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Multimodal Learning Analytics

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Xavier Ochoa
Xavier Ochoa

Presentation at #edumetricas Montevideo, Uruguay 2016

Education
1 of 72
Multimodal Learning Analytics Xavier Ochoa Escuela Superior Politécnica del Litoral #edumetricas, Montevideo, UY 15 April 2016
http://www.slideshare.net/xaoch Download this presentation
(Multimodal) Learning Analytics
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.
Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs) Using transaction-level data to diagnose knowledge gaps and misconceptions Likelihood analysis of student enrollment outcomes using learning environment variables: a case study approach Tracking student progress in a game- like learning environment with a Monte Carlo Bayesian knowledge tracing model
Strong focus on online data Based on the papers it should be called Online-Learning Analytics
Streetlight effect
Where learning is happening?
Why Multimodal Learning Analytics? We should be looking where it is useful to look, not where it is easy
There is learning outside the LMS But it is very messy!
Who is learning?
Who is learning?
Who is learning?
Who is learning? – Traditional way
But there are better ways to assess learning At least theoretically
Who is learning? – Educational Research
How can we approach the problem from a Learning Analytics perspective Measure, collect, analyze and report to understand and optimize
We need to capture learning traces from the real world Look ma, no log files!
In the real world, humans communicate (and leave traces) in several modalities What you say is as important as how you say it 😀
We need to analyze the traces with variable degrees of sophistication And we have to do it automatically as humans are not scalable
We need to provide feedback in the real world Often in a multimodal way too
But…
Which modes are important to understand the learning process? We do not know yet…
Possibilities • What we see • What we hear • How we move • How we write • How we blink • Our pulse • Brain activity? • Our hormones?
What are the relevant features of those signals We do not know yet…
Our current analysis tools are good enough? We do not know yet…
How to present the information (and uncertainty) in a way that is actually useful? We do not know yet…
It is an open (but very dark) field One feels like an explorer
This particular flavor of Learning Analytics is what we called Multimodal Learning Analytics
Multimodal Learning Analytics is related to: • Behaviorism • Cognitive Science • Multimodal Interaction (HCI) • Educational Research (old school one) • Computer Vision • Natural Language Processing • Biosignals Processing • And as many fields as modes you can think of...
Examples
Math Data Corpus
How to (easily) obtain multimodal features? What is already there?
Three Approaches • Literature-based features • Common-sense-based features • “Why not?”-based features
All approaches proved useful Proof that we are in an early stage
Video: Calculator Use (NTCU) et s of s was t , on t hat eived so in- n and lving ed by udent e was score given diffi- ssion, n ex- Mat h at t et direct ion in which it was point ing at using t he rigid t rans- format ions capabilit ies provided by OpenCV . W hile t here were some frames in which t his mat ching was not possible due t o object occlusions or changes in t he illuminat ion of t he calculat or, in general t he described det ect ion t echnique was robust and provided useful posit ion and direct ion dat a. F igur e 1: D et er m in at ion of w hich st udent is usin g
Video: Total Movement (TM) fined as t he number of whit e pixels cont ained in t he binary image out put by t he Codebook algorit hm. T his magnit ude, when comput ed for t he ent ire problem solving session, re- sult s from summing up it s individual values obt ained from each frame t hat compose a problem recording. (a) Original frame (b) Difference frame F igur e 2: R esult s of t he C odeb ook algor it hm .
Video: Distance from center table (DHT) he de- oblem ed t o calcu- par- a spe- ed as e pre- Code- ficant small ondi- where ained s de- inary t ude, n, re- from each head cent roid t o t he cent er of t he t able is calculat ed and t hen, t he average of t hese dist ances is obt ained by prob- lem (see Figure 3). Addit ionally, t he variance of t he average dist ance head t o t able (SD-DHT ), was calculat ed t o det er- mine if a part icipant remains most ly st at ic or varies his or her dist ance t o t he t able. F igur e 3: C alculat ion of t he dist ance of t he st udent ’s
Audio: Processing
Audio: Features • Number of Interventions (NOI) • Total Speech Duration (TSD) • Times Numbers were Mentioned (TNM) • Times Math Terms were Mentioned (TMTM) • Times Commands were Pronounced (TCP)
Digital Pen: Basic Features • Total Number of Strokes (TNS) • Average Number of Points (ANP) • Average Stroke Path Length (ASPL) • Average Stroke Displacement (ASD) • Average Stroke Pressure (ASP)
Digital Pen: Shape Recognition Stronium – Sketch Recognition Libraries
Analysis at Problem level Solving Problem Correctly • Logistic Regression to model Student Solving Problem Correctly • Resulting model was significantly reliable • 60,9% - the problem solving student was identified • 71,8% - incorrectly solved problems were identified
Analysis at Group Level Expertise Estimation • Features were feed to a Classification Tree algorithm • Several variables had a high discrimination power between expert and non-experts • Best discrimination result in 80% expert prediction and 90% non-expert prediction
Expert Estimation over Problems Plateau reached after just 4 problems
Main conclusion: Simple features could identify expertise Faster Writer (Digital Pen) Percentage of Calculator Use (Video) Times Numbers were Mentioned (Audio)
Oral Presentation Quality Corpus
Video Features • 66 facial features were extracted using Luxand software including both eyes and nose tip to estimate the presenter’s gaze.
Kinect features • Identify Common postures
Kinect features • Identify Common postures
Kinect Features Laban’s theory helps to describe human movement using non-verbal characteristics: Spatial aspects of movement Temporal aspects of movement Fluency, smoothness, impulsivity Energy and power Overall activity
EVALUATION METHODOLOGY Extracted Features Human coded Criterion Video Kinect Body and Posture Language Eye Contact
Results: less than 50% accuracy What we were measuring was not was humans were measuring
What is next in MLA?
Mode integration framework for MLA Currently pioneered by Marcelo Worsley
Developing Multimodal Measuring Devices Our Fitbits
Record different learning settings And share them with the community
Conclusions
Multimodal Learning Analytics is not a subset of Learning Analytics Current Learning Analytics is a subset of MLA 
Some problems are easy, some hard But we do not know until we try to solve them
There is a lot of exploring to do And we need explorers
Gracias / Thank you Questions? Xavier Ochoa xavier@cti.espol.edu.ec http://ariadne.cti.espol.edu.ec/xavier Twitter: @xaoch http://www.sigmla.org

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Multimodal Learning Analytics

  • 1. Multimodal Learning Analytics Xavier Ochoa Escuela Superior Politécnica del Litoral #edumetricas, Montevideo, UY 15 April 2016
  • 4. Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.
  • 5. Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs) Using transaction-level data to diagnose knowledge gaps and misconceptions Likelihood analysis of student enrollment outcomes using learning environment variables: a case study approach Tracking student progress in a game- like learning environment with a Monte Carlo Bayesian knowledge tracing model
  • 6. Strong focus on online data Based on the papers it should be called Online-Learning Analytics
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15. Why Multimodal Learning Analytics? We should be looking where it is useful to look, not where it is easy
  • 16. There is learning outside the LMS But it is very messy!
  • 20. Who is learning? – Traditional way
  • 21. But there are better ways to assess learning At least theoretically
  • 22. Who is learning? – Educational Research
  • 23.
  • 24. How can we approach the problem from a Learning Analytics perspective Measure, collect, analyze and report to understand and optimize
  • 25. We need to capture learning traces from the real world Look ma, no log files!
  • 26. In the real world, humans communicate (and leave traces) in several modalities What you say is as important as how you say it 😀
  • 27. We need to analyze the traces with variable degrees of sophistication And we have to do it automatically as humans are not scalable
  • 28. We need to provide feedback in the real world Often in a multimodal way too
  • 30. Which modes are important to understand the learning process? We do not know yet…
  • 31. Possibilities • What we see • What we hear • How we move • How we write • How we blink • Our pulse • Brain activity? • Our hormones?
  • 32. What are the relevant features of those signals We do not know yet…
  • 33. Our current analysis tools are good enough? We do not know yet…
  • 34. How to present the information (and uncertainty) in a way that is actually useful? We do not know yet…
  • 35. It is an open (but very dark) field One feels like an explorer
  • 36. This particular flavor of Learning Analytics is what we called Multimodal Learning Analytics
  • 37. Multimodal Learning Analytics is related to: • Behaviorism • Cognitive Science • Multimodal Interaction (HCI) • Educational Research (old school one) • Computer Vision • Natural Language Processing • Biosignals Processing • And as many fields as modes you can think of...
  • 39.
  • 41. How to (easily) obtain multimodal features? What is already there?
  • 42. Three Approaches • Literature-based features • Common-sense-based features • “Why not?”-based features
  • 43. All approaches proved useful Proof that we are in an early stage
  • 44. Video: Calculator Use (NTCU) et s of s was t , on t hat eived so in- n and lving ed by udent e was score given diffi- ssion, n ex- Mat h at t et direct ion in which it was point ing at using t he rigid t rans- format ions capabilit ies provided by OpenCV . W hile t here were some frames in which t his mat ching was not possible due t o object occlusions or changes in t he illuminat ion of t he calculat or, in general t he described det ect ion t echnique was robust and provided useful posit ion and direct ion dat a. F igur e 1: D et er m in at ion of w hich st udent is usin g
  • 45. Video: Total Movement (TM) fined as t he number of whit e pixels cont ained in t he binary image out put by t he Codebook algorit hm. T his magnit ude, when comput ed for t he ent ire problem solving session, re- sult s from summing up it s individual values obt ained from each frame t hat compose a problem recording. (a) Original frame (b) Difference frame F igur e 2: R esult s of t he C odeb ook algor it hm .
  • 46. Video: Distance from center table (DHT) he de- oblem ed t o calcu- par- a spe- ed as e pre- Code- ficant small ondi- where ained s de- inary t ude, n, re- from each head cent roid t o t he cent er of t he t able is calculat ed and t hen, t he average of t hese dist ances is obt ained by prob- lem (see Figure 3). Addit ionally, t he variance of t he average dist ance head t o t able (SD-DHT ), was calculat ed t o det er- mine if a part icipant remains most ly st at ic or varies his or her dist ance t o t he t able. F igur e 3: C alculat ion of t he dist ance of t he st udent ’s
  • 48. Audio: Features • Number of Interventions (NOI) • Total Speech Duration (TSD) • Times Numbers were Mentioned (TNM) • Times Math Terms were Mentioned (TMTM) • Times Commands were Pronounced (TCP)
  • 49. Digital Pen: Basic Features • Total Number of Strokes (TNS) • Average Number of Points (ANP) • Average Stroke Path Length (ASPL) • Average Stroke Displacement (ASD) • Average Stroke Pressure (ASP)
  • 50. Digital Pen: Shape Recognition Stronium – Sketch Recognition Libraries
  • 51. Analysis at Problem level Solving Problem Correctly • Logistic Regression to model Student Solving Problem Correctly • Resulting model was significantly reliable • 60,9% - the problem solving student was identified • 71,8% - incorrectly solved problems were identified
  • 52. Analysis at Group Level Expertise Estimation • Features were feed to a Classification Tree algorithm • Several variables had a high discrimination power between expert and non-experts • Best discrimination result in 80% expert prediction and 90% non-expert prediction
  • 53. Expert Estimation over Problems Plateau reached after just 4 problems
  • 54. Main conclusion: Simple features could identify expertise Faster Writer (Digital Pen) Percentage of Calculator Use (Video) Times Numbers were Mentioned (Audio)
  • 55.
  • 57. Video Features • 66 facial features were extracted using Luxand software including both eyes and nose tip to estimate the presenter’s gaze.
  • 58. Kinect features • Identify Common postures
  • 59. Kinect features • Identify Common postures
  • 60. Kinect Features Laban’s theory helps to describe human movement using non-verbal characteristics: Spatial aspects of movement Temporal aspects of movement Fluency, smoothness, impulsivity Energy and power Overall activity
  • 62. Results: less than 50% accuracy What we were measuring was not was humans were measuring
  • 63. What is next in MLA?
  • 64. Mode integration framework for MLA Currently pioneered by Marcelo Worsley
  • 66.
  • 67. Record different learning settings And share them with the community
  • 69. Multimodal Learning Analytics is not a subset of Learning Analytics Current Learning Analytics is a subset of MLA 
  • 70. Some problems are easy, some hard But we do not know until we try to solve them
  • 71. There is a lot of exploring to do And we need explorers
  • 72. Gracias / Thank you Questions? Xavier Ochoa xavier@cti.espol.edu.ec http://ariadne.cti.espol.edu.ec/xavier Twitter: @xaoch http://www.sigmla.org