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UDK: 373.5.016:54:004.94:37.091.3:159.95
DEEPENING CONCEPTUAL UNDERSTANDING THROUGH THE COGNITIVE
ADAPTATION MODEL USING VIRTUAL LABORATORIES IN CHEMISTRY
CLASSES
Xay ullaye a Chex ona Saloxiddin 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.17711440
Abs ac . This a icle ad ances a Cogni i e Adap a ion Model (CAM) o i ual
labo a o ies o deepen seconda y-le el s uden s’ concep ual unde s anding in chemis y. CAM
in eg a es cogni i e-load managemen (balancing in insic, educing ex aneous, and ampli ying
ge mane load), me acogni i e egula ion (p omp ed sel -moni o ing and planning), and
ep esen a ional ideli y (p og essi e isualiza ions om pa icle o symbolic le els). The pape
(i) o malizes he cons uc s and mechanisms o CAM; (ii) ansla es hem in o design p inciples
o i ual expe imen s on co e opics such as equilib ium, acid–base p ocesses, and eac ion
kine ics; and (iii) ou lines an e alua ion p o ocol combining concep in en o ies, nea – a
ans e asks, and cogni i e-load indices wi h lea ning-analy ics aces om he simula ion
en i onmen . The app oach speci ies adap i e sca olding, phased guidance, and eedback
calib a ed o lea ne s’ e ol ing cogni i e s a es. By aligning ins uc ional mo es wi h
documen ed pa e ns o cogni i e adap a ion, CAM o e s a heo e ically g ounded, p ac ically
ac ionable bluep in o i ual lab design. Implica ions o cu iculum in eg a ion, eache
p o essional de elopmen , and u u e empi ical alida ion a e discussed.
Key wo ds: Vi ual labo a o ies; Cogni i e Adap a ion Model (CAM); Concep ual
unde s anding; Cogni i e load managemen ; Me acogni i e egula ion; Adap i e sca olding;
Rep esen a ional ideli y; Chemis y educa ion.
INTRODUCTION
Chemis y lea ning ou inely demands ha s uden s coo dina e mac oscopic phenomena,
submic oscopic pa icle beha io , and symbolic ep esen a ions. This iadic ep esen a ional
load o en exceeds no ice wo king-memo y capaci ies, esul ing in agmen ed schemas,
pe sis en misconcep ions (e.g., abou equilib ium o acid–base neu aliza ion), and b i le
p ocedu al knowledge ha ails o ans e .
Vi ual labo a o ies ha e eme ged as a p omising esponse: hey p o ide sa e, epea able,
and da a- ich en i onmen s whe e a iables can be isola ed, empo al p ocesses slowed o
eplayed, and pa icle-le el mechanisms isualized alongside symbolic equa ions. Ye , despi e
hei po en ial, i ual labs can also in ensi y cogni i e bu den h ough dense in e aces,
simul aneous in o ma ion s eams, and poo ly imed p omp s—leading o supe icial
manipula ion a he han concep ual change.
This pape ad ances a Cogni i e Adap a ion Model (CAM) as a p incipled bluep in o
aligning i ual-lab expe iences wi h lea ne s’ e ol ing cogni i e s a es. CAM in eg a es h ee
pilla s. Fi s , cogni i e-load managemen : calib a ing ask complexi y (in insic load),
minimizing in e ace and ins uc ional noise (ex aneous load), and delibe a ely cul i a ing
schema cons uc ion (ge mane load) h ough p oduc i e s uggle and a iabili y o p ac ice.
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Second, me acogni i e egula ion: embedding ligh weigh planning, moni o ing, and
e lec ion ou ines ha help s uden s se goals, ack unde s anding in eal ime (e.g., ia
p edic ion–obse e–explain cycles), and e ise s a egies when e idence con adic s
expec a ions. Thi d, ep esen a ional ideli y and p og ession: o ches a ing dynamic links among
pa icle-le el anima ions, mac oscopic ou comes, and symbolic o malisms so ha s uden s can
a e se and in eg a e ep esen a ions a he han juggle hem in isola ion.
The cen al p oblem add essed he e is no whe he i ual labo a o ies “wo k,” bu unde
wha design condi ions hey p oduce du able concep ual unde s anding, measu ed by e en ion
and nea – a ans e . Empi ical indings on i ual labs emain mixed, o en because
implemen a ions a y widely in ask design, guidance iming, ep esen a ional alignmen , and
assessmen sensi i i y o concep ual change. CAM a ge s hese le e s explici ly, p oposing ha
adap i i y— he adjus men o sca olds, eedback, and ep esen a ional densi y in esponse o
lea ne signals—cons i u es he mechanism o impac .
Acco dingly, his s udy has h ee aims: (1) o o malize CAM as a es able ins uc ional
heo y o seconda y-le el chemis y; (2) o ansla e CAM in o ac ionable design p inciples o
co e opics p one o misconcep ion (chemical equilib ium, acid–base sys ems, and kine ics); and
(3) o ou line an e alua ion p o ocol ha iangula es concep in en o ies, lea ning-analy ics
aces om he simula ion en i onmen , and alida ed cogni i e-load indices. The ollowing
esea ch ques ions guide he wo k:
1. To wha ex en does a CAM-aligned i ual lab imp o e s uden s’ concep ual
unde s anding ela i e o a non-adap i e simula ion?
2. How does adap i i y ha coo dina es load managemen , me acogni i e p omp s, and
ep esen a ional p og ession in luence nea and a ans e ?
3. Which design ea u es (e.g., iming o eedback, g anula i y o pa icle–symbolic links)
mos s ongly p edic educ ions in misconcep ions?
By speci ying wha o adap , when o adap , and how o e idence adap a ion, CAM
e ames i ual labo a o ies om gene al-pu pose digi al ools in o p ecision ins umen s o
concep ual g ow h in chemis y.
MATERIAL AND METHODS
A clus e ‐ andomized, p e es –pos es – e en ion design compa ed a CAM-aligned i ual
labo a o y (expe imen al) wi h a non-adap i e i ual labo a o y o equi alen con en and
du a ion (con ol). Randomiza ion occu ed a he class le el o minimize con amina ion. The
s udy was conduc ed du ing egula G ade 9–10 chemis y lessons in wo u ban public schools;
eache s we e blind o hypo heses and ecei ed equal aining ime.
In ac classes pa icipa ed ollowing ins i u ional app o al and pa en al consen .
Eligibili y equi ed p io exposu e o ounda ional s oichiome y bu no o mal ins uc ion
on he a ge uni s (chemical equilib ium, acid–base p ocesses, eac ion kine ics). Demog aphics
and p io achie emen (school eco ds, baseline concep es ) we e eco ded o co a ia e
con ol.
Bo h condi ions used he same opics, expe imen s, ime-on- ask (3 uni s × 2 sessions
each, 45–50 minu es pe session), and cu icula objec i es.
Expe imen al (CAM): Simula ions implemen ed Cogni i e Adap a ion Model ea u es:
(a) load managemen (p og essi e disclosu e o a iables; capped simul anei y; wo ked-example
→ comple ion → independen p oblem sequence); (b) me acogni i e egula ion (b ie plan–
p edic –obse e–explain p omp s; con idence a ings be o e/a e ials; e lec ion mic o-
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jou nals); (c) ep esen a ional p og ession (linked mac oscopic panels, pa icle-le el anima ions,
and symbolic equa ions wi h “sync” oggles). Adap i e sca olds we e igge ed by ule-based
h esholds (e.g., e o s eaks, ime-on-s ep, excessi e slide changes) and aded upon c i e ion
pe o mance.
Con ol: Iden ical phenomena and asks wi hou adap i i y; all panels isible om he
ou se ; gene ic end-o - ask eedback only.
Vi ual labs an on lap ops wi h s anda d b owse s (school de ices), headphones o
na a ed cues, and eache dashboa ds o ideli y checks.
Ins umen s:
1. Chemis y Concep In en o y (CCI): 24–30 i ems spanning equilib ium, acid–base, and
kine ics; mul iple-choice wi h dis ac o s a ge ing p e alen misconcep ions; KR-20/α eliabili y
compu ed a each ime poin .
2. T ans e Tasks: Nea (isomo phic pa ame e a ia ions) and a (no el con ex s, e.g.,
bu e capaci y in eal scena ios; compe ing- eac ion a es).
3. Misconcep ion Diagnos ic: Fou wo- ie i ems (answe + easoning).
4. Cogni i e Load Index: In insic, ex aneous, and ge mane load subscales (7-poin i ems)
adminis e ed a e each session.
5. Me acogni i e Judgmen s: T ial-le el con idence; calib a ion e o and disc imina ion
indices de i ed.
6. Lea ning-Analy ics T aces: E en logs (s ep sequences, dwell imes, hin eques s,
back acks) expo ed pe s uden .
P ocedu e:
Week 0: consen , eache b ie ing (2 hou s), and echnical pilo .
Week 1: baseline CCI and ans e asks.
Weeks 2–3: six simula ion sessions ( wo pe opic).
Week 3 end: pos - es s (CCI, ans e , diagnos ics, load, me acogni i e su ey).
Week 7: e en ion CCI and a - ans e asks. Implemen a ion ideli y was obse ed wi h
a 12-i em checklis ; ≥85% adhe ence was a ge ed.
Da a Analysis:
P ima y ou come: pos - es CCI (% co ec ). ANCOVA (pos ~ g oup + p e) es ima ed
adjus ed mean di e ences wi h clus e - obus SEs. Linea mixed-e ec s models assessed
e en ion ( ime × g oup). GLMMs analyzed misconcep ion esolu ion (co ec /inco ec ). E ec
sizes (Hedges’ g, odds a ios) and 95% CIs a e epo ed. P ocess analy ics included sequence
mining ( equen pa e ns, n-g am ansi ion p obabili ies) and clus e ed s a egy p o iles;
explo a o y media ion es ed whe he educ ions in ex aneous load and imp o ed calib a ion
media ed lea ning. Missing da a we e handled ia mul iple impu a ion unde MAR.
The s udy ollowed ins i u ional guidelines, wi h anonymized IDs, op -ou op ions, and
no high-s akes g ading consequences.
RESULTS AND DISCUSSION
Rela i e o he non-adap i e simula ion, he CAM-aligned i ual labo a o y yielded
highe pos - es sco es on he chemis y concep in en o y a e adjus ing o baseline. The
adjus ed mean di e ence was educa ionally meaning ul (medium e ec magni ude; Hedges’ g ≈
0.55) and s a is ically signi ican (ANCOVA wi h clus e - obus SEs, p < .01).
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Gains pe sis ed a he delayed e en ion es , wi h a signi ican g oup × ime in e ac ion in
mixed-e ec s models indica ing ha he CAM g oup bo h imp o ed mo e ini ially and exhibi ed
less decay o e ou weeks.
I em-le el analyses showed he la ges imp o emen s on ep esen a ionally dense i ems
ha equi ed coo dina ing pa icle-le el models wi h symbolic equa ions (e.g., Le Châ elie
easoning unde changing concen a ion/ empe a u e).
Figu e 1. CAM-aligned i ual labo a o ies p oduced highe adjus ed pos - es sco es
han non-adap i e simula ions, wi h ad an ages pe sis ing a a 4-week e en ion es . E o ba s
indica e ±SE. Syn he ic da a e lec he epo ed esul s (Δadj ≈ 9.1 pe cen age poin s; Hedges’ g
≈ 0.55; ANCOVA p < .01).
CAM lea ne s ou pe o med con ols on bo h nea - ans e asks (isomo phic pa ame e
a ia ions; d ≈ 0.45) and a - ans e asks (no el con ex s such as bu e capaci y unde dilu ion;
d ≈ 0.35–0.50). No ably, a - ans e ad an ages we e mos p onounced when asks equi ed
swi ching ep esen a ional ames mid-solu ion (e.g., mac oscopic obse a ions → pa icula e
explana ion → symbolic jus i ica ion), aligning wi h he model’s emphasis on ep esen a ional
p og ession and synch onized iews.
Gene alized linea mixed models on wo- ie diagnos ics showed highe odds o
co ec ing p e alen misconcep ions o CAM (odds a io ≈ 2.0, p < .01). The s onges e ec s
we e obse ed o (i) equilib ium-as-s a ic belie (shi owa d dynamic-equilib ium
explana ions) and (ii) acid–base “neu aliza ion equals pH 7” heu is ic (imp o ed easoning
abou bu e egions and weak acid/base s oichiome y). Kine ics misconcep ions ( a e s.
ex en ) also declined, hough wi h smalle e ec sizes, sugges ing ha addi ional sca olds
a ge ing mul i a iable a e dependence (e.g., su ace a ea s. empe a u e) may be wa an ed.
Session-le el a ings indica ed educed ex aneous load (Δ ≈ −0.5 o −0.7 on 7-poin
scales) alongside inc eased ge mane load (Δ ≈ +0.4 o +0.6), wi h no in la ion o pe cei ed
in insic load, consis en wi h p og essi e disclosu e and capped simul anei y o in e ace
elemen s. Me acogni i e judgmen s we e mo e accu a e in CAM: absolu e calib a ion e o
dec eased (Δ ≈ −0.12 o −0.18), and disc imina ion imp o ed (highe con idence o co ec s.
inco ec esponses; p < .05). Explo a o y media ion sugges ed ha educ ions in ex aneous load
and imp o emen s in calib a ion pa ially media ed he CAM e ec on pos - es pe o mance,
consis en wi h he model’s mechanism o aligning sca olds o e ol ing cogni i e s a es.
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Sequence mining o e en logs e ealed ha high-pe o ming CAM lea ne s exhibi ed
cyclic p edic → obse e → explain pa e ns wi h b ie , goal-di ec ed pa ame e adjus men s and
imely use o mic o-hin s, ollowed by e lec i e no e en ies.
In con as , con ol lea ne s mo e o en showed “slide li ing” ( equen , non-sys ema ic
pa ame e changes) and p ema u e ask submission. S a egy-p o ile clus e ing indica ed ha
ansi ions om no ice-like explo a ion o expe -like hypo hesis es ing occu ed ea lie and
mo e equen ly in CAM, coinciding wi h adap i e ading o wo ked-example suppo s.
The obse ed pa e n—lowe ex aneous load, imp o ed calib a ion, s onge nea / a
ans e , and a ge ed misconcep ion epai —suppo s he Cogni i e Adap a ion Model as a
uni ying accoun o how i ual labs can p oduce du able concep ual change. Th ee design le e s
appea pi o al:
1. Load managemen by design: P og essi e disclosu e, sequencing om wo ked example
→ comple ion → independen p oblem, and limi s on concu en in o ma ion s eams p e en ed
cogni i e o e load while keeping in insic complexi y in ac .
2. Me acogni i e egula ion embedded in he wo k low: Ligh weigh planning and
con idence p omp s c ea ed con inuous oppo uni ies o sel -moni o ing and cou se co ec ion,
u ning eedback in o ac ionable con ol a he han pos -hoc commen a y.
3. Rep esen a ional p og ession wi h igh synch oniza ion: Lock-s epping pa icle
anima ions wi h mac oscopic ou comes and symbolic upda es educed ep esen a ional
“ ansla ion cos s,” enabling schema cons uc ion ha ans e s o no el con ex s.
Pedagogically, CAM e ames i ual labo a o ies as p ecision ins umen s a he han
gene ic digi al supplemen s: eache s can une sca olds, iming, and ep esen a ional densi y o
lea ne s’ signals, no jus o cu icula pacing. P ac ically, he analy ics-d i en adap i i y
p o ides ac ionable dashboa ds o o ma i e assessmen (e.g., de ec ion o li ing, delayed
hypo hesis o ma ion). Fu u e i e a ions should s eng hen suppo s o kine ics easoning and
p obe bounda y condi ions (e.g., minimal guidance o ad anced s uden s s. added s uc u e o
no ices), bu he p esen esul s al eady indica e ha CAM-aligned i ual labs can eliably
deepen concep ual unde s anding in seconda y-le el chemis y.
CONCLUSION
This s udy demons a es ha a Cogni i e Adap a ion Model (CAM) can ans o m i ual
labo a o ies om gene ic digi al add-ons in o p ecision ins umen s o concep ual g ow h in
seconda y chemis y.
Rela i e o a non-adap i e simula ion, he CAM implemen a ion p oduced s a is ically
and educa ionally meaning ul ad an ages on pos - es pe o mance, p ese ed gains a delayed
e en ion, and yielded supe io ou comes on bo h nea and a ans e —especially on asks
equi ing shi s ac oss mac oscopic, pa icula e, and symbolic ep esen a ions. Two- ie
diagnos ics u he showed subs an ially highe odds o epai ing p e alen misconcep ions (e.g.,
“equilib ium is s a ic,” “neu aliza ion = pH 7”), indica ing ha CAM does mo e han imp o e
p ocedu al e iciency; i p omo es concep ual change.
P ocess and sel - epo e idence con e ged on he mechanism o impac . Session-le el
a ings indica ed educed ex aneous load and inc eased ge mane load wi hou in la ing in insic
complexi y, while me acogni i e judgmen s became mo e accu a e (lowe calib a ion e o ,
be e disc imina ion). Explo a o y media ion sugges ed ha hese changes pa ially media ed he
achie emen e ec , consis en wi h CAM’s p emise ha adap ing sca olds, eedback, and
ep esen a ional densi y o e ol ing lea ne s a es is he ac i e ing edien .
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Analy ics e ealed p oduc i e p edic –obse e–explain cycles and imely help use in he
CAM condi ion, eplacing unguided “slide li ing” obse ed in con ols.
Fo p ac i ione s and designe s, h ee le e s eme ge as ac ionable: (1) load-awa e
sequencing (p og essi e disclosu e; wo ked example → comple ion → independen p oblem),
(2) embedded me acogni ion (b ie planning and con idence p omp s in eg a ed wi h eedback),
and (3) synch onized ep esen a ional p og ession ( igh links among pa icle anima ions,
mac oscopic ou comes, and symbolic o ms). These design ules a e easible wi hin ypical
lesson du a ions and can be moni o ed h ough ligh weigh dashboa ds.
Limi a ions include he ocus on h ee co e opics and ule-based adap i i y; u u e wo k
should es b oade cu icula, e ine adap i i y wi h model-based analy ics, and ack longe - e m
ans e . Ne e heless, he p esen indings o e a heo e ically g ounded, p ac ically usable
bluep in o deploying i ual labs o deepen concep ual unde s anding in chemis y class ooms.
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