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Richard Baraniuk Connexions Rice University PowerPoint PPT Presentation


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Personalized Learning via Connexions. Richard Baraniuk Connexions Rice University. learning today. inefficient development and feedback. concept silos. poor access to high-quality opportunities. nanotubes. knowledge forms a network. algebra. geometry. art history. proteomics.

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Richard Baraniuk Connexions Rice University

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Richard baraniuk connexions rice university

Personalized Learning

via Connexions

Richard BaraniukConnexions

Rice University


Learning today

learning today

inefficient developmentand feedback

concept silos

poor access to high-quality opportunities


Richard baraniuk connexions rice university

nanotubes

knowledge forms a network

algebra

geometry

art history

proteomics

linguistics


Richard baraniuk connexions rice university

knowledge forms a network

networks enable new means to produce and exploit knowledge


Richard baraniuk connexions rice university

open standards for networks of knowledge


O pen education enablers

open education enablers

technology

web, internet,databases, …

intellectual property

open-source licenses for content

make content easy and safe to share


Connexions cnx org

Connexions (cnx.org)

  • 1200 open textbooks/collections

  • 20000 Lego modules

  • from contributors worldwide

  • in 40+ languages

  • millions of users per monthfrom 190 countries

  • 90 million uses of

  • STEM content


Richard baraniuk connexions rice university

free online: 6million uses to date

iPad/iPhone/Androidvia ePub

$26 in print(627 pages)


Cnx personalized learning

CNXpersonalized learning


Richard baraniuk connexions rice university

openness can disrupt the entire educational enterprise

move beyond factory mindset to individualized, personalizededucation

Sir Ken Robinson


Richard baraniuk connexions rice university

GOAL:

personalize the learning experience


Educational assessment

educational assessment

lack of timely feedback

lack of diverse remedial/enrichment materials

automated feedback

systems expensive and fragile


Richard baraniuk connexions rice university

GOAL:

personalize the learning experience

  • exploit global community of authors/teachers/learners (OER/CNX)

  • replace top-down rules based systems with bottom-up machine learning algorithms

  • bake in cognitive science best practices


Richard baraniuk connexions rice university

Connexions

Connexions


Richard baraniuk connexions rice university

Connexions+

Quadbaseopen sourceassessments database

supporting infrastructure for assessment, interactivity, peer review

Connexions

interactivesimsLablets

Focuspeer review system

Videotutorials


V ersions comments tags licenses roles publishing

versions, comments, tags, licenses, roles, publishing


S olutions

solutions


P rojects for collaboration

projects for collaboration


T ags

tags!


Richard baraniuk connexions rice university

PLS – personalized learning system

QuADopen sourceassessments database

machine learning algs

community

Connexions

interactivesimsLablets

Focuspeer review system

Videotutorials


I nstructor v iew course

instructor view: course


I nstructor v iew lesson

instructor view: lesson


S tudent v iew section

student view: section


S tudent v iew assignment

student view: assignment


S tudent v iew working a problem

student view: working a problem


S tudent feedback

student feedback


V iewing a s tudent s work

viewing a student’s work


L og of student s activities

log of student’s activities

http://cnx.org/m34921/latest

http://cnx.org/m34921/latest


Richard baraniuk connexions rice university

cognitive science

cog sci principles baked into PLS

  • retrieval practice

  • timely and relevant feedback

  • spacing of practice and feedback

    cog sci collaborators

  • Elizabeth Marsh, Andy Butler, Duke U

  • Henry Roediger, Wash U


M achine learning

machine learning

graphical models

learn and encode relationships among content, questions, answers, potential feedback, …

adaptivity

optimize each student’s “learning path” through the graph


Richard baraniuk connexions rice university

PLS architecture

Machine

Learning

Researchers

Duke Cog Sci

PLS

Backend

PLS

Quadbase

Connexions

Lablets

Linkify


A lpha testing

alpha testing

Rice U ELEC301 Signals and Systems

  • homework replacement w/ cog sci (feedback, retrieval practice, repetition, spacing) but no machine learning based adaptivity

    preliminary findings

  • using the PLS for homework promoted better retention and transfer of knowledge on an end-of-semester assessment relative to standard practice

  • magnitude of the benefit was almost equivalent to one letter grade considering completely accurate use of knowledge (no partial credit) and about half of one letter grade considering giving credit for partial knowledge

    summary

  • PLS > standard practice

  • effect size ≈ 1/2 to 1 letter grade


Summary

summary

open architecture for personalized learning

Connexions, OpenStax CollegeQuadbasePLS

built-in machine learning and cognitive science

domain and level agnostic

promising alpha test results

coming soon:PLS integration with Moodle, Sakai, …collaborations in college, K-12


Richard baraniuk connexions rice university

Connexionscnx.org

OpenStax College

openstaxcollege.org

Quadbasequadbase.org

PLS

pls.ricedsp.org

The Personalized Learning System


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