341
1 INTRODUCTION
In the shipping industry, the need for excellent
educationandonotherhand,theusabilityevaluation
of ship manipulation systems and engine
management,leadstotheuseofnewtechnologiesin
educational practice. Specifically, the Marine
Education & Training (MET), the use of simulators
(engine or ship’s bridge) is fact
. Various maritime
educational standards (i.e. STCW, 95, Manila 2011)
allowthesimulatorsandothereducationaltools(i.e.
educational software, MATLAB) use in educational
practice.
The aim for the application of new technology
(simulators, games etc.), in MET is the transport of
capacity, i.e. to adapt the dexterities learned within
the vessel operating training framework.We
assume tha
t the dexterities and the knowledge
learnedintheclassroomcanbeappliedeffectivelyin
reallifesimilarsituations(Tsoumasetal.,2004).
MET follows certain education standards
(STCW’95/Manila 2011) for each specialty (Captain,
Engineer) andfor eachlevel (Aʹ,Bʹ, C’). Its scope is
the acquisit
ion of basic scientific knowledge,
dexterities on execution (navigation, route plotting,
engineering etc.) as well as protecting the ship and
crew (safety issues and environment protection
issues)(IMO,2003,Papachristosetal.,2012,Tsoukalas
etal.,2008).
InMET,inparticular,theuser’ssatisfactionbased
on objective crit
eria poses an important research
subject because via this we can determine the
background explaining the satisfaction phenomena,
recommending at thesame time new considerations
that will expand the up‐to‐date educational
conclusions on the adult education in educational
Experimental Research with Neuroscience Tool in
Maritime Education and Training (MET)
D.Papachristos&N.Nikitakos
UniversityofAegean,Greece
ABSTRACT: The paper argues
f
or the necessity to combine MMR methods (questionnaire, interview)
,
gaze
tracking as neuroscience tool and sentiment/opinion techniques for personal satisfaction analysis at the
maritime and training education (MET) and proposes a practical research approach for this purpose. The
purposeofthispaperistocomparetheresultsfromgazetracker(Faceanalysistool)ofthreeexperiments&
sentimentanalysisoftwoexperimentsforsatisfactionevaluationofthestudents‐users’(subject
ive)satisfaction
of themaritime education via user interface evaluation of several typesof educational software (i.e. engine
simulator,ECDIS,MATLAB).Theexperimentalprocedurepresentedhereisa primaryefforttoresearchthe
emotion analysis (satisfaction) of the users‐students in MET. The gaze tra
cking & sentiment analysis
methodologyappearstobeonesufficientasevaluationtool.Finally,theultimategoalofthisresearchistofind
and test the critical factors that influence the educational practice and user’s satisfaction of MET modern
educationaltools(simulators,ECDISetc.).
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 10
Number 2
June 2016
DOI:10.12716/1001.10.02.17
342
programs and software development (IMO, 2003,
Papachristos,Nikitakos,2010,2011).
The paper argues for the necessity of a mixed
approach to usability and educational evaluation at
the engine room or Ship bridge simulation, and
proposes a practical framework for this purpose. In
particular, we use a multi‐method approach for
the
usability and educational evaluation of maritime
simulatorsandothereducationaltoolsthatcombines
physiologicaldatageneratedfromgazetrackingdata
(neuroscience tool), questionnaires and interviews
and speech recording for measuring emotional user
responses‐lexical analysis. The combination of these
methodsaimsatthegenerationofmeasurableresults
of user
experience complementary assessments
(Papachristosetal.,2012).
Gazetrackinginvolvesdetectingandfollowingthe
directioninwhichapersonlooks.Thedirectionofthe
eye gaze can express the user’s interests; it is a
potential porthole into the current cognitive
processes. Communication through the direction of
the eyes is faster than
any other mode of human
communication. Gaze Tracking has been applied: in
Human Computer Interaction, Advertising,
Communication for disabled, Virtual Reality,
Improvedimage and video communication, Medical
fieldandHumanBehaviorStudy(Arpan,2009).
Eye observation on handiness tests is a rather
promisingnewfieldespeciallyforsystem designers,
as
itmayofferinformationonwhatmayattractuser
attentionandwhicharetheproblematicareasduring
system use. The research area on use of the optical
recordingtoolsisthequestforanexactinterpretation
of the optical measurements, their connection to the
satisfaction and the learning effectiveness for users.
Suggestedresearchaimsatthisdirectionwiththeuse
ofneurosciencemethodsincombinationwiththeuse
of qualitative‐quantitative researches aiming at the
extraction of useful conclusion that will help
simulator system designers to develop the systems
(especially the interface, delivering & organizing
educationmaterial),classdesignerstobetter
organize
material and modern tools use (better planed
educational scenarios that thriftily develop the
trainees abilities but also can offer a more objective
evaluation of their abilities & function as future
captains or mechanics) and finally the expansion of
theadulteducationfieldbyoffering newconclusions
regardingtheuseof
e‐learning(introductionmodes,
evaluation) and possible revision of maritime
education models of the respective apposite
organizations (ΙΜΟ) (Dix et al., 2004, Papachristos,
Nikitakos,2010,2011).
An important factor that can be investigated in
relation to the emotional experience (specifically
satisfactionphenomenon)isthelanguageprocess.The
psychological
research in the language production,
comprehension and development is developed
mainlyafter1960asaresultoflinguist’sN.Chomsky
researchongenerativegrammar.Thepsycholinguistic
research showed that language comprehension and
production is not influenced only from factors not
relatedtotheirlinguisticcomplexitybutalsofromthe
speaker’s/listener’sexisting
knowledgefortheworld
around him/her, as well as by the information
included in the extra linguistic environment (Pinker
and Jackendorff, 2005). Investigating the emotional
gravityofwordsspokenbyaspeakeranddefinedits
emotionalstate(currentorpast)constitutesastateof
the art issue. Most of the
emotional state
categorization suggested concern the English
language. In recent years many sentiment analysis
and opinion mining applications have been
developedtoanalyzeopinions,feelingsandattitudes
aboutproducts,brands,andnews,andthelike(Maks
andVossen,2012).
Generally, this approach is generic, in the sense
that it can be
the starting point for an integrated
usability & educational evaluation of the interactive
technologiesduringin‐situeducation, simulationand
pragmatic ship operation management. Today, in
total the application of neurosciences on education
and especially gaze‐tracking methods are an
important research quest and expansion (Goswami,
2007,Papachristos,Nikitakos,2010).
2 LITERATUREREVIEWANDSCOPE
As more information is integrated on board by
implementing an e‐navigation strategy plan in the
future, graphic user interface (GUI) is likely to be
moresophisticated.Suchsophisticatedequipmentcan
enhance navigational safety if seafarers can operate
equipment, access information and understand it
properly. So,
when seafarers misunderstand
information, sophistication will not lead to
navigationalsafetyandrathermayposerisksonthe
ship.Thus,itisimportanttoestablishamethodology
for usability evaluation (with emphasis on user’s
satisfaction) navigational or engine management
equipment(IMO,2012).
Usability has been defined by ISO 9241 as
“the
extent to which a product can be used by specified
users to achieve specified goals with effectiveness,
efficiency, and satisfaction in a specified context of
use”. It is widely acknowledged that the efficiency
and effectiveness can be measured in an objective
manner, i.e. in specific contexts of use and with
the
participationofrepresentativeusergroups. Theyare
usuallydefinedintermsofmetricslike:tasksuccess,
time‐to‐task, errors, learnability (in repetitive use
tests),etc.;whilepersonalsatisfactionissubjectivein
natureanddependsonthecharacteristicsoftheuser
groupsaddressed(Papachristosetal.,2012,Tullisand
Albert, 2008, Kotzabasis, 2011). Usability testing
procedures used in user‐centered interactionare
designedtoevaluateaproductbytestingitonusers.
Thiscanbeseenasanirreplaceableusabilitypractice,
sinceitgives direct inputon how realusers use the
system. Usability testing focuses on measuring a
human
‐madeproductʹs capacitytomeetitsintended
purpose(Dixetal.,2004,Nielsen,1994).Anumberof
usability methods have been developed and
promotedbydifferentresearchers(NeilsonandMark,
1994,Norman,2006,Ryu,2005).
There is considerable work on the ergonomic &
usability assessment of the human strain
(Torner et
al., 1994) and the design and arrangement of ship
equipment. This work has few applications in
shippingindustry(Petersenetal.,2010) and has not
yet resulted to well established evaluation methods
343
and cases (Wang, 2001). More specifically, these
studies tend to report on usage effects on health,
safetyandmentalworkload;howevertheyofferlittle
guidance on the evaluation methods and/or the
design of the respective technology and equipment
(devices)withrespecttousability(Papachristosetal.,
2012).
Research in Human
‐Computer Interaction (HCI)
has created many methods for improving usability
duringthedesignprocessaswellasattheevaluation
ofinteractiveproducts.Thestudyofusabilityitselfis
extended to include other aspects of the user
experience like accessibility, aesthetics, emotion and
affectandergonomics(Papachristosetal.,2012).
The area of computer simulation has been
successfully applied to the study and modeling of
processes,applicationsandreal‐worldobjects(Rutten
et al., 2012). The simulators constitute a category of
educational software and follow a methodology of
application in instructive practice (Crook, 1994,
Solomonidou, 2001). According to de Jong and
van
Joolingen(1998)acomputersimulationis“aprogram
thatcontainsamodelofasystem(naturalorartificial;
e.g.,equipment)oraprocess”.Theiruseinthescience
ortechnologyeducationhasthepotentialtogenerate
higher learning outcomes in ways not previously
possible(Akpan,2001).Incomparisonwith
textbooks
andlectures, alearningenvironmentwithacomputer
simulation has the advantages that students can
systematicallyexplorehypotheticalsituations,interact
with a simplified version of a process or system,
change the time‐scale of events, and practice tasks
andsolveproblemsinarealisticenvironmentwithout
stress (van Berkum and
de Jong, 1991). A student’s
discovery that predictions are confirmed by
subsequenteventsinasimulation,whenthestudent
understandshowtheseeventsarecaused,canleadto
refinement of the conceptual understanding of a
phenomenon (Windschitl and Andre, 1998). Possible
reasons instigating teachers to use computer
simulations include: the
saving of time, allowing
themtodevotemoretimetothestudentsratherthan
setting up and supervising experimental equipment;
the ease with which experimental variables can be
manipulated, allowing for stating and testing
hypotheses; and provision of ways to support
understandingwithvarying representations, suchas
diagramsandgraphs(Blake
andScanlon,2007).
Specifically, the Maritime Engine Simulation
(MES)allowsthecreationofreal,dynamicsituations
that take place on a ship at sea in a controlled
surroundingwherenavalmachineofficersareableto
(Kluj,2002;Tsoumasetal.,2004):
1 practicenewtechniquesanddexterities
2 shapeopinions
fromteachersandcolleagues
3 transport the theory of a real situations in a safe
operation
4 face several problems simultaneously rather than
successively, can learn by giving priority to
multipleobjectivesunderhighpressuresituations
andchangesituationsaccordingly.
Gaze interaction through eye tracking is an
interface technology that has
great potential. Eye
tracking is a technology that provides analytical
insights for studying human behavior and visua l
attention (Duchowski, 2007). Moreover, it is an
intuitive human–computer interface that especially
enables users with disabilities to interact with a
computer (Nacke et al., 2011). Infrared monitor eye
gaze tracking Human‐
Computer Interaction (HCI),
which is limited by restrictions of user’s head
movement and frequent calibrations etc, is an
important HCI method (Cheng et al., 2010, Hansen
andQiang,2010).Thismethodmeasuringtheeffectof
personalization could be the relationship of users’
actual behavior in a hypermedia environment with
theoriesthat
raisetheissueofindividualpreferences
anddifferences(Tsianosetal.,2009).Thenotionthat
there are individual differences in eye movement
behaviorininformationprocessing hasalreadybeen
supportedataculturallevel (Rayner et al., 2007),at
thelevel ofgenderdifferences (Mueller et al., 2008),
andeven
inrelationtocognitivestyle(verbal‐analytic
versusspatial‐holistic)(GalinandOrnstein,1974).
Internationalbibliographyprovidesmanysources
ontheEye‐trackingresearchineducation(Conatiand
Merten2007).Inthefieldoflearningandinstruction,
eyetrackingusedtobeappliedprimarilyinreading
research with only a few
exceptions in other areas
suchastextandpicturecomprehensionandproblem
solving (Halsanova et al., 2009, Hannus and Hyona,
1999,Hagerty andJust,1993,HyonaandNiemi,1990,
JustandCarpenter,1980,Rayner,1998,VanCogand
Scheiter,2010,Verschaffeletal.,1992).However,this
has changed overrecent
years, eye‐tracking is
startingtobeappliedmoreoften,especiallyinstudies
onmultimedialearning(VanCogandScheiter,2010).
Because eye tracking provides insights in the
allocationofvisualattention,itishighlysuitedforthe
study of differences in intentional processes evoked
by different types of multimedia and
multi‐
representational learning materials (Van Cog and
Scheiter, 2010, Halsanova et al., 2009). For example,
Qu and Johnson (2005), use eye‐tracking for
interaction adaptation within the Virtual Factory
teaching systems (VFTS), an computer tutor for
teaching engineering skills. Eye‐tracking is used to
discern the time the user spends
reading something
fromthetimetheuserspendsthinkingbeforetaking
action,withthegoalofassessingandadaptingtothe
motivationalstatesofstudenteffortandconfusion.
Also, the international bibliography contains
variousapproaches–techniques(sortingalgorithms)
concerning linguistic emotional analyses, which are
followed and are based mainly in
the existence of
word lists or dictionaries with labels of emotional
gravityalongwithapplicationsinmarketing,cinema,
internet,politicaldiscourseetc.Therearestudiesalso
concerning sorting English verbs and French verbs
that state emotions based on conceptual and
structural‐syntactical characteristics. For the Greek
languagethereisa
studyonverbsofGreekthatstate
emotions based on the theoretical framework
“Lexicon‐Grammar” that is quite old and doesn’t
contain data from real language use; there are also
some studies concerning Greek adjectives and verbs
that state emotions and comparison with other
languages (French – Turkish) under the viewpoint:
Structural‐syntactical + conceptual characteristics.
More recent studies in Greek conducted
systematically the noun structures based on the
theoreticalframeworkof“Lexicon‐Grammar”andthe
establishmentofconceptual&syntactical criteria for
344
the distinction and sorting of nouns based on
conceptual‐syntacticalcharacteristicsofthestructures
inwhichtheyappear(Lambovetal.,2011;Shanahan
etal.,2006;Fotopoulouetal.,2009).
The major idea of this paper is to compare the
results from the gaze tracker (face analysis tool) of
three
experiments and sentiment/opinion techniques
oftwoexperimentsofthestudents‐users’(subjective)
satisfaction of the maritime education via user
interface evaluation of several types of educational
software (i.e. engine simulator, ECDIS, MATLAB).
We use a combination of qualitative – quantitative
methodology, on one hand, and the use of a
neuroscience
tool (use biometric tool –face
analysis/gaze tracker) and language techniques, on
the other hand. This aims at the combination of the
positiveaspectsofthecorrespondingmethodologies:
aiming at countable results & variable check
(quantitative, questionnaire use), interpretative,
explanatory (qualitative, interview use) and more
objective measurements by “observation” of the
user’s
physiologicaldata(gazetrackinguse,language
process).
3 METHOD
The optical perception includes the stimulant’s
natural reception from the external world and the
process/explicationofthatstimulant.Theobservation
of eye movement is an established method in many
years now. The eye movements are supposed to
depictthelevelof
cognitiveprocessascreendemands
andconsequentlythe level of facility or difficultyof
its process. Usually, optical measurement
concentrates on the following: (a) the eyes’ focus
points,(b)theeyes’movementpatternsand/or(c)the
pupil’salterations(Dixetal.,2004;Duchowski,2007).
The measurement methodology must fulfill all
three
requirements of the cognitive neuroscience
(experiential verification, operational definition,
repetition) and include data‐tools: (a) Recording
device: might include special glasses with the
recording camera or a web camera, (b) Registration
dataprocess–analysissoftwareand(c)dataprocess
software (Papachristos, Nikitakos, 2010). The
following figure shows the optical
data registration
procedure:
Figure1.TheGazetrackingprocess(asrichpicture)
The elements of the proposed approach include
(Fig.2)(Papachristosetal.,2013):
1 Registration and interpretation of user emotional
states(questionnaires)
2 Opticalrecording(gazetracker)
3 Usability/Satisfaction & Educational assessment
questionnaires
4 Wrap‐upinterviews(emotionalassessments).
Figure2.Thestepsofproposedresearchapproach
Intheexperimenttheopticaldataregistrationwill
beconductedbythe“FaceAnalysis”softwarethatwas
developed by the IVML Lab of the National Technical
UniversityofAthens,inconnectionwithaWebcamera
set on thewhere the subject of the research
(educationalsoftwarei.e.MATLAB)(Asteriadis
etal.,
2009). That particular software records a large
number of variables (42) that concern data on the
formofthefaceaswellbutinthepresentresearchwe
focus only 5 parameters that refer to the user’s eyes
and head movement. The next diagramshows the
software’s optical interface
during the registration
procedure (Fig.3) and a figure for tool’s operation
(Fig.4).
Eyegazevector
Scheduleofeyes
&
head
p
ose
Distancefrom
monitor
Headroll
(
an
g
le
),
HR
X
o
Eyes
Qualityparameter(eyegaze
trucking)values(horizontal)~0:
meanoutofscreen
values(horizontal)→10viewof
~0attentioninscreen
>1closetothescreen
Values>10
o
degrees,(high
mobility)
Values<10
o
degrees(attention
dependingonthescenario
HorizontalLevel,HL
EyeLevel,EL
Descri
p
tion Inter
p
retation
Figure3. Biometric tool parameters interpretation (‘Face
Analysis’)
The formalistic presentation of the tool (Face
Analysis)givesatotaloutputwithaparameterssetof
User’sVisualAttention(VA):
VA={p
i},iϵ[1..5]
345
whop
i:parametersofVA,as
p
1[time]:timerecording
p
2[gv(h,v)]:gazevectorf(horizontal,vertical)
p
3[h_p]:headposef(pitch,yaw)
p
4[d_m]:distanceofmonitor(metric)
p
5[h_r]:headroll(angle)
Figure4.‘FaceAnalysis’software inaction
For sentiment/opinion analysis used a Lexicon Base
(LB). This approach based a Greek Lexicon of
Emotions (“ANTILEXICON”) (Vostanjoglou, 1998).
Supposethefollowparametersforsentiment/opinion
processing(Fig.5):
IndW
+=(∑WS+)/TotNw (2)
IndW
‐=(∑WS‐)/TotNw (3)
W
S+|Wop+=∑W (4)
W
S‐|Wop‐=∑W (5)
whereW:numberwordswithsentimentoropinionload
pertext(positivepolarity+ornegativepolarity‐)
Figure5.Sentimentprocess
The personal satisfaction modeling contains 5
levels(Papachristosetal.,2013):
Very
dissatisfied
dissatisfied neutral Somewhat
satisfied
Very
satisfied
LEVEL
1
LEVEL
2
LEVEL
3
LEVEL
4
LEVEL
5
Figure6.TheSatisfactionlevels
From these levels, we design the Research
Personal Satisfaction Framework (RPSF) (Fig.7)
(Papachristosetal.,2013):
NegativeLevel(Levels1&2)
NeutralLevel(Level3)
PositiveLevel(Levels4&5)
Ne
g
ativeLevel NeutralLevel PositiveLevel
Level 1 AND2 Level3 Level4 AND5
Figure7. The Research Personal Satisfaction Framework
(RPSF)
Finally, we use a combination of methods
(questionnaires, interviews), because in the
international bibliography, the use of multiple
methods of educational evaluation in educational
practiceismoreeffectiveandthecombinatorialuseof
quantitativeandqualitativeapproachesconfinestheir
weaknesses (Brannen, 1995, Bryman, 1995, Patton,
1990, Retalis et al., 2005, Tsianos
et al., 2009).
Specifically, the Mixed Methods Research (MMR)
employsacombinationofqualitativeandquantitative
methods. It has been used as a distinct approach in
thesocialandbehavioralsciencesformorethanthree
decades. MMR is still generating discussions and
debatesaboutitsdefinition,themethodinvolved, and
thestandardsforthequality.Althoughstillevolving,
MMRhasbecomeanestablishapproach.Itisalready
consideredthe 3
rd
researchapproach,alongwiththe
quantitative and qualitative approaches, and has its
own emerging world view, vocabulary, and
techniques(Fidel,2008).
ThePersonal(subjective)Satisfactionisadifficult
measuringfactor.Forthat,weuseamixedtechnique
by using a gaze tracker and language
dimension/sentiment analysis with MMR methods
(questionnaire
&interview),verifying measurements
can be accomplish in order to extract safer
conclusions. The size of samples is small because in
experimentalpsychologybyusingequipment,are20‐
30 participants usually. The size of sample depends
fromnatureofresearch(BorgandGall,1979,Cohen
etal.2008,Papachristosetal.,
2013a,2013b,2013c).
4 ANALYSIS
I.Thedataofgazetrackinganalysiscomefromthree
experimentsforGazetracking(Fig.8)(Papachristoset
al.,2013a,2013b,2013c):
Experiment‐A(E‐A): the execution a didactic
scenario in a MATLAB environment that took
placeinMarineAcademyofAspropyrgos –MAA
(Merchant Faculty).
The random sampling took
placeinJanuary2011intheComputerScienceLab
of MAA. The sample consists of 16 students (15
Male,1Female)thatweresubjectedtothespecific
experimental procedure, completed the
questionnaire and gave interviews (MMR
approach). The scenario is based on the
educational material (according
to the STCW‐95
346
corresponding standard) tutored in the 5
th
semester, aiming at the following educational
goals:
mathematictoolforcontrolsystemsdesign,
controlsystemsmodeling,and
modelanalysisandsimulation
Thescenarioinvolvesthefollowingactivities:
transferfunctions(tf)toMATLAB:
G
1(s)=1/s+1 (6)
G
2(s)=1/s (7)
and computation the total transfer function
(Gtotal(s)). Furthermore, calculating the
response(image) oftheGtotal(s)inwhichthere
isaunitaryfeedback(Η=1)andastepfunction
entrance.
The scenario combines educational goals with the
use of simple implementation commands in the
MATLAB
environment.Videorecordingof5‐18
minperstudent.Weusesimplescaleforusability
assessment.
Experiment‐B(E‐B): the execution scenarios in e‐
navigationenvironments(ECDIS),aiming:
to plan and display the ship’s route for the
intended voyage and to plot and monitor
positionsthroughoutvoyage
followSOLASV/19.2.1.4
ThesamplingwascarriedoutontheJanuary2012
until May 2012, in the Information Technologies
Lab of the National Marine Training Centre of
Piraeus (NMTCP). Participated 3 Marine officers
in experiment and they underwent a specific
procedure (ships travels in different ports) in the
ECDIS lab
room with recording of 23 min per
student. They completed the questionnaires and
wereinterviewedfollowtheresearchmethodology
framework. We use SUS scale (SUS is a simple,
ten‐item scale giving a global view of subjective
assessments of usability) for usability assessment
(Brooke,1996).
Experiment‐C (E‐C): sampling
was carried out
betweenMayandJune2012,intheMarineEngine
System Simulator (MESS) Laboratory of the
National Marine Training Centre of Piraeus
(NMTCP). The samples consisted of 13
professional(MerchantMarine officers) that were
subjected to a specific experimental procedure
(operationmanagement)inengineroomsimulator
ERS 5L90MCL11,
(video recording ~23 minutes
per student), completed the questionnaires and
gave interviews. We use SUS scale for usability
assessmenttoo.
Figure8.DiagramofResultAnalysisforGazetracking
Theresultanalysis:
E‐A:TheVAparametersshows
thegazevertical(p2)is0foralongtime(mean,
median and mode va lues for all satisfaction
scales: Matlab & scenario), which means that
usersfocusenoughtimeoutoffscreen,
in a distance from the monitor
(dist_Monitor
parameter) it is observed that approach the
screen(>1)andkeeparelativelyclosedistance
(valueshomogeneity),
Time
recording parameter (video recording) is
connected to the Satisfaction scale (grows in
lowscaletoupperscale)(Fig.9).
Figure9. Time allocation relative success of didactic
scenario (computing stages G1(s)/G2(s)/G’/Gtot/Time
response)
E‐B: a relationship between Gaze parameter and
Usabilityassessmentofusers.Thegazeparameter
depending from SUS score. It shows attention
increases as assessment from ECDIS software, as
shownthenexttable:
Table1.Correlationbetweenvariables
_______________________________________________
VariableSpearman’sSig. Remark
correlatedrho(2‐tailed)
_______________________________________________
SUSScore–Gaze .393.029 Positive
Verticalparameter(p2)
_______________________________________________
E‐C:wefoundtheVisualAttention(VA)fromthe
“FaceAnalysistool”shows(Tab.2)
growing the attention as satisfaction scenario
increase(meangrowhigh→veryhigh)indist
parameter (distance from monitor, >1 close to
thescreen),
347
growing the attention as satisfaction scenario
increase (mean grow high → very high) in
HeadRollparameter(rollingofthehead–eye
angle from horizontal level, <10 attention
dependingonthescenario,>10highmobility),
growing the attention as satisfaction scenario
increase(meangrowhigh→very
high)inGaze
trackingparameter(Gazeverticalparameter>1
viewthescreen).
Table2.VariationofVAparameters
_______________________________________________
SatisfactionScenario Veryhigh High Medium
(Average)(4male) (8male) (1male)
_______________________________________________
GazeVertical8.287.09 0.21
Dist_Monitor1.121.07 0.99
HeadRoll1.90.49‐2.16
_______________________________________________
II.Thedataofsentiment/opinionanalysiscomefrom
three experiments for Gaze tracking
(Fig.9)(Papachristos et al., 2013d; Papachristos and
Nikitakos,2014):
Figure9.DiagramofResultAnalysisforSentiment Analysis
1 Experiment ECDIS (E‐E): The experimental
procedure for satisfaction phenomenon of the
users‐students in e‐navigation ship’s bridge
environmentECDISinMayandJune2012,inthe
Marine Engine System Simulator (MESS)
LaboratoryoftheNationalMarineTrainingCentre
ofPiraeus(NMTCP)(sample:31Marineofficers).
2 Experiment Engine
Simulator (ES‐E): the sampling
was carried out between January and February
2013. The samples consisted of 6 students‐users.
They were subjected to a specific experimental
procedure (Diesel generator operation) in engine
roomsimulatorandcompletedthequestionnaires
andgaveinterviews(researchapproach).
Wefoundinbothexperiments(E‐
E,ES‐E):
The most used phrase in user’s answers in
sentimentanalysishasthisformat:
Phrase:(mdf|auxiliaryverb)+satisfied (7)
andopinionanalysis:
Phrase:mdf+adjective|noun|verb (8)
Specifically,inE‐Eexperimentfound:
Insentiment/opinionanalysis,weobservethetotal
word of answer’s users depending from
satisfaction(growingthesumofTotalwordsfrom
low→veryhighsatisfaction)inScenario&ECDIS
satisfaction.
The Mdf (modifiers) words (Mean Mdf, using
Mdf) depending from satisfaction (growing from
low→veryhighsatisfaction)inScenario&ECDIS
satisfaction.
The
most used word in sentiment phrases is
“αρκετά(enough)” (ECDIS&Scenariosatisfaction
formuser;sanswers)andthemostusedphrasein
user’s answers has this format: (mdf | auxiliary
verb)+satisfied(verb).
Specifically,inES‐Eexperimentfound:
The Total N / Sum Mdf (Modifiers) Index
depending from
personal satisfaction (growing
fromveryhigh→highsatisfaction)inScenario&
Simulatorsatisfaction.
The most used words in sentiment phrases is
“αρκετά(enough)”&πολύ(alot/very)(simulator&
Scenariosatisfactionform users answers)andthe
most used phrase in user’s answers has this
format:(mdf|auxiliaryverb)+
satisfied(verb)
&mdf+adjective|noun|verb.
Very High personal satisfaction forsimulator
(majority) and high personal satisfactionfor
simulator(majority).
In sentiment/opinion analysis, we observe the
MeanMdfIndexis2approximatelyforallcases.
5 CONCLUSIONS
This experimental procedure is a primary effort to
research
theeducationalandusabilityevaluationwith
emotionanalysis(satisfaction)oftheusers‐studentsin
maritimesimulators.
The main purpose of this research, is the
investigationofpersonalsatisfactionofauserofMET
equipment (Engine room simulator, ECDIS,
MATLAB) via the assistance of Gaze tracker (Face
Analysistool)&sentiment/opinionanalysis,
butalso
othermethodslikeMMR(questionnaires‐interviews).
The results from experiments until now, are
shows:
1 opticalparameters
the correlation between VA parameters and
satisfaction:
timerecorder(p1),
Gazevertical(p2),
DistanceMonitor(p4),and
HeadRoll(p5),
and the correlation between VA
parameters
and usability assessment (SUS scale)(ECDIS
experiment).
2 languageprocess
commonstructuretypeofphraseforsentiment
&opinionanalysis,
observe the total word of answer’s users
dependingfromsatisfaction,and
the most used word in sentiment phrases is
“αρκετά(enough)”
The two techniques (gaze tracking,
sentiment/opinionanalysis)
appearstobeonecapable
of satisfying evaluation tool. The research continues
withthenumeralincreaseofthesampleandthetotal
processing and evaluation of the research findings
(qualitative and quantitative data). The proposed
approach may require further adaptations to
348
accommodate evaluation of particular interactive
systems.
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