Visual Learning Pulse - Final Thesis presentation

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UM supervisorsKurt DriessensPietro BonizziOU supervisorsHendrik DrachslerMaren ScheffelMaastricht, 29th June 2016Daniele DI MITRI presents MSc Thesis in Artificial IntelligenceVisual Learning Pulse Final thesis presentation 2What was done - visual21/09/2015Internship starts21/12/2015Internship endsDesignpre-testExperimentImplement29/06/2016Thesis endsReport01/02/2016Thesis startsPaper submitted to LAK conferenceAnalysis, literature18-25/06/16JTEL summerschool25-29/04/16LAK conferenceCoding31/03/16Announcing PresentationTraining phaseValidation phaseExploitationphaseReporting17/05/1611/04/1630/05/168 monthsof workOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation What was done - numbers31 publication2 conferences 2 software apps6 presentations 9 blog posts 20+ meetings1800 lines of codeOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Learning Analytics & Knowledge Conference 20164Di Mitri, Scheffel, Drachsler, Brner, Ternier 2016 - Learning Pulse: using Wearable Biosensors and Learning Analytics to Investigate and Predict Learning Success in Self - regulated Learning. Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 5Background,meaning, vision5Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Data deluge in Education6Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 7Data-driven approachPicture from tincanapi.comhttps://tincanapi.com/Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Self-regulated learners need support8Self-Regulated Learning no guidance no feedback no supportOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Related work 9Signals, Purdue University Student success, University North DakotaS3, Desire To LearnOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Dimensions of Learning10Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Machine Learning with Human Learning11y = f(X)Learningperformance Predictive ModelInput spaceOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 12Own Approach12Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 13RESEARCH QUESTIONCan we predict learning success out of physiological, activity and weather data?13Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 14Participants 9 PhD students at Welten institute Different disciplines Different OSOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Experiment Timeline1511th to 29th April 20161st phase: TrainingParticipants rate their activity17nd to 27th May 20162nd phase: ValidationParticipants rate their activity + Feedback visualization30th May to 3th June 20163rd phase: ExploitationIndividual and group Feedback visualizationOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 16Input spaceContextBodyActivitiesBody: physiological (heart-rate) and physical responses (steps) - from Fitbit HR Activities: applications used during learning from RescueTimeContext: weather data from OpenWeatherMapOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 17Hypothesis space: the Flow Csikszentmihalyi, 1972Theoretical EmpiricalOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Activity Rating Tool18ProductivityHow productive was last activity?StressHow stressful was last activity?ChallengeHow challenging was last activity?AbilitiesHow prepared did you feel for the activity? FLOWParticipants rate hourly, from 7AM to 7PMA scalable web app!Client: Bootstrap + JquerySever: GoogleApp + PythonVery easy to use!Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 19VLP Data ModelWeather dataDashboardFeedback CubesOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 20Scheffel, M., Ternier, S., & Drachsler, H. (2016). The Dutch xAPI Specification for Learning Activities http://bit.ly/DutchXAPIregExperience API Data storing format for the Learning Record Storehttp://bit.ly/DutchXAPIregOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 21The Data journeyOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation A complicated Architecture22Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Data collection23 PULL data from the 3rd party APIs Make the xAPI triples PUSH data in the LRS Its scalable! No collisions Its fast Its InteroperableLearning Pulse Server +Learning Record StoreOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Data Processing application24Script in Python on a VM which processes data in real timeOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 25Transformed dataset Time Series: tabular representation 5 minutes intervals Enough samples now! Easier view for Machine Learning Signal resampling needed8728observations X29 attributesOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 26(Issue 1) Extract features from TSHeart Rate Variability and Heart Rate Entropy didnt workSOLUTION Mean of the signal Maximum Minimum Standard Deviation Average changeHeart-rate signal for 15 minsOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 27(Issue 2) Reduce sparsityRule based grouping of applicationsSubjects can be comparedApplications used are too sparseLets create application categoriesOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 28(Issue 3) Ladder effectTrade-off: number of samples vs How much bother peopleNO SOLUTIONOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 29(Issue 4) Dependency constraintIndependence constraintKnowing one value of et for one observation does not help us to guess value of et+1 yt = + X t + et cov(et ,et+1) = 0FIXED EffectRANDOM EffectSOLUTION follows...Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Approach 1) Vector Auto Regression30 x0 x1 x2 x... ant0 x x x ... xt1 x x x ... xt... ... ... ... ... ...tp x x x ... xtp+1 ? ? ? ? ?tp+2 ? ? ? ? ?PASTPRESENTFUTURETime intervalsPREPROCESSTimeseries were LOGgedLIMITATIONS Participants need to be treated separately Doesnt work with categorical data Doesnt work with random effectsOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Approach 2) Mixed Effect Linear Model31 x0 x1 x2 ... xn-1 xn g yt0 x x x ... x x 1 yt1 x x x ... x x 1 yt2 x x x ... x x 2 yt... ... ... ... ... ... ... 2 ytp-1x x x x x x 3 ytp ? ? --- --- --- --- x ?Random EffectsFixed Effects GroupTried both Python and R implementationsUsed R-squared for goodness-testLIMITATIONS Poor results Convergence time Mono-output Algebra errorsOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 32Issue: high inter-subject variabilityi.e. Participants have rated very differently Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Visualisations3333Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 34Learner Dashboard 9 personal Dashboard + 1 publicOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 35*Brner, Tabuenca, Storm, Happe, and Specht. 2015Feedback Cubes Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Conclusions3636Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 37Limitations Low accuracy of prediction RQ-answer: YES but prediction accuracy can be improved. Real-time issuesFitbit synchronisations, Virtual Machine performance 3rd party API constraints No great solution for sparse data (manual grouping)Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 38Achievements Real-time system works Data collection was seamless Good dataset for experiments (will be open sourced) Useful insights IoT in Learning Reusable architectureOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation Future ideas3939Open UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 40Modelling sparse data (idea)a1 a2 a3 a4 a5 a6Ft Ft+1 Ft+2Hidden Flow valuesFt+3Random samplinga7 a8Visible applicationsHiddenMarkov Chains+Random samplingOpen UniversiteitWelten InstituteVisual Learning Pulse Final thesis presentation 41Thank you! Q&ALife can only be understood backwards, but it must be lived forwards - Kierkegaard