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Field studiesTraditional field studies require watching usersinteract with the application in their own environments.Users have to be observed completing the course, eitherin person or via Web or phone conferencing.As mentioned earlier, EDUSA combines elements offield studies and usability tests in order to give user mostconvenient environment while performing the evaluationof his/her satisfaction in the background.Time consuming solutionsMany of traditional e-learning assessment methodsare very time consuming. They often require hours(sometimes even weeks) to perform an evaluation cycle(Shilwant and Haggarty 2005). In most cases, theseapproaches require writing reviews, filling questionnairesor evaluation reports manually.Event Monitor shows graphical representation of (1)selected user participation activities; (2) selected systemresponse activities and/or (3) a value calculated fromparticipation and response data according to the selectedmodel to be described in this paper.Event Log shows time, type, and textualrepresentation of (1) selected user participation events,(2) selected system response events. Evaluator can usethe scrollbar to review each of the Event Log entriestogether with its graphical representation within EventMonitor.Figure 1 shows graphical user interface of EDUSAAmbient Usability Assessment Meter.Expensive solutionsPerson-hours of hired usability experts can be veryexpensive. Traditional non-automated e-learning testingsolutions normally depend on experts’ availability.Technology loaded solutionsMaking interviews and the subsequent audio/videopost-production requires usage of correspondingtechnology (video cameras, microphones, tape recorders,etc.). Only well equipped highly professional usabilityevaluation labs have the necessary means to produce thatsort of good quality materials.New approach of assessing learning tasks for multitaskinglearners using EDUSAIntegrated system-wide scanning skillsEDUSA evaluation method emphasizes the organicand functional relation between all parts (tasks) and thewhole system (in broader sense – human, computer andsurrounding ambience).EDUSA acts task independently at the very core ofthe operational system. It connects to the humancomputerinterface to scan all the communicationbetween user and system. The resulting information issearched for programmatically recognizable patterns ofhuman behaviour (e-gestures) and used to identifylearner’s subjective satisfaction with the learningmaterial. Optionally EDUSA can build and exportlearner’s profile that can be later used with other multitaskingevaluation sessions.Figure 1. EDUSA Ambient Usability Assessment Meter.Display at the top of the screen shows the real-timevalues for (1) weighted cumulative value of userparticipation and system response values, (2) degree ofuser participation, (3) degree of system response, (4)current session time and (5) current Event Monitor time.File menu is used to access functionality forloading/saving the (1) environment parameters, (2) userprofiles and (3) session log files.Tools menu is used to access functionality for (1)creating/editing hotspots (evaluator-defined areas insidetask windows that can capture specified user activities),(2) analyzing currently loaded session and (3) generatingsummary (currently in MS Excel file format).Help menu is used to access software documentationand client support files.Annual Proceedings of Vidzeme University College “ICTE in Regional Development”, 200632
Ability to follow user’s multi-tasking activitiesEDUSA analyzes all user and system activities withall tasks learner participates in during the evaluationsession and combines the results with respect to theparticipation levels in every single task.Figure 2 shows ordered lists for four levels of userparticipation in different tasks together with degree ofparticipation in each task.analyze the overall efficiency of learning process“orchestration”.E-Gestures implementation in EDUSAEDUSA currently implements the following set of e-gestures:• User e-gestures: (1) Browsing (2) Discovering (3)Learning (4) Logged off;• System e-gestures: (1) Productive action (2)Unproductive action.Figure 3 represents user e-gestures linked to a timescale. S1 to S4 are called Decision Slots.User e-gesturesFigure 2. EDUSA - Multi-tasking learner’s participationlevels.EDUSA Usability Analyzer allows also viewingordered lists of productive/unproductive peripheral(mouse, keyboard), hardware (CPU, memory) andsoftware (learning tasks, hotspots) activities.Figure 3. e-gestures / Decision slots.Another way to represent e-gestures is shown in figure 4.Ability to handle learning process real-timediscontinuityDuring the evaluation session, EDUSA writes everytask-related action to a XML log file. This way it canhandle both continued and discontinued learning sessionswhile analyzing the recorded data subsequently. EDUSAhas the ability to reconstruct discontinued learning tasksand analyze them by putting in different evaluationcontexts (task scopes, e-gesture sets, etc.).Non-linear approaches to evaluationEDUSA is aware of all learner-computer interactionsprovided by the interface. This allows evaluation of bothlinear and non-linear learning sessions.EDUSA logs learner’s connection times, return ratesand idle times per task window. It keeps track of allpossible combinations of learning “lines” and is able toFigure 4. e-gestures / Decision tree.Figure 4 shows the decision tree learner uses to decideif Learning Screen (LS - page, slide, window scrollregion, etc.) is useful. After entering the LS learnersearches for Good Content Indicators (GCI). GCIs arehighly context dependant. The main context buildingcomponents are: (1) learner’s type characterized byAnnual Proceedings of Vidzeme University College “ICTE in Regional Development”, 200633
Page 1 and 2: ISBN 9984-633-03-9Annual Proceeding
Page 3 and 4: “Development of Creative Human -
Page 5 and 6: TABLE OF CONTENTSINTELLIGENT SYSTEM
Page 7 and 8: INTELLIGENT SYSTEM FOR LEARNERS’
Page 9 and 10: LEARNER 1GROUP OF HUMAN AGENTSLEARN
Page 11 and 12: QuantityQuantityFigure 6. Distribut
Page 13 and 14: LEARNERStructure of theconcept mapL
Page 15 and 16: WEB-BASED INTELLIGENT TUTORING SYST
Page 17 and 18: materials to be presented and which
Page 19 and 20: INFORMATION TECHNOLOGIES AND E-LEAR
Page 21 and 22: correspondence with the course aim
Page 23 and 24: projects and through IT. Hence, it
Page 25 and 26: APPLICATION OF MODELING METHODS IN
Page 27 and 28: can support configuration managemen
Page 29 and 30: The EKD is one of the Enterprise mo
Page 31 and 32: CHANGES TO TRAINING AND PERSPECTIVE
Page 33 and 34: or an end, yet none of these attitu
Page 35 and 36: make decisions. It cannot be volunt
Page 37: logs), data and video conferencing
Page 41 and 42: CONCLUSIONSEDUSA method gives us a
Page 43 and 44: in successful SD. Given this situat
Page 45 and 46: SPATIAL INFORMATIONFor the visualis
Page 47 and 48: MOBILE TECHNOLOGIES USE IN SERVICES
Page 49 and 50: learning environment (Learning Mana
Page 51 and 52: ago only some curricula on Logistic
Page 53 and 54: The Web-based version can be access
Page 55 and 56: Web-portal, which incorporates diff
Page 57 and 58: DO INTELLIGENT OBJECTS AUTOMATICALL
Page 59 and 60: Table 1. Examples for introducing R
Page 61 and 62: workable influencing of the process
Page 63 and 64: are handed over to the objects and
Page 65 and 66: • Basic processes, such as wareho
Page 67 and 68: THE ECR E-COACH: A VIRTUAL COACHING
Page 69 and 70: participating in the workshops and
Page 71 and 72: • Assessment modules enable indiv
Page 73 and 74: with pictures and illustrated graph
Page 75 and 76: ECR Question Banknumber category su
Page 77 and 78: educational programme that follows
Page 79 and 80: DEVELOPMENT OF WEB BASED GRAVITY MO
Page 81 and 82: These results of a model require a
Page 83 and 84: CONCLUSIONSThe main goal of work ha
Page 85 and 86: dimension and included within any o
Page 87 and 88: • Resources sharing by providing
Page 89 and 90:
Pursuant to the guidelines of elect
Page 91 and 92:
tariffs of regulated services have
Page 93 and 94:
INFORMATION TECHNOLOGY FOR MOTIVATI
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difficult to predict when and for w
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Listeners' workon the WebListenersS
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PERSPECTIVES OF WEB PAGE AND E-MAIL
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INCREASE IN THE NUMBER OF INTERNETU
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tourism accommodations (home pages
Page 105 and 106:
interactive relationships with clie
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• The data obtained by the resear
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Central Statistical Bureau of Latvi
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departures for 1995 are taken from
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120100maximumworldminimum806040200-
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140120maximumworldminimum1008060402
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would be a promising extension. Cur
Page 119 and 120:
AN OVERVIEW OF THE AGENT − BASED
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Suitability for social system simul
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6. MASONDescription:MASON is a fast
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Suitability for social system simul
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could be bad particularly when over
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(for 10 repeat &| CCar[]->runfor);P
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• Streaming audio• Collaboratio
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NECESSITY OF NEW LAYERED APPROACH T
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Up to now, there has only been limi
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aaaaa6= −aa2,1 = − a0,3226= −
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∂ u∂x∂ u∂y2 2+ b = 02 2wher
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a6,3= −2030a4,5−130a4,3- - - -
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0,10,20,30,4( )Mag x y y Ge wx2, =
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Example 1. To understand better the
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Therefore, further the following co
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SOLUTION OF THE THREE-DIMENSIONALEQ
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Mag1, m , m , m1 2 3= mm1 m2m32 2 2
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MagMag0, m , m , m1 2 31, m , m , m
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CONCLUSIONSThe basic content of thi
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