Resea ch Pape
Recommended ci a ion: Immonen, P., Äijälä, M., & Naukka inen, J. (2025).
Suppo ing Enginee ing Physics Lea ning wi h Au oma ed Fo ma i e Feedback. In
Kangaslampi, R., Langie, G., Jä inen, H.-M., & Nagy, B. (Eds.), SEFI 53 d Annual
Con e ence. Eu opean Socie y o Enginee ing Educa ion (SEFI), Tampe e,
Finland. DOI: 10.5281/zenodo.17631376.
This Con e ence Pape is b ough o you o open access by he 53 d Annual Con e ence
o he Eu opean Socie y o Enginee ing Educa ion (SEFI) a Tampe e Uni e si y in
Tampe e, Finland. This wo k is licensed unde a C ea i e Commons
A ibu ion-NonComme cial-Sha e Alike 4.0 In e na ional License.
SUPPORTING ENGINEERING PHYSICS LEARNING WITH
AUTOMATED FORMATIVE FEEDBACK
P. Immonen a,
1
, M. Äijälä b, J. Naukka inen c
a LUT Uni e si y, Lappeen an a, Finland, 0000-0002-3286-6840
b LUT Uni e si y, Lappeen an a, Finland, 0000-0002-6626-4207
c LUT Uni e si y, Lappeen an a, Finland, 0000-0001-6029-5515
Con e ence Key A eas: Teaching ma hema ics and physics in enginee ing
educa ion, Digi al ools and AI in enginee ing educa ion
Keywo ds: online-assignmen , eedback, enginee ing physics
ABSTRACT
This a icle in es iga es how s uden s' exam pe o mance is a ec ed by ecei ing
in e ac i e o ma i e eedback on a weekly assignmen . The s udy was conduc ed in
a physics cou se o i s -yea enginee ing s uden s. S uden s we e di ided in o wo
g oups which bo h ac ed as an in e en ion g oup and as a con ol g oup in u n. One
g oup ecei ed a speci ic Q- ac o assignmen wi h au oma ed eedback in a o m o
ies and hin s and a wa e in e e ence assignmen wi hou eedback. The o he
g oup ecei ed a Q- ac o assignmen wi hou eedback and a wa e in e e ence
assignmen wi h ies and hin s. The pe o mance o s uden s in di e en g oups on
weekly assignmen s and he inal exam in he co esponding subjec a ea was
examined. The esul s showed ha he assignmen wi h mul iple ies and hin s
inc eased s uden s' lea ning o he opic.
1
P. Immonen
paula.immonen@lu . i
1 INTRODUCTION
Feedback is an essen ial pa o lea ning and he esea ch on eedback in lea ning is
long-es ablished and di e se. On a gene al le el i has been no ed ha lea ning is
mo e e ec i e he mo e o en eedback is ecei ed (Yong e al. 2021), (K ause,
S a k, and Mandl 2009). Howe e , he e ec s o eedback on lea ning can be e y
di e en depending on he o ma i e o summa i e ole o eedback (B ookha
2018), he ype o eedback (A ali & an de Kleij 2017), he iming o eedback (Pol
e al. 2009), he ocus o eedback (Fy e e al. 2012), he p io knowledge o he
lea ne s (Lee 2017) and many o he ea u es o he lea ning si ua ion.
B ookha (2018) sugges s ha he powe o eedback lies especially in i s o ma i e
use. Shu e (2008) cha ac e izes good o ma i e eedback as imely, alid, ocused,
objec i e, clea , and well ma ched o he ecipien ’s p e ious knowledge le el. A
eedback model o mula ed by Ha ie and Timpe ley (2007) p oposes ele an
eedback ques ions o he eedback’s ecipien o be: “Whe e am I going?” “How am
I going?” and “Whe e o nex ?” The model implies ha eedback should a) highligh
he ask’s goal, b) posi ion how he cu en pe o mance ela es o his goal (i.e. he
gap o disc epancy), and c) p o ide hin s on he nex s eps owa d he objec i e.
Resea ch on eedback on sol ing ma hema ical p oblems o en dis inguishes
be ween speci ic, ask-le el eedback and mo e s a egic p ocess- ela ed o sel -
egula ing eedback and i has been sugges ed ha especially he no ices bene i
om he o me wand he mo e p o icien lea ne s o he la e (Knau e al. 2022).
In e es ingly, Lee’s (2015) me hod o eedback p omp ing he use o Polya’s
p oblem-sol ing me hods imp o ed he lea ning e ec i eness o mode a e o low-
le el achie ing s uden s mo e han he lea ning o he high achie e s, bu his was
suspec ed o be caused by he mo e limi ed scope o imp o emen o he al eady
mo e knowledgeable lea ne s.
Unde some ci cums ances eedback can weaken he lea ning ins ead o suppo ing
i . A ali and an de Kleij (2017) no iced ha an elabo a ion p esen ed a e a
s uden had gi en a co ec answe o a p oblem did no imp o e bu ma ginally
lowe ed he pe o mance on he simila ques ion. This was explained by he
inc eased cogni i e load due o p ocessing o edundan in o ma ion. In a simila
ein, Fy e e al. (2012) disco e ed ha child en wi h some p io knowledge o a
co ec ma hema ical solu ion s a egy bene i ed mo e om explo ing he p oblem
wi hou eedback han wi h i . Also Roll e al. (2014) disco e ed ha wi h child en
o e using help was associa ed wi h poo e lea ning o ma hema ics, sugges ing ha
no ice lea ne s may equi e se e al ies be o e being able o make sense o he
eedback.
Con empo a y online lea ning solu ions p o ide new challenges and oppo uni ies o
eedback. Despi e losing in o ma ion on he ecipien s’ si ua ion and o en seen o be
lacking in di e si y and esponsi eness, i can ha e ma ked bene i s ela ed o
imeliness, (pe cei ed) objec i eness and cla i y in deli e y. E-lea ning pla o ms also
allow he s uden s o y he assignmen s mo e han once, wi h he op ion o gi ing
immedia e eedback, and i needed, e-gene a ing o andomising he ask da a in
be ween he ies (e.g. You s one e al., 2010). This ype o e-lea ning assignmen s,
i.e. mul iple ies eedback wi h hin s ha e been ecen ly ound o enhance lea ning,
by enabling s uden s o lea n om hei mis akes (Schwe e e al., 2022). Such a
ial-and e o lea ning me hod is also p e e ed by he s uden s, acco ding o
B azhkin & S akos, 2023), whose s udy sugges s ha s uden s p e e o ha e h ee
ies o an assignmen , wi h conc e e enough hin s ha allow hem o sol e he
exe cise by hemsel es. On he o he hand, esea ch also shows ha gi ing mo e
han wo ies may be unnecessa y (You s one e al., 2010), and ha mul iple ies
may lead o guessing beha iou and ela ed op imisa ion s a egies ha do no
p omo e lea ning on he desi ed opic (Rhodes & Sa baum, 2015).
This s udy explo es he e ec o ask-speci ic o ma i e eedback on lea ning
p oblem sol ing in an enginee ing physics cou se module. The o ma i e eedback
was buil in o wo physics assignmen s wi h di e en opics bu equal le el o
di icul y, by gi ing he s uden s he maximum o ou consecu i e hin s i he s uden
submi ed a w ong answe . The esea ch ques ion was o mula ed as: How is he
s uden pe o mance on a speci ic opic in an examina ion e ec ed by ecei ing
s uc u ed o ma i e eedback du ing he p ac ice exe cise?
2 METHODOLOGY
2.1 Pa icipan s and desc ip ion o cou se implemen a ion
The s udy in ol ed s uden s om he Finnish enginee ing p og ams in he
depa men s o Ene gy Technology, Mechanical Enginee ing, Elec ical Enginee ing,
and Sus ainabili y Science a he LUT School o Ene gy Sys ems. These s uden s
we e en olled in he physics cou se Basics o Vib a ion and Wa e Mo ion. The
pa icipan s we e p ima ily i s and second-yea s uden s. The s uden s who
pa icipa ed in he s udy we e asked o pe mission o use hei esul s in he s udy.
The Basics o Vib a ion and Wa e Mo ion cou se is wo h wo ECTS c edi s and i
ex ends o se en weeks. The cou se includes lec u es, exe cises, weekly
independen online assignmen s, and a inal exam. The weekly online assignmen s
consis o mul iple-choice ques ions, image in e p e a ion asks, and calcula ion
p oblems. These assignmen s aim o measu e and deepen s uden s' unde s anding
o he week's opics. The asks a e au oma ically g aded and include ea u es such
as immedia e eedback and in e ac i e hin s.
2.2 P ocedu e
In he inal week o he cou se, s uden s we e gi en wo mo e ex ensi e
assignmen s, he esul s o which a e examined in his s udy. The assignmen s we e
olun a y, bu p o ided ex a poin s, making i possible o aise an accep ed g ade.
S uden s we e able o comple e he assignmen s ei he independen ly o oge he
wi h pee s. One assignmen in ol ed de e mining he Q- ac o (quali y ac o ) o a
damped o ced oscilla o , and he o he in ol ed analysing wa e in e e ence. The
opics o assignmen s used o he s udy we e chosen om opics ha ha e been
challenging o s uden s in p e ious yea s.
The assignmen s we e implemen ed and g aded using Moodle's Nume ical Cloze
ques ion. Nume ical Cloze ques ions consis o ex , possible igu es o ables and an
answe box o whe e he s uden en e s hei nume ical answe . (Moodle, 2003)
The s uden s in he cou se we e di ided in o wo g oups, A1 and A2. The numbe o
s uden s in G oup A1 who comple ed he inal week assignmen , pa icipa ed in he
exam, and ga e pe mission o he s udy was NA1 = 32. The co esponding numbe
in G oup A2 was NA2 = 37. Each o he wo assignmen s (Q- ac o and wa e
in e e ence) was implemen ed in wo di e en e sions. In one e sion, he
assignmen ques ions we e di ided in o mul iple pa s and s uden s ha e mul iple
ies wi h hin s (MTH) i hey answe ed inco ec ly. In he o he e sion, he e was
only one y and no eedback (NF). The ini ial alues o he assignmen we e
di e en o s uden s in di e en g oups.
In he assignmen wi h MTH, s uden s ecei ed immedia e eedback on whe he hei
answe was co ec o inco ec . Each s uden had i e ies pe ques ion pa . I he
i s answe was co ec , he s uden ecei ed ull poin s. I no , he s uden ecei ed
a hin . I he s uden answe ed co ec ly:
• on he second y, hey ecei ed 80% o he maximum poin s,
• on he hi d y, hey ecei ed 60% o he maximum poin s,
• on he ou h y, hey ecei ed 40% o he maximum poin s,
• on he i h y, hey ecei ed 20% o he maximum poin s.
The hin s we e cumula i e, wi h he hin ex ge ing p og essi ely longe , mo e
elabo a ed, and adding isual cues a e e y i e a ion (Fig. 1).
Fo example, in he Q- ac o assignmen , he s uden was asked o de e mine om
he able gi en in he assignmen :
• he de ia ion caused by a s a ic cons an o ce on a body, and
• he de ia ion o a body when an ex e nal o ce oscilla es a he esonan
equency.
The i s hin old he s uden ha hese equi ed alues could be de e mined using a
able. I is also explained ha he ampli ude eaches i s maximum alue when he
equency o he ex e nal o ce is equal o he esonan equency.
The second hin addi ionally old he s uden ha he equency o he cons an o ce
is ze o and ha he de ia ion caused by he ex e nal o ce oscilla ing a he esonan
equency is he maximum de ia ion alue in he able.
In addi ion o he p e ious hin s, he hi d and ou h hin s p esen ed he same able
gi en in he assignmen , and highligh he ows in he able om which he equi ed
alues could be de e mined.
G oup A1 s uden s ecei ed mul iple pa s, MTH assignmen ela ed o de e mining
he Q- ac o in he inal week o he cou se, as well as a one y, and NF assignmen
ela ed o wa e in e e ence. G oup A2 s uden s ecei ed mul iple pa s, MTH
assignmen ela ed o wa e in e e ence, as well as a one y, and NF assignmen
ela ed o he Q- ac o . Bo h g oups i s comple ed he mul iple pa , MTH
assignmen , ollowed by he one y and NF assignmen .
Fig. 1. Q- ac o assignmen i s pa , hin s o he i s pa and he sco ing p inciple. The Q-
ac o ask has been di ided in o six pa s. Each pa simila ly con ains i e ies wi h hin s
and he same sco ing p inciple. The i s pa o he Q- ac o assignmen asks o answe s o
i ems a) and b), he second pa o i em c), he hi d pa o i em d), he ou h pa o i em e),
he i h pa o i em ), and he las pa o i em g).
3 RESULTS
3.1 The weekly assignmen
The maximum poin s o he inal week's Q- ac o and wa e in e e ence
assignmen s we e 9 poin s and he maximum o al poin s o he weekly assignmen s
in he inal week was 29. Table 1 shows he mean poin s, s anda d de ia ion,
Welch’s - es s a is ic alues and p- alues and Cohen's d e ec size o he di e en
g oups in he Q- ac o assignmen , wa e in e e ence assignmen and o al poin s.
Table 1. The mean poin s, s anda d de ia ion, di e ences o means, Welch’s - es s a is ic
alues and p- alues and Cohen's d e ec size o he di e en g oups in he Q- ac o
assignmen , wa e in e e ence assignmen and o al poin s.
A1
A2
Di . o
means
Welch’ - es
Cohen’s
d
Mean
SD
Mean
SD
p
Q- ac o
(max. 9)
6.72
2.29
3.62
2.73
|3.09|
5.13
0.000
1.22
Wa e in e .
(max. 9)
3.04
2.50
6.77
2.61
|3.72|
-6.05
0.000
1.46
To al
(max. 29)
19.03
4.62
19.78
5.36
|0.75|
-0.62
0.536
0.15
I can be seen om able 1 ha he e is a signi ican di e ence be ween he mean
poin s o g oups A1 and A2 on he Q- ac o and wa e in e e ence assignmen s bu
he e is no di e ence in he mean o al poin s o he weekly assignmen . Cohen's d
e ec size (> 1) also indica es ha he di e ence be ween g oups A1 and A2 is
s a is ically highly signi ican in he Q- ac o and wa e in e e ence assignmen s.
O cou se, i is expec ed ha he e will be di e ences in he mean poin s o he
di e en g oups, as one g oup has only one y pe ask pa , so hey can only ge 0
o 100% o he poin s and ano he g oup can ge 0, 20%, 40%, 60%, 80% o 100%
o he poin s on he ask pa hanks o mul iple ies.
Figu e 2 shows he pe cen age o s uden s in g oups A1 and A2 who ecei ed hin s
in he Q- ac o ask and he wa e in e e ence ask. Fo bo h g oups, he
pe cen ages o s uden s who ecei ed hin s a e almos he same, excep o s uden s
who ecei ed 4 hin s and hose who did no espond o he ask.
Fig. 2. The pe cen age o s uden s in g oups A1 and A2 who ecei ed hin s in he Q- ac o
assignmen and he wa e in e e ence assignmen .
Now he mos in e es ing hing is whe he mul iple ies ha e helped s uden s lea n
and in e nalize he hings es ed in he assignmen , o i i can ac ually hu hei
lea ning, in case o oo many ies, as obse ed by You s one e al. (2010). This was
examined by es ing s uden s' knowledge wi h a ques ion on he co esponding opic
in he exam.
3.2 The exam
The inal exam o he cou se included ques ions on he co esponding opics. The
inal exam has 5 ques ions, each o which is wo h a maximum o 10 poin s, making
he maximum sco e o he inal exam 50. Ques ion 2 o he inal exam was a
ques ion ela ed o he Q- ac o and ques ion 5 was a ques ion ela ed o wa e
in e e ence. Table 2 p esen s he mean poin s, s anda d de ia ion, Welch’s - es
s a is ic alues and p- alues and Cohen's d e ec size ob ained om he exam
ques ions o he di e en g oups.
Table 2. The mean poin s, s anda d de ia ion, di e ences o means, Welch’s - es s a is ic
alues and p- alues and Cohen's d e ec size o he di e en g oups in he exam ques ions,
sum o poin s o ques ion 1, 3 and 4 and o al poin s o exam.
A1
A2
Di . o
means
Welch’ - es
Cohen’s
d
Mean
SD
Mean
SD
p
Q1
8.39
1.81
8.45
1.61
|0.06|
-0.13
0.894
0.03
Q2 (Q- ac o )
8.77
1.99
7.49
2.81
|1.28|
2.16
0.035
0.52
Q3
8.13
1.58
7.86
1.87
|0.27|
0.63
0.532
0.15
Q4
6.48
3.08
6.69
2.98
|0.21|
-0.29
0.773
0.07
Q5 (Wa e
in e e ence)
6.69
2.22
7.52
1.95
|0.83|
-1.59
0.117
0.40
Q1+Q3+Q4
22.99
4.74
23.00
4.65
|0.01|
-0.01
0.995
0.00
To al
37.28
7.62
37.81
6.95
|0.53|
-0.30
0.765
0.07
I can be seen om able 1 ha he e is a di e ence be ween he mean poin s o
g oups A1 and A2 on he ques ion Q2 and Q5 bu in o he ques ions o in he o al
exam poin s, he e is only e y li le di e ence be ween he g oups' mean poin s.
Cohen's d e ec size (≥ 0.4) also shows ha he di e ence be ween g oups A1 and
A2 is no iceable in ques ions 2 and 5, bu i is no pa icula ly la ge.
In he exam ques ion, he mean poin s be ween he di e en g oups do no di e as
signi ican ly as in he assignmen o he inal week. In he exam ques ions, he
s uden s ha e e u ned an a achmen in which hey ha e p esen ed he solu ion
p inciple hey used in he ques ion. I he s uden 's answe has been inco ec , bu
he p esen ed calcula ion p inciple is co ec , he e has been a possibili y o ge ing
some poin s o he ques ion.
4 DISCUSSION AND CONCLUSIONS
Resul s indica e ha ou model o ask-speci ic o ma i e eedback ope a ionalised
as consecu i e hin s in a Moodle assignmen enhanced s uden s lea ning o
enginee ing physics opics as indica ed by he examina ion esul s. The o e use o
help (Roll e al.2014) and he unnecessa y inc ease o cogni i e load (A ali & an
de Kleij 2017) we e a oided by p o iding hin s only when s uden s submi ed w ong
answe s and discou aging he unnecessa y use o hin s by edac ing exe cise poin s
o each hin use. Al hough he ask-speci ic eedback has been c i icised o
encou aging su ace lea ning, we belie e ha in bachelo le el enginee ing physics
cou se many lea ne s can s ill be conside ed no ices, whose lea ning bene i s om
his kind o eedback. We also hypo hesise ha hese kind o consecu i e hin s in a
complex p oblem can help s uden s also o acqui e app op ia e p ocess- ela ed
knowledge and p o ide oppo uni ies o sel - e lec ion. This, howe e , needs o be
examined u he in ano he s udy.
One signi ican limi a ion o his s udy is ha p o iding eedback in o m o hin s
mean ha he s uden s ecei ing he hin s also ecei ed mo e ies han he con ol
g oup. Thus i is no possible o conclusi ely disce n be ween he lea ning e ec s o
a) p o iding o ma i e eedback and b) p o iding mul iple ies. Since ha ing mul iple
ies be o e p o iding assis ance has been sugges ed o suppo lea ning wi h
child en (Fy e e al. 2012, Roll e al. 2014) i would be good o examine how much
mul iple ies wi hou hin s, o wi h knowledge o esponse (i co ec o inco ec )
eedback only, a ec s uden s' success in exam ques ions co esponding o weekly
assignmen . This could be done o example by di iding s uden s in o h ee g oups,
in which s uden s would ecei e he ollowing assignmen :
1. assignmen wi h mul iple ies and hin s
2. assignmen wi h mul iple ies wi hou hin s
3. assignmen wi hou mul iple ies o hin s
The ob ious implica ion o p ac ice om his esea ch is o encou age eache s o
u ilize he means o i ual lea ning en i onmen s o p o iding s uc u al o ma i e
eedback. Wi h he help o echnology his can be done in a imely way and
ega dless o he class size. Acco ding o esea ch by Schwe e e al. (2022),
assignmen s ha allow o mul iple a emp s and in e ac ions, acili a ed by
echnology, ha e been ound o enhance lea ning. Based on ou p e ious esea ch
(Immonen a al. 2023), s uden s a e also mo i a ed o comple e olun a y online
lea ning assignmen s wi h mul iple ies and hin s e en hough he impac o he
assignmen s on he inal cou se g ade is small.
ACKNOWLEDGEMENTS
An a i icial in elligence applica ion, Google No ebookLM, was used o manage he
e e ence li e a u e, bu no o p oduce he ex . The AI applica ion, Copilo , was used
o English language e inemen o he inal e sion o he manusc ip .
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