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UDK: 37.016:54:004.4:373.5
QUASI-EXPERIMENTAL ANALYSIS OF THE EFFECTIVENESS OF INTEGRATING
THE "CHEMIST 5.0.3" MOBILE APPLICATION INTO CHEMISTRY LESSONS.
Rajabo a Mashhu a Tu diboy qizi
S uden a he Sama kand S a e Pedagogical Ins i u e.
Spi amen Shokh S ee , 166, Sama kand, Uzbekis an.
h ps://doi.o g/10.5281/zenodo.17709341
Abs ac . This s udy conduc s a quasi-expe imen al analysis o he e ec i eness o
in eg a ing he CHEMIST 5.0.3 mobile applica ion in o seconda y-school chemis y lessons.
Two in ac , pa allel classes (G ades 8–9; one expe imen al, one con ol) comple ed a 4–
6 lesson mini-module on solu ions, pH, and bu e s. The expe imen al g oup used CHEMIST
5.0.3 o in e ac i e isualiza ion, guided p oblem sol ing, and mic o-lab planning, while he
con ol g oup ecei ed me hodologically equi alen ins uc ion wi hou he app. Ou comes
included a alida ed concep es (p e/pos and a 2-week e en ion es ), ub ic-sco ed p ac ical
asks (p ocedu e adhe ence, in e p e a ion quali y), a b ie cogni i e-load scale, and a ime-on-
ask–no malized e iciency index. Da a analysis ollowed an ANCOVA amewo k wi h p e- es
as co a ia e and epo ed e ec sizes (Cohen’s d/Hedges’ g); in e - a e ag eemen o ub ic
sco es was es ima ed ia Cohen’s κ, and in e nal consis ency ia KR-20/α. Con en alidi y was
ensu ed h ough expe e iew. The s udy documen s whe he mobile in eg a ion imp o es
concep ual unde s anding, ans e o p ac ice, and lea ning e iciency while main aining
accep able cogni i e load, and discusses me hodological limi a ions and class oom implica ions
o scalable, esou ce-awa e adop ion.
Key wo ds: mobile-assis ed chemis y educa ion; CHEMIST 5.0.3; quasi-expe imen ;
unc ional scien i ic li e acy; ANCOVA; e ec size; cogni i e load; seconda y educa ion.
INTRODUCTION
Mobile-assis ed lea ning has become a p agma ic pa hway o upg ading science
ins uc ion in esou ce-cons ained class ooms, whe e access o ull we -lab acili ies, senso s, o
desk op simula ions is une en. In chemis y, opics such as solu ions, pH, and bu e ac ion
equi e lea ne s o coo dina e mul iple ep esen a ional le els—mac oscopic phenomena
(measu emen s, colo change), submic oscopic models (ions, molecules), and symbolic
o malisms ( o mulas, equa ions). Lea ne s equen ly con la e acid s eng h wi h concen a ion,
misin e p e he loga i hmic na u e o pH, and s uggle o p edic he quali a i e e ec o dilu ion
o acid/base addi ion on bu e s. Ca e ully designed mobile applica ions can supply in e ac i e
isualiza ions, s uc u ed p ac ice, and immedia e eedback ha help s uden s na iga e hese
concep ual hu dles du ing and be ween lessons.
This s udy is amed by (i) cons uc i is iews o lea ning, emphasizing ac i e
knowledge cons uc ion h ough guided inqui y and p oblem sol ing; (ii) he cogni i e heo y o
mul imedia lea ning, which posi s dual channels and limi ed capaci y and, he e o e, p i ileges
cohe ence, signaling, and con igui y in ins uc ional design; and (iii) cogni i e load heo y,
which dis inguishes in insic, ex aneous, and ge mane load. A mobile applica ion can educe
ex aneous load (by emo ing i ele an de ail, o ganizing s eps, and ex e nalizing
ep esen a ions) and inc ease ge mane load (by p omp ing sense-making and sel -explana ion).
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Equally, poo ly aligned in e aces o asks may ele a e ex aneous load (no i ica ions,
nonessen ial ea u es) and dep ess pe o mance. Measu ing cogni i e load alongside lea ning
ou comes is hus no op ional bu necessa y o in e p e e ec s.
Empi ical wo k on mobile lea ning in STEM gene ally epo s small- o-mode a e gains in
immedia e pos - es s when apps p o ide sca olds such as wo ked examples, in e ac i e
simula ions, s epwise hin s, and apid eedback. Fo chemis y speci ically, simula ion-enhanced
ins uc ion appea s mos bene icial on pa icula e-le el easoning and ans e o p ac ical
p oblem sol ing, pa icula ly when class oom ime is s uc u ed a ound sho cycles o
p edic ion–obse a ion–explana ion and when assessmen s include bo h nea - and a - ans e
i ems. Howe e , he e iden ia y base is he e ogeneous: many s udies pool di e en apps and
opics, omi e en ion es s, o ely solely on aw pos - es compa isons wi hou adjus ing o
p io knowledge. Few include a e ag eemen o pe o mance ub ics, and ewe s ill epo
e iciency me ics ha join ly conside accu acy and ime-on- ask. Finally, while quasi-
expe imen s wi h in ac classes a e common in schools, me hodological anspa ency (e.g.,
co a ia e con ol, eliabili y indices, ea men ideli y) is une en.
Agains his backd op, he e is limi ed app-speci ic e idence a ge ing seconda y-le el
ins uc ion on solu ions, pH, and bu e s, wi h concu en a en ion o (a) concep ual
unde s anding, (b) p ac ical pe o mance quali y, (c) e en ion a e a delay, (d) pe cei ed
cogni i e load, and (e) lea ning e iciency (accu acy no malized by ime). The e is also a need
o class oom- ealis ic designs ha ope a e wi hin minimal in as uc u e (sha ed de ices,
in e mi en connec i i y), while epo ing eliabili y/ alidi y e idence and con olling o
baseline di e ences.
The p esen s udy conduc s a quasi-expe imen al e alua ion o in eg a ing he CHEMIST
5.0.3 mobile applica ion in o seconda y-school chemis y lessons on solu ions, pH, and bu e s.
Two in ac , pa allel classes om one school comple ed an equi alen mini-module; he
expe imen al class ecei ed ins uc ion ha sys ema ically embedded CHEMIST 5.0.3 ac i i ies
(in e ac i e isualiza ion, s uc u ed p oblem sol ing, and mic o-lab planning), while he con ol
class ecei ed ma ched ins uc ion wi hou he applica ion.
Speci ically, he s udy aims o:
1. Es ima e he e ec o CHEMIST 5.0.3 in eg a ion on pos -ins uc ion concep ual
unde s anding, adjus ing o p e- es di e ences.
2. Examine sho - e m e en ion wo weeks a e ins uc ion.
3. Compa e p ac ical ask pe o mance using an analy ic ub ic (p ocedu e adhe ence and
in e p e a ion quali y).
4. E alua e pe cei ed cogni i e load and a simple e iciency index (sco e pe minu e) o
con ex ualize pe o mance di e ences.
F om hese aims ollow ou wo king esea ch ques ions:
RQ1: Does in eg a ion o CHEMIST 5.0.3 imp o e pos - es pe o mance ela i e o an
equi alen , non-app ins uc ion when con olling o p e- es ?
RQ2: Do expe imen al-g oup lea ne s e ain mo e knowledge a e a wo-week delay?
RQ3: Does he in eg a ion enhance p ac ical pe o mance quali y on ub ic-sco ed asks?
RQ4: How does he in eg a ion a ec pe cei ed cogni i e load and lea ning e iciency?
Me hodologically, he s udy combines co a ia e-con olled compa isons (ANCOVA)
wi h eliabili y checks (KR-20/α o es s; Cohen’s κ o ub ic sco ing) and epo s s anda dized
e ec sizes (Cohen’s d/Hedges’ g) alongside an e iciency me ic, p o iding a mo e in e p e able
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accoun han aw sco e con as s. P ac ically, i o e s a eplicable, ime-bounded lesson
sequence ha can be enac ed wi h sha ed de ices, wi h ea men ideli y no es and ins umen
appendices o suppo adop ion, adap a ion, and subsequen scaling.
MATERIAL AND METHODS
S udy design. A quasi‐expe imen al, nonequi alen g oups p e es –pos es design wi h a
delayed pos - es ( e en ion) was implemen ed in one public seconda y school. Two in ac ,
pa allel classes (G ades 8–9) we e assigned as expe imen al (app-in eg a ed) and con ol (no
app). The same eache , ime able, and con en sequence we e used ac oss g oups. The
in e en ion comp ised a 4–6 lesson mini-module on solu ions, pH, and bu e s deli e ed o e 2–
3 weeks.
Pa icipan s. Eligibili y equi ed egula en ollmen in he a ge uni and ≥80%
a endance. S uden s wi h p io s uc u ed exposu e o CHEMIST o wi h manda ed al e na i e
assessmen s we e excluded om analysis. Pa en al consen and s uden assen we e ob ained.
Demog aphic a iables (age, sex) we e eco ded o desc ip i e pu poses only.
Ins uc ional ma e ials and in e en ion. The expe imen al g oup used CHEMIST 5.0.3
on sha ed sma phones/ able s (≈1 de ice pe 2–3 s uden s). App-based ac i i ies ollowed sho
cycles o p edic ion–in e ac ion–explana ion: in e ac i e pa icula e-le el isualiza ions (e.g.,
ioniza ion, neu aliza ion), guided p oblem s eps wi h immedia e checks, and mic o-lab planning
(e.g., pH dilu ion se ies). The con ol g oup comple ed me hodologically equi alen asks wi h
p in ed wo ked examples, s a ic diag ams, and eache -led checks. T ea men ideli y was
moni o ed ia a 10-i em checklis comple ed by an independen obse e each lesson ( a ge
adhe ence ≥80%).
P ocedu e. Lesson 1 adminis e ed a 24-i em p e- es (Fo m A). Lessons 2–5 deli e ed
opic ac i i ies. Lesson 6 adminis e ed he 24-i em pos - es (Fo m B) and a ub ic-sco ed
p ac ical ask. A sho e en ion es (Fo m A′ wi h ancho i ems) was adminis e ed wo weeks
la e du ing class. Time-on- ask was eco ded du ing all es adminis a ions.
Measu es. (i) Concep ual unde s anding: 24 ou -op ion MCQs aligned o he h ee s udy
objec s (8 i ems each). Pa allel Fo ms A/B sha ed six ancho i ems o check o m equi alence;
in e nal consis ency was es ima ed ia KR-20/α. (ii) P ac ical pe o mance: analy ic ub ic wi h
wo c i e ia—p ocedu e adhe ence (0–3) and in e p e a ion quali y (0–3)—sco ed independen ly
by wo ained a e s; in e - a e ag eemen summa ized wi h Cohen’s κ and ICC ( wo-way
andom, absolu e ag eemen ). (iii) Cogni i e load: six i ems, i e-poin Like ; composi e mean
epo ed. (i ) Lea ning e iciency: E = (% co ec )/(minu es) compu ed o pos - es and p ac ical
ask.
S a is ical analysis. Diagnos ics included Shapi o–Wilk and Le ene’s es s, ou lie
sc eening (±3 SD), and epo ing o eliabili y (KR-20/α) and a e ag eemen (κ/ICC). The
p ima y analysis was ANCOVA on pos - es sco es wi h g oup as ixed ac o and p e- es as
co a ia e; homogenei y o eg ession slopes was e i ied. Seconda y analyses included
analogous ANCOVA o e en ion and independen -samples es s (o Quade ank-based
ANCOVA/HC3- obus ANCOVA i assump ions we e iola ed) o p ac ical sco es, cogni i e
load, and e iciency. E ec sizes (Cohen’s d/Hedges’ g, pa ial η²) and 95% CIs a e epo ed.
Missing da a ≤5% we e handled by comple e-case analysis; o he wise, mul iple
impu a ion (m=5) included g oup and p e- es .
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E hics. School app o al, pa en al consen , and s uden assen we e ob ained. Pa icipa ion
was olun a y; da a we e anonymized and s o ed secu ely. The in e en ion used s anda d
cu icula con en and posed minimal isk.
RESULTS AND DISCUSSION
A o al o N = 58 s uden s comple ed all phases (expe imen al n = 29, con ol n = 29).
Two s uden s wi hd ew a e Lesson 1 (o e all a i ion 3.3%), wi h no di e en ial loss
be ween g oups (p = 0.77).
In e nal consis ency was accep able (concep es KR-20/α: p e = 0.78, pos = 0.84,
e en ion = 0.81; cogni i e-load scale α = 0.83). P ac ical- ask sco ing me ag eemen a ge s
(Cohen’s κ = 0.79; ICC(2,2) = 0.88, 95% CI 0.78–0.93).
T ea men ideli y a e aged 91% ac oss lessons.
Fig.1. Shows he inc ease in e iciency (E_z) ac oss lessons; each lesson has wo lines
(Con ol/Expe imen al) and a Δ anno a ion be ween hem.
P ima y ou come (pos - es ). ANCOVA (pos ~ g oup + p e) showed a signi ican g oup
e ec , a o ing he expe imen al class: adjus ed mean di e ence Δadj = 9.1 pe cen age poin s
(95% CI 3.9–14.3), F(1, 55) = 12.34, p = 0.001, pa ial η² = 0.18.
S anda dized impac was Cohen’s d = 0.64 (Hedges’ g = 0.63). The homogenei y-o -
slopes es was sa is ied (p = 0.48).
Ancho -i em checks indica ed pa allel- o m compa abili y (|Δ di icul y| ≤ 0.04).
ANCOVA model o mula’s:
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Re en ion. Two weeks la e , he expe imen al g oup e ained mo e o hei lea ning: Δadj
= 6.7 poin s (95% CI 1.6–11.9), F(1, 55) = 6.99, p = 0.010, pa ial η² = 0.11; d = 0.49. This
pa e n sugges s e ec s beyond sho - e m p ac ice, consis en wi h s eng hened concep ual
in eg a ion.
P ac ical pe o mance and e iciency. Blinded ub ic o als (0–6) we e highe o he
expe imen al g oup by 0.8 poin s (SDs pooled; (56) = 2.52, p = 0.014, d = 0.66). A ank-based
Quade ANCOVA (co a ia e = p e- es ) co obo a ed his (p = 0.018).
The e iciency index (sco e/min) imp o ed by ΔE = 0.47 z-uni s (95% CI 0.09–0.85),
(56) = 2.45, p = 0.017, indica ing ha app sca olds sho ened solu ion pa hways while
p ese ing accu acy.
Cogni i e load. Mean pe cei ed load did no di e meaning ully (expe imen al M = 2.86,
SD 0.51; con ol M = 2.96, SD 0.55 on a 1–5 scale), Δ = −0.10 Like uni s, (56) = 0.89, p =
0.38. Hence, he digi al laye did no in oduce excess ex aneous p ocessing.
Explo a o y exposu e–ou come ela ions. Wi hin he expe imen al class, g ea e in-app
in e ac ion (median 6.0 pe lesson; ≈11 minu es pe pai ) modes ly co ela ed wi h pos - es
pe o mance ( = 0.34, p = 0.036) and e iciency ( = 0.31, p = 0.049).
These obse a ional ends in i e cau ion bu hin a a dose– esponse pa e n.
KR-20 and C onbach’s α o mula’s:
Ac oss con e ging indica o s—co a ia e-adjus ed pos - es , delayed e en ion, p ac ical
easoning, and e iciency—in eg a ing CHEMIST 5.0.3 yielded medium educa ional bene i s
wi hou ele a ing cogni i e load.
Mechanis ically, esul s align wi h mul imedia lea ning and cogni i e load heo y: he app
likely educed ex aneous load (signaling, s epwise checks, cohe en ep esen a ions) and
suppo ed ge mane load (sel -explana ion, p edic ion– eedback loops).
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The SAR 1 model was ob ained by combining
he esul s o he ANCOVA model and he KR-
20 and C onbach’s α.
The SAR 2 model (3D) was ob ained by
combining he esul s o he ANCOVA model
and he KR-20 and C onbach’s α.
Fig.2. Combining he esul s o he ANCOVA model and he KR-20 and C onbach’s α,
SAR models 1 and 2 we e ob ained.
Limi a ions include he nonequi alen -g oups design ( esidual con ounding possible), one
eache and school (ex e nal alidi y), and a sho module. None heless, eliabili y e idence, a e
ag eemen , high ideli y, and co a ia e con ol s eng hen in e nal alidi y. Implica ions a e
p ac ical o esou ce-awa e schools: sha ed de ices and mos ly o line use can measu ably
imp o e unde s anding and ans e on solu ions–pH–bu e opics. Replica ion ac oss schools,
ex ension o quan i a i e bu e / i a ion wo k, and ea u e-le el analyses wi hin he app a e
wa an ed.
Fig. 3. Shows he inc ease in lea ning ac oss lessons; each lesson has wo lines
(Con ol/Expe imen al) and a D anno a ion be ween hem.
CONCLUSION
This quasi-expe imen al s udy demons a es ha in eg a ing he CHEMIST 5.0.3 mobile
applica ion in o a sho seconda y-school module on solu ions, pH, and bu e s yields
meaning ul lea ning gains unde ealis ic class oom cons ain s.
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A e adjus ing o p io a ainmen , he expe imen al class ou pe o med he con ol class
on he pos - es by 9.1 pe cen age poin s (p = 0.001; pa ial η² = 0.18; d = 0.64), and main ained
an ad an age on he wo-week e en ion es (Δ = 6.7 poin s, p = 0.010; d = 0.49). P ac ical
pe o mance imp o ed by 0.8/6 ub ic poin s (p = 0.014), and he lea ning e iciency index
inc eased wi hou ele a ing pe cei ed cogni i e load (g oup di e ence non-signi ican ).
Reliabili y indices o es s and a ing scales we e accep able, in e - a e ag eemen exceeded a
p io i h esholds, and ea men ideli y a e aged 91%, suppo ing in e nal alidi y.
Collec i ely, hese esul s indica e ha app-suppo ed ep esen a ions, s epwise guidance,
and p edic ion– eedback cycles can educe ex aneous p ocessing and os e ge mane p ocessing,
imp o ing bo h immedia e unde s anding and sho - e m du abili y. The indings a e ac ionable
o esou ce-awa e schools: sha ed de ices and la gely o line use a e su icien o achie e
measu able bene i s when lessons a e delibe a ely s uc u ed and assessmen is aligned.
Limi a ions include in ac -class alloca ion, a single eache and si e, and a b ie
in e en ion window; esidual con ounding and es ic ed gene alizabili y emain possible.
Fu u e wo k should eplica e ac oss schools and eache s, ex end o quan i a i e
bu e / i a ion opics, and isola e ea u e-le el con ibu ions (e.g., wo ked examples s.
in e ac i e isualiza ions) and dose– esponse ela ions. Despi e hese limi s, he p esen e idence
suppo s inco po a ing CHEMIST 5.0.3 in o ou ine ins uc ion on co e acid–base opics o
enhance concep ual unde s anding, p ac ical easoning, and lea ning e iciency wi hou
addi ional cogni i e bu den.
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