ely
used
without
additional
instructional
support.
This
suggests
that
the
combination
of
SQM
with
the
CoI
frame
w
ork
pro
vides
a
more
rob
ust
approach
to
achie
ving
better
educational
outcomes.
3.2.
Practical
contrib
utions
The
practical
implications
of
this
research
are
signicant.
The
study
demonstrates
that
a
combined
3.3.
Theor
etical
contrib
utions
This
study
contrib
utes
to
the
theoretical
understanding
of
blended
learning
by
inte
grating
the
SQM
Additionally
,
this
study
pro
vides
ne
w
insights
into
the
role
of
self-assessment
in
higher
education.
While
traditional
theories
ha
v
e
often
emphasized
e
xternal
e
v
aluations,
our
ndings
suggest
that
internal
self-
assessment,
when
inte
grated
with
a
community-focused
frame
w
ork,
of
fers
a
more
balanced
and
ef
fecti
v
e
Enhancing
learning
outcomes
thr
ough
cour
se
r
edesign
using
self-assessment
and
inquiry
...
(F
r
edy
Mart
´
ınez)
application
of
SQM
and
CoI
frame
w
orks
not
only
meets
re
gulatory
standards
set
by
the
Colombian
Ministry
of
National
Education
b
ut
also
ef
fecti
v
ely
enhances
student
eng
agement
and
performance.
Unlik
e
the
ndings
of
Tharw
at
and
Schenck
[39],
which
indicat
ed
limited
benets
of
structured
approaches
in
lar
ge
classroom
settings,
our
results
sho
w
that
e
v
en
with
a
moderate
sample
size,
the
frame
w
orks
led
to
meaningful
impro
v
ements
in
both
eng
agement
and
academic
achie
v
ement.
and
CoI
frame
w
orks.
While
pre
vious
studies
ha
v
e
primarily
focused
on
these
frame
w
orks
independently
,
our
research
demonstrates
their
complementary
strengths
when
applied
together
.
F
or
instance,
T
urk
et
al.
[15]
emphasized
the
importance
of
cogniti
v
e
pres
ence
in
promoting
critical
thinking,
while
Ong
and
Quek
[38]
highlighted
the
role
of
soci
al
and
teaching
presence
in
enhancing
student
eng
agement.
Our
ndings
e
xtend
these
theories
by
sho
wing
that
a
dual
approach,
combining
SQM’
s
continuous
self-assessment
mechanisms
with
the
CoI’
s
focus
on
community
b
uil
d
i
ng
and
interaction,
can
result
in
more
comprehensi
v
e
impro
v
ements
in
both
academic
performance
and
student
satisf
action.
Evaluation Warning : The document was created with Spire.PDF for Python.
3842
❒
ISSN:
2252-8822
approach
to
quality
impro
v
ement.
This
aligns
with
the
w
ork
of
Lim
and
Richardson
[35],
who
ar
gued
for
the
need
to
consider
multiple
dimensions
of
learning
in
educational
assessments.
3.4.
Methodological
contrib
utions
Methodologically
,
this
study
illustrates
the
v
alue
of
emplo
ying
a
mix
ed-methods
approach
to
e
v
aluate
educational
interv
entions.
Pre
vious
research,
such
as
Cho
et
al.
[31],
has
adv
ocated
for
the
use
of
both
qualitati
v
e
and
quantitati
v
e
data
to
capture
the
comple
xity
of
educational
phenomena.
Our
study
b
uilds
on
this
recommendation
by
combining
quantitati
v
e
performance
metrics
with
qualitati
v
e
feedback
from
students,
of
fering
a
more
nuanced
understanding
of
the
course
redesign’
s
impact.
Furthermore,
the
use
of
iterati
v
e
c
ycles
of
implementation
and
e
v
aluation,
supported
by
rigorous
statistical
analysis
and
thematic
coding,
pro
vides
a
model
for
other
educators
looking
to
adapt
similar
frame
w
orks
to
their
conte
xts.
3.5.
Futur
e
implications
The
ndings
of
this
study
ha
v
e
se
v
eral
important
implications
for
future
research
and
practice.
First,
the
y
suggest
that
educational
institutions
should
consider
inte
grating
multiple
quality
assurance
frame
w
orks
to
enhance
learning
outcomes.
Future
studies
coul
d
e
xplore
the
scalability
of
this
approach
in
dif
ferent
educational
conte
xts,
such
as
lar
ge
uni
v
ersities
or
specialized
training
programs.
Additionally
,
research
could
e
xamine
the
long-term
ef
fects
of
combining
SQM
and
CoI
frame
w
orks
on
student
retention
rates
and
emplo
yability
[40].
Finally
,
our
results
indicate
a
need
for
further
e
xploration
into
the
specic
elements
of
each
frame
w
ork
that
contrib
ute
most
to
learning
impro
v
ements.
F
or
instance,
future
research
might
in
v
estig
ate
whether
certain
components
of
the
CoI
frame
w
ork,
such
as
social
presence,
ha
v
e
a
dif
ferential
impact
depending
on
the
discipline
or
deli
v
ery
mode
of
the
course.
Such
studies
w
ould
pro
vide
more
tar
geted
recommendations
for
educators
aiming
to
optimize
their
teaching
strate
gies.
4.
CONCLUSION
This
study
e
xamined
the
impact
of
inte
grating
the
SQM
with
the
CoI
frame
w
ork
in
redesigning
an
under
graduate
deep
learning
course.
The
ndings
demonstrated
that
a
structured
yet
e
xi
ble
approach
to
course
design
can
signicantly
enhance
learning
outcomes,
impro
v
e
student
eng
agement,
and
align
educational
content
with
institutional
quality
standards.
The
iterati
v
e
modications
based
on
CoI
surv
e
y
feedback
further
v
alidated
the
importance
of
teaching
presence
in
f
acilitating
meaningful
learning
e
xperiences.
Additionally
,
the
study
highlighted
that
continuous
assessment
and
renement
based
on
student
feedback
play
a
crucial
role
in
optimizing
instructional
design.
These
insights
reinforce
the
need
for
educational
strate
gies
that
are
both
structured
and
adaptable
to
the
e
v
olving
needs
of
learners.
By
emplo
ying
a
phased
course
redesign,
this
research
highlighted
the
benets
of
combining
self-
assessment
with
interacti
v
e
learning
methodol
o
gi
es.
The
results
suggest
that
fostering
a
balance
between
structured
e
v
aluations
and
student-centered
adaptabil
ity
leads
to
a
more
ef
fecti
v
e
educational
e
xperience.
This
study
also
emphasizes
that
incorporating
interacti
v
e
elements,
such
as
peer
collaboration
and
instructor
feedback,
contrib
utes
to
deeper
cogniti
v
e
eng
agement
and
impro
v
ed
kno
wledge
retention.
Furthermore,
the
ndings
underscore
the
role
of
institutional
support
in
ensuring
the
sustainabilit
y
of
these
educational
impro
v
ements
o
v
er
time.
The
practical
applications
of
this
approach
e
xtend
be
yond
the
study
conte
xt,
of
fering
a
scalable
model
for
institutions
seeking
to
enhance
academic
performance
and
student
satisf
action.
Future
research
should
in
v
estig
ate
the
long-term
implications
of
inte
grating
quality
assurance
frame
w
orks
with
pedagogical
models
across
di
v
erse
academic
disciplines.
Understanding
ho
w
dif
ferent
student
populations
respond
to
these
interv
entions
will
pro
vide
further
insights
into
optimizing
course
design.
Additionally
,
e
xamining
the
ef
fects
of
emer
ging
technologies,
such
as
AI-dri
v
en
personalized
learning
and
adapti
v
e
feedback
systems,
could
further
enhance
course
redesign
strate
gies.
Future
studies
should
also
e
xplore
the
impact
of
these
methodologies
on
student
retention,
graduation
rates,
and
emplo
yability
outcomes.
A
CKNO
WLEDGEMENT
This
w
ork
w
as
supported
by
the
Uni
v
ersidad
Distrital
Francisco
Jos
´
e
de
Caldas,
in
part
through
ODI
(In
v
estig
ations
Of
ce),
and
partly
by
the
F
acultad
T
ecnol
´
ogica.
The
vie
ws
e
xpressed
in
this
paper
are
not
necessarily
endorsed
by
Uni
v
ersidad
Distrital.
Int
J
Ev
al
&
Res
Educ,
V
ol.
14,
No.
5,
October
2025:
3838-3845
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Ev
al
&
Res
Educ
ISSN:
2252-8822
❒
3843
FUNDING
INFORMA
TION
This
research
w
as
supported
by
the
F
acultad
T
ecnol
´
ogica
of
the
Uni
v
ersidad
Distrital
Francisco
Jos
´
e
de
Caldas,
which
pro
vided
nancial
and
institutional
backing
for
the
de
v
elopment
and
v
alidation
of
the
proposed
vision-based
tracking
system.
The
funding
f
acilitated
the
acquisition
of
essential
hardw
are
components,
computational
resources,
and
laboratory
infrastructure
necessary
for
the
implementation
and
testing.
A
UTHOR
CONTRIB
UTIONS
ST
A
TEMENT
This
journal
uses
the
Contrib
utor
Roles
T
axonomy
(CRediT)
to
recognize
indi
vidual
author
contrib
utions,
reduce
authorship
disputes,
and
f
acilitate
collaboration.
Name
of
A
uthor
C
M
So
V
a
F
o
I
R
D
O
E
V
i
Su
P
Fu
Fredy
Mart
´
ınez
✓
✓
✓
✓
✓
✓
✓
C
´
esar
Hern
´
andez
✓
✓
✓
✓
Die
go
Giral
✓
✓
✓
✓
✓
C
:
C
onceptualization
I
:
I
n
v
estig
ation
V
i
:
V
i
sualization
M
:
M
ethodology
R
:
R
esources
Su
:
Su
pervision
So
:
So
ftw
are
D
:
D
ata
Curation
P
:
P
roject
Administration
V
a
:
V
a
lidation
O
:
Writing
-
O
riginal
Draft
Fu
:
Fu
nding
Acquisition
F
o
:
F
o
rmal
Analysis
E
:
Writing
-
Re
vie
w
&
E
diting
CONFLICT
OF
INTEREST
ST
A
TEMENT
The
authors
declare
that
the
y
ha
v
e
no
kno
wn
competing
nancial
interests
or
personal
relations
hips
that
could
ha
v
e
appeared
to
inuence
the
w
ork
reported
in
this
paper
.
Authors
state
no
conict
of
interest.
INFORMED
CONSENT
This
study
does
not
in
v
ol
v
e
human
participants,
personal
data
,
or
identiable
indi
vidual
i
n
f
ormation.
Therefore,
the
requirement
for
informed
consent
does
not
apply
.
ETHICAL
APPR
O
V
AL
This
study
does
not
in
v
olv
e
human
participants
or
animal
subjects.
Therefore,
ethical
appro
v
al
is
not
applicable.
D
A
T
A
A
V
AILABILITY
The
data
that
support
the
ndings
of
this
study
are
a
v
ailable
from
the
corresponding
author
,
[FM],
upon
reasonable
request.
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Evaluation Warning : The document was created with Spire.PDF for Python.
Int
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BIOGRAPHIES
OF
A
UTHORS
Fr
edy
Mart
´
ınez
is
an
associate
professor
specializing
in
control,
intelligent
systems,
and
robotics
at
Uni
v
ersidad
Distrital
Francisco
Jos
´
e
de
Caldas
in
Colombia.
He
w
as
appointed
to
this
position
in
2001
and
serv
es
as
the
Director
of
the
ARMOS
research
group
(Modern
Architectures
for
Po
wer
Systems).
Dr
.
Mart
´
ınez
earned
his
Ph.D.
in
Computer
and
Systems
Engineering
from
Uni
v
ersidad
Nacional
de
Colombia.
His
research
interests
include
control
schemes
for
autonomous
robots,
mathematical
modeling,
electronic
instrumentation,
pattern
recognition,
and
multi-agent
systems.
He
is
dedicated
to
adv
ancing
the
eld
through
both
his
research
and
teaching
ef
forts.
He
can
be
contacted
at
email:
fhmartinezs@udistrital.edu.co.
C
´
esar
Her
n
´
andez
is
an
associate
professor
of
T
elecommunications,
Digital
Signal
Processing,
and
Electronics
at
Uni
v
ersidad
Distrital
Francisco
Jos
´
e
de
Caldas
in
Colombia.
He
w
as
appointed
to
this
position
in
2007
and
serv
es
as
the
Director
of
the
SIREC
research
group
(Systems
and
Cogniti
v
e
Netw
orks).
Dr
.
Hern
´
andez
earned
his
Ph.D
.
in
Computer
and
Systems
Engineering
from
Uni
v
ersidad
Nacional
de
Colombia.
His
research
interests
include
telecommunications,
assisti
v
e
technology
,
teleinformat
ics,
and
adv
anced
digital
systems.
Dr
.
Hern
´
andez
is
dedicated
to
adv
ancing
the
eld
through
both
his
research
and
teaching
ef
forts.
He
can
be
contacted
at
email:
cahernandezs@udistrital.edu.co.
Diego
Giral
is
an
assistant
professor
specializing
in
po
wer
systems,
digi
tal
circuits,
programming,
and
electrical
instrumentation
at
Uni
v
ersidad
Distrital
Francisco
Jos
´
e
de
Caldas
in
Colombia.
He
is
an
acti
v
e
researcher
within
the
ARMOS
(Modern
Architectures
for
Po
wer
Systems)
and
SIREC
(Systems
and
Cogniti
v
e
Netw
orks)
research
groups.
Dr
.
Giral
earned
his
doctorate
in
Engineering
from
Uni
v
ersidad
Distrital
Francisco
Jos
´
e
de
Caldas.
His
res
earch
interests
include
distrib
uted
cogniti
v
e
radio
ne
tw
orks
and
spectra
l
decision
models.
He
is
dedicated
to
adv
ancing
the
eld
of
electrical
engineering
through
inno
v
ati
v
e
research
and
teaching.
He
can
be
contacted
at
email:
dagiralr@udistrital.edu.co.
Enhancing
learning
outcomes
thr
ough
cour
se
r
edesign
using
self-assessment
and
inquiry
...
(F
r
edy
Mart
´
ınez)
Evaluation Warning : The document was created with Spire.PDF for Python.