visual factors that significantly impact academic performance

C L I N I C A L R E S E A R C H

The academic performance of children is recognized asone of the major indicators of a strong society.1 - 2 3 H i s-t o r i c a l l y, the western world’s educational system wa s

designed to supply workers for an industrial society. Now,a more service-oriented work force is needed. If childrenare not prepared for the changing service-oriented society,t h ey may not be able to adequately compete for jobs in theglobal economy.1

Not only have the educational needs of Western societychanged, but the speed at which the world moves is tak-ing its toll on both the child and the adult. Our society’s paceis much faster today than it was in previous generations.6Parents who once were invo l ved in child-oriented and schoolactivities find their time taken up with other endeavors. Itis clear that education must adapt to the sociological changesin order to address the needs of this society in the twenty-first century.

Changes are indeed being instituted in education.3 - 5 , 7 , 8 , 1 0 - 1 2

Teacher certification and re-certification, school vouchers forp r i vate schools, mandatory standardized testing, and politi-cal debate at the highest levels clearly demonstrate that aproblem is recognized and solutions are being pursued.4 , 7 - 1 2

Experts agree that educational levels must be comparablefrom school to school and region to region.7,8,10,11 Unlesseducators recognize how their schools are ranked in rela-tion to other schools, they will not be able to direct neededc h a n g e s. Standardized test scores indicate each school’s aca-demic health as well as areas of strength and weakness.2,3

SATs and ACTs are popular standardized tests at the highschool level. The score achieved on these tests can have a

Visual factors that significantly impact academic performance

W.C. Maples, O.D., M.S.

Northeastern State University, College of Optometry, Tahlequah, Oklahoma

Maples WC. Visual factors that significantly impact academicperformance. Optometry 2003;74:35-49

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VOLUME 74/NUMBER 1/JANUARY 2 0 0 3 O P TO M E T RY

Background: Both race and socio-economic status are corre-lated to perf o rmance in the classroom. These two factors areinter-related, since minorities, proportion-wise, are morehighly re p resented in the lower socio-economic strata. Inef-ficient visual skills have been shown to be more prevalentamong minority groups and in low socio-economic groups.These inefficient visual skills impact the students’ learn i n g .This study was undertaken to discover the visual skills thatwere significantly correlated with academic performanceproblems.

Method: A total of 2,659 examinations were perf o rmed on 540children over the course of six examination periods, whichwere administered over three consecutive school years.Socio-economic, racial, and standardized academic per-formance data (Iowa Test of Basic Skills—ITBS) were fur-nished by the families and the school system. The visual anddemographic data from the examinations were then com-pared to performance on the 21 subtests of the ITBS.

Results: Some visual factors were found to be a much betterp redictor of scores on the ITBS than either race or socio-eco-nomic status. Even though the significance of these twodemographic variables was small, race and socio-economicvariables were each significant in about a third of the 21 ITBSscores.

Conclusion: Visual factors are significantly better predictors ofacademic success as measured by the ITBS than is race orsocio-economics. Visual motor activities are better predic-tors of ITBS scores than are binocularity or accommodation.These latter skills were significant predictors also, but to alesser degree.

Key Words: Academic performance, accommodation, binocu -l a r i t y, children, education, learning, race, minorities, testing


tremendous influence on the youngadult’s future.

For many children, these standard-ized tests begin in elementaryschool. One of the elementaryschool standardized tests is theI owa Tests of Basic Skills (ITBS).2 4 - 2 7

It consists of twenty-one subscores.The test sections are administeredbased on the academic grade of thec h i l d. Appendix A provides an anno-tated description of each of the sub-t e s t s.2 5 - 2 7 This test has a longtradition (1935) and is periodicallyre-assessed to ensure that norms arekept current.2 4 The test is appropri-ate to be administered to childrenfrom kindergarten to the eighthg r a d e. It has demonstrated reliabil-ity and va l i d i t y.

L ow socio-economic status hasbeen suggested as a causative fac-tor in lowered academic perform-a n c e.1 3 - 2 1 Racial status and lowsocio-economic status are corre-lated with one another and withpoor academic scores.2 2 It does notf o l l ow that the variables of raceand low socio-economic statuscause poorer academic perform-a n c e, since poor visual skills havebeen shown to be a predictor ofacademic performance and these,in turn, are correlated with raceand socio-economic va r i a b l e s.2 2

Visual factors appear to be moresusceptible to positive modificationthan are either variables of race ors o c i o - e c o n o m i c s. It is possible that,by improving visual skills in these groups, aca-demic scores might increase.

A previous study had demonstrated that certainvisual skills scores were better predictors forfour ITBS subscores than were race and socio-economic groups.2 2 Race was found to be asmall but significant predictor for three va r i-a b l e s, and socio-economic status was found tobe a small but significant predictor for two ofthe four subscores. The purpose of this studywas to evaluate the correlations between the 21ITBS subscores and the results of a three-ye a r

longitudinal, prospective study of visual skillsat the elementary school level. A secondarygoal was to compare the relative significance ofvisual factors to demographic factors—specif-i c a l l y, race and socio-economic status. Thestudy included demographic and visual data ofchildren who are attending public school, firstthrough fifth grades. This article is limited toreporting of the analysis of only the demo-graphic and visual factors that significantly cor-related to the ITBS. Fu r t h e r, a regressionanalysis was performed to investigate the rel-a t i ve significance of these visual variables toeach of the ITBS subscores. Only those meas-

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O P TO M E T RY VOLUME 74/NUMBER 1/JANUARY 2 0 0 3

Data points collected during each visual evaluation

1 . S t u dy site and coded re p re s e n t ation for each subject2 . D ate of birth, age, grade, primary race and sex3 . Dominant eye, dominant hand, and if/when optical

p rescription is wo r n4 . Visual acuity both far and near with each eye and binocu-

larity and habitual correction. All subsequent testing was p e r formed through this prescription. If the prescription was full time, the lenses we re worn full time. If the pre -scription was only for near, the prescription was worn only for near testing.

5 . Disease screening with binocular loupe, tra n s i l l u m i n at o r, and direct ophthalmoscope

6 . C over test both far and near with notation of phoria or tro p i a

7 . Phoria both far and near with the Howell Card (Modified Thorington Technique) and Binocular ±1 D AC/A at near with the near Howell Card

8 . Near Stereo with Wirt circles and autore f ra c t o r, each eye9 . Near point of accommodation blur out and re c overy with

the dominant eye (NPA )1 0 . A c c o m m o d at i ve Rock ±2 D Flippers, monocularly and

binocularly with Po l a roid suppression check for binocular t e s t i n g

1 1 . Nearpoint of conve rgence break and re c over [3 measures (NPC)]

1 2 . Nott re t i n o s c o py1 3 . Prism bar ranges base in/base out at near1 4 . Prism flippers 8 base out/8 base in at near1 5 . Maples Ocular Motor Test, both pursuits and saccades1 6 . D evelopmental Eye Movement Test (DEM)1 7 . Motor Free Visual Pe rception Test (MVPT)1 8 . Wold Sentence Copy Te s t1 9 . Visual Motor Integration Test (Beery)2 0 . C OVD Quality of Life Checklist from both Pa rent and

Te a c h e r2 1 . Socio-Economic Checklist Info r m at i o n2 2 . R e l at i ve placement in class as judged by the teacher2 3 . I owa Test of Basic Skills in the Spring of each ye a r

Box


ures that showed significance when comparedto a subscore of the ITBS at the 0.05 level we r ec o n s i d e r e d.

MethodsA three-year prospective, lon-gitudinal study was under-t a ken to evaluate the visualand academic performance of540 students in three elemen-tary schools in Ta h l e q u a h ,Oklahoma. The students, whowere in the first, second, andthird grades, were followe dfor three ye a r s. Testing ofindividual children was com-pleted when most were in thefirst through the fifth grades.The visual testing was per-formed by licensed optom-e t r i s t s. The same optometristperformed the same set oftests during each test period,minimizing inter-exa m i n e rb i a s. The examiner did noth ave access to previous tests c o r e s. A list of the datapoints gathered on each eva l-uation can be found in theB ox. The testing was per-formed once in the fall andonce in the spring for threeye a r s. Over the three-ye a rp e r i o d, 2,659 evaluations we r ep e r f o r m e d. The subjects we r eprimarily white (401), fol-l owed by Native American(121), black (9), Hispanic (8),and one Oriental. T-tests we r eperformed on the parametrictest data, which allowed com-parison of mean scores. Fo rthe nominal data representedby the socio-economic, racial,and gender factors, a Chisquare (χ2 ) analysis was used.This statistical tool is usefulwhen named groups are beingcompared but the groups donot have a measured andstandard interval betwe e nthem. A Likert scale wa sassumed for the Maples Ocu-lar Motor Test and VisualMotor Integration Test (VMI).

The Likert scale allows the use of interva ll evel statistics (t-test) by assuming standarddistances between data points.2 8 - 3 0

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Regression analysis graph of significant factors related to core battery.Figure 1


ResultsThe Table contains the 21 ITBS subcategories, alongwith the results of the analysis of significant testdata. Only factors that were found significant atthe 0.05 level or better, using the appropriate sta-tistical instrument, were analyzed. A total of 39screening factors were found to be significantlyrelated to at least one of the 21 ITBS subfactors.Appendix B lists significant examination data, whileAppendix C lists each of the examination abbre-viations used in the Ta b l e. A regression analysis ofthese significant screening variables was per-f o r m e d. Each column labeled as an exa m i n a t i o ndata category in the Table contains a number. Eachline represents a ITBS subscore. The presence of

a number in a line/column indicates that the exa m-ination category was significant. The value of thenumber represents the percentage of the ITBS testvariance that was accounted for by that particu-lar visual test result. The sum variance—found atthe end of each line—represents the total amountof variance explained by the significant factors dis-c overed in predicting ITBS performance. The bot-tom numbers of each column represent f i r s t, thesummed variance for that particular test and s e c-o n d, the number of ITBS subscores that were foundto be significant for that particular test.

The ITBS subscores in the Table are listed ino r d e r, from the largest-pooled predictive subfac-

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Table. Visual and demographic variance of significant factors compared to individual ITBS scores

I T B S

C o re Battery 3 5 . 7 9 . 7 03 . 1 04 . 3 — 0 . 8 1 . 7 — — — — — 1 . 5 — — 0 . 9 — —

Math To t a l 3 4 . 7 1 1 . 2 02 . 0 03 . 8 — 0 . 8 2 . 2 0 . 8 — — — — — — — 0 . 9 — —

Language To t a l 3 5 . 2 07 . 3 04 . 2 02 . 5 00 . 9 — — — — — — 2 . 7 0 . 5 1 . 3 — — — —

Reading To t a l 01 . 7 1 1 . 0 2 4 . 8 07 . 7 02 . 7 0 . 6 0 . 4 — 0 . 6 — — 1 . 5 0 . 5 1 . 1 — 0 . 4 — —

Listening Gr Eq 2 4 . 3 01 . 2 — — 1 6 . 1 — — — — — — — — — — 1 . 0 — 4 . 4

Vo c a b u l a ry Gr Eq 0 . 4 2 4 . 5 1 0 . 5 06 . 1 03 . 8 1 . 2 1 . 6 0 . 5 — — — — 0 . 9 — — — — —

Math Computations 3 2 . 4 08 . 8 02 . 9 05 . 2 — — — — — — — — — — — — — —

C o n c e p t s / E s t i m a t e 2 5 . 9 1 0 . 9 02 . 9 05 . 6 1 . 2 — 1 . 9 — — — — — — — — — — —

P ro b l e m / D a t a 2 3 . 6 08 . 4 02 . 1 01 . 9 — 1 . 7 4 . 1 0 . 9 — — — — — — — — — —

Lang Expre s s i o n 2 3 . 7 09 . 7 03 . 5 04 . 3 00 . 9 0 . 6 0 . 4 0 . 7 — — — 2 . 2 1 . 0 — — — — —

C o m p rehens Gr Eq 00 . 9 05 . 9 2 1 . 6 1 0 . 1 01 . 5 — — — 0 . 8 — — 2 . 2 0 . 6 0 . 6 — 0 . 4 — —

Composite Gr Eq — 2 1 . 4 07 . 9 06 . 5 — 1 . 8 1 . 5 — 3 . 0 — — — 1 . 9 — — — — —

Wo rd Analysis 2 6 . 3 04 . 8 — — 01 . 9 2 . 7 — — — 8 . 2 — — — — — — — —

M a p s / D i a g r a m s — 1 3 . 6 — — — 5 . 9 — 7 . 1 — — 3 . 4 — — 3 . 3 — — — —

P u n c t u a t i o n — 1 7 . 9 07 . 0 — 02 . 0 — 2 . 2 — — 3 . 3 — — — — — — — —

Info Source To t a l — 1 6 . 5 03 . 9 03 . 8 — 1 . 7 — 2 . 0 5 . 0 — — — — — — 1 . 4 — —

S c i e n c e — 1 4 . 5 06 . 9 04 . 3 — 2 . 1 — — — — — — — — — — — —

C a p i t a l i z a t i o n — 05 . 2 1 2 . 2 — — — — — — — 2 . 2 — — — — — — —

Ref Materials — 07 . 9 1 1 . 3 — — — — 2 . 8 — — 4 . 0 — — — — — — —

S p e l l i n g — — 1 5 . 2 — 08 . 4 — — — 2 . 1 — — — — — — — — —

Social Studies — — — — 03 . 8 — — — — — — — — — 5 . 4 — 4 . 6 —

T O TA L S 2 6 4 . 8 2 1 0 . 4 1 4 2 . 0 6 6 . 1 4 3 . 2 1 9 . 9 1 6 . 0 1 4 . 8 1 1 . 5 1 1 . 5 9 . 6 8 . 6 6 . 9 6 . 3 5 . 4 5 . 0 4 . 6 4 . 4

No. of

significant factors 1 2 1 9 1 7 1 3 1 1 1 1 9 7 5 2 3 4 7 4 1 6 1 1


tor to the least-pooled predictive factor. Thesescores range from a high of 58.9% (Core Battery)to 19.3% (Social Studies). Figures 1 and 2 graph-ically represent this regression analysis data forthese high and low subscores of the ITBS.

The largest-pooled predictive category, Core Bat-tery, had nine variables that were significantm a r ke r s. The sum total of the variance accountedfor was 58.9% (s e e Table). This category was fol-lowed by seven other ITBS categories in whichat least 50% of the variance was accounted for bythe evaluation. These seven categories were Ma t hTo t a l (58.0%, 10 variables); L a ng u age To t a l(55.4%, nine variables); Re a d i ng To t a l (54.7%, 15

variables); L i s t e n i ng Grade Equivalent (53.9%, ninevariables), Vocabulary Grade Equivalent (51.3%, 11variables); Math Computations (50.6%, five vari-ables); and Concepts and Estimation ( 5 0 . 5 % ,eight variables). Social Studies, on the other hand,with its five va r i a b l e s, was predicted only19.3% of the va r i a n c e. Figure 3 visually representsthese ITBS subscores, which were predicted bythis school examination protocol at least 50% ofthe time.

The ITBS subscore with the largest number of sig-nificant variables was the total reading score,which was predicted by 15 factors (54.7%). Vo c a b-ulary grade equivalent was the next most fre-

3 9


— — — — — — — — — — — — — — — — — 1 . 2 — — — 5 8 . 9 9

— — — — — — — — 0 . 9 — — 0 . 7 — — — — — — — — — 5 8 . 0 1 0

— — — — — — — — — — — — — — — — — — — — 0 . 8 5 5 . 4 9

— — — — — — 0 . 6 — — — — — — — — — 0 . 5 — — 0 . 6 — 5 4 . 7 1 5

— — — — — — 1 . 8 — — — — — — 2 . 0 2 . 0 — — — 1 . 1 — — 5 3 . 9 9

— 1 . 0 — — — — — — — — — — — — — — 0 . 8 — — — — 5 1 . 3 1 1

— — — 1 . 3 — — — — — — — — — — — — — — — — — 5 0 . 6 5

— 1 . 0 1 . 1 — — — — — — — — — — — — — — — — — — 5 0 . 5 8

— — 2 . 8 — — — — — 1 . 6 — — 1 . 6 — — — — — — — — — 4 8 . 7 1 0

— — — — — — — — — — — — — — — — — — — — — 4 7 . 0 1 0

— — — — — — 0 . 5 — — 0 . 6 — — — — — — — — — 0 . 4 — 4 6 . 1 1 3

— — — — — — — — — — — — — — — — — — — — — 4 4 . 0 7

— — — — — — — — — — — — — — — — — — — — — 4 3 . 9 5

2 . 6 — — — — — — 2 . 7 — — 2 . 4 — — — — — — — — — — 4 1 . 0 8

— — — 2 . 4 — — — — — — — — — — — — — — — — — 3 4 . 8 6

— — — — — — — — — — — — — — — — — — — — — 3 4 . 3 7

1 . 7 — — — — — — — — — — — — — — — — — — — — 2 9 . 5 5

— — — — 3 . 5 — — — — 1 . 8 — — 2 . 1 — — 1 . 8 — — — — — 2 8 . 8 7

— — — — — — — — — — — — — — — — — — — — — 2 6 . 0 4

— — — — — — — — — — — — — — — — — — — — — 2 5 . 7 3

— 2 . 0 — — — 3 . 5 — — — — — — — — — — — — — — — 1 9 . 3 5

4 . 3 4 . 0 3 . 9 3 . 7 3 . 5 3 . 5 2 . 9 2 . 7 2 . 5 2 . 4 2 . 4 2 . 3 2 . 1 2 . 0 2 . 0 1 . 8 1 . 3 1 . 2 1 . 1 1 . 0 0 . 8

2 3 2 2 1 1 3 1 2 2 1 2 1 1 1 1 2 1 1 2 1

Table continued


quently predicted ITBS skill bythe test data. The eleven testscores predicted 46.1% of thevariance. Other ITBS subscorespredicted by 10 tests were totalmath score (58.0%), problem solv-ing/data (48.7%), and languagee x p r e s s i o n (47.0%). The ITBSscores that had the fewest sig-nificant screening correlationswere reference materials (with 4)and spelling (with 3).

Of the 21 ITBS variables, racewas significant in nine and s o c i o -economic status was significant ins even. None of the r a c e or s o c i o -economic status regression scoreswas highly predictive of any ofthe ITBS items. The ITBS itemthat scored the highest whencompared to race was problemsolving/data, with 4.1% of thetotal variance being predicted byrace. Socio-economic status (SES)correlations were even lowe r. Ofthe seven significant (SES)screening factors, the highestregression variance was 1.9%,with composite grade equivalent.

The Wold Sentence Copy was themost-robust overall predictor,with a cumulative predictivevalue of 264.8 and 12 ITBS fac-tors. The average variance forthis test was 22.1%. The VMIpredicted some measure of per-formance on 19 of the 21 ITBSscores, with a sum predictivevalue of 210.4. The ave r a g evariance for these 19 items was11.1%. The race factor was pre-dictive on nine of the 21 ITBS subscores. It hada cumulative predictive score of 16.0%. The ave r-age for the nine ITBS scores was 1.8%. The va r i-able of socio-economic status was predictive onseven of the 21 ITBS categories and a sum vari-ance of 6.9%, for an average variance on theseseven scores of 1%.

There were six factors that predicted at least 11or more of the ITBS subskills. These were, in

decending order, the VMI (19), DEM VerticalScore (17), DEM Ratio Score (13), Wold SentenceC o py (12), and the Motor Free Visual Pe r c e p t i o nMemory Sub-Test and Motor Free Visual Per-ception Closure Sub-Test, each with 11.

DiscussionThe two visuo-motor tests, the VMI and the Wo l dSentence Copy, were the most-robust predictorsof academic success in this study of children in

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Regression analysis graph of significant factors related to Social Studies.Figure 2


the first through fifth grades. Race was the sev-enth-best predictor and socio-economic status wa sthe eighth-best predictor. The predictive abilityof the VMI and the Wold Sentence Copy were farbetter than the factors of race or socio-economicstatus. The Wold Sentence Copy, a one-minutetest, is a 16.55 times better predictor of scores onthe ITBS than race and a 38.4 times better pre-dictor of scores on the ITBS than socio-economicfactors. Likewise, the VMI predicts performanceon the ITBS at a 13.15-fold rate better than doesrace. The VMI prediction rate, when comparedto socio-economic status, is 30.48 greater rate.

Other visual tests were also significant predic-tors of ITBS scores, though less robust than theWold and VMI. These include visual acuity,visual-auditory processing, ocular motor, b i n o c u-lar skills, accommodative skills, and refractive sta-t u s. Both near and far visual acuity a n da u t o - r e f r a c t o r (AR) scores were found to corre-late with some academic scores. V i s u a l - v e r b a lp r o c e s s i ng, measured by the Developmental EyeM ovement Test (DEM) vertical time score. Thevertical DEM score requires the individual to

look at the digit and then recall the name of thedigit. This automaticity skill requires a visualsymbol to be converted into a verbal response,a rudimentary form of reading. Ocular motors k i l l s, as measured by the DEM horizontalscore/ratio and the Maples Ocular Motor Te s t ,were also correlated. In the realm of b i n o c u l a rm e a s u r e s, the Howell Card (out of phoropterphorias), near point of convergence and stereoacuity were correlated to scores on the ITBS.L a s t l y, all three measures of a c c o m m o d a t i o n—a m p l i t u d e, l ag, and f a c i l i t y— were found to cor-relate to the academic scores.

An earlier paper reported on four of these 21 ITBSsubscores. It demonstrated the significance ofvision skills in prediction of academic perform-ance.22 The earlier data were collaborated anddramatically expanded by these findings. More-ove r, this information points to a solution hithertonot universally considered: improving visual func-tion to impact learning. Visual skills, and thesymptoms associated with these deficient skills,3 1

can be easily measured and modified by opto-metric techniques.32-36

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Total variance of Iowa Tests of Basic Skills, accounted for by visual evaluation scores.Figure 3


A controlled study should be undertaken to eva l-uate if treatment of the visual factors identifiedin this study would make an impact on academicscores. Therapy would include optical prescrip-t i o n s, ergonomics, and specialized therapeutic pro-c e d u r e s. Such a study would demonstrate if suchtreatment would have a statistically significantimpact on the ITBS scores or on some compara-ble standardized academic test.

Visual skills can be improved.37-39 It is possiblethat improvement of visual skills would be a sign-ficicant part of the solution for this very complexproblem of academic under-performance. Clearly,this problem is multi-factorial, but the improve-ment of ocular motor, binocular, accommodativeand particularly visual motor and visual percep-tual skills could only help the overall picture.

ConclusionsThis article gives evidence that visual motor, ocu-lar motor, binocular, accommodative, and visualperceptual skills are significant factors in childrenwho score poorly on the standardized Iowa Testof Basic Skills educational test. Race and socio-economic factors are less-significant predictors ofsome of the scores on the ITBS.

This article also indicates the need to institute am u l t i - s i t e, prospective, randomized study toinvestigate if children who received optimumoptometric care would improve in their academicstanding, as measured by the ITBS.

AcknowledgmentsH e a rtfelt thanks go to Mr. Richard Hoenes for his statistical assistance. This pro j-ect was funded in part by the Australiasian College of Behavioural Optometry;College of Optometrists in Vision Development; the Optometric Extension Foun-dation Inc.; the American Foundation for Vision Aw a reness; and Mountain StatesC o n g ress of Optometry. The Faculty Research Committee of Nort h e a s t e rn StateUniversity partially funded release time for the writing of this article.

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38. Atzmon D. Po s i t i ve effect of improving relative fusionalvergence on reading and learning disabilities. Binoc Vis1985;1:39-43.

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Corresponding author

W.C. Maples, O.D.Northeastern State University

College of Optometry1001 North Grand Avenue

Tahlequah, Oklahoma 74464

[email protected]

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Appendix A


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Appendix B


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Appendix C

visual factors that significantly impact academic performance

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