The final presentation of the master thesis project Visual Learning Pulse: Flow Prediction and Feedback in Self-Regulated Learning, a project collaboration between the Department of Data Science and Knowledge Engineering of the Univeristy of Maastricht and the Welten Institute of the Open University in the Netherlands.
TITLE:
Visual Learning Pulse: Flow Prediction and Feedback in Self-regulated Learning
ABSTRACT:
Visual Learning Pulse is a Master thesis research project developed in cooperation with the Welten Institute, the Research Centre for Learning, Teaching and Technology at the Open University of the Netherlands, and partially nanced by the European project Learning Analytics Community Exchange (LACE). Visual Learning Pulse explores whether physiological and physical data such as heart rate, step count and weather data if correlated with learning activity data can be used to predict learning success in self-regulated learning settings.
To verify this hypothesis an experiment was opportunely designed, consisting of three phases, lasting six weeks and involving nine participants, each of them wearing a Fitbit HR wrist band and having their application usage recorded during their learning and working activities throughout the day. An ad-hoc infrastructure for longitudinal and multi-modal data was designed and implemented. The data from dierent sources were stored using the Experience API (xAPI) data standard in a cloud distributed database called Learning Record Store.
The participants (doctoral students at the Open Universiteit) - were asked to rate their learning experience through an Activity Rating Tool indicating their perceived level of productivity, stress, challenge and abilities. These self-reported performance indicators were used as training labels for the two algorithms employed for prediction of time series data: the Vector Autoregression and Linear Mixed Eect Model.
A major task of the thesis consisted of developing the software application to pre-process, perform the analysis and generate the predictions on real time, in order to provide timely feedback to the users about their learning performances. Although not showing high overall accuracy, the prediction models were successfully learnt and used in production: in the third phase of the experiment, two visualisations mechanisms were used, the Learner Dashboard and the Feedback Cubes.
In addition, a conceptual paper of Visual Learning Pulse, illustrating setup and overall the rationale was presented at the Learning Analytics & Knowledge conference 2016 in Edinburgh, Scotland and was included in CEUR workshop proceedings.
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Visual Learning Pulse - Final Thesis presentation
1. UM supervisors
Kurt Driessens
Pietro Bonizzi
OU supervisors
Hendrik Drachsler
Maren Scheffel
Maastricht, 29th June 2016
Daniele DI MITRI presents
MSc Thesis in Artificial Intelligence
2. Visual Learning Pulse – Final thesis presentation
2
What was done - visual
21/09/2015
Internship starts
21/12/2015
Internship ends
Design
pre-test
Experim
ent
Implement
29/06/2016
Thesis ends
Report
01/02/2016
Thesis starts
Paper submitted
to LAK conference
Analysis,literature
18-25/06/16JTEL summerschool
25-29/04/16
LAK conference
Coding
31/03/16
Announcing
Presentation
Trainingphase
Validation
phase
Exploitation
phase
Reporting
17/05/16
11/04/16
30/05/16
8 months
of work
3. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
What was done - numbers
3
1 publication
2 conferences
2 software apps
6 presentations
9 blog posts
20+ meetings
1800 lines of code
4. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
Learning Analytics & Knowledge
Conference 2016
4
Di Mitri, Scheffel, Drachsler, Börner, Ternier
2016 - Learning Pulse : using Wearable Biosensors
and Learning Analytics to Investigate and Predict
Learning Success in Self - regulated Learning.
8. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
Self-regulated learners need support
8
Self-Regulated Learning → no guidance → no feedback → no support
9. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
Related work
9
Signals, Purdue University Student success, University North Dakota
S3, Desire To Learn
11. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
Machine Learning with Human Learning
11
y = f(X)
Learning
performance Predictive
Model
Input
space
13. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
13
RESEARCH QUESTION
Can we predict learning success out of
physiological, activity and weather data?
13
14. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
14
Participants
● 9 PhD students at Welten institute
● Different disciplines
● Different OS
15. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
Experiment Timeline
15
11th
to 29th
April 2016
1st phase: “Training”
Participants rate their activity
17nd
to 27th
May 2016
2nd phase: “Validation”
Participants rate their activity +
Feedback visualization
30th
May to
3th
June 2016
3rd phase: “Exploitation”
Individual and group Feedback
visualization
16. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
16
Input space
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
18. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
Activity Rating Tool
18
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!”
20. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
20
Scheffel, M., Ternier, S., & Drachsler, H. (2016). The Dutch xAPI Specification for Learning Activities http://bit.ly/DutchXAPIreg
Experience API Data storing format for the
Learning Record Store
23. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
Data collection
23
● 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
24. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
Data Processing application
24
Script in Python on a VM which processes data in real time
25. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
25
Transformed dataset
● Time Series: tabular representation
● 5 minutes intervals
● Enough samples now!
● Easier view for Machine Learning
● Signal resampling needed
8728
observations
X
29 attributes
26. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
26
(Issue 1) Extract features from TS
Heart Rate Variability and
Heart Rate Entropy… didn’t
work
SOLUTION
● Mean of the signal
● Maximum
● Minimum
● Standard Deviation
● Average change
Heart-ratesignalfor15mins
27. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
27
(Issue 2) Reduce sparsity
Rule based grouping of applications
Subjects can be compared
Applications used are
too sparse
Let’s create
application categories
28. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
28
(Issue 3) Ladder effect
Trade-off:
number of samples
vs
How much
bother people
NO SOLUTION
29. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
29
(Issue 4) Dependency constraint
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...
30. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
Approach 1) Vector Auto Regression
30
x0
x1
x2
x...
an
t0 x x x ... x
t1 x x x ... x
t... ... ... ... ... ...
tp x x x ... x
tp+1 ? ? ? ? ?
tp+2 ? ? ? ? ?
PAST
PRESENT
FUTURE
Time intervals
PREPROCESS
Timeseries were LOGged
LIMITATIONS
● Participants need to
be treated separately
● Doesn’t work with
categorical data
● Doesn’t work with
random effects
31. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
Approach 2) Mixed Effect Linear Model
31
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
Tried both Python and R
implementations
Used R-squared for
goodness-test
LIMITATIONS
● Poor results
● Convergence time
● Mono-output
● Algebra errors
32. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
32
Issue: high inter-subject variability
i.e. Participants have rated very differently
37. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
37
Limitations
● Low accuracy of prediction
RQ-answer: YES but prediction accuracy can be improved.
● Real-time issues
Fitbit synchronisations, Virtual Machine performance
● 3rd party API constraints
● No great solution for sparse data (manual grouping)
38. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
38
Achievements
● Real-time system works
● Data collection was seamless
● Good dataset for experiments (will be open sourced)
● Useful insights IoT in Learning
● Reusable architecture
40. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
40
Modelling sparse data (idea)
a1 a2 a3 a4 a5 a6
Ft
Ft+1
Ft+2
Hidden Flow values
Ft+3
Random sampling
a7 a8
Visible applications
Hidden
Markov
Chains
+
Random sampling
41. Open Universiteit
Welten Institute
Visual Learning Pulse – Final thesis presentation
41
Thank you!
Q&A
“Life can only be understood backwards, but it
must be lived forwards” - Kierkegaard