Indian J Physiol Pharmacol 19%; 40(4): 330-334

COMPUTER-ASSISTED LEARNING PACKAGE FOR FREQUENCYDISTRIBUTION OF PHYSIOLOGICAL VARIABLES

R. BAJPAI* AND A. INDRAYAN

Division of Biostatistics & Medical Informatics,University College of Medical Sciences,Delhi - 110095

(Received on March 2, 1996)

Abstract: There are a variety of ways of picturing a frequency distributiontype, viz, the histogram, the frequency polygon, the smoothed curve and theogive. We have developed a computer package to demonstrate gradual changeof a histogram into a curve. For a given set of data on frequency distributionof, say blood pressure levels in specified categories, this package helps thecomputer to draw bars which gradually rise to the level of the frequencies, andlateron are replaced by the polygon and finally by the frequency curve of theGassussian type on the computer screen pixel by pixel. This thus demonstratesthe meaning and genesis of frequency curves. This package could be veryuseful in learning the concept of frequency curves particularly the Gaussianform.

Key words: statistics frequency distribution gaussian curvecomputer-assisted learning physiological variables

INTRODUCTION

Computer-based methods in medicaleducation are now so well defined and assessedat international level that their use is consideredas the necessary goal for the coming years.Advantages of computer-based education includelocal control over the topic, time, place, and paceof instruction. Learners have different preferredstyles of receiving information and computerprogrammes can be written to appeal toparticular style of learning (1). They caneffectively assist in the organisation ofknowledge, clinical reasoning and competence,and manual skills.

In the past several years, there has been anenormous increase in the number of computer­assisted learning applications. Many medicaleducators and physicians have recognised thepower and utility or hypertextJ hypermedia whichare evolving as important new measures in theeducation of health sciences (2). Hypertext/

*CorresJlonding Author

hypermedia is computer-based means oforganising information that allows one to explorethe connections among related subjects. Userscan choose frames (picture or text on the screen)from an alphabetical index producedautomatically from key words in each frame title,or from hierarchically organised subject index.Links between frames allow users to explorerelated frames and then return to the originalframe. Physicians can add new frames to thesystem so that the specified knowledge can beshared (3). Some developers have incorporatedsimple diagrams, scanned monochrome graphicsor still frame photographs from a laser disk orCD -ROM into their hypertextlhypermediaapplications (4). These technologies have greatlyincreased the role of micro-computer in educationand training.

A hypertext system has also been developedwhich allows easy courseware development withmultiple authors working on a networkedenvironment. The system benefits not only the

Indian ,J Physiol PharmacoJ 199G; 40(4) Autonomic ancl Vasodilatory Dysftmction in NLDDM 329

of microvascular flow studies (2). Clinically alsothis finding is confirmed by grauual claudicationand foot ulcer. In conclusion, our data suggestsexistence of correlation between autonomicdysfunction and peripheral vascular dysfunctionat subclinical level.

ACKNOWLEDGMENTS

The work was accomplished withthe help of grant received from All IndiaInstitute of Medical Sciences, New Delhi during1993-94.

REFERENCES

1. l~ving 0,1, Cl:uke Hr. Diagnosis and managc~ment ordiabetic autonomic neuropathy. Br ivied J 1982;285: 91G-911l.

2. Rendell M, Basisedurn O. Diabetic cutaneousmicroangiopathy. Am J ivIed 1992; 9:~: flU-G18.

3. Gilmore ,IE, Allen ,fA, Hayes JR. 1\ comparison nfperipheral vasoconstrictnr resp"nse~s andc:'II'd iovascular autonomic (llllction tests in diabeticp<ltients. Viaue/ulugia 1~)~)(); ~~:3 : :J50-:35G.

4. Bannister R, J\·lathiRs c.J. Tc,sting alltonomic n,flexes.In Bannister R, eel. Autonomic failure - t\. text bonkor clinical IlislJ1'ders of autonomic ntOrvous systl~m 2ndedn. Oxford: Oxford University Press. 1988; 289-31)7.

5. Das AK, Flynn MD, Edmonds ME, Watkills X.AIJl)nrmal peripheral n8rve function in the dialJeticischRemic limb: evid(·~nce for a specific diabeticisch<lemic neuropathy. In: Ward .I, Got" Y. (cds),Diabetic neurol'ftthy..John Wiley & Sons, Chichester;1990; 235-239.

G. Clarke DF, Ewing D,I, Camphell IW. Diahetic auto­nomic llelll'opathy. Diabetoloffia 1979; 17: 195-212.

7. Bellavere P, Borellow G, Fedele 0, COTllol1l~ C, FerriM. Diagnosis and management (If rliahdic autonomicnemopathy. HI' kJed ,/ 198:J; 287: G1.

8. TandcJIl R, lJajpai HS, Agarwal JK. A comprehensivestudy of autonomic nervous system dysfunction indiahetes mellitus. J Assoc Physicians India, 1985;33 : 2r;~-2G8.

9. Ho r:l', Tang KT, Wang LM, Chang TC, ,hlp TS,Kwok GT. J{d]cx heart rate r8sponse in normal anddiahetic $ubjects relationship to age and skinmicrovascuLH reflexes. Tn: Ward .J, Goto Y, E·ds.Difthetic nell1'opathy.•John Wiley Chichester 1990;~179-:J9:l.

10. SitmpSOn M.J, Wilson S, KarRgiannis P, Edmonds M,Watkins P,I. Progr8ssion of diabetic autonomicnellrolJathy over a decad". Q ,1 iVIed 1990;75 : (;~l5-G1(j.

11. Seshiah V, Venkataraman S, Madhwan R, Hilril1aranHS, Kllmar TV, Sanjeevi CD et al. Alltonomicneuropathy and sexual dysfunction, peripheralneuropathy and foot syndrome in eliabetes mellitus.In: Ward .J, Goto Y, Eels. Diabetic neuropathy NewYork, ,John Wiley 1990: 425-4.28.

12. Sudkvist G. Autonomic nervous function inasymptomatic diabetic patients with signsof peripheral neuropathy. Diabetes Care 19K l;10 : 529-537.

Indian ,J Physiol Ph:'\rmacol lSiSi(;; 1U(1) Coml'utcr-ctssisterl Leaning f'ack"i''' :1;ll

student trying to relate material between courses,but also assist the faculty in the development ofappropriate instructional materials as well ascourse goals and objectives (5).

Computer-assisted learning (CAL) packageshave been developed in almost all biomedicaldisciplines, viz, physiology (6,7,8), bacteriology(9), pathology (10), pharmacology (11), radiology(12), anaesthesiology (13), haematology (14),psychology (15), medical decision-making (16),CME (17), three-dimensional imaging (18),learning in medicine (19), public health (20) anddrawing bar graphs (21) to mention a few.

Computers have also began to find a placein the education of health care staff (22, 23).They offer a cost-effective means of orientinglarge number of personnels to a hospitalinformation system. Equally important, adecentralised self-administrated, self-pacedtutorial allow professionals to get trillning whereand when they want it, with as little or as muchreinforcement as described.

It is very easy to discover the weakness inthe computer assisted learning program bytesting with students, so that conversation withthe computer can be imporved from time totime. As a result both interaction andinformation presented are improved. Also thecomputer-student interaction allows bettergraphic facilities because the computer can drawbetter pictures than those drawn mannually.Programmes can be developed even to providecomputer based training to the teachers toupgrade and update their knowledge.

The major challenge in computer basedlearning is indeed the availability of thesoftware. We at Diyjsion of Biostatistics andMedical Informatics of the University Collegeof Medical Sciences in Delhi developed a partof a computer-assisted learning system inBiostatistics which suits our medical educationcurriculum.

Medical students are invariably taughtabout the Gaussian form of statisticaldistribution which many physiological variables

follow. This is a theoretical form of distributionwhich is only approximately obtained in practicethat too almost always in the form of gronpeddata. We have developed a computer basedlesson which draws histogram for a given 'et ofdata in the first step and gTadually replaces itby a polygon. It is finally replaced by a gaussiancurve with mean and standard deviation Siuneas obtained from the e1ata. This gradual step­wise replacement seem to help the medicalstudents to clearly visualise the concept ofgaussian form -also called normality.

Software

This package has four easy-to-underst:mdand easy-to-operate modules as follows:

(i) Data entry,(ii) Drawing of a histogram,

(iii) Its gradual replacement by the polygon(iv) Smoothening of polygon by a frequency

curve of Gaussian form

The first module allows the student to enterthe data on any physiological variable, e.g.,number of subjects in different blood pressurecategories. These categories must be of equalwidth. The package lLln..i a chi-square test inthe background and displays one of the followingmessages depending upon the data evaluation:

(a) "The number of categories are either veryfew (less than 5) or too many (more than11) - please reenter the data."

(b) "The frequencies in many categories are toosmall to judge the adequacy of Gaussianform of the distribution - please reenter thedata with larger frequencies." This messageis displayed when more than one categoryhas expected frequency less than 5. Notethat tIllS is the well known condition for chi­square test to be valid.

(c) "The data do not follow a Gaussian form ofthe distribution and so the frequency curvedrawn will not be a good approximation tothe data."

(d) "The data follow on approximate Gaussianform of distribution - please proceed ahead."

332 Bajpai et al Indian .J Physiol Pharmacol 1996; 40(4)

Fig. 1: Three Sl\CCl~Ssiv", views of gnulual changl' ofhisLogram i.ntl) a Caussian curve;.

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the bars reached is proportional to the frequencyin the categories and the frequency in eachcategory is also displayed. In our CAL package,small gap between the bars in the histogram isinserted for better understanding and clarity ofthe picture on the screen. This gap does notaffect the frequency polygon, distribution curve,mean and SD of the data under use.

After the histogram is drawn, the message"Strike any key when ready" is displayed on thescreen. On pressing any key, the CAL packageenters in third module. The third modulegradually joins the middle points of the tops ofthe adjacent bars of the histogTam by a linewhich terminates on the x-axis (Fig. Ia). Thusthe polygon is completed and the same messageon the screen "Strike any key when ready" isdisplayed again. When we press any key, thehistogram gradually starts disappearing leavingonly the polygon on the screen.

Our CAL package again prompts the user topress any key. \Vhen pressed, it enters the fourthmodule. This module computes the mean andSD from the data and use these to draw aGaussian curve. This can be clearly seen (Fig Ib)as being "super-imposed" on the polygon - awell-known concept in this context. If we pressany key again, the polygon gradually disappearsand only the Gaussian curve is finally left onthe screen (Fig. Ic). In fitting the curve, themean and standard deviation of the observeddistribution are treated as population mean andpopulation standard deviation.

DISCUSSION

Whether the teachers like it or not, the timehas arrived for exploiting the enormouscapabilities of computers to assist the teaching­learning process. The advantages CAL offers forout weigh the cost involved and it is beingincreasingly accepted around the world at leastas a supplementary method.

Statistical concepts are generally perceivedby medical profession as a difficult entity. Thereis sometimes tendency to avoid these concepts.At the same time there is a growing realization

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Note that in the case of either (a) or Cb), thesystem goes back to the data entry module whichalso allows to exit. In the case of (c), the studentcan either proceed ahead after becoming awareof the caution or can exit. The case Cd) offers noproblem at all and in fact ideally suited for theprogramme to work.

Indian J Physiol Pharmacol 1996; 40(4) Computer-assisted Leaning Package 333

that statistical methods are essential tool to dealwith the profound uncertainties that medicinehas to face every day. These methods help toreach to valid and reliable decisions despite suchuncertainties (24). Understanding the conceptof statistical distribution of data, particularlythe Normal or Gaussian form, is among the firststeps to reach to the other statistical methodssuch as tests of significance (25).

The package developed by us provides anavenue for medical students to learn theconcepts of frequency distribution, particularlythe bell shaped Gaussian form. It brings homethe point that the frequencies observed inpractice in different categories of values ofphysiological variables are an approximation toa smooth function. By actual visualdemonstration, the concept is likely to beembedded in the mind which otherwise couldremain only as a philosophy and may even beforgotten. Some students have tried thispackage and have given a very positive feedback but full scale scientific investigations arerequired in future to assess the usefullness andimpact of this package.

We have not yet incorporated into thispackage the features which could help to

actually see that (mean - 2SD, mean + 2SD)limits really cover 95% of the subjects if theform of distribution is Gaussian, and that eachtail has nearly 2.5% subjects. This is underprocess. We may lateron add the concepts ofother forms of distribution into this package aswell as other related topics such as location ofmean, median and mode.

Whenever a student converse with a teacher,it is a small or generally one time conversation.The teacher is seldom consulted again and againbecause of hesitation and fear of disturbing him.With the help of this package, the student caninteract and learn as many times as he needs.Such type of packages can also be developed inother topics of medical sciences in differentsubjects such as physiology, biochemistryand pharmacology. Such packages should bedeveloped jointly by the medical and computerexperts. The computers should ideally beconnected and interlinked through a network sothat, by their common opinion, standarisedpackages, which suits all the medical collegesaccording to their syllabus, can be developed.Since learning is repetitive process, computer­assisted learning (CAL) packages could be veryuseful.

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