Download - 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