Determinants of demand on paid online courses

The study of the history of online education in the world and Russia. The study of basic generations of distance generation. Characteristics of the main online platforms. Analysis of the definition of important requirements for paid interactive courses.

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Department of Management

Determinants of demand on paid online courses

Saint-Petersburg School of Economics and Management

Zalberg Dina Aleksandrovna

Saint-Petersburg 2018


It sms s lng g - th tim whn w usd t tk clsss t th cllgs nd ndd t ctully gt ut f th hus t lrn smthing nw. Nwdys this prblm is slvd with th xistnc f th Intrnt nd nlin ductin. ccrding t Frbs rsrch, in 2015 th siz f nlin lrning mrkt ws pprximtly vr 165 bln $ nd btwn 2016 nd 2023 it is likly t grw by 5% nd qul mr thn $240 bln.

Sinc -lrning is gining its ppulrity s fst, it is imprtnt t hv tl which wuld hlp t distinguish th fctrs which hv th mst influnc n th ppulrity f th curss. By ppulrity hr w mn th number f ppl which ch curs ttrcts. Shuld it b th tpic, r th lngug, r th xistnc f vids nd thr visul cntnt, r th uthrs f th curs (which univrsity is rspnsibl fr this curs)? This is what will be researched in this paper.

Ky wrds: nlin curss, distnt ductin, -lrning, dmnd, dtrminnts f dmnd


nlin curss r bcming mr nd mr ppulr vr th yrs nd th univrsitis r sking wys t invlv nlin lrning in thir lrning prgrms. Hwvr, t tk prt in this prcss thy nd t hv thrugh undrstnding f hw this mrkt wrks nd wht fctrs r th dtrminnts f dmnd. Sinc -lrning ffrs th pprtunity t cut dwn th budgt (spcilly in cmprisn with th mny spnt n trditinl ductin - just think f ll th ppr nd pns nd dsks nd bks!) nd mks lrning much sir nd flxibl, th industry cntinus t s cnstnt nd stdy grwth.

Srv s th dfinitin fr nlin lrning in this ppr:

nlin lrning is th us f Intrnt t ccss lrning mtrils; t intrct with th cntnt, instructr nd thr lrnrs; nd t btin supprt during th lrning prcss in rdr t cquir knwldg, t cnstruct prsnl mning, nd t grw frm th lrning xprinc. (M. lly, 2002)

During 2017 nlin ductin mrkt xcdd 165 bln dllrs, nd it kps grwing. Thrfr, thr is ncssity t filtr nlin curss (t stimt thm). It is ssntil t dtrmin th min fctrs which influnc th ppulrity f th curs nd th munt f ppl it will ttrct. Ths factors r wht I m plnning t rsrch in this ppr. This ppr will b usful fr nlin pltfrms such s Cursr, dX, Udmy, Lctrium tc. Thus th trgt udinc f this rsrch is implmntrs f nlin curss nd pltfrms.

Th im f this ppr is t giv rcmmndtins t th nlin pltfrms but ths fctrs. In rdr t fulfill this im it is ncssry t prfrm svrl tsks:

1. Cllct th dt frm th pltfrm Udemy cncrning 100 curss (th dt will b fr this yr)

2. nlyz th dt using prgrm SPSS (regression nlysis)

3. Bsd n th significncy f fctrs, build mdl

4. Frmult rcmmndtins fr th pltfrm

Th rsrch qustin f th ppr is Which fctrs influnc th munt f ppl signing fr th curs?. Th bjct is nlin ductin, nd th subjct is fctrs influncing th munt f ppl signing fr th curs.

Th structur f th ppr is th fllwing:

a. Intrductin

b. Thrticl bckgrund

c. Rsrch

d. Rsults

e. Cnclusin

f. Tbls

Chptr 1. Thrticl bckgrund

Th histry f ductin ruts bck int th ncint Grc. Fr mny yrs it dvlpd purly in th sm frm using bks nd dsks nd pns with lng bring lcturs t th univrsitis whn prfssr kpt writing n brd with th chlk nd n n culd ctully dtrmin wht h ws writing This kpt ging n nd n, nd thn th Intrnt ws invntd. T tll th truth, Intrnt cmpltly chngd th prcptin f ductin. Du t its invntin, it bcm pssibl t gin nw skills nd knwldg withut lving th hus simply by using nlin curss.

1.1 Th histry f nlin ductin in th wrld

In 1840 in th UK Isc Pitmn bcm th first prsn in th wrld t intrduc distnt ductin - h strtd tching studnts stngrphy using mil. It ws ls in th UK tht th first univrsity ffring distnt ductin pprd - th Univrsity f Lndn llwd studnts frm thr univrsitis t pss his xms. Frm 1850 ths xms bcm pssibl fr cndidts ll vr th wrld n mttr whr thy studid. This situtin ld t th crtin f diffrnt cllgs which ffrd studnts t tk up thir curss vi mil in ccrdnc with th univrsity prgrm.

In Pnnsylvni, US, th dily nwsppr strtd publishing studying mtrils in rdr t mlirt mining tchniqus nd t prvnt ccidnts n th minry. Ths rticls gind such succss tht in 1891 thy wr turnd int th curs which ltr srvd s mdl fr lrning prgrms fr thr curss. Willim Hrpr which in th US is cnsidrd t b fthr f distnt lrning crtd th first univrsity dprtmnt f distnt lrning in th Univrsity f Chicg by xprimnting with th xtrcurriculr tching in Bptist thlgicl sminry. nd in 1906 th distnt lrning vi mil ws intrducd in th Univrsity f Wiscnsin.

ustrli ws ls n f th rly cuntris t intrduc distnt lrning. It ws in 1914 tht th gvrnmnt rgnizd ductin vi mil fr kids in primry schl wh livd fr frm schls. Studnts frm tching cllg in Mlburn tught thm using mil. Sn middl schls nd tchnicl cllgs ls tk up this prctic.

Distnt ductin ls bcm kind f th fminist gnd. In 1870s wmn wr still nt llwd t study in th US, s nn llit Ticknr rgnizd Ticknr's Scity crting distnt ductin prgrm fr wmn vi mil. Hr prgrm ws bsd n th nglish prgrm ncurgmnt fr Hm Study. This ws brkthrugh in wmn rights nd ductin.

ftr 1895 (th invntin f rdi) distnt ductin sw nw hrizns. Nw it bcm pssibl t study nt nly vi mil but ls vi tw-wy rdi. Th Univrsity f Pnnsylvni is cnsidrd t b th first n t us rdi in its lrning prctics. In 1934 th Univrsity f Iw lunchd th first ductinl rdi chnnl in th wrld which still xists tdy.

Thn, in 1933 th tlvisin ws invntd, nd by 1950s TV ductin bcm rthr ppulr mng US nd urpn univrsitis.

In 1960s distnt ductin rcivd intrntinl rcgnitin nd gind th supprt f UNSC. In 1962 th prim ministr f nglnd dclrd tht thy r crting s clld wirlss univrsity which wuld unit ll th ductinl institutins using distnt lrning. Thrfr in 1969 th pn Univrsity ws crtd in nglnd, it still xists nd functins tdy, tching mr thn 200 000 studnts ll vr th wrld.

In 1960s IBM crtd spcil prgrm Curswritr which ws usd fr distnt ductin. It ws usd in th lbrt Univrsity frm 1968 t 1980s fr 17 distnt curss.

Sinc th Intrnt ws invntd, distnt ductin (nw - nlin ductin) strtd t dvlp by lps nd bunds.

1.2 Th histry f nlin ductin in Russi

In ur cuntry distnt ductin bgn t dvlp in 1917 ftr th rvlutin ccurrd. USSR crtd systm f ductin which ws bsd n cnsulttins. This systm implid distnt ductin whr prfssr nd studnt did nt s ch thr. This systm suggstd curss n diffrnt lvls. In 1960s 11 distnt univrsitis wr pnd in th USSR lng with fcultis f distnt ductin.

In th bginning distnt ductin lkd lik this - firstly univrsitis cnductd lcturs with mndtry ttndnc. Thr th dscriptin f curs nd th ncssry mtrils wr givn. During th curs studnt studid th infrmtin individully nd t th sm tim thy cn cll r writ th prfssr. By th nd f th yr r smstr studnts hd t cm t th univrsity t pss th xms.

This systm kpt dvlping in th USSR, but with its dcy th dvlpmnt f distnt ductin diminishd. Th min rsn f this phnmnn ws crisis bth in cnmics nd plitics. Hwvr, vn 1990s sw svrl stgs f distnt ductin dvlpmnt. In 1993 th subsidiry f urpn Schl f rprtr ductin ws pnd in Russi. This schl ffrd th wy f lrning nglish vi tps with diffrnt lvls f lngug knwldg. ftr pssing th curs yu gind crtifict. Mny ppl wr ttrctd by this systm f ductin sinc it ws innvtiv nd ngging spcilly in cmprisn with trditinl lngug lrning.

Th nxt stg f distnt lrning ws intrctin with UNSC. This hlpd t build mr thrugh systm f ductin nd rciv cprtin in trms f dvlpmnt f distnt ductin. Th gvrnmnt pnd Mscw Tchnlgicl Univrsity which ffrd ductin n diffrnt distnt prgrms. In 2000 it rcivd ccrdittin, nd nw it kps dvlping nd grwing.

Frm th bginning f th XXI cntury lt f big cmpnis hv bn using distnt ductin t trin thir mplys. This cmplis with Simmns (2002) dfinitin f nlin ductin ( min dlivry mthd t trin mplys). Min cmpnis implmnting distnt ductin in thir trining systm r Russin Rilwys (), SvrStl', Nrylsk Nikl tc.

In 2005 Russi mngd t chiv th intrntinl lvl in trms f distnt ductin. It ws thn tht dvncd Distributd Lrning sscitin fficilly dclrd tht th tril prid fr Russin systm f distnt ductin is fficilly vr nd it full cmplis with th intrntinl stndrd SCRM 1.2. This is th stndrd which is supprtd by ll th mnufcturrs f distnt ductin systms.

1.3 Generations of distant education

This trminlgy ws intrducd by Grrisn (1985) nd Nippr (1989, p.63). It is bsd n th typ f th min dt strg.

First gnrtin minly usd hnd-writtn nd printd mtrils which wr snt by mil hwvr ftr typgrphy ws invntd it bcm pssibl t publish chp bks. lng with thm usully spcil lrning bks wr publishd cntining lists f litrtur nd qustins f studnts. This frm is still cmmnly usd in nwdys ductin prctics (cs tchnlgis)

Scnd gnrtin pprd whn th pn Univrsity in th UK ws crtd (in 1969). Prfssrs usd diffrnt tchnlgis hwvr minly thy cncntrtd n printd mtrils. T ris th fficincy f lrning prcss pn Univrsity crtd spcil lrning mtrils purly fr distnt ductin. Prfssrs intrctd with studnts using printd mtrils lng with rdi nd vidtps (ltr udi tps). ftr th invntin f tlphn it bcm pssibl t cmmunict with th prfssr using tchnicl wys nt nly vi mil. During studying in this univrsity it ws cmmn t ttnd fc-t-fc cnsulttins lng with shrt-trm curss t th plc f rsidnc.

Third gnrtin strtd with th crtin f cmputr mthds f lrning. Ths mthds ffr cmmunictin using txts, grphics nd visuls. t this tim yu cn study bth in synchrnus wy (t th sm tim - fr xmpl, during vidcnfrncs) r in synchrnus wy (nt t th sm tim - with th us f -mil, Intrnt r tlcnfrnc).

ll f ths mthds (s clld gnrtins) cn b cmbind r usd sprtly. In ny wy, distnt ductin hlps t s th lrning prcss nd t minimiz studnts `csts.

Mdls f distnt ductin

During th rsrch 5 mdls f distnt ductin wr dfind:

a. xtrnl dgr prgrm. This mdl is fr studnts nd pupils wh r nt bl t ttnd n-cmpus lssns. ctully, this is n xtrmurl frm f study.

b. Univrsity ductin. Systm f ductin fr studnts wh r studying distntly using IT nd cmputr tlcmmunictins. Studnts r ls ffrd printd mtrils, udi- nd vidtps nd CDs crtd by lding prfssrs f th univrsitis.

c. ductin bsd n cllbrtin f svrl univrsitis. This mdl mks ductin mr prfssinl nd qulifid.

d. utnmus ductin systms. ductin is fully bsd n TV r rdi prgrms, CD discs, nd dditinl printd mtrils.

e. Infrml intgrtd ductin bsd n multimdi prgrms. Ths r slf-ductin prgrms. Thir trgt udinc is dults (ths wh did nt mng t grdut frm schl). Such prjcts cn b prt f fficil ductin prgrm r spcilly rintd n crtin ductin im.

1.4 Min nlin pltfrms

Nt vryn knws tht first MC pprd lng bfr th wll-knwn Cursr, Udcity nd dX wr brn. In 2000 th Univrsity f Clumbi strtd ffring nlin curss by fmus lctrs t vryn (fr mny, f curs). Th univrsity spnt $20 mln n Fthm (th nm f this prjct) hwvr it did nt ttrct th munt f ppl tht ws plnnd nd clsd in 2003. Thn xfrd, Yl nd Stnfrd rgnizd th llLrn pltfrm which livd sinc 2001 till 2006, nd its hundrd curss ttrctd but 10 000 ppl. In 2003-2004 th UK gvrnmnt spnt 62 millin punds n th UKU prjct which ws suppsd t unit 12 British univrsitis. Unfrtuntly, th prjct did nt liv lng; it did withut gthring vn 1000 studnts. Mst f th curss which wr ffrd by ths pltfrms cst mny, but thy wr vry much lik wht ws invntd 10 yrs ltr.

Lt us nw lk wht is th situtin with nlin pltfrms tdy.

Th ldr in trms f nlin ductin is Cursr, which ws crtd by prfssrs frm Stnfrd Univrsity. Bst univrsitis publish thir curss (nwdys thir numbr xcds 400) n Cursr. Coursera has contracts with universities all over the world - USA, China, Spain, even Higher School of Economics has their courses published on Coursera. Thr r curss in nglish, Chins, Spnish, Frnch, Russin nd Prtugus, hwvr BBYY Lngug Srvics is nw crting srvic which llws vluntrs t trnslt subtitls t th curss nlin. Cursr ds nt spciliz n ny xct sphr f knwldg; it cntins curss but physics, mthmtics, humn, mdicl, cmputr nd cnmic scinc s wll s businss and sociology. Curss lst up t 10 wks; ls th pltfrm ffrs vid lcturs, txts, hm tsks nd finl xms. Cursr hs limit in trms f curss vilbility - curss r vilbl nly during dfinit tim prids.

Dx is nthr fmus cmpny which spcilizs in nlin lrning. It is nn-prfit rgniztin which ws crtd by prfssrs frm Hrvrd Univrsity, MIT nd Brkly Cllg. Th hd ffic f Dx is bsd in Cmbridg, Msschustts. n this pltfrm lcturs r dividd int mduls with ch lsting up t 10 minuts. Lcturs cntin tsks which hlp t chck hw th studnt undrstnds th infrmtin. Sm f th curss in Dx r bsd n uniqu sftwr, which ws crtd spcilly fr crtin tpics r instructin mthds. Dx cllbrts with fmus IT cmpnis which sll thir sftwr t hlp Dx crt thir curss. Thus, thy signd cntrct with VMwr Inc. s tht thy wuld supply thir sftwr fr Hrvrd curs Bsics f Cmputr Scinc. Studnts f this curs gin ccss t VMwr Wrksttin 9 nd VMwr Fusin 5. Such cprtin llws thm t lunch Linux, Windws tc. n thir cmputrs.

This pltfrm my ls b usd s lbrtry whr yu cn cllct dt t ssss hw th studnts study.

nthr fmus nlin pltfrm is Udcity. It ws ls crtd by Stnfrd prfssrs s wll s Cursr. 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. Udcity curss includ lcturs, vids, tsts nd hm tsks. Vids lst but 5 minuts r lss. Udcity stimults studnt's prctiv stnc, his/hr prticiptin in cdmic scity in prticulr vi ctivity rting. Th pltfrm llws t publish qustins nd t cnsult n frums. Frum is plc whr studnts sk qustins, hlp ch thr, tlk but thir ttitud twrds th curs nd ls studnts cn cmmunict with thr studnts t frm studying grups nd rgniz rl-tim mtings t cmmunict nd lrn.

Udcity in cmprisn with Cursr hs mildr plitics f rgistrtin nd tim mngmnt. distinctiv ftur f Udcity is vilbility f curss, i.. curss r vilbl t ny tim ftr thy r publishd. Th curss hv ddlins (n cn wtch vids t ny tim during th wk, but by th nd f th wk yu hv t cmplt th hm tsk), hwvr n cn strt th curs t ny tim. This pprch is vry suitbl fr wrking ppl sinc thy cn listn t th lcturs t ny tim nd thy will nt intrfr with thir wrk.

Udcity hs n spcific pint - thy hv rthr shrt vid lcturs. Thy lst frm 2 t 6 minuts; if thy lst lngr th uthrs mk puss with tsts, quizzs nd puzzls s tht th ductin wuld b mr individul.

nthr imprtnt pint which distinguishs Udcity frm ll th thr nlin pltfrms in gd wy is th fct tht Udcity frms CV n vry grdut nd ccrding t his wishs snds th CV t ptntil mplyrs. Th CV cntins nt nly mrk fr th finl xm but ls studnt's ctivity during th curs.

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

Ths wr the frign nlin pltfrms. Hwvr, in Russi thr r ls svrl pltfrms which wr crtd slly by Russin prfssrs. n f thm is Lctrium.

Lctrium ws crtd in 2009 by Ykv Smv nd his wif lxndr Skrdumva. 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 () 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



Price (dollars)

Length of videos (hours)

Age of the course (months)

Introduction to accounting

13 143




20 hours

9 months

Excel Crash Course: Master Excel for Financial analysis

10 123




3,5 hours

19 months

Advanced Executive Recruitment & Hiring





2,5 hours

31 months

Marketing: how to promote your business effectively





3,5 hours

35 months

Business: Fast Business Growth

5 852


Business strategies


5 hours

10 months

Online Business Strategies for Total Beginners

21 035


Business strategies


1 hour

32 months

The Complete Financial Analyst Course

49 526




13 hours

12 months

SMM Mastery

27 719




9 hours

13 months

Viral Marketing - 8 steps online

4 233




2 hours

14 months

How to motivate your team

1 675




0,5 hour

33 months

Complete English Course - English Speaking & Grammar

7 742




9 hours

14 months

Spanish for Beginners

3 980




3,5 hours

6 months

German for Beginners

3 996




2,5 hours

10 months

Conversational French

10 689




2,5 hours

34 months

Learn Italian Language

2 008




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.


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.

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