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Learning Pulse - paper presentation at LAK17

Daniele Di Mitri
Daniele Di Mitri
Daniele Di MitriResearch group leader at DIPF | Leibniz Institute for Research and Information in Education

This is the presentation of the paper "Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data" which I delivered at the Learning Analytics and Knowledge conference 2017 in Vancouver, Canada. http://dl.acm.org/citation.cfm?id=3027447&CFID=912205331&CFTOKEN=43442860

Learning Pulse - paper presentation at LAK17

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Learning Pulse
D. Di Mitri, M. Scheffel, H. Drachsler, D. Börner, S. Ternier, M. Specht
A machine learning approach for
predicting performance in self-regulated
learning using multimodal data
Paper presentation at LAK17
15th March 2017, Vancouver, Canada
Outline
1. Background, context, vision
2. Our approach
3. Data collection
4. Data analysis
5. Conclusions
Pagina 2
Data deluge in education
Pagina 3
Collecting learning experiences
Picture from tincanapi.com
Pagina 4
Pagina 5
Learning happening across spaces
Context: Self Regulated Learning
Self-Regulated Learning → no guidance → no feedback → no support
Pagina 6

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Learning Pulse - paper presentation at LAK17

  • 1. Learning Pulse D. Di Mitri, M. Scheffel, H. Drachsler, D. Börner, S. Ternier, M. Specht A machine learning approach for predicting performance in self-regulated learning using multimodal data Paper presentation at LAK17 15th March 2017, Vancouver, Canada
  • 2. Outline 1. Background, context, vision 2. Our approach 3. Data collection 4. Data analysis 5. Conclusions Pagina 2
  • 3. Data deluge in education Pagina 3
  • 4. Collecting learning experiences Picture from tincanapi.com Pagina 4
  • 6. Context: Self Regulated Learning Self-Regulated Learning → no guidance → no feedback → no support Pagina 6
  • 7. Vision: machine learning approach y = f(X) Learning Performance (output space) Predictive Model Multimodal Data (input space) Pagina 7
  • 9. Research questions (RQ-MAIN) How can we store, model and analyse multimodal data to predict performance in human learning? (RQ1) Which architecture allows the collection and storage of multimodal data in a scalable and efficient way? (RQ2) What is the best way to model multimodal data to apply supervise machine learning techniques? (RQ3) Which machine learning model is able to produce learner specific predictions on multimodal data? Pagina 9
  • 10. Participants • 9 PhD students at Welten institute • Different disciplines • Different working setups: – Time – Tasks – Operating systems Pagina 10
  • 11. Experimental timeline Pagina 11 Phase 0 Pre-test System architecture tested Phase 1 Training 3 weeks of data collection Phase 2 Validation 2 weeks of data collection and prediction
  • 12. Input space – multimodal data Pagina 12 Context Body Activities Body: physiological (heart-rate) and physical responses (steps) - from Fitbit HR Activities: applications used during learning from RescueTime Context: weather data from OpenWeatherMap
  • 13. Output space – Flow Csikszentmihalyi, 1972 Pagina 13 Theoretical Empirical
  • 14. Activity Rating Tool Productivity How productive was last activity? Stress How stressful was last activity? Challenge How challenging was last activity? Abilities How prepared did you feel for the activity? FLOW Participants rate hourly, from 7AM to 7PM A scalable web app! Client: Bootstrap + Jquery Sever: GoogleApp + Python “Very easy to use!” Pagina 14
  • 17. Berg, A., Scheffel, M., Drachsler, H., Ternier, S. & Specht, M. (2016). The Dutch xAPI Experience. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK’16), April 25-29, 2016, Edinburgh, UK. Data storing format for the Learning Record StoreExperience API
  • 20. Data collection • PULL data from the 3rd party APIs • Make the xAPI triples • PUSH data in the LRS • It’s scalable! • No collisions • It’s fast • It’s Interoperable Learning Pulse Server + Learning Record Store Pagina 20
  • 21. Data Processing Application Script in Python running on a VM which processes data in real time Pagina 21
  • 23. Transformed dataset • Time Series: tabular representation • 5 minutes intervals • Enough samples now! • Easier view for Machine Learning • Signal resampling needed 9410 observations X 29 attributes Pagina 23
  • 24. Issue 1) Feature extraction from Time Series Heart Rate Variability and Heart Rate Entropy… didn’t work SOLUTION • Mean of the signal • Maximum • Minimum • Standard Deviation • Average change Heart-ratesignalfor15mins Pagina 24
  • 25. Issue 2) Activity data very sparse Rule based grouping of applications Learners’ activity can be compared! Applications used are too sparse SOLUTION Let’s create application categories Pagina 25
  • 26. Issue 3) Number of labels available Trade-off: number of labels vs Seamlessness of the data collections NO SOLUTION Pagina 26
  • 27. Issue 5) Random vs continuous data Independence constraint Knowing one value of et for one observation does not help us to guess value of et+1 yt = α + βX t + et cov(et ,et+1) = 0 FIXED Effect RANDOM Effect SOLUTION follows... Pagina 27
  • 28. Mixed Effect Linear Model x0 x1 x2 ... xn-1 xn g y t0 x x x ... x x 1 y t1 x x x ... x x 1 y t2 x x x ... x x 2 y t... ... ... ... ... ... ... 2 y tp -1 x x x x x x 3 y tp ? ? --- --- --- --- x ? Random EffectsFixed Effects Group Used R-squared for goodness-test LIMITATIONS ● Convergence time ● Mono-output Pagina 28
  • 29. Issue 6) Inter-subject variability i.e. Participants have rated very differently SOLUTION Predictions are normalised wrt each learner xnew = (xmax – xmin) *xi/100 + xmin Pagina 29
  • 31. RQ1) Architecture The architecture developed was able of: 1. Importing great number of sensor data in xAPI format; 2. combining sensor data with self-reports 3. programmatically transform xAPI data 4. train predictive models & reuse them 5. save the predictions to compare with actual values Pagina 31
  • 32. RQ2) Represent multimodal data • Multiple Instance Representation • Each learning sample is a 5 minute interval • It’s suitable for machine learning Pagina 32
  • 33. RQ3) Machine learning model • Linear Mixed Effect Models allow 1. taking into account data specific to each learner 2. distinguish between fixed and random effects 3. Take categorical data into account. Pagina 33
  • 34. Limitations • Low accuracy of predictions R-Square tests Stress: 0.32, Challenge: 0.22, Flow score: 0.16, Abilities: 0.08, Productivity: 0.05. • Real-time issues Fitbit synchronisation, Virtual Machine performance • 3rd party API constraints • No great solution for grouping activity data (manual grouping) Pagina 34
  • 35. Opportunities • Data driven • Real Time feedback • Visualisations can show feedback • Seamless data collection • Multimodal dataset for reserach • Reusable architecture Pagina 35 *Börner, Tabuenca, Storm, Happe, and Specht. 2015 Example visualisation: The Feedback Cube*
  • 36. Q&A Thanks for listening! Daniele Di Mitri ddm@ou.nl @dimstudi0 Pagina 36 Check my poster!