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Learning Analytics Cockpit for MOOC Platforms

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Abstract and Figures

Within the sector of education, Learning Analytics (LA) has become an interdisciplinary field aiming to support learners and teachers in their learning process. Most standard tools available for Learning Analytics in Massive Open Online Courses (MOOCs) do not cater to the individual's conception of where Learning Analytics should provide them with insights and important key figures. We propose a prototype of a highly configurable and customizable Learning Analytics Cockpit for MOOC-platforms. The ultimate goal of the cockpit is to support administrators, researchers, and especially teachers in evaluating the engagement of course participants within a MOOC. Furthermore, comparing learner's individual activity to course wide average scores should enhance the self-assessment of students, motivate their participation, and boost completion rates. Therefore, several metrics were defined which represent and aggregate learner's activity. From this predefined list, stakeholders can customize the cockpit by choosing from multiple visualization widgets. Although, the current prototype focuses only on a minimal group of stakeholders, namely administrators and researchers. Therefore, it is designed in a modular, highly configurable and customizable way to ensure future extensibility. It can be strongly carried out that customization is integral to deepen the understanding of Learning Analytic tools and represented metrics, to enhance the student's learning progress.
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Learning Analytics Cockpit for MOOC-platforms
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Karin Maier
Educational Technology
Graz University of Technology, Münzgrabenstraße 35a, 8010 Graz, Austria Europe
karin.maier@student.tugraz.at
Philipp Leitner
Educational Technology
Graz University of Technology, Münzgrabenstraße 35a, 8010 Graz, Austria Europe
philipp.leitner@tugraz.at
Martin Ebner
Educational Technology
Graz University of Technology, Münzgrabenstraße 35a, 8010 Graz, Austria Europe
martin.ebner@tugraz.at
Abstract
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Introduction
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J%'12(/,4'03'"3,)#0,3'$("'1E234/,34'4#%#'#**"3*#%)(+'#+4'."(E311)+*')+'%23'0#EG*"(/+48''
V23"3'I/1%'03'#'/+)%\%31%34'4#%#'#**"3*#%)(+8'
6(4/,#"'#+4'E(+$)*/"#0,3'1-1%3I'431)*+')1'#,1('#'."3E(+4)%)(+8'
V23'!#120(#"4'431)*+')1'E2((13'$("'#'$,3^)0,3'E/1%(I)[#%)(+8'
])+#,,-&'#+')+%3"#E%)X3'X)1/#,'"3."313+%#%)(+'($'#**"3*#%34'4#%#'12(/,4'03'.(11)0,38'
Concept
V23'LA(Cockpit'."(%(%-.3'12(/,4'03')+%3*"#0,3')+%('%23'6HHF'.,#%$("I'=6((4,3B'#1'3#1),-'#1'
.(11)0,3&'*/#"#+%33)+*'I#)+%#)+#0),)%-'#1'Z3,,'#1'3^%3+4)0),)%-8'L#134'(+'%23'"3a/)"3I3+%1'%23'
."(%(%-.3'2#1'033+'4)X)434')+%('%23'$(,,(Z)+*'I#)+'E(I.(+3+%15'%23'4#%#'#**"3*#%)(+&'I3%")E1'%('
)43+%)$-'%23'G3-'#1.3E%1'($'%23'LA(Cockpit&'#+4'1/013a/3+%,-'%23'X)1/#,)[#%)(+'($'%23'G3-'$)*/"31')+'#'
4#120(#"4',#-(/%8'
@8 !#%#'D**"3*#%)(+5'V23'LA(Cockpit'+3341'%('*#%23"')%1'4#%#13%1'$"(I'%23'.,#%$("I'4#%#0#13_1'
%#0,318'6((4,3'1%("31'#+4'."(c3E%1'%23'/13"'#E%)X)%-'#+4'1-1%3I'3X3+%1')+'4)$$3"3+%'%#0,318'
e3*/,#"'#+4'1E234/,34'4#%#'#**"3*#%)(+'E#+'03'4(+3'Z)%2'6((4,3_1'1E234/,34'%#1G1'Z2)E2'
#"3'3113+%)#,,-'E"(+c(01'"/+'$"(I'%23'96N')+1%#+E3')%13,$8':,/*)+1'E#+'#44'%23)"'(Z+'%#1G1'%('
%2#%&'%23'."(%(%-.3'#441')%1'E"(+c(0'$("'4#),-'4#%#'#**"3*#%)(+8'
>8 63%")E15'V23'."(%(%-.3'$(E/131'(+'#'I)+)I#,'*"(/.'($'1%#G32(,43"1&'+#I3,-'#4I)+)1%"#%("1'
#+4'"313#"E23"18'kd(Z'#E%)X3'#"3'/13"1'(+'%23'.,#%$("Il_'("'kJ1'%23'a/)['/134'#%'#,,l_'\'V2313'
!"#$%&'(")*)+#,,-'./0,)1234')+5'6#)3"&'78&'93)%+3"&':8&';'<0+3"&'68'=>?@AB8'C93#"+)+*'D+#,-%)E1'F(EG.)%'$("'6HHF':,#%$("I1C8'J+'
Emerging(Trends(in(Learning(Analytics8'93)43+&'K)343",#+435'L"),,'M'N3+138'4()5'2%%.15OO4()8("*O@?8@@PQOARSA??TQAA>RQU?@T'
#+4'1)I),#"'a/31%)(+1'#")13'#+4'E#+'03'#+1Z3"34')+'#'E,3#"'#+4'1)I.,3'Z#-8'V23'I#)+'
I(%)X#%)(+'$("'%23'LA(Cockpit'I3%")E1')1'%('43$)+3'G3-'#1.3E%1'(+'2(Z'%('I3#1/"3'%23'
,3#"+3"1_',3X3,'($'3+*#*3I3+%8'
6(1%'($'%23',3#"+)+*'I#+#*3I3+%'1-1%3I1&')+E,/4)+*'6((4,3&'"3E("4',3#"+3"1_'#E%)X)%)31'0-'
*#%23")+*')+$("I#%)(+'#0(/%'3X3+%1'#+4'1%(")+*'%23I')+'3^2#/1%)X3',(*'%#0,318':"(E311)+*'
%23I')+%('#'2)*23"',3X3,'($'#01%"#E%)(+')I."(X31')+1)*2%')+%('%23',3#"+)+*'."(E3118'=6/+([\
63")+('3%'#,8&'>?@QB8'V23',3#"+3"_1'032#X)(/"'E#+'03'13.#"#%34')+%('4)$$3"3+%'E#%3*(")31'
4)"3E%,-'"3,#%34'%('%23'($$3"34'#E%)X)%)31')+'%23'E(/"131'($'%23'6HHF8'J+'%23'."(%(%-.3&'Z3'
$(E/1'(+'#'*,(0#,'#01%"#E%)(+',3X3,'\'#**"3*#%)+*'G3-'#1.3E%1'.,#%$("I'Z)43'#+4'."(.(13'%2"33'
0#1)E'I3%")E18'
6@5'9(*)+1'(X3"'%)I3'
6>5'm/)['N%#%)1%)E1'
6Q5']("/I':(1%']"3a/3+E-'
V23'4#%#'#**"3*#%)(+'Z),,'2#..3+'4#),-&'3X3+')$'%23'X)1/#,)[#%)(+'I)*2%'."(X)43'#'Z33G,-'%)I3'$"#I38'
H+3'($'%23'"3a/)"3I3+%1'Z#1'"3,)#0),)%-'#+4'Z)%2'4#),-'#**"3*#%)(+'#1'E"(+c(0'Z3'E#+'*/#"#+%33'#+'
3$$)E)3+%'#+4'43%#),34',(**)+*'."(E3118'!)1%)+E%'/13"')+$("I#%)(+')1'+(%',(**34&'(+,-'#**"3*#%34'
+/I03"1'#"3'.#"%'($'%23'I3%")E18']/"%23"&'#,,',(*'$),31'#"3'43,3%34')+'#'."3\43$)+34'%)I3')+%3"X#,'%('
E(I.,-'Z)%2'.")X#E-'"3*/,#%)(+18':")X#E-'#+4'3%2)E1'#"3'#'43,)E#%3'%(.)E'Z23+'#..,-)+*',3#"+)+*'
#+#,-%)E1')+'6HHF1'#+4'#1'#,,'4#%#'#"3'/13"\"3,#%34')%'2#1'%('03'2#+4,34'Z)%2'E(+$)43+%)#,)%-'#+4'
E#/%)(/1+311'=:#"4(';'N)3I3+1&'>?@TB8'
Design reflections
V23'$3#%/"31'#*"334'(+')+'%23'E(+E3.%'"3a/)"3'%23'LA(Cockpit'%('."(X)43'#'2)*2\,3X3,'(X3"X)3Z'($'
#X#),#0,3'I3%")E1'#1'Z3,,'#1'%23'$),%3"'("'E/1%(I)[#%)(+'(.%)(+18'D'4#120(#"4'431)*+'Z),,'."(X)43'#'
$#I),)#"'3+X)"(+I3+%8'<#E2'I3%")E')1'#'1I#,,'.)3E3'($'*"(/.34')+$("I#%)(+'X)1/#,)[34'X)#'#'E2#"%'#+4'
3#E2'E2#"%')1'3+E#.1/,#%34')+%('#'Z)4*3%'=V#"#*2)'3%'#,&'>?@@B8'W)4*3%1'I/1%'E(+1)1%'($'#'23#43"&'
$),%3"\#"3#&'#+4'%23'E2#"%'E#+X#1')%13,$'#+4'03'1/1E3.%)0,3'%('E/1%(I)[#%)(+&'"31)[)+*&'#+4'4"#**)+*'(+'
%23'4#120(#"48'V2)1'#,,(Z1'03%%3"'/13"'3^.3")3+E3'#+4'$(E/1'(+'E3"%#)+'.#"%1'($'%23'X)1/#,)[34'4#%#'
#+4'I3%")E18''
V23'/13"'E#+'#""#+*3'3#E2'Z)4*3%'%('%23)"'."3$3"3+E31&'"31)[)+*&'#+4'4"#**)+*'13X3"#,'($'%2(13'
Z)4*3%1'(+'%23'4#120(#"4'I)*2%',3#4'%('(X3",#.'03%Z33+'%23I8'V23"3$("3&'%23'LA(Cockpit'E(+1%"#)+1'
"31)[)+*'#+4'2)4)+*'3113+%)#,')+$("I#%)(+'032)+4'#+(%23"'Z)4*3%'0-'1+#..)+*'0#EG'%('#,,(Z34'1)[3'("'
.(1)%)(+8'6(X3I3+%'($'%23',#0(^'E(+%#)+3"1')1'E(+1%"#)+34'%('%23',3$%'#+4'")*2%'($'%23'E/""3+%'
#X#),#0,3'.#*3'1)[3'($'%23'6((4,3'%23I3'$"(I')6((j'%('."3X3+%'2(")[(+%#,'1E"(,,)+*8'V23'0(%%(I'
"31)[31'4-+#I)E#,,-'Z)%2'%23'4"#*'($'#'Z)4*3%8'V2#%'Z#-'%23'4#120(#"4')1'G3.%'/+E,/%%3"34'Z2),1%'1%),,'
03)+*'4-+#I)E')+'#""#+*3I3+%'($'%23'Z)4*3%1'
Use Cases
D'%-.)E#,'/13"'1311)(+'Z)%2'%23'LA(Cockpit'E#+'E(+1)1%'($'X#")(/1'#E%)(+18'J+'%2)1'13E%)(+'Z3'E"3#%34'
%23'I(1%')I.("%#+%'/13'E#1315'
9(*')+'%('%23'1-1%3I5'6((4,3')%13,$'"3a/)"31'#',(*)+&'%23'E"343+%)#,'/.(+',(*)+')1'E23EG348'J$'
%23'E#.#0),)%)31'#"3'."313+%&'%23-'Z),,'133'#',)+G'%('%23'.,/*)+')+'%23'1)43'+#X)*#%)(+'%"33')+'%23'
D4I)+)1%"#%)(+'L,(EG8'V23'."(%(%-.3'LA(Cockpit')1'(+,-'#EE311)0,3'$("'/13"1'Z)%2'%23'
#4I)+)1%"#%("'"(,38'
e34)"3E%'%('4#120(#"45'D'E,)EG'/.(+'%2#%'+#X)*#%)(+')%3I'"34)"3E%1'%('#'+3Z'.#*3&'%23'
4#120(#"4',#-(/%8'd3#43"'#+4'$((%3"'($'%23'(")*)+#,'6((4,3'%23I3'#"3'G3.%&'#,,'(%23"'
+#X)*#%)(+'3,3I3+%1'#"3'2)443+8'
!"#$%&'(")*)+#,,-'./0,)1234')+5'6#)3"&'78&'93)%+3"&':8&';'<0+3"&'68'=>?@AB8'C93#"+)+*'D+#,-%)E1'F(EG.)%'$("'6HHF':,#%$("I1C8'J+'
Emerging(Trends(in(Learning(Analytics8'93)43+&'K)343",#+435'L"),,'M'N3+138'4()5'2%%.15OO4()8("*O@?8@@PQOARSA??TQAA>RQU?@T'
!)1.,#-34'Z)4*3%15'V23'1%#%3&'%23'.,/*)+'3^)%34',#1%'1311)(+&')1'"3E(X3"34&'#,,'Z)4*3%1'#+4'%23)"'
$),%3"'."3$3"3+E31'."313+%')+'%23'4#%#0#13'#"3',(#4348'V23'E/""3+%'#X#),#0,3'4#%#')+'%23'
.,/*)+_1'%#0,31')1',(#4348'
F2#+*35'D44)+*'("'43,3%)+*'Z)4*3%1'Z)%2'%23'."(X)434'I3+/'(.%)(+18']),%3"'4#%#'Z)%2'
."(X)434'$),%3"'(.%)(+18'
N311)(+5'9(**)+*'(/%'$"(I'%23'/13"'("'#'%)I3'(/%'0-'%23'1-1%3I'*/#"#+%331'+('#EE311'%('
/+#/%2(")[34'/13"18'
Implementation
Overall architecture
V23'3#1)31%'Z#-'%('."(X)43'+3Z'$/+E%)(+#,)%-'Z2),3'1%),,'*/#"#+%33)+*'I#)+%#)+#0),)%-'$("'6((4,3')1'0-'
Z")%)+*'.,/*)+18'V23"3'#"3'I#+-'."343$)+34'%-.31'#X#),#0,3n')%'Z#1'43E)434'%(')I.,3I3+%'#',(E#,'
.,/*)+8'V2)1'%-.3'$3#%/"31'%23'I(1%'$"334(I'$("'43X3,(.3"1&'#1')%')I.(131'+('"31%")E%)(+1'(+'
1%"/E%/"3&'431)*+&'#+4'E#.#0),)%)318'
N%"/E%/"#,,-&'#'6((4,3'.,/*)+')1'#'$(,43"'($':d:'1E").%1'=#1'Z3,,'#1'FNN&'f#X#NE").%&'3%E8')$'+3E311#"-B8'
6((4,3'E("3'E(II/+)E#%31'Z)%2'%23'.,/*)+'0-',((G)+*'$("'.#"%)E/,#"'3+%"-'.()+%1&'($%3+'43$)+34')+'
%23'$),3',)08.2.'Z)%2)+'%23'.,/*)+8'
Widgets implementation
<#E2'Z)4*3%'($'%23'LA(Cockpit'2(,41'(+3'($'%23'X)1/#,)[#%)(+1'($'%23'I3%")E1'6@'%('6Q8'V23-'#,,'12#"3'
#'E(II(+'1%"/E%/"3'0/%'+334'4)$$3"3+%'E2#"%)+*'(.%)(+1'=E2#"%'%-.3&'#^31&'%)%,31B8'D+'#01%"#E%'$#E%("-'
431)*+'.#%%3"+'#,,(Z1'3$$)E)3+%'E"3#%)(+'($'Z)4*3%1'Z)%2'*/#"#+%334'E(II(+'$/+E%)(+#,)%-')+'%23'
#01%"#E%'$/+E%)(+1'#+4'4)$$3"3+%'E(+E"3%3')I.,3I3+%#%)(+1'=$)*/"3'>B8'
D44'])*'>'23"3'
])*/"3'>5'o69'($'D01%"#E%']#E%("-':#%%3"+'$("'F2#"%8.2.'
V23'#01%"#E%'E,#11'E2#"%8.2.'2(,41'I3I03"'X#")#0,31'$("')4&'E#+X#1')4&'^.(1&'-.(1&'23)*2%&'Z)4%2&'
4#%#13%&',#03,13%&'#+4'#44)%)(+#,'E2#"%)+*'(.%)(+1'1/E2'#1'E2#"%'%)%,3'#+4'%23'E(""31.(+4)+*'*3%%3"'
$/+E%)(+18']/"%23"')%1'E(+1%"/E%("'E(+1%"/E%'=)4&'%-.3&'^.(1&'-.(1&'Z)4%2&'23)*2%B'#+4'$/+E%)(+1'%('
4)1.,#-'#+4'"3+43"'%23'Z)4*3%1'Z)%2'6((4,3_1')+%3*"#%34'I(4/,3&'#+4'$/+E%)(+1'%('13%/.'%23'dV69'
3,3I3+%1'($'#'Z)4*3%',#0(^8'
V23'E,#11'E2#"%$#E%("-8.2.'2#1'#'$/+E%)(+'E(+1%"/E%D,,]"(I!0#13=B&'E#,,34'$"(I')+43^8.2.'/.(+'1%#"%'
($'%23'.,/*)+8'D,,'Z)4*3%1'4)1.,#-34'(+'%23'4#120(#"4'#"3'a/3")34'$"(I'%23'LA(Cockpit'%#0,3'
4#120(#"48'V23'$#E%("-'E#,,'%23+'E"3#%31'%23'E(""31.(+4)+*'Z)4*3%'%-.38'
<#E2'Z)4*3%'."3.#"31'#'dV69i'E#+X#1'3,3I3+%'$("'%23'X)1/#,)[#%)(+&'%23'E2#"%')%13,$')1'."(X)434'0-'
%23'F2#"%8c1',)0"#"-8'V23'0#1)E'1%"/E%/"3'($'1/E2'#'E2#"%8)1'%23'1#I3'$("'3#E2'%-.3'($'X)1/#,)[#%)(+&'
%(*3%23"'Z)%2'%23'$#E%("-'431)*+'.#%%3"+')%')1'.(11)0,3'%('E(+$)*/"3'3#E2'E2#"%'%(')%1'4)1.,#-34'4#%#8'
V23'#""#-'4#%#'E(+1)1%1'($',#03,1'#+4'4#%#13%18'W)%2'%23'(.%)(+1'#""#-')%')1'.(11)0,3'%('E(+$)*/"3'#'
X#")3%-'($'4)1.,#-'(.%)(+1'1/E2'#1'#^31&',#03,1&'%)%,31'("'%((,%).18'LA(Cockpit'I#G31'23#X-'/13'($'%2)1'
E(+$)*/"#%)(+'%('4-+#I)E#,,-'#44'E(+%3+%'#+4'1%-,)+*'(.%)(+1'%('3#E2'Z)4*3%8'
D423")+*'%('%2)1'1%"/E%/"3'0-')+)%)#,,-'E"3#%)+*'(+,-'#'0#1)E'E2#"%'#+4'#44)+*'$/"%23"'(.%)(+1'Z)%2'
f#X#NE").%'4-+#I)E#,,-',#%3"&'%23'3^%3+4)0),)%-'($'%23'."(%(%-.3')1'*/#"#+%3348'
D44)+*'+3Z'Z)4*3%1'=#+4'%23"3$("3'I3%")E1B'%('%23'E("3'($'%23'."(%(%-.3')1'1)I.,3&'03E#/13'%23'0#1)E'
0/),4)+*'0,(EG1'1%#-'%23'1#I3'#+4'(+,-'%23'13.#"#%3'X)1/#,)[#%)(+'."(.3"%)31'+334'%('03'13%8'
!"#$%&'(")*)+#,,-'./0,)1234')+5'6#)3"&'78&'93)%+3"&':8&';'<0+3"&'68'=>?@AB8'C93#"+)+*'D+#,-%)E1'F(EG.)%'$("'6HHF':,#%$("I1C8'J+'
Emerging(Trends(in(Learning(Analytics8'93)43+&'K)343",#+435'L"),,'M'N3+138'4()5'2%%.15OO4()8("*O@?8@@PQOARSA??TQAA>RQU?@T'
Cockpit Interaction
D$%3"'%23'LA(Cockpit'4#120(#"4',(#41&'#,,'%23'4)1.,#-34'E2#"%'4#%#')1'/.\4#%34'Z)%2'DfDj'"3a/31%18'
V23"3'#"3'+('I/,%).,3'.#*3'"3,(#41'+3E311#"-'%('X)1/#,)[3'%23'Z)4*3%18'
J+'I("3'43%#),&'/.(+'"34)"3E%'%('%23'LA(Cockpit')+43^'.#*3&'%23'4#120(#"4',#-(/%')1'13%'/.8'V23'.,/*)+'
E23EG1')+')%1'4#120(#"4'%#0,3'Z2)E2'Z)4*3%1'#"3'."313+%348']("'3#E2'($'%2(13&'%23'E2#"%'$#E%("-'
E"3#%31'%23',#0(^'E(+%#)+3"'#+4')%1'23#43"\&'$),%3"\'#+4'E2#"%'3,3I3+%1'#+4'/131'6((4,3_1'"3+43"3"'%('
4)1.,#-'%23'Z)4*3%18'V23'4)1.,#-'I3%2(4'E#,,1'%23'E(""31.(+4)+*'f#X#NE").%'$/+E%)(+1'$("'%23'$),%3"'#+4'
E2#"%'3,3I3+%1&'Z2)E2')+'%/"+'/13'%23'."(X)434'DfDj'$/+E%)(+#,)%-'%('$3%E2'%23'E2#"%'4#%#'$"(I'%23'
E(""31.(+4)+*'%#0,318'
]("'#'03%%3"'(X3"X)3Z'($'%23'Z)4*3%_1'4-+#I)E',(#4)+*'."(E311'%23'13a/3+E3'4)#*"#I'=$)*/"3'QB',)1%1'
%23')+X(,X34'13"X3"'#+4'E,)3+%'$),31'#+4'%23'DfDj'"3a/31%1')+'03%Z33+'%23I8'
D44'])*8'Q'23"3'
])*/"3'Q5'N3a/3+E3'!)#*"#I'($'W)4*3%'F"3#%)(+'
Discussion
J+'*3+3"#,&')%'E#+'03'.()+%34'(/%'%2#%'%23'X)1/#,)[#%)(+1'($'%23'I3%")E1'23,.'%('*3%'#'$)"1%')I."311)(+'($'
%23'(+,)+3'E(/"1318'V2)1')I."311)(+')1'/134'%(',((G'$("'#'E(+1.)E/(/1+311'($'#'1.3E)$)E'E(/"138'
K("I#,,-'E(/"131'032#X3'"#%23"'%-.)E#,'b'$("'3^#I.,3'(+'6(+4#-'I(1%'($'%23',3#"+3"1',(*'(+'#+4'
*3%%)+*'$3Z3"'#,(+*'%23'E(/"13'%)I3'.3")(48'6(1%'($'%23',3#"+3"1'+3X3"'4('#'.(1%')+'#'$("/I'#+4'I(1%'
($'%23'.(1%3"1'+(%'I("3'%2#+'%Z('.(1%)+*18'V23"3'#"3'#,"3#4-'#'+/I03"'($'1%/4)31'E#""-)+*'(/%'%23'
4"(.\(/%\."(0,3I'=72#,),';'<0+3"&'>?@TB'("'#44"311)+*'%23',311')+%3"#E%)(+'=9#EG+3"'3%'#,8&'>?@PB8'
W)%2'%23'23,.'($'%2)1'I3%")E1')%'E#+'03'133+'X3"-'$#1%')$'#'6HHF'032#X31',)G3'(%23"'(+318'H+'%23'(%23"'
1)43')$'#'4)4#E%)E#,'I3#1/"3')1'%#G3+'%2)1'E#+',3#4'%('#'+(+\%-.)E#,'*"#.28']("'3^#I.,3&')+'%23'
./0,)E#%)(+'($'72#,),'3%'#,8'=>?@RB'%23'/13'($'p#I)$)E#%)(+\3,3I3+%1'Z)%2)+'6HHF1')1'431E")034'#+4'
%2#%'%23'+/I03"'($'4(+3'a/)[[31')+E"3#13'X(+'Z33G'@'%('Z33G'>8'N)I),#"'3$$3E%1'E#+'03'133+'0-'%23'
/13'($'H.3+'L#4*31'#1'E3"%)$)E#%)(+'=7(..';'<0+3"&'>?@RB8''
])*/"3'T'."313+%1'%23'Z)4*3%'$("'%23'%)I31.#+'($'(+3'($'(/"'6HHF1&'%23'NJ6HHF'Z)%2'(X3"'@Q??'
.#"%)E).#+%18'J%'1%#"%34'#%'%23'A%2'HE%(03"'>?@R'#+4'%23'4/"#%)(+'Z#1'S'Z33G18'W23"30-&'3X3"-'Z33G'#'
I(4/,3')1'I#43'#EE311)0,3'%('%23'.#"%)E).#+%1'($'%23'E(/"138'V23'0#1)E'I3%")E'($'%23'logins(over(time'
12(Z1'%23'+/I03"'($'4)1%)+E%'1/EE311$/,',(**34')+'3X3+%1'($'%23'1-1%3I8'V23'X)1/#,)[#%)(+'$("'6@')1'#'
,)+3'E2#"%&'4/3'%(')%1'%3I.("#,'E2#"#E%3"n'%2)1'I3%")E')1'$),%3"#0,3'.3"'%)I3'$"#I38'V23',)+3'E2#"%'
."313+%1'%2#%'I("3'.3(.,3'#"3'#E%)X3'#%'%23'03*)++)+*'($'3#E2'Z33G'Z23+'#'+3Z'13E%)(+'Z#1'I#43'
#EE311)0,3'#+4'43E"3#131'4"#1%)E#,,-'/+%),'%23'3+48'D44)%)(+#,,-&')%'12(Z1'%23'(X3"#,,'#E%)X)%-'$("'%23'
.#"%)E).#+%1'43E"3#13'(X3"'%23'4/"#%)(+'($'%23'E(/"138'
!"#$%&'(")*)+#,,-'./0,)1234')+5'6#)3"&'78&'93)%+3"&':8&';'<0+3"&'68'=>?@AB8'C93#"+)+*'D+#,-%)E1'F(EG.)%'$("'6HHF':,#%$("I1C8'J+'
Emerging(Trends(in(Learning(Analytics8'93)43+&'K)343",#+435'L"),,'M'N3+138'4()5'2%%.15OO4()8("*O@?8@@PQOARSA??TQAA>RQU?@T'
'
])*/"3'T5'W)4*3%'$("',(*)+1'(X3"'%)I3'Z)%2'$),%3"'13%'$("'%23'4/"#%)(+'($'%23'NJ6HHF'
V23'+3^%'I3%")E'($'%23'."(%(%-.3'"3."313+%1'%23'#E%)X)%-')+'$("/I1'=6QB8'J%'E(/+%1'%23'+/I03"'($'
/13"1'.3"'+/I03"'($'$("/I'.(1%1'#+4'$("/I1'#"3'#%%")0/%34'%('#'E(/"138'W23"30-&'#'E(/"13'E#+'
E(+%#)+'I("3'%2#+'(+3'$("/I8'V23'E"(+'%#1G'4#),-'#**"3*#%31'%23'4)1%")0/%)(+'.3"'E(/"13'Z)%2(/%'
4)"3E%'#%%")0/%)(+'%(')+4)X)4/#,'/13"18'V23')+$("I#%)(+')1'$),%3"#0,3'.3"'E(/"13'Z2)E2'#,,(Z1'1)43\0-\
1)43'E(I.#")1(+')+'#'4(/*2+/%'E2#"%8'
])*/"3'i'."313+%1'%23'E(I.#")1(+'($'%23'$("/I'.(1%'$"3a/3+E-'03%Z33+'%2"33'4)$$3"3+%'6HHF18'J%'
E#+'03'133+'%2#%'%23'6HHF'h!#1'J+%3"+3%')+'I3)+3I'o+%3"")E2%g'2(,41'I/E2'I("3'.(1%)+*1'#1'%23'
(%23"'(+318']/"%23"I("3&'%23"3'#"3'I("3',3#"+3"1'.(1%)+*'I("3'%2#+'>'.(1%)+*1'%2#+')+'#'%-.)E#,'
6HHF8'J$'#'E,(13"',((G')1'4(+3&')%'E#+'03'.()+%34'(/%'%2#%')+'%2)1'6HHF'%23'%3#E23"1'/134'%23'$("/I'
)+%3+1)X3,-'$("'#+'3^E2#+*3'03%Z33+',3#"+3"1'#+4'."(I.%34'%23I'%('%#,G'#0(/%'%23)"'.3"1(+#,'
3^.3")3+E31')+'4)$$3"3+%'1E2((,'1)%/#%)(+18'V2)1',3#41'%('I#+-'.3"1(+#,'1%#%3I3+%1'#+4'4)1E/11)(+1'
#+4'I/1%'03'133+'#1'#'4)4#E%)E#,'I3#1/"3I3+%8'
D44'])*8'i'23"3'
])*/"3'i5'W)4*3%'Z)%2'%23'E(I.#")1(+'($'%23'.(1%'$"3a/3+E-'03%Z33+'%2"33'4)$$3"3+%'6HHF1'
V23',#1%'I3%")E'=6>B')1'#0(/%'a/)['1%#%)1%)E18'm/)[[31'#"3'($%3+'.#"%'($'#'E(/"13'#+4'"3a/)"3'%23',3#"+3"'
%('.#"%)E).#%3'#+4'$)+)12'%23I'1/EE311$/,,-8'9D'F(EG.)%'4#),-'#**"3*#%31'%23'1%#%3'%23',3#"+3"1'#"3')+&'
$("'3#E2'a/)['$"(I'%23'%#0,3'a/)['#+4'a/)['#%%3I.%18'V23'E(/+%'($'/13"1'.3"'a/)['$("'%23'1%#%31'($'
$)+)1234'#+4')+'."(E311'#"3'X)1/#,)[34')+'#'1%#EG34'0#"'E2#"%8'
J+'$)*/"3'P'%23"3'#"3'1(I3'a/)[[31&'Z2)E2'#"3'4"#I#%)E#,,-'4)$$3"3+%'%('%23'"31%'#+4'."(X)43'#'2)*2'
$)+)12)+*'"#%38'9((G)+*'#',)%%,3'0)%'E,(13"&')%'E#+'03'133+'%2#%'%2(13'a/)[[31'#"3'.#"%'($'6HHF1'Z)%2'
(0,)*#%("-'3^#I18'J+'(%23"'Z("41'%2(13'a/)[[31'#"3'I#+4#%("-'$("'#'2)*23"'+/I03"'($'1%/43+%18'
V23"3$("3&'%2)1',3#41'%('%23'1%#%3I3+%'%2#%'%23'/13'($'6HHF1'Z)%2)+'#'E/"")E/,/I',3#41'%('#'2)*23"'
E(II)%I3+%'($'%23',3#"+3"18'
!"#$%&'(")*)+#,,-'./0,)1234')+5'6#)3"&'78&'93)%+3"&':8&';'<0+3"&'68'=>?@AB8'C93#"+)+*'D+#,-%)E1'F(EG.)%'$("'6HHF':,#%$("I1C8'J+'
Emerging(Trends(in(Learning(Analytics8'93)43+&'K)343",#+435'L"),,'M'N3+138'4()5'2%%.15OO4()8("*O@?8@@PQOARSA??TQAA>RQU?@T'
'
])*/"3'P5'W)4*3%'$("'a/)['#%%3I.%1&'Z2)E2'(..(131'/13"1'Z)%2'1%#%3'in(progress'%('1%#%3'finished''
])+#,,-&')%'E#+'03'133+'%2#%'%23'X)1/#,)[#%)(+'($'%23'I3%")E1'12(/,4',3#4'%('#'$)+#,'"3.("%'$("'%3#E23"1'#1'
Z3,,'#1'#4I)+)1%"#%("18'V3#E23"1'Z),,'+334'%2#%'%('*3%'#+')+$("I#%)(+'#0(/%'2(Z'%23)"'E(/"13'
.3"$("I34&'%('133'2(Z',3#"+3"1'3+*#*34'Z)%2'%23)"',3#"+)+*'E(+%3+%1&'#+4')$'%23"3'#"3'."(0,3I1'
(EE/"")+*'("'X)E3'X3"1#'#'4)4#E%)E#,'#.."(#E2'Z("G1'a/)%3'Z3,,8'D4I)+)1%"#%("1'E#+'133')$'#'E(/"13'
032#X31'+("I#,'("'/+\+("I#,8'V23-'E#+',3#"+'Z2)E2'4)4#E%)E#,'#.."(#E231'#"3'($')+%3"31%'$("'
$("%2E(I)+*'6HHF1'#+4'Z2)E2'12(/,4'+(%'03'/134'#+-I("38'V2)1'"3.("%'Z),,'03'(+3'($'%23'+3^%'1%3.1'
$("'%23'4#120(#"48''
Conclusion
V23'H.3+'N(/"E3',3#"+)+*'I#+#*3I3+%'1-1%3I'6((4,3'."(X)431'#'1/)%#0,3'3+X)"(+I3+%'%('43.,(-'#'
Z("G)+*'."(%(%-.3'($'#',3#"+)+*'#+#,-%)E1'.,/*)+8'J+'%23'E(+%3^%'($'6HHF1'#+4'%23'%#"*3%'/13"'*"(/.'
($'%23'%((,')%')1'X3"-')I.("%#+%'%('E#"3$/,,-'E2((13'%23'4#%#&'Z2)E2'12(/,4'03'#**"3*#%34'#+4'
4)1.,#-348'93#"+)+*'D+#,-%)E1'/131'#'13%'($'%((,1'#+4'%23'$(E/1'Z#1'+(%'(+,-'#0(/%'Z2#%'%('X)1/#,)[3'
0/%'#,1('2(Z'%('X)1/#,)[3'#**"3*#%34'4#%#8'
V23'X)1/#,)[#%)(+1'($'%23'I3%")E1'#"3'4-+#I)E&')+%3"#E%)X3&'#+4'#,,(Z'%23'/13"'%('a/)EG,-'133'
E(23"3+E31'$"(I'0)*'4#%#13%1'(%23"Z)13'+(%'*"#1.#0,38'V23'"313#"E2'Z("G'12(Z1'%2#%'3X3+')+'#'
."(%(%-.3'1/E2'#1'%23'LA(Cockpit'#..3#,)+*'X)1/#,)[#%)(+1'#"3'#'G3-'E(I.(+3+%'Z23+'%"#+1$3"")+*'
1%#%)1%)E#,')+$("I#%)(+'#+4'%"#+1$3"")+*',3#"+)+*'#+#,-%)E1'G+(Z,34*38'
V23'."(I)1)+*'$3340#EG'$("'%23'LA(Cockpit'Z),,'2(.3$/,,-'$(1%3"'$/"%23"',3#"+)+*'#+#,-%)E1'Z)%2'%2)1'
%((,8'V23'."(%(%-.3&'E"#$%34'Z)%2'3^%3+1)0),)%-')+'I)+4&'12(/,4'#,,(Z'$/"%23"'a/#+%)$)#0,3')+1)*2%')+'%23'
,3#"+3"_1'032#X)(/"'#+4'."(E311'($',3#"+)+*'Z)%2'6HHF1'(+'%23')6((j'.,#%$("I8'
V23'LA(Cockpit'12(Z1'%2#%'3X3+'#'1I#,,'13%'($'1%#G32(,43"1'#+4'/13"1'03+3$)%'$"(I'#..,-)+*'0#1)E'
,3#"+)+*'#+#,-%)E18'!#%#'X)1/#,)[#%)(+'($'E/1%(I)[34'I3%")E1'#,,(Z1')+%3"."3%#%)(+'($'4#%#'E(23"3+E31'
(%23"Z)13'+(%'#EE311)0,3')+'#+'3$$)E)3+%'#+4'/13"\$")3+4,-'Z#-8'
V23'."(%(%-.3')1'#+'3^%3+4)0,3'#+4'E(+$)*/"#0,3'0#13&'%23"3$("3')%')1'1)I.,3'%('#44'$/"%23"',3#"+)+*'
#+#,-%)E1'E#.#0),)%)318'V2313'#44)%)(+#,'I3%")E1'E#+'E(X3"'#'Z)43'"#+*3'($'#E%)X)%)318'F(/"131'%2#%'($$3"'
X)43(',3E%/"31'E#+'."(X)43'I(%)X#%)(+'$("'#**"3*#%)+*'G3-'#1.3E%1'1/E2'#1'h+/I03"'($'X)43(',311(+1'
Z#%E234g8'
6("3'$),%3"'(.%)(+1'0"3#G)+*'%23'#**"3*#%34'4#%#'$"(I'#'1-1%3I\Z)43'4(Z+'%('E(/"13\Z)43',3X3,'Z),,'
$/"%23"')I."(X3'%23'/13"'3^.3")3+E3'($'LA(Cockpit8'
!"#$%&'(")*)+#,,-'./0,)1234')+5'6#)3"&'78&'93)%+3"&':8&';'<0+3"&'68'=>?@AB8'C93#"+)+*'D+#,-%)E1'F(EG.)%'$("'6HHF':,#%$("I1C8'J+'
Emerging(Trends(in(Learning(Analytics8'93)43+&'K)343",#+435'L"),,'M'N3+138'4()5'2%%.15OO4()8("*O@?8@@PQOARSA??TQAA>RQU?@T'
<^%3+4)+*'%23'I3%")E1'Z),,'#,1('03'(+3'($'%23'I(1%'."(I)1)+*'+3^%'1%3.1'$("'%23'LA(Cockpit8'F2#+*)+*'
%23'*"#+/,#")%-'($'4)1.,#-34'4#%#'*)X31'%23'1%#G32(,43"1'#44)%)(+#,')+1)*2%')+'%23',3#"+3"1_'032#X)(/"'
)+'%23'E(+%3^%'($'%23')6((j'.,#%$("I8'
]/%/"3'$3#%/"31'($'%23'LA(Cockpit'12(/,4'+(%'(+,-'E,(13'%23'$3340#EG',((.'%('23,.',3#"+3"1')+'%23)"'
,3#"+)+*'."(E3118'H+'(+3'2#+4&'E(I.#")1(+'%('#X3"#*3'1E("31'I)*2%'."(X)43'#+')+E3+%)X3'%('%23'
,3#"+3"'#+4'23,.'%23I'1/EE311$/,,-'E(I.,3%3'%23)"'6HHF18'H+'%23'(%23"&'%23'."(X)434',3#"+)+*'
I#%3")#,')+'6HHF1'#1'.#"%'($'%23',3#"+)+*'."(E311'E#+'03+3$)%'$"(I'(.%)I)[#%)(+'4/3'%(',3#"+)+*'
#+#,-%)E18'W3,,\4(E/I3+%34'#+4')+%3"#E%)X3',3#"+)+*'I#%3")#,'3+E(/"#*31',3#"+3"'#E%)X)%-'#1'Z3,,8'
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2%%.15OO3")G4/X#,8Z("4."3118E(IO>?@>O?@OQ?O,3#"+)+*\#+#,-%)E1\#+4\34/E#%)(+#,\4#%#\I)+)+*O'=,#1%'
X)1)%34'6#"E2'>?@SB'
<0+3"&'68&'V#"#*2)&'L8&'N#"#+%)&'D8&';'NE2q+&'N8'=>?@iB8'N3X3+'$3#%/"31'($'1I#"%',3#"+)+*'#+#,-%)E1'\'
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34)%)(+8'e3%")3X34'$"(I5'2%%.15OOZZZ8,3#"+%3E2,)08("*O.OTSAPT'=,#1%'X)1)%34'6#"E2'>?@SB'
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In(Innovations(for(community(services:(16th(international(conference&')TE1'>?@P8'4()5@?8@??ROARS\Q\
Q@A\TATPP\@'@''
72#,),&'68&'<0+3"&'68&';'D4I)"##,&'W8'=>?@RB8'd(Z'E#+'p#I)$)E#%)(+'JI."(X3'6HHF'N%/43+%1'
<+*#*3I3+%l8'J+'."(E334)+*1'($'%23'</"(.3#+'F(+$3"3+E3'(+'p#I3'L#134'93#"+)+*&'p"#[&'D/1%")#&'
=..8'S@A\S>SB'
7(..&'68&'<0+3"&'68'=>?@RB'9#'E3"%)$)E#E)r+'43',(1'6HHF8'`3+%#c#1&'431#$s(1'-'3^.3")3+E)#1'."tE%)E#18'
e3X)1%#'31.#u(,#'43'.34#*(*s#8'`(,8'Ri&'K('>PP&'>?@R&'..8'SQ\@??8'JNNK'??QT\ATP@'
!"#$%&'(")*)+#,,-'./0,)1234')+5'6#)3"&'78&'93)%+3"&':8&';'<0+3"&'68'=>?@AB8'C93#"+)+*'D+#,-%)E1'F(EG.)%'$("'6HHF':,#%$("I1C8'J+'
Emerging(Trends(in(Learning(Analytics8'93)43+&'K)343",#+435'L"),,'M'N3+138'4()5'2%%.15OO4()8("*O@?8@@PQOARSA??TQAA>RQU?@T'
9#EG+3"&'<8&'72#,),&'68&'<0+3"&'68'=>?@PB'd(Z'%('$(1%3"'$("/I'4)1E/11)(+1'Z)%2)+'6HHF15'D'E#13'1%/4-8'
J+%3"+#%)(+#,'f(/"+#,'($'DE#43I)E'e313#"E2')+'<4/E#%)(+8'>=>B&'?@\@Q8'!HJ5'@?8@RASiO)c#"38Q@TQ>'
9#"1(+&'H8'=@ASPB8'J+$("I#%)(+'1-1%3I1'."(%(%-.)+*8'J+'Proceedings(Interez(HP(3000(Conference'=..8'
Qi@\QPTB8'e3%")3X34'$"(I5'2%%.5OOZZZ8(.3+I.38E(IOE1,."(E334Od:JjSPO:Qi@8.4$'=,#1%'X)1)%34'
6#"E2'>?@SB'
93)%+3"&':8&'72#,),&'68&';'<0+3"&'68'=>?@RB8'93#"+)+*'#+#,-%)E1')+'2)*23"'34/E#%)(+'\'#',)%3"#%/"3'"3X)3Z8'
J+'Learning(analytics:(Fundaments,(applications,(and(trends8'4()5@?8@??ROARS\Q\Q@A\i>ARR\P'
6/+([\63")+(&':8&'e/).3"3['`#,)3+%3&'f8&';'7,((1'!3,*#4(&'F8'=>?@QB8'J+$3"")+*'2)*23"',3X3,',3#"+)+*'
)+$("I#%)(+'$"(I',(Z',3X3,'4#%#'$("'%23'G2#+'#E#43I-'.,#%$("I8'J+'Proceedings(of(the(third(
international(conference(on(learning(analytics(and(knowledge8'4()5@?8@@TiO>TP?>AP8>TP?Q@S''
K/+#I#G3"'f"&'f8']8&'F23+&'68&';':/"4)+&'V8'!8'=@AA?B8'N-1%3I1'43X3,(.I3+%')+')+$("I#%)(+'1-1%3I1'
"313#"E28'Journal(of(management(information(systems&'R=QB&'SA\@?P8'
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... At Graz University of Technology (TU Graz) the organizational unit Educational Technology has intensive experience in learning analytics and visualizations, including for our Austria-wide MOOC platform iMooX.at (Maier, Leitner & Ebner, 2019;Leitner, Maier & Ebner 2020), for the university-wide learning management system TeachCenter and through numerous international research cooperation (De Laet et al. 2018a, De Laet et al., 2018b. When students expressed the wish to get a better and easier overview of their study progress, we were happy to comply. ...
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At Graz University of Technology (TU Graz, Austria), the learning management system based on Moodle (https://moodle.org/ – last accessed February 10, 2021) is called TeachCenter. Together with a campus management system – called TUGRAZonline – it is the main infrastructure for digital teaching and general study issues. As central instances for both teachers and students, various services and support for them are offered. The latest developments include the design and implementation of a study progress dashboard for students. This dashboard is intended to provide students a helpful overview of their activities: It shows their academic performance in ECTS compared to the average of their peers, their own study progress, and the official study recommendation as well as the progress in the various compulsory and optional courses. The first dashboard prototype was introduced to all computer science students in May 2020, and a university-wide rollout started in December 2020. The chapter describes design considerations and design development work, implementation, as well as the user feedback on the implementation. Finally, the authors present recommendations as guidelines for similar projects based on their experience and students’ feedback and give an outlook for future development and research.
... Therefore, we tried out different possibilities to analyze and visualize some results using widgets and a dashboard. Teachers as well as learners can take those visualizations to optimize their MOOCs or their learning behavior [16]. Fig. 4 shows an example of a Learning Analytics dashboard. ...
Conference Paper
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iMooX.at is the Austrian MOOC platform founded in 2014. This platform offers free, openly licensed online courses for all, so called Massive Open Online Course (MOOCs). It aims to offer university education in an innovative and digital way.In this article, we will briefly look at the history of the platform and its main milestones till now. Finally, a few possible development steps will be pointed out and discussed.
... But some LA implementations allows direct support of learning. For example, the LA cockpit developed for learning management system Moodle [22] shows live activities within the course frame and therefore allows to react on special patterns such as very low activities in the discussion forum or very low grades in the quizzes of an accumulation of wrong answers in a specific quiz item. ...
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This paper discusses the general thesis that massive open online courses (in short MOOC), open educational resources (in short OER) and learning analytics are an impactful trio for future education, especially if combined. The contribution bases upon our practical experience as service providers and researchers in the department “Educational Technology” at Graz University of Technology (TU Graz) in Austria. The team members provide support to lecturers, teachers and researchers in these addressed fields for several years now, for example as host of the MOOC platform iMooX.at, providing only OER since 2015. Within this contribution, we will show, against some doubtful or conflicting opinions and positions, that (a) MOOCs are opening-up education; (b) learning analytics give insights and support learning, not only online learning, if implemented in MOOCs; and (c) that OER has the potential for sustainable resources, innovations and even more impact, especially if implemented in MOOCs.
... Elle est aussi définie par la norme ISO 9241-1159 comme « le degré selon lequel un produit peut être utilisé, par des utilisateurs identifiés, pour atteindre des buts définis avec efficacité, efficience et satisfaction, dans un contexte d'utilisation spécifié ». D'après les travaux menés par Maier et al., (2019) l'utilisabilité des applications est indispensable pour améliorer l'expérience des apprenants. L'utilisabilité doit être examinée et étudiée, principalement mais pas exclusivement, dans le but d'aider : ...
Thesis
Les MOOC (Massive Open Online Course) ou CLOM en français (Cours en Ligne Ouvert et Massif) représentent l'une des technologies émergentes pour l'apprentissage et l'enseignement en ligne. Dans ce contexte caractérisé par sa pluralité, son hétérogénéité, sa massivité et son ouverture, les enseignants conçoivent les contenus et les activités d'apprentissage avant qu'ils ne soient dispensés aux apprenants, et ne tiennent pas ainsi compte de leurs besoins et préférences. Une conception non adaptée aux profils des apprenants peut vraisemblablement empêcher les apprenants et les enseignants d'atteindre leurs objectifs d'apprentissage et d'enseignement. En effet, une meilleure prise de conscience de la diversité des besoins d'apprentissage des apprenants par les enseignants leur donne de meilleures chances de les satisfaire et de répondre à leurs besoins. Un des aspects de la diversité qui s'est avéré avoir des répercussions importantes sur l'enseignement et l'apprentissage est celui des styles d'apprentissage. Ces derniers constituent un ensemble de facteurs qui donnent une large orientation à l'apprentissage et rendent ainsi la même méthode d'enseignement appréciée par certains apprenants et rejetée par d'autres. Dans ce sens, les MOOC introduisent de nombreux composants qui ont un impact sur l'apprentissage, et différents types d’apprenants en apprécient différemment. Par exemple, pour apprendre certains apprenants préfèrent utiliser les forums de discussion, d'autres visionner les vidéos, et parfois même lire des pages web. Afin de prendre en compte ces préférences et face aux milliers d'apprenants qui interagissent avec ces plateformes MOOC, l'analyse des traces des apprenants offre une opportunité sans précédent pour mieux identifier leurs styles d’apprentissage. L'objectif de cette thèse est de proposer une approche automatique, basée sur l’analyse des traces et qui utilise les algorithmes d'apprentissage automatique, pour la prédiction des styles d'apprentissage des apprenants dans une perspective d'aider les enseignants à mobiliser des stratégies d'enseignement appropriées. Les données analysées sont issues du cours edX intitulé « Statistical Learning » dispensé à l'hiver 2015 et à l'hiver 2016 par la plateforme open source Lagunita de Stanford (fondée sur la plateforme Open edX). L’approche proposée est basée sur la théorie du style d'apprentissage, le modèle de style d'apprentissage Felder-Silverman (FSLSM) a été adopté en raison de ses dimensions distinctes et indépendantes, un indice qui peut servir à décrire plus en détail les styles d'apprentissage de chaque apprenant. Nous visons à travers ce travail de recherche à définir le modèle prédictif approprié qui puisse fournir des résultats fiables en termes d'identification des styles d'apprentissage. À l’issue des résultats obtenus, nous proposons un outil de visualisation, appelé MOOCLS (MOOC Learning Styles), destiné à aider les enseignants et les concepteurs pédagogiques à se faire une idée précise de la diversité des styles d'apprentissage des apprenants inscrits dans leurs MOOC. Les résultats d’expérimentation obtenus montrent que la compréhension des styles d'apprentissage des apprenants est une pierre angulaire pour 1) adapter les expériences pédagogiques, 2) améliorer la satisfaction des besoins éducatifs, et 3) enrichir les expériences d'apprentissage.
... Taking into account guidelines and best practices from our previous research (Maier, Leitner & Ebner, 2019), we have extended our framework to include also web analytics. Our overall goal for our LA Cockpit is to close the information gap that teachers in MOOCs have compared to real classroom learning situations and examines what can be derived from recorded activity traces in online learning environments. ...
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Massive open online courses (MOOCs) provide anyone with Internet access the chance to study at university level for free. In such learning environments and due to their ubiquitous nature, learners produce vast amounts of data representing their learning process. Learning Analytics (LA) can help identifying, quantifying, and understanding these data traces. Within the implemented web-based tool, called LA Cockpit, basic metrics to capture the learners’ activity for the Austrian MOOC platform iMooX were defined. Data is aggregated in an approach of behavioral and web analysis as well as paired with state-of-the-art visualization techniques to build a LA dashboard. It should act as suitable tool to bridge the distant nature of learning in MOOCs. Together with the extendible design of the LA Cockpit, it shall act as a future proof framework to be reused and improved over time. Aimed toward administrators and educators, the dashboard contains interactive widgets letting the user explore their datasets themselves rather than presenting categories. This supports the data literacy and improves the understanding of the underlying key figures, thereby helping them generate actionable insights from the data. The web analytical feature of the LA Cockpit captures mouse activity in individual course-wide heatmaps to identify regions of learner’s interest and help separating structure and content. Activity over time is aggregated in a calendar view, making timely reoccurring patterns otherwise not deductible, now visible. Through the additional feedback from the LA Cockpit on the learners’ behavior within the courses, it will become easier to improve the teaching and learning process by tailoring the provided content to the needs of the online learning community.
... A variety of strategies to tackle this problem has emerged. We can provide students with a level of personalisation, enabling them to configure widgets presented in dashboards [54]; or with charts enhanced with textual prompts [21]. An explainable AI approach [96] would involve helping people understand what the system knows about them [6]. ...
Conference Paper
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Massive Open Online Courses (MOOCs) require students' motivation either intrinsically or extrinsically to complete any of its courses. Even though MOOCs enjoy great popularity and bring many benefits to the educational community, some concerns arise with MOOC advancement. In fact, MOOCs are affected by low completion rate and face issues with respect to interactivity and student engagement along MOOC duration, which may convert student excitement to boredom and then drop out at any stage. A key result of research in the past couple of years has proved that students' engagement in MOOCs is strongly related to their activities online. These activities are related to the interaction between student and logging in the MOOC, reading and writing in the MOOC discussion forum, watching videos and doing quizzes. In this research paper, we present our research in deploying a gamification mechanic in MOOCs to increase student engagement. The gamification approach relies on weekly feedback to drive student intrinsic and extrinsic motivation. Following learning analytics on students' data from a MOOC offered in 2014, 2015, and 2016, the outcome of this approach showed an obvious increase in students' activity and engagement in discussion forums, login frequency and quiz trials. The active students' cohort allotment has increased in comparison with previous versions of the same MOOC as well as the completion rate has incremented up to 26% of the total number of participants.
Article
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Discussion forums are an essential part to foster interaction among teachers and students, as well as students and students, in virtual learning settings. If interaction can be enhanced, this has a positive influence on motivation and finally also on dropout rates. These days, a special form of online courses, so‐ called MOOCs (Massive Open Online Courses), are popping up massively. Those courses are characterized by a high number of students. In this paper, we would like to examine discussion forums and their role concerning interaction. Therefore, Gilly Salmon's well‐known Five stage model is taken and adapted to MOOCs based on a case study. As a method, we tracked learners' data through learning analytics applications and concluded that there is a positive correlation between reading from one side and writing in forums from the other side.
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The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper focuses on the link between the learning strategies identified in the trace data and student reported approaches to learning. The paper reports on the findings of a study conducted in the scope of an undergraduate engineering course (N=144) that followed a flipped classroom design. The study found that learning strategies extracted from trace data can be interpreted in terms of deep and surface approaches to learning. The detected significant links with self-report measures are with small effect sizes for both the overall deep approach to learning scale and the deep strategy scale. However, there was no observed significance linking the surface approach to learning and surface strategy nor were there significant associations with motivation scales of approaches to learning. The significant effects on academic performance were found, and consistent with the literature that used self-report instruments showing that students who followed a deep approach to learning had a significantly higher performance.
Conference Paper
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Research on learning dashboards aims to identify what data is meaningful to different stakeholders in education, and how data can be presented to support sense-making processes. This paper summarizes the main outcomes of a systematic literature review on learning dashboards, in the fields of Learning Analytics and Educational Data Mining. The query was run in five main academic databases and enriched with papers coming from GScholar, resulting in 346 papers out of which 55 were included in the final analysis. Our review distinguishes different kinds of research studies as well as different aspects of learning dashboards and their maturity in terms of evaluation. As the research field is still relatively young, many of the studies are exploratory and proof-of-concept. Among the main open issues and future lines of work in the area of learning dashboards, we identify the need for longitudinal research in authentic settings, as well as studies that systematically compare different dashboard design options.
Conference Paper
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Within the evolution of technology in education, Learning Analytics has reserved its position as a robust technological field that promises to empower instructors and learners in different educational fields. The 2014 horizon report (Johnson et al., 2014), expects it to be adopted by educational institutions in the near future. However, the processes and phases as well as constraints are still not deeply debated. In this research study, the authors talk about the essence, objectives and methodologies of Learning Analytics and propose a first prototype life cycle that describes its entire process. Furthermore, the authors raise substantial questions related to challenges such as security, policy and ethics issues that limit the beneficial appliances of Learning Analytics processes.
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Learning Analytics (LA) is an emerging field; the analysis of a large amount of data helps us to gain deeper insights into the learning process. This contribution points out that pure analysis of data is not enough. Building on our own experiences from the field, seven features of smart learning analytics are described. From our point of view these features are aspects that should be considered while deploying LA.
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Many MOOCs initiatives continue to report high attrition rates among distance education students. This study investigates why students dropped out or failed their MOOCs. It also provides strategies that can be implemented to increase the retention rate as well as increasing overall student satisfaction. Through studying literature, accurate data analysis and personal observations, the most significant factors that cause high attrition rate of MOOCs are identified. The reasons found are lack of time, lack of learners’ motivation, feelings of isolation and the lack of interactivity in MOOCs, insufficient background and skills, and finally hidden costs. As a result, some strategies are identified to increase the online retention rate, and will allow more online students to graduate.
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To process low level educational data in the form of user events and interactions and convert them into information about the learning process that is both meaningful and interesting presents a challenge. In this paper, we propose a set of high level learning parameters relating to total use, efficient use, activity time distribution, gamification habits, or exercise-making habits, and provide the measures to calculate them as a result of processing low level data. We apply these parameters and measures in a real physics course with more than 100 students using the Khan Academy platform at Universidad Carlos III de Madrid. We show how these parameters can be meaningful and useful for the learning process based on the results from this experience.
Conference Paper
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This paper provides an evaluation of the current state of the field of learning analytics through analysis of articles and citations occurring in the LAK conferences and identified special issue journals. The emerging field of learning analytics is at the intersection of numerous academic disciplines, and therefore draws on a diversity of methodologies, theories and underpinning scientific assumptions. Through citation analysis and structured mapping we aimed to identify the emergence of trends and disciplinary hierarchies that are influencing the development of the field to date. The results suggest that there is some fragmentation in the major disciplines (computer science and education) regarding conference and journal representation. The analyses also indicate that the commonly cited papers are of a more conceptual nature than empirical research reflecting the need for authors to define the learning analytics space. An evaluation of the current state of learning analytics provides numerous benefits for the development of the field, such as a guide for under-represented areas of research and to identify the disciplines that may require more strategic and targeted support and funding opportunities.
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Recently, interest in how this data can be used to improve teaching and learning has also seen unprecedented growth and the emergence of the field of learning analytics. In other fields, analytics tools already enable the statistical evaluation of rich data sources and the identification of patterns within the data. These patterns are then used to better predict future events and make informed decisions aimed at improving outcomes (Educause, 2010). This paper reviews the literature related to this emerging field and seeks to define learning analytics, its processes, and its potential to advance teaching and learning in online education.