ArticlePDF Available

Self‐Regulated Learning strategies in world's first MOOC in Implant Dentistry

Authors:

Abstract and Figures

Background Massive Open Online Courses (MOOCs) have been often described as a major innovation Higher Education, but their application in the teaching of clinical disciplines is still very limited, while there is a lack of scientific evaluations in this domain. The aim of this study was to investigate learners’ behaviors and correlate patterns of Self‐Regulated Learning (SRL) with performance and achievement during a MOOC in Implant Dentistry. Methods The clickstream data of learners from the first run of the MOOC Implant dentistry by The University of Hong Kong, was modelled and quantified based on Zimmerman's SRL model. The data was quantitatively analysed by means of k‐means clustering for evidence of five SRL behavioural indicators of student activity. The clusters identified were then correlated with student engagement and learning performance. Results 7608 individuals enrolled, 5014 engaged (active learners 65.90%), 1277 of them (25.47%) completed the course and 1232 purchased a certificate. Two major groups of learners emerged: Attentive (n=1433) who were more likely to follow the prescribed pathway in the MOOC and Auditors (n=3581) who accessed content selectively. There was significantly higher engagement, achievement and completion rates among Attentive than Auditors. Both groups included subcategories (Browser, Digger, Test‐driven, Sampler, Persistent) which might reflect different SRL strategies. Conclusion A MOOC in a clinical discipline can achieve high enrollment and completion rates as compared to current benchmarks. There appears to be a wide diversity of learning behaviors among learners, with two however dominant patterns. Learners with a linear learning pathway achieved significantly higher grades and completion rates than those who accessed content irregularly and selectively. Such differences however might be influenced by the demographic and professional background of the learner, as well as their motivation to attending the MOOC. Certain learning behaviors, in particular how learners access content in relation to assessments might be closer related to SRL. This article is protected by copyright. All rights reserved.
Content may be subject to copyright.
Eur J Dent Educ. 2019;1–8. wileyonlinelibrary.com/journal/eje  
|
 1
© 2019 John Wiley & Sons A/S.
Published by John Wiley & Sons Ltd
Received:31August2018 
|
  Accepted:29January2019
DOI : 10.1111/eje .1242 8
ORIGINAL ARTICLE
Self‐regulated learning strategies in world's first MOOC in
implant dentistry
Min Lan1| Xiangyu Hou2| Xinyu Qi3| Nikos Mattheos4
1Information Technology in St udies , Facult y
of Educat ion, Th e Univer sity of Hong Kong,
Hong Kong, China
2Electrical and Electronic Engineering,
Faculty of Engineering, The Universit y of
Hong Kong, Hong Kong, China
3Technology‐Enriched Learning
Initiat ive, The U niversity of Ho ng Kong,
Hong Kong, China
4ImplantDentis try,Pros thodontics,Facult y
of Dentis try, The U niversity of Ho ng Kong,
Hong Kong, China
Correspondence
NikosMat theos ,Prosthodont ics,Prince
PhillipD entalH ospital,HongKo ng,China.
Email: nikos@mattheos.net
Abstract
Background: MassiveOpenOnlineCourses(MOOCs)havebeenoftendescribedas
amajorinnovationHigherEducation,buttheirapplicationintheteachingofclinical
discipline s is still very lim ited, while there is a l ack of scientific eva luations in this
domain. The aim of this study was to investigate learners' behaviours and correlate
patternsofSelf‐RegulatedLearning(SRL)withperformanceandachievementduring
aMOOCinImplantDentistry.
Methods: The clickstream data of learners from the first run of the MOOC Implant
dentistry by The University of Hong Kong were modelled and quantified based on
Zimmerman's SRL model. The data were quantitatively analysed by means of k‐means
clustering for evidence of five SRL behavioural indicators of student activity. The
clusters identified were then correlated with student engagement and learning
performance.
Results: Atotalof7608individualsenrolled,5014engaged(activelearners65.90%),
1277ofthem(25.47%)completedthecourseand1232purchasedacertificate.Two
majorgroupsoflearnersemerged:Attentive(n=1433)whoweremorelikelytofol
low the presc ribed pathway in the MOO C and Auditors (n=3581) who accessed
content selectively. There was significantly higher engagement, achievement and
completion ratesamong Attentivethan Auditors. Both groupsincludedsubcatego
ries(Browser,Digger,Test‐driven,Sampler,Persistent)whichmightreflectdifferent
SRL strategies.
Conclusion: AMOOCinaclinicaldisciplinecanachievehighenrolmentandcomple
tionratesascomparedtocurrentbenchmarks.Thereappearstobeawidediversity
of learning behaviours among learners, with two however dominant patterns.
Learners with a linear learning pathway achieved significantly higher grades and
completion ratesthanthosewhoaccessedcontentirregularlyandselectively.Such
differenceshowevermightbeinfluencedbythedemographicandprofessionalback
ground of the learner, as well as their motivation to attending the MOOC. Certain
learning behaviours, inparticularhowlearnersaccesscontentinrelation toassess
ments might be closer related to SRL.
KEYWORDS
elearning,implantdentistry,massiveopenonlinecourse,selfregulatedlearning
2 
|
   LAN et AL.
1 | INTRODUCTION
AMassive Open Online Course (MOOC) is an open online learn
ing environment which can simultaneously cater for an unlimited
numberoflearners.Thetermisreportedtohavebeenintroduced
in 20081 and quickly gained traction, describing a learning instru
mentthatof ferslearnersandtutorsanopenandcontinualformof
networking beyond the limits of conventional online courses.2,3 A
large number of MOOCs are available on an ever‐growing array of
subjec ts, with th e most est ablished on line platfo rms repor ted to
reach more than 101 million learners worldwide.2 The recent devel
opmentofMOOCshasbeenhailedbymanyeducatorsasasignifi
cantinnovationwithinteachingandlearning,withthepotentialto
revolutionisethelandscapeofuniversityeducationworldwide.1‐3
Despite the rapid growth of MOOCs in most university dis
cipline s, expansion in cli nical science s has been slow. The rol e of
openandonlinelearningintheteachingofclinicaldisciplines,such
as Medicine and Dentistry, has been debated, with educators being
dividedwhen assessing the potentialof MOOCs.4,5 It is clear that
MOOCs are not to replace dominant models of clinical educa
tion such as residencies and face‐to‐face teaching environment s.
However, some educators believe MOOCs could become the trans
formative pedagogy to help dental schools overcome many of the
presentchallenges,suchasincreasingstudenttuitionanddebt,de
creasing funds for faculty salaries and associated faculty shortages,
aswellasthehighcostofclinicoperations.6
Implant D entistry is o ne of the most rece nt and rapidly de
veloping clinical subjects within Oral Health Sciences, evolving
around th e replacement of mis sing teeth with sur gically placed
endosseoustitanium implants.TheteachingofImplantDentistry
in the undergraduate curricula is limited, and it varies significantly
between regions and institutions, with universities having many
difficultiestoadequatelyimplementit.7, 8 A s a result, the majority
of dental graduates worldwide start their careers with a ver y lim
itedunder standing ofdental implants as part of comprehensive
ca re .Evenlong‐p rac ticin gd en ti sts wh ow er en ev er taughtimp la nt
dentistry in theircurricula havelittleopportunity to reach flexi
ble,quality‐assuredandunbiasededucationinthisdiscipline.The
lackof humanand materialresourcesand expertiseisrepeatedly
cited as one of t he obsta cles for the im plement ation of Impla nt
Dentistry to the undergraduate curriculum to the required ex
tend.9,10 In that sense, sharing of teaching resources and content
atagloballevelthrough open andonline learninghasbeenoften
proposedas apossible remedyto addresssuchdeficiencies.11 As
ofthetimethisprojectwaslaunched(2015),therewasnoMOOC
attemptedfortheteachingofclinicaldisciplinesinDentistry,while
fewMOOCshadbeenlaunchedwithindisciplinesofclinicalmed
icine.12,13 The methodological challenge therefore was evident, to
design a robust pedagogical framework but also to produce evi
dence ofthelearningimpactfortheteaching of clinical sciences
with this medium. Under such a massive and autonomous learning
environment, students’ self‐monitoring and organisation become
of paramount importance, demanding a higher self‐regulated
learning ability. According to Zimmerman,14 the degree to which
students are metacognitively, motivationally and behaviourally
active in steering their own learning is described as self‐regulated
learning (SRL). As a MOOC caters for large numbers of learners
with diverse motivation, professional background and learning
needs, the SRL ability greatly varies among learners, some of who
arestudents,whileothersarepracticingcliniciansunderdifferent
settings,expertiseandcompetence.
1.1 | Aims
The aim of this study was to evaluate learners’ dynamics and learn‐
ingbehavioursduringthefirstrunofaMOOCinImplantDentistry.
Inpar ticular,thestudyaimedto investigatepatternsofSRLbehav
iours throughdata miningandinvestigatecorrelations of such pat
ternswiththelearner'sactivit yandperformance.
2 | MATERIAL AND METHODS
ThisstudywasapprovedbytheHKU/HAWestClusterInstitutional
ReviewBoardwithreferencenumberUW16‐1005.
2.1 | Pedagogic framework and structure
The authors used Zimmerman's SRL model,14inordertod eve lopthe
pedagogicalframeworkoftheMOOC.ZimmermanapproachedSRL
fromacognitivepoint,whereSRLischaracterisedbythreephases:
• Forethoughtphase(F ):taskanalysisandself‐motivation
• Performancephase(P):self‐controlandself‐observation
• Self‐reflectionphase(S):self‐judgmentandself‐reaction
Using the three phases of the SRL theory as scaffold, a multidis
ciplinary learningenvironment was designed whichcould foster the
growthofeffectivelearninginthreelearning“pathways,”developinga
verticallyandhorizontallyintegratedmatrix:
• Theoreticalfundamentalsandfoundationknowledge,mainlypre
sented through short seminars, supported by discussion boards
suggestedreadingsandassessedbyMultipleChoiceQuizzes.
• Clinica l applications a nd procedures , mainly present ed through
practical tutorials, clinicalvideos and casestudies,supportedby
discussion boards suggested readings and assessed by Multiple
ChoiceQuizzes.
• Applicationof knowledge, competences, decisionmaking and
guided re flection , by means of thre e tailor‐made “v irtual” p a
tientsseekinghelp.Thiswasalsosuppor tedbydiscussionsand
assessment was done through non‐graded peer and self‐as
sessment.Eachpatient ispresentedinitiallyat the startofthe
course and theproblemsareunfolding gradually asthecourse
advances, while certain tasks are requested by the students
at each st age. The model was a dopted from the “ Interactive
    
|
 3
LAN e t AL.
Examination”concept,15 during which the student s engage in a
guided process withanactualpatient problem, define thepa
rameter s of the proble m and propose s olutions base d on the
theoretical and experiential content. A peer solution is then
utilised tokickstartaguidedreflection,aimingtohelp thestu
dents identify strengths and weaknesses in their own thinking
and define new learning objectives.15
2.2 | Learning content
The content a nd learning objec tives of the MOOC wer e derived from
thecompetenciesdefinedbytheEuropeanConsensusWorkshopin
the university te aching of Implant Dentistr y16 for the graduating
dentist.Fifty‐twodistinct competencies (11majorand 41support
ing) were e ncoded into 28 le arning obj ectives , which were con se
quently mapped tothethree learning pathwaysandorganisedin5
modulescomposedof17lessons(Figure1).
Anevidentbut non‐binding “prescribed”learningpathwaywas
established, which encouraged the learners to follow a sequential
access to th e different c omponent s of the course c ompleting o ne
module per week. Gradual advancement on all three pathways
through the 5 weeks of the course was engineered through regu
lar “checkp oints” and me ntoring by expe rienced tuto rs and peer s.
Inaddition,the threepathways were colour coded throughoutthe
course's interface with distinct colours maintained for each one for
example.invideo backgrounds, notesetc Learners remained how
ever free atall timesto access anycontent intheir ownpreferred
sequence and timing.
Twenty internationally acknowledged experts in respective
fields of Implant Dentistr y were invited to contribute according to
theirexpertisebydevelopingcontentasdirectedbythemapofthe
identifiedcompetencesandlearningobjectives.TheMOOCranfor
fourroundsonCoursera(www.coursera.org)during2016‐2017.The
analyse s in this study a re based on the s ample form the f irst run
whichtookplacefrom25thOctoberto5thDecember2016.
2.3 | Data collection
The numbers of learners enrolled in the course, as well as the num‐
berofcoursecompletersandcertificateholderswererecorded.The
clickstreamdataofallenrolledlearnerswerecollected,representing
userinterac tionswiththecontentwithinthelearningplatform.Such
data are matched with the unique user ID and include any course
item clicked or accessed by the user, in the sequence and the time
it occurred.
2.4 | Data analysis
The course was organised in 5 modules, each of which has a num
berof lessons.Eachlessonwascomposedbya varietyofresources
such as lectures, clinical procedures videos, discussion boards and
a graded assessment. The way the students navigated the content
was recorded in their individual clickstream. The clickstream data
were modelled and quantified based on the Zimmerman's SRL model
The data were analysed for evidence of five SRL indicators of stu
dentactivityperformance,pointingtostudentlearningpatterns:(a)
Time man agement (T M), (b) Tasks trategy (com prehensio n) (TS), (c)
FIGURE 1 Structure and organisation of the course
4 
|
   LAN et AL.
Self‐instruction(assessmentdriven) (SI‐ad)(d) Self‐instruction(sys
tematic le arning) (SI‐ sl) and (e) Applic ation (AP). Def initions of the
SRLind icatorsu sedca nbeseeninTable1.Engagement(EN)wasde
finedasthenumberofcompletedlessonsoutofthetotalnumberof
less onsacce ss eda nd wasexpr ess ed inper ce nt a ge fr om0%to10 0 % .
Scores on t he basis of the comple ted graded cour se assessment s
werecalculatedforeachlearnerandexpressedasafinalgrade(per
centage pa ssing grade: 75% ‐ max imum possibl e 100%). A Py thon
script was programmed in Jupy ter web‐based platform (Project
Jupyt er,jupy ter.org,version5.4 .0).Q uantitati vely,thetoolkitofpan
das,counter,sklearn(eg,k‐meanclustering),and n‐gram wereused
to identif y clusters of learners based on the indicators. The identified
clusterswerequalitativelyclassifiedusingtheconstant‐comparative
method , a technique t hat is typi cally reco mmended fo r systemat ic
reviewpursuits.17 Students behaviour for each indicator was defined
as“highlevel”iftheclickstreamsuggestedascoreabovetheaverage
and “low level” if belowthe average, based onthe defined metrics
foreachindicator.Pearsoncorrelationwasusedtoinvestigatecorre
lationsbetweenlearners’behavioursandperformance,(grades,en
gagementcompletion rates).Mann‐WhitneyU test and Chi‐square
testforindependent samplesrun onSPSS (IBM, Armonk, NY,USA,
version24)wereusedtoinvestigatesignificantdifferencesinpreva
lenceofpatternsamongthedif ferentidentifiedclustersoflearners.
3 | RESULTS
3.1 | Enrolment and completion
During the first run, 7608 individuals enrolled in the MOOC. Out of
them,5014learners(65.90%)executedatleastoneactioninteract
ingwith thecontent thus becoming “active learners,”and2957of
them(58.97%)completedat leastone assessment.A total of1277
learners completedt hecourse, while 1232 ofthem went further
to purchase a certif icate. Completion rate was 25.47% of active
learnersor 16.78%ofthe enrolled learners.A certificate was pur
chased by 96.48% of thelearners who qualified (completers). The
first certificate was acquired already on the first day after the start
ofthecourse,while35learnerscompletedallcourseassessments
bytheendofthefirstweek(Figure2).Themajorityofcompletions
and certificates were achieved before the 5th and last week of the
course.
The results of the correlation of the indicators can be seen in
Table 2. Time management( TM)was moderately and negatively
correlated with engagement (EN) (r =−0.303 , P<0.01) and also
to the final grade (r =−0.300, P<0.01). The self‐instruction of
systematic learning was highlyand positively correlated withthe
final grade (r =0. 841, P<0.01), com pletion (r =0.792, P<0.01),
earningcertificate(r =0.781,P<0.01)andengagement(r =0.880,
P<0.01).
3.2 | Patterns of Self‐Regulated Behaviour
K‐means clustering resulted in the identification of eight clusters
amongparticipantsintheMOOC(Table2).Thelearners'typeswere
furt her organis ed in two major p atterns in te rms of their lea rning
engagement on activities, namely “Auditors” (n=3581, 71.42%)
and “Attentive” (n=1433 , 28.58%). Audi tor describe s particip ants
whodemonstratedinterestinspecificpart softhecontentandt ypi
callydidnotfollowthe prescribed sequenceofinstructionaldesign
(cluster s 2, 3, 4, 5, 7,8). A ttentive de scribes par ticipant s who ap
pearedtofollowtheprescribedpathway,typicallyincludingpassing
theassessments insequence andcompletionofthecourse.Based
TABLE 1 Definitions of the SRL indicators
Indicator Definition Quantification Significance
Timemanagement( TM) The number of m odules the
participantaccessedpernumber
of days logged in
The number of modules
accessed(1‐5)/thenumberof
thedaysparticipantlog gedin
(1‐3 5)
The higher the indicator, the more
likelyisthestudenttohavespread
attendance over more modules on
fewer days logged in
Taskstrategyforcomprehen
sion(TS)
Theratioofreplaysofindividual
lectures from the total lectures
theparticipantaccessed
The times of video lectures
replayed/thetotalnumberof
the video lectures accessed
The higher the indicator, the more
likelytheparticipantwastorevisit
andreplayvideolectures
Self‐instruction(assessment
driven)(SI‐ad)
Theratioofreplaysofthevideo
lectures after a quiz out of in the
total number of the assessments
theparticipanttook
Thenumberofreplaysofvideo
lectures immediately after an
assessment/the number of the
assessmentstheparticipant
took
The higher the indicator, the more
likely the learner to demonstrate
assessment‐driven learning
behaviours
Self‐instruction of systematic
learning(SI‐sl)
The ratio of the number of lessons
followedintheprescribed
sequence out of total lessons the
participantaccessed
The number of lessons accessed
intheprescribedsequence/the
total number of lessons the
participantsaccessed
The higher the indicator, the more
likelytheparticipanttohave
followedtheprescribedsequence
of the inst ructional de sign
Applic ation(AP) The ratio of the discussions
accessed related to the clinical
problemsset,outofnumberof
clinicalproblemsattended
The number of discussion
threads attended related to the
clinicalproblem/thenumberof
clinicalproblemsattended
The higher the indicator, the more
likely the student to be engaged in
theclinicalproblemsandrelated
discussion
    
|
 5
LAN e t AL.
on the demonstrated behaviours, Auditorsincluded(a)Browser,(b)
Test‐driven,and(c)Digger,whileAttentiveincluded(e)Samplerand
(f)Persistentlearners.
Browserreferstoparticipantswhowentthroughafewactivi
ties mainly at the initial modules of the course, which they did not
revisit such as the participantsof clusters 2, 4 and 8. Te s t‐dr ive n
describesparticipantswhoappearedtorevisitactivitiesespecially
after takinggraded assessments,suchastheparticipants ofclus
ter 7. Diggercharacterises participantswhowere focused in few
specificmaterials such as videolectures,which they however ac
cessedrepeatedlymultipletimes,suchastheparticipantsofclus
ter 3 and 5. Among the Attentive learners, Samplers were students
who did not revisit already accessed content, even after taking as
sessments as in cluster 1. Persistent were learners who were more
frequentlyrevisitingcontent,inparticularrevisitinglecturesafter
taking assessments, such as cluster 6. In terms of achievement, sig
nificant differences were found in final grades, completion rates
as well as acquisition of certificates for learners of the different
clusters(Table3).
4 | DISCUSSION
MOOCs are no longer a novelty in the Higher Education land
scape,withthemos tpopularplatformscollectivel yreachingm ore
than 81 million students already. Nevertheless, learners' behav
iour and dynamics within these new learning environments remain
poorly understood,asresearch remainsstillscarce. Withregards
tohealth care,althoughthere is at present agrowing number of
MOOCs,publishedstudiesarever yfew andmerelyreportingex
pert opinions4 or descriptive case studies 18.Althoughpeer‐re
viewed pub lications based on MOOCs in healthcare disciplines
are emerging, published studies so far discuss mainly non‐clini
calMOOCsindomainssuchasPharmacologyandAlliedHealth,18
MedicineasBusiness,19 Dentistry for non‐dentists,20 while there
isst il la sca rc ity onas cie nt ifi ce va luati onsforcli ni c al di scipl ines. It
becomesthereforeimperativetoapproachsuchlearningenviron
ments in a scientific and systematic manner, in order to determine
th ep ot entia lr ol eofs uchle arnin gp lat for ms in theteac hingf ra me
workofclinicaldisciplines.
FIGURE 2 Numbersofcompletersatdifferentstagesofthecourse.A xisYisexpressedinlogarithmicscale
Cluster TM TS SI‐ad SI‐sl AP Types Types in ge neral
1Low Low Low High Low Sampler Attentive
2High Low Low Low Low Browser Auditor
3Low High High Low Low Digger Auditor
4High Low Low Low Low Browser Auditor
5Low High High Low Low Digger Auditor
6Low Low High High Low Lucubrator Attentive
7High Low High Low Low Doer Auditor
8High Low Low Low Low Browser Auditor
TABLE 2 The eight clusters identified
bytheconst ant‐comparativeanalysis,
further organised in two overriding
patterns“Auditors” and “Attentive”
6 
|
   LAN et AL.
It is diff icult to benchm ark enrolme nt and completio n rates in
a MOOC, as the figures var y significantly among courses and dis‐
cipline s and the drop out r ated is typic ally very hig h. Completio n
ratesamongMOOCstypicallyvarybetween2%and11%.Inhealth
care,arecentreporton4non‐clinicalMOOCsrepor tedhigherthan
the average completion rates, placed between 4.3% and 11%.18
Conclusively,onecannotethatincomparisontothecurrentbench
marks,ImplantDentistrywaswellattendedandachievedexception
ally high completion rate,pointingtoafavourablepotentialofthis
learningplatformfortheteachingofclinicaldisciplines.
Engagement is term dif ficult to define and even more to assess
ina MOOCenvironment, as the termimplies not onlyquantitative
butalsoqualitativecharacteristics.Insomereports,engagementis
define d as the percenta ge of learners th at performe d an "action"
amongthe completenumber ofstudentsenrolled.18 As this action
couldhowever implyjusta singleclickinavideolecture,whatthis
indicator mainly shows is rather how many students enrol without
everbotheringtojustclickonceanythingin the course.Itisappar
ent that we need much better indicators in order to understand and
assesstheactualengagementofstudentswiththelearningexperi
ence. At the same time, there is a limitation in what clickstream data
can offe r,wit hout being sup plemented wi th qualitat ive data from
eachuser.Unfortunately,inCourserait provedto be impossible to
collect background data from the learners which could be analysed
inparallel with the clickstreamand help uspinpointpat terns toac
tuallearnersandtheirdemographicandprofessionalbackground.
One of the major challenges today in evaluating MOOCs is to
find the a ppropriate ins truments , metrics and i ndicators to as sess
the success of such a course. Traditional metrics and assessments
based on completion and student grades do not completely ap
proach suchmassive, student‐driveneducational environments, as
it become s obvious that in p articula r non‐novice prof essionals are
pursuingtheirownspecificlearningobjectivesinattendingMOOCs,
which are not necessarily met by the teacher‐defined pathways to
completion.Ratherthanfocusingoncoursecompletionorpercent
age of resources accessed, the authors defined engagement as the
amountofless onscomplete dou tofallles sonsaccesse d.Thiswasan
attempttocaptureamor equalitativea spectofengagement,aseach
lesson included a variety of resources, videos, discussions boards and
assessment. Attending resources, discussions and assessment even
if in one focused area of the course, might define a different level of
engagementthanaccessing multiple lessonsandmodules but with
out utilis ing all possib ilities of th e learning e nvironment , especia lly
the more interactive ones such as discussions and assessments.
It was obviousthat learners approached the course in very
different ways and there was a wide diversity in all indicators
that define learner's behaviour. The analysis of the clickstream
by clustering certain indicators allowed the identification of some
major patterns of navigation and interaction with the content.
Significantcorrelationswerefoundbetweencertainlearningpat
ternsandstudent'sachievementin termsof gradesandcomple
tion.Atthe same time,asthedemographicandprofessionaldata
ofthe studentswerenotavailable,itwas notpossibletoidentif y
TABLE 3 Distributionoflearnersandengagement,performanceandcompletionratesforeachoftheclusters
Types Sub‐types N
Completed at
least one test Completers Certificate holders EN
Final Grade
(mean) Grade range 1234 5
Auditor 1.Browser 3125 1176(37.63%) 7(0. 2%) 6(0.2%) 9.7 % 0.07 0 ‐ 0.93
2. Digger 195 92(47.18%) 40(20.5%) 40(20.5%) 25.5% 0.25 0 ‐ 1 *
3. Test‐Driven 261 238(91.19%) 37(14.2%) 36(13.8%) 25.4% 0. 26 0 ‐ 1 **
Tot a l 3581 1506(42.06%) 84(2 .3%) 82(2.3%) 11.7% 0.09 0 ‐ 1
Attentive 4.Sampler 973 940(96.61%) 791(81.3%) 766(78.7%) 6 9. 2% 0.87 0.4 0 ‐ 1 ***
5.Persistent 460 451(98.04%) 402(87.4%) 384(83.5%) 69.4 % 0.88 0.33 ‐ 1 ***0.077
Tot a l 1433 1391(97.07%) 1193(83.3%) 1150(8 0.3%) 69. 2% 0.88 0.33 ‐ 1 *
EG,Engagement(%)
FinalGr ade=group'smean(%)
*Meansdif ferenceoffinalgradebetweentypesatthesignificantlevel(P <0.01)
    
|
 7
LAN e t AL.
the types of learners populating each cluster and investigate
their individual objectives and motivation attending the MOOC.
There was a clear division between two patterns: a sequential
navigation of the content as directed by the instructional design
(Atten tive) and a more ir regular and se lective acc ess of specifi c
areas(Auditors).S tu de nt swithat te nt ivepat te rn sa ch ie ve dsignifi
cantly highergradesandwerethemajorityofthecompleters. In
somestudies,suchdistinctpatternshavebeenrelatedtotheSRL
ability of the learner. Littlejohn, et al21inter viewed32MOO Cpar
ticipants who were classified as higher and lowerself‐regulated
learne r (SRLer) base d on an SRL sur vey.T hey found that h igher
SRLearnerswerelesslikelytofollowalinearprogressionthrough
the MOOC , while lower SR Learners were more likely to follow the
course inastructuredway.Nevertheless,in the case of Implant
Dentistry Auditors and Attentive behaviours are also likely to re
flec tdiffere ncesint he pr of ess io nalback gro undan de xp eri enceof
the learners, rather than SRL ability. As was obvious from the non‐
identifiabledemographics available,thecourse was attended by
adiversegroupoflearnersrangingfromundergraduatestudents,
fresh gr aduates and junior clinicians to senior an d experienced
pract itioners. T he first, b eing at a novice level a nd less famili ar
withtheentirecontentweremorelikelyto follow theprescribed
pathway,while thelatter, beingexperienced were more likely to
selectively attendorinteractin theirareas ofinterest.Forexpe
riencedclinicianssuchareascouldbetopicsrelatedtotheirfocus
ofpractice,contentofcertaintype (eg,clinicalprocedurevideos)
orinputfromcertainlecturers.Themotivationforcompletionand
certification might have also been dif ferent among learners with
di ffe ren tp r of ess ion albac k gro und ,aspu rch asi ngave rif ied MO O C
certificate might have been of much higher value to students and
novice learners than experienced and already established pro
fessionals. This might be one of the reasons why so many of the
completersactually completed the course and acquiredthecer
tificatesmuchearlierthantheprescribedtime. Lookingcloser at
31 learner s who complet ed all assessm ents with on one day, we
couldfindthat26wereat tentive andonlyfiveauditors.The per
centage of certificate acquisitions was exceptionally high for a
MOOC,regardlessofthebackgroundofthosewhopu rchasedthe
certificates.This might pointasignificant potentialofthis learn
ingplatforminaccreditationandcredentialingandreflectawider
needespeciallywithin clinical disciplines. Basedontheavailable
demographics offeredfromCourserainAugust2018,64%ofen
rolled le arners were male, w hile 51%of al l learners w ere in the
agebracketbetween 25and34.Furthermore,35%ofthemwere
fulltimestudents,while40%wereemployedfulltime.Intermsof
educ at io n, 20 %a lr ea dyha da Ma st er sd egreeand6%aDoc to rate .
Unfort unately, these dat a are offer ed as a sample b ased on the
morethan3000 0student senrolled uptoAugust 2018andcan
not be in anyway correlated to the actual sample used in this
study,asidentificationofuserswasnotpossible.
Even if Auditor s and Attent ive might refl ect diff erent profes
sional backgrounds, the sub categories might be closer to demon
strating actual SRL, as they describe more specific patterns of
ap pro ach ingth eco nt ent andas ses sme nts. As t udyba sed ona nS RL
su r v eya n dcl i c kstre a mda t a from 4 8 31 M OOCp a r t ici p a ntss h owe d
thathigherSRLearnersweremorelikelytorevisitpreviouslystud
iedcoursematerials,especiallyafterthecourseassessment.22
There is at p resent litt le knowledge as to t he selecti on of the
ideal indicators in order to study engagement and learners' be‐
haviours through clickstream data. The five indicators modelled for
this stu dy were chosen a mong many possib le combinatio ns, in an
attempt t o identify cer tain behav iours based on t he perform ance
phase in th e Zimmerman's m odel. Alth ough there is a ce rtain ris k
of bias in the selection of the five indicators, the fact that some sig‐
nificant differences were finally identified among the achievement
and per formance in ea ch cluster sug gests that th e modelling wa s
relevant to the aims of the study. In future research, these indica‐
torscould be replicated, modified and enriched, hopefully allowing
a deeper u nderstand ing of how to best anal yse clickstr eam data.
Furthermore, theavailability of anonymised demographic and pro
fessionaldata or learners willempower clickstreamdata to offera
muchdeeperunderstandingoflearners'behavioursinaMOOCand
platformsthatofferMOOCsneedtoseriouslyconsiderhowtomake
suchdataavailableforresearchpurposes.
Inconc lusio n,the ex per ien ce wi thI mp lan tD entis trysho we dthat
a MOOC can be a favourable learning environment for the teach
ing of clinic al disciplin es and can at tract a wi de diversit y of learn
ers, ach ieving much hi gher comple tion and cer tificat ion rates th an
typically reported. There appear two fundamental approaches in
the navigation and interaction with the content, one of a sequen
tial engagement and one of more targeted and selec tive access of
resources. The first learning patternissignificantlycorrelated with
higherengagement,performanceandcompletionratesthanthelat
terintheMOOCcontext.Thesepatternsmightbeinfluencedbythe
demogr aphic and profe ssional back ground of the le arners, as wel l
as their motivation for attending the MOOC. Certain subcategories
however, in particular how learners access content in relation to
assessments might be closer related to SRL. In the future, research
based on data from clickstreams combined with an analysis of the de
mograp hi c,prof essionalandmotivation alba ck gro un dofthelear ne rs
couldhelpusbetterunderstandlearners'behavioursandthepoten
tialofMOOCsintheteachingofclinicaldisciplinesinparticular.
ORCID
Nikos Mattheos https://orcid.org/0000‐00017358‐7496
REFERENCES
1. Skiba DJ. Dis ruption in high er education: M assively open on line
courses(MOOCs).Nurs Educ Perspect.2012;33(6):416.
2. Shah D.By The Numb ers: MOOCs in 2018: C lass Central. 2018
[Available at: https://www.class‐central.com/report/mooc‐
stats‐2018/. Accessed D ecember 11, 2018.
 3. Kaplan AM, HaenleinM. Highereducation and the digit al revolu
tion:AboutMOOCs,SPOCs,socialmedia,andtheCookieMons ter.
Bus Horiz.2016;59(4):441‐450.
8 
|
   LAN et AL.
4. Kearney RC , Premaraj S, Smith BM, Olson GW, Williamson AE,
Romanos G.Massiveopenonlinecoursesindentaleducation:t wo
viewpoints: viewp oint 1:massive openonlinecoursesof fer trans
formativetechnologyfordental educationand viewpoint2: mas
siveopenonline coursesarenot readyfor primetime. J Dent Educ.
2016;80(2):121‐127.
 5. WalshK.Massive Open Online Courses on Health andMedicine:
Will They BeSustainable? J Med Int Res. 2014;16(8):e197.https://
doi.org/10.2196/jmir.3798.
6. Walker MP, Duley SI, Beach MM, et al. Dental education eco
nomics: challenges and innovative strategies. J Dent Educ.
20 08 ;72(12) :1440 ‐1449.
 7. MattheosN,deBruynH ,HultinM,et al. Developingimplantden
tistr y education in Europe: the continuumfrom undergraduate to
postgraduateeducationandcontinuingprofessionaldevelopment.
Eur J Dent Educ.2014;18(Suppl1):3‐10.
 8. MattheosN,Albrekt ssonT,BuserD,etal.Teachingandassessment
ofimplantdentis tr yinundergraduateandpostgraduateeducation:a
Europeanconsensus.Eur J Dent Educ.200 9;13(Suppl.1):10‐17.
 9. KooleS, De Bruyn H.Contemporar y undergraduate implant den
tistry education, a systematic review. Eur J Dent Educ .2014;18(Suppl.
1):11‐2 3.
10. KooleS,VandewegheS,MattheosN,DeBruynH.Implantdentistry
inEurope:5yearsaftertheADEEconsensusreport.Eur J Den t Educ.
2014;18(Suppl.1):43‐51.
11. Mattheos N, St efanovic N , Apse P, et al. Potent ial of inform ation
technology in dental education. Eur J Dent Educ. 2008;12(Suppl
1):85‐92.
12. Henningsohn L, Renstrom KL. Introduction to Urolog y. Karolinska
Institute, edX. Available at: https://www.edx.org/course/
introduction‐to‐urology.
13. Du E.Introduction to cataract surgery. Universit y of Michigan.
Coursera. Available at: https://www.coursera.org/learn/cataract‐
sur ge r y.
14. Zimmerman M‐P.Construct validation ofas trategy modelof stu
dent self‐regulated learning. J Educ Psychol.1988;80(3):28 4.
15. Mattheos N, Nat test ad A, Falk‐Nilsson E, Attström R. The inter ac‐
tiveexamination:assessing students' self‐assessmentability. Med
Educ. 2004;38:378‐389.
16. Sanz M.Shapira L. Competencies in implant therapy for the den
tal gra duate. Approp riate educatio nal methods. Eur J Dent Educ.
2009;13(Suppl.1):36‐43.
17. Hew KF, Cheung WS. Students’ an d instructor s’ use of massive
openonlinecourses (MOOCs): Motivations and Challenges.Educ
Res Rev. 2014;1 2:45‐5 8.
18. MaxwellWD, FabelPH,DiazV,etal.Massiveopenonlinecourses
inU.S.healthcareeducation:PracticalTconsiderationsandlessons
learned from implementation. Curr Pharm Teach Learn. 20 18;10 :
73 6 ‐74 3 .
19. RobinsonR.Deliveringamedicalschoolelectivewithmassiveopen
onlinecourse(MOOC)technolog y.Pee rJ. 2016;4:e2343.
20. StokesCW,TowersAC,JinksPV,SymingtonA.DiscoverDentistr y:
encouragingwiderparticipationindentistryusingamassiveopen
onlinecourse(MOOC).Br Dent J.2015;219(2):81.
21. Littlejohn A , HoodN, Milligan C, MustainP.Learning in MOOCs:
Motivations and self‐regulated learning in MOO Cs. The Internet a nd
Higher Education. 2016;29:40 ‐48.
22. Kizilcec RF, Pérez‐Sanagustín M, Maldonado JJ. Self‐regulated
learningstrategiespredict learnerbehavior and goalattainment in
MassiveOpenOnlineCourses.Comput Educ. 2017;10 4:18‐33 .
How to cite this article:LanM,HouX,QiX,MattheosN.
Self‐regulated learning strategies in world's first MOOC in
implantdentistr y.Eur J Dent Educ. 2019;00:1–8. h t tp s : //d o i.
org /10.1111/ej e.1 2428
... 4 Dentistry is no exception, and has made leaps and bounds in the development and application of associated technologies, such as haptic and virtual simulation, augmented reality for clinical training and incorporation of massive open online courses (MOOCs). 5,6 In the diverse context of healthcare education, e-learning has been found to be at least as effective as traditional instructor-led classroom activities. [3][4][5] However, despite the growing body of evidence, the use of e-learning is highly variable amongst medical and dental schools. ...
... [3][4][5] However, despite the growing body of evidence, the use of e-learning is highly variable amongst medical and dental schools. 6,7 Although computer-based technology is commonly leveraged in teaching and learning, undergraduate dental education is typically conducted in a physical face-to-face manner. 8 Whilst students consider e-learning as a positive supplement to traditional methods of learning, teaching staff often harbour passive or even negative attitudes towards e-learning. ...
Article
Introduction The COVID-19 pandemic has necessitated an unprecedented shift from face-to-face teaching to e-learning. Previous surveys revealed the negative impact of COVID-19 on dental education and the physical and psychological well-being of dental students. This qualitative study aimed to investigate the perspectives of dental educators towards e-learning during the pandemic, and the impact of this experience on their future adoption of e-learning. Methods Semi-structured interviews with dental educators from the National University of Singapore were conducted over Zoom. Audio-recordings were transcribed verbatim and subjected to thematic analysis. Data saturation was reached. Consolidated Criteria for Reporting Qualitative Research (COREQ) was followed. Results 15 out of 22 (68%) eligible dental educators were interviewed. Educators had minimal prior e-learning experience. They encountered difficulties in engaging students, assessing students’ understanding and adapting their teaching. A practical challenge was to ensure the well-rounded training of competent dentists with adequate patient-interaction skills through e-learning. Self-motivation of the audience, class size, type of teaching and complexity of the material were perceived as factors influencing the suitability of the e-learning format. Educators reported an increased confidence after this emergency e-learning experience. Some considered sustaining or expanding e-learning in their future teaching practice and highlighted the need for continued investment and institutional support, training on the pedagogy of e-learning modalities, and curriculum redesign to accommodate blended learning approaches. Conclusions Although the shift to e-learning during COVID-19 pandemic presented a myriad of challenges, dental educators gained experience and confidence which may accelerate the pace of future e-learning adoption and innovation.
... For example, Hmedna et al. (2019) use clustering analysis, DT, RF, K-nearest neighbor and a neural network to propose a predictive model for learning styles that can distinguish between learners in terms of their degree of preference for each learning style. Also, cluster analysis was used by Hmedna et al. (2019), Khalil and Ebner (2017), Lan et al. (2019) and Wang et al. (2018) to propose an approach and make a prediction to enhance educational process mining centered on the collected data from logs and learners' engagement. ...
Article
Full-text available
Purpose The general goal of this paper is to help educators understand the importance of MOOC training to school teachers and their hypothetical value for predicting the use of teaching strategies in the face-to face-classroom teaching. With this purpose, the study is guided by two research questions: (1) Are there different patterns of preferences in teaching strategies among school teachers when they participate in MOOC training? (2) To what extent the attributes selected from the data set to visualize patterns are suitable for the formation of models? Design/methodology/approach Peer instruction (PI) and think-pair-share (TPS) strategies might bring positive outcome during classroom teaching. When introduced properly to school teachers, these strategies help students see reason beyond the answers by sharing with other students their response and thus learning from each other. This study aims to use educational data mining (EDM) techniques to visualize patterns and propose models based on the teaching strategies training to be used in face-to-face classroom teaching. The data set includes five attributes extracted from school teachers' Massive Open Online Courses (MOOC) training interaction data. All analysis and visualization were performed using Python, and the models were evaluated using fivefold cross-validation. The modeling performance of three different algorithms (decision tree, random forest and K -means) was tested on the data set. The results of model accuracy were presented as a confusion matrix. The experimental results indicate that the random forest (RF) algorithm outperforms decision tree (DT) and K -means algorithms with an accuracy of 96.4%. Findings This visualization information on the grouping of school teachers based on the teaching strategies serves as an essential reference for school teachers choosing between the two types of strategies within their face-to-face classroom settings. Teachers may use the finding obtained for an initial understanding of which strategies will fit well on their classroom teaching based on their subject majors. Moreover, the classification accuracy rates of DT and RF algorithms were the highest and considered highly significant to allow developing predictive models for similar EDM cases and provide a positive effect on the learning environment. Research limitations/implications This visualization information on the grouping of school teachers based on the teaching strategies serves as an essential reference for school teachers choosing between the two types of strategies within their face-to-face classroom settings. Teachers may use the finding obtained for an initial understanding of which strategies will fit well on their classroom teaching based on their subject majors. Unlike predicting different patterns of preferences in teaching strategies among school teachers when they participate in MOOC training, using visualization was found much more comfortable, less complicated and more time-efficient for small data sets. Moreover, the classification accuracy rates of decision tree and random forest algorithms were the highest and considered highly significant to allow developing predictive models for similar educational data mining cases and provide a positive effect on the learning environment. Practical implications DT classifier in this study ranks first before model optimization, but second after model optimization in terms of accuracy. Therefore, the goodness of the indicators needs to be further studied to devise a reasonable intervention. Social implications A different group of school teachers attending training on teaching strategies in a different online platform is required in future research to cross-validate these study findings. Originality/value The authors declare that this submission is their own work and to the best of their knowledge it contains no materials previously published or written by another person, or substantial proportions of material that have been accepted for the award of any other degree at any other educational institution.
... MOOCs break the routine of the traditional teaching mode, stimulate students' enthusiasm for learning English grammar, and develop their divergent thinking to learn by themselves. Their ideas and questions are uploaded on the network platform and answered immediately, improving their learning efficiency (Lan et al., 2019). The platform focuses on the application of information technology to English grammar teaching. ...
Article
Full-text available
The study aims to explore the roles of Massive Open Online Courses (MOOCs) based on deep learning in college students’ English grammar teaching. The data are collected using a survey. After the experimental data are analyzed, it is found that students have a low sense of happiness and satisfaction and are unwilling to practice oral English and learn language points in English learning. They think that college English learning only meets the needs of CET-4 and CET-6 and does not take it as the ultimate learning goal. After the necessity and problems in English grammar teaching are discussed, the advantages of flipped classrooms of MOOCs are discussed in English grammar teaching. A teaching platform is constructed to study the foreign language teaching mode under MOOCs, and classroom teaching is combined with the advantages of MOOCs following the principle of “teaching students according to their personalities” to improve the listening, speaking, reading, writing, and translation skills of foreign language majors. The results show that high-quality online teaching resources and the deep learning-based teaching environment can provide a variety of interactive tools, by which students can communicate with their peers and teachers online. Sharing open online communication, classroom discussion, and situational simulation can enhance teachers’ deep learning ability, like the ability to communication and transfer thoughts. Constructivism with interaction as the core can help students grasp new knowledge easily. Extensive communication and interaction are important ways for learning and thinking. The new model provides students with profound learning experience, expands the teaching resources of MOOCs around the world, and maximizes the interaction between online and offline teachers and students, making knowledge widely rooted in the campus and realizing the combination of online resources and campus classroom teaching. Students can learn the knowledge through autonomous learning and discussion before class, which greatly broadens the learning time and space. In the classroom and after class, the internalization and sublimation of knowledge are completed through group cooperation, inquiry learning, scenario simulation, display, and evaluation, promoting students to know about new knowledge and highlighting the dominant position of students.
... MOOCs have been reported to favor participants with higher education (23). Previous studies have also found that MOOC students have the advantages of higher educational credentials (38) and that most MOOC students are graduates with a bachelor's degree, while the remainder are older "continuing learners" (16,(39)(40)(41). There are even some interesting studies suggesting that parents' literacy has an influence on the completion rates of MOOCs (20). ...
Article
Full-text available
Objectives: During the pandemic, quarantine has led to the lockdown of many physical educational institutions. Thus, massive open online courses (MOOCs) have become a more common choice for participants. MOOCs are often flagged as supplemental methods to educational disparities caused by regional socioeconomic distribution. However, dissenters argue that MOOCs can exacerbate the digital divide. This study aimed to compare the participants' performance before and after the outbreak of COVID-19, analyze the impact of the epidemic on online education of cosmetic dermatology from the view of the regional socioeconomic distribution, and investigate whether MOOCs exacerbate the digital divide in the COVID-19 epidemic. Methods: The study was conducted in participants of the MOOC course Appreciation and Analysis of Cosmetics from January 2018 to December 2020. Based on the platform data and official socioeconomic statistics, correlation of multivariate analysis was used to determine the factors related to the number of total participants. A panel regression model and stepwise least squares regression analysis (STEPLS) were employed to further analyze the relationship between GDP, population, number of college students and number of total participants in different years in the eastern, central and western regions of China. Results: The number of total participants in 2020 surged 82.02% compared with that in 2019. Completion rates were generally stable in 2018 and 2019 before the COVID-19 pandemic and significantly decreased in 2020 after the outbreak of the pandemic. GDP was the most important socioeconomic factor that determined the total number of participants and it was positively related to the total number of participants before and after the outbreak of the pandemic. The number of college students was unrelated to the total number of participants before the epidemic, and after the outbreak of COVID-19 in 2020, the number became positively related in all regions of China. Conclusions: This study shows that the epidemic pushes more people to choose MOOCs to study cosmetic dermatology, and online education could exacerbate rather than reduce disparities that are related to regional and socioeconomic status in the cosmetic field in the COVID-19 pandemic.
... 12 This combined model is able to fully mobilize the enthusiasm and participation of students and effectively improve their ability to learn and find and solve problems independently. [13][14][15][16] There is timely interaction in the MOOC process, including interaction between students and interaction between teachers and students. ...
Article
Full-text available
Background: This study aims to evaluate the feasibility, acceptability, and effectiveness of massive open online courses (MOOCs) in combination with flipped classroom teaching in the standard training of resident physicians (resident physician trainees). Methods: A total of 110 resident physician trainees enrolled in 2018, with a major in Internal Medicine, were selected and divided into a control group (n = 55) who experienced traditional teaching methods and an experimental group (n = 55) who experienced MOOCs plus flipped classroom teaching. Their post-class test scores and satisfaction questionnaires were compared. Results: The test scores (80.60 ± 7.65) of resident physician trainees in the experimental group were higher than those of the control group (77.05 ± 8.08), and the difference was statistically significant (P < 0.05). The experimental group trainees were highly satisfied with the MOOC + flipped classroom program. Conclusion: MOOCs in combination with flipped classroom teaching can increase the effectiveness of teaching in the standard training for resident physicians and trainees' comprehensive clinical diagnosis and treatment ability.
... It is predicted that this sudden change in education will have a significant impact on education worldwide, even after this pandemic ends [55]. A previous study found that enhanced self-regulation or self-efficacy could boost the efficacy of online learning [59]. We need to prepare the changeable educational stream to improve students' self-efficacy and clinical reasoning. ...
Article
Full-text available
Clinical reasoning is a vital competence for nursing students, as it is required for solving problems arising in complex clinical situations. Identifying the factors that influence nursing students’ clinical reasoning competence in the social context can help their implicit educational needs. Therefore, this study aimed to determine the factors associated with developing clinical reasoning competency among undergraduate nursing students. In total, 206 senior nursing students were included in this study. Self-reported measures were used to obtain data on participants’ clinical reasoning competence, problem-solving abilities, academic self-efficacy, and level of clinical practicum stress. Relationships among continuous variables were analyzed using Pearson’s correlation coefficients. A multiple linear regression analysis was conducted to identify factors related to clinical reasoning competence. Our findings show that participants with better problem-solving abilities and academic self-efficacy perceived themselves as having higher levels of clinical reasoning competence. Nursing students with lower clinical practicum stress reported higher clinical reasoning competence. Significant factors identified were younger age and subcategories of problem-solving ability such as problem clarification, alternative solution development, planning/implementation, and self-regulated efficacy. Our findings highlight essential factors necessary for developing a nursing curriculum that contributes to professional nurses’ clinical reasoning competence.
... The conceptualization of self-study includes the following: it should be self-initiated and focused; improvement-aimed; interactive; and it should include multiple, mainly qualitative, methods 3 . Under this autonomous learning environment, students` self-monitoring and organization become of paramount importance, demanded a self-regulated learning ability 4,5 . The degree to which students are metacognitively, motivationally and behaviourally active in steering their own learning is described as self-regulated learning 6 . ...
Article
Full-text available
Este artigo objetivou descrever brevemente uma experiência com a metodologia de estudo autônomo realizada no Programa de Graduação Internacional de Odontologia (IDDP) em uma Universidade no Canadá. Esta abordagem encorajou o aprendizado autorregulado dos estudantes. Devido à pandemia COVID-19 as aulas “on-site” da faculdade de odontologia foram descontinuadas. Os estudantes do programa IDDP eram permitidos frequentar o prédio da faculdade somente para realizar as atividades pré-clínicas (e eram supervisionados por apenas um professor de cada vez). Como a turma de 2020 do programa IDDP era pequena (2 alunos), o diretor do programa e os professores consideraram que o estudo autônomo seria uma ideia apropriada. Percebeu-se que os estudantes tiveram sucesso em autorregular o aprendizado. Como por exemplo: usaram suas anotações, monitoraram a compreensão do material teórico disponibilizado, fizeram perguntas etc. A experiência com os estudantes do programa IDDP mostrou que a oportunidade de estudo autônomo se caracterizou como um ambiente favorável para os professores usarem nas disciplinas pré-clínicas.
Article
Full-text available
The outbreak of the two-year corona virus has made a great difference on existing methods of learning and instruction. Online education has become a crucial role to maintain non-stop learning after the post-epidemic period. The advanced technologies and growing popularity of network equipment have made it easy to deploy remote connections. However, teachers still face challenges when they actually implement distance courses. During the learning process, the quality of learning can be improved if the researchers consider multiple factors, including emotions, attitudes, engagement, cognition, neuroscientific and cultural psychology. After analyzing these factors, instructors can have better understanding of students’ mental building and cognitive understanding in their process of learning, and be familiar with the way of interaction with students and appropriately adjust their teaching. Therefore, the current study established a learning system that aimed to understand learners’ emotional signals during learning by applying the adaptive-feedback emotional computing technology. The purpose of the system was to allow learners to (1) self-examine their learning condition, (2) enhance their self-directed learning, (3) help learners who are in negative learning emotions or settings to lower anxieties, and (4) promote their learning attitudes and engagement. Result showed that the system with the adaptive-feedback emotional computing technology has significantly improved the learning effectiveness, lowered learning anxieties and increased students’ self-directed learning.
Article
Health disparities disproportionately effect patients in racial and ethnic minority groups, and these disparities are linked to economic, environmental, and social disadvantage. It is widely known that health disparities impact patients with allergic and immunologic conditions, yet universal and comprehensive training in health disparities is lacking. More robust educational opportunities are needed to fully equip trainees with tools to recognize and develop effective strategies to reduce the burden of health disparities. Also, there are no universal standards or requirements for professional medical boards in their respective maintenance of certification programs that will ensure ongoing training for practicing providers that will help them identify and manage individual or societal issues such as social determinants that contribute to health disparities. Further, the long-term impact of systematic discrimination, implicit and overt bias, and medical mistrust among populations most often effected by disparities compounds the complexity of the methods and types of training that is desperately needed to overcome health disparities. We provide a commentary on important topics that should be addressed during Allergy and Immunology training and beyond. We further highlight strategies and tools that should be utilized to tackle this important issue effecting millions of patients under our specialty care. It is past time for us to go beyond the bedside and comprehensively integrate health disparities training in our fellowship programs and in our practices.
Article
Background With the recent challenges due to the Coronavirus 2019 outbreak, distance learning has been largely introduced in healthcare sciences curricula, and universities have been called upon to share learning opportunities with each other to ensure continuity of education and delivery of new graduates to the health system. However, decisions about its introduction should be supported by up-to-date evidence capable of providing an overview of available knowledge. Objectives To map the (a) state of research on massive open online courses in undergraduate and postgraduate health sciences education, (b) evaluation methods and tools used to measure learning outcomes, and (c) factors increasing their effectiveness as documented to date. Design A rapid review following the preferred reporting items for systematic reviews and meta-analysis guidelines. Methods PubMed, the Cumulative Index to Nursing and Allied Health Literature, Cochrane, Scopus, PsycInfo and Medline (via Ovid) were searched. Primary studies reporting one or more massive open online course (1) devoted to undergraduate and/or postgraduate students in nursing and healthcare sciences (2), written in English (3) with abstract available (4) and published up to February 18th, 2020 were all included. After having assessed the need for a review and the topic itself (a), the literature search was performed (b), studies were screened and selected (c), data was extracted (d), and the findings were summarised (e). Results Thirty-six studies emerged with mainly an explorative/descriptive or case study design. The courses have been developed mainly by universities alone or in collaboration with institutions mainly in US, Sweden and the UK. Their delivery has been performed at multi-national levels, mainly in English, and with a number of participants ranging from 45 to > 23,000. The duration spanned from two weeks to six months on clinical topics (e.g., emergency medicine) to methods (e.g., statistics). The target audience has been mainly mixed, including students, healthcare professionals, and lay citizens. Evaluation methods and tools have been described in 28 studies, and multiple-choice questions were most frequently adopted. Factors affecting the effectiveness of massive open online courses have been identified analysing the courses themselves and the participants. Conclusion Massive open online courses have recently started to be studied in healthcare sciences: these can be useful to educate students, mainly as elective courses, and to educate a massive audience, thus embodying the third mission of the university. The complexity of factors increasing effectiveness suggests the need for a multidisciplinary approach both in their design and implementation.
Article
Full-text available
The use of information technology (IT) in dentistry is far ranging. In order to produce a working document for the dental educator, this paper focuses on those methods where IT can assist in the education and competence development of dental students and dentists (e.g. e-learning, distance learning, simulations and computer-based assessment). Web pages and other information-gathering devices have become an essential part of our daily life, as they provide extensive information on all aspects of our society. This is mirrored in dental education where there are many different tools available, as listed in this report. IT offers added value to traditional teaching methods and examples are provided. In spite of the continuing debate on the learning effectiveness of e-learning applications, students request such approaches as an adjunct to the traditional delivery of learning materials. Faculty require support to enable them to effectively use the technology to the benefit of their students. This support should be provided by the institution and it is suggested that, where possible, institutions should appoint an e-learning champion with good interpersonal skills to support and encourage faculty change.
Article
Full-text available
Introduction: The educational technology of massive open online courses (MOOCs) has been successfully applied in a wide variety of disciplines and are an intense focus of educational research at this time. Educators are now looking to MOOC technology as a means to improve professional medical education, but very little is known about how medical MOOCs compare with traditional content delivery. Methods: A retrospective analysis of the course evaluations for the Medicine as a Business elective by fourth year medical students at Southern Illinois University School of Medicine (SIU-SOM) for the 2012-2015 academic years was conducted. This course was delivered by small group discussions for 2012-2014 and delivered via MOOC technology in 2015. Learner ratings were compared between the two course delivery methods using routinely collected course evaluations. Results: Course enrollment has ranged from 6-19 students per year in the 2012-2015 academic years. Student evaluations of the course are favorable in the areas of effective teaching, accurate course objectives, meeting personal learning objectives, recommending the course to other students, and overall when rated on a 5 point Likert scale. Ratings show no statistically significant difference between the small group or MOOC format versions of the course (p = 1.00 for all comparisons). Discussion: Students found this elective to be an effective means of meeting their personal learning objectives when delivered in a small group discussion format or by using MOOC technology. The primary advantage of this new course format is flexibility of time and place for learners, allowing them to complete the course objectives when convenient for them. The course evaluations indicate this is a change that is acceptable to the target audience. Conclusions: This study shows that learner evaluations of a fourth year medical school elective course do not significantly differ when delivered in small group discussions or via MOOC technology. This suggests that MOOCs may be a reasonable format to deliver medical school courses.
Article
Full-text available
Introduction: The educational technology of massive open online courses (MOOCs) has been successfully applied in a wide variety of disciplines and are an intense focus of educational research at this time. Educators are now looking to MOOC technology as a means to improve professional medical education, but very little is known about how medical MOOCs compare with traditional content delivery. Methods: A retrospective analysis of the course evaluations for the Medicine as a Business elective by fourth-year medical students at Southern Illinois University School of Medicine (SIU-SOM) for the 2012–2015 academic years was conducted. This course was delivered by small group flipped classroom discussions for 2012–2014 and delivered via MOOC technology in 2015. Learner ratings were compared between the two course delivery methods using routinely collected course evaluations. Results: Course enrollment has ranged from 6–19 students per year in the 2012–2015 academic years. Student evaluations of the course are favorable in the areas of effective teaching, accurate course objectives, meeting personal learning objectives, recommending the course to other students, and overall when rated on a 5-point Likert scale. The majority of all student ratings (76–95%) of this elective course are for the highest possible choice (Strongly agree or Excellent) for any criteria, regardless if the course was delivered via a traditional or MOOC format. Statistical analysis of these ratings suggests that the Effective Teacher and Overall Evaluations did not statistically differ between the two delivery formats. Discussion: Student ratings of this elective course were highly similar when delivered in a flipped classroom format or by using MOOC technology. The primary advantage of this new course format is flexibility of time and place for learners, allowing them to complete the course objectives when convenient for them. The course evaluations suggest this is a change that is acceptable to the target audience. Conclusions: This study suggests that learner evaluations of a fourth-year medical school elective course do not significantly differ when delivered by flipped classroom group discussions or via MOOC technology in a very small single center observational study. Further investigation is required to determine if this delivery method is an acceptable and effective means of teaching in the medical school environment.
Article
Full-text available
Distance learning—that is, providing education to students who are separated by distance and in which the pedagogical material is planned and prepared by educational institutions—is a topic of regular interest in the popular and business press. In particular, MOOCs (Massive Open Online Courses), which are open-access online courses that allow for unlimited participation, as well as SPOCs (Small Private Online Courses), are said to have revolutionized universities and the corporate education landscape. In this article we provide a nuanced analysis of the phenomenon of online distance learning. We first provide an overview of its historical evolution, and subsequently define and classify key concepts. We further discuss in detail the optimal target group in terms of participating students and teaching professors and propose corresponding frameworks for driving intrinsic student motivation and for choosing a successful online teacher. We also outline the benefits that institutions can achieve by offering online distance learning. Finally, we speak about the specific connection between online distance learning and social media by focusing on the difference between MOOCs based on traditional lecture formats (xMOOCs) and connectivist cMOOCs.
Article
Background and purpose Massive Open Online Courses (MOOCs) offer an innovative approach to pharmacy education and are expected to challenge traditional pedagogy and foundational knowledge acquisition practices. A survey of the literature reveals no current publications describing implementation of MOOCs in pharmacy education and limited information about MOOC implementation in other healthcare disciplines in the United States. Educational activity and setting A few colleges of pharmacy (COPs) and other health professions’ educational programs have recently started offering MOOCs. Findings Herein we provide an overview of MOOCs and describe the early implementation stages of MOOCs being conducted at two COPs, an interprofessional MOOC, and a variety of MOOCs offered by a public health program. This overview and the four case studies on MOOC implementation in healthcare education provide practical information about course development, descriptions of selected course engagement outcomes, insight into lessons learned by the institutions, and practical considerations for development of future MOOCs. Discussion MOOCs prompt diversification of models of teaching and learning, transformation of pedagogical frameworks, and innovation in the scholarship of teaching and learning. Summary MOOCs offer exciting opportunities to distribute knowledge on a massive and global scale to a diverse population of learners.
Article
Individuals with strong self-regulated learning (SRL) skills, characterized by the ability to plan, manage and control their learning process, can learn faster and achieve higher grades compared to those with weaker SRL skills. SRL is critical in learning environments that provide low levels of support and guidance, as is commonly the case in Massive Open Online Courses (MOOCs). Learners can be trained to engage in SRL and further supported by facilitating prompts, activities, and tools. However, effective implementation of learner support systems in MOOCs requires an understanding of which SRL strategies are most effective and how these strategies manifest in learner behavior. Moreover, identifying learner characteristics that are predictive of weaker SRL skills can advance efforts to provide targeted support without obtrusive survey instruments. We investigated SRL in a sample of 4831 learners across six MOOCs based on individual records of overall course achievement, interactions with course content, and survey responses. Results indicated that goal setting and strategic planning predicted attainment of personal course goals, while help seeking appeared to be counterproductive. Learners with stronger SRL skills were more likely to revisit previously studied course materials, especially course assessments. Several learner characteristics, including demographics and motivation, predicted learners’ SRL skills. We discuss implications and next steps towards online learning environments that provide targeted support and guidance.
Article
This point/counterpoint article discusses the strengths and weaknesses of incorporating Massive Open Online Courses (MOOCs) into dental education, focusing on whether this relatively new educational modality could impact traditional dental curricula. Viewpoint 1 asserts that MOOCs can be useful in dental education because they offer an opportunity for students to learn through content and assessment that is delivered online. While specific research on MOOCs is limited, some evidence shows that online courses may produce similar learning outcomes to those in face-to-face courses. Given that MOOCs are intended to be open source, there could be opportunities for dental schools with faculty shortages and financial constraints to incorporate these courses into their curricula. In addition to saving money, dental schools could use MOOCs as revenue sources in areas such as continuing education. Viewpoint 2 argues that the hype over MOOCs is subsiding due in part to weaker than expected evidence about their value. Because direct contact between students, instructors, and patients is essential to the dental curriculum, MOOCs have yet to demonstrate their usefulness in replacing more than a subset of didactic courses. Additionally, learning professionalism, a key component of health professions education, is best supported by mentorship that provides significant interpersonal interaction. In spite of the potential of early MOOC ideology, MOOCs in their current form require either further development or altered expectations to significantly impact dental education.
Article
Massive open online courses (MOOCs) require individual learners to be able to self-regulate their learning, determining when and how they engage. However, MOOCs attract a diverse range of learners, each with different motivations and prior experience. This study investigates the self-regulated learning (SRL) learners apply in a MOOC, in particular focusing on how learners' motivations for taking a MOOC influence their behaviour and employment of SRL strategies. Following a quantitative investigation of the learning behaviours of 788 MOOC participants, follow-up interviews were conducted with 32 learners. The study compares the narrative descriptions of behaviour between learners with self-reported high and low SRL scores. Substantial differences were detected between the self-described learning behaviours of these two groups in five of the sub-processes examined. Learners' motivations and goals were found to shape how they conceptualised the purpose of the MOOC, which in turn affected their perception of the learning process.
Article
This paper describes how a relatively new style of online learning, a massive open online course (MOOC), may be used to raise aspirations and widen participation in dental professions. A MOOC was designed and run with the aim of engaging prospective students of dental professions in learning and discussion. Over 4,200 learners signed up, and 450 students fully completed this first run of the course. The course attracted a significantly younger demographic than is typical for MOOCs, and nearly a third who responded to the pre-course survey reported they were doing the course specifically as preparation for a dental degree. The approach also provided a platform for public engagement on the subject of dentistry with participants, both dental professionals and members of the public, contributing to discussion around the learning materials from around the world, providing a unique, internationalised perspective of oral healthcare for learners. This study shows that there is genuine potential for MOOCs to involve people from disadvantaged backgrounds in higher education by offering free, accessible, enjoyable and engaging educational experiences. The data gives us cautious optimism that these courses can play a significant role within a platform of other WP interventions.