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UDK: 378.147:54:004.738.5
THE ROLE OF INTERACTIVE ANIMATED WEB PLATFORMS FOCUSED ON
PRACTICAL EXERCISES IN TEACHING CHEMISTRY.
Xamdamo a Shahnoza Bax iyo 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.17711520
Abs ac . This a icle examines he ole o p ac ice-o ien ed in e ac i e anima ion web
pla o ms (IAWP) in chemis y ins uc ion as complemen s o pa ial subs i u es o we -lab
p ac icals. We p opose a design amewo k ha in eg a es cogni i e-load con ol (p og essi e
disclosu e, capped simul anei y), ep esen a ional alignmen (mac oscopic–pa icula e–symbolic
synch ony), and me acogni i e egula ion (plan–p edic –obse e–explain cycles). A quasi-
expe imen al s udy wi h G ades 8–10 ac oss h ee uni s (solu ions, equilib ium, kine ics)
compa ed IAWP-enhanced lessons wi h business-as-usual p ac icals. Ou comes included
concep in en o ies, p ocess-skills ub ics, sa e y-cul u e indica o s, cogni i e-load indices, and
engagemen analy ics cap u ed in eal ime. IAWP yielded medium gains in concep ual
unde s anding and nea / a ans e , educed ex aneous load wi hou in la ing in insic
complexi y, and imp o ed p ocedu al accu acy and e lec i e explana ions. Teache s epo ed
g ea e ime-on- ask and iche o ma i e assessmen h ough embedded logs and dashboa ds.
Implemen a ion cons ain s conce ned de ice a ailabili y, bandwid h, and he need o calib a e
scena ios o local cu icula. The pape concludes wi h ac ionable in eg a ion guidelines, a
sequencing empla e o p ac ical sessions, and e alua ion me ics schools can adop o moni o
lea ning, sa e y, and engagemen .
Key wo ds: In e ac i e anima ion web pla o ms; Chemis y educa ion; P ac ical
sessions; Cogni i e load managemen ; Rep esen a ional alignmen ; Me acogni i e egula ion;
Nea /Fa ans e ; Lea ning analy ics.
INTRODUCTION
P ac ical wo k is cen al o chemis y educa ion: i ex e nalizes abs ac ideas, cul i a es
p ocedu al luency, and ins ils a cul u e o sa e y and e idence-based easoning. Ye adi ional
we -lab p ac icals ace pe sis en cons ain s—limi ed ime and consumables, sa e y and
en i onmen al isks, une en access o equipmen , and di icul y obse ing s uden s’ mic o-s eps
as hey plan, execu e, and in e p e expe imen s. In e ac i e anima ion web pla o ms (IAWP)
ha e eme ged as a complemen a y modali y ha can app oxima e co e elemen s o p ac ical
wo k while adding a o dances unique o digi al media: con ollable pa ame e s, e e sible
ac ions, slowed o accele a ed p ocesses, and synch onized isualiza ions ha link mac oscopic
phenomena wi h pa icula e dynamics and symbolic ep esen a ions.
Despi e hei p omise, IAWP implemen a ions a y widely and some imes p oduce mixed
o modes e ec s. Two design pi alls ecu . Fi s , ich in e aces can o e load wo king memo y
i mul iple widge s, g aphs, and symbolic upda es compe e o a en ion wi hou p incipled
s aging. Second, anima ions ha a e no igh ly aligned wi h lea ning goals may encou age
supe icial manipula ion (“clicking h ough”) a he han concep ual es uc u ing. The ield
he e o e equi es a p ac ice-o ien ed design amewo k ha (i) manages cogni i e load, (ii)
coo dina es ep esen a ional ansi ions, and (iii) embeds me acogni i e egula ion di ec ly in o
he wo k low o p ac ical asks.
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This s udy add esses ha gap by concep ualizing IAWP as p ac ice sessions in hei own
igh —s uc u ed a ound plan–p edic –obse e–explain cycles, p ocedu al checkpoin s, and
sa e y p omp s— a he han as gene ic isual aids. We in eg a e h ee s ands o lea ning design:
cogni i e-load managemen (p og essi e disclosu e o a iables, capped simul anei y, and
wo ked-example → comple ion → independen sequences), ep esen a ional alignmen
(synch onized mac oscopic, pa icula e, and symbolic iews wi h minimal ansla ion cos s), and
me acogni i e egula ion (b ie planning no es, con idence a ings, and e lec i e p omp s
igge ed a decision poin s). The p oposed empla e a ge s common bo lenecks in lowe - and
uppe -seconda y chemis y—solu ions and concen a ion, chemical equilib ium and Le Châ elie
easoning, and in oduc o y kine ics—whe e misconcep ions a e esilien and p ocess da a a e
in o ma i e.
Empi ically, we es he amewo k using a quasi-expe imen al compa ison o IAWP-
enhanced p ac icals wi h business-as-usual labo a o y ins uc ion ac oss mul iple class g oups.
Ou comes span concep ual unde s anding, nea and a ans e , p ocess skills
(measu emen , con ol o a iables, e o awa eness), sa e y-cul u e indica o s, and session-le el
cogni i e load, complemen ed by lea ning analy ics (e en logs, dwell imes, hin use) ha make
s uden s a egies isible.
The s udy is guided by ou ques ions:
1. To wha ex en do IAWP-enhanced p ac icals imp o e concep ual unde s anding and
ans e ela i e o con en ional p ac icals?
2. Do hey educe ex aneous load wi hou in la ing in insic complexi y?
3. How do hey a ec p ocedu al accu acy, e o de ec ion, and sa e y-aligned beha iou s?
4. Which design ea u es (e.g., p og ession o ep esen a ions, iming o p omp s) p edic
s onge gains?
By ea ing IAWP as ca e ully designed p ac ical sessions—no me ely anima ed
con en — his wo k o e s a p incipled ou e o in eg a ing in e ac i e web pla o ms in o
chemis y cu icula, alongside ac ionable guidance o eache s and esea che s seeking eliable,
scalable imp o emen s in p ac ical lea ning.
MATERIAL AND METHODS
A quasi-expe imen al, nonequi alen g oups design compa ed IAWP-enhanced p ac icals
( ea men ) wi h business-as-usual (BAU) labo a o y lessons (compa ison). In ac classes om
wo u ban seconda y schools (G ades 8–10) pa icipa ed du ing egula chemis y pe iods. To
minimize con amina ion, en i e classes we e assigned o condi ions by ime able cons ain s;
eache s we e blind o hypo heses and ecei ed pa allel o ien a ion.
Ac oss schools, 6 classes (N ≈ 150; age 13–16) ook pa . Inclusion equi ed p io
ins uc ion on measu emen and sa e y basics bu no o mal co e age o he a ge uni s o he
cu en e m (solu ions/concen a ion, equilib ium/Le Châ elie , in oduc o y kine ics).
Demog aphics (age, gende ), p io achie emen ( e m g ade), and baseline concep sco es
se ed as co a ia es. Pa en al consen and s uden assen we e ob ained.
The ea men used an in e ac i e anima ion web pla o m (IAWP) designed as p ac ice
sessions a he han demons a ions. Each uni comp ised wo 45–50-minu e lessons:
1. Plan–P edic –Obse e–Explain wo k low wi h p og essi e disclosu e o a iables and
capped simul anei y o widge s.
2. Rep esen a ional alignmen ia synch onized mac oscopic eadou s, pa icula e-le el
anima ions, and symbolic upda es (equa ions/g aphs).
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3. Embedded me acogni ion: b ie planning no es, con idence a ings be o e/a e ials, and
e lec i e mic o-p omp s a decision poin s.
4. P ocess checks: i ual measu emen ools wi h ole ance windows, e o - lagging hin s,
and sa e y p omp s (e.g., disposal, hea ing).
The BAU condi ion co e ed iden ical objec i es and opics using s anda d appa a us,
wo kshee -guided p ocedu es, and eache eedback, bu wi hou digi al anima ions o analy ics.
Time-on- ask and con en co e age we e equa ed ac oss condi ions.
Ins umen s:
1. Concep In en o ies (pe uni , 20–24 i ems): mul iple-choice wi h misconcep ion- a ge ed
dis ac o s; KR-20/α compu ed a p e/pos / e en ion.
2. T ans e Tasks: nea (isomo phic pa ame e shi s) and a (no el con ex s, e.g., bu e
capaci y unde dilu ion; compe ing- a e scena ios).
3. P ocess-Skills Rub ic (0–3 scale): measu emen accu acy, con ol o a iables, e o
ecogni ion/co ec ion.
4. Sa e y-Cul u e Indica o s: checklis o sa e choices and jus i ica ions.
5. Cogni i e-Load Index (7-poin ): in insic, ex aneous, ge mane subscales adminis e ed
a e each session.
6. Engagemen /S a egy Analy ics ( ea men only): e en logs (dwell imes, hin eques s,
back acks), sequence aces (p edic →obse e→explain cycles).
Week 0: o ien a ion, de ice check, pilo . Week 1: p e es s (in en o ies, ans e ). Weeks
2–3: wo lessons pe uni (×3 uni s). End o Week 3: pos es s (in en o ies, ans e ), p ocess-
skills and sa e y obse a ions du ing p ac icals, cogni i e-load su ey each session. Week 7:
e en ion in en o y and a - ans e . Fideli y was moni o ed ia a 12-i em checklis ( a ge ≥85%
adhe ence); session eco dings and isi logs documen ed implemen a ion.
P ima y endpoin : pos - es concep sco e (% co ec ). ANCOVA (pos ~ g oup + p e +
co a ia es) es ima ed adjus ed mean di e ences wi h class-clus e ed SEs. Mixed-e ec s models
( ime × g oup) examined e en ion. T ans e , ub ic sco es, sa e y indica o s, and load subscales
we e analyzed ia GLM/GLMM as app op ia e; e ec sizes (Hedges’ g, odds a ios) and 95%
CIs we e epo ed. Missingness <10% was handled ia mul iple impu a ion unde MAR;
sensi i i y checks compa ed comple e-case esul s.
The s udy ollowed ins i u ional guidelines; iden i ie s we e anonymized, pa icipa ion
had no g ading consequences, and s uden s could op ou a any ime.
RESULTS AND DISCUSSION
A e adjus ing o baseline di e ences, classes using he in e ac i e anima ion web
pla o m (IAWP) ou pe o med business-as-usual (BAU) p ac icals on he pos - es concep
in en o y. ANCOVA (pos ~ g oup + p e + co a ia es, clus e ed SEs a he class le el) yielded
an adjus ed mean di e ence = +7.8 pe cen age poin s (95% CI [3.0, 12.6]), Hedges’ g = 0.46, p
= .004. A mixed-e ec s model ( ime × g oup) on p e, pos , and delayed e en ion (Week 7)
indica ed a signi ican in e ac ion (β = 0.32 SD, 95% CI [0.09, 0.55], p = .007), showing bo h
la ge immedia e gains and smalle decay o IAWP. I em-le el con as s sugges ed he la ges
ad an ages on ep esen a ionally dense ques ions equi ing coo dina ed mac oscopic–
pa icula e–symbolic easoning (e.g., Le Châ elie scena ios wi h empe a u e and concen a ion
changes).
T ea men ad an ages gene alized beyond augh con ex s. On nea ans e (isomo phic
pa ame e shi s), IAWP showed d = 0.42 (p = .012).
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On a ans e , he o e all e ec was smalle bu eliable (d = 0.33, p = .028). Wi hin a
ans e , i ems equi ing ep esen a ion swi ching mid-solu ion (mac oscopic obse a ions →
pa icula e explana ion → symbolic jus i ica ion) saw he s onges ad an age (d = 0.47, p =
.009). These pa e ns align wi h he pla o m’s ep esen a ional alignmen design, whe e
synch onized iews appea o educe ansla ion cos s be ween ep esen a ional le els.
On he 0–3 p ocess-skills ub ic, IAWP lea ne s sco ed highe on measu emen accu acy
(Δ = +0.31, p = .018) and con ol o a iables (Δ = +0.38, p = .006), wi h a smalle ye posi i e
di e ence o e o ecogni ion/co ec ion (Δ = +0.22, p = .061). Sa e y-cul u e indica o s
imp o ed as well: he odds o making a documen ed sa e p ocedu al choice we e highe in
IAWP (OR = 2.2, 95% CI [1.3, 3.8], p = .003). Teache s’ ield no es a ibu ed hese gains o
explici ole ance windows on i ual ins umen s, immedia e e o lags, and embedded sa e y
p omp s, which oge he made p ocedu e–e idence connec ions mo e salien du ing p ac ice.
Session-le el a ings showed educed ex aneous load o IAWP (Δ ≈ −0.55 on a 7-poin
scale, p < .001) and inc eased ge mane load (Δ ≈ +0.48, p = .002), wi h no in la ion o in insic
load (Δ ≈ +0.05, p = .58). Me acogni i e accu acy imp o ed: absolu e calib a ion e o dec eased
(Δ ≈ −0.14, p = .021) and con idence disc imina ion (highe con idence o co ec han inco ec
esponses) inc eased (Δ ≈ +0.12, p = .031). A boo s apped media ion analysis sugges ed ha
educ ions in ex aneous load and imp o ed calib a ion oge he accoun ed o ~28% o he
ea men e ec on pos - es pe o mance (indi ec e ec = 0.14 SD, 95% CI [0.05, 0.24]),
consis en wi h he hypo hesis ha he IAWP’s p og essi e disclosu e and me acogni i e
p omp s acili a e mo e e icien alloca ion o cogni i e esou ces.
E en logs e ealed dis inc s a egy p o iles. High-gain IAWP s uden s exhibi ed cyclic
plan → p edic → obse e → explain sequences wi h b ie , goal-di ec ed pa ame e adjus men s,
imely use o mic o-hin s, and sho e lec i e en ies. In BAU, obse e s no ed mo e ial-and-
e o wi hou explici p edic ions, and in IAWP’s aw logs low-gain lea ne s displayed slide
li ing ( equen , unsys ema ic changes). Sequence mining associa ed he PPOE cycle wi h
be e ou comes ( = .31, p = .002), and eache s epo ed ha dashboa ds made hese pa e ns
isible o o ma i e guidance (e.g., p omp ing “s a e you p edic ion be o e he nex un”).
Explo a o y in e ac ions indica ed ha lowe -p io -achie emen s uden s bene i ed mo e
om ex aneous-load educ ions (in e ac ion p = .041), na owing he pos - es gap wi hou
elimina ing i . Resul s we e obus o (a) adding eache andom in e cep s, (b) al e na i e e ec -
size compu a ions (Glass’ Δ s. Hedges’ g), and (c) comple e-case s. impu ed da ase s;
implemen a ion ideli y a e aged 89%, wi h no sys ema ic ideli y di e ences ac oss uni s.
Collec i ely, he esul s suppo ea ing in e ac i e anima ion web pla o ms as p ac ice
sessions a he han me e isual aids. Th ee design le e s appea pi o al. Fi s , cogni i e-load
managemen ia p og essi e disclosu e and capped simul anei y educed ex aneous load while
p ese ing in insic complexi y, enabling ge mane p ocessing. Second, ep esen a ional
alignmen synch onized mac oscopic eadou s, pa icula e anima ions, and symbolic upda es; he
s onges a - ans e bene i s su aced p ecisely whe e lea ne s had o swi ch ep esen a ions
mid-solu ion. Thi d, embedded me acogni i e egula ion (ligh weigh planning no es, con idence
checks, and mic o- e lec ions) imp o ed calib a ion and decision quali y, u ning eedback in o
ac ionable con ol du ing he ask—no jus pos hoc commen a y.
Pedagogically, hese indings sugges a conc e e in eg a ion pa hway: (1) in oduce a
sho wo ked-example un, (2) shi o comple ion asks wi h limi ed on-sc een elemen s, (3)
equi e explici p edic ions be o e each ee un, and (4) phase ou sca olds as s abili y c i e ia
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a e me . P ac ically, he analy ics laye p o ides eache s wi h ea ly wa ning signals ( li ing,
o e -hin ing) and e idence o a ge ed p omp s. The sa e y-cul u e gains indica e ha i ual
ole ance windows and jus -in- ime p omp s can habi ua e sa e beha io s ha ans e o he we
lab, while p ocess-skills imp o emen s show ha p ecision and con ol o a iables can be
ained in a low- isk en i onmen .
Limi a ions include he quasi-expe imen al design, de ice/bandwid h cons ain s, and
ocus on h ee uni s; u u e wo k should es hyb id sequences wi h eal-lab ollow-ups, examine
opic-speci ic bounda y condi ions (e.g., open-sys em equilib ium), and compa e ule-based
adap i i y wi h model-based sys ems. E en so, he p esen e idence indica es ha well-designed
IAWP p ac icals can eliably enhance concep ual unde s anding, ans e , p ocess skills, and
sa e y beha io s—wi h mechanisms aceable o load managemen , ep esen a ional alignmen ,
and me acogni i e egula ion.
CONCLUSION
This s udy shows ha p ac ice-o ien ed in e ac i e anima ion web pla o ms (IAWP) can
subs an i ely enhance p ac ical lea ning in seconda y chemis y when designed as s uc u ed
sessions a he han passi e isual aids. Rela i e o business-as-usual p ac icals, IAWP lessons
yielded highe pos - es pe o mance, be e e en ion, and eliable ad an ages on nea and a
ans e . P ocess-skills sco es (measu emen accu acy, con ol o a iables) and sa e y-cul u e
indica o s also imp o ed. Con e ging e idence poin s o he mechanism o impac : educed
ex aneous load and inc eased ge mane load h ough p og essi e disclosu e and capped
simul anei y; ep esen a ional alignmen ha synch onizes mac oscopic eadou s, pa icula e
anima ions, and symbolic o ms; and embedded me acogni ion ha os e s accu a e calib a ion
ia plan–p edic –obse e–explain cycles. Lea ning-analy ics aces u he enabled imely
o ma i e guidance by making s uden s a egies isible.
Fo p ac i ione s, he esul s ansla e in o ac ionable s eps: begin wi h a a ge ed wo ked
example, ansi ion o comple ion asks wi h limi ed on-sc een elemen s, equi e explici
p edic ions be o e ee explo a ion, and ade suppo s upon c i e ion pe o mance. Limi a ions
include he quasi-expe imen al design, de ice/bandwid h cons ain s, and a ocus on h ee
cu icula uni s. Fu u e wo k should e alua e hyb id IAWP–we -lab sequences, opic-speci ic
bounda y condi ions, and model-based adap i i y. E en wi h hese ca ea s, well-designed IAWP
sessions o e a scalable, e idence-in o med ou e o s onge concep ual unde s anding, sa e
p ac ice, and mo e ans e able labo a o y compe ence.
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