Personalization & Adaptivity

Turning Data into Personalized Student Experiences

George Siemens, PhDMay 14, 2013Presented to

IMS Global

Technique: Baker and Yacef (2009) five primary areas of analysis:

- Prediction- Clustering- Relationship mining- Distillation of data for human judgment- Discovery with models

Application: Bienkowski, Feng, and Means (2012) five areas of LA/EDM application:

- Modeling user knowledge, behavior, and experience- Creating profiles of users- Modeling knowledge domains- Trend analysis- Personalization and adaptation

LA approach Example

Techniques 

Modeling Attention metadata

  Learner modeling

  Behavior modeling

  User profile development

Relationship Mining Discourse analysis

  Sentiment analysis

  A/B Testing

  Neural networks

Knowledge Domain Modeling

Natural language processing

  Ontology development

  Assessment (matching user knowledge with knowledge domain)

Siemens 2013: Adapted from Bienkowski et al, 2012, Baker & Yacef, 2009, Baker & Siemens 2013

LA approach Example

Applications 

Trend Analysis and Prediction

Early warning, risk identification

  Measuring impact of interventions

  Changes in learner behavior, course discussions, identification of error propagation

Personalization/Adaptive learning

Recommendations: content and social connections

  Adaptive content provision to learners

  Attention metadata

Structural analysis Social network analysis

  Latent semantic analysis

  Information flow analysisSiemens 2013: Adapted from Bienkowski et al, 2012, Baker & Yacef, 2009, Baker & Siemens 2013

Context

The Conference Board & McKinsey & Co

McKinsey Quarterly, 2012

Increasing diversity of student profiles

The U.S. is now in a position when less than half of students could be considered fulltime students. In other words, students who can attend campus five days a week nine-to-five, are now a minority.

(Bates, 2013)

Increasingly: learning across traditional boundaries (i.e. work, outside of classroom, hobby)

Ok, on to adaptivity, personalization

LA approach Example

Applications 

Trend Analysis and Prediction

Early warning, risk identification

  Measuring impact of interventions

  Changes in learner behavior, course discussions, identification of error propagation

Personalization/Adaptive learning

Recommendations: content and social connections

  Adaptive content provision to learners

  Attention metadata

Structural analysis Social network analysis

  Latent semantic analysis

  Information flow analysisSiemens 2013: Adapted from Bienkowski et al, 2012, Baker & Yacef, 2009, Baker & Siemens 2013

Personalization as the holy grail of learning

(btw – this isn’t new)

Rich, 1979All those CMU folks Fischer, 2001

How does it work?

First, a knowledge domain is mapped

http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004803

http://drunks-and-lampposts.com/2012/06/13/graphing-the-history-of-philosophy/

http://drunks-and-lampposts.com/2012/06/13/graphing-the-history-of-philosophy/

http://linkeddata.org/

(again, not new)

Novak, 1990 (concept mapping)Semantic web: Berners-Lee, Hendler, Lassila, 2001Brusilovsky, 2001

Next, the learner is modeled/profiled

Cognitive stylesCognitive modelsLearning preferences (by various criteria)Tutors (cognitive, intelligent)

(Also, not new)

Anderson, Corbett, Koedinger, Pelletier, 1995That shady learning styles literature Burns, 1989

Knowledge domain + learner profile/knowledge +

? = Personalization!

The ? varies: from algorithms to pixie dust to chicken bones

State of Wisconsin, 2012

State of Wisconsin, 2012

So, what about creative processes?

AI/ML/analytics aren’t useful here, are they?

“We’ve been interested in pushing computing to a new direction, computational creativity. We’re trying to draw on data sets, not just to make inferences about the world, but to create new things you’ve never seen”

Lav Varshney on Watson

http://www.fastcodesign.com/1672444/try-a-recipe-devised-by-ibms-supercomputer-chef

“An Ecuadorian strawberry dessert algorithmically maximized for pleasantness”

http://www.fastcodesign.com/1672444/try-a-recipe-devised-by-ibms-supercomputer-chef

“For as much as $20,000 per script…a team of analysts compare the story structure and genre of a draft script with those of released movies, looking for clues to box-office success.”

The need to sensemake

Sensemaking

“Sensemaking is a motivated, continuous effort to understand connections . . . in order to anticipate their trajectories and act effectively”

(Klein et al. 2006)

or

“Sensemaking is about labeling and categorizing to stabilize the streaming of experience”

(Weick et al. 2005: 411)

We socially sensemake through stories, narratives, knowledge exchange, discourse

We turn to technical approaches when the data exceeds our capacity to create social discourse

around it

But, in fairness, once we technically sensemake, we turn to narrative to share

Adaptivity/Personalization addresses these quadrants

The future of work is in these quadrants

LA interoperability

Open Learning Analytics

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Twitter/Gmail: gsiemens