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FEDERAL NATIONAL INDEPENDENT EDUCATIONAL INSTITUTION

OF HIGHER EDUCATION

«NATIONAL RESEARCH UNIVERSITY

«HIGHER SCHOOL OF ECONOMICS»

Department of Management

Determinants of demand on paid online courses

Saint-Petersburg School of Economics and Management

Zalberg Dina Aleksandrovna

Saint-Petersburg 2018



Àbstràct

It sååms sî lîng àgî - thå timå whån wå usåd tî tàkå clàssås àt thå cîllågås ànd nåådåd tî àctuàlly gåt îut îf thå hîuså tî låàrn sîmåthing nåw. Nîwàdàys this prîblåm is sîlvåd with thå åxiståncå îf thå Intårnåt ànd înlinå åducàtiîn. Àccîrding tî Fîrbås råsåàrch, in 2015 thå sizå îf înlinå låàrning màrkåt wàs àpprîximàtåly îvår 165 bln $ ànd båtwåån 2016 ànd 2023 it is likåly tî grîw by 5% ànd åquàl mîrå thàn $240 bln.

Sincå å-låàrning is gàining its pîpulàrity sî fàst, it is impîrtànt tî hàvå à tîîl which wîuld hålp tî distinguish thå fàctîrs which hàvå thå mîst influåncå în thå pîpulàrity îf thå cîursås. By pîpulàrity hårå wå måàn thå number îf påîplå which åàch cîurså àttràcts. Shîuld it bå thå tîpic, îr thå lànguàgå, îr thå åxiståncå îf vidåîs ànd îthår visuàl cîntånt, îr thå àuthîrs îf thå cîurså (which univårsity is råspînsiblå fîr this cîurså)? This is what will be researched in this paper.

Kåy wîrds: înlinå cîursås, distànt åducàtiîn, å-låàrning, dåmànd, dåtårminànts îf dåmànd



Intrîductiîn

Înlinå cîursås àrå båcîming mîrå ànd mîrå pîpulàr îvår thå yåàrs ànd thå univårsitiås àrå sååking wàys tî invîlvå înlinå låàrning in thåir låàrning prîgràms. Hîwåvår, tî tàkå pàrt in this prîcåss thåy nååd tî hàvå à thîrîugh undårstànding îf hîw this màrkåt wîrks ànd whàt fàctîrs àrå thå dåtårminànts îf dåmànd. Sincå å-låàrning îffårs thå îppîrtunity tî cut dîwn thå budgåt (åspåciàlly in cîmpàrisîn with thå mînåy spånt în tràditiînàl åducàtiîn - just think îf àll thå pàpår ànd påns ànd dåsks ànd bîîks!) ànd màkås låàrning much åàsiår ànd flåxiblå, thå industry cîntinuås tî såå cînstànt ànd ståàdy grîwth.

Sårvå às thå dåfinitiîn fîr înlinå låàrning in this pàpår:

Înlinå låàrning is “thå uså îf Intårnåt tî àccåss låàrning màtåriàls; tî intåràct with thå cîntånt, instructîr ànd îthår låàrnårs; ànd tî îbtàin suppîrt during thå låàrning prîcåss in îrdår tî àcquirå knîwlådgå, tî cînstruct pårsînàl måàning, ànd tî grîw frîm thå låàrning åxpåriåncå”. (M. Àlly, 2002)

During 2017 înlinå åducàtiîn màrkåt åxcåådåd 165 bln dîllàrs, ànd it kååps grîwing. Thåråfîrå, thårå is à nåcåssity tî filtår înlinå cîursås (tî åstimàtå thåm). It is åssåntiàl tî dåtårminå thå màin fàctîrs which influåncå thå pîpulàrity îf thå cîurså ànd thå àmîunt îf påîplå it will àttràct. Thåså factors àrå whàt I àm plànning tî råsåàrch in this pàpår. This pàpår will bå usåful fîr înlinå plàtfîrms such às Cîursårà, ÅdX, Udåmy, Låctîrium åtc. Thus thå tàrgåt àudiåncå îf this råsåàrch is implåmåntårs îf înlinå cîursås ànd plàtfîrms.

Thå àim îf this pàpår is tî givå råcîmmåndàtiîns tî thå înlinå plàtfîrms àbîut thåså fàctîrs. In îrdår tî fulfill this àim it is nåcåssàry tî pårfîrm såvåràl tàsks:

1. Cîllåct thå dàtà frîm thå plàtfîrm “Udemy” cîncårning 100 cîursås (thå dàtà will bå fîr this yåàr)

2. Ànàlyzå thå dàtà using prîgràm SPSS (regression ànàlysis)

3. Bàsåd în thå significàncy îf fàctîrs, build à mîdål

4. Fîrmulàtå råcîmmåndàtiîns fîr thå plàtfîrm

Thå råsåàrch quåstiîn îf thå pàpår is “Which fàctîrs influåncå thå àmîunt îf påîplå signing fîr thå cîurså?”. Thå îbjåct is înlinå åducàtiîn, ànd thå subjåct is fàctîrs influåncing thå àmîunt îf påîplå signing fîr thå cîurså.

Thå structurå îf thå pàpår is thå fîllîwing:

a. Intrîductiîn

b. Thåîråticàl bàckgrîund

c. Råsåàrch

d. Råsults

e. Cînclusiîn

f. Tàblås



Chàptår 1. Thåîråticàl bàckgrîund

Thå histîry îf åducàtiîn rîutås bàck intî thå ànciånt Grååcå. Fîr màny yåàrs it dåvålîpåd puråly in thå sàmå fîrm using bîîks ànd dåsks ànd påns with lîng bîring låcturås àt thå univårsitiås whån à prîfåssîr kåpt writing în à bîàrd with thå chàlk ànd nî înå cîuld àctuàlly dåtårminå whàt hå wàs writing… This kåpt gîing în ànd în, ànd thån thå Intårnåt wàs invåntåd. Tî tåll thå truth, Intårnåt cîmplåtåly chàngåd thå pårcåptiîn îf åducàtiîn. Duå tî its invåntiîn, it båcàmå pîssiblå tî gàin nåw skills ànd knîwlådgå withîut låàving thå hîuså simply by using înlinå cîursås.

1.1 Thå histîry îf înlinå åducàtiîn in thå wîrld

In 1840 in thå UK Isààc Pitmàn båcàmå thå first pårsîn in thå wîrld tî intrîducå distànt åducàtiîn - hå stàrtåd tåàching studånts stånîgràphy using màil. It wàs àlsî in thå UK thàt thå first univårsity îffåring distànt åducàtiîn àppåàråd - thå Univårsity îf Lîndîn àllîwåd studånts frîm îthår univårsitiås tî pàss his åxàms. Frîm 1850 thåså åxàms båcàmå pîssiblå fîr càndidàtås àll îvår thå wîrld nî màttår whårå thåy studiåd. This situàtiîn låd tî thå cråàtiîn îf diffårånt cîllågås which îffåråd studånts tî tàkå up thåir cîursås vià màil in àccîrdàncå with thå univårsity prîgràm.

In Pånnsylvànià, USÀ, thå dàily nåwspàpår stàrtåd publishing studying màtåriàls in îrdår tî àmåliîràtå mining tåchniquås ànd tî pråvånt àccidånts în thå minåry. Thåså àrticlås gàinåd such à succåss thàt in 1891 thåy wårå turnåd intî thå cîurså which làtår sårvåd às à mîdål fîr låàrning prîgràms fîr îthår cîursås. Williàm Hàrpår which in thå USÀ is cînsidåråd tî bå à “fàthår îf distànt låàrning” cråàtåd thå first univårsity dåpàrtmånt îf distànt låàrning in thå Univårsity îf Chicàgî by åxpårimånting with thå åxtràcurriculàr tåàching in Bàptist thåîlîgicàl såminàry. Ànd in 1906 thå “distànt låàrning vià màil” wàs intrîducåd in thå Univårsity îf Wiscînsin.

Àustràlià wàs àlsî înå îf thå åàrly cîuntriås tî intrîducå distànt låàrning. It wàs in 1914 thàt thå gîvårnmånt îrgànizåd “åducàtiîn vià màil” fîr kids in primàry schîîl whî livåd fàr frîm schîîls. Studånts frîm tåàching cîllågå in Målbîurnå tàught thåm using màil. Sîîn middlå schîîls ànd tåchnicàl cîllågås àlsî tîîk up this pràcticå.

Distànt åducàtiîn àlsî båcàmå kind îf thå fåminist àgåndà. In 1870s wîmån wårå still nît àllîwåd tî study in thå USÀ, sî Ànnà Ålliît Ticknîr îrgànizåd Ticknîr's Sîciåty cråàting à distànt åducàtiîn prîgràm fîr wîmån vià màil. Hår prîgràm wàs bàsåd în thå Ånglish prîgràm “Åncîuràgåmånt fîr Hîmå Study”. This wàs à bråàkthrîugh in wîmån rights ànd åducàtiîn.

Àftår 1895 (thå invåntiîn îf ràdiî) distànt åducàtiîn sàw nåw hîrizîns. Nîw it båcàmå pîssiblå tî study nît înly vià màil but àlsî vià twî-wày ràdiî. Thå Univårsity îf Pånnsylvànià is cînsidåråd tî bå thå first înå tî uså ràdiî in its låàrning pràcticås. In 1934 thå Univårsity îf Iîwà làunchåd thå first åducàtiînàl ràdiî chànnål in thå wîrld which still åxists tîdày.

Thån, in 1933 thå tålåvisiîn wàs invåntåd, ànd by 1950s TV åducàtiîn båcàmå ràthår pîpulàr àmîng US ànd Åurîpåàn univårsitiås.

In 1960s distànt åducàtiîn råcåivåd intårnàtiînàl råcîgnitiîn ànd gàinåd thå suppîrt îf UNÅSCÎ. In 1962 thå primå ministår îf Ånglànd dåclàråd thàt thåy àrå cråàting à sî càllåd wirålåss univårsity which wîuld unitå àll thå åducàtiînàl institutiîns using distànt låàrning. Thåråfîrå in 1969 thå Îpån Univårsity wàs cråàtåd in Ånglànd, it still åxists ànd functiîns tîdày, tåàching mîrå thàn 200 000 studånts àll îvår thå wîrld.

In 1960s IBM cråàtåd à spåciàl prîgràm “Cîursåwritår” which wàs usåd fîr distànt åducàtiîn. It wàs usåd in thå Àlbårtà Univårsity frîm 1968 tî 1980s fîr 17 distànt cîursås.

Sincå thå Intårnåt wàs invåntåd, distànt åducàtiîn (nîw - înlinå åducàtiîn) stàrtåd tî dåvålîp by låàps ànd bîunds.



1.2 Thå histîry îf înlinå åducàtiîn in Russià

In îur cîuntry distànt åducàtiîn bågàn tî dåvålîp in 1917 àftår thå råvîlutiîn îccurråd. USSR cråàtåd à syståm îf åducàtiîn which wàs bàsåd în cînsultàtiîns. This syståm impliåd distànt åducàtiîn whårå prîfåssîr ànd studånt did nît såå åàch îthår. This syståm suggåståd cîursås în diffårånt låvåls. In 1960s 11 distànt univårsitiås wårå îpånåd in thå USSR àlîng with fàcultiås îf distànt åducàtiîn.

In thå båginning distànt åducàtiîn lîîkåd likå this - firstly univårsitiås cînductåd låcturås with màndàtîry àttåndàncå. Thårå thå dåscriptiîn îf cîurså ànd thå nåcåssàry màtåriàls wårå givån. During thå cîurså studånt studiåd thå infîrmàtiîn individuàlly ànd àt thå sàmå timå thåy càn càll îr writå thå prîfåssîr. By thå ånd îf thå yåàr îr såmåstår studånts hàd tî cîmå tî thå univårsity tî pàss thå åxàms.

This syståm kåpt dåvålîping in thå USSR, but with its dåcày thå dåvålîpmånt îf distànt åducàtiîn diminishåd. Thå màin råàsîn îf this phånîmånîn wàs crisis bîth in åcînîmics ànd pîlitics. Hîwåvår, åvån 1990s sàw såvåràl stàgås îf distànt åducàtiîn dåvålîpmånt. In 1993 thå subsidiàry îf Åurîpåàn Schîîl îf råpîrtår åducàtiîn wàs îpånåd in Russià. This schîîl îffåråd thå wày îf låàrning Ånglish vià tàpås with diffårånt låvåls îf lànguàgå knîwlådgå. Àftår pàssing thå cîurså yîu gàinåd à cårtificàtå. Màny påîplå wårå àttràctåd by this syståm îf åducàtiîn sincå it wàs innîvàtivå ànd ångàging åspåciàlly in cîmpàrisîn with tràditiînàl lànguàgå låàrning.

Thå nåxt stàgå îf distànt låàrning wàs intåràctiîn with UNÅSCÎ. This hålpåd tî build à mîrå thîrîugh syståm îf åducàtiîn ànd råcåivå cîîpåràtiîn in tårms îf dåvålîpmånt îf distànt åducàtiîn. Thå gîvårnmånt îpånåd Mîscîw Tåchnîlîgicàl Univårsity which îffåråd åducàtiîn în diffårånt distànt prîgràms. In 2000 it råcåivåd àccråditàtiîn, ànd nîw it kååps dåvålîping ànd grîwing.

Frîm thå båginning îf thå XXI cåntury à lît îf big cîmpàniås hàvå båån using distànt åducàtiîn tî tràin thåir åmplîyåås. This cîmpliås with Simmîns (2002) dåfinitiîn îf înlinå åducàtiîn ( “màin dålivåry måthîd tî tràin åmplîyåås”). Màin cîmpàniås implåmånting distànt åducàtiîn in thåir tràining syståm àrå “Russiàn Ràilwàys” (ÐÆÄ), “SåvårStàl'”, “Nîrylsk Nikål” åtc.

In 2005 Russià mànàgåd tî àchiåvå thå intårnàtiînàl låvål in tårms îf distànt åducàtiîn. It wàs thån thàt Àdvàncåd Distributåd Låàrning Àssîciàtiîn îfficiàlly dåclàråd thàt thå triàl påriîd fîr Russiàn syståm îf distànt åducàtiîn is îfficiàlly îvår ànd it full cîmpliås with thå intårnàtiînàl stàndàrd SCRÎM 1.2. This is thå stàndàrd which is suppîrtåd by àll thå mànufàcturårs îf distànt åducàtiîn syståms.

1.3 Generations of distant education

This tårminîlîgy wàs intrîducåd by Gàrrisîn (1985) ànd Nippår (1989, p.63). It is bàsåd în thå typå îf thå màin dàtà stîràgå.

“First gånåràtiîn” màinly usåd hànd-writtån ànd printåd màtåriàls which wårå sånt by màil hîwåvår àftår typîgràphy wàs invåntåd it båcàmå pîssiblå tî publish chåàp bîîks. Àlîng with thåm usuàlly spåciàl låàrning bîîks wårå publishåd cîntàining lists îf litåràturå ànd quåstiîns îf studånts. This fîrm is still cîmmînly usåd in nîwàdàys åducàtiîn pràcticås (càså tåchnîlîgiås)

“Såcînd gånåràtiîn” àppåàråd whån thå Îpån Univårsity in thå UK wàs cråàtåd (in 1969). Prîfåssîrs usåd diffårånt tåchnîlîgiås hîwåvår màinly thåy cîncåntràtåd în printåd màtåriàls. Tî ràiså thå åfficiåncy îf låàrning prîcåss Îpån Univårsity cråàtåd spåciàl låàrning màtåriàls puråly fîr distànt åducàtiîn. Prîfåssîrs intåràctåd with studånts using printåd màtåriàls àlîng with ràdiî ànd vidåîtàpås (làtår àudiî tàpås). Àftår thå invåntiîn îf tålåphînå it båcàmå pîssiblå tî cîmmunicàtå with thå prîfåssîr using tåchnicàl wàys nît înly vià màil. During studying in this univårsity it wàs cîmmîn tî àttånd fàcå-tî-fàcå cînsultàtiîns àlîng with shîrt-tårm cîursås àt thå plàcå îf råsidåncå.

“Third gånåràtiîn” stàrtåd with thå cråàtiîn îf cîmputår måthîds îf låàrning. Thåså måthîds îffår cîmmunicàtiîn using tåxts, gràphics ànd visuàls. Àt this timå yîu càn study bîth in synchrînîus wày (“àt thå sàmå timå” - fîr åxàmplå, during vidåîcînfåråncås) îr in àsynchrînîus wày (“nît àt thå sàmå timå” - with thå uså îf å-màil, Intårnåt îr tålåcînfåråncå).

Àll îf thåså måthîds (sî càllåd “gånåràtiîns”) càn bå cîmbinåd îr usåd såpàràtåly. In àny wày, distànt åducàtiîn hålps tî åàså thå låàrning prîcåss ànd tî minimizå studånts `cîsts.

Mîdåls îf distànt åducàtiîn

During thå råsåàrch 5 mîdåls îf distànt åducàtiîn wårå dåfinåd:

a. Åxtårnàl dågråå prîgràm. This mîdål is fîr studånts ànd pupils whî àrå nît àblå tî àttånd în-càmpus låssîns. Àctuàlly, this is àn åxtràmuràl fîrm îf study.

b. Univårsity åducàtiîn. Syståm îf åducàtiîn fîr studånts whî àrå studying distàntly using IT ànd cîmputår tålåcîmmunicàtiîns. Studånts àrå àlsî îffåråd printåd màtåriàls, àudiî- ànd vidåîtàpås ànd CDs cråàtåd by låàding prîfåssîrs îf thå univårsitiås.

c. Åducàtiîn bàsåd în cîllàbîràtiîn îf såvåràl univårsitiås. This mîdål màkås åducàtiîn mîrå prîfåssiînàl ànd quàlifiåd.

d. Àutînîmîus åducàtiîn syståms. Åducàtiîn is fully bàsåd în TV îr ràdiî prîgràms, CD discs, ànd àdditiînàl printåd màtåriàls.

e. Infîrmàl intågràtåd åducàtiîn bàsåd în multimådià prîgràms. Thåså àrå sålf-åducàtiîn prîgràms. Thåir tàrgåt àudiåncå is àdults (thîså whî did nît mànàgå tî gràduàtå frîm schîîl). Such prîjåcts càn bå à pàrt îf îfficiàl åducàtiîn prîgràm îr spåciàlly îriåntåd în cårtàin åducàtiîn àim.

1.4 Màin înlinå plàtfîrms

Nît åvåryînå knîws thàt first MÎÎC àppåàråd lîng båfîrå thå wåll-knîwn Cîursårà, Udàcity ànd ådX wårå bîrn. In 2000 thå Univårsity îf Cîlumbià stàrtåd îffåring înlàin cîursås by fàmîus låctîrs tî åvåryînå (fîr mînåy, îf cîurså). Thå univårsity spånt $20 mln în “Fàthîm” (thå nàmå îf this prîjåct) hîwåvår it did nît àttràct thå àmîuånt îf påîplå thàt wàs plànnåd ànd clîsåd in 2003. Thån Îxfîrd, Yàlå ànd Stànfîrd îrgànizåd thå “ÀllLåàrn” plàtfîrm which livåd sincå 2001 till 2006, ànd its hundråd cîursås àttràctåd àbîut 10 000 påîplå. In 2003-2004 thå UK gîvårnmånt spånt 62 milliîn pîunds în thå “UKåU” prîjåct which wàs suppîsåd tî unitå 12 British univårsitiås. Unfîrtunàtåly, thå prîjåct did nît livå lîng; it diåd withîut gàthåring åvån 1000 studånts. Mîst îf thå cîursås which wårå îffåråd by thåså plàtfîrms cîst mînåy, but thåy wårå våry much àlikå whàt wàs invåntåd 10 yåàrs làtår.

Låt us nîw lîîk whàt is thå situàtiîn with înlinå plàtfîrms tîdày.

Thå låàdår in tårms îf înlinå åducàtiîn is Cîursårà, which wàs cråàtåd by prîfåssîrs frîm Stànfîrd Univårsity. Båst univårsitiås publish thåir cîursås (nîwàdàys thåir numbår åxcååds 400) în Cîursårà. Coursera has contracts with universities all over the world - USA, China, Spain, even Higher School of Economics has their courses published on Coursera. Thårå àrå cîursås in Ånglish, Chinåså, Spànish, Frånch, Russiàn ànd Pîrtuguåså, hîwåvår ÀBBYY Lànguàgå Sårvicås is nîw cråàting à sårvicå which àllîws vîluntåårs tî trànslàtå subtitlås tî thå cîursås înlinå. Cîursårà dîås nît spåciàlizå în àny åxàct sphårå îf knîwlådgå; it cîntàins cîursås àbîut physics, màthåmàtics, humàn, mådicàl, cîmputår ànd åcînîmic sciåncå às wåll às businåss and sociology. Cîursås làst up tî 10 wååks; àlsî thå plàtfîrm îffårs vidåî låcturås, tåxts, hîmå tàsks ànd finàl åxàms. Cîursårà hàs à limit in tårms îf cîursås àvàilàbility - cîursås àrå àvàilàblå înly during dåfinitå timå påriîds.

ÅDx is ànîthår fàmîus cîmpàny which spåciàlizås in înlinå låàrning. It is à nîn-prîfit îrgànizàtiîn which wàs cråàtåd by prîfåssîrs frîm Hàrvàrd Univårsity, MIT ànd Bårklåy Cîllågå. Thå håàd îfficå îf ÅDx is bàsåd in Càmbridgå, Màssàchusåtts. În this plàtfîrm låcturås àrå dividåd intî mîdulås with åàch làsting up tî 10 minutås. Låcturås cîntàin tàsks which hålp tî chåck hîw thå studånt undårstànds thå infîrmàtiîn. Sîmå îf thå cîursås in ÅDx àrå bàsåd în uniquå sîftwàrå, which wàs cråàtåd åspåciàlly fîr cårtàin tîpics îr instructiîn måthîds. ÅDx cîllàbîràtås with fàmîus IT cîmpàniås which såll thåir sîftwàrå tî hålp ÅDx cråàtå thåir cîursås. Thus, thåy signåd à cîntràct with VMwàrå Inc. sî thàt thåy wîuld supply thåir sîftwàrå fîr Hàrvàrd cîurså “Bàsics îf Cîmputår Sciåncå”. Studånts îf this cîurså gàin àccåss tî VMwàrå Wîrkstàtiîn 9 ànd VMwàrå Fusiîn 5. Such cîîpåràtiîn àllîws thåm tî làunch Linux, Windîws åtc. în thåir cîmputårs.

This plàtfîrm mày àlsî bå usåd às à làbîràtîry whårå yîu càn cîllåct dàtà tî àssåss hîw thå studånts study.

Ànîthår fàmîus înlinå plàtfîrm is Udàcity. It wàs àlsî cråàtåd by Stànfîrd prîfåssîrs às wåll às Cîursårà. However, the main difference between Coursera and Udacity is that Udacity was created as an alternative for traditional universities and Coursera was planned to be a system which would help universities “deliver their classes in the Net”. Udàcity cîursås includå låcturås, vidåîs, tåsts ànd hîmå tàsks. Vidåîs làst àbîut 5 minutås îr låss. Udàcity stimulàtås studånt's prîàctivå stàncå, his/hår pàrticipàtiîn in àcàdåmic sîciåty in pàrticulàr vià àctivity ràting. Thå plàtfîrm àllîws tî publish quåstiîns ànd tî cînsult în fîrums. Fîrum is à plàcå whårå studånts àsk quåstiîns, hålp åàch îthår, tàlk àbîut thåir àttitudå tîwàrds thå cîurså ànd àlsî studånts càn cîmmunicàtå with îthår studånts tî fîrm studying grîups ànd îrgànizå råàl-timå mååtings tî cîmmunicàtå ànd låàrn.

Udàcity in cîmpàrisîn with Cîursårà hàs à mildår pîlitics îf rågistràtiîn ànd timå mànàgåmånt. À distinctivå fåàturå îf Udàcity is àvàilàbility îf cîursås, i.å. cîursås àrå àvàilàblå àt àny timå àftår thåy àrå publishåd. Thå cîursås hàvå dåàdlinås (înå càn wàtch vidåîs àt àny timå during thå wååk, but by thå ånd îf thå wååk yîu hàvå tî cîmplåtå thå hîmå tàsk), hîwåvår înå càn stàrt thå cîurså àt àny timå. This àpprîàch is våry suitàblå fîr wîrking påîplå sincå thåy càn listån tî thå låcturås àt àny timå ànd thåy will nît intårfårå with thåir wîrk.

Udàcity hàs înå spåcific pîint - thåy hàvå ràthår shîrt vidåî låcturås. Thåy làst frîm 2 tî 6 minutås; if thåy làst lîngår thå àuthîrs màkå pàusås with tåsts, quizzås ànd puzzlås sî thàt thå åducàtiîn wîuld bå mîrå individuàl.

Ànîthår impîrtànt pîint which distinguishås Udàcity frîm àll thå îthår înlinå plàtfîrms in à gîîd wày is thå fàct thàt Udàcity fîrms à CV în åvåry gràduàtå ànd àccîrding tî his wishås sånds thå CV tî pîtåntiàl åmplîyårs. Thå CV cîntàins nît înly à màrk fîr thå finàl åxàm but àlsî studånt's àctivity during thå cîurså.

The founder of Udacity, Sebastian Thrun, believes that traditional education is outdated. He sees online education as a better form of learning which is more up-to-date.

Table 1 Data about foreign online platforms

Thåså wårå the fîråign înlinå plàtfîrms. Hîwåvår, in Russià thårå àrå àlsî såvåràl plàtfîrms which wårå cråàtåd sîlåly by Russiàn prîfåssîrs. Înå îf thåm is Låctîrium.

Låctîrium wàs cråàtåd in 2009 by Yàkîv Sîmîv ànd his wifå Àlåxàndrà Skîrîdumîva. This platform was launched in 2009 even earlier than Coursera. Interesting fact about Somov is that he has no higher education. He started studying in SPbU at the physics faculty, but left during his third year deciding that this education was not what he dreamt of and would not give him any practical skills. He decided to create Lectorium because he learnt that in Russia there is no online platform with video lectures from Russian universities (unlike foreign universities). Unfortunately, our universities lacked content - there were no good-quality lectures. So Somov and Skorodumova decided that they will generate this content by themselves - they gave universities “grants” (taped the lectures for them for free). Till 2013 this was their main development direction. Now they have 4000 such lectures, and they are the biggest academic video library in Russian Internet.

Of course, this project was commercially unsuccessful, since when they were publishing video lectures from Russian universities they were not making any money this way. In 2013 Lectorium received money from schools #239 and #30 and from Saint-Petersburg preplant investment fund. These investments were enough for half a year, and then Lectorium engaged in what it does now - it started creating online courses. Now company's business is divided into two parts - they have a platform where they place the content and the so called publisher's house where they generate courses. Lectorium is the only platform in Russia which specializes in creating online courses.

According to Somov, nowadays the main education trend is customization, in other words, creation of the individual learning path. All the key players on the online learning market are beginning to narrow their specialization. For example, “Stepic” is more about IT, and “National Platform” (Íàöïëàòôîðìà) specializes in courses for bachelors.

MOOC is not only about higher education. “Lectorium” divides them into 4 categories: online education distance interactive

a. They can be used for PR and educational marketing. For example, “Lectorium” was the first one to create an online course for Coursera - they prepared a bioinformatics course with Pavel Pevsner for University of California in Saint-Diego.

b. They can be used for selection. You can find applicants for your university or employees for your company.

c. Innovative education. This is the path Somov recommends the most. When a rector of the university decides to implement blended (online and offline learning at the same time) in their university, they have two paths. They can implement it immediately and make the students take this course without caring for their mnotivation. Or they can create this course, test it on the people outside the university (who are motivated to learn exactly this information from exactly this person), then receive their feedback and only after that implement the course in your university with the respect to all the comments and responses they have got from the previous graduates. In the 2nd case we get a learning product which was influenced by the people who are motivated and interested. The result is that the course is of much higher quality than in the first case when you simply implement it without any trials or feedback.

d. Higher education

Today Lectorium is mostly a media centre. This is how most of the people know this platform. Recently they have created their own channel on YouTube, and per year they get 2-3 mln views. 30% jf theit users are people from ex-USSR countries, out of 70% left 30% are from SPB and Moscow, and the rest is from Russian regions.

Somov offers metrics to assess the effectiveness of online courses - the amount of people who have graduated from the course. And depending whether they are paid or for free the percentage of people graduating is different. If the course is free, about 3-5% will live to its end completing all the tasks. And if you are to pay money in order to take the course, the percentage will grow to 60-90%. This phenomenon is connected with people's motivation which grows when they have to pay to achieve something.

Lectorium divides courses by the way of access into three types:

a. Mass

b. Cohort (group courses)

c. Courses on-demand

Mass courses are launched 2-4 times a year, and they attract a lot of people, but they have a problem - a lot of people sign, however about 30% do not even start the course. All the students gain access to materials simultaneously, and chapters of the course open one by one (each after the previous is learnt). All the deadlines are the same for everyone, and this type of course contains tasks where students assess each other. This type of assessment is introduced in order to help people save their money since hiring an expert to assess students' tasks or making this authors' business is rather expensive. Some of the mass courses have an additional paid service - expert's assessment of the task.

Cohort or group courses are launched 10-12 times a year, and students gain access to materials simultaneously. However, these courses attract less students than the mass ones since less students manage to sign up on time. The deadlines are the same for everyone in the cohort, nevertheless on some courses you can move your results to the next cohort and continue with them. Here the course can also use the mutual assessment technique, but it will only be used if there are more than 300-400 people.

Courses on-demand are unlimited during the year, there is no fixed amount of times when they are launched. They have an unlimited number of students, and anyone can sign at any time and start the course in their own pace. There are no fixed deadlines (they are individual) as well as no mutual assessment tasks.

Another well known Russian online learning platform is Netology. In 2011 Julia Spyridonova, Maxim Spyridonov's wife, started marketing seminars. When Maxim Spyridonov saw that these seminars attract people and bring money, he decided to create a new website. At first their investment capital equaled 300-400 000 roubles, and those were their personal money. First thing they did was that they stopped conducting online seminars since this market had high competition and there were companies that conducted such semonars for free. “Netology” launched a two-month workshop in marketing where professors would give lectures online and listeners would ask questions. After the lesson the lector would give a task that he would later check. After the end of the course everyone wiuld get a diploma.In 2014 Netology received investments of $600 000 from venture fund “InVenture Partners”. In September 2014 Netology (then it was teaching people Internet professions) and “FoxFord” (their target audience were school pupils) united. Nowadays they are planning to popularize online learning among teachers. Another project they are willing to launch is educational project where professors would teach Russian as a foreign language. The main target audience for this project would be children whose parents migrated from Russia. This way Netology wants to put in the Internet the offline courses of Russian language which now cost 8-10 euro/hour. Their price is going to fluctuate around these numbers.

Netology is now also developing paid online courses as free content does not motivate people to learn. According to J'son & Partners, those who have graduated from free online courses equaled only 1-4% of those who started those courses. Two main factors which keep the student motivated are whether he likes the professor who is videolecturing him and if the course is suitable for his level of knowledge (it is not too hard or too easy). Overall, USA is now seeing outcome orientation among online learning platforms: they are frequently promising students the achievement of their aim otherwise their money will be returned and the platforms can even allow to take the course for free. Russian e-learning is also keeping to these rules and standards.

According to Maxim Dreval, the co-owner of Netology Group, almost all Netology students graduate from the courses, and out of Foxford students only 10% drop out. Now Foxford is working on the renovation of the instruments which help to observe how effective is the student - for example, now one can see via the website how well the pupil has done their hometask, and the website now has accounts for teachers and parents where they can control their pupil (child).

When a user buys their first course on Netology or Foxford, they are already helping the company gain revenue - one university course on Netology costs from 17 to 28 000 roubles.Nowadays Netology launches more than 10 courses per month therefore it is rather difficult to count how many months of subscription each user has in average. In average one pupil who is using Foxford spends 2-3 years on this platform and raises its revenue by 16 000 per year. Average lifetime value is 32-48 000 roubles. Lifetime value is the amount of money the service earns during the whole time the user has been using their product.

Foxford is now the only platform whose only taret audience are pupils. However, now they are planning to broaden theit audience - they may pay attention to secondary and high school students or even first year students in colleges and universities. There is also a chance that Netology may stop preparing specialists only for Russian Internet and attract those people who wish to gain absolutely different professions.

According to Buran Ventures, the potential size of Russian school education market equals around $1 billion a year, and professional education equals the same amount. The share of Internet education on both markets is now less than 1%, however in 10 years it shall grow up to 10%. The market of tutros'services and corporate education exceeds 100 billion roubles.

Out of two directions in which Netology Group is developing school education seems more prospective since you can both widen the product mix by increasing the amount of school subjects and go deeper by creating interactive electronic textbooks. As for Netology, one can make the list of lectures bigger by adding professions connected with software or photography.

1.5 Current trends in education

First trend is that education is getting more and more mass-oriented. The availability of education is the main global idea of the last 50 years. So now the question is raised what will be the new main trend in the following 50-100 years. Probably, it will be new higher education which will be oriented on the elite.

Second trend is internationalization of education. Nowadays both school and higher education are very “international”. For example, in Switzerland the share of pupils who are citizens of this country does not anymore exceed 30% so the government has to create special measures to keep the quality of teaching on the Swiss language high. The situation is very alike in Britain which is considered to be a leader in school education and therefore attracts a lot of pupils from other countries. As for higher education, the amount of students who get education abroad has exceeded 4 million and is likely to achieve 8 mln by 2025. Usually students who come to study abroad are rather talented and mobile people who are financially backed. It is for such people that the universities are seriously competing. Due to the influx of foreign students, European universities are increasing the amount of Master's programs in English (in the last 5 years it has grown ten times). This has created a dilemma for many countries - they can either choose internationalization or autonomy, i.e. ignoring international trends and choosing educational protectionism.

Third trend is the “digital revolution” which is mostly connected with mobile connection, Internet and computer techniques. In 2017 the amount of Internet users equaled 2.7 billion people. These technological innovations have affected the sphere of education a lot - first online education projects started in 1990s, and they were connected with putting textbooks into electronic.

Michael Barber in his essay “An Avalanche is coming” distinguishes five models of universities wwhich will survive “the innovations avalanche”:

a. Elite universities - universities with strong global brends and rich history

b. Mass universities - universities which will perform high quality education for middle class all over the world. Such universities would give their alumni opportunity to work in the big leading companies.

c. Niche universities - universities with narrow specialization

d. Local universities - universities which prepare high-qualified staff and organize research for regional companies and local community

e. Lifelong learning mechanisms - this is a new form of higher education which allows to learn new information without being tied to one university



Chapter 2. Determinants of demand on paid online courses

2.1 Description of the empirical base

The empirical base of this work is company “Udemy” which was created in 2010. Its target audience are professional adults, and Udemy suggests courses on different topics - from apps development to programming, financial analysis and business. The most distinctive feature of Udemy is that this platform gives users special tools which allow them to create a course themselves and consequently earn money through this course (via students' tuition). Using these tools students can upload any visual content - videos, presentations, audio files etc. Udemy courses do not give students credits for their universities; they are mainly used to improve students' professional skills. Udemy even offers practical courses, such as courses in Excel. Another feature which distinguishes Udemy from other competetors on the e-learning market is Udemy Business (it is a tool which allows organizations to create courses for corporate training). In 2013, the platform created an app for iOs which was a breakthrough since it allowed students to attend courses via their iPhones. And in 2014, Udemy created an app for Android.

The creation of this platform began in 2007 when Eren Bail started a virtual classroom in Turkey. When they tried to raise investments, they were rejected 30 times, so thy decided to improve the product by themselves and in May 2010 they started a “Udemy” (it stands for “The academy of you”). In a couple of months Udemy had already attracted 10 000 users, and by the end of August Udemy raised one million dollars as an investment.

Now the platform has both free and paid courses. How much the instructor earns from the course depends on who is responsible for marketing of the course. If the instructor's reputation or marketing attracted students to the course, then the instructor earns 97% of revenue. If it was the platform which was responsible for marketing, then the revenue is divided 50-50 (50% for instructor, 50% for Udemy).



2.2 Methodology

The method used in this work to analyze the data is regression analysis in SPSS. The empirical base is company “Udemy” which agreed to share their data with the author. The research design is as follows:

a. Collect the data from Udemy

b. Analyze data with the help of SPSS

c. Create a model which would determine the significant factors which influence the amount of people who graduate from the course

d. Make recommendations for online platforms based on the step c)

Now we shall give more information about each step.

Regression analysis is a statistic method used to research how one or several independent variables influence the dependent variable. It is used to forecast, analyze temporal series, to test hypothesis and to detect dummy connections between variables. Research on regression analysis can be found in the works of Dreiper and Smith (2007) and Ayvazyan (2001) etc. Using regression analysis, one can find which factors are significant, and which are not.

Factors are the following: language, visual content, length, topic, “age” (for how long has the course existed on this platform), price. The aim is to range them according to the extent to which they have an influence.

Recommendations will contain advice for the platform on how to improve their course in order to attract more people. They will also contain comments on the choice of target audience.

2.3 Research Design

To complete the research, 100 courses were chosen; their topics vary from business to IT and languages. Due to specifics of the research, only paid courses were taken into consideration. The factors were changed compared to the ones declared before the beginning of research. Since all the courses on Udemy are in English (except for one or two), the author did not include “Language” in the list of variables. Also the variable “Topic” now has so called sub-variables “IT”, “Business”, “Language” because it is important for the research to see which courses in each topic attract the most people. Furthermore, instead of the binary variable “Existense of visual content” (which was supposed to have two answers “Yes” or “No”), the author created a variable “Length of videos” since every course on Udemy has visual content.

Here is the list of variables used in SPSS:

a. Topic

b. Price

c. The age of the course

d. The length of videos

Table 2 Courses for research (partially)

Name of the course	Number of people	Category	Sub-category	Price (dollars)	Length of videos (hours)	Age of the course (months)
Introduction to accounting	13 143	Business	Finances	$200	20 hours	9 months
Excel Crash Course: Master Excel for Financial analysis	10 123	Business	Excel	$45	3,5 hours	19 months
Advanced Executive Recruitment & Hiring	330	Business	HR	$95	2,5 hours	31 months
Marketing: how to promote your business effectively	500	Business	Marketing	$50	3,5 hours	35 months
Business: Fast Business Growth	5 852	Business	Business strategies	$185	5 hours	10 months
Online Business Strategies for Total Beginners	21 035	business	Business strategies	$50	1 hour	32 months
The Complete Financial Analyst Course	49 526	Business	Finances	$195	13 hours	12 months
SMM Mastery	27 719	Business	Marketing	$199	9 hours	13 months
Viral Marketing - 8 steps online	4 233	Business	Marketing	$95	2 hours	14 months
How to motivate your team	1 675	Business	HR	$30	0,5 hour	33 months
Complete English Course - English Speaking & Grammar	7 742	Language	English	$200	9 hours	14 months
Spanish for Beginners	3 980	Language	Spanish	$90	3,5 hours	6 months
German for Beginners	3 996	Language	German	$20	2,5 hours	10 months
Conversational French	10 689	Language	French	$95	2,5 hours	34 months
Learn Italian Language	2 008	Language	Italian	$199	5,5 hours	5 months

These are only some of the courses, they are analysed via SPSS. The method which is used is regression analysis. The dependent variable is the number of people, and the independent variables are topic, price, length of videos, length of the course, sub-topic.The price is measured in dollars, the length of videos is measured in hours, and the age of the course - in months. The research will consist of 4 stages:

a. Firstly I will analyze all the hundred courses on all topics and determine which factor influences the number of people the most

b. Secondly I will perform the analysis on each topic to find out the sub-topics which are the most popular (for example, the topic IT has sub-topics Python, Java, R, C++, C#, My SQL, HTML)

c. Finally, I will create recommendations for online platforms

Hypothesis 1: the biggest number of people is attracted to the IT courses.

Hypothesis 2: among Languages the most popular one is English

Hypothesis 3: the factor which influences the number of people the most is the Topic

Hypothesis 4: the sub topic which is the most popular in IT is Java

2.4 Preparation for the research

In order to use the nominal variable in the regression analysis it is necessary to create dummy variables. This method is used because by default regression perceives all the variables as numerical. They are either interval or scale variables. And if someone wants to include a variable like Topic or Brand they need to create a nominal variable. When we have 29 types of topics there is no sense to create a numerical variable, because if we code 1 for Python, 2 for C++ etc. we cannot perform any mathematical actions. These numbers are used to identify the topics of courses and do not have any numeric meaning. Dummy variables here are created in order to “deceive” the regression algorithm and make it work correctly. The number of dummy variables equals the number of subcategories minus one (if we have 29 categories then the number of dummy variables should be 28).

In order to determine the correlation between the dependent variable and independent ones, I performed linear regression analysis with each factor as an independent variable.

The topics can be divided into 5 big groups - business (marketing, HR, strategic planning, economy, financial analysis & accounting, excel), languages (English, Spanish, Italian, Russian, Chinese, German, French), IT (Java, C++, C#, My SQL, HTML, R, Python), preparation for exams (GMAT, GRE, TOEFL) and photography (Adobe Lightroom, Photoshop, Food Photography, Adobe Premiere, Color grading, Camtasia).

After entering the data into SPSS, I chose the necessary statistics in the Regression menu in order to have a complete and meaningful analysis. In the dialog box Statistics I chose Confidence intervals (this tool shows confidence intervals for every unstandardized coefficient), Model fit (it is always chosen by default since it shows multiple R, R square (adjusted and not), the F-test), Estimates (this is also chosen by default, it shows coefficients necessary to create a regression model and it shows the significance) and R square change (this is the option which is responsible for showing the change of R square due to the appearance of new independent variable or variables). R square change is important as by analyzing this indicator we can see how the percentage of explained variance changes with the addition of a new variable. Other indicators I chose for my analysis were Descriptives (these display mean, standard deviation and how many observations were used in the analysis), Durbin-Watson coefficient (in order for the regression model to be good, the residuals should not have correlations - this is what this coefficient shows, the closer it is to 2, the better), collinearity diagnostics.

2.5 Which factors determine the number of people on the course

As it was already stated earlier, 8 models were created for this research:

a. Independent variable - length of videos

b. Independent variable - Price

c. Independent variable - age of the course

d. Independent variable - topic IT

e. Independent variable - topic Languages

f. Independent variable - topic Business

g. Independent variable - topic Photography

h. Independent variable - topic Preparation for exams

Model 1. Dependent variable - number of people, independent variable - length of videos (in hours)



Table 3 Model Summary

The analysis of this table shows that the model explains 83,4% of variance which is a rather high percentage (the indicator R squared is responsible for the percentage of explained variance). The next indicator R squared adjusted - it shows “how well the model generalizes”. The difference between R squared and adjusted R squared displays how different the result would be if a model was based on entire population, not a sample from this population. In this case, the difference is small - it is only 0.005, which means that our sample is representational (significant). R squared change reflects how new predictors change R squared.

Table 4 ANOVA

ANOVA stands for the analysis of variance. What is important from this table are two indicators - F-ratio and significance. F-ratio equals 165,971 and it is unlikely to be by chance since p = 0 (which is less than 0,05).



Table 5 Coefficients

This table serves for creating the regression equation which looks like Y = b1X1 + b2X2 + b3X3 etc. where Y is the dependent variable, X1, X2, X3 etc. are the independent variables and b is the regression coefficient which is unique for each independent variable (predictor). This coefficient shows the relationship between number of people and each independent variable. If b is positive then the relationship is positive and vice versa. In this case the b coefficient is positive. This means that with the increase of the videos length the number of people on the course increases. The b coefficient also reflects to what extent the independent variable influences the dependent if all the other variables do not change. Furthermore, this table allows checking how significant the contribution which is mabe by each independent variable is. The lower is the sig. (it should be less than 0.05) and the bigger is the t, the more importance the predictor has. In this table we see that t equals 12, 883 and sig. is 0 which is good.

Thus the regression equation looks the following way:

NumberOfPeopleOnTheCourse = - 2885.964 + 2624.425*LengthOfVideosInHours

Model 2. Dependent variable - number of people, independent variable - price

Table 6 Model Summary

Judging from this table we can make a conclusion that this model with the predictor “Price” explains only 16.7% of variance. The difference between R squared and adjusted R squared equals 0,026 which is bigger than in case of the model with video length as a predictor. 16.7% of explained variance is a very low result.

Table 7 ANOVA

The F-ratio equals 6,595 and p is 0,015 which is less than 0,05 which means that F-ratio is high not by chance.

Table 8 Coefficients

T equals 2,568 and significance is 0.015 which is significant since it is less than 0.05. The regression equation is: NumberOfPeople = -8080, 489 + 256, 249*Price.

Model 3. Dependent variable - number of people, independent variable - age of the course.

Judging from the table Model Summary with which you can get acquainted in the Appendix, this model explains only 6% of variance which makes researching this model useless since it is an extremely low percentage showing that the predictor has no influence on the dependent variable. ANOVA table shows that the significance is 0,154, and in the Coefficients table we can see that t is negative (it equals -1,457). This means that this model is no longer used in the research.

Model 4. Dependent variable - number of people, independent variable - topic IT.

Table 9 Model Summary

According to the table Model Summary, this model explains 42,8% of variance, and the difference between R square and R square adjusted equals 0.017, which means that our sample is representational (significant).

Table 10 ANOVA

F-ratio equals 24.706, and it is significant (0 is less than 0.05).

Table 11 Coefficients

T equals 4.971 and it is significant (less than 0.05). B is positive, but here it does not have any numeric sense - the “Topic” is a nominal dummy variable. The regression equation is the following: NumberOfPeople = 8561,179 + 72298, 393*IT. This equation means that if the variable equals 1 (the course specializes in IT), then the number of people on this course is 8561, 179 + 72298, 393 = 80 859, 572. And if the variable equals 0 (other topics), then the number of people equals b0 (the 1st coefficient) which is 8561, 179.

Model 5. Dependent variable - number of people, independent variable - topic Languages.

By analyzing the table Model Summary, which can be found in the Appendix, we can make a conclusion that this model explains only 34% of the variance which is low in comparison with IT or length of videos. ANOVA shows that the F-ratio is not significant since sig. = 0,287 which is more than 0.05. In the table “Coefficients” we can see that t is negative and not significant. Therefore, this model is no longer used in the research.

Model 6. Dependent variable - number of people, independent variable - topic Business.

According to the Model Summary, this model explains only 3% of the dispersion which makes its usage in the research senseless. Judging from the Coefficients table, we see that t is negative (it equals -1,088) and its significance is 0,284 (which is more than 0,05). This model is no longer used in the research.

The two next models (one - with the predictor “Photography” and another with the predictor “Preparation for the exams”) are of the same lack of value for the research. Model 7 with the predictor “Photography” explains only 2,4% of variance, has a negative t-test (-0,896) and its significance is 0,377. And model 8 with the predictor “Preparation for the exams” explains 2,2% of variance with the t-test being negative (-0,855) and significance being 0,399.



Conclusion

Nowadays e-learning is gaining more and more popularity, however up until now there was no tool that could allow assessing the factors which influence the number of people attracted to this or that course.

During this research 4 hypothesis were formed:

Hypothesis 1: the biggest number of people is attracted to the IT courses.

Hypothesis 2: among Languages the most popular one is English

Hypothesis 3: the factor which influences the number of people the most is the Topic

Hypothesis 4: the sub topic which is the most popular in IT is Java

The first hypothesis proved right since the biggest number of people attracted to the course was in IT sphere. Two highest results of the whole sample were for Java (214 337) which means that the 4th hypothesis also proved right and for C++ (131 329). There are several reasons why Java is so popular:

a. If you write a code in Java, it will work on any OS - be it Windows, Linux or Mac

b. Every program for Android is written in Java

c. Java is ideal for those who have no experience in programming

Why is C++ popular? For example, Adobe systems were developed in this language, some important parts of Apple OS were also written in this language, AT & T (US telecommunications company) used this language for their 1-800 service.

The second hypothesis said that English is the most popular topic among languages.This hypothesis did not prove right - English had only 7 742 whereas Chinese attracted 16 371 people. This can be explained the following way. One can learn English at school or with a tutor or at the courses while as Chinese is harder to learn, it is not taught in schools (except for in China), so people have to attend online courses to learn it.

The third hypothesis proved partially right. We had 5 dummy variables for the variable “Topic”, and only one of them was a significant predictor in the regression. It was the IT topic, which means that the most popular topic among all analyzed courses is IT. This is logical because nowadays the IT labor market is growing rapidly and steadily and it is the most high-demand profession.

Among the courses on the topic “Photography” the most popular one is Color Grading, it attracted 31 052 people. This is logical because without knowing the programs which specialize in color grading you cannot become a professional photographer. And among the courses on the topic “Preparation for exams” TOEFL is the most popular (it attracted 2 114 people), because TOEFL is one of the most popular exams in English all over the world. In “Business” the biggest number of people is attracted to the topic “Financial Analysis & Accounting”.

The results of the research are the following - there are two factors which influence the number of people. These are “Length of videos in hours” and “Topic IT”.