Multimodal Learning Analytics

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This document discusses multimodal learning analytics (MmLA), which analyzes learning traces from multiple data sources to understand learning. It describes the rationale for MmLA as clickstream data alone provides limited insights. The process involves sensing data from tools like cameras and microphones, perceiving learning traces using techniques like pose detection and speech recognition, feature extraction, data fusion, and providing feedback. Examples of MmLA systems that provide automatic feedback on oral presentations are presented. Directions for MmLA include using non-intrusive sensors at scale, artificial sensing to augment humans, and connecting it to educational research to gain deeper insights into learning contexts.

Multimodal
Learning Analytics
Xavier Ochoa
New York University
Rationale, Process,
Example and Direction

2
Get the Slides
https://tinyurl.com/mmlaLASER

Learning analytics is the
measurement, collection, analysis
and reporting of data about
learners and their contexts, for
purposes of understanding and
optimizing learning and the
environments in which it occurs.

5
Images in public domain. Obtained from Wikimedia Foundation.

6
Images in public domain. Obtained from Wikimedia Foundation.

7
Images in public domain. Obtained from Wikimedia Foundation.

Abundant digital
traces of
student actions
Data is there and it is ready to be used

Streetlight Effect
in Learning Analytics
Image taken from The Streetlight Effect—Is There Light at the End of the Tunnel?
Åke Lernmark, Diabetes. Apr 2015, 64 (4) 1105-1107

We are reaching the
limits of what
clickstream data can
tell us
Maybe it is time to look in a
different place

Multimodal Learning
Analytics
MmLA
Going beyond digital traces

Multimodal
Learning
Analytics
Analyzing learning traces in
multiple modalities to support
learning process stakeholders

The machine
should be closer to
the human
Sense like a human
Perceive like a human
Think like a human
Compute like a machine
Communicate with humans

01
Persiving
Feature Extraction
Analyzing
Fusion
Sensing
Recording
02 03
Feedback
Multimodal
Interfaces
04

Sense like a human
Sense the world through multiple modalities

Sensors
Cameras
Depth Camera
Eye Tracker
Microphones / Arrays
Position Tracker
Pulse / GSR
Digital Pens
EEG

Perceive like a human
Detect learning traces in those modalites

Video – Posture - OpenPose
https://colab.research.google.com/drive/1Ap3MH00-0GGXM2td3twXP7pUp7GIXlO2?usp=sharing

Video – Facial Landmarks – MediaPipe
https://colab.research.google.com/drive/1hwMhkRlf70cTnD_nlVCnMwyGIqTXXuY8?usp=sharing

Video – Hands – MediaPipe
https://colab.research.google.com/drive/1ZudZqefYiovVBsLuQpCa46MJ1gUQbusP

Video – Holistic - MediaPipe
https://colab.research.google.com/drive/18I5T7RJdRG3L6ZigiZ4zYTgDodpsdT5i?usp=sharing

Video – Object Detection - YOLO
https://colab.research.google.com/drive/1yKaFjhbbyN8eKnPhjtr7BM_My1apSeBI?usp=sharing

Audio – Speech Recognition – NVIDIA
Nemo
https://colab.research.google.com/drive/1k9-q-OgBwjYbuLbNWXkJ40rGt5flztPn?usp=sharing

Audio – Speech Diarization – Pyannote
https://colab.research.google.com/drive/1_A5C8xNW2TDDyoPkOO3Fcw-uuCF0lO_c?usp=sharing

EZ-MMLA: https://mmla.gse.harvard.edu/
Try it Yourself

Compute like a
machine
Being able to handle and simplify large amounts of data

Communicate with
humans
Use multimodal ways to interact

Collaboration
Constructs
Traces
Behavioral
Indicators
Modalities
Sensors
Feature
Extraction
Feedback
Analytics
Collaboration
Feedback
THEORY
TOOL
PEDAGOGICAL
TECHNOLOGICAL
+ Independent + Dependent
CONTEXT

RAP System
(Oral Presentation Automatic Feedback)
Ochoa, et al. The RAP System: Automatic Feedback of Oral Presentation Skills using Multimodal Analysis and Low-Cost Sensors
Learning Analytics and Knowledge Conference 2018

OpenOPAF
Open Source Oral Presentation Feedback

Multimodal Learning
Analytics is not a
just a sub-field of of
Learning Analytics
It is its natural evolution

Tendency to use
non-intrusive
sensors
Make it scalable
and practical

Using Artificial
Sensing
Not replacing humans,
but augmenting them

MmLA could help us
bridge Learning
Analytics with more
traditional
Educational
Research
Focus on the same learning contexts

You could be a
MmLA researcher
and practitioner!
Join SoLAR
CrossMmLA SIG

CREDITS: This presentation template was
created by Slidesgo, including icons by Flaticon,
and infographics & images by Freepik.
THANKS!
Xavier Ochoa
xavier.ochoa@nyu.edu
http://wp.nyu.edu/xavier_ochoa
Twitter: @xaoch

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What's a Research Roadmap For? Why do we need one? How can we avoid the usual trap of making bold promises to do X, Y and Z, then hope that our previous promises will not be remembered the next time we apply for funds to do X, Y and Z? How can we produce a sensible, well informed roadmap? Originally presented at the euCognition Research Roadmap discussion in Munich on 12 Jan 2007 This suggests a way to avoid tempting dead ends (repeating old promises that proved unrealistic) by examining many long term goals, including describing existing human and animal competences not yet achieved by robots, then working backwards systematically by investigating requirements for those competences, and requirements for meeting those requirements, etc. Insread of generating a single linear roadmap this should produce a partially ordered network of intermediate targets, leading back, to short term goals that may be achievable starting from where we are. Such a roadmap will inevitably have mistakes: over-optimistic goals, missing preconditions, unrecognised opportunities. But if the work is done in many teams in a fully open manner with as much collaboration as possible, it should be possible to make faster, deeper, progress than can be achieved by brain-storming discussions of where we can get in a few years.

Learning Analytics: Today, Tomorrow, and When We Get Flying Cars #psuweb Conf...

Megan Bowe

This document discusses learning analytics, which involves measuring, collecting, analyzing, and reporting data from educational contexts to understand and optimize learning. Learning analytics uses techniques from various fields to analyze data collected during instruction and administration. Educational data mining develops methods to analyze teaching and learning data to test theories and inform practice. The document outlines challenges in defining goals and accessing relevant data from various sources for learning analytics. It also discusses moving from today's focus on basic metrics to a more holistic view integrating diverse data sources, and envisions future advances in learning analytics through standardized measures and predictive capabilities to recommend optimal learning experiences.

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