teaching and learning environment

courageouscellistAI and Robotics

Oct 29, 2013 (3 years and 7 months ago)

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Using analytics to improve the
teaching and learning environment

George Siemens

November 21, 2011

Sydney, Australia

Data Intensive University Forum


A university where staff and students
understand data and, regardless of its volume
and diversity, can use it and reuse it, store and
curate it, apply and develop the analytical tools
to interpret it
.


We’re living in data.

We’re all
doing

analytics.

Next
-
Generation Analytics.

Analytics is growing along three key dimensions:

(1) From
traditional offline analytics to in
-
line embedded analytics
.
This
has been the focus for many efforts in the past and will continue to be an important focus for
analytics.

(2)From
analyzing historical data to explain what happened to
analyzing historical and real
-
time

data from multiple systems to simulate and
predict the future.

Over the next three years, analytics will mature along a third
dimension,


(3) from
structured and simple data analyzed by individuals to analysis
of complex information of many types

(text, video, etc…) from many systems
supporting a collaborative decision process that brings multiple people together to analyze,
brainstorm and make decisions.

Data reveals

our
sentiments
,

our
attitudes
,

our
social connections
,

our
intentions
,

and
what we might do next
.


Learning
Analytics

Business
Intelligence

Big Data

EDM

Statistical
methods

Intelligent
Tutors

Personalization

Adaptive
learning

Roots of learning analytics

A
nalytics processes

Data Sources

Repositories

Tools and

Monitoring

Analytics

Methods

Permissions

LMS, library,

social media,
support services,
mobiles, profile,
attendance

Data

warehouse
(institutional,
national)

Dashboards,
visualization,
query

& drill
down,
automated
monitoring,
“quantified self”
moni瑯ring

Pr敤ic瑩ve

course
-
pa瑨Ⱐ
social n整eorkⰠ
da瑡 miningⰠ
l敡rn敲 profile

AdminⰠfacul瑹Ⱐ
l敡rn敲s,

r数or瑩ng
ag敮ci敳

Siemens, Long, 2011. EDUCUASE Review

1. Data Trails

2
. Machine
-
human readable content

3
. Learner Profile Development

4. Analytics tools and Methods

5
. Prediction & Intervention

6
. Adapting and personalizing

Siemens, Long, 2011. EDUCAUSE Review

Open Learning Analytics

Challenge:

O
rganizational

capacity building

for analytics
deployment and use

Why invest in analytics?

1.
Unbox the “black box of learning”


2.
Identify students at the margins


3.
Adapt teaching process to context/learners


4.
Target support resources to those who need it

5.
Personalize and adapt content


6.
More effective planning and allocation of
institutional resources


7.
(in the future) Restructure education
processes to account for
the architecture of
information
today:
social, network,
fragmented participatory



Knewton

analyzes learning materials based on thousands
of data points

concepts, structure, difficulty level, media
format

and uses sophisticated algorithms to piece
together the perfect bundle of content for each student,
every day
.


The more students who use the platform, the more
accurate it
becomes.”

Predictive Analytics Reporting

Check my activity

Open online course: Learning Analytics

January 23
-

March 17, 2012



http://www.solaresearch.org/


Simon

Buckingham Shum

Shane Dawson

Erik Duval

Dragan

Gasevic

George Siemens


change.mooc.ca


Twitter: gsiemens


www.elearnspace.org/blog


http://www.solaresearch.org
/



Learning Analytics & Knowledge 2012:

Vancouver


http://lak12.sites.olt.ubc.ca/