Analysing health professionals learning interactions in online social networks: A social network analysis approach

Anal y sin g health professionals' l earning i nteracti ons in onlin e social networks: A social netw ork anal y sis approac h XIN LI a, b, 1 , KATH L EEN GRAY a, b , KARIN VER SPO OR a, b , STEPH EN BA RN ETT c a Health and Biomed ical Inform a tic s Resear ch Cen tre b Depar tment o f Computing and In formation Systems, School of Engineeri ng , Univer sity of Mel bourne c Genera l Practi ce Acad emic Un it, Graduat e Schoo l of Med icine, Universi ty of Woll ongong , Australia A bstract Online Soci al Netw orking may be a w ay to support h ealt h professionals' need for continuous learning throug h interaction w it h peers and experts. Unders t anding and e valua ting such l earning is i mportan t but dif ficult, an d Social Netw ork A nalysis (SNA) of fers a solution. Th is pape r demonstra tes how SNA can be used to study le vels of participat ion as w ell as the patterns of interactions tha t tak e pla ce am ong heal th pro fessionals in a l arge on line professi onal learning netw ork. Our analysis has show n t hat the ir learning networ k is highly centralis ed and l o osely connected . T he le vel of participa tion is l ow in general, and most interactions are stru ctured around a sma ll se t of users consisting of moderators and c or e members. Th e struc tural patterns of interaction indicat es t here is a chance of small group lea rning occurr ing and r equires further investigati on t o ide n tify those potential learnin g groups. Th is first stage of analysis , to be f ollow ed by l on gitudinal stu dy of the dynamics of interaction and com pl em ented by content analys i s of their discussion , ma y contribu te to greater s oph istication in the analys is and utilis ation of ne w environmen ts for h ealth professiona l learn ing. Key w o rds : Social n etwork ana lysis, n etworked lear ning, health pr ofessio nal education 1. In t roduction As m edica l kno w ledge is expa nding and healt h ca re delive ry is be coming more co mplex, healt h p r o fe ssionals must co mmit to continuous l ea rning to mainta in up- to -da te kn ow ledge an d sk i lls. O ne approac h cl aimed to meet thei r learning and dev elopment needs is throug h the use o f Online So cial Netwo rks (OSN) [1] . OSN has bee n f ound usef ul t o reduce prof essiona l isolatio n and support any t ime-an y w her e peer- to -peer inte ra ctio n at scale [2, 3]. Also, it contrib utes to the dev elo pment of pr of essional netwo r ks and imp rov es Continuing P r of essio n al De ve l o pment (CPD) [4] . How e ver, the r e is a la ck o f understandi ng abo ut h ow lea rning o cc ur s in OSN, maki ng it dif fic ult to desig n and fac ilitate this ty pe of learning. T he re ar e ack now ledge d challe nges in dev elopin g eff e ctiv e online di scus sions fo r h ea lth profe ssionals [5] . To r ealise the full potenti al of OSN fo r health pro fe ssionals’ learni ng, the I n stitute o f Medicine [6] sugges ts ev aluati ng t h is ty pe of learni ng and i nt eg r ating ef f ective approaches into the ma instream of CPD. Althoug h u n de r standing and ev aluating this ty pe of learnin g is impo rtant, traditio nal evaluatio n approac h es are uns uited to the task. They may focus more on learni n g outco mes, w her eas the le arning oc curring i n OSN h as g reater emp h asis on the sharing of opin io n s and th e validatio n of curr e n t prac tic e, and th e r efo r e the fo cus of evaluation is m o r e on learning proce sses [7] . To study h ealt h profe ssio na ls' lea rning in their OSN , the first step is t o i de n tify an d unde rstand the inte r act ions wit hin t heir lea rning e n viro nment, as t his he lps u ndersta nd their le arni ng b eha v iours w h ic h p r o vide s valuab le insights i nt o lea rn ing results [4 ] . So cial Netwo rk A naly sis (SNA) has already pr ov en to be an effec tive technique to analy s e interactio n in o nline lea rning enviro nm e n ts [8 , 9]. Us in g SNA, the n ode s (learne r s, or learni ng r eso urces) and t ies (relations hips) in netwo r ks can b e vis uali se d an d an aly sed using qua ntitativ e measu res and grap h ica l r ep r esentat ions in o rder t o examine the flo w o f inte r act ions. The aim of this pape r is to demo n st rate how SNA can be used to stud y levels of par ticipatio n as we ll as the patterns o f int erac tions that take place among health p rof ess i onals in a l arge o n lin e prof essiona l lea rning netwo r k. 2. Background and Related Work Rec ent technolo gical changes, in pa rticul ar netw ork tec hn o logies such as OSN , have reorga nised h ow we learn and bro ugh t us netw orke d learning . Go ody ear [10] def in es n etw orked l ea rning a s t h e learning in w hi c h info rmation an d co mmunicatio ns technology is use d t o p r o mote co nn ectio n s betwe en l ea rners, and betw e en a learni ng n etw o rk and its 1 Correspondi ng Author. learning r eso ur ces . The study of n etwo rked lea rning aims t o under st and th e learni ng proc ess by in ve stigating h ow peo ple deve l op and mainta in a w eb of s o cial r ela tions for their learning; i t fo cuses on the dive rsity of social relatio nships (rat h e r than t h e dev elopment of long - lasting relatio nships), as w ell as th e value t his c r eates fo r learni ng. Bas ed o n the t heory of soc ial co nstructiv ism, learni n g b est o ccurs thr o ugh soc ial interac tions an d co mmunicatio n be tween learne rs [11] . T h e lea rning th eo ry called conne ctivism stat es that l ea rning r esides in n etw orks. Th es e n etw orks are f ormed f rom t he so cial inte r a ctio n s be tw een l ea rners . T he t heory e mpha sises the i mpo rtance of fin d ing new co nn ectio ns; the lea rning p roce ss be gin s w ith estab lishing a nd finding new co n nections [12] . The applica tion o f SNA to learning is still at a very early stage [13] . In particul ar , th e r e have b een ve r y few studies co nducted in the co n tex t of in f ormal learning fo r prof ess i onal dev el opment in any spec ific fie ld. SNA was employ ed to captu r e an d analy s e tr ace s o f teachers' info r mal learni ng occ urring in their so cial -prof es sional netwo r ks [14] . SNA was also used to un de rstand th e flow of experiential know ledge s haring amo n g h ealt h profe ssionals wit hin a paed iatric pai n discuss ion fo r um [15] . H owev er, bo t h studie s w ere limited in t h ei r analy sis, and bas ed on sm all numb ers. 3. Methods 3.1. Data Data fo r this study come s f r o m an online p r o fe ssional learning n etwo r k used by more than 10,000 h ealt h prof essiona ls during the period 2009 -2014 . Th e onli ne lea rning n etw o rk is a cc ess ed by health prof e ssio na ls only . All doc tors w ere ve rified by ent e ring their Medical Regist ra tio n details , whic h we re the n c hecked agai nst the r egist ration datab ase be fore users gai n ed ent r y . The online co mmunity h as fo r um s set up w h ich allo w th e doc tors discussing industry issue s, sharing bes t practice s and promoti ng conve rsation wit hin the h e a lth co mmu n ity . T o pics of di sc ussio n te nd t o be highly clinically focused, for examp le, cardiov ascular, rheumato logy , der matolo gy , e tc. 3.2. M e asures The inte r act ions of h ealt h prof e ssio na ls withi n the forum were analy sed usin g diffe rent SNA m easu res, whic h includes both mathe matical an d visual approac h es. As Table 1 depict s , mathematica l analy sis involve s using fo ur n etw ork -leve l structu ral measu res (de nsity , centralisation, dia meter an d average path l engt h) and three individual-le v el cen tral ity measu res (deg ree, betw eenn ess , and close ness). The n etwo r k st ructu ral measu res reve al th e participat ion lev el and co nn ectiv ity in the e ntir e ne tw ork. The ce n trality m easures prov i de info rma tio n a b out the a ct ivity levels of th e individu al users, along w ith th e ov erall ac tivity stat us o f the n etw ork; they help understa nd how in te ra ct ions take place by summ arisi ng th e indiv idual users /thread-lev el characteri stics. Lastly , visual analy sis w as d one by r epresenting the relatio nships b etwe en users/threads thr o ugh g raphs to e nri c h t he findings of math em atical analy sis. Table 1 – D escriptiv e Def initions of All SNA Measu res Used in This Stu dy SNA m easu re Descriptiv e definitio n Netwo r k structu ral measu res Density The numb er of pr ese nt relatio nships as a ratio of the pos sib le n umb er of relatio nships in a n etw ork; rep resents the ov er all co nnection be tween users/thre ads. Centralisa tion The exte n t to w hi c h the connec ted ness is fo cused around a particu lar user/thread. Diamete r The lo ngest path b etwe en an y pair o f use rs/thr ea ds in a netw ork. Av erage path lengt h The ave rage pat h be twee n any pair of use r s/threads in a netw o rk. Centrality measu res Degree centrali ty The numb er of direct relatio n ships a user/thread has wit h others in a n etw ork, w hi ch p rovide s an indicatio n of thei r popul arity an d influe nce . Be tweenn es s cent rality The numb er of tim es a use r/thread sits o n the shorte st path linki ng two other users/thre ads to gether in a ne tw ork; it hel ps ide ntify im po r tant use r s/threads. Clos eness ce ntralit y How quickly a user/thread c an reach all ot her use r s/threads w ith in the enti r e netwo r k; it prov ides an indic ati o n of the spee d of in fo r matio n dist r ibutio n . 3.3. Procedure An online discuss ion fo rum is a t y pe of 2-m o de n etwo r k, w hich re p resents h ow a cto rs are tied to p articular ev ents (i.e. an acto r -by -event n etw ork) . A co m mon met hod of anal y sin g a 2-mo de netw ork is to t ransfo r m the data into two 1-mode netwo r ks [16] . One is an actor netwo r k, in thi s case , crea ted from th e fo r um use r s. A tie i s c r eated be twee n them if they both co m munic ate on th e same thr e a d. A nothe r is an ev ent n etw o r k, c r eated from disc ussio n threads . A tie is created be tween two thr e ads if the same use r co m municate d o n bo t h of th e m. Acco r ding to this, o nce the fo r um ’s data is extracted throug h SQL queries, it is struc tured i nt o two matrices fo r analy sis. The stat net lib rar y in R w as used fo r th e analy sis a nd vi sualisatio n of int e r actio n s. F irstly , t h e netwo r k structu ral measu res w ere a pplied t o bo t h use r (a cto r ) and thr ead (ev en t) n etw orks, and the n ce ntrality an aly sis w as perfo r med fo r both of the n etw o rks. Th e results of density , ce ntralisatio n, and ce ntrality analy sis we r e normalise d fo r the adap tation to 2-mode n etw o rks [17] and to a [0, 1] sca le f or simple r interpre tatio n . 4. Results and Disc ussi on 4.1. N e twork A ctivities Overview In orde r to an aly s e t he o nlin e pro fess ional learning n etw o rk for h ealt h prof e ssio na ls, basic statistic s we r e extra cte d fi rst to prov i de basic understa nding o f activity levels w ith in the n etw ork . There ar e a total of 10056 r egiste r ed use r s in th e forum, ma inly w i th backg r ou nd s o f Ge neral Practice (n=763 2), but also including Nu r s ing (n=775), Ca rdi olo g y (n=125) , and Ge n e r al Medicine (n=122). In the study period 2009-2014 , th e re w ere 40 forums, with t otal of 723 threads and 7089 po sts. 621 users pos ted at least once . Of t hese 621, m os t users ma de less th an 100 pos ts; fiv e ma de m o r e tha n 200 posts, h ow ever, three of th em are know n to ha v e f ormal roles as mode rato rs. W h en the post co unt fo r eac h fo r um was a naly sed, we fo un d pos t ev ents not only varied ov er time b ut were dif ferent across diff er ent f orum s. Politic s/IT/adm inistratio n, Doc to r s ’ l ife , and Cardiov ascula r /vasc ular we re the mos t po pula r fo r ums. A numb er of topics w ere discusse d in 2 011 but stopped after 2012. Discuss ion on spec ialised topic s (e.g. P aediat ri c, Ne ur o logy ) was n ot ve r y i ntense but periodic, in particul ar, in the ye ar 2012 and 2 014. 4.2. N e twork Structural M easures Tab le 2 prese nts t he netwo r k structural m easu r es fo r bo th use r and thr e a d netwo r ks. Wit h r egard to the use r netwo r k, the density sco r e of 0.04 indica tes a low level of participa tion and connec tion amo n g the users i n the n etw o r k. Th e ce ntr alisatio n sco re of 0. 59 indi ca tes t hat the interactio n is centralised i n a small set of users and a g reat amount o f use rs are not engaged and interact as little as pos sible . A diameter of 5. 00 and average pat h l engt h of 2.17 in dica tes that the users are not ve r y cl os e t o one an other, w hich co n f irms th e low density of th e n etw ork, meaning that th e use rs may not easily reach eac h o the r a nd share know ledge. The thread n etw ork has a m uc h higher de nsit y of 0.40 compar ed t o the use r n etw ork, which may impl y that the sa me users initiated a large n umb er of thr eads, and that those thr e a ds we r e co nn ec ted throug h th em . The ce n tralisatio n sc ore of 0.46 indicates that mos t thr eads we re attended equally and only a few attracted mo re attentio n than ot her s . The diameter of 4. 00 and ave ra ge p ath l e ngt h of 1. 66 in dica te that the threads sit close to each o th e r in ge neral, how e ve r so me ar e dista nt from each ot h er. This implies some threads we re initia ted and comme n ted by diffe r ent set of users and so th e re i s a cha nce of small group learning oc curring. Table 2 – N etwo rk Structu r al Measu res Netwo rk structu ral measu r e User (N=621) Thread (N =723 ) Density 0.04 0.40 Centralisa tion 0.59 0.46 Diamete r 5.00 4.00 Av erage path lengt h 2.17 1.66 4.3. Central ity M easures Figu r e 2 presents th e ce n trality di stribut ions fo r the users. A s show n , degree dist ributio n is quite skew ed, ran g ing from 0.001 to 0.42 w ith a median of 0.003. It is a highly ce ntralise d netwo r k, with a minority of users w ho ha v e degree of 0.20 or abov e pr oduc ing th e b ulk of the disc ussio n with th e netw ork. These users we r e fo und to include moderato rs and co re m emb ers. Mode rat o r s are those that initiate and most ly encourage di sc ussio ns, co r e m emb ers ar e self-directed learners w h o initiate and p articipate most disc ussio n s. Clos eness centrality r anges from 0.26 t o 0.59 w ith a media n of 0.37. Mo st of t he users ar e not ve ry close to each oth e r, co nfirming that the n etw ork is loo se. How e ver, most users are simil arly positioned, w hi ch implies that th e user s ha ve pote nt ial a cc es s to learn f r o m eac h ot her. Be tweenn es s centrality ha s a hig h ly skew ed distri but io n. The re are a la rge numbe r o f users wh o have 0.00 betw e enness . Only a few user s ha ve b etwe enn ess centrality of 0.02 or a bov e. Median betw ee n ness centrality is 4.83886e-06 whic h is ve r y low, sh ow in g that m o st o f th e users are a part of a clu ster; th e cluste r fo rm s the co r e of t he n etw o rk and ar e n ot well co nnected e ach ot her. This low co n n ectiv ity further emphas ises the v ery weak particip atio n in the fo rum s. Figu r e 2 – Th e Centra lity Dist ribu tions fo r the Users Figure 3 depicts the ce ntrality distrib ution s f or the thr e a ds. De gr ee dist ributio n r ange s from 0.002 to 0.05 wit h a median of 0.006, indic ating the re is n o o ne thread that ev er yo n e co m mented on . The thread netw ork is not ce ntralised co m par ed to the user netwo r k. Th ere ar e only a f ew t hreads w ith r elat ive l y h ighe r deg rees of 0.024 o r a bov e, as the majo r ity of threads had few er than 13 co mme nt s . Clos eness centrality ran ges from 0.43 to 0.59 w ith a media n of 0. 45, indicat ing that most o f the thr ea ds sit clo se to each other, and ar e s imila rly positioned. Thi s suggests th at users w ho participated in o n e learni n g t o pic did n ot fi nd it diff icult to particip a te i n other lea rning to pics. Be tweenn es s ce ntrality ran ges from 0.00 to 0.02 wit h a media n of 0.001. Thoug h a large numbe r o f t hr eads hav e 0.00 be tweenn es s, this n umb er is muc h lo w er than in the user netwo r k. More than 20% of t he threads have b etweenness ce ntr ality of 0.003 or abov e, sh ow in g that th e thr eads ar e independent, an d the r e i s n o one pa rticular thr ead required to initiate o ther thre ads, or to kee p ot her threads go ing. Figu r e 3 – Th e Centra lity Dist ribu tion s for the Th reads 4.4. N e twork Vis ua lisati on Figu r e 4 prese n ts the visualisat ion of the 2-m ode n etw ork, whic h has nodes of use r and thre ad. We optimis ed the lay out by appl y in g the l ay out al gorit hm that di rects most co nn ected nodes in to the ce n tre o f th e g raph. As show n , the re ar e only a small numbe r of users si tting in the ce ntr e of threa ds who we re really enga ged and active l y parti ci pating in threads: a b out sev en active use rs c ontroll ed mos t threads wher e c omments we re poste d. Inte resting ly , th ere w as o n e user (who is not i n the cent ral) initiated mo re than 20 t hread s but had n o respo nses. Figu r e 4 – 2- m od e Networ k Figu r e 5 p resents the visualisa tion of user and t hread ne two r ks sepa rately . Give n the lar g e size of the netw o r k (users=62 1, threads=723) , we t hinned bo t h n etw o r ks by display in g only th os e ties tha t satisf y a cut-off point (spec ific all y , we kept only those ti es t hat have a tie w eigh t gre ater tha n the mea n tie we ig ht plus one sta ndard dev ia tion). As s h o w n , the user n etw ork (node size repr es ents th e n umb er of t hreads that the use r cont ri b uted) is quite centralise d. The co nversatio n mostly occurs among a fe w active users and th e r est are n ot e ngaged much. The thre ad n etwo r k is generally decentr alised. Some threads rece ive d more attention tha n o the rs, but we re quit e independe nt of each other. All of these f indings are co nfirmed by the netw ork structural and ce n tral ity anal y sis r esul ts. Figu r e 5 – 1-Mode N etwo rk 5. Conclusions and Future Work OSN has potenti al a s an inn o vative approac h to info r mal l ea rning fo r prof ess ional dev elo pm ent of h ea lth p r o fe ssionals. How e ver, we n eed to g ain a clear understa nding o f how th e proce ss of onlin e interactio n can be co n sidered to be educ ational. This study takes th e f ir st step by analy sin g the i nteractio n s oc curring in a large o nline prof es si o na l lea rn ing netwo r k f or health p rofe ssionals using SNA. We t ransfo r med the online lea rning n etw ork (a disc ussio n fo rum) into a user and thread n etw ork, and analy s ed the in teractio n s within two n etw o r ks r es pec tively using n etw o rk -l ev el structural and individu al-lev el ce ntrality m easu r es. We c oncluded that th e pa rticipatio n lev el is low in ge n eral and the learning n etw o r k is highl y ce n tralised and loo sel y co nn ecte d. This findi n g is co nsiste n t with ot h e r r ese ar c h [18, 19] that ha s fo un d ev i dence of a s mall set of users produci ng the bulk of t he discuss ion w ithin o n line co mmu nities. In o u r stu dy , the s mall s et o f users c o n sists of mode rators and co r e memb ers. Since they control w hat k n o w ledge a n d info r mat ion is s hared, engagi ng t h em is esse ntial to furth e rin g the i nt e ractio n or lea rning proce ss in the n etw ork. In additio n, the struc tural patte rns o f int e ra ctio n indi ca te that the r e is a c han ce of sma ll g r o up learni ng o ccurring; th is r equi r es furt h e r in ve stigatio n to i dentify potenti al lea rning groups. Due to li mitatio n s of th e data sou r ce, w e we r e unable to trac k passive users (i.e. thos e who read but do n ot pa rticipate in any discussio n) who are li kely to gain value from the fo ru m and may n ee d suppo rt on th ei r lea rn ing activitie s. Fut ure studies s hould i nvestigate t heir inte ractio ns with lea rning reso ur ce s if suc h dat a is ob t ainab le. This study pr ov i des an indication of the pa rticipatio n l ev el an d the patterns of inte r act ions wit h in the fo rum; h ow ever, it doe s n ot neces sarily in fo r m to what extent th es e interactio n s impact learning. Th e r efo r e, it r emai ns diff i cult to int e rpret learning based on th e patte rns o f in te ractions. Longitud inal n etw ork an aly sis has bee n suggeste d to ov erco me this challe nge by stud y i ng the inte ractio n c han ges ov er t ime and the d r ivi ng facto rs be hind such cha nges [20] . Comple mented by content analy sis of t he discus sions in the fo r um, suc h ana ly sis ma y h elp t o explain how h ealth profe ssionals' know ledge is co n structed a nd influe n ced by t heir interac tio ns. Studies such as these are expec ted t o contribute to greate r sophistica tio n in the analy sis of n ew en vironme n ts fo r health profe ssional learni ng, and thus to more ef fec tive des ign a nd o peratio n of such lea rning e n vi r o nments. 6. References [1] Gorham, R., et al ., Social Media and Health Educati on: What the Literatur e Says. The Journal of Distance Educatio n, 2012 . 26 (2). [2] Moorhead, S.A., et al., A New Dimension of Hea lth Care: Systema t ic Review of the Uses , Benefits, and Limitations of Social Media for Health Commu nication. Jo urnal of Medic al I nternet R ese ar ch, 2013. 15 (4): p . e85. [3] Ch eston, C. C. , T.E. Fli ck i n ge r , and M.S. Chiso lm, Social media use in m edica l educ ation: a systemat ic review . Acade mic Medici ne, 2013. 88 (6) : p. 893-901. [4] Sandar s , J., P. Jay e, a nd K . 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