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learning skills and attentional parameters and (2)material’s type characterized by interactivity parameters(game-like material, reading/viewing material, etc.).E-Gestures and Good Content IndicatorsGood electronic learning materials have (1) goodcontent and (2) good content organization (navigation,design style guides, etc.).Strategies of finding good content are very differentfor each learner. Learners have different knowledge,different skills, different learning objectives, differentlearning paths, and different positions within theparticular learning path. All this must lead to varyingperception of what a good content (1) for a specificstudent (2) at a specific time actually mean?As the victory march of smart and fast Internet searchengines has shown, the most commonly used techniqueof finding good content is using keywords. Therefore,keywords are probably the today’s top ranked GoodContent Indicators. But as presumably every googler hasnoticed not every GCI points to a really valuable content.Table 1 shows how EDUSA’s interpreter processesuser’s e-gestures.DecisionSlot Nr.Table 1. Interpretation of e-gestures.e-GestureDescription of impliedUser actions1 Browsing 1. User has entered a newLearning Screen (LS -page, slide, window scrollregion, etc.)2. User browses the LS forGood Content Indicators(GCI)3. User finds no GCI4. User leaves the LS2 Discovering 3. User finds GCI4. User evaluates GCI’s ifthere is really good contentbehind them5. User finds no goodcontent behind the GCI6. User leaves the LS3 Learning 5. User finds good contentbehind the GCI6. User consumes goodcontent = learns (readstext, views image or video,explores diagram, etc.)7. After learning userleaves the LS4 Logged off 7. After learning user doesnot leave the LS (gone fora coffee break?)EXPERIMENTAL RESULTS OF E-COURSEEVALUATION USING EDUSAFigure 5 shows the experimental data and calculatedrelative probability of participation intervals ininteractive multimedia e-course SQL Fundamentals usingEDUSA method: 1 – Learning curve, 2 – Discoverycurve, 3 – Additive curve (Discovery curve + Learningcurve).Figure 5. EDUSA measurement of e-course delivery -relative probability of observation intervals in interactivemultimedia e-course SQL Fundamentals.Log file data for the subsequent measurements wererecorded during the testing session in Liepaja Academyof Pedagogy/Latvia with 25 participants.The subject for tests was SQL Fundamentals e-coursedeveloped at Riga Technical University DistanceEducation Study Centre.Participants explored units 1 to 6 from altogether 14course units. Browsing the web, reading e-mails, chattingand similar multi-tasking activities were explicitlypermitted.Measurements were calculated using time intervals of250ms. In order to interpret the results, user participationevents (interactivities with computer: mouse clicks,mouse move events, key press events, etc.) wereevaluated.Experimental data shows the presence of twoprobability distributions. Participation events withinshorter time intervals, approx. 3 to 8s, were interpreted asDiscovering events (see: Discovering curve, Figure 5),participation events with longer time intervals, approx. 8to 25s, were interpreted as Learning events (see:Learning curve, Figure 5).Additive curve was calculated to assess the selected 2-components model of multi-tasking evaluation accordingto the recorded data. Both components were described asnormal distribution curves that allowed goodinterpretation of the results of experiment.Annual Proceedings of Vidzeme University College “ICTE in Regional Development”, 200634